ASSOCIATION ANALYSIS OF GALECTIN GENE PROMOTER
POLYMORPHISMS WITH MULTIPLE CANCERS
Dean Wu
B.S., University of California, Santa Cruz, 2004
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF SCIENCE
in
BIOLOGICAL SCIENCES
(Molecular and Cellular Biology)
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SPRING
2011
ASSOCIATION ANALYSIS OF GALECTIN GENE PROMOTER
POLYMORPHISMS WITH MULTIPLE CANCERS
A Thesis
by
Dean Wu
Approved by:
__________________________________, Committee Chair
Nicholas N. Ewing, Ph.D.
__________________________________, Second Reader
Brett Holland, Ph.D.
__________________________________, Third Reader
Fu-Tong Liu, M.D., Ph.D.
Date: ____________________________
ii
Student: Dean Wu
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
__________________________, Graduate Coordinator
Susanne Lindgren, Ph.D.
Department of Biological Sciences
iii
___________________
Date
Abstract
of
ASSOCIATION ANALYSIS OF GALECTIN GENE PROMOTER
POLYMORPHISMS WITH MULTIPLE CANCERS
by
Dean Wu
Galectins are a family of carbohydrate-binding proteins with diverse functions in
a wide range of cellular processes. A number of galectins play roles in tumorigenesis and
cancer progression, and altered expression of various galectins has been observed in
numerous malignancies. As promoter polymorphisms have been linked to expression
differences, the goal of this thesis was to utilize public genome databases in order to
locate single nucleotide polymorphisms (SNPs) in human galectin promoters, particularly
those overlapping putative transcription factor and methylation sites, and investigate any
possible association with cancer susceptibility.
It was hypothesized that galectin promoter SNPs overlapping methylation and
transcription factor binding sites may be associated with various cancers exhibiting
iv
altered galectin expression. In order to test this hypothesis, the objectives of this project
are summarized as follows:
1: Parse the Oncomine database for expression differences in galectins 1-4 and 7-10
between cancerous and normal tissue.
2: Locate SNPs in upstream regulatory regions of genes coding for the above galectins
using the International HapMap Project database.
3: Screen SNP sequences for putative overlapping transcription factor binding sites in
upstream regulatory regions using fSNP, Consite, and TESS (TRANSFAC and IMD)
search platforms.
4: Locate individual CpG sites overlaying upstream SNPs. Search for known CpG islands
coinciding with the SNP sites using the UCSC Genome Browser and inspect sequences
for putative CpG islands using CpG Island Searcher, CpG Plot, and CpG Island Explorer.
5: Screen Illumina whole-genome SNP data archived at the Gene Expression Omnibus
for association between presence of galectin promoter SNPs and cancers showing
significant galectin expression differences.
v
A possible association between the rs3763959 polymorphism upstream of the
galectin-9 start site and human breast carcinoma was observed, along with a more
tenuous but still statistically significant association of the galectin-1 upstream
polymorphism rs4820294 with a single melanoma dataset.
_______________________, Committee Chair
Nicholas N. Ewing, Ph.D.
vi
DEDICATION
This thesis is dedicated to the anonymous study participants – many of whom succumbed
to their cancers – who provided tissue samples for the genotyping projects that
contributed data to the databases this thesis drew so heavily from.
vii
ACKNOWLEDGMENTS
First, I would like to thank Dr. Fu-Tong Liu for his great generosity in allowing me the
use of his laboratory facilities. The Liu lab members have offered valuable insights,
particularly Dr. Daniel Hsu who first introduced me to Oncomine, GEO, and other online
databases. I would also like to thank the other members of my committee, Dr. Nicholas
Ewing and Dr. Brett Holland, for their indispensable help and support. Finally, this work
would not be possible without the many researchers cited herein who made the data from
their projects publicly available.
viii
TABLE OF CONTENTS
Page
Dedication .................................................................................................................. vii
Acknowledgments..................................................................................................... viii
List of Tables ............................................................................................................ xix
List of Figures ............................................................................................................. xx
INTRODUCTION ........................................................................................................ 1
Overview of the galectin family ....................................................................... 1
Galectin structure and carbohydrate binding .................................................... 2
Intracellular functions of galectins.................................................................... 7
Extracellular functions of galectins .................................................................. 8
Galectin-modulated immunoregulation .......................................................... 11
Galectins and cancer ....................................................................................... 14
Transcriptional regulation of galectin genes ................................................... 16
OBJECTIVES ............................................................................................................. 19
MATERIALS AND METHODS ................................................................................ 20
Oncomine ........................................................................................................ 20
SNP selection using the HapMap SNP database ............................................ 21
Transcription factor binding site location ....................................................... 22
Location of CpG sites and islands .................................................................. 23
Genomic data and association analysis ........................................................... 24
RESULTS ................................................................................................................... 28
ix
Differential expression of galectins in Oncomine cancers ............................. 28
Promoter SNPs located on HapMap database ................................................ 31
Multiple predicted transcription factor binding sites ...................................... 33
Putative methylation sites coinciding with galectin promoter SNPs .............. 35
Significant genotype and allele frequency differences uncovered ................. 37
DISCUSSION ............................................................................................................. 41
Galectin-9 and breast cancer ........................................................................... 42
E2F binding site at rs3763959 ........................................................................ 43
Methylation at rs3763959 ............................................................................... 43
Conclusions and future directions ................................................................... 44
APPENDICES ............................................................................................................ 47
Appendix A. Oncomine results ................................................................................. 48
1. List of individual Oncomine datasets cited ................................................. 48
2. Normal vs. cancer, LGALS1 nonlymphoid/noncolon ................................ 51
a. Expression differences, p-value threshold of 1E-2 ......................... 51
b. Individual studies, expression difference significant at 0.9 lognormalized expression units ............................................................ 53
3. Normal vs. cancer, LGALS2 nonlymphoid/noncolon ................................ 59
a. Expression differences, p-value threshold of 1E-2 ......................... 59
b. Individual studies, expression difference significant at 0.9 lognormalized expression units ............................................................ 61
4. Normal vs. cancer, LGALS3 nonlymphoid/noncolon ................................ 62
x
a. Expression differences, p-value threshold of 1E-2 ......................... 62
b. Individual studies, expression difference significant at 0.9 lognormalized expression units .............................................................64
5. Normal vs. cancer, LGALS4 nonlymphoid/noncolon ................................ 71
a. Expression differences, p-value threshold of 1E-2 ......................... 71
b. Individual studies, expression difference significant at 0.9 lognormalized expression units ............................................................ 73
6. Normal vs. cancer, LGALS7 nonlymphoid/noncolon ................................ 80
a. Expression differences, p-value threshold of 1E-2 ......................... 80
b. Individual studies, expression difference significant at 0.9 lognormalized expression units ............................................................ 82
7. Normal vs. cancer, LGALS8 nonlymphoid/noncolon ................................ 90
a. Expression differences, p-value threshold of 1E-2 ......................... 90
b. Individual studies, expression difference significant at 0.9 lognormalized expression units ............................................................ 92
8. Normal vs. cancer, LGALS9 nonlymphoid/noncolon ................................ 99
a. Expression differences, p-value threshold of 1E-2 ......................... 99
b. Individual studies, expression difference significant at 0.9 lognormalized expression units .......................................................... 101
9. Normal vs. cancer, CLC/LGALS10 nonlymphoid/noncolon ................... 104
a. Expression differences, p-value threshold of 1E-2 ....................... 104
b. Individual studies, expression difference significant at 0.9 lognormalized expression units .......................................................... 106
Appendix B. Association analysis, genotype frequencies ...................................... 110
1. rs428007 (CLC/LGALS10) ..................................................................... 111
xi
a. GSE16019: Chen M et al. (2009) ................................................. 111
b. GSE13282: Gordan et al. (2008) ................................................. 112
c. GSE19189: Letouze et al. (2010) ................................................. 113
d. GSE10506: Nancarrow et al. (2008) ............................................ 114
e. GSE18799: Popova et al. (2009) .................................................. 115
f. GSE9003: Stark & Hayward (2007) ............................................. 116
g. GSE19177: Waddell et al. (2010) ................................................ 117
2. rs929039 (LGALS1) ................................................................................ 118
a. GSE16019: Chen M et al. (2009) ................................................. 118
b. GSE13282: Gordan et al. (2008) ................................................. 119
c. GSE19189: Letouze et al. (2010) ................................................. 120
d. GSE10506: Nancarrow et al. (2008) ............................................ 121
e. GSE18799: Popova et al. (2009) .................................................. 122
f. GSE9003: Stark & Hayward (2007) ............................................. 123
g. GSE19177: Waddell et al. (2010) ................................................ 124
3. rs2235338 (LGALS2) .............................................................................. 125
a. GSE21168: Castillo et al. (2010) ................................................. 125
b. GSE16019: Chen M et al. (2009) ................................................ 126
c. GSE13282: Gordan et al. (2008) .................................................. 127
d. GSE19189: Letouze et al. (2010) ................................................. 128
e. GSE10506: Nancarrow et al. (2008) ............................................ 129
xii
f. GSE18799: Popova et al. (2009) .................................................. 130
g. GSE9003: Stark & Hayward (2007) ............................................ 131
h. GSE19177: Waddell et al. (2010) ................................................ 132
4. rs3763959 (LGALS9) .............................................................................. 133
a. GSE21168: Castillo et al. (2010) ................................................. 133
b. GSE16019: Chen M et al. (2009) ................................................ 134
c. GSE13282: Gordan et al. (2008) .................................................. 135
d. GSE19189: Letouze et al. (2010) ................................................. 136
e. GSE10506: Nancarrow et al. (2008) ............................................ 137
f. GSE18799: Popova et al. (2009) .................................................. 138
g. GSE9003: Stark & Hayward (2007) ............................................ 139
h. GSE19177: Waddell et al. (2010) ................................................ 140
5. rs4820294 (LGALS1) .............................................................................. 141
a. GSE21168: Castillo et al. (2010) ................................................. 141
b. GSE16019: Chen M et al. (2009) ................................................ 142
c. GSE13282: Gordan et al. (2008) .................................................. 143
d. GSE19189: Letouze et al. (2010) ................................................. 144
e. GSE10506: Nancarrow et al. (2008) ............................................ 145
f. GSE18799: Popova et al. (2009) .................................................. 146
g. GSE9003: Stark & Hayward (2007) ............................................ 147
h. GSE19177: Waddell et al. (2010) ................................................ 148
6. rs10403583 (LGALS4) ............................................................................ 149
xiii
a. GSE21168: Castillo et al. (2010) ................................................. 149
b. GSE16019: Chen M et al. (2009) ................................................ 150
c. GSE13282: Gordan et al. (2008) .................................................. 151
d. GSE19189: Letouze et al. (2010) ................................................. 152
e. GSE10506: Nancarrow et al. (2008) ............................................ 153
f. GSE18799: Popova et al. (2009) .................................................. 154
g. GSE9003: Stark & Hayward (2007) ............................................ 155
h. GSE19177: Waddell et al. (2010) ................................................ 156
7. rs10489789 (LGALS8) ............................................................................ 157
a. GSE16019: Chen M et al. (2009) ................................................. 157
b. GSE13282: Gordan et al. (2008) ................................................. 158
c. GSE19189: Letouze et al. (2010) ................................................. 159
d. GSE10506: Nancarrow et al. (2008) ............................................ 160
e. GSE18799: Popova et al. (2009) .................................................. 161
f. GSE9003: Stark & Hayward (2007) ............................................. 162
g. GSE19177: Waddell et al. (2010) ................................................ 163
Appendix C. Association analysis, allele frequencies ............................................ 164
1. rs428007 (CLC/LGALS10) ..................................................................... 165
a. GSE16019: Chen M et al. (2009) ................................................. 165
b. GSE13282: Gordan et al. (2008) ................................................. 166
c. GSE19189: Letouze et al. (2010) ................................................. 167
xiv
d. GSE10506: Nancarrow et al. (2008) ............................................ 168
e. GSE18799: Popova et al. (2009) .................................................. 169
f. GSE9003: Stark & Hayward (2007) ............................................. 170
g. GSE19177: Waddell et al. (2010) ................................................ 171
2. rs929039 (LGALS1) ................................................................................ 172
a. GSE16019: Chen M et al. (2009) ................................................. 172
b. GSE13282: Gordan et al. (2008) ................................................. 173
c. GSE19189: Letouze et al. (2010) ................................................. 174
d. GSE10506: Nancarrow et al. (2008) ............................................ 175
e. GSE18799: Popova et al. (2009) .................................................. 176
f. GSE9003: Stark & Hayward (2007) ............................................. 177
g. GSE19177: Waddell et al. (2010) ................................................ 178
3. rs2235338 (LGALS2) .............................................................................. 179
a. GSE21168: Castillo et al. (2010) ................................................. 179
b. GSE16019: Chen M et al. (2009) ................................................ 180
c. GSE13282: Gordan et al. (2008) .................................................. 181
d. GSE19189: Letouze et al. (2010) ................................................. 182
e. GSE10506: Nancarrow et al. (2008) ............................................ 183
f. GSE18799: Popova et al. (2009) .................................................. 184
g. GSE9003: Stark & Hayward (2007) ............................................ 185
h. GSE19177: Waddell et al. (2010) ................................................ 186
4. rs3763959 (LGALS9) .............................................................................. 187
xv
a. GSE21168: Castillo et al. (2010) ................................................. 187
b. GSE16019: Chen M et al. (2009) ................................................ 188
c. GSE13282: Gordan et al. (2008) .................................................. 189
d. GSE19189: Letouze et al. (2010) ................................................. 190
e. GSE10506: Nancarrow et al. (2008) ............................................ 191
f. GSE18799: Popova et al. (2009) .................................................. 192
g. GSE9003: Stark & Hayward (2007) ............................................ 193
h. GSE19177: Waddell et al. (2010) ................................................ 194
5. rs4820294 (LGALS1) .............................................................................. 195
a. GSE21168: Castillo et al. (2010) ................................................. 195
b. GSE16019: Chen M et al. (2009) ................................................ 196
c. GSE13282: Gordan et al. (2008) .................................................. 197
d. GSE19189: Letouze et al. (2010) ................................................. 198
e. GSE10506: Nancarrow et al. (2008) ............................................ 199
f. GSE18799: Popova et al. (2009) .................................................. 200
g. GSE9003: Stark & Hayward (2007) ............................................ 201
h. GSE19177: Waddell et al. (2010) ................................................ 202
6. rs10403583 (LGALS4) ............................................................................ 203
a. GSE21168: Castillo et al. (2010) ................................................. 203
b. GSE16019: Chen M et al. (2009) ................................................ 204
c. GSE13282: Gordan et al. (2008) .................................................. 205
xvi
d. GSE19189: Letouze et al. (2010) ................................................. 206
e. GSE10506: Nancarrow et al. (2008) ............................................ 207
f. GSE18799: Popova et al. (2009) .................................................. 208
g. GSE9003: Stark & Hayward (2007) ............................................ 209
h. GSE19177: Waddell et al. (2010) ................................................ 210
7. rs10489789 (LGALS8) ............................................................................ 211
a. GSE16019: Chen M et al. (2009) ................................................. 211
b. GSE13282: Gordan et al. (2008) ................................................. 212
c. GSE19189: Letouze et al. (2010) ................................................. 213
d. GSE10506: Nancarrow et al. (2008) ............................................ 214
e. GSE18799: Popova et al. (2009) .................................................. 215
f. GSE9003: Stark & Hayward (2007) ............................................. 216
g. GSE19177: Waddell et al. (2010) ................................................ 217
Appendix D. SNP genotypes in the GSE11976 dataset .......................................... 218
Appendix E. G-test results for Hardy-Weinburg equilibrium ................................. 219
1. rs428007 (CLC/LGALS10) ..................................................................... 219
2. rs929039 (LGALS1) ................................................................................ 220
3. rs2235338 (LGALS2) .............................................................................. 221
4. rs3763959 (LGALS9) .............................................................................. 222
5. rs4820294 (LGALS1) .............................................................................. 223
6. rs10403583 (LGALS4) ............................................................................ 224
7. rs10489789 (LGALS8) ............................................................................. 225
xvii
Literature Cited ......................................................................................................... 226
xviii
LIST OF TABLES
Page
Table 1. Relevant Illumina cancer SNP datasets archived at the Gene Expression
Omnibus ........................................................................................................ 26
Table 2. Differential expression of galectins in nonlymphatic tissue (cancer vs.
normal), from Oncomine ................................................................................29
Table 3. Illumina BeadArray SNPs in upstream promoter regions of galectin
genes .............................................................................................................. 32
Table 4. Predicted transcription factor binding sites overlapping with SNPs (both
reference and nonreference alleles) in upstream promoter regions of
galectin genes ................................................................................................ 34
Table 5. Individual CpG sites and putative CpG islands at SNP locations ................ 36
Table 6. SNPs exhibiting significant genotype and/or allele frequency differences
in study datasets when compared to HapMap CEU population only............ 39
xix
LIST OF FIGURES
Page
Figure 1. The carbohydrate-binding domain (CRD) of galectin-3 displayed in 3D
Mol-Viewer, Vector NTI Advance 10.3.1 demo mode (©2007
Invitrogen Corporation, http://www.invitrogen.com); shown with ligand
(dark structure) at upper right (PDB ID: 2nmo, from Collins et al., 2007) ... 3
Figure 2. Structural categorization of galectins showing prototype (1, 2, 5, 7, 10,
11, 13, 14, 15, 16, 17), tandem repeat (4, 6, 8, 9, 12), and chimeric (3)
galectins ......................................................................................................... 5
Figure 3. Galectin-3 molecules can associate with each other at the N-terminus
(left), leading to pentamer formation (right). ................................................. 6
Figure 4. Homodimers of prototype galectins (light circles) bind to glycans (black
crosses) to form lattices. ................................................................................ 9
Figure 5. Tandem-repeat galectins containing two different CRDs (light and dark
circles, connected by linker regions) can form lattice structures with
bivalent ligands displaying distinct saccharide groups, shown here as
crosses with differently-colored arms. ......................................................... 10
xx
1
INTRODUCTION
Overview of the galectin family
The galectins are a family of carbohydrate-binding proteins distinguished by the
criteria of binding to -galactosides (Massa et al., 1993) and/or the presence of
characteristic conserved sequence elements in the carbohydrate-binding domain (CRD)
(Liu, 2000; Nakahara & Raz, 2006). This protein family is believed to be ancient, and is
present in chordates, nematodes, insects, sponges, and fungi with the exception of
budding yeast (Boulianne et al., 2000). Houzelstein et al. (2008) hypothesized that a
series of multiple gene duplication events resulted in the divergence of vertebrate
galectins.
The first galectin to be described was found by Teichberg et al. (1975) who
isolated electrolectin from the electric organ of the gymnotid electric "eel" Electrophorus
electricus. It was not until additional galectins had been discovered (Barondes et al.,
1994a) that the term "galectin" was adopted to describe this protein family. Granulocytic
bodies containing Charcot-Leyden crystal protein (CLC, also known as galectin-10) were
discovered by Jean-Martin Charcot in the 19th century, although the carbohydratebinding properties of CLC were not noted until much later (Leonidas et al., 1995).
Galectins are synthesized on free ribosomes (Rabinovich et al., 2002) and lack a
transmembrane domain or a classical secretory signal sequence (Barondes et al., 1994b;
Liu, 2000), although they are secreted across the plasma membrane and intracellular
membranes (Nakahara et al., 2006; Ochieng et al., 2004) in addition to localizing in the
cytoplasm. Because galectins are able to recognize and bind cell surface glycoproteins
2
and glycolipids (Guévremont et al., 2004; Yang et al., 2008), extracellular galectins can
induce cross-linking of surface glycoproteins on adjacent cells of the same or different
types (Brewer et al., 2002; Lagana et al., 2006) and thereby trigger transmembrane
signaling cascades (Stillmann et al., 2006; He & Baum, 2006; Camby et al., 2006). In
addition to their extracellular functions, galectins are known to operate in a variety of
intracellular regulatory networks (Liu et al., 2002). Involvement of extracellular galectin
cross-linking has further been proposed in host-pathogen interactions (Rabinovich &
Toscano, 2009).
Galectin structure and carbohydrate binding
The carbohydrate-binding sites of galectin CRDs (Figure 1) are able to
accommodate galactoside saccharides of varying composition, although each galectin
exhibits fine specificities for certain saccharides (Barondes et al., 1988; Nakahara et al.,
2005). Three consecutive exons code for the CRD in known mammalian galectin genes,
with the middle exon accounting for the vast majority of the conserved carbohydratebinding residues characteristic of the galectin family (Barondes et al., 1994b).
Crystallographic analysis of CRD structure indicates that this domain typically is around
130 amino acids in length, with a highly conserved secondary structure consisting of fiveto six-stranded  sheets (Rabinovich et al., 2002).
3
Figure 1. The carbohydrate-binding domain (CRD) of galectin-3 displayed in 3D Mol-Viewer,
Vector NTI Advance 10.3.1 demo mode (©2007 Invitrogen Corporation, http://www.invitrogen.com);
shown with ligand (dark structure) at upper right (PDB ID: 2nmo, from Collins et al., 2007).
4
Galectins can be broadly divided into three structural types (Figure 2) based on
the organization of their protein domains (Rabinovich et al., 2002). Prototype galectins
(galectins 1, 2, 5, 7, 10, 11, and 13-17; Than et al., 2009) consist of two identical CRDs
and may exist as monomers or noncovalent homodimers. In contrast, tandem repeat-type
galectins (galectins 4, 6, 8, 9, and 12) consist of two distinct CRDs joined in tandem by a
single linker region (Hsu & Liu, 2004). Finally, galectin-3 is the sole known chimeratype galectin (Mazurek et al., 2000). Structurally, galectin-3 is a 31-kd protein consisting
of a single CRD at the C-terminus and an N-terminus composed of mostly short tandem
repeats with no carbohydrate-binding function (Liu et al., 2002). This N terminus, unique
to galectin-3, is the site of self-association with other galectin-3 molecules (Figure 3) to
form pentamers (Hsu et al., 1992; Yang et al., 2008) and also has been proposed to play a
role in intracellular localization (Gong et al., 1999).
5
Figure 2. Structural categorization of galectins showing prototype (1, 2, 5, 7, 10, 11, 13, 14, 15,
16, 17), tandem repeat (4, 6, 8, 9, 12), and chimeric (3) galectins.
6
Figure 3. Galectin-3 molecules can associate with each other at the N-terminus (left), leading to
pentamer formation (right).
7
Intracellular functions of galectins
Studies have demonstrated a wide variety of intracellular functions of galectins,
some of which may be independent of their carbohydrate-binding activity (Yang et al.,
2008). It has been shown that galectins -1 and -3 play a role in pre-mRNA splicing
(Vyakarnam et al., 1997; Liu, 2005), and both galectins are associated with the SMN
complex during snRNP assembly (Park et al., 2001). Intracellular galectins have further
been demonstrated to influence apoptosis, cell growth, and cell cycle regulation (Liu et
al., 2002; Hernandez & Baum, 2002). Expression of galectin-3 confers resistance to
apoptosis (Yoshii et al., 2002), although this lectin is able to induce apoptosis when
delivered extracellularly (Stillmann et al., 2006, Fukumori et al., 2003). Galectin-3 shares
considerable sequence similarity with the known apoptosis-suppressing molecule BCL-2
which it has been shown to interact with in vitro (Yang et al., 1996). The exact
mechanism for this inhibition of apoptosis has not yet been elucidated, though a number
of possibilities have been suggested (Nakahara et al., 2005). However, galectin-3 has
been shown to translocate to the perinuclear mitochondrial membrane where it prevents
mitochondrial damage and inhibits the release of cytochrome c (Yu et al., 2002;
Fukumori et al., 2006) [a known component of the mitochondrial apoptotic pathway
released in response to pro-apoptotic stimuli (Cereghetti & Scorrano, 2006; Dimmer &
Scorrano, 2006)]. Other pathways may also be involved in galectin-3 mediated apoptotic
regulation (Liu, 2000; Fukumori et al., 2004; Oka et al., 2005). In contrast, studies have
shown intracellular galectin-7 to promote apoptosis in epithelial cells, possibly by
affecting the expression of apoptotic regulators (Liu et al., 2002).
8
Extracellular functions of galectins
Many extracellular functions of galectins are related to their cross-linking ability.
Inherent structural characteristics of galectins facilitate the formation of extensive
galectin-glycan lattices (Yang et al., 2008). Homodimerization in prototype galectins and
the heterodimeric structure of tandem-repeat galectins enable simultaneous bivalent
saccharide binding (Brewer, 2002), while galectin-3 molecules can self-associate to form
pentamers (Figure 3). In both cases, the binding of galectins with multivalent
carbohydrates can result in lattice formation (Brewer et al., 2002, Yang et al., 2008).
These lattices (Figures 4 and 5) have been shown to function in the organization of
plasma membrane domains as well as signaling regulation (Garner & Baum, 2008), and
raft associated galectin-3 is believed to modulate dendritic cell migration (Hsu et al.,
2009b). Braccia et al. (2003) found that galectin-4 surface lattices stabilized lipid raft
formation in intestinal cell microvilli, and other studies have shown a role for galectin-3
lattices in apical sorting of non-raft glycoproteins (Delacour et al., 2006). Galectin-3 has
been determined to play a major role in macrophage function, localizing in phagocytic
cups and phagosomes (Sano et al., 2003) including mycobacterial phagosomes (Beatty et
al., 2002); in addition, extracellular galectin-3 is a powerful chemoattractant in human
monocytes and macrophages (Sano et al., 2000; Kuwabara et al., 2003). Galectin-3deficient macrophages showed a markedly diminished phagocytic capability, both in
vitro and in vivo (Sano et al., 2003).
9
Figure 4. Homodimers of prototype galectins (light circles) bind to glycans (black crosses) to form
lattices.
10
Figure 5. Tandem-repeat galectins containing two different CRDs (light and dark circles,
connected by linker regions) can form lattice structures with bivalent ligands displaying distinct saccharide
groups, shown here as crosses with differently-colored arms.
11
Galectins can facilitate cell adhesion by binding to glycoproteins and glycolipids
located on adjacent cells and/or the extracellular matrix (Yang et al., 2008); though early
research showed that the binding of cell-surface galectin-1 (Gu et al., 1994), galectin-3
(Sato & Hughes, 1992), and galectin-8 (Hadari et al., 2000) was capable of inhibiting
adhesion across a range of cell types by interfering with laminin-integrin interaction.
Numerous ligands, bound by different galectins, have been identified in many different
cell types as involved in cell-cell or cell-matrix adhesion (Kuwabara et al., 2003).
Galectin-3 binding has been linked to neutrophil activation (Nieminen et al., 2005) as
well as mediating adhesion and extravasation in neutrophils (Sato et al., 2002).
Galectin-modulated immunoregulation
Various other immune functions have been attributed to extracellular galectin
interactions (Rabinovich et al., 2002). Galectins have been shown to play a role in
inflammation and inflammatory response regulation both in vitro and in vivo, suggesting
important roles in both adaptive and innate immunity (Rabinovich et al., 1999b; Zuberi et
al., 1994; Bernardes et al., 2006). Extracellular galectin-1 has been shown to inhibit
activation of T lymphocytes and induce arrest and apoptosis in already-activated T cells
(Perillo et al., 1995; Nguyen et al., 2001) via modulation of T-cell receptor signaling
(Garner & Baum, 2008). Cell-surface galectin-1 in T-cells binds to a discreet set of
glycoproteins including CD3, CD4, CD7, CD43, and CD45 which are known to be
involved in T-cell development and activation; and galectin-1 interaction with these
12
glycoproteins in galectin-glycan lattices has been implicated in the modulation of T-cell
receptor signaling (Liu et al., 2008).
Galectin-3 has also been shown to regulate lymphocyte function (Peng et al.,
2008), and galectin-3 cross-linking and lattice formation has been proposed to inhibit Tcell activation by interfering with T-cell receptor clustering (Demetriou et al., 2001). In
T-cells, galectin-3 takes on a dual role with regard to apoptosis; intracellularly, galectin-3
not only plays an anti-apoptotic function but promotes T-cell proliferation (Dhirapong et
al., 2009), whereas extracellular galectin-3 induces apoptosis as mentioned previously.
