15 years of GWAS discovery: Realizing the promise

Abstract

It has been 15 years since the advent of the genome-wide association study (GWAS) era. Here, we review how this experimental design has realized its promise by facilitating an impressive range of discoveries with remarkable impact on multiple fields, including population genetics, complex trait genetics, epidemiology, social science, and medicine. We predict that the emergence of large-scale biobanks will continue to expand to more diverse populations and capture more of the allele frequency spectrum through whole-genome sequencing, which will further improve our ability to investigate the causes and consequences of human genetic variation for complex traits and diseases.

Figure 1

Average sample size and average number of genome-wide significant (GWS) loci per publication for each year during the 15 years history of GWAS discoveries The data were extracted from 5,771 GWAS publications that used a genome-wide genotyping array and shared their summary statistics on GWAS Catalog before November 8, 2022.

Figure 2

Effect sizes of polygenic scores increase with sample size (A–D) Each panel corresponds to one of four height polygenic scores derived from independent genome-wide significant SNPs identified in Lango-Allen et al. (2010) (A), Wood et al. (2014) (B), Yengo et al. (2018) (C), and Yengo et al. (2022) (D). Note the difference between the panels in the scale of the y axes on the right, indicating the increasing precision of the height polygenic scores as the discovery sample sizes increase. Each polygenic score is scaled to have a mean of 0 and a variance of 1. Error bars indicate standard errors of the mean. (A), (B), and (D) use data from 14,587 unrelated participants of the UK Biobank (not included in the discovery GWAS), while (C) uses data from 8,235 unrelated participants from the Health and Retirement Study not included in Yengo et al. (2018). The number of SNPs used in each polygenic score is reported in the legend of each panel (top-left) and were based for Lango-Allen et al. (2010) and Wood et al. (2014) on a reanalysis by Yengo et al. (2022) based on the HapMap 3 SNP panel. Each polygenic score was binned into 12 groups defined as: below −2.5, (−2.5,−2.0), (−2.0,−1.5), (−1.5,−1.0), (−1.0,−0.5), (−0.5,0.0), (0.0,0.5), (0.5,1.0), (1.0,1.5), (1.5,2.0), (2.0,2.5) and above >2.5. Height differences are expressed on the z axis against the lowest group (defined). Each panel represents a histogram of the height polygenic score (x axis) with the percentage of the individuals in each group represented on the y axis.

Figure 3

Proportional increase in sample size (R_N(p)) relative to common variant GWAS (p_REF = ½) required for detecting rare variant associations with GWAS-by-WGS (A and B) (A) and (B) show R_N(p) as a function of the frequency p (varied between 0.01% and 1%) and the parameter S (varied between −1 and 0), respectively.