From the time of the first genome-wide association study (GWAS) on macular degeneration (form of blindness that can occur with increasing age) in 2005, there have now been more than 3,000 GWAS studies published, selecting more than 1,000 traits, and reporting on tens of thousands of “genetic risk variants”. These results have increased our understanding of the genetic architecture (please recall that this means ‘the underlying genetic basis of a phenotypic trait and its properties of variability’) of traits. Occasionally, GWAS results have led to further insight into disease mechanisms — such as autophagy (destruction of damaged cellular components occurring in vacuoles within the cell) for Crohn disease, immunodeficiency for rheumatoid arthritis, and transcriptome regulation, via FOXA2, in pancreas and liver for type-2 diabetes.
After 14 years of GWAS, we now know that the majority of studied traits represent contribution of countless genes (i.e. highly polygenic) and influenced by many genetic small-effect single-nucleotide variants (SNVs) — with unequal genetic architectures across the various traits. However, fundamental questions remain unanswered: [a] whether all genetic variants or genes in the human genome are associated with at least one, many, or even all, traits; and [b] whether polygenic effects for specific traits are functionally clustered, or randomly scattered across, the genome. Such knowledge would greatly enhance our understanding of how genetic variation leads to trait variation and trait correlations. Whereas GWAS primarily aim to discover SNVs associated with specific traits, current availability of vast amounts of GWAS results allow investigation of these general questions.
With this in mind, authors [see attached article] compiled a catalog of 4,155 GWAS results across 2,965 unique traits from 295 studies (https://atlas.ctglab.nl) — including publicly available GWAS and new results for 600 traits from the UK Biobank. These GWAS results were used in the current study to: [a] chart the extent of pleiotropy (a single gene’s contribution to two or more apparently unrelated traits) at the trait-associated locus, gene, SNV, and gene-set levels, [b] characterize the nature of trait-associated variants (i.e. distribution of effect-size, minor allele frequency (MAF) and biological functionality of trait-associated or credible SNVs); and [c] investigate genetic architecture across a variety of traits and domains in terms of SNP heritability and trait polygenicity.
Authors show that trait-associated loci cover more than half the genome, and 90% of these overlap with loci from multiple traits (i.e. they are pleiotropic). Authors found that potential causal SNVs are enriched in coding and flanking regions, as well as in regulatory elements, and show variation in polygenicity and discoverability of traits. These data provide insights into how genetic variation contributes to variation of any complex disease (e.g. schizophrenia, obesity, type-2 diabetes, response to drugs, environmental toxicants) or quantitative trait (e.g. height, blood pressure, body mass index).
Nat Genet Sept 2019; 51: 1339–1348