Galectins have been demonstrated to modulate cytokine secretion and T-cell
activation and development (Hsu et al., 2009a), including that of T helper cells (Ilarregui
et al., 2005). Activated T helper cells differentiate into TH1 and TH2 effector cells
depending on the cytokines present, with TH1 cells associated with inflammation and
delayed-type hypersensitivity and TH2 associated with parasite clearance and allergy
(Toscano et al., 2007). Galectin-1 was shown to inhibit pro-inflammatory cytokines
(Rabinovich et al., 1999a), resulting in a shift towards a TH2 response (Santucci et al.,
2003; Motran et al., 2008). Moreover, TH2 cells were not as susceptible to galectin-1
induced cell death as were TH1 cells (Toscano et al., 2007). Galectin-2 has also been
indicated to favor a TH2 response (Ilarregui et al., 2005). Additionally, Zuberi et al.
(2004) suggested a T helper regulatory role for galectin-3, noting that galectin-3
knockout mice exhibited reduced TH2 and elevated TH1 response following antigen
challenge and subsequent airway inflammation. However, Cortegano et al. (1998)
reported that exogenous galectin-3 inhibited IL-5, a major TH2 cytokine, in certain cell
13
lines. The aforementioned galectin-modulated effects on T-cell response have been
implicated in allergic and autoimmune inflammation (Rabinovich et al., 2002;
Rabinovich et al., 2007).
Galectin cross-linking has been linked to the regulation of immune synapse
formation between T-cells and antigen presenting cells (Laderach et al., 2010). In
already-activated CD4-positive T-cells, galectin-3 was observed to localize to the
cytoplasmic side of the immunological synapse, suggesting that this galectin destabilizes
synapse formation by promoting T-cell receptor downregulation via an intracellular
pathway (Chen HY et al., 2009). In contrast, galectin-1 expressed on the surface of
stromal cells has been suggested to bind to pre-B-cell receptor, thereby aiding in the
formation of the pre-B-cell/stromal cell synapse and facilitating signaling during B-cell
development (Gauthier et al., 2002).
Saccharide binding by galectins has been shown to play a role in host-pathogen
interactions (Rabinovich and Toscano, 2009), either by directly modulating pathogen
recognition and invasion through interaction with pathogen saccharides at the cell surface
(Beatty et al., 2002; Vray et al., 2004; Okumura et al., 2008) or by affecting immune
responses through various signaling pathways (Hsu et al., 2006). Moreover, host galectin
functions may also be exploited by bacterial, viral, or parasitic pathogens. Galectin-3 has
been demonstrated in mice to contribute to Toxoplasma gondii intracellular survival by
downmodulating cell death in infected host neutrophils (Alves et al., 2010). A number of
studies have demonstrated the ability of viral infection to affect galectin expression
and/or secretion (Fogel et al., 1999; King et al., 2009). In the case of human T-cell
14
lymphotropic virus type 1 (HTLV-1), not only is expression of galectin-1 and -3
upregulated by viral infection (Hsu et al., 1996; Gauthier et al., 2008), but galectin-1 has
been implicated in the HTLV-1 infection process by facilitating cell-cell interaction
through crosslinking of cell-surface glycoproteins (Gauthier et al., 2008).
Galectins and cancer
The extensive and varied roles played by galectins in immunity, cell-cycle
regulation, apoptosis, and adhesion have drawn attention to possible functions of
galectins in carcinogenesis and cancer progression (Lahm et al., 2004). Galectins can
influence tumorigenesis intracellularly by interacting with Ras-subfamily proteins and
other cell cycle regulators (Yang et al., 2008). Galectins-1 and -3 have also been shown
to promote angiogenesis through intracellular (Thijssen et al., 2006) and extracellular
processes, respectively (Yang et al., 2008). Furthermore, galectin-mediated effects,
including but not limited to extracellular glycoprotein binding, have been linked to tumor
cell migration and metastasis (Zou et al., 2005; Yang et al., 2008). Expression differences
are seen in many galectins across a wide range of cancers when compared with normal
tissue (Xu et al., 1995 & 2000; Plzak et al., 2004; Prieto et al., 2006).
So far, the roles of galectins -1 and -3 in tumorigenesis and cancer progression
have been widely researched. As previously mentioned, galectin-1 has been shown to
induce apoptosis of effector T-cells (Perillo et al., 1995), thereby influencing cancer cell
survival via modulation of anti-tumor T-cell responses. This induction of apoptosis may
occur via multiple pathways, both intracellular and extracellular (Salatino & Rabinovich,
15
2011). More directly, pro-apoptotic effects of galectin-1 may be manifested in cancer
cells themselves (van den Brûle et al., 2004), although this is strongly dependent upon
other factors such as altered regulation of glycosyltransferase genes (Valenzuela et al.,
2007) that affect galectin-1 binding to surface glycoproteins and thereby also affect the
triggering of downstream apoptotic signaling cascades (Hsu et al., 2006).
Galectin-3 is a known apoptotic regulator with both suppressive and inductive
effects on apoptosis (Hsu et al., 2006), and increased galectin-3 expression has been
observed in many different cancer types (van den Brûle et al., 2004). Increased
expression of this galectin has also been associated with poor prognosis in many cancers,
probably as a result of increased metastatic and invasive potential due to heightened
adhesion (Rabinovich et al., 2002). However, decreased galectin-3 expression has been
correlated with aggressive phenotype in some cancers (Castronovo et al., 1996),
indicating that other factors may also be involved in the regulation of metastasis by
galectin-3, and Matarrese et al. (2000) proposed that overexpression of galectin-3 may in
fact prevent metastasis in some cancers by inhibiting detachment of potentially metastatic
tumor cells via improved cell-cell adhesion. Diminished expression of galectin-3 has
been reported in multiple cancers (Danguy et al., 2002, van den Brûle et al., 2004),
suggesting markedly differing effects of galectin-3 depending on cancer type, tumor
environment, and intracellular versus extracellular expression of galectin-3 (Rabinovich
et al., 2002).
Although not studied in as much detail as galectins -1 and -3, other galectins have
been shown or suspected to function in tumor growth and development (Danguy et al.,
16
2002). Similarly to galectin-1, the effects of galectin-2 upon T-cell development and
survival have been linked to tumor progression (Salatino & Rabinovich, 2011). Galectin2 can modulate immune tolerance to tumors by triggering caspase-dependent apoptotic
pathways, thereby inducing programmed death of activated T-cells (Sturm et al., 2004).
Galectin-7 has also been proposed to negatively regulate tumor progression by
suppressing proliferation (Saussez and Kiss, 2006). Hadari et al. (2000) reported that
galectin-8-induced inhibition of adhesion also resulted in the induction of apoptosis in
human cancer cells. Additionally, a number of studies have noted a correlation between
increased galectin-9 expression in tumors and a reduction in invasiveness, and galectin-9
has also been found to induce apoptosis and cell cycle arrest in adult T-cell leukemia by
reducing the expression of various regulatory factors (Rabinovich et al., 2007). Thus,
galectin-9 has been considered to be a prognostic factor in cancer progression (Yamauchi
et al., 2006; Nobumoto et al., 2008).
Transcriptional regulation of galectin genes
Transcriptional regulation of galectin expression occurs at upstream promoter
regions (Chiariotti et al., 2004). The human galectin-3 gene (LGALS3) promoter has
been functionally characterized (Kadrofske et al., 1998), although a search of the
publications referenced at the PubMed literature database reveals that considerably less
research has been carried out regarding other galectin promoters. Unlike other galectin
genes, LGALS3 contains an internal promoter element in its second intron (Raimond et
al., 1995) regulating expression of the galectin-3 internal gene GALIG which encodes the
17
unrelated mitochondrial-targeted protein mitogaligin (Duneau et al., 2005). Regulation of
this internal gene occurs independently of LGALS3 regulation (Guittaut et al., 2001).
In humans, cytosines adjacent to guanines (CpG sites) are usually methylated,
thereby inhibiting transcription. Methylation of promoter CpGs can lead to transcription
alteration via gene silencing (Saxonov et al., 2006). However, regions of higher CG
density known as CpG islands, often located near transcription start sites, are
unmethylated in expressed genes. Single-nucleotide polymorphisms (SNPs) overlaying
CpG islands or individual CpG sites may affect transcription by altering methylation
patterns, and changes in promoter methylation have been shown to affect galectin
expression in various cancers (Ruebel et al., 2005; Ahmed et al., 2007; Demers et al.,
2009) such that hypermethylation of the LGALS3 promoter in prostate adenocarcinoma
has been proposed as a marker for early detection (Ahmed, 2010).
Overlap of SNPs with transcription factor binding sites may also affect gene
expression (Ameur et al., 2009). It has been shown that even single-nucleotide genomic
variations in regulatory regions may have significant effects on transcription factor
binding ability, resulting in differences in gene expression levels, although the effects of
genetic variation differ depending on the DNA binding motifs present in different
transcription factors (Kasowski et al., 2010). Despite the relative paucity of studies in this
area, a number of regulatory elements have been found in galectin promoters (Chiariotti
et al., 2004).
Here, it is reported that in silico analytical techniques point towards a possible
association between the rs3763959 polymorphism upstream of the galectin-9 start site
18
and human breast carcinoma. No previous work has been done regarding potential effects
of galectin promoter polymorphisms on cancer susceptibility, although association
studies have been done for promoters in other genes involved in apoptotic pathways
(Cerhan et al., 2008; Cordano et al., 2005; Novak et al., 2009; Enjuanes et al., 2008) and
a LGALS2 coding region polymorphism has been linked to ischemic stroke (Yamada et
al., 2008) and myocardial infarction in some (Ozaki et al., 2004) but not all (Mangino et
al., 2007; Sedlacek et al., 2007) human populations. Moreover, a search of the PubMed
citation database reveals that no published in silico analyses using GEO data have been
carried out with regard to galectin promoter SNPs, further demonstrating the originality
of this project.
19
OBJECTIVES
1: Parse the Oncomine database for expression differences in galectins 1-4 and 7-10
between cancerous and normal tissue.
2: Locate SNPs in upstream regulatory regions of genes coding for the above eight
galectins using the International HapMap Project database.
3: Screen SNP sequences for putative overlapping transcription factor binding sites in
upstream regulatory regions using fSNP, Consite, and TESS (TRANSFAC and IMD)
search platforms.
4: Use MEGA alignment software to locate individual CpG sites overlaying upstream
SNPs. Search for known CpG islands coinciding with the SNP sites using the UCSC
Genome Browser, then inspect sequences for putative CpG islands using CpG Island
Searcher, CpG Plot, and CpG Island Explorer.
5: Determine if there is any association between presence of galectin promoter SNPs and
cancers showing significant galectin expression differences by screening Illumina wholegenome SNP data archived at the Gene Expression Omnibus.
20
MATERIALS AND METHODS
All work was carried out in the laboratory of Dr. Fu-Tong Liu (University of
California Davis Medical Center, Department of Dermatology) at the UC Davis Medical
Center.
Oncomine
Oncomine (http://www.oncomine.org) is an online microarray database,
containing expression data from numerous studies (Rhodes et al., 2004). Differential
expression analyses enable side-by-side comparisons of gene expression between normal
and cancerous tissues for a wide variety of malignancies (Rhodes et al., 2007). Oncomine
data is organized by study and tissue type. Analyses may compare expression of a
particular gene in cancer versus normal tissue, normal versus normal, cancer versus
cancer, or by other criteria such as molecular alteration or patient prognosis (Rhodes et
al., 2007). Gene expression is presented in log2 transformed expression units. Expression
profiles are visualized as boxplots showing the median, first and third quartiles, and 10th
and 90th percentiles in addition to outliers. To allow flexibility in significance testing, pvalue cutoffs can be set at various levels by the user.
The Oncomine database was screened for expression of galectins 1-4 and 7-10 in
multiple non-lymphatic cancers in comparison with normal tissue. Although a
conservative p-value threshold of 1E-4 was initially chosen for stringency as per
Duhagon et al. (2010), it was felt that this eliminated too many Oncomine studies and the
p-value threshold was subsequently altered to 1E-2. Expression differences for Oncomine
21
datasets were obtained by subtracting median cancer expression from median noncancer
expression. The resultant value is positive if overexpressed in normal tissue and negative
if overexpressed in cancerous tissue. For individual datasets, expression differences in
excess of 0.9 log-normalized expression units were considered significant. This threshold
was chosen as it was broad enough to guarantee a range of suitable GEO datasets, while
retaining a level of stringency matching or exceeding similar Oncomine comparisons in
the published literature (Draheim et al., 2010; Tang et al., 2010).
SNP selection using the HapMap SNP database
The International HapMap Project (http://www.hapmap.org) database was used to
locate SNPs in galectin promoter regions. This project is a large-scale global
collaborative effort involving multiple research groups aimed at cataloging SNP
variations in the human genome (International Hapmap Consortium, 2003 & 2005). The
initial Phase 1 haplotype map was created by genotyping 270 individuals from four
widely separated populations: Japanese in Tokyo (JPT), residents of Utah with
Northern/Western European ancestry (CEU), Yoruba in Ibadan, Nigeria (YRI), and
Beijing residents of Han ancestry (CHB). It should be cautioned that due to recent mass
migration to urban areas as well as the inclusion of university students in the CHB
population, the composition of HapMap populations may not necessarily be
representative of the local populations in the areas surveyed (He et al., 2009). All SNPs
included in HapMap conformed to Hardy-Weinberg equilibrium (HWE) expectations
(exact test, p-value of 0.001 or less) for individual populations sampled (International
22
Hapmap Consortium, 2003 & 2005); although due to population stratification this is not
necessarily true when multiple populations are aggregated.
The default minor allele frequency (MAF) cutoff for Phase I HapMap is 0.05 for
each of the four populations sampled, a threshold intended to screen out rare alleles and
simplify haplotype mapping (International Hapmap Consortium, 2003 & 2005). In order
to maintain consistency this cutoff was used for SNP selection even for later additions to
the database, excluding rarer SNPs or those restricted to a single population; a 0.05 or
0.01 MAF threshold is often used for clinical studies since detection and analysis of less
common alleles may require unrealistically large sample sizes (Nebert et al., 2008). The
HapMap database was consulted to locate SNPs within 2kb upstream of LGALS1-4, 710, and 12 start sites. The LGALS3 internal regulatory region was ignored because, as
previously mentioned, it controls transcription of the GALIG internal gene and has not
been shown to affect regulation of LGALS3 itself (Chiariotti et al., 2004). The presence
of each selected SNP from HapMap was confirmed using the UCSC Genome Browser
(http://genome.ucsc.edu) at the University of California, Santa Cruz.
Transcription factor binding site location
Four online tools were used to detect putative transcription factor (TF) binding
sites coinciding with SNPs (in both the reference and nonreference alleles) in galectin
promoter regions: TFSearch (via fSNP at http://compbio.cs.queensu.ca/F-SNP/), the
TRANSFAC 6.0 and IMD databases (via TESS, the Transcriptional Element Search
System at UPenn; http://www.cbil.upenn.edu/cgi-bin/tess/tess?RQ=WELCOME), and the
23
Consite platform at the University of Bergen (http://asp.ii.uib.no:8090/cgibin/CONSITE/consite/). Multiple tools were used due to the uncertainty associated with
inherent limitations in site scanning algorithms (Carmack et al., 2007). Even with this
measure it is cautioned that in silico TF binding site recognition models remain prone to
various errors and therefore may not accurately reflect real-life processes (Hannenhalli,
2008); furthermore, unlike CpG sites, replacement of a single nucleotide in a TF binding
site may not sufficiently alter the site to affect or prevent binding. Nonetheless, these
search platforms have been used to find candidate sites in multiple published studies (Li
et al., 2009; Banerjee & Nandagopal, 2007). It should be noted that TFSearch relies on
the earlier TRANSFAC 3.3 which is several years old; the current version is 7.0 as of
2005. However, the results do not show a great deal of difference between the 3.3 and 6.0
versions. The default significance thresholds (85% for all with the exception of Consite
which was set to 80%) were used for each program.
Location of CpG sites and islands
In silico detection algorithms have been used to find CpG sites and islands in past
studies (Wang & Leung, 2004; Li et al., 2006). Three of these search platforms were used
to investigate CpG islands in the vicinity of galectin promoter SNPs: CpG Island
Searcher (http://cpgislands.usc.edu; web-based), EMBOSS CpG Plot
(http://www.ebi.ac.uk/Tools/emboss/cpgplot/index.html; web-based), and CpG Island
Explorer (http://bioinfo.hku.hk/cpgieintro.html; downloaded Java application). Individual
CpG sites were confirmed using MEGA alignment software (http://megasoftware.net).
24
As the definition of the length of a CpG island varies by author (Takai & Jones, 2002),
both the default (55%GC, 0.65 obs/exp, 500 bp length, 100bp gap for CpG Island
Searcher; 100bp window, 0.6 obs/exp ratio, 50% GC, 200bp length for CpG Plot;
50%GC, 0.6 obs/exp, 500bp length for CpG Island Explorer) and least stringent (50%GC,
0.6 obs/exp, 200 bp length, 100bp gap for CpG Island Searcher; 100bp window, 0.6
obs/exp ratio, 30% GC, 50bp length for CpG Plot; 50%GC, 0.6 obs/exp, 200bp length for
CpG Island Explorer) settings were used for each program.
Genomic data and association analysis
The NBCI’s Gene Expression Omnibus (GEO; accessed at
http://www.ncbi.nlm.nih.gov/gds) is an extensive public database of expression data from
a diverse range of published and unpublished studies, including whole-genome studies.
As Illumina (http://www.illumina.com) is the only manufacturer to include the majority
of galectin promoter SNPs of interest, only datasets derived from those studies performed
using Illumina bead arrays were used. This had the added benefit of obviating the need
for meta-analytical techniques, since nearly identical arrays could be analyzed using a
log-likelihood test (G-test). One drawback is that not all the SNPs in question are
included on the Illumina arrays, but these are the most comprehensive of the more
commonly-used systems available.
Genomic SNP data from Illumina BeadArrays covering bladder cancer,
esophageal adenocarcinoma, lung squamous cell carcinoma, melanoma, and renal clear
cell carcinoma were extracted from studies archived at the GEO, as significant
25
differential expression had previously been found in these cancers using Oncomine. In
addition, galectin promoter SNPs were investigated in breast carcinoma since, as
mentioned in the introduction, multiple studies have pointed to galectin-9 as a prognostic
factor in this disease (Yamauchi et al., 2006; Irie et al., 2005). In the two datasets
(GSE13282, Gordan et al., 2008; GSE16019, Chen M et al., 2009) where the authors
failed to include genotype calls, these were obtained manually by calculating the B allele
frequency as per LaFramboise et al. (2010).
Genotype and allele frequencies were compared between the GEO datasets (Table
1) and both individual and aggregated HapMap Phase 1 populations as controls, with
Williams’ corrected G-test for independence used to determine association as per Viviani
Anselmi et al. (2008) under a null hypothesis of no association and p-values of 0.05 or
less considered significant (Daly, 2009). A Williams’ corrected two-degree G-test for
goodness-of-fit was used to determine Hardy-Weinberg equilibrium as per McDonald
(2009) at a p-value threshold of 0.05 or less. In each particular cancer of interest, analyses
were still performed on promoter SNPs in galectins that did not exhibit significant
differential expression for that cancer, since these were still useful as negative controls.
26
Table 1. Relevant Illumina cancer SNP datasets archived at the Gene Expression Omnibus. It
should be noted that rs929039, rs10489789, and rs428007 are not featured on the Illumina HumanOmni1Quad array used in Castillo et al. (2010).
Study
Letouze et al. (2010)
Popova et al. (2009)
Waddell et al. (2010)
Staaf et al. (2008)
Nancarrow et al. (2008)
Gordan et al. (2008)
Chen M et al. (2009)
Castillo et al. (2010)
Stark & Hayward (2007)
GEO accession no.
GSE19189
GSE18799
GSE19177
GSE11976
GSE10506
GSE13282
GSE16019
GSE21168
GSE9003
Tissue type
Bladder
Breast
Breast
Breast
Esophagus
Kidney
Kidney
Lung
Melanoma
Sample size
20
21
34
HCC1395 cell line
23 adenocarcinoma
21 ccRCC
80 paired ccRCC
4 lung SCC
76 melanoma cell lines
Illumina array
HumanCNV370-QuadV3_C
HumanHap300-Duov2
HumanCNV370-Duov1
HumanCNV370-Duov1
HumanHap300
HumanHap550-Duov3
HumanHap300-Duov2
HumanOmni1-Quad
HumanHap300
27
Williams’-corrected G-tests were carried out using Microsoft Excel
(http://www.microsoft.com) software as per McDonald (2009). G-test degrees of freedom
were 1 and 2 respectively for allele and genotype frequency testing. Williams’ corrected
G values were compared against a chi-square distribution table (Mann, 2004) and
thresholds of 3.841 or greater and 5.991 or greater were selected for 1 and 2 degrees of
freedom, respectively.
28
RESULTS
Differential expression of galectins in Oncomine cancers
In order to find possible cancer types for galectin promoter SNP association
analysis, the Oncomine database was screened for differential expression of galectins 1-4
and 7-10 in multiple cancers in comparison with normal tissue. At a significance
threshold consisting of a difference in expression in excess of 0.9 log-normalized
expression units, twenty-one cancers of interest (Table 2) were found, some of which
were genotyped at studies archived at the NCBI Gene Expression Omnibus.
29
Table 2. Differential expression of galectins in nonlymphatic tissue (cancer vs. normal), from
Oncomine. Galectin-3 is excluded here due to lack of LGALS3 promoter SNPs on most Illumina BeadChip
arrays.
Malignancy
LGALS1
bladder
brain, glioblastoma
up
brain, glioblastoma multiforme
brain, glioma
brain, oligodendroglioma
down
endometrium
down
esophagus
head-neck
up
kidney
up
liver
lung adenocarcinoma
lung SCC
melanoma
up
mesothelioma
ovarian
pancreas, adenocarcinoma
pancreas, ductal carcinoma
prostate
salivary
seminoma/testis
tongue SCC
LGALS2
LGALS4
LGALS7
LGALS8
up
LGALS9
CLC
up
down
down
up
up
down
down
down
down
up
down
up
down
down
up
up
down
up
up
up
down
up
down
down
up
up
30
Oncomine data from Table 2 is from individual studies as follows:
Bladder: Dyrskjøt et al., 2004
Brain, glioblastoma: Bredel et al., 2005
Brain, glioblastoma multiforme: Liang et al., 2005; Shai et al., 2003
Brain, glioma: Rickman et al., 2001
Brain, oligodendroglioma: Bredel et al., 2005
Endometrium: Mutter et al., 2001
Esophagus: Wang et al., 2006
Head-neck: Chung et al., 2004; Ginos et al., 2004; Talbot et al., 2005; Toruner et al., 2004
Kidney: Lenburg et al., 2003
Liver: Chen et al., 2002
Lung adenocarcinoma: Garber et al., 2001
Lung squamous-cell carcinoma (SCC): Garber et al., 2001; Wachi et al., 2005
Melanoma: Haqq et al., 2005; Talantov et al., 2005
Mesothelioma: Gordon et al., 2005
Ovarian cancer: Hendrix et al., 2006
Pancreas, adenocarcinoma: Iacobuzio-Donahue et al., 2003
Pancreas, ductal carcinoma: Ishikawa et al., 2005
Prostate: Varambally et al., 2005
Salivary gland: Frierson et al., 2002
Seminoma/testis: Korkola et al., 2006; Skotheim et al., 2005; Sperger et al., 2003
Tongue squamous-cell carcinoma (SCC): Talbot et al., 2005
31
In general the data obtained from the Oncomine differential expression analyses
was consistent with previous reports, although in some cases the degree of expression
difference did not entirely match what had been published in previous studies
(Valenzuela et al., 2007). Significant overexpression of galectin-4 was noted in cancers
of the liver (Chen et al., 2002) and esophagus, although galectin-7 was greatly
underexpressed in esophageal adenocarcinoma (Wang et al., 2006). It should be noted
that using the p-value threshold of 1E-2 may have resulted in the elimination of some
studies, although this threshold had been altered from an earlier, even more stringent,
threshold of 1E-4.
Promoter SNPs located on HapMap database
In total, seven galectin promoter SNPs were located on the HapMap Phase 1
database which are also present in the commonly used high-resolution Illumina
BeadArray (Table 3). The presence of each selected SNP was confirmed using the UCSC
Genome Browser (http://genome.ucsc.edu). One CLC (galectin-10) promoter
polymorphism (rs428007) was found not to be in Hardy-Weinberg equilibrium in the
HapMap Phase 1 aggregated population; however, due to previously mentioned
population stratification, this is not considered to be significant.
32
Table 3. Illumina BeadArray SNPs in upstream promoter regions of galectin genes.
Gene
LGALS1
LGALS1
LGALS2
LGALS4
LGALS8 (long)
LGALS9
CLC/LGALS10
SNP
rs4820294
rs929039
rs2235338
rs10403583
rs10489789
rs3763959
rs428007
Location
chr22:36400989
chr22:36401457
chr22:36295826
chr19:43983611
chr1:234746800
chr17:22981461
chr19:44913207
Alleles (ref/other)
G/A
T/C
G/A
A/G
G/A
A/G
C/T
33
Multiple predicted transcription factor binding sites
The default significance thresholds were used for each search platform; this was
85% for all except Consite which was set to 80%. Only four SNPs showed putative sites
that were predicted by two or more programs (Table 4).
34
Table 4. Predicted transcription factor binding sites overlapping with SNPs (both reference and
nonreference alleles) in upstream promoter regions of galectin genes. Only SNPs with sites predicted by
multiple platforms are shown.
Gene
SNP
FSNP/TFSearch
(TRANSFAC
3.3)
LGALS1
rs929039
ref:cap, c-Myb
LGALS2
rs2235338
other:c-Myb
LGALS9
rs3763959
CLC
rs428007
Consite
TESS/TRANSFAC
6.0
TESS/IMD
Sites
predicted
by three
platforms
(no TF
changes)
(no TF
changes)
ref:c-Myb; other:cMyb
other:c-Myb
(none)
other:cMyb
other:cMyb
other:E2F
other:E2F
(no TF changes
found)
(none)
other:E2F
ref:C/EBPb;
other:C/EBPb
ref:USF,
Max;
other:Thing1E47
ref:Lmo2, GATA-3,
GATA-2, AREB6,
cap, E12, E47,
ITF-2, Tal-1;
other:HiNF-A, cap
ref:LBP-1,
CP2
other:LBP1, CP2, cMyb
ref:CACbinding;
other:E2FDRTF
ref:Tal-1,
RFX2, NFX3, NFuE4, E2A,
E12, LBP1;
other:MBF1
Sites
predicted
by two or
more
platforms
ref: c-Myb
(none)
ref:Tal-1,
E12
35
Putative methylation sites coinciding with galectin promoter SNPs
No unusually GC-rich regions were found using the default settings (55%GC,
0.65 obs/exp, 500 bp length, 100bp gap for CpG Island Searcher; 100bp window, 0.6
obs/exp ratio, 50% GC, 200bp length for CpG Plot; 50%GC, 0.6 obs/exp, 500bp length
for CpG Island Explorer) on any of the programs, although this was not unexpected as the
UCSC genome browser (http://genome.ucsc.edu) did not list any CpG islands at any of
the SNP sites. However, when the applications were run again using the least stringent
settings (50%GC, 0.6 obs/exp, 200 bp length, 100bp gap for CpG Island Searcher; 100bp
window, 0.6 obs/exp ratio, 30 min % GC, 50bp length for CpG Plot; 50%GC, 0.6 obs/exp
200bp length for CpG Island Explorer), a number of putative islands were found at or
near SNP locations (Table 5). In addition, most SNPs with the exception of rs10403583
and rs428007 were found to overlap individual CpG sites.
36
Table 5. Individual CpG sites and putative CpG islands at SNP locations.
Gene
LGALS1
LGALS1
LGALS2
LGALS4
LGALS8
(long)
LGALS9
CLC/LGA
LS10
SNP
rs4820
294
rs9290
39
rs2235
338
rs1040
3583
rs1048
9789
rs3763
959
rs4280
07
CpG site at
SNP location
Putative islands, CpG
Island Searcher
Putative islands, CpG
Island Explorer
(none)
Putative islands,
CpG Plot
other
(downstream)
ref, other
(downstream)
ref
other (downstream)
other
ref
(none)
ref
(none)
(none)
(none)
ref, other
(none)
ref
(none)
(none)
(none)
other
(none)
other
(none)
(none)
(none)
(none)
(none)
other
(none)
37
Significant genotype and allele frequency differences uncovered
Association analysis using Williams’ corrected G-test revealed significant
differences in both genotype (Appendix B, 1 to 7) and allele (Appendix C, 1 to 7)
frequencies at galectin promoter SNP locations between individual GEO cancer datasets
and both individual and aggregated HapMap controls. No association analysis could be
conducted on the Staaf (2008) breast dataset GSE11976 as it consisted only of the
HCC1395 breast carcinoma line. The Castillo (2010) lung dataset, GSE21168, only
included four tumor samples and used a slightly different Illumina array that excluded
some of the investigated promoter polymorphisms; only rs2235338 (LGALS2),
rs3763959 (LGALS9), rs4820294 (LGALS1), and rs10403583 (LGALS4) were included;
therefore results obtained from statistical analysis of GSE21168 are presumed to be less
robust than those involving other GEO datasets.
