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

Journal article

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.

Authors: Mai, TT; Turner, P; Corander, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: BMC Bioinformatics
• Volume: 22
• Issue: 1
• Article number: 164
• Publication date: 2021-03-27
• Acceptance date: 2021-03-15
• DOI: 10.1186/s12859-021-04079-7
• EISSN: 1471-2105
• Language: English
• Keywords: FFR; boosting; linear model; antimicrobial resistance; heritability