Heritability curves: a local measure of heritability

TL;DR

This paper proposes a novel local heritability measure that captures nonlinear trait dependence, allowing for trait-specific heritability analysis, demonstrated on birth weight and BMI data.

Contribution

It introduces heritability curves that relax the assumption of a single heritability value, enabling analysis of nonlinear dependence structures.

Findings

Birth weight heritability varies with trait value, lower at extremes.

BMI heritability shows no significant heterogeneity across trait values.

The method effectively captures nonlinear heritability patterns.

Abstract

This paper introduces a new measure of heritability which relaxes the classical assumption that the degree of heritability of a continuous trait can be summarized by a single number.This measure can be used in situations where the trait dependence structure between family members is nonlinear, in which case traditional mixed effects models and covariance (correlation) based methods are inadequate. Our idea is to combine the notion of a correlation curve with traditional correlation based measures of heritability, such as the formula of Falconer. For estimation purposes, we use a multivariate Gaussian mixture, which is able to capture non-linear dependence and respects certain distributional constraints. We derive an analytical expression for the associated correlation curve, and investigate its limiting behaviour when the trait value becomes either large or small. The result is a measure of heritability that varies with the trait value. When applied to birth weight data on Norwegian mother father child trios, the conclusion is that low and high birth weight are less heritable traits than medium birth weight. On the other hand, we find no similar heterogeneity in the heritability of Body Mass Index (BMI) when studying monozygotic and dizygotic twins.