Most of these associations between promoter SNPs and cancers are probably
spurious, despite their apparent statistical significance. The majority of putative
associations occur in comparisons using either non-European HapMap populations or the
aggregated HapMap Phase 1 poulation as control groups. For galectin promoter SNPs,
there are significant differences in allele and genotype frequencies between different
HapMap populations (see Appendices B and C), and it has been shown that differences in
allele frequencies between even populations in relatively close geographical proximity
(Sedlacek et al., 2007) can result in difficulties extrapolating the results of association
studies from one population to another. The breast cell line used in the GSE11976 (Staaf
et al., 2008) study was derived from a female of European descent, and the GSE16019
38
(Chen M et al., 2009) dataset samples were derived from patients of mostly white and
Hispanic ancestry. The other GEO cancer datasets do not explicitly list the ethnic
background of the study participants; however, as most of the studies (GSE21168:
Castillo et al., 2010; GSE13282: Gordan et al., 2008; GSE19189: Letouze et al., 2010;
GSE10506: Nancarrow et al., 2008; GSE18799: Popova et al., 2009) involved hospital
patients in regions with a predominantly European-descended demographic majority, it
can be inferred that the HapMap CEU population is a better control for these datasets
than either the non-European populations or the aggregated HapMap population.
When non-CEU controls are excluded, five SNPs show significant differences in
genotype and/or allele frequency in bladder carcinoma (GSE19189: Letouze et al., 2010),
breast carcinoma (GSE18799: Popova et al., 2009; GSE19177: Waddell et al., 2010),
esophageal adenocarcinoma (GSE10506: Nancarrow et al., 2008), and melanoma
(GSE9003: Stark & Hayward, 2007) datasets (Table 6).
39
Table 6. SNPs exhibiting significant genotype and/or allele frequency differences in study datasets
when compared to HapMap CEU population only.
SNP
rs4820294
(LGALS1)
rs2235338
(LGALS2)
rs10403583
(LGALS4)
rs10489789
(LGALS8)
rs3763959
(LGALS9)
Letouze
GSE19189
Nancarrow
GSE10506
(none)
Genotype only
(none)
(none)
(none)
Popova
GSE18799
Stark
GSE9003
Genotype
only
Waddell
GSE19177
Genotype and
allele
(none)
(none)
Allele only
(none)
Allele only
Genotype only
(none)
Genotype
only
Genotype and
allele
(none)
(none)
(none)
(none)
(none)
(none)
Genotype only
(none)
Genotype only
40
As previously mentioned, statistical testing could not be carried out on the
GSE11976 dataset (Staaf et al., 2008) due to the fact that it consisted of only one sample.
Nonetheless, SNP genotype data was extracted from this dataset (Appendix D).
The single LGALS9 upstream SNP analyzed, rs3763959, was found not to
conform to Hardy-Weinberg equilibrium expectations at a p-value threshold of 0.05 in
the single GSE9003 melanoma dataset (Stark & Hayward, 2007) or in the two breast
cancer datasets GSE18799 (Popova et al., 2009) and GSE19177 (Waddell et al., 2010)
downloaded from GEO. In the Staaf (2008) dataset, GSE11976, the genotype at
rs3763959 was G/G (Appendix D), further hinting at a link, although this dataset
consisted of only a single breast cancer cell line (HCC1395).
A number of additional SNPs [rs4820294 (LGALS1) in GSE10506 (Nancarrow et
al., 2008) and GSE9003 (Stark & Hayward, 2007), rs10403583 (LGALS4) in GSE9003
(Stark & Hayward, 2007)] were also not in Hardy-Weinberg equilibrium at p<0.05 in
some datasets (Appendix E, 1 to 7); however, of these galectins, only the putative
rs4820294/melanoma link showed any correlation with the Oncomine differential
expression data (Table 2). Interestingly, this SNP showed a significant genotype
frequency difference between the GSE9003 study population and the HapMap CEU
population (Table 6).
41
DISCUSSION
The above results indicate a statistically significant genotypic association of the
LGALS9 upstream polymorphism rs3763959 with breast carcinoma. This association is
observed in two different datasets, GSE18799 (Popova et al., 2009) and GSE19177
(Waddell et al., 2010). The distribution of this SNP is significantly skewed toward the
G/G genotype and does not conform to Hardy-Weinberg expectations in the two study
populations, although the allele frequencies do not significantly differ from the HapMap
CEU population in either dataset.
Significant deviation from Hardy-Weinberg equilibrium may be a sign of
consanguinity in a sampled population, or may indicate genotyping or procedural error
(Talseth et al., 2006). It is not likely that the observed deviation from expected HardyWeinberg distribution in the breast cancer datasets derives from genotyping error, since
this deviation occurs in two datasets from different studies, both of which utilized
Illumina BeadArray genotyping kits. Furthermore, the Popova et al. (2009) and Waddell
et al. (2010) patient populations were from different countries (France and Australia,
respectively, although fourteen Dutch and United States samples were included in the
Waddell study), decreasing the possibility of consanguinity. Therefore, this discrepancy
may be more likely to indicate an actual correlation (Gyorffy et al., 2003) between the
rs3763959 polymorphism and breast carcinoma, although other possibilities must not be
ruled out.
A statistically significant association of the LGALS1 upstream polymorphism
rs4820294 was also observed in connection with the GSE9003 (Stark & Hayward, 2007)
42
melanoma dataset, and it is worth noting that the rs4820294 SNP also does not conform
to Hardy-Weinberg expectations in this dataset. However, it should be cautioned that as
this dataset derives from a single study, genotyping or methodological errors are less
easily ruled out.
Galectin-9 and breast cancer
Galectin-9, or ecalectin, was at first identified as an eosinophil chemoattractant
(ECA) and activator (Matsumoto et al., 1998). This galectin has been described as having
anti-metastatic potential in breast cancer (Yamauchi et al., 2006), melanoma (Kageshita
et al., 2002), oral squamous cell carcinoma (Kasamatsu et al., 2005), and lung cancer
(Nobumoto et al., 2008). Inhibition of metastasis most likely occurs, at least in some
cancers, by galectin-9 competitively blocking adhesion to the extracellular matrix
(Nobumoto et al., 2008). Anti-proliferative activity has also been observed in multiple
myeloma cells via the JNK and p38 MAP kinase signaling pathways (Kobayashi et al.,
2010).
As previously mentioned, galectin-9 is considered a prognostic factor in breast
cancer development (Yamauchi et al., 2006; Nobumoto et al., 2008), although
interestingly none of the Oncomine datasets surveyed revealed significant galectin-9
expression differences between normal and cancerous breast tissue. In breast cancer,
increased expression of this galectin was inversely associated with distant metastasis (Irie
et al., 2005). However, in contrast to previous studies involving melanoma cells
(Kageshita et al., 2002); Irie et al. (2005) found no detectable amounts of galectin-9 on
43
the surface of breast cancer cells despite high cytoplasmic expression, suggesting that
antimetastatic functions of galectin-9 may involve different processes depending on
cancer type. As of this writing, the mechanisms of galectin-9 antimetastatic activity in
breast cancer have still not been elucidated.
E2F binding site at rs3763959
Multiple search platforms predict an E2F binding site for the nonreference G
allele of rs3763959. The E2F family consists of both transcription activators and
repressors with diverse functions in the regulation of cell proliferation and apoptosis
(Chen HZ et al., 2009). Paradoxically, increased expression of both the transcriptional
activator E2F1 (Han et al., 2003) and the transcriptional inhibitor E2F4 (Rakha et al.,
2004) has been associated with poor prognosis in metastatic breast cancer. To explain this
discrepancy, it was suggested that normally antagonistic E2F proteins could compensate
for each other in the formation of complexes with members of the pocket protein family,
which are known to directly associate with E2Fs and regulate their activity (Chen HZ et
al., 2009). Given the wide range of roles played by the many E2F family proteins, any
speculation of a link between the putative E2F binding site at the G allele of rs3763959
and specific E2F family members is well beyond the scope of this study.
Methylation at rs3763959
The nonreference G allele of rs3763959 coincides with an individual CpG site.
Although it may be tempting to speculate that a link exists between this CpG site and the
44
possible association of the G/G genotype with breast carcinoma, there is no direct
evidence to support such a conclusion.
Furthermore, as mentioned previously (Table 5), either the reference or
nonreference allele of the majority of SNPs analyzed in this study overlap CpG sites.
Sixty to seventy percent of CpG dinucleotides are methylated in the genomes of
mammalian somatic cells, and most unmethylated CpG sites are not isolated but rather
are located in CpG islands in promoters of actively transcribed housekeeping genes (Hartl
& Jones, 2009). Although it is possible that the aforementioned CpG sites are methylated,
without solid data regarding the methylation status of individual CpG sites in all
individuals genotyped in both the GEO datasets and the HapMap populations, it is
premature to suggest the possibility that an altered methylation state in the G allele may
affect expression, especially as there is no evidence from Oncomine database of
differential expression of galectin-9.
Conclusions and future directions
It should be cautioned that, because of the extremely divergent functions played
by galectins in processes affecting cancer development, it would be unwise and
premature to speculate on any individual explanation for the observations reported here.
As correlation does not equate to causation, the role of factors other than the specific
SNPs analyzed here cannot entirely be ruled out. It is concievable that other
polymorphisms which had not been included in the HapMap database, but were located
in the vicinity of the analyzed HapMap SNPs (and hence may be inherited together with
45
them due to linkage effects), might actually contribute to expression regulation itself.
Furthermore, both the Popova (2009) and Waddell (2010) patient populations consisted
of fewer than fifty individuals. A study done on a larger scale could alleviate this issue
(Manolio, 2010).
Though an entirely in silico strategy for SNP association analysis may seem
somewhat theoretical, this project utilized procedures commonly used in patient studies
(Viviani Anselmi et al., 2008) and used datasets derived from previous human studies,
thereby obviating the cost and ethical concerns associated with large-scale patient data
collection. However, the results of this study should be considered somewhat preliminary
rather than conclusive, and must be confirmed by further research.
A case-comparison study involving larger numbers of subjects and controls could
be performed to further confirm the associations postulated by this project. This would
have several advantages in that the use of higher-resolution arrays could locate promoter
SNPS not listed in the HapMap database but which may possibly affect transcription
regulation; moreover, high-throughput array-based methylation assays such as the
Illumina Infinium array (http://www.illumina.com) are now available which can
determine the methylation status of individual CpG sites. Fine-scale genetic mapping of
galectin promoters could also provide a better picture of genetic linkage in these regions.
Nevertheless, when planning any future studies, the potential benefits must be balanced
by taking into consideration both the high cost of labor and/or equipment associated with
genotyping studies and the increasing privacy concerns of patients involved in human
genetic research.
46
In summary, it is reported here that through the analysis of public-domain data, a
possible genotypic association between the LGALS9 upstream polymorphism rs3763959
and breast carcinoma has been uncovered, although further research must be undertaken
to confirm the relevance of this finding.
47
APPENDICES
48
APPENDIX A
Oncomine results
1. List of individual Oncomine datasets cited
Bhattacharjee_Lung: Bhattacharjee et al., 2001
Boer_Renal: Boer et al., 2001
Bredel_Brain_2: Bredel et al., 2005
Buchholz_Pancreas: Buchholz et al., 2005
Chen_Liver: Chen et al., 2002
Chung_Head-Neck: Chung et al., 2004
Cromer_Head-Neck: Cromer et al., 2004
Dhanasekaran_Prostate: Dhanasekaran et al., 2001
Dyrskjot_Bladder_3: Dyrskjøt et al., 2004
French_Brain: French et al., 2005
FriersonHF_Salivary-gland: Frierson et al., 2002
Garber_Lung: Garber et al., 2001
Ginos_Head-Neck: Ginos et al., 2004
Gordon_Mesothelioma: Gordon et al., 2005
Graudens_Colon: Graudens et al., 2006
Haqq_Melanoma: Haqq et al., 2005
Hendrix_Ovarian: Hendrix et al., 2006
Hoek_Melanoma: Hoek et al., 2006
Huang_Thyroid: Huang et al., 2001
49
Iacobuzio-Donahue_Pancreas: Iacobuzio-Donahue et al., 2003
Iacobuzio-Donahue_Pancreas_2: Iacobuzio-Donahue et al., 2003
Ishikawa_Pancreas: Ishikawa et al., 2005
Korkola_Seminoma: Korkola et al., 2006
Lancaster_Ovarian: Lancaster et al., 2004
Lapointe_Prostate: Lapointe et al., 2004
Lenburg_Renal: Lenburg et al., 2003
Liang_Brain: Liang et al., 2005
Logsdon_Pancreas: Logsdon et al., 2003
Luo_Prostate: Luo et al., 2001
Luo_Prostate_2: Luo et al., 2002
Mutter_Endometrium: Mutter et al., 2001
Quade_Uterus: Quade et al., 2004
Richardson_Breast_2: Richardson et al., 2006
Rickman_Brain: Rickman et al., 2001
Sanchez-Carbayo_Bladder_2: Sanchez-Carbayo et al., 2006
Shai_Brain: Shai et al., 2003
Skotheim_Multi-cancer: Skotheim et al., 2005
Sperger_Others: Sperger et al., 2003
Sun_Brain: Sun et al., 2006
Talantov_Melanoma: Talantov et al., 2005
Talbot_Lung: Talbot et al., 2005
50
Tomlins_Prostate: Tomlins et al., 2007
Toruner_Head-Neck: Toruner et al., 2004
Varambally_Prostate: Varambally et al., 2005
Wachi_Lung: Wachi et al., 2005
Wang_Esophagus: Wang et al., 2006
Welsh_Ovarian: Welsh et al., 2001a
Welsh_Prostate: Welsh et al., 2001b
Yu_Prostate: Yu et al., 2004
51
2. Normal vs. cancer, LGALS1 nonlymphoid/noncolon
a. Expression differences, p-value threshold of 1E-2
Figure A2.1a. LGALS1 expression in nonlymphoid, noncolon malignancies (higher expression in normal
tissue). For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the
paired bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is
organized in descending order based on median expression in cancer.
52
Figure A2.1b. LGALS1 expression in nonlymphoid, noncolon malignancies (higher expression in cancer).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
53
b. Individual studies, expression difference significant at 0.9 log-normalized expression units
Figure A2.2a. LGALS1expression differences in oligodendroma, normal vs. cancer (Bredel et al., 2005),
from Oncomine (p = 7.40E-04). Thick bars indicate median expression value; error bars show 10th and
90th percentiles.
54
Figure A2.2b. LGALS1 expression differences in head/neck, normal vs. cancer (Chung et al., 2004), from
Oncomine (p = 3.1E-10). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
55
Figure A2.2c. LGALS1 expression differences in kidney, normal vs. cancer (Lenburg et al., 2003), from
Oncomine (p = 4.1E-5). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
56
Figure A2.2d. LGALS1 expression differences in endometrium, normal vs. cancer (Mutter et al., 2001),
from Oncomine (p = 6.1E-4). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
57
Figure A2.2c. LGALS1 expression differences in melanoma, normal vs. cancer (Talantov et al., 2005),
from Oncomine (p = 2.4E-10). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
58
Figure A2.2d. LGALS1 expression differences in head-neck, normal vs. cancer (Toruner et al., 2004), from
Oncomine (p = 3.0E-3). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
59
3. Normal vs. cancer, LGALS2 nonlymphoid/noncolon
a. Expression differences, p-value threshold of 1E-2
Figure A3.1a. LGALS2 expression in nonlymphoid, noncolon malignancies (higher expression in normal).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
60
Figure A3.1b. LGALS2 expression in nonlymphoid, noncolon malignancies (higher expression in cancer).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
61
b. Individual studies, expression difference significant at 0.9 log-normalized expression units
Figure A3.2a. LGALSx expression differences in pancreatic ductal carcinoma, normal vs. cancer (Ishikawa
et al., 2005), from Oncomine (p = 3.0E-3). Thick bars indicate median expression value; error bars show
10th and 90th percentiles.
62
4. Normal vs. cancer, LGALS3 nonlymphoid/noncolon
a. Expression differences, p-value threshold of 1E-2
Figure A4.1a. LGALS3 expression in nonlymphoid, noncolon malignancies (higher expression in normal).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
63
Figure A4.1b. LGALS3 expression in nonlymphoid, noncolon malignancies (higher expression in cancer).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
64
b. Individual studies, expression difference significant at 0.9 log-normalized expression units
Figure A4.2a. LGALS3 expression differences in lung, normal vs. cancer (Bhattacharjee et al., 2001), from
Oncomine (p = 3.0E-10). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
65
Figure A4.2b. LGALS3 expression differences in glioblastoma, normal vs. cancer (Bredel et al., 2005),
from Oncomine (p = 3.8E-8). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
66
Figure A4.2c. LGALS3 expression differences in small cell lung cancer, normal vs. cancer (Garber et al.,
2001), from Oncomine (p = 2.0E-3). Thick bars indicate median expression value; error bars show 10th and
90th percentiles.
67
Figure A4.2d. LGALS3 expression differences in melanoma, normal vs. cancer (Haqq et al., 2005), from
Oncomine (p = 2.3E-5). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
68
Figure A4.2e. LGALS3 expression differences in pancreatic adenocarcinoma, normal vs. cancer
(Iacobuzio-Donahue et al., 2003), from Oncomine (p = 3.6E-6). Thick bars indicate median expression
value; error bars show 10th and 90th percentiles.
69
Figure A4.2f. LGALS3 expression differences in prostate, normal vs. cancer (Lapointe et al., 2004), from
Oncomine (p = 6.3E-10). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
70
Figure A4.2g. LGALS3 expression differences in prostate, normal vs. cancer (Luo et al., 2001), from
Oncomine (p = 1.0E-3). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
71
5. Normal vs. cancer, LGALS4 nonlymphoid/noncolon
a. Expression differences, p-value threshold of 1E-2
Figure A5.1a. LGALS4 expression in nonlymphoid, noncolon malignancies (higher expression in normal).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
72
Figure A5.1b. LGALS4 expression in nonlymphoid, noncolon malignancies (higher expression in cancer).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
73
b. Individual studies, expression difference significant at 0.9 log-normalized expression units
Figure A5.2a. LGALS4 expression differences in liver, normal vs. cancer (Chen et al., 2002), from
Oncomine (p = 3.00E-5). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
74
Figure A5.2b. LGALS4 expression differences in lung adenocarcinoma, normal vs. cancer (Garber et al.,
2001), from Oncomine (p = 6.00E-03). Thick bars indicate median expression value; error bars show 10th
and 90th percentiles.
75
Figure A5.2c. LGALSx expression differences in lung squamous cell carcinoma, normal vs. cancer (Garber
et al., 2001), from Oncomine (p = 2.00E-03). Thick bars indicate median expression value; error bars show
10th and 90th percentiles.
76
Figure A5.2d. LGALS4 expression differences in ovarian, normal vs. cancer (Hendrix et al., 2006), from
Oncomine (p = 4.00E-04). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
77
Figure A5.2e. LGALS4 expression differences in pancreatic adenocarcinoma, normal vs. cancer
(Iacobuzio-Donahue et al., 2003), from Oncomine (p = 1.30E-04). Thick bars indicate median expression
value; error bars show 10th and 90th percentiles.
78
Figure A5.2f. LGALS4 expression differences in glioblastoma multiforme, normal vs. cancer (Liang et al.,
2005), from Oncomine (p = 2.00E-3). Thick bars indicate median expression value; error bars show 10th
and 90th percentiles.
79
Figure A5.2g. LGALS4 expression differences in esophagus, normal vs. cancer (Wang et al., 2006), from
Oncomine (p = 5.00E-3). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
80
6. Normal vs. cancer, LGALS7 nonlymphoid/noncolon
a. Expression differences, p-value threshold of 1E-2
Figure A6.1a. LGALS7 expression in nonlymphoid, noncolon malignancies (higher expression in normal).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
81
Figure A6.1b. LGALS7 expression in nonlymphoid, noncolon malignancies (higher expression in cancer).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
82
b. Individual studies, expression difference significant at 0.9 log-normalized expression units
Figure A6.2a. LGALS7 expression differences in lung squamous cell carcinoma, normal vs. cancer (Garber
et al., 2001), from Oncomine (p = 5.00E-03). Thick bars indicate median expression value; error bars show
10th and 90th percentiles.
83
Figure A6.2b. LGALS7 expression differences in melanoma, normal vs. cancer (Haqq et al., 2005), from
Oncomine (p = 7.80E-06). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
84
Figure A6.2c. LGALS7 expression differences in melanoma, normal vs. cancer (Talantov et al., 2005),
from Oncomine (p = 1.10E-19). Thick bars indicate median expression value; error bars show 10th and
90th percentiles.
85
Figure A6.2d. LGALS7 expression differences in tongue squamous cell carcinoma, normal vs. cancer
(Talbot et al., 2005), from Oncomine (p = 2.80E-08). Thick bars indicate median expression value; error
bars show 10th and 90th percentiles. Note that this tongue tissue data was included as part of a lung study.
86
Figure A6.2e. LGALS7 expression differences in head-neck, normal vs. cancer (Toruner et al., 2004), from
Oncomine (p = 5.00E-03). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
87
Figure A6.2f. LGALS7 expression differences in prostate, normal vs. cancer (Varambally et al., 2005),
from Oncomine (p = 3.00E-03). Thick bars indicate median expression value; error bars show 10th and
90th percentiles.
88
Figure A6.2g. LGALS7 expression differences in lung squamous cell carcinoma, normal vs. cancer (Wachi
et al., 2005), from Oncomine (p = 1.00E-02). Thick bars indicate median expression value; error bars show
10th and 90th percentiles.
89
Figure A6.2h. LGALS7 expression differences in esophagus, normal vs. cancer (Wang et al., 2006), from
Oncomine (p = 2.30E-09). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
90
7. Normal vs. cancer, LGALS8 nonlymphoid/noncolon
a. Expression differences, p-value threshold of 1E-2
Figure A7.1a. LGALS8 expression in nonlymphoid, noncolon malignancies (higher expression in normal).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
91
Figure A7.1b. LGALS8 expression in nonlymphoid, noncolon malignancies (higher expression in cancer).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
92
b. Individual studies, expression difference significant at 0.9 log-normalized expression units
Figure A7.2a. LGALS8 expression differences in bladder, normal vs. cancer (Dyrskjøt et al., 2004), from
Oncomine (p = 4.1E-9). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
93
Figure A7.2b. LGALS8 expression differences in mesothelioma, normal vs. cancer (Gordon et al., 2005),
from Oncomine (p = 8.0E-3). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
94
Figure A7.2c. LGALS8 expression differences in ovarian, normal vs. cancer (Hendrix et al., 2006), from
Oncomine (p = 1.2E-10). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
95
Figure A7.2d. LGALS8 expression differences in testis, normal vs. cancer (Korkola et al., 2006), from
Oncomine (p = 7.6E-9). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
96
Figure A7.2e. LGALS8 expression differences in testis, normal vs. cancer (Skotheim et al., 2005), from
Oncomine (p = 4.0E-3). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
97
Figure A7.2f. LGALS8 expression differences in head-neck, normal vs. cancer (Toruner et al., 2004), from
Oncomine (p = 3E-3). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
98
Figure A7.2g. LGALS8 expression differences in lung squamous cell carcinoma, normal vs. cancer (Wachi
et al., 2005), from Oncomine (p = 6.4E-4). Thick bars indicate median expression value; error bars show
10th and 90th percentiles.
99
8. Normal vs. cancer, LGALS9 nonlymphoid/noncolon
a. Expression differences, p-value threshold of 1E-2
Figure A8.1a. LGALS9 expression in nonlymphoid, noncolon malignancies (higher expression in normal).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
100
Figure A8.1b. LGALS9 expression in nonlymphoid, noncolon malignancies (higher expression in cancer).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
101
b. Individual studies, expression difference significant at 0.9 log-normalized expression units
Figure A8.2a. LGALS9 expression differences in glioblastoma, normal vs. cancer (Bredel et al., 2005),
from Oncomine (p = 5.9E-4). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
102
Figure A8.2b. LGALS9 expression differences in pancreatic adenocarcinoma, normal vs. cancer
(Iacobuzio-Donahue et al., 2003), from Oncomine (p = 1.9E-7). Thick bars indicate median expression
value; error bars show 10th and 90th percentiles.
103
Figure A8.2c. LGALSx expression differences in testis, normal vs. cancer (Sperger et al., 2003), from
Oncomine (p = 3.3E-4). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
104
9. Normal vs. cancer, CLC/LGALS10 nonlymphoid/noncolon
a. Expression differences, p-value threshold of 1E-2
Figure A9.1a. LGALS10 expression in nonlymphoid, noncolon malignancies (higher expression in
normal). For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the
paired bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is
organized in descending order based on median expression in cancer.
105
Figure 9.1b. LGALS10 expression in nonlymphoid, noncolon malignancies (higher expression in cancer).
For each paired Oncomine dataset, median expression in normal tissue is shown by the left of the paired
bars, and median expression in cancerous tissue is shown by the right of the paired bars. Data is organized
in descending order based on median expression in cancer.
106
b. Individual studies, expression difference significant at 0.9 log-normalized expression units
Figure A9.2a. LGALS10 expression differences in salivary, normal vs. cancer (Frierson et al., 2002), from
Oncomine (p = 2.8E-4). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
107
Figure A9.2b. LGALS10 expression differences in lung adenocarcinoma, normal vs. cancer (Garber et al.,
2001), from Oncomine (p = 3.00E-13). Thick bars indicate median expression value; error bars show 10th
and 90th percentiles.
108
Figure A9.2c. LGALS10 expression differences in lung squamous cell carcinoma, normal vs. cancer
(Garber et al., 2001), from Oncomine (p = 2.10E-07). Thick bars indicate median expression value; error
bars show 10th and 90th percentiles.
109
Figure A9.2d. LGALS10 expression differences in glioma, normal vs. cancer (Rickman et al., 2001), from
Oncomine (p = 4E-3). Thick bars indicate median expression value; error bars show 10th and 90th
percentiles.
110
APPENDIX B
Association analysis, genotype frequencies
For each SNP, genotype frequencies were compared between datasets archived at
the Gene Expression Omnibus (noted here by GEO accession number and associated
research article, eg. GSE16019: Chen M et al. (2009)) and both individual and aggregated
HapMap Phase 1 populations as controls, with Williams’ corrected G-test for
independence used to determine association. Statistical relevance of individual
association analyses is shown in each table below.
111
1. rs428007 (CLC/LGALS10)
a. GSE16019: Chen M et al. (2009)
Genotype
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
T/T: 18/77 = 0.234
T/C: 30/77 = 0.390
C/C: 29/77 = 0.377
HapMap Phase 1
(aggr.)
T/T: 79/395 = 0.200
T/C: 166/395 = 0.420
C/C: 150/395 = 0.380
GSE16019 vs.
HapMap aggr.
18
30
29
79
166
150
18
30
29
11
46
55
18
30
29
32
42
10
18
30
29
33
44
9
1
15.138
5.162E-04
17.487
1.595E-04
GSE16019 vs.
HapMap YRI
HapMap YRI
T/T: 3/113 = 0.027
T/C: 34/113 = 0.301
C/C: 76/113 = 0.673
3.567E-02
GSE16019 vs.
HapMap JPT
HapMap JPT
T/T: 33/86 = 0.384
T/C: 44/86 = 0.512
C/C: 9/86 = 0.105
6.667
GSE16019 vs.
HapMap CHB
HapMap CHB
T/T: 32/84 = 0.381
T/C: 42/84 = 0.500
C/C: 10/84 = 0.119
7.827E-01
GSE16019 vs.
