GWASs are hypothesis-free study designs in which a panel (usually hundreds of thousands or millions) of genetic variants are each systematically tested for association with a single trait (which could be a health-related or disease outcome). GWASs have the primary objectives of identifying specific variants that can be used for prediction of the trait, and for highlighting genes or loci that are relevant to the aetiology of the trait. Most genetic effects are small (especially those of common genetic variants), so GWASs need to be performed using large biobanks or collaborations between many studies in which the GWAS results from all studies are meta-analysed. Control for confounding due to population structure, the use of strict p-value thresholds and replication of findings in independent samples are key features of reliable GWASs. Commonly, GWAS results are visualised using a Manhattan plot, which shows the -log10(p-value) of each single nucleotide polymorpism (SNP) by the location of that SNP in the genome by chromosome. All variants that associate with the trait at a genome-wide significance p-value threshold are then commonly used as genetic instrumental variables (IVs) in MR studies.
Potential limitations include: 1) genome-wide significance p-value thresholds are based on a Bonferroni correction that leads to small genetic effect estimates being biased upwards due to winner’s curse. The use of these discovery effect estimates in MR, rather than those obtained through independent replication can lead to biased effect estimates; 2) the function of genetic variants identified from GWASs (i.e., known genome-wide hits) may be unknown; therefore, particularly for complex traits (such as body mass index), where there is likely to be a chain of effects or associations from the gene to the trait, there could be a strong potential for horizontal pleiotropy; 3) with sample sizes growing ever larger, the risk of subtle population stratification or intergenerational (dynastic) effects leading to false positive or biased GWAS findings is growing. Potential strengths include: 1) Most GWASs will highlight genetic variants that replicate in (large) independent studies; and 2) where several variants are identified as potential IVs for a trait, several statistical methods, each with differing assumptions, can be used and results compared across them.
References
- Visscher PM, Wray NR, Zhang Q, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. American journal of human genetics 2017; 101: 5-22.
- Haworth S, Mitchell R, Corbin L, et al. Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis. Nature Communications 2019; 10: 333.
- Bush WS, Moore JH. Chapter 11: Genome-Wide Association Studies. PLoS Computational Biology 2012; 8: e1002822.