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Review
. 2017 Jul 6;101(1):5-22.
doi: 10.1016/j.ajhg.2017.06.005.

10 Years of GWAS Discovery: Biology, Function, and Translation

Peter M Visscher  1 , Naomi R Wray  2 , Qian Zhang  3 , Pamela Sklar  4 , Mark I McCarthy  5 , Matthew A Brown  6 , Jian Yang  2
Affiliations
Review

10 Years of GWAS Discovery: Biology, Function, and Translation

Peter M Visscher et al. Am J Hum Genet. .

Abstract

Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.

Keywords: SNP; auto-immune disease; genome-wide association study; heritability; obesity; schizophrenia.

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Figures

Figure 1
Figure 1
Minimum Sample Sizes for Detecting Trait-SNP Associations from Imputed and WGS Data Required sample sizes for detecting association were calculated with (Equation A1), (Equation A2), (Equation A3), (Equation A4), (Equation A5) under the assumption of a type I error rate of 5 × 10−8, 80% power, and Hardy-Weinberg equilibrium. Effect sizes (β) are in phenotypic standard deviation units. For genotyped SNPs imputed to a fully sequenced reference, we have used the average imputation Rimp2 values reported by the Haplotype Reference Consortium in their Figure S3. This is a conservative estimate of imputation accuracy because it is based on a less dense genotyping array. For the WGS data, we have assumed no sequencing errors. Note that for some combinations of allele frequency and effect size, the required minimum number of individuals for detecting association exceeds 100 million.
Figure 2
Figure 2
GWAS SNP-Trait Discovery Timeline Data used for generating the graph were taken from the GWAS Catalogue. SNPs and traits were selected according to the following filters. SNPs were selected with a p value < 5 × 10−8. For each trait with two or more selected SNPs, SNPs were removed if they had an LD r2 > 0.5 (calculated from 1000 Genomes phase 3 data) with another selected SNPs and their p value was larger. For each year of discovery, only the top three traits and diseases with the largest number of SNPs are labeled in the circle.
Figure 3
Figure 3
Examples of Links between GWAS Discoveries and Drugs
See this image and copyright information in PMC

References

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