HapMap CEU
HapMap CEU
T/T: 11/112 = 0.098
T/C: 46/112 = 0.411
C/C: 55/112 = 0.491
0.49
18
30
29
G and p-values are Williams’-corrected
3
34
76
26.608
1.668E-06
112
b. GSE13282: Gordan et al. (2008)
Genotype
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
T/T: 1/21 = 0.048
T/C: 12/21 = 0.571
C/C: 8/21 = 0..381
HapMap Phase 1
(aggr.)
T/T: 79/395 = 0.200
T/C: 166/395 = 0.420
C/C: 150/395 = 0.380
GSE13282 vs.
HapMap aggr.
1
12
8
79
166
150
1
12
8
11
46
55
1
12
8
32
42
10
1
12
8
33
44
9
1
13.618
1.104E-03
14.615
6.705E-04
GSE13282 vs.
HapMap YRI
HapMap YRI
T/T: 3/113 = 0.027
T/C: 34/113 = 0.301
C/C: 76/113 = 0.673
3.819E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
T/T: 33/86 = 0.384
T/C: 44/86 = 0.512
C/C: 9/86 = 0.105
1.925
GSE13282 vs.
HapMap CHB
HapMap CHB
T/T: 32/84 = 0.381
T/C: 42/84 = 0.500
C/C: 10/84 = 0.119
1.213E-01
GSE13282 vs.
HapMap CEU
HapMap CEU
T/T: 11/112 = 0.098
T/C: 46/112 = 0.411
C/C: 55/112 = 0.491
4.219
1
12
8
G and p-values are Williams’-corrected
3
34
76
5.406
6.700E-02
113
c. GSE19189: Letouze et al. (2010)
Genotype
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
T/T: 1/20 = 0.050
T/C: 11/20 = 0.550
C/C: 8/20 = 0.400
HapMap Phase 1
(aggr.)
T/T: 79/395 = 0.200
T/C: 166/395 = 0.420
C/C: 150/395 = 0.380
GSE19189 vs.
HapMap aggr.
1
11
8
79
166
150
1
11
8
11
46
55
1
11
8
32
42
10
1
11
8
33
44
9
1
13.422
1.217E-03
14.451
7.278E-04
GSE19189 vs.
HapMap YRI
HapMap YRI
T/T: 3/113 = 0.027
T/C: 34/113 = 0.301
C/C: 76/113 = 0.673
4.875E-01
GSE19189 vs.
HapMap JPT
HapMap JPT
T/T: 33/86 = 0.384
T/C: 44/86 = 0.512
C/C: 9/86 = 0.105
1.437
GSE19189 vs.
HapMap CHB
HapMap CHB
T/T: 32/84 = 0.381
T/C: 42/84 = 0.500
C/C: 10/84 = 0.119
1.582E-01
GSE19189 vs.
HapMap CEU
HapMap CEU
T/T: 11/112 = 0.098
T/C: 46/112 = 0.411
C/C: 55/112 = 0.491
3.688
1
11
8
G and p-values are Williams’-corrected
3
34
76
4.516
1.046E-01
114
d. GSE10506: Nancarrow et al. (2008)
Genotype
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
T/T: 2/20 = 0.100
T/C: 11/20 = 0.550
C/C: 7/20 = 0.350
HapMap Phase 1
(aggr.)
T/T: 79/395 = 0.200
T/C: 166/395 = 0.420
C/C: 150/395 = 0.380
GSE10506 vs.
HapMap aggr.
2
11
7
79
166
150
2
11
7
11
46
55
2
11
7
32
42
10
2
11
7
33
44
9
1
9.046
1.086E-02
9.909
7.052E-03
GSE10506 vs.
HapMap YRI
HapMap YRI
T/T: 3/113 = 0.027
T/C: 34/113 = 0.301
C/C: 76/113 = 0.673
4.946E-01
GSE10506 vs.
HapMap JPT
HapMap JPT
T/T: 33/86 = 0.384
T/C: 44/86 = 0.512
C/C: 9/86 = 0.105
1.408
GSE10506 vs.
HapMap CHB
HapMap CHB
T/T: 32/84 = 0.381
T/C: 42/84 = 0.500
C/C: 10/84 = 0.119
3.975E-01
GSE10506 vs.
HapMap CEU
HapMap CEU
T/T: 11/112 = 0.098
T/C: 46/112 = 0.411
C/C: 55/112 = 0.491
1.845
2
11
7
G and p-values are Williams’-corrected
3
34
76
6.924
3.137E-02
115
e. GSE18799: Popova et al. (2009)
Genotype
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
T/T: 3/21 = 0.143
T/C: 9/21 = 0.429
C/C: 9/21 = 0.429
HapMap Phase 1
(aggr.)
T/T: 79/395 = 0.200
T/C: 166/395 = 0.420
C/C: 150/395 = 0.380
GSE18799 vs.
HapMap aggr.
3
9
9
79
166
150
3
9
9
11
46
55
3
9
9
32
42
10
3
9
9
33
44
9
1
10.376
5.583E-03
11.591
3.041E-03
GSE18799 vs.
HapMap YRI
HapMap YRI
T/T: 3/113 = 0.027
T/C: 34/113 = 0.301
C/C: 76/113 = 0.673
8.005E-01
GSE18799 vs.
HapMap JPT
HapMap JPT
T/T: 33/86 = 0.384
T/C: 44/86 = 0.512
C/C: 9/86 = 0.105
0.445
GSE18799 vs.
HapMap CHB
HapMap CHB
T/T: 32/84 = 0.381
T/C: 42/84 = 0.500
C/C: 10/84 = 0.119
7.906E-01
GSE18799 vs.
HapMap CEU
HapMap CEU
T/T: 11/112 = 0.098
T/C: 46/112 = 0.411
C/C: 55/112 = 0.491
0.47
3
9
9
G and p-values are Williams’-corrected
3
34
76
5.87
5.313E-02
116
f. GSE9003: Stark & Hayward (2007)
Genotype
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
T/T: 7/73 = 0.096
T/C: 25/73 = 0.342
C/C: 41/73 = 0.562
HapMap Phase 1
(aggr.)
T/T: 79/395 = 0.200
T/C: 166/395 = 0.420
C/C: 150/395 = 0.380
GSE9003 vs.
HapMap aggr.
7
25
41
79
166
150
7
25
41
11
46
55
7
25
41
32
42
10
7
25
41
33
44
9
1
40.617
1.514E-09
44.172
2.560E-10
GSE9003 vs.
HapMap YRI
HapMap YRI
T/T: 3/113 = 0.027
T/C: 34/113 = 0.301
C/C: 76/113 = 0.673
6.225E-01
GSE9003 vs.
HapMap JPT
HapMap JPT
T/T: 33/86 = 0.384
T/C: 44/86 = 0.512
C/C: 9/86 = 0.105
0.948
GSE9003 vs.
HapMap CHB
HapMap CHB
T/T: 32/84 = 0.381
T/C: 42/84 = 0.500
C/C: 10/84 = 0.119
7.860E-03
GSE9003 vs.
HapMap CEU
HapMap CEU
T/T: 11/112 = 0.098
T/C: 46/112 = 0.411
C/C: 55/112 = 0.491
9.692
7
25
41
G and p-values are Williams’-corrected
3
34
76
4.832
8.928E-02
117
g. GSE19177: Waddell et al. (2010)
Genotype
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
T/T: 6/31 = 0.164
T/C: 11/31 = 0.355
C/C: 14/31 = 0.452
HapMap Phase 1
(aggr.)
T/T: 79/395 = 0.200
T/C: 166/395 = 0.420
C/C: 150/395 = 0.380
GSE19177 vs.
HapMap aggr.
6
11
14
79
166
150
6
11
14
11
46
55
6
11
14
32
42
10
6
11
14
33
44
9
1
13.8
1.008E-03
15.551
4.199E-04
GSE19177 vs.
HapMap YRI
HapMap YRI
T/T: 3/113 = 0.027
T/C: 34/113 = 0.301
C/C: 76/113 = 0.673
3.948E-01
GSE19177 vs.
HapMap JPT
HapMap JPT
T/T: 33/86 = 0.384
T/C: 44/86 = 0.512
C/C: 9/86 = 0.105
1.859
GSE19177 vs.
HapMap CHB
HapMap CHB
T/T: 32/84 = 0.381
T/C: 42/84 = 0.500
C/C: 10/84 = 0.119
7.164E-01
GSE19177 vs.
HapMap CEU
HapMap CEU
T/T: 11/112 = 0.098
T/C: 46/112 = 0.411
C/C: 55/112 = 0.491
0.667
6
11
14
G and p-values are Williams’-corrected
3
34
76
10.127
6.323E-03
118
2. rs929039 (LGALS1)
a. GSE16019: Chen M et al. (2009)
Genotype
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
T/T: 34/75 = 0.453
T/C: 34/75 = 0.453
C/C: 7/75 = 0.093
HapMap Phase 1
(aggr.)
T/T: 205/396 = 0.518
C/T: 165/396 = 0.417
C/C: 26/396 = 0.066
GSE16019 vs.
HapMap aggr.
34
34
7
205
165
26
34
34
7
51
50
12
34
34
7
49
34
1
34
34
7
39
41
6
1
7.022
2.987E-02
0.314
8.547E-01
GSE16019 vs.
HapMap YRI
HapMap YRI
T/T: 66/113 = 0.584
C/T: 40/113 = 0.354
C/C: 7/113 = 0.062
9.584E-01
GSE16019 vs.
HapMap JPT
HapMap JPT
T/T: 39/86 = 0.453
C/T: 41/86 = 0.477
C/C: 6/86 = 0.070
0.085
GSE16019 vs.
HapMap CHB
HapMap CHB
T/T: 49/84 = 0.583
C/T: 34/84 = 0.405
C/C: 1/84 = 0.002
5.125E-01
GSE16019 vs.
HapMap CEU
HapMap CEU
T/T: 51/113 = 0.451
C/T: 50/113 = 0.442
C/C: 12/113 = 0.106
1.337
34
34
7
G and p-values are Williams’-corrected
66
40
7
3.102
2.120E-01
119
b. GSE13282: Gordan et al. (2008)
Genotype
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
T/T: 6/18 = 0.333
T/C: 10/18 = 0.556
C/C: 2/18 = 0.111
HapMap Phase 1
(aggr.)
T/T: 205/396 = 0.518
C/T: 165/396 = 0.417
C/C: 26/396 = 0.066
GSE13282 vs.
HapMap aggr.
6
10
2
205
165
26
6
10
2
51
50
12
6
10
2
49
34
1
6
10
2
39
41
6
1
5.238
7.288E-02
0.939
6.253E-01
GSE13282 vs.
HapMap YRI
HapMap YRI
T/T: 66/113 = 0.584
C/T: 40/113 = 0.354
C/C: 7/113 = 0.062
6.395E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
T/T: 39/86 = 0.453
C/T: 41/86 = 0.477
C/C: 6/86 = 0.070
0.894
GSE13282 vs.
HapMap CHB
HapMap CHB
T/T: 49/84 = 0.583
C/T: 34/84 = 0.405
C/C: 1/84 = 0.002
3.211E-01
GSE13282 vs.
HapMap CEU
HapMap CEU
T/T: 51/113 = 0.451
C/T: 50/113 = 0.442
C/C: 12/113 = 0.106
2.272
6
10
2
G and p-values are Williams’-corrected
66
40
7
3.667
1.599E-01
120
c. GSE19189: Letouze et al. (2010)
Genotype
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
T/T: 7/19 = 0.368
T/C: 10/19 = 0.526
C/C: 2/19 = 0.105
HapMap Phase 1
(aggr.)
T/T: 205/396 = 0.518
C/T: 165/396 = 0.417
C/C: 26/396 = 0.066
GSE19189 vs.
HapMap aggr.
7
10
2
205
165
26
7
10
2
51
50
12
7
10
2
49
34
1
7
10
2
39
41
6
1
4.53
1.038E-01
0.541
7.630E-01
GSE19189 vs.
HapMap YRI
HapMap YRI
T/T: 66/113 = 0.584
C/T: 40/113 = 0.354
C/C: 7/113 = 0.062
7.866E-01
GSE19189 vs.
HapMap JPT
HapMap JPT
T/T: 39/86 = 0.453
C/T: 41/86 = 0.477
C/C: 6/86 = 0.070
0.48
GSE19189 vs.
HapMap CHB
HapMap CHB
T/T: 49/84 = 0.583
C/T: 34/84 = 0.405
C/C: 1/84 = 0.002
4.520E-01
GSE19189 vs.
HapMap CEU
HapMap CEU
T/T: 51/113 = 0.451
C/T: 50/113 = 0.442
C/C: 12/113 = 0.106
1.588
7
10
2
G and p-values are Williams’-corrected
66
40
7
2.845
2.411E-01
121
d. GSE10506: Nancarrow et al. (2008)
Genotype
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
T/T: 13/20 = 0.65
T/C: 4/20 = 0.20
C/C: 3/20 = 0.15
HapMap Phase 1
(aggr.)
T/T: 205/396 = 0.518
C/T: 165/396 = 0.417
C/C: 26/396 = 0.066
GSE10506 vs.
HapMap aggr.
13
4
3
205
165
26
13
4
3
51
50
12
13
4
3
49
34
1
13
4
3
39
41
6
1
7.159
2.789E-02
5.391
6.751E-02
GSE10506 vs.
HapMap YRI
HapMap YRI
T/T: 66/113 = 0.584
C/T: 40/113 = 0.354
C/C: 7/113 = 0.062
1.191E-01
GSE10506 vs.
HapMap JPT
HapMap JPT
T/T: 39/86 = 0.453
C/T: 41/86 = 0.477
C/C: 6/86 = 0.070
4.256
GSE10506 vs.
HapMap CHB
HapMap CHB
T/T: 49/84 = 0.583
C/T: 34/84 = 0.405
C/C: 1/84 = 0.002
1.090E-01
GSE10506 vs.
HapMap CEU
HapMap CEU
T/T: 51/113 = 0.451
C/T: 50/113 = 0.442
C/C: 12/113 = 0.106
4.432
13
4
3
G and p-values are Williams’-corrected
66
40
7
2.74
2.541E-01
122
e. GSE18799: Popova et al. (2009)
Genotype
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
T/T: 7/19 = 0.369
T/C: 8/19 = 0.421
C/C: 4/19 = 0.211
HapMap Phase 1
(aggr.)
T/T: 205/396 = 0.518
C/T: 165/396 = 0.417
C/C: 26/396 = 0.066
GSE18799 vs.
HapMap aggr.
7
8
4
205
165
26
7
8
4
51
50
12
7
8
4
49
34
1
7
8
4
39
41
6
1
9.363
9.265E-03
2.808
2.456E-01
GSE18799 vs.
HapMap YRI
HapMap YRI
T/T: 66/113 = 0.584
C/T: 40/113 = 0.354
C/C: 7/113 = 0.062
4.846E-01
GSE18799 vs.
HapMap JPT
HapMap JPT
T/T: 39/86 = 0.453
C/T: 41/86 = 0.477
C/C: 6/86 = 0.070
1.449
GSE18799 vs.
HapMap CHB
HapMap CHB
T/T: 49/84 = 0.583
C/T: 34/84 = 0.405
C/C: 1/84 = 0.002
1.282E-01
GSE18799 vs.
HapMap CEU
HapMap CEU
T/T: 51/113 = 0.451
C/T: 50/113 = 0.442
C/C: 12/113 = 0.106
4.109
7
8
4
G and p-values are Williams’-corrected
66
40
7
4.652
9.769E-02
123
f. GSE9003: Stark & Hayward (2007)
Genotype
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
T/T: 30/64 = 0.469
T/C: 28/64 = 0.438
C/C: 6/64 = 0.094
HapMap Phase 1
(aggr.)
T/T: 205/396 = 0.518
C/T: 165/396 = 0.417
C/C: 26/396 = 0.066
GSE9003 vs.
HapMap aggr.
30
28
6
205
165
26
30
28
6
51
50
12
30
28
6
49
34
1
30
28
6
39
41
6
1
6.187
4.534E-02
0.392
8.220E-01
GSE9003 vs.
HapMap YRI
HapMap YRI
T/T: 66/113 = 0.584
C/T: 40/113 = 0.354
C/C: 7/113 = 0.062
9.560E-01
GSE9003 vs.
HapMap JPT
HapMap JPT
T/T: 39/86 = 0.453
C/T: 41/86 = 0.477
C/C: 6/86 = 0.070
0.09
GSE9003 vs.
HapMap CHB
HapMap CHB
T/T: 49/84 = 0.583
C/T: 34/84 = 0.405
C/C: 1/84 = 0.002
6.463E-01
GSE9003 vs.
HapMap CEU
HapMap CEU
T/T: 51/113 = 0.451
C/T: 50/113 = 0.442
C/C: 12/113 = 0.106
0.873
30
28
6
G and p-values are Williams’-corrected
66
40
7
2.238
3.266E-01
124
g. GSE19177: Waddell et al. (2010)
Genotype
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
T/T: 13/32 = 0.406
T/C: 11/32 = 0.344
C/C: 8/32 = 0.25
HapMap Phase 1
(aggr.)
T/T: 205/396 = 0.518
C/T: 165/396 = 0.417
C/C: 26/396 = 0.066
GSE19177 vs.
HapMap aggr.
13
11
8
205
165
26
13
11
8
51
50
12
13
11
8
49
34
1
13
11
8
39
41
6
1
15.892
3.541E-04
6.439
3.998E-02
GSE19177 vs.
HapMap YRI
HapMap YRI
T/T: 66/113 = 0.584
C/T: 40/113 = 0.354
C/C: 7/113 = 0.062
1.461E-01
GSE19177 vs.
HapMap JPT
HapMap JPT
T/T: 39/86 = 0.453
C/T: 41/86 = 0.477
C/C: 6/86 = 0.070
3.847
GSE19177 vs.
HapMap CHB
HapMap CHB
T/T: 49/84 = 0.583
C/T: 34/84 = 0.405
C/C: 1/84 = 0.002
9.932E-03
GSE19177 vs.
HapMap CEU
HapMap CEU
T/T: 51/113 = 0.451
C/T: 50/113 = 0.442
C/C: 12/113 = 0.106
9.224
13
11
8
G and p-values are Williams’-corrected
66
40
7
8.194
1.662E-02
125
3. rs2235338 (LGALS2)
a. GSE21168: Castillo et al. (2010)
Genotype
frequencies
Castillo,
GSE21168
GSE21168
HapMap
G1
p-value1
G/G: 2/4 = 0.500
A/G: 1/4 = 0.250
A/A: 1/4 = 0.250
HapMap Phase 1
(aggr.)
G/G: 127/396 = 0.321
A/G: 186/396 = 0.470
A/A: 83/396 = 0.210
GSE21168 vs.
HapMap aggr.
2
1
1
127
186
83
2
1
1
12
60
41
2
1
1
41
36
7
2
1
1
23
39
24
1
0.878
6.447E-01
0.589
7.449E-01
GSE21168 vs.
HapMap YRI
HapMap YRI
G/G: 51/113 = 0.451
A/G: 51/113 = 0.451
A/A: 11/113 = 0.097
2.255E-01
GSE21168 vs.
HapMap JPT
HapMap JPT
G/G: 23/86 = 0.267
A/G: 39/86 = 0.453
A/A: 24/86 = 0.279
2.979
GSE21168 vs.
HapMap CHB
HapMap CHB
G/G: 41/84 = 0.488
A/G: 36/84 = 0.429
A/A: 7/84 = 0.083
6.966E-01
GSE21168 vs.
HapMap CEU
HapMap CEU
G/G: 12/113 = 0.106
A/G: 60/113 = 0.531
A/A: 41/113 = 0.363
0.723
2
1
1
G and p-values are Williams’-corrected
51
51
11
0.838
6.577E-01
126
b. GSE16019: Chen M et al. (2009)
Genotype
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
G/G: 13/76 = 0.171
A/G: 40/76 = 0.526
A/A: 23/76 = 0.303
HapMap Phase 1
(aggr.)
G/G: 127/396 = 0.321
A/G: 186/396 = 0.470
A/A: 83/396 = 0.210
GSE16019 vs.
HapMap aggr.
13
40
23
127
186
83
13
40
23
12
60
41
13
40
23
41
36
7
13
40
23
23
39
24
1
23.705
7.121E-06
2.2
3.329E-01
GSE16019 vs.
HapMap YRI
HapMap YRI
G/G: 51/113 = 0.451
A/G: 51/113 = 0.451
A/A: 11/113 = 0.097
3.910E-01
GSE16019 vs.
HapMap JPT
HapMap JPT
G/G: 23/86 = 0.267
A/G: 39/86 = 0.453
A/A: 24/86 = 0.279
1.878
GSE16019 vs.
HapMap CHB
HapMap CHB
G/G: 41/84 = 0.488
A/G: 36/84 = 0.429
A/A: 7/84 = 0.083
1.721E-02
GSE16019 vs.
HapMap CEU
HapMap CEU
G/G: 12/113 = 0.106
A/G: 60/113 = 0.531
A/A: 41/113 = 0.363
8.124
13
40
23
G and p-values are Williams’-corrected
51
51
11
22.195
1.515E-05
127
c. GSE13282: Gordan et al. (2008)
Genotype
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
G/G: 5/20 = 0.25
A/G: 7/20 = 0.35
A/A: 8/20 = 0.4
HapMap Phase 1
(aggr.)
G/G: 127/396 = 0.321
A/G: 186/396 = 0.470
A/A: 83/396 = 0.210
GSE13282 vs.
HapMap aggr.
5
7
8
127
186
83
5
7
8
12
60
41
5
7
8
41
36
7
5
7
8
23
39
24
1
10.766
4.594E-03
1.131
5.681E-01
GSE13282 vs.
HapMap YRI
HapMap YRI
G/G: 51/113 = 0.451
A/G: 51/113 = 0.451
A/A: 11/113 = 0.097
1.853E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
G/G: 23/86 = 0.267
A/G: 39/86 = 0.453
A/A: 24/86 = 0.279
3.372
GSE13282 vs.
HapMap CHB
HapMap CHB
G/G: 41/84 = 0.488
A/G: 36/84 = 0.429
A/A: 7/84 = 0.083
1.830E-01
GSE13282 vs.
HapMap CEU
HapMap CEU
G/G: 12/113 = 0.106
A/G: 60/113 = 0.531
A/A: 41/113 = 0.363
3.397
5
7
8
G and p-values are Williams’-corrected
51
51
11
9.878
7.162E-03
128
d. GSE19189: Letouze et al. (2010)
Genotype
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
G/G: 3/19 = 0.159
A/G: 8/19 = 0.421
A/A: 8/19 = 0.421
HapMap Phase 1
(aggr.)
G/G: 127/396 = 0.321
A/G: 186/396 = 0.470
A/A: 83/396 = 0.210
GSE19189 vs.
HapMap aggr.
3
8
8
127
186
83
3
8
8
12
60
41
3
8
8
41
36
7
3
8
8
23
39
24
1
13.482
1.181E-03
1.756
4.156E-01
GSE19189 vs.
HapMap YRI
HapMap YRI
G/G: 51/113 = 0.451
A/G: 51/113 = 0.451
A/A: 11/113 = 0.097
6.548E-01
GSE19189 vs.
HapMap JPT
HapMap JPT
G/G: 23/86 = 0.267
A/G: 39/86 = 0.453
A/A: 24/86 = 0.279
0.847
GSE19189 vs.
HapMap CHB
HapMap CHB
G/G: 41/84 = 0.488
A/G: 36/84 = 0.429
A/A: 7/84 = 0.083
9.456E-02
GSE19189 vs.
HapMap CEU
HapMap CEU
G/G: 12/113 = 0.106
A/G: 60/113 = 0.531
A/A: 41/113 = 0.363
4.717
3
8
8
G and p-values are Williams’-corrected
51
51
11
12.325
2.107E-03
129
e. GSE10506: Nancarrow et al. (2008)
Genotype
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
G/G: 5/20 = 0.25
A/G: 5/20 = 0.25
A/A: 10/20 = 0.5
HapMap Phase 1
(aggr.)
G/G: 127/396 = 0.321
A/G: 186/396 = 0.470
A/A: 83/396 = 0.210
GSE10506 vs.
HapMap aggr.
5
5
10
127
186
83
5
5
10
12
60
41
5
5
10
41
36
7
5
5
10
23
39
24
1
16.065
3.247E-04
3.904
1.420E-01
GSE10506 vs.
HapMap YRI
HapMap YRI
G/G: 51/113 = 0.451
A/G: 51/113 = 0.451
A/A: 11/113 = 0.097
5.011E-02
GSE10506 vs.
HapMap JPT
HapMap JPT
G/G: 23/86 = 0.267
A/G: 39/86 = 0.453
A/A: 24/86 = 0.279
5.987
GSE10506 vs.
HapMap CHB
HapMap CHB
G/G: 41/84 = 0.488
A/G: 36/84 = 0.429
A/A: 7/84 = 0.083
2.013E-02
GSE10506 vs.
HapMap CEU
HapMap CEU
G/G: 12/113 = 0.106
A/G: 60/113 = 0.531
A/A: 41/113 = 0.363
7.811
5
5
10
G and p-values are Williams’-corrected
51
51
11
15.484
4.342E-04
130
f. GSE18799: Popova et al. (2009)
Genotype
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
G/G: 7/19 = 0.368
A/G: 8/19 = 0.421
A/A: 4/19 = 0.210
HapMap Phase 1
(aggr.)
G/G: 127/396 = 0.321
A/G: 186/396 = 0.470
A/A: 83/396 = 0.210
GSE18799 vs.
HapMap aggr.
7
8
4
127
186
83
7
8
4
12
60
41
7
8
4
41
36
7
7
8
4
23
39
24
1
2.327
3.124E-01
0.815
6.653E-01
GSE18799 vs.
HapMap YRI
HapMap YRI
G/G: 51/113 = 0.451
A/G: 51/113 = 0.451
A/A: 11/113 = 0.097
2.776E-02
GSE18799 vs.
HapMap JPT
HapMap JPT
G/G: 23/86 = 0.267
A/G: 39/86 = 0.453
A/A: 24/86 = 0.279
7.168
GSE18799 vs.
HapMap CHB
HapMap CHB
G/G: 41/84 = 0.488
A/G: 36/84 = 0.429
A/A: 7/84 = 0.083
9.008E-01
GSE18799 vs.
HapMap CEU
HapMap CEU
G/G: 12/113 = 0.106
A/G: 60/113 = 0.531
A/A: 41/113 = 0.363
0.209
7
8
4
G and p-values are Williams’-corrected
51
51
11
1.733
4.204E-01
131
g. GSE9003: Stark & Hayward (2007)
Genotype
frequencies
Stark, GSE9003
GSE9003
G1
HapMap
p-value1
G/G: 11/72 = 0.153
A/G: 31/72 = 0.431
A/A: 30/72 = 0.417
HapMap Phase 1
(aggr.)
G/G: 127/396 = 0.321
A/G: 186/396 = 0.470
A/A: 83/396 = 0.210
GSE9003 vs.
HapMap aggr.
11
31
30
127
186
83
11
31
30
12
60
41
11
31
30
41
36
7
11
31
30
23
39
24
1
32.821
7.465E-08
4.605
1.000E-01
GSE9003 vs.
HapMap YRI
HapMap YRI
G/G: 51/113 = 0.451
A/G: 51/113 = 0.451
A/A: 11/113 = 0.097
3.746E-01
GSE9003 vs.
HapMap JPT
HapMap JPT
G/G: 23/86 = 0.267
A/G: 39/86 = 0.453
A/A: 24/86 = 0.279
1.964
GSE9003 vs.
HapMap CHB
HapMap CHB
G/G: 41/84 = 0.488
A/G: 36/84 = 0.429
A/A: 7/84 = 0.083
3.106E-04
GSE9003 vs.
HapMap CEU
HapMap CEU
G/G: 12/113 = 0.106
A/G: 60/113 = 0.531
A/A: 41/113 = 0.363
16.154
11
31
30
G and p-values are Williams’-corrected
51
51
11
32.49
8.808E-08
132
h. GSE19177: Waddell et al. (2010)
Genotype
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
G/G: 6/34 = 0.176
A/G: 19/34 = 0.559
A/A: 9/34 = 0.265
HapMap Phase 1
(aggr.)
G/G: 127/396 = 0.321
A/G: 186/396 = 0.470
A/A: 83/396 = 0.210
GSE19177 vs.
HapMap aggr.
6
19
9
127
186
83
6
19
9
12
60
41
6
19
9
41
36
7
6
19
9
23
39
24
1
12.597
1.839E-03
1.417
4.924E-01
GSE19177 vs.
