机器学习时代细菌性状遗传力的推断
Inferring the heritability of bacterial traits in the era of machine learning
AI总结：
该研究系统总结了机器学习在遗传力推断中的最新进展，聚焦细菌基因组应用，通过合成数据与真实病原体数据分析测试多种方法性能，发现不同方法表现差异大，需针对目标生物遗传结构定制方法以提升推断效果。
摘要：
遗传力定量是遗传学的一项核心需求，可用于评估加性遗传变异对目标性状变异的贡献程度。评估性状遗传力的传统计算方法是在数量遗传学领域发展起来的。然而，大样本量的现代群体基因组学兴起，推动了多种基于机器学习的遗传力推断新方法的发展。本文系统总结了可用于遗传力推断的机器学习领域最新进展。我们重点关注这些方法在细菌基因组中的应用——在细菌中，遗传力对于理解抗生素耐药性、毒力等表型具有关键作用，而随着抗菌药物耐药性发生率不断上升，这些表型的重要性尤为突出。通过构建一个纳入了全基因组连锁不平衡真实模式的遗传力模型（针对一种高频重组的细菌病原体），我们测试了多种不同推断方法的性能，其中也包括GCTA（基因组复杂性状分析工具）。除了合成数据基准测试外，我们还针对多种细菌病原体的抗生素耐药性状开展了方法比较。基准测试与真实数据分析的结果表明，不同方法的性能差异极大，且提示遗传力推断若能针对目标生物的特定遗传结构定制方法，很可能会得到性能提升。
英文摘要：
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.
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