Nho et al.
Supplementary information
Whole-exome sequencing and imaging genetics identify functional variants for rate of
change in hippocampal volume in Mild Cognitive Impairment
Kwangsik Nho1, PhD, Jason J. Corneveaux2, BS, Sungeun Kim1,3, PhD, Hai Lin3, BS, Shannon
L. Risacher1, PhD, Li Shen1,3, PhD, Shanker Swaminathan1,4, PhD, Vijay K. Ramanan1,4, BS,
Yunlong Liu3,4, PhD, Tatiana Foroud1,3,4, PhD, Mark H. Inlow5, PhD, Ashley L. Siniard2, BS,
Rebecca A. Reiman2, BS, Paul S. Aisen6, MD, Ronald C. Petersen7, MD, Robert C. Green8, MD,
MPH, Clifford R. Jack7, MD, Michael W. Weiner9,10, MD, Clinton T. Baldwin11, PhD, Kathryn
Lunetta12, PhD, Lindsay A. Farrer11,13, PhD, for the Multi-Institutional Research on Alzheimer
Genetic Epidemiology (MIRAGE) Study, Simon J. Furney14,15,16, PhD, Simon Lovestone14,15,16,
PhD, Andrew Simmons14,15,16, PhD, Patrizia Mecocci17, MD, Bruno Vellas18, MD, Magda
Tsolaki19, MD, Iwona Kloszewska20, MD, Hilkka Soininen21, MD, for the AddNeuroMed
Consortium, Brenna C McDonald1, PsyD, Martin R. Farlow22, MD, Bernardino Ghetti, MD23, for
the Indiana Memory and Aging Study, Matthew J. Huentelman2, PhD, Andrew J. Saykin1,3,4,22*,
PsyD, for the Alzheimer's Disease Neuroimaging Initiative (ADNI)**
1
Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University
School of Medicine, Indianapolis, IN, USA;
2
Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, AZ,
USA;
3
Center for Computational Biology and Bioinformatics, Indiana University School of Medicine,
Indianapolis, IN, USA;
4
Department of Medical and Molecular Genetics, Indiana University School of Medicine,
Indianapolis, IN, USA;
1
Nho et al.
5
Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA;
6
Department of Neurosciences, University of California, San Diego, San Diego, CA, USA;
7
Mayo Clinic, Rochester, MN, USA;
8
Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard
Medical School, Boston, MA, USA;
9
Departments of Radiology, Medicine and Psychiatry, University of California, San Francisco,
San Francisco, CA, USA;
10
Department of Veterans Affairs Medical Center, San Francisco, CA, USA;
11
Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA;
12
Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA;
13
Genetic Epidemiology Center, Boston University School of Medicine, Boston, MA, USA;
Institute of Psychiatry, King’s College London, London, UK;
14
15
NIHR Biomedical Research Centre for Mental Health at South London and Maudsley NHS
Foundation Trust and Institute of Psychiatry, King’s College London, UK;
16
NIHR Biomedical Research Unit for Dementia at South London and Maudsley NHS
Foundation Trust and Institute of Psychiatry, King’s College London, UK;
17
Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy;
18
Toulouse Gerontopole University Hospital, Universite Paul Sabatier, INSERM U 558,
Toulouse, France;
19
Third Department of Neurology, Aristotle University of Thessaloniki, Thessaloniki, Greece;
20
Department of Old Age Psychiatry & Psychotic Disorders, Medical University of Lodz, Lodz,
Poland;
2
Nho et al.
21
Department of Neurology, University of Eastern Finland, Kuopio University Hospital, Kuopio,
Finland;
22
Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
23
Department of Pathology and Laboratory Medicine, Indiana University School of Medicine,
Indianapolis, IN, USA
*
Please address correspondence to:
Andrew J. Saykin, PsyD
Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University
School of Medicine, Indianapolis, IN, USA
Phone: 317-963-7501; Fax: 317-963-7547; E-mail: asaykin@iu.edu
Running title: Whole exome sequencing in MCI
**Data used in preparation of this article were obtained from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within
the ADNI contributed to the design and implementation of ADNI and/or provided data but did not
participate in analysis or writing of this report. A complete listing of ADNI investigators can be
found at: http://adni.loni.ucla.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
3
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Supplementary Table ST1 WES sample demographics
Slow group
Rapid group
P
Number of samples
8
8
−
Age at baseline
74.9±6.6
74.0±8.0
0.82
Education (years)
15.4±2.4
15.4±2.4
1
Slope
0.15±0.97
-5.27±1.59
<0.001
Average APC
0.16±0.99
-4.99±1.71
<0.001
MMSE
27.9±1.8
26.6±2.1
0.22
Abbreviation: APC, Annual percentage of change; MMSE, Mini-mental state examination.
