 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  .
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 .
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Background:
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Results:
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"boosting heritability"
``boosting heritability"
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several
the
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to a more stable
in general to a stable and and accurate
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both simulated data and real
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Sptreptococcus pneumoniae
Sptreptococcus pneumoniae
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applicability
attractive features
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. Conclusions: Boosting is shown to offer a reliable and practically useful tool for inference about heritability