Work

Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting

Year: 2021

Type: article

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.

Source: BMC Bioinformatics

DOI: 10.1186/s12859-021-04079-7

Authors: The Tien Mai, Paul Turner, Jukka Corander

Institutions: University of Oslo, Angkor Hospital for Children, University of Oxford, Cambodia-Oxford Medical Research Unit, University of Helsinki

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,490

Funder: H2020 European Research Council