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

TL;DR

This paper introduces a novel heritability estimation method called 'boosting heritability' that combines multiple recent techniques and sample splitting to produce stable, accurate estimates in high-dimensional genetic models.

Contribution

It proposes a generic heritability inference strategy using multiple sample splitting, improving stability and accuracy over existing methods, especially in high-dimensional settings.

Findings

Demonstrated effectiveness on simulated data

Validated on real antibiotic resistance data

Produced stable and accurate heritability estimates

Abstract

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 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.