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Supplementary Table ST2 ADNI-1 sample demographics
Slow group
Rapid group
All WES sample
All MCI
All ε3/ε3
8
8
16
347
316
Age
74.9 (6.6)
74.0 (8.0)
74.4 (7.1)
74.9 (7.3)
76.3 (7.2)
education
15.4 (2.4)
15.4 (2.4)
15.4 (2.4)
15.7 (3.0)
15.9 (3.0)
Sex (M, F)
8, 0
8, 0
16, 0
217, 130
183, 133
Handedness (R, L)
8, 0
8, 0
16, 0
313, 34
284, 32
ApoEGrp (+,-)
0, 8
0, 8
0, 16
199, 148
0, 316
MMSE
27.9 (1.8)
26.6 (2.1)
27.3 (2.0)
27.1 (1.8)
27.3 (2.6)
Global CDR
0.5 (0.0)
0.5 (0.0)
0.5 (0.0)
0.50 (0.03)
0.3 (0.3)
CDR-Sum of Boxes
1.4 (0.7)
1.6 (0.9)
1.5 (0.8)
1.6 (0.9)
1.4 (1.7)
RAVLT total score
31.1 (4.2)
24.9 (5.9)
28.0 (5.9)
30.9 (9.3)
35.0 (11.6)
Boston Naming Test
26.6 (2.8)
25.9 (4.3)
26.3 (3.5)
25.5 (4.1)
25.9 (5.0)
Fluency-Animals
17.9 (5.6)
13.4 (3.9)
15.6 (5.2)
16.1 (4.9)
17.3 (6.2)
Fluency-Vegetables
12.3 (2.5)
9.1 (2.3)
10.7 (2.8)
10.8 (3.5)
12.0 (4.7)
Trail A
52.4 (40.6)
45.0 (23.4)
48.7 (32.2)
44.1 (22.0)
46.5 (28.6)
Trail B
128.9 (86.4)
160.3 (80.4)
144.6 (82.2)
130.1 (74.0)
121.4 (78.1)
N
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Supplementary Table ST3 Sequencing and mapping statistics for 16 exomes
SubjID
Total
reads
Aligned
reads
Aligned
bases
HQ aligned
Q20 bases
(M)
(M)
(B)
(B)
Mismatch
rate
Mean
target
coverage
Fold
enrichment
PCT target
bases 30X
08AD6520
184.2
152.7
11.9
11.4
0.0038
162
41
0.82
08AD6533
159.8
132.6
10.4
9.9
0.0037
139
40
0.80
08AD6543
214.1
175.6
13.8
13.2
0.0036
185
40
0.82
08AD6611
148.7
122.2
9.5
9.1
0.0037
126
40
0.79
08AD6795
118.5
98.1
7.7
7.4
0.0035
103
40
0.77
08AD6863
138.0
113.8
9.0
8.6
0.0035
123
41
0.78
08AD7023
125.2
103.7
8.2
7.9
0.0035
113
41
0.77
08AD7133
145.4
121.1
9.5
9.2
0.0035
126
40
0.79
08AD6596
169.7
140.1
10.9
10.5
0.0038
146
40
0.80
08AD6663
214.0
176.7
13.7
13.2
0.0038
179
39
0.82
08AD6679
157.5
130.3
10.2
9.7
0.0037
135
40
0.79
08AD6786
165.1
135.6
10.5
10.1
0.0038
137
39
0.80
08AD6804
90.6
115.0
9.1
8.7
0.0035
119
39
0.79
08AD6858
178.2
146.3
11.5
11.1
0.0035
153
40
0.81
08AD7092
179.6
149.7
11.8
11.3
0.0035
155
39
0.81
08AD7125
191.9
159.6
12.6
12.1
0.0035
170
40
0.82
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Supplementary Table ST4 Variants identified by whole-exome sequencing
SNV
Exonic
Indel
50,396
Exonic
613
Non-synonymous
25,144
Frameshift
382
Synonymous
25,234
Non-frameshift
231
Unknown
18
Splicing
945
Splicing
82
Intronic
29,236
Intronic
3,082
Intergenic
1,785
Intergenic
165
Others
7,037
Others
602
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Supplementary Table ST5 Exonic variants unique to the rapid group (cases) but not found in the slow group (controls).
