New Approaches to Large-Scale
Data Integration:
Across Variant Types and Across -Omics
Nancy J. Cox, Ph.D.
The University of Chicago
http://genemed.bsd.uchicago.edu
Overview
• Why are we obtaining largely negative
results in sequence studies of common
disease?
- Rationale for results
- Integrating rare and common variants
• Integration across transcriptome and
genome
- Some integration
- Better integration
- Really cool integration
G C A C G G T
T
T G T
T
C C C A G C T A G C
G G T
T C G T
A T C T
G C A C G G T
T G T
T
T C C C
T A A A C T
C C C T G G G C G T
T
T
Evidence that Sequencing Will Yield
Discoveries Accounting for Substantial
Heritability for Common Diseases with
Complex Transmission
Results of Sequencing Studies
• Lipids and cardiovascular
phenotypes
- Small number of new genes
- Rediscover known genes
• Schizophrenia
- No new genes
- Some enrichment of rare variants in prespecified gene set
• Type 2 diabetes and related traits
- Few new genes, some enrichment in sets
Relationship Between MAF and
Effect Size
Lobo, I. (2008) Multifactorial inheritance and genetic disease. Nature Education 1(1):5
Relationship between MAF and
Effect Size
Effect
size
q
Relationship between MAF and
Effect Size
Effect
size
q
Relationship between MAF and
Effect Size
Effect
size
q
Relationship between MAF and
Effect Size
But WHY? What is
wrong with the
way we were
thinking?
Effect
size
q
Gene
Protein
Gane
Pretein
Selection
Gane
Pretein
Gepe
Proteen
T2D
Selection
Gepe
Proteen
T2D
Selection
Serious, early onset disease
Be
optimistic
about data
integration!
Relating Variation to Phenotype
Genotyping
Sequencing
Relating Variation to Phenotype
Common
Variants
Rare
Variants
Genome
Interrogation
Galton, 1889
Polygenic Load
Rare Variant Burden
Polygenic Load
Rare Variant Burden
Polygenic Load
Rare Variant Burden
Polygenic Load
Rare Variant Burden
Implications of Inverse
Axis of Risk
• Study design – families are rare, but
subjects with GWAS are not
- Sequence affecteds with low polygenic load and
unaffecteds with high polygenic load
• Analysis and interpretation of existing
sequencing data
- Weighting polygenic load to distinguish
contributory de novo from rest
- Incorporating into general analysis of rare
variants to improve power
Complex Traits
Type 1 Diabetes
Overall
0.48
0.06
Crohns Disease
0.50 0.07
Concentration of Heritability
• Smaller numbers of eQTLs (3-30K)
account for 30-60% of heritability
estimated for all variants after QC
(150-600K)
• Observed across autoimmune and
inflammatory diseases, bipolar
disorder (brain), T2D
(muscle+adipose)
• Improved prediction?
nformation
rom largescale data
PrediXscan
• Build large-scale predictors of gene
expression within and across
tissues
• Validate predictors in independent
data
• Apply to phenotype data with
genome variation to identify genes
with significant differences in
predicted gene expression
Known Crohns Disease Genes
Whole Blood Predictors
Gene
SLC22A5
CARD9
SOX4
ZGPAT
ERAP2
IL18RAP
GCKR
IL23R
TNFSF11
UBE2L3
KLF6
T-Stat
-4.03
3.71
3.43
-3.42
3.15
2.95
2.80
2.78
2.63
2.44
-2.39
Cerebellum Predictors
Gene
ATG16L1
GSDMB
PTPRK
C5orf56
CCDC88B
TAGAP
PTRF
CCNY
ERAP2
SBNO2
DNMT3A
T-Stat
-6.35
-3.39
3.23
-2.97
2.96
-2.83
-2.83
2.65
2.58
2.55
-2.55
Cox Lab
Eric Gamazon
Lea Davis
(Bridget)
Anna
Tikhomirov
Jason Torres
Keston AquinoMichaels
Carolyn Jumper
Anuar Konkashbaev
Anna Pluzhnikov
Vasily Trubetskoy
Colleagues & Collaborators
Bob Grossman
Dan Nicolae
M. Eileen Dolan
Haky Im
Chun-yu Liu
Andrey Rzhetsky
Acknowledgements
The GTEx Consortium Investigators (GTEx Pilot phase)
•
