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

TL;DR

This paper reviews recent machine learning methods for inferring heritability in bacterial traits, highlighting their variable performance and the importance of tailoring approaches to specific genetic architectures, especially for antibiotic resistance.

Contribution

It systematically compares machine learning approaches for heritability estimation in bacteria, including benchmarking on synthetic and real data, emphasizing the need for organism-specific method tuning.

Findings

Performance of methods varies widely across bacterial traits.

Tailoring heritability inference methods improves accuracy.

Benchmarking highlights strengths and limitations of current approaches.

Abstract

Quantification of heritability is a fundamental desideratum in genetics, which allows an assessment of the contribution of additive genetic variation to the variability of a trait of interest. The traditional computational approaches for assessing the heritability of a trait have been developed in the field of quantitative genetics. However, the rise of modern population genomics with large sample sizes has led to the development of several new machine learning based approaches to inferring heritability. In this paper, we systematically summarize recent advances in machine learning which can be used to infer heritability. We focus on an application of these methods to bacterial genomes, where heritability plays a key role in understanding phenotypes such as antibiotic resistance and virulence, which are particularly important due to the rising frequency of antimicrobial resistance. By designing a heritability model incorporating realistic patterns of genome-wide linkage disequilibrium for a frequently recombining bacterial pathogen, we test the performance of a wide spectrum of different inference methods, including also GCTA. In addition to the synthetic data benchmark, we present a comparison of the methods for antibiotic resistance traits for multiple bacterial pathogens. Insights from the benchmarking and real data analyses indicate a highly variable performance of the different methods and suggest that heritability inference would likely benefit from tailoring of the methods to the specific genetic architecture of the target organism.