  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.
