Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting

Background Heritability is a central measure in genetics quantifying how much of the variability observed in a trait is attributable to genetic differences. Existing methods for estimating heritability are most often based on random-effect models, typically for computational reasons. The alternative of using a fixed-effect model has received much more limited attention in the literature. Results In this paper, we propose a generic strategy for heritability inference, termed as boosting heritability, by combining the advantageous features of different recent methods to produce an estimate of the heritability with a high-dimensional linear model. Boosting heritability uses in particular a multiple sample splitting strategy which leads in general to a stable and accurate estimate. We use both simulated data and real antibiotic resistance data from a major human pathogen, Sptreptococcus pneumoniae, to demonstrate the attractive features of our inference strategy. Conclusions Boosting is shown to offer a reliable and practically useful tool for inference about heritability.

Automated summary

This study proposes a generic strategy called boosting heritability, which combines advantageous features of different recent methods to estimate heritability using a high-dimensional linear model. The use of a multiple sample splitting strategy in boosting heritability generally leads to a stable and accurate estimate. Results from simulated data and real antibiotic resistance data demonstrate that boosting offers a reliable and practically useful tool for inference about heritability.