HapMap YRI
HapMap YRI
G/G: 51/113 = 0.451
A/G: 51/113 = 0.451
A/A: 11/113 = 0.097
4.202E-01
GSE19177 vs.
HapMap JPT
HapMap JPT
G/G: 23/86 = 0.267
A/G: 39/86 = 0.453
A/A: 24/86 = 0.279
1.734
GSE19177 vs.
HapMap CHB
HapMap CHB
G/G: 41/84 = 0.488
A/G: 36/84 = 0.429
A/A: 7/84 = 0.083
1.919E-01
GSE19177 vs.
HapMap CEU
HapMap CEU
G/G: 12/113 = 0.106
A/G: 60/113 = 0.531
A/A: 41/113 = 0.363
3.302
6
19
9
G and p-values are Williams’-corrected
51
51
11
10.948
4.194E-03
133
4. rs3763959 (LGALS9)
a. GSE21168: Castillo et al. (2010)
Genotype
frequencies
Castillo,
GSE21168
GSE21168
HapMap
G1
p-value1
A/A: 0/4 = 0.000
A/G: 3/4 = 0.750
G/G: 1/4 = 0.250
HapMap Phase 1
(aggr.)
A/A: 44/395 = 0.111
A/G: 152/395 = 0.385
G/G: 199/395 = 0.504
GSE21168 vs.
HapMap aggr.
0
3
1
44
152
199
0
3
1
23
55
35
0
3
1
10
38
36
0
3
1
8
36
41
1
1.498
4.728E-01
1.559
4.586E-01
GSE21168 vs.
HapMap YRI
HapMap YRI
A/A: 3/13 = 0.027
A/G: 23/113 = 0.204
G/G: 87/113 = 0.770
4.125E-01
GSE21168 vs.
HapMap JPT
HapMap JPT
A/A: 8/85 = 0.094
A/G: 36/85 = 0.424
G/G: 41/85 = 0.482
1.771
GSE21168 vs.
HapMap CHB
HapMap CHB
A/A: 10/84 = 0.119
A/G: 38/84 = 0.452
G/G: 36/84 = 0.429
3.604E-01
GSE21168 vs.
HapMap CEU
HapMap CEU
A/A: 23/113 = 0.204
A/G: 55/113 = 0.487
G/G: 35/113 = 0.310
2.041
0
3
1
G and p-values are Williams’-corrected
3
23
87
2.784
2.486E-01
134
b. GSE16019: Chen M et al. (2009)
Genotype
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
A/A: 14/77 = 0.182
A/G: 37/77 = 0.481
G/G: 26/77 = 0.338
HapMap Phase 1
(aggr.)
A/A: 44/395 = 0.111
A/G: 152/395 = 0.385
G/G: 199/395 = 0.504
GSE16019 vs.
HapMap aggr.
14
37
26
44
152
199
14
37
26
23
55
35
14
37
26
10
38
36
14
37
26
8
36
41
1
1.967
3.740E-01
4.584
1.011E-01
GSE16019 vs.
HapMap YRI
HapMap YRI
A/A: 3/13 = 0.027
A/G: 23/113 = 0.204
G/G: 87/113 = 0.770
8.945E-01
GSE16019 vs.
HapMap JPT
HapMap JPT
A/A: 8/85 = 0.094
A/G: 36/85 = 0.424
G/G: 41/85 = 0.482
0.223
GSE16019 vs.
HapMap CHB
HapMap CHB
A/A: 10/84 = 0.119
A/G: 38/84 = 0.452
G/G: 36/84 = 0.429
2.116E-02
GSE16019 vs.
HapMap CEU
HapMap CEU
A/A: 23/113 = 0.204
A/G: 55/113 = 0.487
G/G: 35/113 = 0.310
7.711
14
37
26
G and p-values are Williams’-corrected
3
23
87
38.117
5.284E-09
135
c. GSE13282: Gordan et al. (2008)
Genotype
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
A/A: 8/21 = 0.381
A/G: 6/21 = 0.286
G/G: 7/21 = 0.333
HapMap Phase 1
(aggr.)
A/A: 44/395 = 0.111
A/G: 152/395 = 0.385
G/G: 199/395 = 0.504
GSE13282 vs.
HapMap aggr.
8
6
7
44
152
199
8
6
7
23
55
35
8
6
7
10
38
36
8
6
7
8
36
41
1
6.806
3.327E-02
8.645
1.327E-02
GSE13282 vs.
HapMap YRI
HapMap YRI
A/A: 3/13 = 0.027
A/G: 23/113 = 0.204
G/G: 87/113 = 0.770
1.527E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
A/A: 8/85 = 0.094
A/G: 36/85 = 0.424
G/G: 41/85 = 0.482
3.758
GSE13282 vs.
HapMap CHB
HapMap CHB
A/A: 10/84 = 0.119
A/G: 38/84 = 0.452
G/G: 36/84 = 0.429
1.035E-02
GSE13282 vs.
HapMap CEU
HapMap CEU
A/A: 23/113 = 0.204
A/G: 55/113 = 0.487
G/G: 35/113 = 0.310
9.141
8
6
7
G and p-values are Williams’-corrected
3
23
87
22.491
1.307E-05
136
d. GSE19189: Letouze et al. (2010)
Genotype
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
A/A: 4/19 = 0.211
A/G: 7/19 = 0.368
G/G: 8/19 = 0.421
HapMap Phase 1
(aggr.)
A/A: 44/395 = 0.111
A/G: 152/395 = 0.385
G/G: 199/395 = 0.504
GSE19189 vs.
HapMap aggr.
4
7
8
44
152
199
4
7
8
23
55
35
4
7
8
10
38
36
4
7
8
8
36
41
1
1.059
5.889E-01
1.701
4.272E-01
GSE19189 vs.
HapMap YRI
HapMap YRI
A/A: 3/13 = 0.027
A/G: 23/113 = 0.204
G/G: 87/113 = 0.770
5.907E-01
GSE19189 vs.
HapMap JPT
HapMap JPT
A/A: 8/85 = 0.094
A/G: 36/85 = 0.424
G/G: 41/85 = 0.482
1.053
GSE19189 vs.
HapMap CHB
HapMap CHB
A/A: 10/84 = 0.119
A/G: 38/84 = 0.452
G/G: 36/84 = 0.429
4.853E-01
GSE19189 vs.
HapMap CEU
HapMap CEU
A/A: 23/113 = 0.204
A/G: 55/113 = 0.487
G/G: 35/113 = 0.310
1.446
4
7
8
G and p-values are Williams’-corrected
3
23
87
10.601
4.989E-03
137
e. GSE10506: Nancarrow et al. (2008)
Genotype
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
A/A: 3/16 = 0.188
A/G: 4/16 = 0.25
G/G: 9/16 = 0.563
HapMap Phase 1
(aggr.)
A/A: 44/395 = 0.111
A/G: 152/395 = 0.385
G/G: 199/395 = 0.504
GSE10506 vs.
HapMap aggr.
3
4
9
44
152
199
3
4
9
23
55
35
3
4
9
10
38
36
3
4
9
8
36
41
1
2.296
3.173E-01
2.101
3.498E-01
GSE10506 vs.
HapMap YRI
HapMap YRI
A/A: 3/13 = 0.027
A/G: 23/113 = 0.204
G/G: 87/113 = 0.770
1.294E-01
GSE10506 vs.
HapMap JPT
HapMap JPT
A/A: 8/85 = 0.094
A/G: 36/85 = 0.424
G/G: 41/85 = 0.482
4.089
GSE10506 vs.
HapMap CHB
HapMap CHB
A/A: 10/84 = 0.119
A/G: 38/84 = 0.452
G/G: 36/84 = 0.429
4.733E-01
GSE10506 vs.
HapMap CEU
HapMap CEU
A/A: 23/113 = 0.204
A/G: 55/113 = 0.487
G/G: 35/113 = 0.310
1.496
3
4
9
G and p-values are Williams’-corrected
3
23
87
5.269
7.175E-02
138
f. GSE18799: Popova et al. (2009)
Genotype
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
A/A: 4/18 = 0.222
A/G: 3/18 = 0.167
G/G: 11/18 = 0.611
HapMap Phase 1
(aggr.)
A/A: 44/395 = 0.111
A/G: 152/395 = 0.385
G/G: 199/395 = 0.504
GSE18799 vs.
HapMap aggr.
4
3
11
44
152
199
4
3
11
23
55
35
4
3
11
10
38
36
4
3
11
8
36
41
1
5.416
6.667E-02
5.061
7.962E-02
GSE18799 vs.
HapMap YRI
HapMap YRI
A/A: 3/13 = 0.027
A/G: 23/113 = 0.204
G/G: 87/113 = 0.770
2.169E-02
GSE18799 vs.
HapMap JPT
HapMap JPT
A/A: 8/85 = 0.094
A/G: 36/85 = 0.424
G/G: 41/85 = 0.482
7.662
GSE18799 vs.
HapMap CHB
HapMap CHB
A/A: 10/84 = 0.119
A/G: 38/84 = 0.452
G/G: 36/84 = 0.429
1.160E-01
GSE18799 vs.
HapMap CEU
HapMap CEU
A/A: 23/113 = 0.204
A/G: 55/113 = 0.487
G/G: 35/113 = 0.310
4.308
4
3
11
G and p-values are Williams’-corrected
3
23
87
7.064
2.925E-02
139
g. GSE9003: Stark & Hayward (2007)
Genotype
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
A/A: 20/67 = 0.299
A/G: 22/67 = 0.328
G/G: 25/67 = 0.373
HapMap Phase 1
(aggr.)
A/A: 44/395 = 0.111
A/G: 152/395 = 0.385
G/G: 199/395 = 0.504
GSE9003 vs.
HapMap aggr.
20
22
25
44
152
199
20
22
25
23
55
35
20
22
25
10
38
36
20
22
25
8
36
41
1
7.676
2.154E-02
10.345
5.670E-03
GSE9003 vs.
HapMap YRI
HapMap YRI
A/A: 3/13 = 0.027
A/G: 23/113 = 0.204
G/G: 87/113 = 0.770
1.028E-01
GSE9003 vs.
HapMap JPT
HapMap JPT
A/A: 8/85 = 0.094
A/G: 36/85 = 0.424
G/G: 41/85 = 0.482
4.549
GSE9003 vs.
HapMap CHB
HapMap CHB
A/A: 10/84 = 0.119
A/G: 38/84 = 0.452
G/G: 36/84 = 0.429
9.142E-04
GSE9003 vs.
HapMap CEU
HapMap CEU
A/A: 23/113 = 0.204
A/G: 55/113 = 0.487
G/G: 35/113 = 0.310
13.995
20
22
25
G and p-values are Williams’-corrected
3
23
87
37.827
6.109E-09
140
h. GSE19177: Waddell et al. (2010)
Genotype
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
A/A: 10/31 = 0.323
A/G: 6/31 = 0.194
G/G: 15/31 = 0.484
HapMap Phase 1
(aggr.)
A/A: 44/395 = 0.111
A/G: 152/395 = 0.385
G/G: 199/395 = 0.504
GSE19177 vs.
HapMap aggr.
10
6
15
44
152
199
10
6
15
23
55
35
10
6
15
10
38
36
10
6
15
8
36
41
1
9.22
9.952E-03
10.104
6.397E-03
GSE19177 vs.
HapMap YRI
HapMap YRI
A/A: 3/13 = 0.027
A/G: 23/113 = 0.204
G/G: 87/113 = 0.770
1.111E-02
GSE19177 vs.
HapMap JPT
HapMap JPT
A/A: 8/85 = 0.094
A/G: 36/85 = 0.424
G/G: 41/85 = 0.482
8.999
GSE19177 vs.
HapMap CHB
HapMap CHB
A/A: 10/84 = 0.119
A/G: 38/84 = 0.452
G/G: 36/84 = 0.429
5.517E-03
GSE19177 vs.
HapMap CEU
HapMap CEU
A/A: 23/113 = 0.204
A/G: 55/113 = 0.487
G/G: 35/113 = 0.310
10.4
10
6
15
G and p-values are Williams’-corrected
3
23
87
20.261
3.985E-05
141
5. rs4820294 (LGALS1)
a. GSE21168: Castillo et al. (2010)
Genotype
frequencies
Castillo,
GSE21168
GSE21168
HapMap
G1
p-value1
G/G: 0/3 = 0.000
A/G: 1/3 = 0.333
A/A: 2/3 = 0.667
HapMap Phase 1
(aggr.)
G/G: 203/392 = 0.518
A/G: 163/392 = 0.416
A/A: 26/392 = 0.066
GSE21168 vs.
HapMap aggr.
0
1
2
203
163
26
0
1
2
50
50
12
0
1
2
49
34
1
0
1
2
38
40
6
1
6.956
3.087E-02
5.51
6.361E-02
GSE21168 vs.
HapMap YRI
HapMap YRI
G/G: 66/112 = 0.589
A/G: 39/112 = 0.348
A/A: 7/112 = 0.062
8.729E-02
GSE21168 vs.
HapMap JPT
HapMap JPT
G/G: 38/84 = 0.452
A/G: 40/84 = 0.476
A/A: 6/84 = 0.071
4.877
GSE21168 vs.
HapMap CHB
HapMap CHB
G/G: 49/84 = 0.583
A/G: 34/84 = 0.405
A/A: 1/84 = 0.012
5.426E-02
GSE21168 vs.
HapMap CEU
HapMap CEU
G/G: 50/112 = 0.446
A/G: 50/112 = 0.446
A/A: 12/112 = 0.107
5.828
0
1
2
G and p-values are Williams’-corrected
66
39
7
6.122
4.684E-02
142
b. GSE16019: Chen M et al. (2009)
Genotype
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
G/G: 34/70 = 0.486
A/G: 29/70 = 0.414
A/A: 7/70 = 0.100
HapMap Phase 1
(aggr.)
G/G: 203/392 = 0.518
A/G: 163/392 = 0.416
A/A: 26/392 = 0.066
GSE16019 vs.
HapMap aggr.
34
29
7
203
163
26
34
29
7
50
50
12
34
29
7
49
34
1
34
29
7
38
40
6
1
6.664
3.572E-02
0.767
6.815E-01
GSE16019 vs.
HapMap YRI
HapMap YRI
G/G: 66/112 = 0.589
A/G: 39/112 = 0.348
A/A: 7/112 = 0.062
8.772E-01
GSE16019 vs.
HapMap JPT
HapMap JPT
G/G: 38/84 = 0.452
A/G: 40/84 = 0.476
A/A: 6/84 = 0.071
0.262
GSE16019 vs.
HapMap CHB
HapMap CHB
G/G: 49/84 = 0.583
A/G: 34/84 = 0.405
A/A: 1/84 = 0.012
6.191E-01
GSE16019 vs.
HapMap CEU
HapMap CEU
G/G: 50/112 = 0.446
A/G: 50/112 = 0.446
A/A: 12/112 = 0.107
0.959
34
29
7
G and p-values are Williams’-corrected
66
39
7
2.068
3.556E-01
143
c. GSE13282: Gordan et al. (2008)
Genotype
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
G/G: 7/15 = 0.467
A/G: 7/15 = 0.467
A/A: 1/15 = 0.067
HapMap Phase 1
(aggr.)
G/G: 203/392 = 0.518
A/G: 163/392 = 0.416
A/A: 26/392 = 0.066
GSE13282 vs.
HapMap aggr.
7
7
1
203
163
26
7
7
1
50
50
12
7
7
1
49
34
1
7
7
1
38
40
6
1
1.359
5.069E-01
0.011
9.945E-01
GSE13282 vs.
HapMap YRI
HapMap YRI
G/G: 66/112 = 0.589
A/G: 39/112 = 0.348
A/A: 7/112 = 0.062
8.851E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
G/G: 38/84 = 0.452
A/G: 40/84 = 0.476
A/A: 6/84 = 0.071
0.244
GSE13282 vs.
HapMap CHB
HapMap CHB
G/G: 49/84 = 0.583
A/G: 34/84 = 0.405
A/A: 1/84 = 0.012
9.291E-01
GSE13282 vs.
HapMap CEU
HapMap CEU
G/G: 50/112 = 0.446
A/G: 50/112 = 0.446
A/A: 12/112 = 0.107
0.147
7
7
1
G and p-values are Williams’-corrected
66
39
7
0.763
6.828E-01
144
d. GSE19189: Letouze et al. (2010)
Genotype
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
G/G: 7/20 = 0.350
A/G: 10/20 = 0.500
A/A: 3/20 = 0.150
HapMap Phase 1
(aggr.)
G/G: 203/392 = 0.518
A/G: 163/392 = 0.416
A/A: 26/392 = 0.066
GSE19189 vs.
HapMap aggr.
7
10
3
203
163
26
7
10
3
50
50
12
7
10
3
49
34
1
7
10
3
38
40
6
1
7.065
2.923E-02
1.342
5.112E-01
GSE19189 vs.
HapMap YRI
HapMap YRI
G/G: 66/112 = 0.589
A/G: 39/112 = 0.348
A/A: 7/112 = 0.062
7.022E-01
GSE19189 vs.
HapMap JPT
HapMap JPT
G/G: 38/84 = 0.452
A/G: 40/84 = 0.476
A/A: 6/84 = 0.071
0.707
GSE19189 vs.
HapMap CHB
HapMap CHB
G/G: 49/84 = 0.583
A/G: 34/84 = 0.405
A/A: 1/84 = 0.012
2.555E-01
GSE19189 vs.
HapMap CEU
HapMap CEU
G/G: 50/112 = 0.446
A/G: 50/112 = 0.446
A/A: 12/112 = 0.107
2.729
7
10
3
G and p-values are Williams’-corrected
66
39
7
4.061
1.313E-01
145
e. GSE10506: Nancarrow et al. (2008)
Genotype
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
G/G: 14/22 = 0.636
A/G: 3/22 = 0.136
A/A: 5/22 = 0.227
HapMap Phase 1
(aggr.)
G/G: 203/392 = 0.518
A/G: 163/392 = 0.416
A/A: 26/392 = 0.066
GSE10506 vs.
HapMap aggr.
14
3
5
203
163
26
14
3
5
50
50
12
14
3
5
49
34
1
14
3
5
38
40
6
1
14.099
8.678E-04
10.23
6.006E-03
GSE10506 vs.
HapMap YRI
HapMap YRI
G/G: 66/112 = 0.589
A/G: 39/112 = 0.348
A/A: 7/112 = 0.062
1.501E-02
GSE10506 vs.
HapMap JPT
HapMap JPT
G/G: 38/84 = 0.452
A/G: 40/84 = 0.476
A/A: 6/84 = 0.071
8.398
GSE10506 vs.
HapMap CHB
HapMap CHB
G/G: 49/84 = 0.583
A/G: 34/84 = 0.405
A/A: 1/84 = 0.012
6.529E-03
GSE10506 vs.
HapMap CEU
HapMap CEU
G/G: 50/112 = 0.446
A/G: 50/112 = 0.446
A/A: 12/112 = 0.107
10.063
14
3
5
G and p-values are Williams’-corrected
66
39
7
7.14
2.816E-02
146
f. GSE18799: Popova et al. (2009)
Genotype
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
G/G: 6/17 = 0.353
A/G: 8/17 = 0.471
A/A: 3/17 = 0.176
HapMap Phase 1
(aggr.)
G/G: 203/392 = 0.518
A/G: 163/392 = 0.416
A/A: 26/392 = 0.066
GSE18799 vs.
HapMap aggr.
6
8
3
203
163
26
6
8
3
50
50
12
6
8
3
49
34
1
6
8
3
38
40
6
1
7.202
2.730E-02
1.663
4.354E-01
GSE18799 vs.
HapMap YRI
HapMap YRI
G/G: 66/112 = 0.589
A/G: 39/112 = 0.348
A/A: 7/112 = 0.062
6.627E-01
GSE18799 vs.
HapMap JPT
HapMap JPT
G/G: 38/84 = 0.452
A/G: 40/84 = 0.476
A/A: 6/84 = 0.071
0.823
GSE18799 vs.
HapMap CHB
HapMap CHB
G/G: 49/84 = 0.583
A/G: 34/84 = 0.405
A/A: 1/84 = 0.012
2.418E-01
GSE18799 vs.
HapMap CEU
HapMap CEU
G/G: 50/112 = 0.446
A/G: 50/112 = 0.446
A/A: 12/112 = 0.107
2.839
6
8
3
G and p-values are Williams’-corrected
66
39
7
3.837
1.468E-01
147
g. GSE9003: Stark & Hayward (2007)
Genotype
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
G/G: 38/56 = 0.679
A/G: 10/56 = 0.179
A/A: 8/56 = 0.143
HapMap Phase 1
(aggr.)
G/G: 203/392 = 0.518
A/G: 163/392 = 0.416
A/A: 26/392 = 0.066
GSE9003 vs.
HapMap aggr.
38
10
8
203
163
26
38
10
8
50
50
12
38
10
8
49
34
1
38
10
8
38
40
6
1
15.232
4.925E-04
13.574
1.128E-03
GSE9003 vs.
HapMap YRI
HapMap YRI
G/G: 66/112 = 0.589
A/G: 39/112 = 0.348
A/A: 7/112 = 0.062
2.166E-03
GSE9003 vs.
HapMap JPT
HapMap JPT
G/G: 38/84 = 0.452
A/G: 40/84 = 0.476
A/A: 6/84 = 0.071
12.27
GSE9003 vs.
HapMap CHB
HapMap CHB
G/G: 49/84 = 0.583
A/G: 34/84 = 0.405
A/A: 1/84 = 0.012
1.079E-03
GSE9003 vs.
HapMap CEU
HapMap CEU
G/G: 50/112 = 0.446
A/G: 50/112 = 0.446
A/A: 12/112 = 0.107
13.663
38
10
8
G and p-values are Williams’-corrected
66
39
7
6.829
3.289E-02
148
h. GSE19177: Waddell et al. (2010)
Genotype
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
G/G: 13/27 = 0.481
A/G: 8/27 = 0.296
A/A: 6/27 = 0.222
HapMap Phase 1
(aggr.)
G/G: 203/392 = 0.518
A/G: 163/392 = 0.416
A/A: 26/392 = 0.066
GSE19177 vs.
HapMap aggr.
13
8
6
203
163
26
13
8
6
50
50
12
13
8
6
49
34
1
13
8
6
38
40
6
1
12.068
2.396E-03
5.154
7.600E-02
GSE19177 vs.
HapMap YRI
HapMap YRI
G/G: 66/112 = 0.589
A/G: 39/112 = 0.348
A/A: 7/112 = 0.062
2.067E-01
GSE19177 vs.
HapMap JPT
HapMap JPT
G/G: 38/84 = 0.452
A/G: 40/84 = 0.476
A/A: 6/84 = 0.071
3.153
GSE19177 vs.
HapMap CHB
HapMap CHB
G/G: 49/84 = 0.583
A/G: 34/84 = 0.405
A/A: 1/84 = 0.012
4.496E-02
GSE19177 vs.
HapMap CEU
HapMap CEU
G/G: 50/112 = 0.446
A/G: 50/112 = 0.446
A/A: 12/112 = 0.107
6.204
13
8
6
G and p-values are Williams’-corrected
66
39
7
5.145
7.634E-02
149
6. rs10403583 (LGALS4)
a. GSE21168: Castillo et al. (2010)
Genotype
frequencies
Castillo,
GSE21168
GSE21168
HapMap
G1
p-value1
A/A: 0/3 = 0.000
A/G: 1/3 = 0.333
G/G: 2/3 = 0.667
HapMap Phase 1
(aggr.)
A/A: 12/396 = 0.030
A/G: 108/396 = 0.273
G/G: 276/396 = 0.697
GSE21168 vs.
HapMap aggr.
0
1
2
12
108
276
0
1
2
1
35
77
0
1
2
1
16
67
0
1
2
0
13
73
1
0.108
9.474E-01
0.432
8.057E-01
GSE21168 vs.
HapMap YRI
HapMap YRI
A/A: 10/113 = 0.088
A/G: 44/113 = 0.389
G/G: 59/113 = 0.522
9.935E-01
GSE21168 vs.
HapMap JPT
HapMap JPT
A/A: 0/86 = 0.000
A/G: 13/86 = 0.151
G/G: 73/86 = 0.849
0.013
GSE21168 vs.
HapMap CHB
HapMap CHB
A/A: 1/84 = 0.012
A/G: 16/84 = 0.190
G/G: 67/84 = 0.798
9.470E-01
GSE21168 vs.
HapMap CEU
HapMap CEU
A/A: 1/113 = 0.009
A/G: 35/113 = 0.031
G/G: 77/113 = 0.681
0.109
0
1
2
G and p-values are Williams’-corrected
10
44
59
0.462
7.937E-01
150
b. GSE16019: Chen M et al. (2009)
Genotype
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
A/A: 3/79 = 0.038
A/G: 25/79 = 0.316
G/G: 51/79 = 0.646
HapMap Phase 1
(aggr.)
A/A: 12/396 = 0.030
A/G: 108/396 = 0.273
G/G: 276/396 = 0.697
GSE16019 vs.
HapMap aggr.
3
25
51
12
108
276
3
25
51
1
35
77
3
25
51
1
16
67
3
25
51
0
13
73
1
4.733
9.381E-02
10.673
4.813E-03
GSE16019 vs.
HapMap YRI
HapMap YRI
A/A: 10/113 = 0.088
A/G: 44/113 = 0.389
G/G: 59/113 = 0.522
3.953E-01
GSE16019 vs.
HapMap JPT
HapMap JPT
A/A: 0/86 = 0.000
A/G: 13/86 = 0.151
G/G: 73/86 = 0.849
1.856
GSE16019 vs.
HapMap CHB
HapMap CHB
A/A: 1/84 = 0.012
A/G: 16/84 = 0.190
G/G: 67/84 = 0.798
6.774E-01
GSE16019 vs.
HapMap CEU
HapMap CEU
A/A: 1/113 = 0.009
A/G: 35/113 = 0.031
G/G: 77/113 = 0.681
0.779
3
25
51
G and p-values are Williams’-corrected
10
44
59
3.714
1.561E-01
151
c. GSE13282: Gordan et al. (2008)
Genotype
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
A/A: 1/21 = 0.048
A/G: 6/21 = 0.286
G/G: 14/21 = 0.667
HapMap Phase 1
(aggr.)
A/A: 12/396 = 0.030
A/G: 108/396 = 0.273
G/G: 276/396 = 0.697
GSE13282 vs.
HapMap aggr.
1
6
14
12
108
276
1
6
14
1
35
77
1
6
14
1
16
67
1
6
14
0
13
73
1
1.574
4.552E-01
3.744
1.538E-01
GSE13282 vs.
HapMap YRI
HapMap YRI
A/A: 10/113 = 0.088
A/G: 44/113 = 0.389
G/G: 59/113 = 0.522
6.005E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
A/A: 0/86 = 0.000
A/G: 13/86 = 0.151
G/G: 73/86 = 0.849
1.02
GSE13282 vs.
HapMap CHB
HapMap CHB
A/A: 1/84 = 0.012
A/G: 16/84 = 0.190
G/G: 67/84 = 0.798
9.144E-01
GSE13282 vs.
HapMap CEU
HapMap CEU
A/A: 1/113 = 0.009
A/G: 35/113 = 0.031
G/G: 77/113 = 0.681
0.179
1
6
14
G and p-values are Williams’-corrected
10
44
59
1.506
4.710E-01
152
d. GSE19189: Letouze et al. (2010)
Genotype
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
A/A: 0/18 = 0.000
A/G: 3/18 = 0.167
G/G: 15/18 = 0.833
HapMap Phase 1
(aggr.)
A/A: 12/396 = 0.030
A/G: 108/396 = 0.273
G/G: 276/396 = 0.697
GSE19189 vs.
HapMap aggr.
0
3
15
12
108
276
0
3
15
1
35
77
0
3
15
1
16
67
0
3
15
0
13
73
1
0.302
8.598E-01
0.003
9.988E-01
GSE19189 vs.
HapMap YRI
HapMap YRI
A/A: 10/113 = 0.088
A/G: 44/113 = 0.389
G/G: 59/113 = 0.522
5.353E-01
GSE19189 vs.
HapMap JPT
HapMap JPT
A/A: 0/86 = 0.000
A/G: 13/86 = 0.151
G/G: 73/86 = 0.849
1.25
GSE19189 vs.
HapMap CHB
HapMap CHB
A/A: 1/84 = 0.012
A/G: 16/84 = 0.190
G/G: 67/84 = 0.798
3.725E-01
GSE19189 vs.
