Abstract
BackgroundHeritability 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.ResultsIn 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.ConclusionsBoosting is shown to offer a reliable and practically useful tool for inference about heritability.
Highlights
Whereas genome-wide association studies (GWAS) represent the primary tool for determining the genetic basis of a phenotype/trait of interest, quantifying the contribution of genetic factors to the variation of a phenotype plays in addition an important role in many studies
Since heritability is a concept detailing the additive variance of a trait which is in a certain sense based on a statistical model, heritability estimation is dependent on the specified model [12]
Using these effect sizes to estimate the heritability would bring insight on the heritability corresponding to the selected covariates and clearly provide useful ways to understand the genetic architecture of a trait
Summary
Whereas genome-wide association studies (GWAS) represent the primary tool for determining the genetic basis of a phenotype/trait of interest, quantifying the contribution of genetic factors to the variation of a phenotype plays in addition an important role in many studies. In Section “Model and definition” we present the linear model that relates a trait with a genotype matrix, narrow-sense heritability is defined together with some discussion regarding the fixed-effect vs random-effect approach for estimation.
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