Abstract

For most complex traits, results from genome-wide association studies show that the proportion of the phenotypic variance attributable to the additive effects of individual SNPs, that is, the heritability explained by the SNPs, is substantially less than the estimate of heritability obtained by standard methods using correlations between relatives. This difference has been called the “missing heritability”. One explanation is that heritability estimates from family (including twin) studies are biased upwards. Zuk et al. revisited overestimation of narrow sense heritability from twin studies as a result of confounding with non-additive genetic variance. They propose a limiting pathway (LP) model that generates significant epistatic variation and its simple parametrization provides a convenient way to explore implications of epistasis. They conclude that over-estimation of narrow sense heritability from family data (‘phantom heritability’) may explain an important proportion of missing heritability. We show that for highly heritable quantitative traits large phantom heritability estimates from twin studies are possible only if a large contribution of common environment is assumed. The LP model is underpinned by strong assumptions that are unlikely to hold, including that all contributing pathways have the same mean and variance and are uncorrelated. Here, we relax the assumptions that underlie the LP model to be more biologically plausible. Together with theoretical, empirical, and pragmatic arguments we conclude that in outbred populations the contribution of additive genetic variance is likely to be much more important than the contribution of non-additive variance.

Highlights

  • A finding from genome-wide association studies for most complex traits is that the proportion of the phenotypic variance attributable to the additive effects of individual SNPs, i.e. the heritability explained by the SNPs, is substantially less than the estimate of heritability obtained from correlations of relatives using family data

  • It is impossible to disentangle the contribution of nonadditive genetic variance and common variance from twin data, there are some bounds on these parameters as illustrated in Figure 1, generated under the basic limiting pathway (LP) model, which is shown to yield more extreme non-additive genetic variance than many extended LP models

  • The LP model provides a useful framework to explore the possible contribution of non-additive genetic variance to complex traits

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Summary

Introduction

A finding from genome-wide association studies for most complex traits is that the proportion of the phenotypic variance attributable to the additive effects of individual SNPs, i.e. the heritability explained by the SNPs, is substantially less than the estimate of heritability obtained from correlations of relatives using family data. Many explanations for this so-called ‘missing heritability’ have been proposed [1,2,3,4,5]. Estimates of heritability for disease traits from twin cohorts (collected in restricted clinical settings) may be higher than those estimated from national cohort data, these differences most likely reflecting environmental factors including clinical practice [12]

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