omnigenicity

Wray NR, Wijmenga C, Sullivan PF, Yang J & Visscher PM 2018 Common disease is more complex than implied by the core gene omnigenic model. Cell 173:1573-1580.

• Boyle et al. (2017b) [...] introduce the term "omnigenic," (omni = "all") in acknowledgment of the very large number of genetic loci contributing to disease risk
• a key feature of the omnigenic model is the classification of genes as "peripheral" (which are generally regulatory in cellular networks and contribute to risk for many diseases and therefore to pleiotropy) or "core" (which are more disease specific with biologically interpretable roles)
• a defining feature of the omnigenic hypothesis is that only a modest number of genes or pathways have specific roles in the etiology of a specific disease
• these core genes, if mutated or deleted, have the strongest functional effects

• the key point of distinction of the omnigenic hypothesis is the emphasis on the importance of core genes
• types of genes detected in rare variant studies—which can detect highly deleterious variants with large effect sizes—play more direct roles in complex disease than do genes identified from GWASs based on common variants
• a consequence of the model is to focus experimental designs on discovery of rare variants
• this conclusion implies a simpler gene-disease biology than we have empirical evidence for
• one conclusion from sequencing genomes from healthy individuals was the high level of redundancy/robustness in the human genome
• most apparently normal humans have ~100 loss-of-function mutations (MacArthur et al., 2012)

• the core/peripheral properties align closely with those of older conceptualizations considering the relative importance of rare/common variants (Pritchard, 2001; Pritchard and Cox, 2002)
• the omnigenic model is partly a reframing of older ideas while trying to accommodate the empirical evidence that confirms polygenicity and a role of risk variants from across the allelic spectrum
• a closer look at the definition of the core gene is warranted

• for common disorders, the largest WES studies conducted to date have not been sufficiently powered to detect the effect sizes that exist in nature
• for type 2 diabetes, the conclusion from analysis of WES (7,380 cases) was "large-scale sequencing does not support the idea that lower-frequency coding variants have a major role in predisposition"

• understanding the consequences of polygenicity for individuals also links into an understanding of epistasis, the interacting effects of risk loci
• expectations for the role of epistasis in complex genetic disease are confusing and confused
• molecular biology studies provide unequivocal evidence that gene-gene interactions are common and impart a strong desire to undertake studies to detect epistatic associations
• quantitative genetic theory suggests that contributions from non-additive effects to phenotypic variation in the population and differences between people are small
• these differing viewpoints are accommodated under a polygenic genetic architecture
• the only way to reconcile disease that impacts only a small fraction of the population with a genetic architecture of many risk loci is to have a highly non-linear relationship between probability of disease and burden of risk alleles (Slatkin, 2008)
• the statistical genetics community prefers to say that complex disease is underpinned by genetic effects working additively in liability to risk
• for a molecular geneticist studying samples from diseased and healthy individuals, interactions between genetic effects are indeed implied, but on a scale that is challenging to study, both in terms of number of contributing genes and uniqueness of individuals

• whether the goal is discovery of rare variants or common variants, sample sizes are a key limiting factor for furthering our understanding of polygenic diseases
• increasing sample size remains a research priority

• Boyle et al. omnigenic core gene Perspective has been widely interpreted as a call for a research focus on cell-specific gene regulatory networks

• to assume that a limited number of core genes are key to our understanding of common disease may underestimate the true biological complexity, which is better represented by systems genetics and network approaches