Boosting Heritability: Estimating the Genetic Component of Phenotypic Variation with Multiple Sample Splitting

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2021 Mar 28

PMID 33773584

Citations 4

Authors

The Tien Mai

Paul Turner

Jukka Corander

Affiliations

Soon will be listed here.

Abstract

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 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.

Citing Articles

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Insights into the influence of diet and genetics on feed efficiency and meat production in sheep.

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Inferring the heritability of bacterial traits in the era of machine learning.

Mai T, Lees J, Gladstone R, Corander J Bioinform Adv. 2023; 3(1):vbad027.

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Mallawaarachchi S, Tonkin-Hill G, Croucher N, Turner P, Speed D, Corander J NAR Genom Bioinform. 2022; 4(1):lqac011.

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