HapMap CEU
HapMap CEU
A/A: 1/113 = 0.009
A/G: 35/113 = 0.031
G/G: 77/113 = 0.681
1.975
0
3
15
G and p-values are Williams’-corrected
10
44
59
7.354
2.530E-02
153
e. GSE10506: Nancarrow et al. (2008)
Genotype
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
A/A: 0/21 = 0
A/G: 6/21 = 0.286
G/G: 15/21 = 0.714
HapMap Phase 1
(aggr.)
A/A: 12/396 = 0.030
A/G: 108/396 = 0.273
G/G: 276/396 = 0.697
GSE10506 vs.
HapMap aggr.
0
6
15
12
108
276
0
6
15
1
35
77
0
6
15
1
16
67
0
6
15
0
13
73
1
0.871
6.469E-01
1.818
4.029E-01
GSE10506 vs.
HapMap YRI
HapMap YRI
A/A: 10/113 = 0.088
A/G: 44/113 = 0.389
G/G: 59/113 = 0.522
8.794E-01
GSE10506 vs.
HapMap JPT
HapMap JPT
A/A: 0/86 = 0.000
A/G: 13/86 = 0.151
G/G: 73/86 = 0.849
0.257
GSE10506 vs.
HapMap CHB
HapMap CHB
A/A: 1/84 = 0.012
A/G: 16/84 = 0.190
G/G: 67/84 = 0.798
5.793E-01
GSE10506 vs.
HapMap CEU
HapMap CEU
A/A: 1/113 = 0.009
A/G: 35/113 = 0.031
G/G: 77/113 = 0.681
1.092
0
6
15
G and p-values are Williams’-corrected
10
44
59
4.704
9.518E-02
154
f. GSE18799: Popova et al. (2009)
Genotype
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
A/A: 2/21 = 0.095
A/G: 8/21 = 0.381
G/G: 11/21 = 0.524
HapMap Phase 1
(aggr.)
A/A: 12/396 = 0.030
A/G: 108/396 = 0.273
G/G: 276/396 = 0.697
GSE18799 vs.
HapMap aggr.
2
8
11
12
108
276
2
8
11
1
35
77
2
8
11
1
16
67
2
8
11
0
13
73
1
6.22
4.460E-02
10.316
5.753E-03
GSE18799 vs.
HapMap YRI
HapMap YRI
A/A: 10/113 = 0.088
A/G: 44/113 = 0.389
G/G: 59/113 = 0.522
1.285E-01
GSE18799 vs.
HapMap JPT
HapMap JPT
A/A: 0/86 = 0.000
A/G: 13/86 = 0.151
G/G: 73/86 = 0.849
4.103
GSE18799 vs.
HapMap CHB
HapMap CHB
A/A: 1/84 = 0.012
A/G: 16/84 = 0.190
G/G: 67/84 = 0.798
2.225E-01
GSE18799 vs.
HapMap CEU
HapMap CEU
A/A: 1/113 = 0.009
A/G: 35/113 = 0.031
G/G: 77/113 = 0.681
3.006
2
8
11
G and p-values are Williams’-corrected
10
44
59
0.011
9.945E-01
155
g. GSE9003: Stark & Hayward (2007)
Genotype
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
A/A: 5/72 = 0.069
A/G: 11/72 = 0.153
G/G: 56/72 = 0.778
HapMap Phase 1
(aggr.)
A/A: 12/396 = 0.030
A/G: 108/396 = 0.273
G/G: 276/396 = 0.697
GSE9003 vs.
HapMap aggr.
5
11
56
12
108
276
5
11
56
1
35
77
5
11
56
1
16
67
5
11
56
0
13
73
1
3.717
1.559E-01
7.634
2.199E-02
GSE9003 vs.
HapMap YRI
HapMap YRI
A/A: 10/113 = 0.088
A/G: 44/113 = 0.389
G/G: 59/113 = 0.522
7.654E-03
GSE9003 vs.
HapMap JPT
HapMap JPT
A/A: 0/86 = 0.000
A/G: 13/86 = 0.151
G/G: 73/86 = 0.849
9.745
GSE9003 vs.
HapMap CHB
HapMap CHB
A/A: 1/84 = 0.012
A/G: 16/84 = 0.190
G/G: 67/84 = 0.798
4.154E-02
GSE9003 vs.
HapMap CEU
HapMap CEU
A/A: 1/113 = 0.009
A/G: 35/113 = 0.031
G/G: 77/113 = 0.681
6.362
5
11
56
G and p-values are Williams’-corrected
10
44
59
13.499
1.171E-03
156
h. GSE19177: Waddell et al. (2010)
Genotype
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
A/A: 0/32 = 0.000
A/G: 6/32 = 0.188
G/G: 26/32 = 0.813
HapMap Phase 1
(aggr.)
A/A: 12/396 = 0.030
A/G: 108/396 = 0.273
G/G: 276/396 = 0.697
GSE19177 vs.
HapMap aggr.
0
6
26
12
108
276
0
6
26
1
35
77
0
6
26
1
16
67
0
6
26
0
13
73
1
0.484
7.851E-01
0.21
9.003E-01
GSE19177 vs.
HapMap YRI
HapMap YRI
A/A: 10/113 = 0.088
A/G: 44/113 = 0.389
G/G: 59/113 = 0.522
4.082E-01
GSE19177 vs.
HapMap JPT
HapMap JPT
A/A: 0/86 = 0.000
A/G: 13/86 = 0.151
G/G: 73/86 = 0.849
1.792
GSE19177 vs.
HapMap CHB
HapMap CHB
A/A: 1/84 = 0.012
A/G: 16/84 = 0.190
G/G: 67/84 = 0.798
2.249E-01
GSE19177 vs.
HapMap CEU
HapMap CEU
A/A: 1/113 = 0.009
A/G: 35/113 = 0.031
G/G: 77/113 = 0.681
2.984
0
6
26
G and p-values are Williams’-corrected
10
44
59
11.126
3.837E-03
157
7. rs10489789 (LGALS8)
a. GSE16019: Chen M et al. (2009)
Genotype
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
G/G: 63/75 = 0.840
A/G: 10/75 = 0.133
A/A: 2/75 = 0.027
HapMap Phase 1
(aggr.)
G/G: 298/384 = 0.776
G/A: 74/384 = 0.193
A/A: 12/384 = 0.031
GSE16019 vs.
HapMap aggr.
63
10
2
298
74
12
63
10
2
83
27
1
63
10
2
78
2
0
63
10
2
81
2
0
1
8.752
1.258E-02
9.11
1.051E-02
GSE16019 vs.
HapMap YRI
HapMap YRI
G/G: 56/110 = 0.509
G/A: 43/110 = 0.391
A/A: 11/110 = 0.100
1.478E-01
GSE16019 vs.
HapMap JPT
HapMap JPT
G/G: 81/83 = 0.976
G/A: 2/83 = 0.024
A/A: 0/83 = 0
3.824
GSE16019 vs.
HapMap CHB
HapMap CHB
G/G: 78/80 = 0.975
G/A: 2/80 = 0.025
A/A: 0/80 = 0
4.480E-01
GSE16019 vs.
HapMap CEU
HapMap CEU
G/G: 83/111 = 0.748
G/A: 27/111 = 0.243
A/A: 1/111 = 0.009
1.606
63
10
2
G and p-values are Williams’-corrected
56
43
11
22.172
1.533E-05
158
b. GSE13282: Gordan et al. (2008)
Genotype
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
G/G: 16/21 = 0.762
G/A: 4/21 = 0.19
A/A: 1/21 = 0.048
HapMap Phase 1
(aggr.)
G/G: 298/384 = 0.776
G/A: 74/384 = 0.193
A/A: 12/384 = 0.031
GSE13282 vs.
HapMap aggr.
16
4
1
298
74
12
16
4
1
83
27
1
16
4
1
78
2
0
16
4
1
81
2
0
1
6.597
3.694E-02
6.723
3.468E-02
GSE13282 vs.
HapMap YRI
HapMap YRI
G/G: 56/110 = 0.509
G/A: 43/110 = 0.391
A/A: 11/110 = 0.100
5.641E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
G/G: 81/83 = 0.976
G/A: 2/83 = 0.024
A/A: 0/83 = 0
1.145
GSE13282 vs.
HapMap CHB
HapMap CHB
G/G: 78/80 = 0.975
G/A: 2/80 = 0.025
A/A: 0/80 = 0
9.361E-01
GSE13282 vs.
HapMap CEU
HapMap CEU
G/G: 83/111 = 0.748
G/A: 27/111 = 0.243
A/A: 1/111 = 0.009
0.132
16
4
1
G and p-values are Williams’-corrected
56
43
11
4.535
1.036E-01
159
c. GSE19189: Letouze et al. (2010)
Genotype
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
G/G: 16/21 = 0.762
G/A: 4/21 = 0.19
A/A: 1/21 = 0.048
HapMap Phase 1
(aggr.)
G/G: 298/384 = 0.776
G/A: 74/384 = 0.193
A/A: 12/384 = 0.031
GSE19189 vs.
HapMap aggr.
16
4
1
298
74
12
16
4
1
83
27
1
16
4
1
78
2
0
16
4
1
81
2
0
1
6.597
3.694E-02
6.723
3.468E-02
GSE19189 vs.
HapMap YRI
HapMap YRI
G/G: 56/110 = 0.509
G/A: 43/110 = 0.391
A/A: 11/110 = 0.100
5.641E-01
GSE19189 vs.
HapMap JPT
HapMap JPT
G/G: 81/83 = 0.976
G/A: 2/83 = 0.024
A/A: 0/83 = 0
1.145
GSE19189 vs.
HapMap CHB
HapMap CHB
G/G: 78/80 = 0.975
G/A: 2/80 = 0.025
A/A: 0/80 = 0
9.361E-01
GSE19189 vs.
HapMap CEU
HapMap CEU
G/G: 83/111 = 0.748
G/A: 27/111 = 0.243
A/A: 1/111 = 0.009
0.132
16
4
1
G and p-values are Williams’-corrected
56
43
11
4.535
1.036E-01
160
d. GSE10506: Nancarrow et al. (2008)
Genotype
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
G/G: 18/21 = 0.857
G/A: 2/21 = 0.095
A/A: 1/21 = 0.048
HapMap Phase 1
(aggr.)
G/G: 298/384 = 0.776
G/A: 74/384 = 0.193
A/A: 12/384 = 0.031
GSE10506 vs.
HapMap aggr.
18
2
1
298
74
12
18
2
1
83
27
1
18
2
1
78
2
0
18
2
1
81
2
0
1
3.31
1.911E-01
3.378
1.847E-01
GSE10506 vs.
HapMap YRI
HapMap YRI
G/G: 56/110 = 0.509
G/A: 43/110 = 0.391
A/A: 11/110 = 0.100
2.407E-01
GSE10506 vs.
HapMap JPT
HapMap JPT
G/G: 81/83 = 0.976
G/A: 2/83 = 0.024
A/A: 0/83 = 0
2.848
GSE10506 vs.
HapMap CHB
HapMap CHB
G/G: 78/80 = 0.975
G/A: 2/80 = 0.025
A/A: 0/80 = 0
5.132E-01
GSE10506 vs.
HapMap CEU
HapMap CEU
G/G: 83/111 = 0.748
G/A: 27/111 = 0.243
A/A: 1/111 = 0.009
1.334
18
2
1
G and p-values are Williams’-corrected
56
43
11
9.407
9.063E-03
161
e. GSE18799: Popova et al. (2009)
Genotype
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
G/G: 14/21 = 0.667
G/A: 6/21 = 0.286
A/A: 1/21 = 0.048
HapMap Phase 1
(aggr.)
G/G: 298/384 = 0.776
G/A: 74/384 = 0.193
A/A: 12/384 = 0.031
GSE18799 vs.
HapMap aggr.
14
6
1
298
74
12
14
6
1
83
27
1
14
6
1
78
2
0
14
6
1
81
2
0
1
10.701
4.746E-03
10.881
4.337E-03
GSE18799 vs.
HapMap YRI
HapMap YRI
G/G: 56/110 = 0.509
G/A: 43/110 = 0.391
A/A: 11/110 = 0.100
5.510E-01
GSE18799 vs.
HapMap JPT
HapMap JPT
G/G: 81/83 = 0.976
G/A: 2/83 = 0.024
A/A: 0/83 = 0
1.192
GSE18799 vs.
HapMap CHB
HapMap CHB
G/G: 78/80 = 0.975
G/A: 2/80 = 0.025
A/A: 0/80 = 0
5.819E-01
GSE18799 vs.
HapMap CEU
HapMap CEU
G/G: 83/111 = 0.748
G/A: 27/111 = 0.243
A/A: 1/111 = 0.009
1.083
14
6
1
G and p-values are Williams’-corrected
56
43
11
1.843
3.979E-01
162
f. GSE9003: Stark & Hayward (2007)
Genotype
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
G/G: 59/73 = 0.808
G/A: 12/73 = 0.164
A/A: 2/73 = 0.027
HapMap Phase 1
(aggr.)
G/G: 298/384 = 0.776
G/A: 74/384 = 0.193
A/A: 12/384 = 0.031
GSE9003 vs.
HapMap aggr.
59
12
2
298
74
12
59
12
2
83
27
1
59
12
2
78
2
0
59
12
2
81
2
0
1
11.387
3.368E-03
11.827
2.703E-03
GSE9003 vs.
HapMap YRI
HapMap YRI
G/G: 56/110 = 0.509
G/A: 43/110 = 0.391
A/A: 11/110 = 0.100
3.296E-01
GSE9003 vs.
HapMap JPT
HapMap JPT
G/G: 81/83 = 0.976
G/A: 2/83 = 0.024
A/A: 0/83 = 0
2.22
GSE9003 vs.
HapMap CHB
HapMap CHB
G/G: 78/80 = 0.975
G/A: 2/80 = 0.025
A/A: 0/80 = 0
8.328E-01
GSE9003 vs.
HapMap CEU
HapMap CEU
G/G: 83/111 = 0.748
G/A: 27/111 = 0.243
A/A: 1/111 = 0.009
0.366
59
12
2
G and p-values are Williams’-corrected
56
43
11
17.492
1.591E-04
163
g. GSE19177: Waddell et al. (2010)
Genotype
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
G/G: 21/29 = 0.724
G/A: 6/29 = 0.207
A/A: 2/29 = 0.069
HapMap Phase 1
(aggr.)
G/G: 298/384 = 0.776
G/A: 74/384 = 0.193
A/A: 12/384 = 0.031
HapMap CEU
G/G: 83/111 = 0.748
G/A: 27/111 = 0.243
A/A: 1/111 = 0.009
HapMap CHB
G/G: 78/80 = 0.975
G/A: 2/80 = 0.025
A/A: 0/80 = 0
GSE19177 vs.
HapMap aggr.
21
6
2
21
6
2
21
6
2
298
74
12
83
27
1
78
2
0
21
6
2
81
2
0
1
GSE19177 vs.
HapMap CEU
2.677
2.622E-01
GSE19177 vs.
HapMap CHB
12.323
2.109E-03
12.62
1.818E-03
GSE19177 vs.
HapMap YRI
HapMap YRI
G/G: 56/110 = 0.509
G/A: 43/110 = 0.391
A/A: 11/110 = 0.100
6.316E-01
GSE19177 vs.
HapMap JPT
HapMap JPT
G/G: 81/83 = 0.976
G/A: 2/83 = 0.024
A/A: 0/83 = 0
0.919
21
6
2
G and p-values are Williams’-corrected
56
43
11
4.353
1.134E-01
164
APPENDIX C
Association analysis, allele frequencies
For each SNP, allele frequencies were compared between datasets archived at the
Gene Expression Omnibus (noted here by GEO accession number and associated
research article, eg. GSE16019: Chen M et al. (2009)) and both individual and aggregated
HapMap Phase 1 populations as controls, with Williams’ corrected G-test for
independence used to determine association. Statistical relevance of individual
association analyses is shown in each table below.
165
1. rs428007 (CLC/LGALS10)
a. GSE16019: Chen M et al. (2009)
Allele
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
T: 66/154 = 0.429
C: 88/154 = 0.571
HapMap Phase 1
(aggr.)
T: 324/790 = 0.410
C: 466/790 = 0.590
GSE16019 vs.
HapMap aggr.
66
88
324
466
66
88
68
156
66
88
106
62
66
88
110
62
1
13.247
2.730E-04
14.585
1.340E-04
GSE16019 vs.
HapMap YRI
HapMap YRI
T: 40/226 = 0.177
C: 186/226 = 0.823
1.302E-02
GSE16019 vs.
HapMap JPT
HapMap JPT
T: 110/172 = 0.640
C: 62/172 = 0.360
6.167
GSE16019 vs.
HapMap CHB
HapMap CHB
T: 106/168 = 0.631
C: 62/168 = 0.369
6.722E-01
GSE16019 vs.
HapMap CEU
HapMap CEU
T: 68/224 = 0.304
C: 156/224 = 0.696
0.179
66
88
G and p-values are Williams’-corrected
40
186
28.401
9.861E-08
166
b. GSE13282: Gordan et al. (2008)
Allele
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
T: 14/42 = 0.333
C: 28/42 = 0.667
HapMap Phase 1
(aggr.)
T: 324/790 = 0.410
C: 466/790 = 0.590
GSE13282 vs.
HapMap aggr.
14
28
324
466
14
28
68
156
14
28
106
62
14
28
110
62
1
11.962
5.430E-04
12.743
3.573E-04
GSE13282 vs.
HapMap YRI
HapMap YRI
T: 40/226 = 0.177
C: 186/226 = 0.823
7.053E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
T: 110/172 = 0.640
C: 62/172 = 0.360
0.143
GSE13282 vs.
HapMap CHB
HapMap CHB
T: 106/168 = 0.631
C: 62/168 = 0.369
3.210E-01
GSE13282 vs.
HapMap CEU
HapMap CEU
T: 68/224 = 0.304
C: 156/224 = 0.696
0.985
14
28
G and p-values are Williams’-corrected
40
186
4.759
2.915E-02
167
c. GSE19189: Letouze et al. (2010)
Allele
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
T: 13/40 = 0.325
C: 27/40 = 0.675
HapMap Phase 1
(aggr.)
T: 324/790 = 0.410
C: 466/790 = 0.590
GSE19189 vs.
HapMap aggr.
13
27
324
466
13
27
68
156
13
27
106
62
13
27
110
62
1
12.16
7.884E-01
12.929
3.235E-04
GSE19189 vs.
HapMap YRI
HapMap YRI
T: 40/226 = 0.177
C: 186/226 = 0.823
7.884E-01
GSE19189 vs.
HapMap JPT
HapMap JPT
T: 110/172 = 0.640
C: 62/172 = 0.360
0.072
GSE19189 vs.
HapMap CHB
HapMap CHB
T: 106/168 = 0.631
C: 62/168 = 0.369
2.819E-01
GSE19189 vs.
HapMap CEU
HapMap CEU
T: 68/224 = 0.304
C: 156/224 = 0.696
1.158
13
27
G and p-values are Williams’-corrected
40
186
4.125
4.225E-02
168
d. GSE10506: Nancarrow et al. (2008)
Allele
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
T: 15/40 = 0.375
C: 25/40 = 0.625
HapMap Phase 1
(aggr.)
T: 324/790 = 0.410
C: 466/790 = 0.590
GSE10506 vs.
HapMap aggr.
15
25
324
466
15
25
68
156
15
25
106
62
15
25
110
62
1
8.488
3.575E-03
9.129
2.516E-03
GSE10506 vs.
HapMap YRI
HapMap YRI
T: 40/226 = 0.177
C: 186/226 = 0.823
3.799E-01
GSE10506 vs.
HapMap JPT
HapMap JPT
T: 110/172 = 0.640
C: 62/172 = 0.360
0.771
GSE10506 vs.
HapMap CHB
HapMap CHB
T: 106/168 = 0.631
C: 62/168 = 0.369
6.596E-01
GSE10506 vs.
HapMap CEU
HapMap CEU
T: 68/224 = 0.304
C: 156/224 = 0.696
0.194
15
25
G and p-values are Williams’-corrected
40
186
7.056
7.900E-03
169
e. GSE18799: Popova et al. (2009)
Allele
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
T: 15/42 = 0.357
C: 27/42 = 0.643
HapMap Phase 1
(aggr.)
T: 324/790 = 0.410
C: 466/790 = 0.590
GSE18799 vs.
HapMap aggr.
15
27
324
466
15
27
68
156
15
27
106
62
15
27
110
62
1
10.111
3.147E-01
10.827
1.000E-03
GSE18799 vs.
HapMap YRI
HapMap YRI
T: 40/226 = 0.177
C: 186/226 = 0.823
4.990E-01
GSE18799 vs.
HapMap JPT
HapMap JPT
T: 110/172 = 0.640
C: 62/172 = 0.360
0.457
GSE18799 vs.
HapMap CHB
HapMap CHB
T: 106/168 = 0.631
C: 62/168 = 0.369
4.958E-01
GSE18799 vs.
HapMap CEU
HapMap CEU
T: 68/224 = 0.304
C: 156/224 = 0.696
0.464
15
27
G and p-values are Williams’-corrected
40
186
6.181
1.291E-02
170
f. GSE9003: Stark & Hayward (2007)
Allele
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
T: 39/146 = 0.267
C: 107/146 = 0.733
HapMap Phase 1
(aggr.)
T: 324/790 = 0.410
C: 466/790 = 0.590
GSE9003 vs.
HapMap aggr.
39
107
324
466
39
107
68
156
39
107
106
62
39
107
110
62
1
42.546
6.904E-11
45.03
1.940E-11
GSE9003 vs.
HapMap YRI
HapMap YRI
T: 40/226 = 0.177
C: 186/226 = 0.823
4.495E-01
GSE9003 vs.
HapMap JPT
HapMap JPT
T: 110/172 = 0.640
C: 62/172 = 0.360
0.572
GSE9003 vs.
HapMap CHB
HapMap CHB
T: 106/168 = 0.631
C: 62/168 = 0.369
8.950E-04
GSE9003 vs.
HapMap CEU
HapMap CEU
T: 68/224 = 0.304
C: 156/224 = 0.696
11.033
39
107
G and p-values are Williams’-corrected
40
186
4.209
4.021E-02
171
g. GSE19177: Waddell et al. (2010)
Allele
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
T: 23/62 = 0.718
C: 39/62 = 0.629
HapMap Phase 1
(aggr.)
T: 324/790 = 0.410
C: 466/790 = 0.590
GSE19177 vs.
HapMap aggr.
23
39
324
466
23
39
68
156
23
39
106
62
23
39
110
62
1
12.307
4.513E-04
13.241
2.739E-04
GSE19177 vs.
HapMap YRI
HapMap YRI
T: 40/226 = 0.177
C: 186/226 = 0.823
3.202E-01
GSE19177 vs.
HapMap JPT
HapMap JPT
T: 110/172 = 0.640
C: 62/172 = 0.360
0.988
GSE19177 vs.
HapMap CHB
HapMap CHB
T: 106/168 = 0.631
C: 62/168 = 0.369
5.452E-01
GSE19177 vs.
HapMap CEU
HapMap CEU
T: 68/224 = 0.304
C: 156/224 = 0.696
0.366
23
39
G and p-values are Williams’-corrected
40
186
9.683
1.860E-03
172
2. rs929039 (LGALS1)
a. GSE16019: Chen M et al. (2009)
Allele
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
T: 102/150 = 0.680
C: 48/150 = 0.320
HapMap Phase 1
(aggr.)
T: 575/792 = 0.726
C: 217/792 = 0.274
GSE16019 vs.
HapMap aggr.
102
48
575
217
102
48
152
74
102
48
132
36
102
48
119
53
1
4.528
3.334E-02
0.052
8.196E-01
GSE16019 vs.
HapMap YRI
HapMap YRI
T: 172/226 = 0.761
C: 54/226 = 0.239
8.795E-01
GSE16019 vs.
HapMap JPT
HapMap JPT
T: 119/172 = 0.692
C: 53/172 = 0.308
0.023
GSE16019 vs.
HapMap CHB
HapMap CHB
T: 132/168 = 0.786
C: 36/168 = 0.214
2.566E-01
GSE16019 vs.
HapMap CEU
HapMap CEU
T: 152/226 = 0.673
C: 74/226 = 0.327
1.287
102
48
G and p-values are Williams’-corrected
172
54
2.951
8.582E-02
173
b. GSE13282: Gordan et al. (2008)
Allele
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
T: 22/36 = 0.611
C: 14/36 = 0.389
HapMap Phase 1
(aggr.)
T: 575/792 = 0.726
C: 217/792 = 0.274
GSE13282 vs.
HapMap aggr.
22
14
575
217
22
14
152
74
22
14
132
36
22
14
119
53
1
4.423
3.546E-02
0.852
3.560E-01
GSE13282 vs.
HapMap YRI
HapMap YRI
T: 172/226 = 0.761
C: 54/226 = 0.239
4.760E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
T: 119/172 = 0.692
C: 53/172 = 0.308
0.508
GSE13282 vs.
HapMap CHB
HapMap CHB
T: 132/168 = 0.786
C: 36/168 = 0.214
1.488E-01
GSE13282 vs.
HapMap CEU
HapMap CEU
T: 152/226 = 0.673
C: 74/226 = 0.327
2.084
22
14
G and p-values are Williams’-corrected
172
54
3.317
6.857E-02
174
c. GSE19189: Letouze et al. (2010)
Allele
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
T: 24/38 = 0.632
C: 14/38 = 368
HapMap Phase 1
(aggr.)
T: 575/792 = 0.726
C: 217/792 = 0.274
GSE19189 vs.
HapMap aggr.
24
14
575
217
24
14
152
74
24
14
132
36
24
14
119
53
1
3.658
5.580E-02
0.503
4.782E-01
GSE19189 vs.
HapMap YRI
HapMap YRI
T: 172/226 = 0.761
C: 54/226 = 0.239
6.249E-01
GSE19189 vs.
HapMap JPT
HapMap JPT
T: 119/172 = 0.692
C: 53/172 = 0.308
0.239
GSE19189 vs.
HapMap CHB
HapMap CHB
T: 132/168 = 0.786
C: 36/168 = 0.214
2.207E-01
GSE19189 vs.
HapMap CEU
HapMap CEU
T: 152/226 = 0.673
C: 74/226 = 0.327
1.5
24
14
G and p-values are Williams’-corrected
172
54
2.627
1.051E-01
175
d. GSE10506: Nancarrow et al. (2008)
Allele
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
T: 30/40 = 0.75
C: 10/40 = 0.25
HapMap Phase 1
(aggr.)
T: 575/792 = 0.726
C: 217/792 = 0.274
GSE10506 vs.
HapMap aggr.
30
10
575
217
30
10
152
74
30
10
132
36
30
10
119
53
1
0.229
6.323E-01
0.53
4.666E-01
GSE10506 vs.
HapMap YRI
HapMap YRI
T: 172/226 = 0.761
C: 54/226 = 0.239
3.267E-01
GSE10506 vs.
HapMap JPT
HapMap JPT
T: 119/172 = 0.692
C: 53/172 = 0.308
0.962
GSE10506 vs.
HapMap CHB
HapMap CHB
T: 132/168 = 0.786
C: 36/168 = 0.214
7.401E-01
GSE10506 vs.
HapMap CEU
HapMap CEU
T: 152/226 = 0.673
C: 74/226 = 0.327
0.11
30
10
G and p-values are Williams’-corrected
172
54
0.023
8.795E-01
176
e. GSE18799: Popova et al. (2009)
Allele
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
C: 16/38 = 0.421
T: 22/38 = 0.579
HapMap Phase 1
(aggr.)
T: 575/792 = 0.726
C: 217/792 = 0.274
GSE18799 vs.
HapMap aggr.
22
16
575
217
22
16
152
74
22
16
132
36
22
16
119
53
1
6.343
1.178E-02
1.717
1.901E-01
GSE18799 vs.
HapMap YRI
HapMap YRI
T: 172/226 = 0.761
C: 54/226 = 0.239
2.700E-01
GSE18799 vs.
HapMap JPT
HapMap JPT
T: 119/172 = 0.692
C: 53/172 = 0.308
1.217
GSE18799 vs.
HapMap CHB
HapMap CHB
T: 132/168 = 0.786
C: 36/168 = 0.214
5.991E-02
GSE18799 vs.
