Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting
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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. In this paper, we propose a generic strategy for heritability inference, termed as "boosting heritability", by combining several 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 to a more stable estimate. We use antibiotic resistance data from a major human pathogen, Sptreptococcus pneumoniae, to demonstrate the applicability of our inference strategy.
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