Inferring the Nature of Missing Heritability in Human Traits Using Data from the GWAS Catalog

Abstract

Thousands of genes responsible for many diseases and other common traits in humans have been detected by Genome Wide Association Studies (GWAS) in the last decade. However, candidate causal variants found so far usually explain only a small fraction of the heritability estimated by family data. The most common explanation for this observation is that the missing heritability corresponds to variants, either rare or common, with very small effect, which pass undetected due to a lack of statistical power. We carried out a meta-analysis using data from the NHGRI-EBI GWAS Catalog in order to explore the observed distribution of locus effects for a set of 42 complex traits and to quantify their contribution to narrow-sense heritability. With the data at hand, we were able to predict the expected distribution of locus effects for 16 traits and diseases, their expected contribution to heritability, and the missing number of loci yet to be discovered to fully explain the familial heritability estimates. Our results indicate that, for 6 out of the 16 traits, the additive contribution of a great number of loci is unable to explain the familial (broad-sense) heritability, suggesting that the gap between GWAS and familial estimates of heritability may not ever be closed for these traits. In contrast, for the other 10 traits, the additive contribution of hundreds or thousands of loci yet to be found could potentially explain the familial heritability estimates, if this were the case. Computer simulations are used to illustrate the possible contribution from nonadditive genetic effects to the gap between GWAS and familial estimates of heritability.

Genetic risk factors of ME/CFS: a critical review

“…Case sample sizes of ∼10 2 are well powered only to identify common (MAF ~ 10%) variants with very strong effects (effect size β ~ 1), whereas sample sizes of 10 4 are required to identify MAF ~ 10% variants with weaker effects ( β ~ 0.1) ( 32 ). This explains why GWAS often employ cohort sizes of ~10 4 –10 6 , and why when studies increase their cohort sizes they tend to discover larger numbers of lower-effect loci ( 35 ).…”

Genetic control of non-genetic inheritance in mammals: state-of-the-art and perspectives

“…Other than polygenic contributions to complex traits and complex genetic interactions (such as epistasis), several additional phenomena have been proposed as potential underlying causes of the missing heritability including intrinsic GWAS experimental limitations, heritability overestimations, variants with small effect-size, epigenetic mechanisms, gene/environment interaction and acquired inheritance, and many others (Lopez-Cortegano and Caballero 2019 ; Trerotola et al 2015 ; Young 2019 ).…”

Lifestyle Risk Score for aggregating multiple lifestyle factors: Handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions

“…Thanks to strong collaborations, many large-scale genome-wide association studies (GWAS) have successfully identified many genetic determinants described to explain part of the pathophysiological mechanism underlying a wide range of traits. Despite these efforts and increased sample sizes, the explained variability of many traits is relatively small and only a small proportion of the familial heritability can be explained by the candidate variants found (Evangelou et al, 2018;López-Cortegano & Caballero, 2019;Manolio et al, 2009).…”