HapMap CEU
HapMap CEU
T: 152/226 = 0.673
C: 74/226 = 0.327
3.54
22
16
G and p-values are Williams’-corrected
172
54
5.029
2.493E-02
177
f. GSE9003: Stark & Hayward (2007)
Allele
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
T: 88/128 = 0.688
C: 40/128 = 0.313
HapMap Phase 1
(aggr.)
T: 575/792 = 0.726
C: 217/792 = 0.274
GSE9003 vs.
HapMap aggr.
88
40
575
217
88
40
152
74
88
40
132
36
88
40
119
53
1
3.621
5.705E-02
0.006
9.383E-01
GSE9003 vs.
HapMap YRI
HapMap YRI
T: 172/226 = 0.761
C: 54/226 = 0.239
7.733E-01
GSE9003 vs.
HapMap JPT
HapMap JPT
T: 119/172 = 0.692
C: 53/172 = 0.308
0.083
GSE9003 vs.
HapMap CHB
HapMap CHB
T: 132/168 = 0.786
C: 36/168 = 0.214
3.735E-01
GSE9003 vs.
HapMap CEU
HapMap CEU
T: 152/226 = 0.673
C: 74/226 = 0.327
0.792
88
40
G and p-values are Williams’-corrected
172
54
2.223
1.360E-01
178
g. GSE19177: Waddell et al. (2010)
Allele
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
T: 37/64 = 0.578
C: 27/64 = 0.422
HapMap Phase 1
(aggr.)
T: 575/792 = 0.726
C: 217/792 = 0.274
GSE19177 vs.
HapMap aggr.
37
27
575
217
37
27
152
74
37
27
132
36
37
27
119
53
1
9.502
2.052E-03
2.614
1.059E-01
GSE19177 vs.
HapMap YRI
HapMap YRI
T: 172/226 = 0.761
C: 54/226 = 0.239
1.676E-01
GSE19177 vs.
HapMap JPT
HapMap JPT
T: 119/172 = 0.692
C: 53/172 = 0.308
1.904
GSE19177 vs.
HapMap CHB
HapMap CHB
T: 132/168 = 0.786
C: 36/168 = 0.214
1.540E-02
GSE19177 vs.
HapMap CEU
HapMap CEU
T: 152/226 = 0.673
C: 74/226 = 0.327
5.87
37
27
G and p-values are Williams’-corrected
172
54
7.762
5.336E-03
179
3. rs2235338 (LGALS2)
a. GSE21168: Castillo et al. (2010)
Allele
frequencies
Castillo,
GSE21168
GSE21168
HapMap
G1
p-value1
G: 5/8 = 0.625
A: 3/8 = 0.375
HapMap Phase 1
(aggr.)
G: 440/792 = 0.556
A: 352/792 = 0.445
GSE21168 vs.
HapMap aggr.
5
3
440
352
5
3
84
142
5
3
118
50
5
3
85
87
1
0.194
6.596E-01
0.498
4.804E-01
GSE21168 vs.
HapMap YRI
HapMap YRI
G: 153/226 = 0.677
A: 73/226 = 0.323
1.684E-01
GSE21168 vs.
HapMap JPT
HapMap JPT
G: 85/172 = 0.494
A: 87/172 = 0.506
1.897
GSE21168 vs.
HapMap CHB
HapMap CHB
G: 118/168 = 0.702
A: 50/168 = 0.298
7.005E-01
GSE21168 vs.
HapMap CEU
HapMap CEU
G: 84/226 = 0.372
A: 142/226 = 0.628
0.148
5
3
G and p-values are Williams’-corrected
153
73
0.087
7.680E-01
180
b. GSE16019: Chen M et al. (2009)
Allele
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
G: 66/152 = 0.434
A: 86/152 = 0.566
HapMap Phase 1
(aggr.)
G: 440/792 = 0.556
A: 352/792 = 0.445
GSE16019 vs.
HapMap aggr.
66
86
440
352
66
86
84
142
66
86
118
50
66
86
85
87
1
23.626
1.170E-06
1.163
2.809E-01
GSE16019 vs.
HapMap YRI
HapMap YRI
G: 153/226 = 0.677
A: 73/226 = 0.323
2.247E-01
GSE16019 vs.
HapMap JPT
HapMap JPT
G: 85/172 = 0.494
A: 87/172 = 0.506
1.474
GSE16019 vs.
HapMap CHB
HapMap CHB
G: 118/168 = 0.702
A: 50/168 = 0.298
6.139E-03
GSE16019 vs.
HapMap CEU
HapMap CEU
G: 84/226 = 0.372
A: 142/226 = 0.628
7.509
66
86
G and p-values are Williams’-corrected
153
73
21.922
2.840E-06
181
c. GSE13282: Gordan et al. (2008)
Allele
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
G: 17/40 = 0.425
A: 23/40 = 0.575
HapMap Phase 1
(aggr.)
G: 440/792 = 0.556
A: 352/792 = 0.445
GSE13282 vs.
HapMap aggr.
17
23
440
352
17
23
84
142
17
23
118
50
17
23
85
87
1
10.316
1.319E-03
0.617
4.322E-01
GSE13282 vs.
HapMap YRI
HapMap YRI
G: 153/226 = 0.677
A: 73/226 = 0.323
5.271E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
G: 85/172 = 0.494
A: 87/172 = 0.506
0.4
GSE13282 vs.
HapMap CHB
HapMap CHB
G: 118/168 = 0.702
A: 50/168 = 0.298
1.086E-01
GSE13282 vs.
HapMap CEU
HapMap CEU
G: 84/226 = 0.372
A: 142/226 = 0.628
2.574
17
23
G and p-values are Williams’-corrected
153
73
8.859
2.916E-03
182
d. GSE19189: Letouze et al. (2010)
Allele
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
G: 14/38 = 0.368
A: 24/38 = 0.632
HapMap Phase 1
(aggr.)
G: 440/792 = 0.556
A: 352/792 = 0.445
GSE19189 vs.
HapMap aggr.
14
24
440
352
14
24
84
142
14
24
118
50
14
24
85
87
1
14.222
1.625E-04
1.973
1.601E-01
GSE19189 vs.
HapMap YRI
HapMap YRI
G: 153/226 = 0.677
A: 73/226 = 0.323
9.748E-01
GSE19189 vs.
HapMap JPT
HapMap JPT
G: 85/172 = 0.494
A: 87/172 = 0.506
0.001
GSE19189 vs.
HapMap CHB
HapMap CHB
G: 118/168 = 0.702
A: 50/168 = 0.298
4.773E-01
GSE19189 vs.
HapMap CEU
HapMap CEU
G: 84/226 = 0.372
A: 142/226 = 0.628
0.505
14
24
G and p-values are Williams’-corrected
153
73
12.633
3.790E-04
183
e. GSE10506: Nancarrow et al. (2008)
Allele
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
G: 15/40 = 0.375
A: 25/40 = 0.625
HapMap Phase 1
(aggr.)
G: 440/792 = 0.556
A: 352/792 = 0.445
GSE10506 vs.
HapMap aggr.
15
25
440
352
15
25
84
142
15
25
118
50
15
25
85
87
1
14.26
1.592E-04
1.846
1.742E-01
GSE10506 vs.
HapMap YRI
HapMap YRI
G: 153/226 = 0.677
A: 73/226 = 0.323
9.643E-01
GSE10506 vs.
HapMap JPT
HapMap JPT
G: 85/172 = 0.494
A: 87/172 = 0.506
0.002
GSE10506 vs.
HapMap CHB
HapMap CHB
G: 118/168 = 0.702
A: 50/168 = 0.298
2.627E-02
GSE10506 vs.
HapMap CEU
HapMap CEU
G: 84/226 = 0.372
A: 142/226 = 0.628
4.938
15
25
G and p-values are Williams’-corrected
153
73
12.651
3.754E-04
184
f. GSE18799: Popova et al. (2009)
Allele
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
G: 22/38 = 0.579
A: 16/38 = 0.421
HapMap Phase 1
(aggr.)
G: 440/792 = 0.556
A: 352/792 = 0.445
GSE18799 vs.
HapMap aggr.
22
16
440
352
22
16
84
142
22
16
118
50
22
16
85
87
1
2.061
1.511E-01
0.886
3.466E-01
GSE18799 vs.
HapMap YRI
HapMap YRI
G: 153/226 = 0.677
A: 73/226 = 0.323
1.782E-02
GSE18799 vs.
HapMap JPT
HapMap JPT
G: 85/172 = 0.494
A: 87/172 = 0.506
5.614
GSE18799 vs.
HapMap CHB
HapMap CHB
G: 118/168 = 0.702
A: 50/168 = 0.298
7.773E-01
GSE18799 vs.
HapMap CEU
HapMap CEU
G: 84/226 = 0.372
A: 142/226 = 0.628
0.08
22
16
G and p-values are Williams’-corrected
153
73
1.339
2.472E-01
185
g. GSE9003: Stark & Hayward (2007)
Allele
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
G: 53/144 = 0.368
A: 91/144 = 0.632
HapMap Phase 1
(aggr.)
G: 440/792 = 0.556
A: 352/792 = 0.445
GSE9003 vs.
HapMap aggr.
53
91
440
352
53
91
84
142
53
91
118
50
53
91
85
87
1
35.414
2.666E-09
5.069
2.436E-02
GSE9003 vs.
HapMap YRI
HapMap YRI
G: 153/226 = 0.677
A: 73/226 = 0.323
9.436E-01
GSE9003 vs.
HapMap JPT
HapMap JPT
G: 85/172 = 0.494
A: 87/172 = 0.506
0.005
GSE9003 vs.
HapMap CHB
HapMap CHB
G: 118/168 = 0.702
A: 50/168 = 0.298
3.345E-05
GSE9003 vs.
HapMap CEU
HapMap CEU
G: 84/226 = 0.372
A: 142/226 = 0.628
17.211
53
91
G and p-values are Williams’-corrected
153
73
34.16
5.076E-09
186
h. GSE19177: Waddell et al. (2010)
Allele
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
G: 31/68 = 0.456
A: 37/68 = 0.544
HapMap Phase 1
(aggr.)
G: 440/792 = 0.556
A: 352/792 = 0.445
GSE19177 vs.
HapMap aggr.
31
37
440
352
31
37
84
142
31
37
118
50
31
37
85
87
1
12.268
4.608E-04
0.285
5.934E-01
GSE19177 vs.
HapMap YRI
HapMap YRI
G: 153/226 = 0.677
A: 73/226 = 0.323
2.167E-01
GSE19177 vs.
HapMap JPT
HapMap JPT
G: 85/172 = 0.494
A: 87/172 = 0.506
1.526
GSE19177 vs.
HapMap CHB
HapMap CHB
G: 118/168 = 0.702
A: 50/168 = 0.298
1.154E-01
GSE19177 vs.
HapMap CEU
HapMap CEU
G: 84/226 = 0.372
A: 142/226 = 0.628
2.479
31
37
G and p-values are Williams’-corrected
153
73
10.554
1.159E-03
187
4. rs3763959 (LGALS9)
a. GSE21168: Castillo et al. (2010)
Allele
frequencies
Castillo,
GSE21168
GSE21168
HapMap
G1
p-value1
A: 3/8 = 0.375
G: 5/8 = 0.625
HapMap Phase 1
(aggr.)
A: 240/790 = 0.304
G: 550/790 = 0.696
GSE21168 vs.
HapMap aggr.
3
5
240
550
3
5
101
125
3
5
58
110
3
5
52
118
1
0.028
8.671E-01
0.153
6.957E-01
GSE21168 vs.
HapMap YRI
HapMap YRI
A: 29/226 = 0.128
G: 197/226 = 0.872
6.947E-01
GSE21168 vs.
HapMap JPT
HapMap JPT
A: 52/170 = 0.306
G: 118/170 = 0.694
0.154
GSE21168 vs.
HapMap CHB
HapMap CHB
A: 58/168 = 0.345
G: 110/168 = 0.655
6.801E-01
GSE21168 vs.
HapMap CEU
HapMap CEU
A: 101/226 = 0.447
G: 125/226 = 0.553
0.17
3
5
G and p-values are Williams’-corrected
29
197
2.563
1.094E-01
188
b. GSE16019: Chen M et al. (2009)
Allele
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
A: 65/154 = 0.422
G: 89/154 = 0.578
HapMap Phase 1
(aggr.)
A: 240/790 = 0.304
G: 550/790 = 0.696
GSE16019 vs.
HapMap aggr.
65
89
240
550
65
89
101
125
65
89
101
125
65
89
58
110
1
0.229
6.323E-01
2
1.573E-01
GSE16019 vs.
HapMap YRI
HapMap YRI
A: 29/226 = 0.128
G: 197/226 = 0.872
6.323E-01
GSE16019 vs.
HapMap JPT
HapMap JPT
A: 58/168 = 0.345
G: 110/168 = 0.655
0.229
GSE16019 vs.
HapMap CHB
HapMap CHB
A: 101/226 = 0.447
G: 125/226 = 0.553
4.865E-03
GSE16019 vs.
HapMap CEU
HapMap CEU
A: 101/226 = 0.447
G: 125/226 = 0.553
7.929
65
89
G and p-values are Williams’-corrected
29
197
41.98
9.221E-11
189
c. GSE13282: Gordan et al. (2008)
Allele
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
A: 22/42 = 0.524
G: 20/42 = 0.476
HapMap Phase 1
(aggr.)
A: 240/790 = 0.304
G: 550/790 = 0.696
GSE13282 vs.
HapMap aggr.
22
20
240
550
22
20
101
125
22
20
58
110
22
20
52
118
1
4.379
3.638E-02
6.684
9.728E-03
GSE13282 vs.
HapMap YRI
HapMap YRI
A: 29/226 = 0.128
G: 197/226 = 0.872
3.620E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
A: 52/170 = 0.306
G: 118/170 = 0.694
0.831
GSE13282 vs.
HapMap CHB
HapMap CHB
A: 58/168 = 0.345
G: 110/168 = 0.655
4.242E-03
GSE13282 vs.
HapMap CEU
HapMap CEU
A: 101/226 = 0.447
G: 125/226 = 0.553
8.177
22
20
G and p-values are Williams’-corrected
29
197
28.875
7.720E-08
190
d. GSE19189: Letouze et al. (2010)
Allele
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
A: 15/38 = 0.395
G: 23/38 = 0.605
HapMap Phase 1
(aggr.)
A: 240/790 = 0.304
G: 550/790 = 0.696
GSE19189 vs.
HapMap aggr.
15
23
240
550
15
23
101
125
15
23
58
110
15
23
52
118
1
0.323
5.698E-01
1.074
3.000E-01
GSE19189 vs.
HapMap YRI
HapMap YRI
A: 29/226 = 0.128
G: 197/226 = 0.872
5.502E-01
GSE19189 vs.
HapMap JPT
HapMap JPT
A: 52/170 = 0.306
G: 118/170 = 0.694
0.357
GSE19189 vs.
HapMap CHB
HapMap CHB
A: 58/168 = 0.345
G: 110/168 = 0.655
2.493E-01
GSE19189 vs.
HapMap CEU
HapMap CEU
A: 101/226 = 0.447
G: 125/226 = 0.553
1.327
15
23
G and p-values are Williams’-corrected
29
197
13.346
2.590E-04
191
e. GSE10506: Nancarrow et al. (2008)
Allele
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
A: 10/32 = 0.313
G: 22/32 = 0.688
HapMap Phase 1
(aggr.)
A: 240/790 = 0.304
G: 550/790 = 0.696
GSE10506 vs.
HapMap aggr.
10
22
240
550
10
22
101
125
10
22
58
110
10
22
52
118
1
0.128
7.205E-01
0.005
9.436E-01
GSE10506 vs.
HapMap YRI
HapMap YRI
A: 29/226 = 0.128
G: 197/226 = 0.872
1.481E-01
GSE10506 vs.
HapMap JPT
HapMap JPT
A: 52/170 = 0.306
G: 118/170 = 0.694
2.092
GSE10506 vs.
HapMap CHB
HapMap CHB
A: 58/168 = 0.345
G: 110/168 = 0.655
9.165E-01
GSE10506 vs.
HapMap CEU
HapMap CEU
A: 101/226 = 0.447
G: 125/226 = 0.553
0.011
10
22
G and p-values are Williams’-corrected
29
197
5.994
1.435E-02
192
f. GSE18799: Popova et al. (2009)
Allele
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
A: 11/36 = 0.306
G: 25/36 = 0.694
HapMap Phase 1
(aggr.)
A: 240/790 = 0.304
G: 550/790 = 0.696
GSE18799 vs.
HapMap aggr.
11
25
240
550
11
25
101
125
11
25
58
110
11
25
52
118
1
0.208
6.483E-01
<0.001
<1
GSE18799 vs.
HapMap YRI
HapMap YRI
A: 29/226 = 0.128
G: 197/226 = 0.872
1.086E-01
GSE18799 vs.
HapMap JPT
HapMap JPT
A: 52/170 = 0.306
G: 118/170 = 0.694
2.575
GSE18799 vs.
HapMap CHB
HapMap CHB
A: 58/168 = 0.345
G: 110/168 = 0.655
<1
GSE18799 vs.
HapMap CEU
HapMap CEU
A: 101/226 = 0.447
G: 125/226 = 0.553
<0.001
11
25
G and p-values are Williams’-corrected
29
197
6.203
1.275E-02
193
g. GSE9003: Stark & Hayward (2007)
Allele
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
A: 62/144 = 0.430
G: 72/144 = 0.500
HapMap Phase 1
(aggr.)
A: 240/790 = 0.304
G: 550/790 = 0.696
GSE9003 vs.
HapMap aggr.
62
72
240
550
62
72
101
125
62
72
58
110
62
72
52
118
1
4.267
3.886E-02
7.81
5.196E-03
GSE9003 vs.
HapMap YRI
HapMap YRI
A: 29/226 = 0.128
G: 197/226 = 0.872
7.706E-01
GSE9003 vs.
HapMap JPT
HapMap JPT
A: 52/170 = 0.306
G: 118/170 = 0.694
0.085
GSE9003 vs.
HapMap CHB
HapMap CHB
A: 58/168 = 0.345
G: 110/168 = 0.655
4.035E-04
GSE9003 vs.
HapMap CEU
HapMap CEU
A: 101/226 = 0.447
G: 125/226 = 0.553
12.516
62
72
G and p-values are Williams’-corrected
29
197
48.535
3.244E-12
194
h. GSE19177: Waddell et al. (2010)
Allele
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
A: 26/62 = 0.419
G: 36/62 = 0.581
HapMap Phase 1
(aggr.)
A: 240/790 = 0.304
G: 550/790 = 0.696
GSE19177 vs.
HapMap aggr.
26
36
240
550
26
36
101
125
26
36
58
110
26
36
52
118
1
1.051
3.053E-01
2.541
1.109E-01
GSE19177 vs.
HapMap YRI
HapMap YRI
A: 29/226 = 0.128
G: 197/226 = 0.872
6.995E-01
GSE19177 vs.
HapMap JPT
HapMap JPT
A: 52/170 = 0.306
G: 118/170 = 0.694
0.149
GSE19177 vs.
HapMap CHB
HapMap CHB
A: 58/168 = 0.345
G: 110/168 = 0.655
6.607E-02
GSE19177 vs.
HapMap CEU
HapMap CEU
A: 101/226 = 0.447
G: 125/226 = 0.553
3.378
26
36
G and p-values are Williams’-corrected
29
197
22.99
1.628E-06
195
5. rs4820294 (LGALS1)
a. GSE21168: Castillo et al. (2010)
Allele
frequencies
Castillo,
GSE21168
GSE21168
HapMap
G1
p-value1
G: 1/6 = 0.167
A: 5/6 = 0.833
HapMap Phase 1
(aggr.)
G: 569/784 = 0.726
A: 215/784 = 0.274
GSE21168 vs.
HapMap aggr.
1
5
569
215
1
5
150
74
1
5
132
36
1
5
116
52
1
8.894
2.861E-03
6.189
1.285E-02
GSE21168 vs.
HapMap YRI
HapMap YRI
G: 171/224 = 0.763
A: 53/224 = 0.237
1.654E-02
GSE21168 vs.
HapMap JPT
HapMap JPT
G: 116/168 = 0.690
A: 52/168 = 0.310
5.745
GSE21168 vs.
HapMap CHB
HapMap CHB
G: 132/168 = 0.786
A: 36/168 = 0.214
6.915E-03
GSE21168 vs.
HapMap CEU
HapMap CEU
G: 150/224 = 0.670
A: 74/224 = 0.330
7.295
1
5
G and p-values are Williams’-corrected
171
53
8.256
4.062E-03
196
b. GSE16019: Chen M et al. (2009)
Allele
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
G: 97/140 = 0.693
A: 43/140 = 0.307
HapMap Phase 1
(aggr.)
G: 569/784 = 0.726
A: 215/784 = 0.274
GSE16019 vs.
HapMap aggr.
97
43
569
215
97
43
150
74
97
43
132
36
97
43
116
52
1
3.418
6.449E-02
0.002
9.643E-01
GSE16019 vs.
HapMap YRI
HapMap YRI
G: 171/224 = 0.763
A: 53/224 = 0.237
6.444E-01
GSE16019 vs.
HapMap JPT
HapMap JPT
G: 116/168 = 0.690
A: 52/168 = 0.310
0.213
GSE16019 vs.
HapMap CHB
HapMap CHB
G: 132/168 = 0.786
A: 36/168 = 0.214
4.288E-01
GSE16019 vs.
HapMap CEU
HapMap CEU
G: 150/224 = 0.670
A: 74/224 = 0.330
0.626
97
43
G and p-values are Williams’-corrected
171
53
2.171
1.406E-01
197
c. GSE13282: Gordan et al. (2008)
Allele
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
G: 21/30 = 0.7
A: 9/30 = 0.3
HapMap Phase 1
(aggr.)
G: 569/784 = 0.726
A: 215/784 = 0.274
GSE13282 vs.
HapMap aggr.
21
9
569
215
21
9
150
74
21
9
132
36
21
9
116
52
1
0.927
3.356E-01
0.011
9.165E-01
GSE13282 vs.
HapMap YRI
HapMap YRI
G: 171/224 = 0.763
A: 53/224 = 0.237
6.005E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
G: 116/168 = 0.690
A: 52/168 = 0.310
0.11
GSE13282 vs.
HapMap CHB
HapMap CHB
G: 132/168 = 0.786
A: 36/168 = 0.214
7.604E-01
GSE13282 vs.
HapMap CEU
HapMap CEU
G: 150/224 = 0.670
A: 74/224 = 0.330
0.093
21
9
G and p-values are Williams’-corrected
171
53
0.541
4.620E-01
198
d. GSE19189: Letouze et al. (2010)
Allele
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
G: 24/40 = 0.600
A: 16/40 = 0.400
HapMap Phase 1
(aggr.)
G: 569/784 = 0.726
A: 215/784 = 0.274
GSE19189 vs.
HapMap aggr.
24
16
569
215
24
16
150
74
24
16
132
36
24
16
116
52
1
5.41
2.002E-02
1.154
2.827E-01
GSE19189 vs.
HapMap YRI
HapMap YRI
G: 171/224 = 0.763
A: 53/224 = 0.237
4.001E-01
GSE19189 vs.
HapMap JPT
HapMap JPT
G: 116/168 = 0.690
A: 52/168 = 0.310
0.708
GSE19189 vs.
HapMap CHB
HapMap CHB
G: 132/168 = 0.786
A: 36/168 = 0.214
9.744E-02
GSE19189 vs.
HapMap CEU
HapMap CEU
G: 150/224 = 0.670
A: 74/224 = 0.330
2.747
24
16
G and p-values are Williams’-corrected
171
53
4.289
3.836E-02
199
e. GSE10506: Nancarrow et al. (2008)
Allele
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
G: 31/44 = 0.705
A: 13/44 = 0.295
HapMap Phase 1
(aggr.)
G: 569/784 = 0.726
A: 215/784 = 0.274
GSE10506 vs.
HapMap aggr.
31
13
569
215
31
13
150
74
31
13
132
36
31
13
116
52
1
1.218
2.698E-01
0.033
8.559E-01
GSE10506 vs.
HapMap YRI
HapMap YRI
G: 171/224 = 0.763
A: 53/224 = 0.237
6.515E-01
GSE10506 vs.
HapMap JPT
HapMap JPT
G: 116/168 = 0.690
A: 52/168 = 0.310
0.204
GSE10506 vs.
HapMap CHB
HapMap CHB
G: 132/168 = 0.786
A: 36/168 = 0.214
7.616E-01
GSE10506 vs.
HapMap CEU
HapMap CEU
G: 150/224 = 0.670
A: 74/224 = 0.330
0.092
31
13
G and p-values are Williams’-corrected
171
53
0.653
4.190E-01
200
f. GSE18799: Popova et al. (2009)
Allele
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
G: 20/34 = 0.588
A: 14/34 = 0.411
HapMap Phase 1
(aggr.)
G: 569/784 = 0.726
A: 215/784 = 0.274
GSE18799 vs.
HapMap aggr.
20
14
569
215
20
14
150
74
20
14
132
36
20
14
116
52
1
5.312
2.118E-02
1.281
2.577E-01
GSE18799 vs.
HapMap YRI
HapMap YRI
G: 171/224 = 0.763
A: 53/224 = 0.237
3.605E-01
GSE18799 vs.
HapMap JPT
HapMap JPT
G: 116/168 = 0.690
A: 52/168 = 0.310
0.836
GSE18799 vs.
HapMap CHB
HapMap CHB
G: 132/168 = 0.786
A: 36/168 = 0.214
9.491E-02
GSE18799 vs.
HapMap CEU
HapMap CEU
G: 150/224 = 0.670
A: 74/224 = 0.330
2.789
20
14
G and p-values are Williams’-corrected
171
53
4.254
3.916E-02
201
g. GSE9003: Stark & Hayward (2007)
Allele
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
G: 86/112 = 0.769
A: 26/112 = 0.232
HapMap Phase 1
(aggr.)
G: 569/784 = 0.726
A: 215/784 = 0.274
GSE9003 vs.
HapMap aggr.
86
26
569
215
86
26
150
74
86
26
132
36
86
26
116
52
1
0.123
7.258E-01
2.016
1.556E-01
GSE9003 vs.
HapMap YRI
HapMap YRI
G: 171/224 = 0.763
A: 53/224 = 0.237
6.089E-02
GSE9003 vs.
HapMap JPT
HapMap JPT
G: 116/168 = 0.690
A: 52/168 = 0.310
3.513
GSE9003 vs.
HapMap CHB
HapMap CHB
G: 132/168 = 0.786
A: 36/168 = 0.214
3.425E-01
GSE9003 vs.
HapMap CEU
HapMap CEU
G: 150/224 = 0.670
A: 74/224 = 0.330
0.901
86
26
G and p-values are Williams’-corrected
171
53
0.008
9.287E-01
202
h. GSE19177: Waddell et al. (2010)
Allele
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
G: 34/54 = 0.630
A: 20/54 = 0.370
HapMap Phase 1
(aggr.)
G: 569/784 = 0.726
A: 215/784 = 0.274
GSE19177 vs.
HapMap aggr.
34
20
569
215
34
20
150
74
34
20
132
36
34
20
116
52
1
4.931
2.638E-02
0.672
4.124E-01
GSE19177 vs.
HapMap YRI
HapMap YRI
G: 171/224 = 0.763
A: 53/224 = 0.237
5.808E-01
GSE19177 vs.
HapMap JPT
HapMap JPT
G: 116/168 = 0.690
A: 52/168 = 0.310
0.305
GSE19177 vs.
HapMap CHB
HapMap CHB
G: 132/168 = 0.786
A: 36/168 = 0.214
1.407E-01
GSE19177 vs.
HapMap CEU
HapMap CEU
G: 150/224 = 0.670
A: 74/224 = 0.330
2.17
34
20
G and p-values are Williams’-corrected
171
53
3.759
5.252E-02
203
6. rs10403583 (LGALS4)
a. GSE21168: Castillo et al. (2010)
Allele
frequencies
Castillo,
GSE21168
GSE21168
HapMap
G1
p-value1
A: 1/6 = 0.167
G: 5/6 = 0.833
HapMap Phase 1
(aggr.)
A: 132/792 = 0.167
G: 660/792 = 0.833
GSE21168 vs.
HapMap aggr.
1
5
132
660
1
5
37
109
1
5
18
150
1
5
13
159
1
0.148
7.005E-01
0.384
5.355E-01
GSE21168 vs.