chr
1
22
7
7
20
2
1
1
1
3
1
17
1
22
18
2
1
3
7
8
14
20
21
3
1
16
14
15
2
SNV ID
genename
rs1136410
PARP1
rs9610775
CARD10
rs6949082
HYAL4
rs3735400
ANLN
rs34323943
ZNF217
rs1042821
MSH6
rs857685
OR10Z1
rs12731961
CAPN9
rs41273491
OR6Y1
rs4685076
TMEM43
rs12568050
ZNF648
rs3744395
TEKT1
rs857725
SPTA1
rs68178377 FAM116B
rs17732496
TXNDC2
rs2276551
FAM179A
rs12564283
ZNF648
rs2340917
TMEM43
rs1129771
SKAP2
rs17335870
SPAG1
rs17180350
SPTB
rs6063966
ZNF217
rs6586239
RIPK4
rs12163565
DNAH1
rs11209026
IL23R
rs113388806 TNRC6A
rs45527334
SAMD15
rs62026667
SHF
rs1800440
CYP1B1
functional only in case
probably
6
probably
6
benign
6
probably
5
probably
5
probably
5
possible
5
possible
5
benign
5
benign
5
benign
5
benign
5
benign
5
benign
5
benign
5
benign
5
benign
5
benign
5
benign
5
benign
5
benign
5
benign
5
benign
5
benign
5
probably
4
probably
4
probably
4
probably
4
probably
4
homo
1
1
2
1
0
0
1
1
2
1
0
0
1
1
0
0
0
1
0
1
1
0
2
1
1
0
0
0
1
hetero
5
5
4
4
5
5
4
4
3
4
5
5
4
4
5
5
5
4
5
4
4
5
3
4
3
4
4
4
3
chr
1
6
1
11
17
8
2
9
7
20
1
4
4
2
14
3
22
20
16
20
1
7
8
15
15
15
21
21
8
SNV ID
rs855314
rs12191414
rs17369441
rs11222085
rs2289530
rs1137806
rs33993717
rs818711
rs34111764
rs55786258
rs15786
rs147709711
rs4963
rs35228363
rs17776256
rs2227998
rs760749
rs34419428
rs4988483
rs56057707
rs34554682
rs5763
rs11545077
rs7170637
rs11549015
rs16963151
rs2837029
rs11575939
genename
PGM1
POM121L2
AXDND1
ADAMTS8
CCDC40
FAM83H
MFSD9
BSPRY
PARP12
ZNF831
ATP13A2
FRAS1
ADD1
ALK
C14orf101
XPC
MOV10L1
SLCO4A1
SSTR5
ZNF831
MAEL
TBXAS1
GGH
CYFIP1
EHD4
ATP8B4
LCA5L
SH3BGR
functional only in case
possible
4
possible
4
possible
4
possible
4
possible
4
possible
4
possible
4
possible
4
possible
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
benign
4
homo
1
0
0
0
1
2
1
0
0
0
1
0
1
1
0
0
0
0
1
0
0
0
2
0
1
0
0
0
hetero
3
4
4
4
3
2
3
4
4
4
3
4
3
3
4
4
4
4
3
4
4
4
2
4
3
4
4
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Nho et al.
Functional: Polyphen2 was used to predict potential impact on protein structure or function of missense variants.
Homo, hetero: number of subjects who are homozygous or heterozygous for the minor allele.
