Missing Heritability of Complex Traits and Diseases

Abstract

Genome‐wide association studies (GWASs) have identified thousands of genetic variants involved in complex traits and diseases, but these only explain a minor fraction of the heritability. New methodologies that scrutinise the GWAS data indicate where the missing heritability might be found. About half of the heritability is still hidden in the GWAS data as this concerns common variants with small effects. Furthermore, a large part is still missing because this involves rare variants, which cannot be detected by GWAS due to low linkage disequilibrium. Nevertheless, even estimates of the most sophisticated methods do not fully reach the total genetic contributions derived from twin and family studies, suggesting that these heritabilities may be overestimated due to violation of the underlying assumptions or heterogeneity in heritability estimation across populations. Future variant discovery for complex traits and diseases will capture an ever larger part of the genetic predisposition and eventually bring health care applications within reach.

Key Concepts

  • Genome‐wide association studies (GWAS) have so far been able to only explain a minority of the heritability of complex traits or diseases, a large part is missing.
  • The missing heritability can be divided in hidden heritability, still‐missing heritability and phantom heritability.
  • Part of the missing heritability is hidden in the GWAS data meaning that due to lack of power the underlying common genetic variants have not yet been identified.
  • About half of the heritability of complex traits or diseases is expected to be caused by common variants and can ultimately be found by GWAS.
  • The still‐missing heritability can be explained by rare and structural genetic variants, dominance effects and epistasis.
  • The effects of dominance and epistasis are likely not very large.
  • Heritability estimates from twin and family studies may be overestimated due to violation of underlying model assumptions and therefore cause phantom heritability.
  • Heritability is likely also missing due to heterogeneity of effects between and within populations, as estimates from twin and family studies are calculated in homogeneous populations.

Keywords: missing heritability; genome‐wide association study; genetic risk scores; polygenic risk scores; hidden heritability; common SNP heritability; still‐missing heritability; phantom heritability; heterogeneity

Figure 1. Illustration of the total and missing heritability concepts and the different sources involved. In dark blue colour is the part of the heritability that can ultimately be explained by GWAS (i.e. common SNP heritability), which is composed of variance that can be explained by identified genetic variants (green slices) and the hidden heritability (lighter blue colour and blue slices). Hidden heritability will become smaller as larger GWAS become available and more genetic variants are identified. If the sample size for GWAS would be infinite, all hidden heritability could be found. The part of the heritability that cannot be explained by GWAS (i.e. still‐missing heritability (shown in red colours)) contains rare variants, structural variations, dominance (D) and epistasis (GxG). The phantom heritability describes the inflation of the heritability estimates from twin and family studies and includes inadequate accounting for dominance effects (D) and epistasis (GxG) as well as shared environment (including GxC interaction and G‐E correlation). The sum of the hidden, the still‐missing, and the phantom heritability is the missing heritability, which is the gap between phenotypic variance explained by current GWAS (through genetic (GRS) and polygenic risk scores (PRS)) and the total heritability as estimated from family or twin studies.
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Nolte, Ilja Maria, Tropf, Felix Christian, and Snieder, Harold(Mar 2019) Missing Heritability of Complex Traits and Diseases. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0028223]