Estimation of effect size distribution from genome-wide association studies and implications for future discoveries
By Ju-Hyun Park, S. Wacholder, M. Gail, U. Peters, K. Jacobs, Stephen J. Chanock, N. Chatterjee. Nature Genetics, 2010
692 citations recorded by the source corpus.
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.
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/421d5450ddd044cb53171eb69b3877a9896290ee
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Citations in the corpus
- Prioritizing candidate disease genes by network-based boosting of genome-wide association data. (2011, background): “Second, only a very small fraction of the genetic heredity of most diseases studied so far can be explained by the SNPs identified by GWAS (Park et al. 2010).”
- Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, background): “In the genetic context, a single SNP usually has very small predictability of a complex trait [12, 31, 41, 46].”
- Correlation and Causation in the Study of Personality (2012, background): “Because an individual genetic variant is likely to have a very small effect, extremely large samples are required to detect it (Park et al., 2010).”
- Phenomics: the next challenge (2010, result): “Similar results have been obtained for other complex human traits, including Crohn's disease, breast, prostate and colorectal cancers, lupus erythematosus, type 1 and type 2 diabetes, lipid metabolism and heart attack”