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Supplementary Figure SF1. Forest plots of meta-analysis result for rs1136410
(a) Left Hippocampal Volume
Studies
Standardized Mean Difference (95% CI)
ADNI-1
ADNI-GO-2
IMAS
AddNeuroMed
0.253
0.319
-0.292
0.391
(0.001, 0.504)
(0.015, 0.623)
(-0.971, 0.387)
(-0.001, 0.782)
MIRAGE
0.219 (-0.063, 0.501)
Overall
0.253 (0.109, 0.398)
(b) Right Hippocampal Volume
Studies
ADNI-1
ADNI-GO-2
IMAS
AddNeuroMed
MIRAGE
Overall
Standardized Mean Difference (95% CI)
0.139
0.293
0.046
0.279
0.070
0.170
(-0.112, 0.390)
(-0.011, 0.596)
(-0.630, 0.722)
(-0.111, 0.669)
(-0.211, 0.352)
(0.027, 0.315)
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Supplementary Figure SF2. Mean difference of left hippocampal volume at baseline for
rs1136410
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Supplementary Figure SF3. Mean difference of right hippocampal volume at baseline for
rs1136410
12
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Supplementary Methods
Alzheimer’s Disease Neuroimaging Initiative (ADNI)
The initial phase (ADNI-1) was launched in 2003 to test whether serial magnetic resonance
imaging (MRI), position emission tomography (PET), other biological markers, and clinical and
neuropsychological assessment could be combined to measure the progression of MCI and
early AD. ADNI was intended to aid researchers and clinicians develop new treatments for MCI
and early AD, monitor their effectiveness, and lessen the time and cost of clinical trials. This
multi-site longitudinal study was supported by the National Institute on Aging (NIA), the National
Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration
(FDA), private pharmaceutical companies and non-profit organizations. The ADNI participants
were recruited from 59 sites across the U.S. and Canada and include approximately 200
cognitively normal older individuals (healthy controls (HC)), 400 patients diagnosed with MCI,
and 200 patients diagnosed with early probable AD aged 55-90 years. ADNI-1 has been
extended to its subsequent phases (ADNI-GO and ADNI-2) for follow-up for existing participants
and additional new enrollments. Genotyping was performed using the Human610-Quad
BeadChip for the ADNI-1 participants and the HumanOmni Express BeadChip for participants
initially enrolled in ADNI-GO or ADNI-2. Inclusion and exclusion criteria, clinical and
neuroimaging protocols, and other information about ADNI have been published previously and
can be found at www.adni-info.org.1-3 Demographic information, raw scan data, APOE and
GWAS genotypes, neuropsychological test scores, and diagnostic information are available
from the ADNI clinical data repository (http://www.loni.ucla.edu/ADNI/).
AddNeuroMed Consortium
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Nho et al.
The AddNeuroMed study is a prospective and longitudinal multicenter collaboration for the
discovery of novel biomarkers for AD.4, 5 Data were collected from six medical centers across
Europe. Informed consent was obtained for all subjects, and the study was approved by the
relevant institutional review board at each data acquisition site. A total of 281subjects (94 AD,
96 MCI, and 91 healthy controls (HC)) were genotyped and had MRI scans. All samples from
the AddNeuroMed study were genotyped using the Illumina Human610-Quad BeadChips. Their
genotyping was conducted as described previously.5 APOE genotyping was separately obtained
using standard methods to yield the APOE allele defining SNPs (rs429358, rs7412).5
Multi-Institutional Research on Alzheimer Genetic Epidemiology (MIRAGE) Study
The MIRAGE study is a family-based genetic epidemiological study of AD, which contains
primarily discordant sibling pairs. Enrollment, data collection, and diagnostic procedures were
described in detail elsewhere.6 The MIRAGE participants were genotyped on the Illumina
Infinium Human 610-Quad BeadChip (approximately 2/3 of participants) and the Illumina
Infinium HumanCNV370-Duo BeadChip (the remaining participants). Genotyping for the APOE
ε2/ε3/ε4 alleles were performed separately.7 We first chose individuals with APOE ε3/ε3 from all
MIRAGE participants and then chose one person from each family at random.
Indiana Memory and Aging Study (IMAS)
The IMAS is an IRB-approved ongoing project on memory circuitry in AD and MCI at the Indiana
University School of Medicine. The sample included individuals with significant cognitive
complaints without performance deficits, amnestic MCI, healthy controls, and AD. Genotyping
was performed using the HumanOmni Express BeadChip and APOE genotyping was separately
obtained using standard methods to yield the APOE allele defining SNPs (rs429358, rs7412).
14
Nho et al.
Participant recruitment, selection criteria, and characterization were described in detail
elsewhere.8
Genotyping and Quality Control Procedures
All subjects used in this study are participants of ADNI. Written informed consent was obtained
at the time of enrollment for imaging and genetic sample collection and protocols of consent
forms were approved by each participating sites’ Institutional Review Board (IRB). A majority of
ADNI-1 participants (818 out of 822) were genotyped using the Illumina Human610-Quad
BeadChip. Their genotyping was conducted as described previously.9 A total of 818 subjects
(188 AD, 401 MCI and 229 HC at baseline) were included in the present analyses. We
performed standard quality control procedures for genetic markers and subjects as described
previously.9, 10 Since population stratification is known to cause spurious association in disease
studies, we restricted our analyses to only subjects that clustered with CEU (Utah residents with
Northern and Western European ancestry from the CEPH collection) + TSI (Toscani in Italia)
populations using HapMap 3 genotype data and the multidimenstional scaling (MDS) analysis
(www.hapmap.org). Consequently, 750 individuals and 531,096 SNPs passed all quality control
tests and the total genotyping rate in the remaining subjects was > 99.5%.