cancer Human Biobank (caHUB)
•
Biospecimen Source Sites (BSS)
•
•
•
•
•
John Lonsdale, Jeffrey Thomas, Mike Salvatore, Rebecca Phillips, Edmund Lo, Saboor
Shad,
National Disease Research Interchange, Philadelphia, PA
Richard Hasz, Gift of Life Donor Program, Philadelphia, PA
Gary Walters, LifeNet Health, Virginia Beach, VA
Nancy Young, Albert Einstein Medical Center, Philadelphia, PA
•
Laura Siminoff (ELSI Study), Heather Traino, Maghboeba Mosavel, Laura Barker,
Virginia
•
•
Commonwealth University, Richmond, VA
Barbara Foster, Mike Moser, Ellen Karasik, Bryan Gillard, Kimberley Ramsey, Roswell
Park
•
•
Cancer Institute, Buffalo, NY
Susan Sullivan, Jason Bridge, Upstate New York Transplant Service, Buffalo, NY
•
Comprehensive Biospecimen Resource (CBR)
•
•
Scott Jewell, Dan Rohr, Dan Maxim, Dana Filkins, Philip Harbach, Eddie Cortadillo, Bree
Berghuis, Lisa Turner, Melissa Hanson, Anthony Watkins, Brian Smith, Van Andel
Institute, Grand Rapids, MI
•
Pathology Resource Center (PRC)
•
•
•
•
Leslie Sobin, James Robb, SAIC-Frederick, Inc., Frederick, MD
Phillip Branton, National Cancer Institute, Bethesda, MD
John Madden, Duke University, Durham, NC
Jim Robb, Mary Kennedy, College of American Pathologists, Northfield, IL
•
Comprehensive Data Resource (CDR)
•
Greg Korzeniewski, Charles Shive, Liqun Qi, David Tabor, Sreenath Nampally, SAICFrederick, Inc., Frederick, MD
•
caHUB Operations Management
•
Steve Buia, Angela Britton, Anna Smith, Karna Robinson, Robin Burges, Karna
Robinson,
Kim Valentino, Deborah Bradbury, SAIC-Frederick, Inc., Frederick, MD
Kenyon Erickson, Sapient Government Services, Arlington, VA
•
•
•
Brain Bank
Laboratory, Data Analysis, and Coordinating Center (LDACC)
Kristin Ardlie, Gad Getz, co-PIs; David DeLuca, Taylor Young, Ellen Gelfand, Tim Sullivan, Yan Meng,
Ayellet Segre, Jules Maller, Pouya Kheradpour, Luke Ward, Daniel MacArthur, Manolis Kellis, The
Broad Institute of Harvard and MIT, Inc., Cambridge, MA
Statistical Methods Development (R01)
Jun Liu, co-PI, Harvard University, Boston, MA, USA
Jun Zhu, co-PI; Zhidong Tu, Bin Zhang, Mt Sinai School of Medicine, New York, NY
Nancy Cox, Dan Nicolae, co-PIs; Eric Gamazon, Haky Im, Anuar Konkashbaev, University of
Chicago, Chicago, IL
Jonathan Pritchard, PI; Matthew Stevens, Timothèe Flutre, Xiaoquan Wen, University of
Chicago, Chicago, IL
Emmanouil T. Dermitzakis, co-PI; Tuuli Lappalainen, Pedro Ferreira, University of Geneva, Geneva,
Switzerland
Roderic Guigo, co-PI; Jean Monlong, Michael Sammeth, Center for Genomic Regulaton,
Barcelona, Spain
Daphne Koller, co-PI; Alexis Battle, Sara Mostafavi, Stanford University, Palo Alto, CA
Mark McCarthy, co-PI; Manuel Rivas, Andrew Morris, Oxford University, Oxford, United Kingdom
Ivan Rusyn, Andrew Nobel, Fred Wright, Co-PIs; Andrey Shabalin, University of North Carolina Chapel Hill, Chapel Hill, NC
US National Institutes of Health
NCBI dbGaP
Mike Feolo, Steve Sherry, Jim Ostell, Nataliya Sharopova, Anne Sturcke, National Center for
Biotechnology Information, National Library of Medicine, Bethesda, MD
Program Management
Leslie Derr, Office of Strategic Coordination (Common Fund), Office of the Director, National
Institutes of Health, Bethesda, MD
Eric Green, Jeffery P. Struewing, Simona Volpi, Joy Boyer, Deborah Colantuoni, National
Human Genome Research Institute, Bethesda, MD
Thomas Insel, Susan Koester, A. Roger Little, Patrick Bender, Thomas Lehner, National Institute
of Mental Health, Bethesda, MD
Jim Vaught, Sherry Sawyer, Nicole Lockhart, Chana Rabiner, Joanne Demchok, National
Cancer Institute, Bethesda, MD