National Human Genome Research Institute Home - Nancy’s … · 2014. 8. 1. · Nancy’s $100M...
Transcript of National Human Genome Research Institute Home - Nancy’s … · 2014. 8. 1. · Nancy’s $100M...
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Nancy J. Cox, Ph.D.
The University of Chicago
http://genemed.bsd.uchicago.edu
Nancy’s $100M Project
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Common Variants
Rare Variants
Relating Variation to Phenotype
Genome Interrogation
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Rare Variant Burden
Polygenic Load
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Rare Variant Burden
Polygenic Load
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Implications of Inverse Axis of Risk
• Study design - Sequence affecteds with low polygenic load and
unaffecteds with high polygenic load - Sequencing 25K in these tails yields power
comparable to sequencing 50-100K • 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
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Year 1 • $20M for genotyping samples with $50
biobanking chips with GWAS and exome chip content - Emphasis on biobanks, existing cohorts, all
clinical trials with samples not yet having GWAS (to be added to all existing GWAS data)
- Expand impact by partnering with other NIH disease and private foundations
- Prioritize use cases that enable reimbursement for CLIA / CAP cheap chips (pharmacogenomics, diagnostic oddysseys)
• Goal: 2-3M samples with sufficient genome interrogation for characterizing polygenic load for all common disease
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With 2,000,000 Subjects • Expect > 400,000 with diagnoses for
diseases with 20% lifetime risk • Expect > 200,000 with diagnoses for
diseases with 10% lifetime risk • Expect > 100,000 with diagnoses for
diseases with 5% lifetime risk • Expect > 20,000 with diagnoses for diseases
with a 1% lifetime risk
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Rare Variant Burden
Polygenic Load
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Rare Variant Burden
Polygenic Load
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Rare Variant Burden
Polygenic Load
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Partner with Pharma
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Rare Variant Burden
Polygenic Load
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Rare Variant Burden
Polygenic Load
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Rare Variant Burden
Polygenic Load
Partner with other NIH institutes, private disease foundations
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Years 2-5 • Sequence individuals in the “tails” for
all common diseases • Build “use cases” for reimbursement of
CLIA / CAP whole genome sequencing • Explore serial transcriptomics for
clinical utility - Any circumstance with “watchful waiting” - Diagnostic oddysseys
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Deliverables • Sufficiently powered but highly efficient
studies of rare variants in common disease
• Additional opportunities for research - Combine EMR usage, biomarkers, with polygenic
scores and rare variants at genes shown to contribute to disease risk to improve prediction of common disease
- Use available data to characterize environmental factors impacting risk “at the tails” – cost-effective prevention
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Cox Lab
Anna Tikhomirov
Anna Pluzhnikov Anuar Konkashbaev
Eric Gamazon
Vasily Trubetskoy
Lea Davis (Bridget) Jason Torres
Keston Aquino-Michaels Carolyn Jumper
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Colleagues & Collaborators
Dan Nicolae M. Eileen Dolan
Bob Grossman
Haky Im
Chun-yu Liu
Andrey Rzhetsky
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Information from large-scale data
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Relationship Between MAF and Effect Size
Lobo, I. (2008) Multifactorial inheritance and genetic disease. Nature Education 1(1):5
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Relationship between MAF and Effect Size
q
Effect size
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Relationship between MAF and Effect Size
q
Effect size
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Relationship between MAF and Effect Size
q
Effect size
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Relationship between MAF and Effect Size
q
Effect size
But WHY? What is wrong with the way we were
thinking?
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Gene Protein
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Gane Pretein
Selection
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Gane Pretein
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Gepe Proteen
Selection
T2D
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Gepe Proteen
Selection
T2D
Serious, early onset disease