Whole exome sequencing analysis
Targeted capture and exome sequencing: Whole-exome sequencing was performed on
blood-derived genomic DNA samples obtained from 16 MCI patients. Sequences were enriched
through hybridization using the Agilent’s SureSelect Human All Exon 50Mb kit following the
manufacturer’s protocol (www.genomics.agilent.com/). The Agilent kit captured an exome that
was approximately 50Mb in size, covering ~21,000 genes. These samples could then be
sequenced together on one lane of the flowcell and segregated later for analysis using their
molecular bar-codes as tags. Samples were sequenced across multiple flowcell lanes to
15
Nho et al.
account for any possible lane effects. These libraries were sequenced on the Illumina
HiSeq2000 using paired-end read chemistry and read lengths of at least 105bp
(www.illumina.com). Each sample was sequenced two times and the two sequencing data sets
were merged after alignment.
Sequence alignment: For each lane in the DNA sequencing output, the resulting Illumina qseq
files were converted into fastq files, a text-based format for storing both sequence reads and
their corresponding quality information in Phred format. Short-read sequences in the targeted
region were mapped to the NCBI reference human genome (build 37.64) with the BWA software
(Burrows-Wheeler Aligner), allowing for up to two mismatches in each read.11 The quality check
on raw sequence data showed that as expected, the 5’ end of each read contained significantly
more error than the 3’ end, i.e., error rates increased with the position along sequencing read.
Hence, during the alignment, we used only bases with Phred Quality > 15 in each read to
include soft clipping of low-quality bases. After the alignment, only those uniquely mapped pairend reads were retained. After completing initial alignment, the alignment was refined by locally
realigning of any suspicious reads likely to require realignment using Genome Analysis ToolKit
(GATK) (http://www.broadinstitute.org/gsa/wiki/index.php/GATK). After local realignment, reads
from molecular duplicates were removed. The reported base calling quality scores obtained
from the sequencer were re-calibrated to account for covariates of base errors such as
sequencing technology and machine cycle. Finally, the realigned reads were written to a
SAM/BAM file for further analysis.
Variant calling and gene annotation: The GATK module, UnifiedGenotyper, was used for
multi-sample variant calling: single nucleotide variants (SNVs) and short insertion/deletions
(indels). Variant quality scores were re-calibrated using a variational Bayes Gaussian mixture
model to estimate the probability that each variant was a true polymorphism in the samples.12
Integrative Genome Viewer (IGV) was used to perform the visual inspection of variants, when
16
Nho et al.
necessary (http://www.broadinstitute.org/igv/). All the variants were annotated with the
ANNOVAR software (http://www.openbioinformatics.org/annovar/), which also assigned each
variant to a coding sequence, untranslated region, intron or intergenic regions. Polyphen2
(http://genetics.bwh.harvard.edu/pph2/) and SIFT13 were used to predict potential impact of
missense variants on protein structure or function of missense variants as indicated by the
posterior probability that a given variant was damaging.
Comparison of identified sequence SNVs to genotyping chip data
Our 16 sequencing samples were also genotyped on the Illumina Human610-Quad BeadChip.
As an accuracy test, we analyzed all variants identified by WES and previously obtained
genotypes from the same individuals. Sequence genotype calls were extracted by Vcftools
(http://vcftools.sourceforge.net/). We calculated the concordance between variants identified
WES and overlapping genotyped variants on the array.
Identification of variants associated with rate of hippocampal volume loss
Among the targeted exonic genetic variants identified from WES, we identified variants carried
only in the rapid group by selecting variants (SNV and short indels) in coding regions for which
at least four of eight subjects in the rapid group had at least one alternative allele, but where
eight subjects in the slow group had same alleles at the locus as the reference.
Imputation
Of newly identified variants, rs1136410 (not-genotyped on the chip) and missing genotypes for
rs9610775 (genotyped on the chip) were imputed using the IMPUTE v2 software package
(https://mathgen.stats.ox.ac.uk/impute/impute.html). The reference panel consisted of
haplotypes from the CEU panels of the HapMap 3 release 2 data (www.hapmap.org) and the
17
Nho et al.
1000 Genomes Project pilot 1 data (www.1000genomes.org). We imposed a posterior
probability equal to 0.90 as the threshold to accept the imputed genotypes.
