Park, JH, Wacholder, S, Gail, MH, Peters, U, Jacobs, KB, Chanock, SJ et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat. Genet. 42, 570-575

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Rockville, Maryland, USA.
Nature Genetics (Impact Factor: 29.65). 07/2010; 42(7):570-5. DOI: 10.1038/ng.610
Source: PubMed

ABSTRACT We report a set of tools to estimate the number of susceptibility loci and the distribution of their effect sizes for a trait on the basis of discoveries from existing genome-wide association studies (GWASs). We propose statistical power calculations for future GWASs using estimated distributions of effect sizes. Using reported GWAS findings for height, Crohn's disease and breast, prostate and colorectal (BPC) cancers, we determine that each of these traits is likely to harbor additional loci within the spectrum of low-penetrance common variants. These loci, which can be identified from sufficiently powerful GWASs, together could explain at least 15-20% of the known heritability of these traits. However, for BPC cancers, which have modest familial aggregation, our analysis suggests that risk models based on common variants alone will have modest discriminatory power (63.5% area under curve), even with new discoveries.

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    • "This is not uncommon in GWAS, for which the majority of the SNPs discovered have relatively small effect sizes [32]. Such SNPs explain a small percentage of total heritability [33] [34], implying that additional factors such as gene-environment interactions and rare variants [7] could explain some of the missing heritability [33]. This is the second SNP in this region for which pleiotropic effects have been discovered; rs6983267 is associated with colorectal and prostate cancer [26] [35]. "
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    • "Fortunately, whole-genome sequencing and genome-wide association (GWA) studies now make it possible to identify segregating alleles that affect complex phenotypes such as body height, diabetes, schizophrenia, and even longevity (Jeck et al., 2012), but GWA studies suffer from numerous challenges, and these are further compounded in analyses of lifespan. First, alleles identified in GWA studies typically explain just 0.1–1.0% of the variation in complex traits (Park et al., 2010). Second, the genetic basis of lifespan appears, at least in part, to differ between the sexes (Burger & Promislow, 2004). "
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    • "Results are shown for four complex traits: two disease and two quantitative phenotypes. The graph assumes that the number of loci detected increases linearly with increasing sample size (data are from Frayling et al., 2007; Lango Allen et al., 2010; Loos et al., 2008; Park et al., 2010; Scuteri et al., 2007; Speliotes et al., 2010; Thorleifsson et al., 2009; Wen et al., 2012; Willer et al., 2009 "
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