Heritability measures the proportion of phenotypic variation attributable to genetic factors. In addition to a shared nuclear genetic component, a number of additional variance components, such as spousal correlation, sibship, household and maternal effects, may have strong contributions to inter-individual phenotype variation. In humans, the confounding effects of these components on heritability have not been studied thoroughly. We sought to obtain unbiased heritability estimates for complex traits in the presence of multiple variance components and also to estimate the contributions of these variance components to complex traits. We compared regression and variance component methods to estimate heritability in simulations when additional variance components existed. We then revisited heritability for several traits in Framingham Heart Study (FHS) participants. Using simulations, we found that failure to account for or misclassification of necessary variance components yielded biased heritability estimates. The direction and magnitude of the bias varied depending on a variance structure and an estimation method. Using the best fitted models to account for necessary variance components, we found that heritability estimates for most FHS traits were overestimated, ranging from 4 to 47 %, when we compared models that considered necessary variance components to models that only considered familial relationships. Spousal correlation explained 14–36 % of phenotypic variation in several anthropometric and lifestyle traits. Maternal and sibling effects also contributed to phenotypic variation, ranging from 3 to 5 % and 4 to 7 %, respectively, in several anthropometric and metabolic traits. Our findings may explain, in part, the missing heritability for some traits.
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