Imaging processing
T1-weighted brain MRI scans were acquired using a sagittal 3D MP-RAGE sequence following
the ADNI MRI protocol.14 As detailed in previous studies,2, 3 two widely employed automated
MRI analysis techniques were independently used to process MRI scans: whole-brain voxelbased morphometry (VBM) (http://www.fil.ion.ucl.ac.uk/spm/) and FreeSurfer V4/V5 software
(http://surfer.nmr.mgh.harvard.edu/). Using SPM5 and standard SPM5 template, scans were coregistered to a standard T1 template image, bias corrected, and segmented into grey matter
(GM), white matter (WM), and cerebrospinal fluid (CSF) compartments. Unmodulated GM
density maps were then normalized to MNI atlas space (1x1x1 mm voxel size) and smoothed
with 10 mm full-width at half-maximum (FWHM) Gaussian kernel. FreeSurfer was used to
process and extract brain-wide target MRI imaging phenotypes (region volume and cortical
thickness) by automated segmentation and parcellation. The cortical surface was reconstructed
to measure thickness at each vertex on surface. The cortical thickness was calculated by taking
the Euclidean distance between the grey/white boundary and the grey/cerebrospinal fluid (CSF)
boundary at each vertex on surface.15 For surface-based comparison of the cortical thickness,
all individual cortical surfaces were registered to a common surface template, which was an
average created from all healthy control subjects. The cortical thickness was smoothed with 10
mm FWHM Gaussian kernel to improve the signal-to-noise ratio and statistical power.
Imaging genetics analysis
Surface-based analysis: Genotype effects of candidate SNVs identified by WES on cortical
thickness measures were investigated using multivariate analysis of cortical thickness. We
employed the SurfStat software to perform surface-based analysis and constructed general
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linear models (GLM) (http://www.math.mcgill.ca/keith/surfstat/) using age at baseline, gender,
years of education, intracranial volume (ICV), and SNV as independent variables. We examined
genetic effects of a candidate SNV on cortical thickness both across diagnosis groups and
within each diagnosis group. SurfStat used the random field theory (RFT) correction method for
a correction for multiple comparisons at a 0.05 level of significance.
Voxel-based analysis: We used SPM5 (http://www.fil.ion.ucl.ac.uk/spm/) to perform statistical
analyses on a voxel-by-voxel basis and an explicit grey matter (GM) mask to restrict analysis to
GM regions. A multiple regression model was performed to determine the additive effect of the
minor allele of SNVs using the same covariates as those used in the surface-based analysis:
age at baseline, gender, years of education, intracranial volume (ICV). In order to detect the
diagnosis effects, we performed the same statistical model analysis before and after controlling
the diagnosis status. Voxel-level significance P values of 0.05 (uncorrected) and minimum
cluster size k=27 contiguous voxels were used to display clusters significantly affected by
genotype.
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Supporting Acknowledgements
Data collection and sharing for this project was funded by the Alzheimer's Disease
Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is
funded by the National Institute on Aging, the National Institute of Biomedical Imaging and
Bioengineering, and through generous contributions from the following: Abbott; Alzheimer’s
Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca;
Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.;
Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated
company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; Janssen Alzheimer
Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research
& Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis
Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical
Company. The Canadian Institutes of Health Research is providing funds to support ADNI
clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the
National Institutes of Health (www.fnih.org). The grantee organization is the Northern California
Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease
Cooperative Study at the University of California, San Diego. ADNI data are disseminated by
the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was
also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation.
Samples from the National Cell Repository for AD (NCRAD), which receives government
support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute
on Aging (AIG), were used in this study. Additional support for data analysis was provided by
NLM K99 LM011384, NIA R01 AG19771, P30 AG10133-18S1, NCI R01 CA101318, NLM R01
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LM011360, IIS-1117335, and RC2 AG036535, and U01 AG032984 from the NIH, Foundation
for the NIH, grant #87884 from the Indiana Economic Development Corporation (IEDC), and
NINDS (R01NS059873). The AddNeuroMed study was funded by the European Union as part
of the FP6 InnoMed program. SF, AS and SL were supported by the Biomedical Research
Centre for Mental Health at South London and Maudsley NHS Foundation Trust and Institute of
Psychiatry, King’s College London, the Biomedical Research Unit for Dementia at South London
and Maudsley NHS Foundation Trust and Institute of Psychiatry, King’s College London and
Alzheimer’s Research UK.
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