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<jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>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.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>In this paper, we propose a generic strategy for heritability inference, termed as <jats:italic>“boosting heritability”</jats:italic>, 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, <jats:italic>Sptreptococcus pneumoniae</jats:italic>, to demonstrate the attractive features of our inference strategy.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>Boosting is shown to offer a reliable and practically useful tool for inference about heritability.</jats:p> </jats:sec>

Original publication





BMC Bioinformatics


Springer Science and Business Media LLC

Publication Date