These GEITP pages have continued to examine genome-wide association studies (GWAS) because they are central to our theme of gene-environment interactions. Common (complex) diseases (e.g. obesity, schizophrenia) and other multifactorial traits (e.g. height, body mass index, response to drugs or environmental toxicants) are heritable and reflect the contribution of hundreds/thousands of small-effect genes (i.e. highly polygenic). There are usually no large-effect common single-nucleotide variants (SNVs), and heritability is evenly spread across thousands of small-effect SNVs. The polygenic distribution of heritability presents a challenge, because small-effect SNVs are difficult to detect (leading to ‘missing heritability’, which these GEITP pages have discussed previously) and they are difficult to interpret.
One factor — contributing to the polygenic distribution of heritability is the complexity of the underlying biology: many genes and regions of the genome have a non-zero phenotypic effect if mutated (i.e. a mutation in many of these genes does have SOME effect). A plausible explanation for the large mutational target is that cellular networks are densely connected, such that nearly every gene, expressed in a relevant cell-type, has a small phenotypic effect (the ‘omnigenic model’). One implication of this model is that disease genes with direct phenotypic effects would explain a minority of disease heritability.
Although biological complexity clearly contributes to the polygenicity of complex traits, negative selection (also called ‘purifying selection’, is the selective removal of alleles that are bad for the host) may also be a critical factor. Biological complexity determines the effect-size distribution of new SNVs. Authors postulated that this distribution — in contrast to that of heritability — might be dominated by a relatively small number of large-effect genes and loci. In the absence of negative selection, the resulting distribution of heritability would be highly concentrated in these large-effect regions; hence, only moderately polygenic (see Fig. 1 of attached article). In the presence of negative selection, however, heritability explained by any single SNV is limited, and mutations in these large-effect regions would not be so common. As a result, heritability would be spread much more evenly across large- and small-effect regions alike. Authors refer to this phenomenon (negative selection causing the distribution of heritability to be extremely polygenic) — as flattening.
To quantify the effects of flattening, authors [see attached article] introduced a mathematical definition of polygenicity, which describes how evenly the heritability of a trait is spread across the genome. Authors then developed a method which validated that this definition produces robust estimates. Analyzing 33 complex traits, authors determined that heritability is spread ~4x more evenly among common SNVs than among low-frequency SNVs. This difference — together with evolutionary modeling of new mutations — suggests that multifactorial traits would be orders of magnitude less polygenic, were it not for the influence of negative selection. These data suggest that, for most multifactorial traits, those genes and loci having the most critical biological effects — often differ from those with the strongest common-variant associations. 😊
Am J Hum Genet Oct 2019; 105: 456-476