The third strategy seeks to implicate genes directly, by carrying the association tests at the level of annotated functional elements in the first place.
There are numerous methods that aggregate GWAS summary statistics at the level of genes, often by combining them with data from expression quantitative trait locus (eQTL) studies or functional annotations of genes and pathways. To arrive at more interpretable, actionable discoveries, another commonly used strategy is to prioritize genes (or other functional entities) rather than variants. However, even following fine-mapping, many of the significant GWAS associations remain without any known biological mechanistic interpretation. Fine-mapping of GWAS summary statistics often relies on functional annotations of the genome, under the assumption that functional entities are more likely to be causal. The most common strategy is the fine-mapping of raw GWAS results. Three major strategies are commonly used for prioritizing the most likely entities (e.g., variants or genes) causally affecting the phenotype. Due to linkage disequilibrium (LD) and population stratification, even when a genomic locus is robustly implicated with a phenotype, pinning the exact causal variants is a convoluted task. This limiting factor is especially crucial when dealing with rare variants of small effect sizes. Among the key factors is its limited statistical power, partly caused by the large number of tested variants across the genome. ĭespite the enormous impact of GWAS, inherent difficulties still limit its success. The UK Biobank (UKBB) is a flagship project of these efforts, having recruited a cohort of over 500,000 individuals, each with a full genotype and thousands of curated phenotypes (including medical history, lab tests, a variety of physical measures and comprehensive lifestyle questionnaires). Nowadays, thanks to the rapid development of large-scale biobanks with well-genotyped and well-phenotyped cohorts, conducting case-control studies has become easier than ever. In the past decade, the method has implicated numerous variant-phenotype associations and driven important scientific discovery. Genome-wide association studies (GWAS) seek to robustly link genetic loci with diseases and other heritable traits.
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