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: 29.35). 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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    ABSTRACT: No single-nucleotide polymorphisms (SNPs) specific for aggressive prostate cancer have been identified in genome-wide association studies (GWAS).
    European Urology 09/2014; 67(4). DOI:10.1016/j.eururo.2014.09.020 · 13.94 Impact Factor
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    • "We can then easily calculate the effect size (β) of each SNP. The effect of the ith SNP on the trait is given by its contribution to the genetic variance of the trait (Park et al., 2010): "
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    ABSTRACT: The switch to a modern lifestyle in recent decades has coincided with a rapid increase in prevalence of obesity and other diseases. These shifts in prevalence could be explained by the release of genetic susceptibility for disease in the form of gene-by-environment (GxE) interactions. Yet, the detection of interaction effects requires large sample sizes, little replication has been reported, and a few studies have demonstrated environmental effects only after summing the risk of GWAS alleles into genetic risk scores (GRSxE). We performed extensive simulations of a quantitative trait controlled by 2500 causal variants to inspect the feasibility to detect gene-by-environment interactions in the context of GWAS. The simulated individuals were assigned either to an ancestral or a modern setting that alters the phenotype by increasing the effect size by 1.05-2-fold at a varying fraction of perturbed SNPs (from 1 to 20%). We report two main results. First, for a wide range of realistic scenarios, highly significant GRSxE is detected despite the absence of individual genotype GxE evidence at the contributing loci. Second, an increase in phenotypic variance after environmental perturbation reduces the power to discover susceptibility variants by GWAS in mixed cohorts with individuals from both ancestral and modern environments. We conclude that a pervasive presence of gene-by-environment effects can remain hidden even though it contributes to the genetic architecture of complex traits.
    Frontiers in Genetics 07/2014; 5:225. DOI:10.3389/fgene.2014.00225
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    • "For haplotypes, the ‘allele substitution’ effect depends on the haplotype that is set to zero. Briefly, the genetic variance calculated by this method determines the contribution of the SNP or haplotype to the additive genetic variance based on its estimated effect and haplotype/allele frequency under Hardy-Weinberg equilibrium and an additive polygenic model [30]. "
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    ABSTRACT: Background To better understand the genetic determination of udder health, we performed a genome-wide association study (GWAS) on a population of 2354 German Holstein bulls for which daughter yield deviations (DYD) for somatic cell score (SCS) were available. For this study, we used genetic information of 44 576 informative single nucleotide polymorphisms (SNPs) and 11 725 inferred haplotype blocks. Results When accounting for the sub-structure of the analyzed population, 16 SNPs and 10 haplotypes in six genomic regions were significant at the Bonferroni threshold of P ≤ 1.14 × 10-6. The size of the identified regions ranged from 0.05 to 5.62 Mb. Genomic regions on chromosomes 5, 6, 18 and 19 coincided with known QTL affecting SCS, while additional genomic regions were found on chromosomes 13 and X. Of particular interest is the region on chromosome 6 between 85 and 88 Mb, where QTL for mastitis traits and significant SNPs for SCS in different Holstein populations coincide with our results. In all identified regions, except for the region on chromosome X, significant SNPs were present in significant haplotypes. The minor alleles of identified SNPs on chromosomes 18 and 19, and the major alleles of SNPs on chromosomes 6 and X were favorable for a lower SCS. Differences in somatic cell count (SCC) between alternative SNP alleles reached 14 000 cells/mL. Conclusions The results support the polygenic nature of the genetic determination of SCS, confirm the importance of previously reported QTL, and provide evidence for the segregation of additional QTL for SCS in Holstein cattle. The small size of the regions identified here will facilitate the search for causal genetic variations that affect gene functions.
    Genetics Selection Evolution 06/2014; 46(1):35. DOI:10.1186/1297-9686-46-35 · 3.82 Impact Factor
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