Machine learning and statistical methods for molecular quantitative trait loci

Barbara E. Engelhardt

doi:10.1038/s41576-026-00990-y

2026-09-25

PRE

Barbara E. Engelhardt *

Joshua S. Weinstock *

Sarah K. Nyquist *

Alexis Battle *

DOI：10.1038/s41576-026-00990-y

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摘要

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生物学的一个重要目标是理解遗传变异如何转化为分子及更广泛的表型变异。数量性状位点（QTL）作图研究旨在通过统计学方法检验遗传变异与表型变异之间的关系，其中分子QTL（molQTL）通过捕捉遗传变异对分子性状（如基因表达或染色质可及性）的影响，作为感兴趣的可变表型。技术进步为molQTL作图方法提供了更多的分子表型进行测试，以及更大的研究队列，后者通过提供更强的统计功效来关联遗传变异与这些表型。随着这些进步，统计学模型——以及日益增加的机器学习方法——正在被开发以改进标准的molQTL作图方法、下游分析及其他molQTL研究方面，从而更深入地了解连接遗传与表型变异的机制，特别是在基因调控背景下。分子数量性状位点（molQTL）为非编码变异如何塑造复杂性状和疾病提供了关键见解。在本综述中，作者概述了用于molQTL作图和下游分析的统计学与机器学习方法，包括混杂因素校正、精细作图和共定位，以及将多效性、上位性和因果机制整合到molQTL模型中。

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