HapMap YRI
HapMap YRI
A: 64/226 = 0.283
G: 162/226 = 0.717
6.353E-01
GSE21168 vs.
HapMap JPT
HapMap JPT
A: 13/172 = 0.076
G: 159/172 = 0.924
0.225
GSE21168 vs.
HapMap CHB
HapMap CHB
A: 18/168 = 0.107
G: 150/168 = 0.893
1
GSE21168 vs.
HapMap CEU
HapMap CEU
A: 37/226 = 0.164
G: 109/226 = 0.836
0
1
5
G and p-values are Williams’-corrected
64
162
0.393
5.307E-01
204
b. GSE16019: Chen M et al. (2009)
Allele
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
A: 31/158 = 0.196
G: 127/158 = 0.804
HapMap Phase 1
(aggr.)
A: 132/792 = 0.167
G: 660/792 = 0.833
GSE16019 vs.
HapMap aggr.
31
127
132
660
31
127
37
109
31
127
18
150
31
127
13
159
1
5.043
2.473E-02
10.456
1.223E-03
GSE16019 vs.
HapMap YRI
HapMap YRI
A: 64/226 = 0.283
G: 162/226 = 0.717
2.334E-01
GSE16019 vs.
HapMap JPT
HapMap JPT
A: 13/172 = 0.076
G: 159/172 = 0.924
1.42
GSE16019 vs.
HapMap CHB
HapMap CHB
A: 18/168 = 0.107
G: 150/168 = 0.893
3.768E-01
GSE16019 vs.
HapMap CEU
HapMap CEU
A: 37/226 = 0.164
G: 109/226 = 0.836
0.781
31
127
G and p-values are Williams’-corrected
64
162
3.826
5.046E-02
205
c. GSE13282: Gordan et al. (2008)
Allele
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
A: 8/42 = 0.19
G: 34/42 = 0.81
HapMap Phase 1
(aggr.)
A: 132/792 = 0.167
G: 660/792 = 0.833
GSE13282 vs.
HapMap aggr.
8
34
132
660
8
34
37
109
8
34
37
109
8
34
18
150
1
0.723
3.952E-01
1.894
1.688E-01
GSE13282 vs.
HapMap YRI
HapMap YRI
A: 64/226 = 0.283
G: 162/226 = 0.717
3.952E-01
GSE13282 vs.
HapMap JPT
HapMap JPT
A: 18/168 = 0.107
G: 150/168 = 0.893
0.723
GSE13282 vs.
HapMap CHB
HapMap CHB
A: 37/226 = 0.164
G: 109/226 = 0.836
6.957E-01
GSE13282 vs.
HapMap CEU
HapMap CEU
A: 37/226 = 0.164
G: 109/226 = 0.836
0.153
8
34
G and p-values are Williams’-corrected
64
162
1.617
2.035E-01
206
d. GSE19189: Letouze et al. (2010)
Allele
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
A: 3/36 = 0.083
G: 33/36 = 0.917
HapMap Phase 1
(aggr.)
A: 132/792 = 0.167
G: 660/792 = 0.833
GSE19189 vs.
HapMap aggr.
3
33
132
660
3
33
37
109
3
33
37
109
3
33
13
159
1
5.618
1.778E-02
0.024
8.769E-01
GSE19189 vs.
HapMap YRI
HapMap YRI
A: 64/226 = 0.283
G: 162/226 = 0.717
1.778E-02
GSE19189 vs.
HapMap JPT
HapMap JPT
A: 13/172 = 0.076
G: 159/172 = 0.924
5.618
GSE19189 vs.
HapMap CHB
HapMap CHB
A: 37/226 = 0.164
G: 109/226 = 0.836
1.579E-01
GSE19189 vs.
HapMap CEU
HapMap CEU
A: 37/226 = 0.164
G: 109/226 = 0.836
1.994
3
33
G and p-values are Williams’-corrected
64
162
7.742
5.395E-03
207
e. GSE10506: Nancarrow et al. (2008)
Allele
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
A: 6/42 = 0.143
G: 36/42 = 0.857
HapMap Phase 1
(aggr.)
A: 132/792 = 0.167
G: 660/792 = 0.833
GSE10506 vs.
HapMap aggr.
6
36
132
660
6
36
37
109
6
36
18
150
6
36
13
159
1
0.389
8.232E-01
1.616
4.457E-01
GSE10506 vs.
HapMap YRI
HapMap YRI
A: 64/226 = 0.283
G: 162/226 = 0.717
3.015E-01
GSE10506 vs.
HapMap JPT
HapMap JPT
A: 13/172 = 0.076
G: 159/172 = 0.924
2.398
GSE10506 vs.
HapMap CHB
HapMap CHB
A: 18/168 = 0.107
G: 150/168 = 0.893
6.837E-01
GSE10506 vs.
HapMap CEU
HapMap CEU
A: 37/226 = 0.164
G: 109/226 = 0.836
0.166
6
36
G and p-values are Williams’-corrected
64
162
3.946
1.390E-01
208
f. GSE18799: Popova et al. (2009)
Allele
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
A: 12/42 = 0.286
G: 30/42 = 0.714
HapMap Phase 1
(aggr.)
A: 132/792 = 0.167
G: 660/792 = 0.833
GSE18799 vs.
HapMap aggr.
12
30
132
660
12
30
37
109
12
30
18
150
12
30
13
159
1
7.366
6.647E-03
11.505
6.941E-04
GSE18799 vs.
HapMap YRI
HapMap YRI
A: 64/226 = 0.283
G: 162/226 = 0.717
6.792E-01
GSE18799 vs.
HapMap JPT
HapMap JPT
A: 13/172 = 0.076
G: 159/172 = 0.924
0.171
GSE18799 vs.
HapMap CHB
HapMap CHB
A: 18/168 = 0.107
G: 150/168 = 0.893
6.551E-02
GSE18799 vs.
HapMap CEU
HapMap CEU
A: 37/226 = 0.164
G: 109/226 = 0.836
3.392
12
30
G and p-values are Williams’-corrected
64
162
0.001
9.748E-01
209
g. GSE9003: Stark & Hayward (2007)
Allele
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
A: 21/144 = 0.146
G: 123/144 = 0.854
HapMap Phase 1
(aggr.)
A: 132/792 = 0.167
G: 660/792 = 0.833
GSE9003 vs.
HapMap aggr.
21
123
132
660
21
123
37
109
21
123
18
150
21
123
13
159
1
1.044
3.069E-01
3.964
4.648E-02
GSE9003 vs.
HapMap YRI
HapMap YRI
A: 64/226 = 0.283
G: 162/226 = 0.717
2.188E-02
GSE9003 vs.
HapMap JPT
HapMap JPT
A: 13/172 = 0.076
G: 159/172 = 0.924
5.255
GSE9003 vs.
HapMap CHB
HapMap CHB
A: 18/168 = 0.107
G: 150/168 = 0.893
5.307E-01
GSE9003 vs.
HapMap CEU
HapMap CEU
A: 37/226 = 0.164
G: 109/226 = 0.836
0.393
21
123
G and p-values are Williams’-corrected
64
162
9.754
1.789E-03
210
h. GSE19177: Waddell et al. (2010)
Allele
frequencies
Waddell,
GSE19177
GSE19188
HapMap
G1
p-value1
A: 6/64 = 0.094
G: 58/64 = 0.906
HapMap Phase 1
(aggr.)
A: 132/792 = 0.167
G: 660/792 = 0.833
GSE19177 vs.
HapMap aggr.
6
58
132
660
HapMap CHB
A: 18/168 = 0.107
G: 150/168 = 0.893
6
58
6
58
37
109
18
150
6
58
13
159
1
5.587E-03
GSE19177 vs.
HapMap CHB
0.089
9.248E-01
0.196
6.580E-01
GSE19177 vs.
HapMap YRI
HapMap YRI
A: 64/226 = 0.283
G: 162/226 = 0.717
7.679
GSE19177 vs.
HapMap JPT
HapMap JPT
A: 13/172 = 0.076
G: 159/172 = 0.924
1.071E-01
GSE19177 vs.
HapMap CEU
HapMap CEU
A: 37/226 = 0.164
G: 109/226 = 0.836
2.597
6
58
G and p-values are Williams’-corrected
64
162
11.289
7.797E-04
211
7. rs10489789 (LGALS8)
a. GSE16019: Chen M et al. (2009)
Allele
frequencies
Chen,
GSE16019
GSE16019
HapMap
G1
p-value1
G: 136/150 = 0.907
A: 14/150 = 0.093
HapMap Phase 1
(aggr.)
G: 670/768 = 0.872
A: 98/768 = 0.128
GSE16019 vs.
HapMap aggr.
136
14
670
98
136
14
193
29
136
14
158
2
136
14
164
2
1
11.101
8.628E-04
11.567
6.713E-04
GSE16019 vs.
HapMap YRI
HapMap YRI
G: 155/220 = 0.295
A: 65/220 = 0.705
2.672E-01
GSE16019 vs.
HapMap JPT
HapMap JPT
G: 164/168 = 0.988
A: 2/166 = 0.012
1.231
GSE16019 vs.
HapMap CHB
HapMap CHB
G: 158/160 = 0.988
A: 2/160 = 0.013
2.288E-01
GSE16019 vs.
HapMap CEU
HapMap CEU
G: 193/222 = 0.869
A: 29/222 = 0.131
1.448
136
14
G and p-values are Williams’-corrected
155
65
23.461
1.275E-06
212
b. GSE13282: Gordan et al. (2008)
Allele
frequencies
Gordan,
GSE13282
GSE13282
HapMap
G1
p-value1
G: 36/42 = 0.857
A: 6/42 = 0.143
HapMap Phase 1
(aggr.)
G: 670/768 = 0.872
A: 98/768 = 0.128
X vs. HapMap
aggr.
36
6
670
98
36
6
193
29
36
6
158
2
36
6
164
2
1
10.297
1.332E-03
10.569
1.150E-03
X vs. HapMap
YRI
HapMap YRI
G: 155/220 = 0.295
A: 65/220 = 0.705
8.339E-01
X vs. HapMap
JPT
HapMap JPT
G: 164/168 = 0.988
A: 2/166 = 0.012
0.044
X vs. HapMap
CHB
HapMap CHB
G: 158/160 = 0.988
A: 2/160 = 0.013
7.800E-01
X vs. HapMap
CEU
HapMap CEU
G: 193/222 = 0.869
A: 29/222 = 0.131
0.078
36
6
G and p-values are Williams’-corrected
155
65
4.554
3.284E-02
213
c. GSE19189: Letouze et al. (2010)
Allele
frequencies
Letouze,
GSE19189
GSE19189
HapMap
G1
p-value1
G: 29/40 = 0.725
A: 11/40 = 0.275
HapMap Phase 1
(aggr.)
G: 670/768 = 0.872
A: 98/768 = 0.128
GSE19189 vs.
HapMap aggr.
29
11
670
98
29
11
193
29
HapMap JPT
G: 164/168 = 0.988
A: 2/166 = 0.012
29
11
29
11
158
2
164
2
1
3.125E-02
25.896
3.603E-07
GSE19189 vs.
HapMap JPT
26.457
2.695E-07
GSE19189 vs.
HapMap YRI
HapMap YRI
G: 155/220 = 0.295
A: 65/220 = 0.705
4.639
GSE19189 vs.
HapMap CHB
HapMap CHB
G: 158/160 = 0.988
A: 2/160 = 0.013
1.795E-02
GSE19189 vs.
HapMap CEU
HapMap CEU
G: 193/222 = 0.869
A: 29/222 = 0.131
5.601
29
11
G and p-values are Williams’-corrected
155
65
0.068
7.943E-01
214
d. GSE10506: Nancarrow et al. (2008)
Allele
frequencies
Nancarrow,
GSE10506
GSE10506
HapMap
G1
p-value1
G: 38/44 = 0.864
A: 4/44 = 0.091
HapMap Phase 1
(aggr.)
G: 670/768 = 0.872
A: 98/768 = 0.128
GSE10506 vs.
HapMap aggr.
38
4
670
98
38
4
193
29
38
4
158
2
38
4
164
2
1
5.344
2.079E-02
5.509
1.892E-02
GSE10506 vs.
HapMap YRI
HapMap YRI
G: 155/220 = 0.295
A: 65/220 = 0.705
5.189E-01
GSE10506 vs.
HapMap JPT
HapMap JPT
G: 164/168 = 0.988
A: 2/166 = 0.012
0.416
GSE10506 vs.
HapMap CHB
HapMap CHB
G: 158/160 = 0.988
A: 2/160 = 0.013
5.297E-01
GSE10506 vs.
HapMap CEU
HapMap CEU
G: 193/222 = 0.869
A: 29/222 = 0.131
0.395
38
4
G and p-values are Williams’-corrected
155
65
8.482
3.587E-03
215
e. GSE18799: Popova et al. (2009)
Allele
frequencies
Popova,
GSE18799
GSE18799
HapMap
G1
p-value1
G: 34/42 = 0.810
A: 8/42 = 0.190
HapMap Phase 1
(aggr.)
G: 670/768 = 0.872
A: 98/768 = 0.128
GSE18799 vs.
HapMap aggr.
34
8
670
98
34
8
193
29
34
8
158
2
34
8
164
2
1
15.863
6.810E-05
16.246
5.563E-05
GSE18799 vs.
HapMap YRI
HapMap YRI
G: 155/220 = 0.295
A: 65/220 = 0.705
3.297E-01
GSE18799 vs.
HapMap JPT
HapMap JPT
G: 164/168 = 0.988
A: 2/166 = 0.012
0.95
GSE18799 vs.
HapMap CHB
HapMap CHB
G: 158/160 = 0.988
A: 2/160 = 0.013
2.715E-01
GSE18799 vs.
HapMap CEU
HapMap CEU
G: 193/222 = 0.869
A: 29/222 = 0.131
1.209
34
8
G and p-values are Williams’-corrected
155
65
2.027
1.545E-01
216
f. GSE9003: Stark & Hayward (2007)
Allele
frequencies
Stark, GSE9003
GSE9003
HapMap
G1
p-value1
G: 130/146 = 0.890
A: 16/146 = 0.110
HapMap Phase 1
(aggr.)
G: 670/768 = 0.872
A: 98/768 = 0.128
GSE9003 vs.
HapMap aggr.
130
16
670
98
130
16
193
29
130
16
158
2
130
16
164
2
1
14.087
1.745E-04
14.643
1.299E-04
GSE9003 vs.
HapMap YRI
HapMap YRI
G: 155/220 = 0.295
A: 65/220 = 0.705
5.468E-01
GSE9003 vs.
HapMap JPT
HapMap JPT
G: 164/168 = 0.988
A: 2/166 = 0.012
0.363
GSE9003 vs.
HapMap CHB
HapMap CHB
G: 158/160 = 0.988
A: 2/160 = 0.013
5.419E-01
GSE9003 vs.
HapMap CEU
HapMap CEU
G: 193/222 = 0.869
A: 29/222 = 0.131
0.372
130
16
G and p-values are Williams’-corrected
155
65
18.783
1.465E-05
217
g. GSE19177: Waddell et al. (2010)
Allele
frequencies
Waddell,
GSE19177
GSE19177
HapMap
G1
p-value1
G: 48/58 = 0.828
A: 10/58 = 0.172
HapMap Phase 1
(aggr.)
G: 670/768 = 0.872
A: 98/768 = 0.128
GSE19177 vs.
HapMap aggr.
48
10
670
98
48
10
193
29
48
10
158
2
48
10
164
2
1
17.16
3.436E-05
17.657
2.645E-05
GSE19177 vs.
HapMap YRI
HapMap YRI
G: 155/220 = 0.295
A: 65/220 = 0.705
4.266E-01
GSE19177 vs.
HapMap JPT
HapMap JPT
G: 164/168 = 0.988
A: 2/166 = 0.012
0.632
GSE19177 vs.
HapMap CHB
HapMap CHB
G: 158/160 = 0.988
A: 2/160 = 0.013
3.471E-01
GSE19177 vs.
HapMap CEU
HapMap CEU
G: 193/222 = 0.869
A: 29/222 = 0.131
0.884
48
10
G and p-values are Williams’-corrected
155
65
3.763
5.240E-02
218
APPENDIX D
SNP genotypes in the GSE11976 dataset
Galectin upstream SNP genotypes for GSE11976 (Staaf et al., 2008), HCC1395
breast carcinoma cell line.
Gene
SNP
Location
Alleles (ref/other)
LGALS1
LGALS1
LGALS2
LGALS4
LGALS8 (long)
LGALS9
CLC/LGALS10
rs4820294
rs929039
rs2235338
rs10403583
rs10489789
rs3763959
rs428007
chr22:36400989
chr22:36401457
chr22:36295826
chr19:43983611
chr1:234746800
chr17:22981461
chr19:44913207
G/A
T/C
G/A
A/G
G/A
A/G
C/T
219
APPENDIX E
G-test results for Hardy-Weinberg equilibrium
1. rs428007 (CLC/LGALS10)
Genotype
frequencies
GSE16019,
Chen
T/T: 18/77 = 0.234
T/C: 30/77 = 0.390
C/C: 29/77 = 0.377
Observed
Expected
G1
p-value1
18
30
29
14.143
37.714
25.143
3.201
0.202
1
12
8
2.333
9.333
9.333
1.813
0.404
1
11
8
2.113
8.775
9.113
1.393
0.498
2
11
7
2.813
9.375
7.813
0.596
0.742
3
9
9
2.679
9.643
8.679
0.09
0.956
7
25
41
5.209
28.582
39.209
1.095
0.578
6
11
14
4.266
14.468
12.267
1.729
0.421
GSE13282,
Gordan
T/T: 1/21 = 0.048
T/C: 12/21 = 0.571
C/C: 8/21 = 0..381
GSE19189,
Letouze
T/T: 1/20 = 0.050
T/C: 11/20 = 0.550
C/C: 8/20 = 0.400
GSE10506,
Nancarrow
T/T: 2/20 = 0.100
T/C: 11/20 = 0.550
C/C: 7/20 = 0.350
GSE18799,
Popova
T/T: 3/21 = 0.143
T/C: 9/21 = 0.429
C/C: 9/21 = 0.429
GSE9003, Stark
T/T: 7/73 = 0.096
T/C: 25/73 = 0.342
C/C: 41/73 = 0.562
GSE19177,
Waddell
T/T: 6/31 = 0.164
T/C: 11/31 = 0.355
C/C: 14/31 = 0.452
1
G and p-values are Williams’-corrected
220
2. rs929039 (LGALS1)
Genotype
frequencies
GSE16019,
Chen
T/T: 34/75 = 0.453
T/C: 34/75 = 0.453
C/C: 7/75 = 0.093
Observed
Expected
G1
p-value1
34
34
7
34.680
32.640
7.680
0.13
0.937
6
10
2
6.722
8.556
2.722
0.504
0.777
7
10
2
7.579
8.842
2.579
0.332
0.847
13
4
3
11.250
7.500
1.250
3.855
0.146
7
8
4
6.368
9.263
3.368
0.341
0.843
30
28
6
30.250
27.500
6.250
0.021
0.99
13
11
8
10.695
15.609
5.695
2.754
0.252
GSE13282,
Gordan
T/T: 6/18 = 0.333
T/C: 10/18 = 0.556
C/C: 2/18 = 0.111
GSE19189,
Letouze
T/T: 7/19 = 0.368
T/C: 10/19 = 0.526
C/C: 2/19 = 0.105
GSE10506,
Nancarrow
T/T: 13/20 = 0.65
T/C: 4/20 = 0.20
C/C: 3/20 = 0.15
GSE18799,
Popova
T/T: 7/19 = 0.369
T/C: 8/19 = 0.421
C/C: 4/19 = 0.211
GSE9003, Stark
T/T: 30/64 = 0.469
T/C: 28/64 = 0.438
C/C: 6/64 = 0.094
GSE19177,
Waddell
T/T: 13/32 = 0.406
T/C: 11/32 = 0.344
C/C: 8/32 = 0.25
1
G and p-values are Williams’-corrected
221
3. rs2235338 (LGALS2)
Genotype
frequencies
GSE21168,
Castillo
A/A: 0/4 = 0.000
A/G: 3/4 = 0.750
G/G: 1/4 = 0.250
Observed
Expected
G1
p-value1
0
3
1
1.563
1.875
0.563
1.652
0.438
14
37
26
14.329
37.342
24.329
0.017
0.991
8
6
7
3.613
9.775
6.613
3.835
0.147
4
7
8
2.579
8.842
7.579
0.994
0.608
3
4
9
2.813
9.375
7.813
2.605
0.272
4
3
11
6.368
9.263
3.368
6.306
0.043
20
22
25
9.753
33.493
28.753
7.791
0.02
10
6
15
7.066
16.868
10.066
11.649
0.002954
GSE16019,
Chen
A/A: 14/77 = 0.182
A/G: 37/77 = 0.481
G/G: 26/77 = 0.338
GSE13282,
Gordan
A/A: 8/21 = 0.381
A/G: 6/21 = 0.286
G/G: 7/21 = 0.333
GSE19189,
Letouze
A/A: 4/19 = 0.211
A/G: 7/19 = 0.368
G/G: 8/19 = 0.421
GSE10506,
Nancarrow
A/A: 3/16 = 0.188
A/G: 4/16 = 0.25
G/G: 9/16 = 0.563
GSE18799,
Popova
A/A: 4/18 = 0.222
A/G: 3/18 = 0.167
G/G: 11/18 = 0.611
GSE9003, Stark
A/A: 20/67 = 0.299
A/G: 22/67 = 0.328
G/G: 25/67 = 0.373
GSE19177,
Waddell
A/A: 10/31 = 0.323
A/G: 6/31 = 0.194
G/G: 15/31 = 0.484
1
G and p-values are Williams’-corrected
222
4. rs3763959 (LGALS9)
Genotype
frequencies
GSE21168,
Castillo
A/A: 0/4 = 0.000
A/G: 3/4 = 0.750
G/G: 1/4 = 0.250
Observed
Expected
G1
p-value1
0
3
1
0.563
1.875
1.563
1.652
0.438
14
37
26
13.718
37.565
25.718
0.017
0.991
8
6
7
5.762
10.476
4.762
3.835
0.147
4
7
8
2.961
9.079
6.961
0.994
0.608
3
4
9
1.563
6.875
7.563
2.605
0.272
4
3
11
1.681
7.639
8.681
6.306
0.043
20
22
25
14.343
33.313
19.343
7.791
0.02
10
6
15
5.452
15.097
10.452
11.649
0.002954
GSE16019,
Chen
A/A: 14/77 = 0.182
A/G: 37/77 = 0.481
G/G: 26/77 = 0.338
GSE13282,
Gordan
A/A: 8/21 = 0.381
A/G: 6/21 = 0.286
G/G: 7/21 = 0.333
GSE19189,
Letouze
A/A: 4/19 = 0.211
A/G: 7/19 = 0.368
G/G: 8/19 = 0.421
GSE10506,
Nancarrow
A/A: 3/16 = 0.188
A/G: 4/16 = 0.25
G/G: 9/16 = 0.563
GSE18799,
Popova
A/A: 4/18 = 0.222
A/G: 3/18 = 0.167
G/G: 11/18 = 0.611
GSE9003, Stark
A/A: 20/67 = 0.299
A/G: 22/67 = 0.328
G/G: 25/67 = 0.373
GSE19177,
Waddell
A/A: 10/31 = 0.323
A/G: 6/31 = 0.194
G/G: 15/31 = 0.484
1
G and p-values are Williams’-corrected
223
5. rs4820294 (LGALS1)
Genotype
frequencies
GSE21168,
Castillo
G/G: 0/3 = 0.000
A/G: 1/3 = 0.333
A/A: 2/3 = 0.667
Observed
Expected
G1
p-value1
0
1
2
0.083
0.833
2.083
0.165
0.921
34
29
7
33.604
29.793
6.604
0.049
0.976
7
7
1
7.350
6.300
1.350
0.184
0.912
7
10
3
7.200
9.600
3.200
0.035
0.983
14
3
5
10.920
9.159
1.920
9.539
0.008486
6
8
3
5.882
8.235
2.882
0.013
0.993
38
10
8
33.018
19.964
3.018
12.305
0.002128
13
8
6
10.704
12.593
3.704
3.497
0.174
GSE16019,
Chen
G/G: 34/70 = 0.486
A/G: 29/70 = 0.414
A/A: 7/70 = 0.100
GSE13282,
Gordan
G/G: 7/15 = 0.467
A/G: 7/15 = 0.467
A/A: 1/15 = 0.067
GSE19189,
Letouze
G/G: 7/20 = 0.350
A/G: 10/20 = 0.500
A/A: 3/20 = 0.150
GSE10506,
Nancarrow
G/G: 14/22 = 0.636
A/G: 3/22 = 0.136
A/A: 5/22 = 0.227
GSE18799,
Popova
G/G: 6/17 = 0.353
A/G: 8/17 = 0.471
A/A: 3/17 = 0.176
GSE9003, Stark
G/G: 38/56 = 0.679
A/G: 10/56 = 0.179
A/A: 8/56 = 0.143
GSE19177,
Waddell
G/G: 13/27 = 0.481
A/G: 8/27 = 0.296
A/A: 6/27 = 0.222
1
G and p-values are Williams’-corrected
224
6. rs10403583 (LGALS4)
Genotype
frequencies
GSE21168,
Castillo
A/A: 0/3 = 0.000
A/G: 1/3 = 0.333
G/G: 2/3 = 0.667
Observed
Expected
G1
p-value1
0
1
2
0.083
0.833
2.083
0.165
0.921
3
25
51
3.041
24.918
51.041
0.000856
1
1
6
14
0.762
6.476
13.762
0.104
0.949
0
3
15
0.125
2.750
15.125
0.273
0.872
0
6
15
0.429
5.143
15.429
0.974
0.615
2
8
11
1.714
8.571
10.714
0.089
0.957
5
11
56
1.531
17.938
52.531
8.162
0.017
0
6
26
0.281
5.438
26.281
0.609
0.737
GSE16019,
Chen
A/A: 3/79 = 0.038
A/G: 25/79 = 0.316
G/G: 51/79 = 0.646
GSE13282,
Gordan
A/A: 1/21 = 0.048
A/G: 6/21 = 0.286
G/G: 14/21 = 0.667
GSE19189,
Letouze
A/A: 0/18 = 0.000
A/G: 3/18 = 0.167
G/G: 15/18 = 0.833
GSE10506,
Nancarrow
A/A: 0/21 = 0
A/G: 6/21 = 0.286
G/G: 15/21 = 0.714
GSE18799,
Popova
A/A: 2/21 = 0.095
A/G: 8/21 = 0.381
G/G: 11/21 = 0.524
GSE9003, Stark
A/A: 5/72 = 0.069
A/G: 11/72 = 0.153
G/G: 56/72 = 0.778
GSE19177,
Waddell
A/A: 0/32 = 0.000
A/G: 6/32 = 0.188
G/G: 26/32 = 0.813
1
G and p-values are Williams’-corrected
225
7. rs10489789 (LGALS8)
Genotype
frequencies
GSE16019,
Chen
G/G: 63/75 = 0.840
A/G: 10/75 = 0.133
A/A: 2/75 = 0.027
Observed
Expected
G1
p-value1
63
10
2
61.653
12.693
0.653
2.407
0.3
16
4
1
15.429
5.143
0.429
0.822
0.663
12
5
3
10.513
7.975
1.513
2.617
0.27
18
2
1
17.190
3.619
0.190
2.521
0.284
14
6
1
13.762
6.476
0.762
0.104
0.949
59
12
2
57.877
14.247
0.877
1.435
0.488
21
6
2
19.862
8.276
0.862
1.806
0.405
GSE13282,
Gordan
G/G: 16/21 = 0.762
G/A: 4/21 = 0.19
A/A: 1/21 = 0.048
GSE19189,
Letouze
G/G: 12/20 = 0.600
G/A: 5/20 = 0.250
A/A: 3/20 = 0.15
GSE10506,
Nancarrow
G/G: 18/21 = 0.857
G/A: 2/21 = 0.095
A/A: 1/21 = 0.048
GSE18799,
Popova
G/G: 14/21 = 0.667
G/A: 6/21 = 0.286
A/A: 1/21 = 0.048
GSE9003, Stark
G/G: 59/73 = 0.808
G/A: 12/73 = 0.164
A/A: 2/73 = 0.027
GSE19177,
Waddell
G/G: 21/29 = 0.724
G/A: 6/29 = 0.207
A/A: 2/29 = 0.069
1
G and p-values are Williams’-corrected
226
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