Presentation on theme: "Synthesizing results across multiple GWA studies and integrating them into the existing knowledge base Marta Gwinn, MD, MPH National Office of Public Health."— Presentation transcript:
Synthesizing results across multiple GWA studies and integrating them into the existing knowledge base Marta Gwinn, MD, MPH National Office of Public Health Genomics CDC Adding Genome-Wide Association to Population Studies Boston, June 22, 2007 GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG
Data Information Knowledge?
Synthesizing and integrating results of GWA Review replication of genetic associations –Identifying associations vs. measuring effects –Methodological issues Describe network approaches –Human Genome Epidemiology Network (HuGENet) –Other examples Discuss two important results GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG
* HuGE Published Literature gene-disease association gene-environment interaction meta-analysis 32 Genetic association studies of unrelated persons Extracted from PubMed, *
“We suggest that this rapid, early succession of extreme findings may be called the Proteus phenomenon after the mythologic god who rapidly metamorphosed himself to very different figures.”
Published literature scan Systematic reviews Strengthened reporting Network collaboration HuGENet Canada Human Genome Epidemiology (HuGE) GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG
HuGENet Network of Networks Single teams Single studies Published and unpublished data Systematic reviews Meta-analyses Field-wide synopses Feedback Reporting Synthesis Grading Commentary, Nature Genetics 38, (2006) A road map for efficient and reliable human genome epidemiology STREGA 6/2006 Handbook 3/2006 Venice 11/2006 Atlanta 1/ /2005
Replication of genetic associations Heterogeneity –unmeasured factors Statistical uncertainty –sampling variability –low power Bias –all the usual epidemiologic biases –publication bias GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG -- including exposures -- to detect small effects -- among many comparisons
Replication of genome-wide associations Heterogeneity –unmeasured factors Statistical uncertainty –sampling variability –low power Bias –all the usual epidemiologic biases –publication bias GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG -- genetic background -- meta-analysis, prior information -- statistical methods -- transparency -- enhanced access to data
HuGE Systematic Reviews and Meta-analysis GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG Handbook for systematic reviews / meta-analyses Online database of systematic reviews / meta-analyses – >50 reviews published in collaboration with 10 journals – citation database of ~550 meta-analyses Proposed guidance for reporting association data Proposed criteria for evaluating evidence for association
Synthesizing results of GWA studies: different from candidate gene studies? Study priorities may differ –GWA: find novel associations –candidate gene: measure effect size Most differences are a matter of degree –Type 1 errors, type 2 errors –harmonization among studies: phenotyping, genotyping methods –population stratification All the usual epidemiologic biases prevail GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG
Meta-analysis of GWAs? Improve power to measure small effects Assess heterogeneity among GWAs Methodological challenges –use of different genotyping platforms / different SNPs –harmonization of phenotypic data –treatment of replication samples within same GWA Good for horizontal integration—only one dimension of evidence GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG
Ioannidis JP. Commentary: grading the credibility of molecular evidence for complex diseases. Int J Epidemiol 2006
Bridge “cottage industry” with “Big Science” Domain experts may share: – specific knowledge (e.g., phenotype definitions) – awareness of current research problems – funding sources Many networks already exist – NCI Cancer Genetic Markers of Susceptibility (CGEMS ) – NINDS Human Genetics Repository – International Collaborative Study on Genetic Susceptibility to Environmental Carcinogens (GSEC) – Preterm Birth International Collaborative (PREBIC) Why a “Network of Networks?” GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG Hoover RN. The evolution of epidemiologic research: from cottage industry to "big" science. Epidemiology Jan 2007;18:13-7.
CARD15 (NOD2) and Crohn’s disease positional cloning, candidate gene studies - risk genotype prevalence 0.3, relative risk 3, 40 - disease risk CFH and age-related macular degeneration genome-wide association study - risk genotype prevalence 0.2, relative risk 5, 7 - disease risk 0.05 (by age 60) GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG A Tale of Two Associations * Note: all estimates are approximate!
Genetic Associations with Crohn Disease HuGE Published Literature, June 2007 **as of June 2007 ** * *indicates number of meta-analyses, including 3 meta-analyses of CARD
Early success –“Ideal” combination of genotype prevalence, effect size and population disease risk? –Key insights into pathogenesis, phenotype Unmet expectations, translation frustrations –No replication in some populations (Japanese) –Disappointing clinical trials (infliximab) Help from GWA GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG CARD15 (NOD2) and Crohn’s disease
Crohn’s disease: help from GWA Cardon L. Delivering new disease genes. Science Dec 2006;314: Perspective on: Duerr RH, et al. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science Dec 2006:314;
Genetic Associations with Macular Degeneration HuGE Published Literature, June 2007 **as of June 2007 ** * *includes 2 meta-analyses in each group
Early success of GWA Insight into pathogenesis and progression –CFH already related to kidney disease, new focus in cardiovascular disease –Interaction with smoking and BMI Translation? –No interaction with anti-oxidant vitamins/zinc supplementation (AREDS rx) –No utility for screening GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG CFH and Age-related macular degeneration
Why? –increase probability that positive associations are true –increase power to examine small associations, less common variants –zero in on causal variants –start translating data into knowledge How? –careful documentation, complete reporting, collaboration –cumulative review of evidence –integration with other epidemiologic data, as well as knowledge from other fields Synthesizing and integrating results of GWA GTCGACTGGAGTGTCTGTGAATTGACTTTTGTTGCCAGTTGGCAGCGGCAGAAGCAGCAAAGCCCGGCCAACAGCAACAAGCTCCTGCCAGATCCCAAAAGCAAACACG
Hokusai, Mt. Fuji Off Kanagawa, Not a tsunami… Not a tsunami-- Hiroshige, A View of Eitai Bridge and Tsukuda Island, 1857 …but a rising tide that lifts all boats.