Data from: A practical introduction to random forest for genetic association studies in ecology and evolution

Large genomic studies are becoming increasingly common with advances in sequencing technology, and our ability to understand how genomic variation influences phenotypic variation between individuals has never been greater. The exploration of such relationships first requires the identification of associations between molecular markers and phenotypes. Here we explore the use of Random Forest (RF), a powerful machine learning algorithm, in genomic studies to discern loci underlying both discrete and quantitative traits, particularly when studying wild or non-model organisms. RF is becoming increasingly used in ecological and population genetics because, unlike traditional methods, it can efficiently analyze thousands of loci simultaneously and account for non-additive interactions. However, understanding both the power and limitations of Random Forest is important for its proper implementation and the interpretation of results. We therefore provide a practical introduction to the algorithm and its use for identifying associations between molecular markers and phenotypes, discussing such topics as data limitations, algorithm initiation and optimization, as well as interpretation. We also provide short R tutorials as examples, with the aim of providing a guide to the implementation of the algorithm. Topics discussed here are intended to serve as an entry point for molecular ecologists interested in employing Random Forest to identify trait associations in genomic data sets.

随着测序技术的迭代升级，大规模基因组研究愈发普及，我们解析基因组变异如何影响个体间表型变异的能力也达到了前所未有的高度。对这类关联关系的探索，首先需要明确分子标记与表型之间的相关性。本文探讨了高性能机器学习算法随机森林（Random Forest, RF）在基因组研究中的应用，用于识别调控离散性状与数量性状的基因座，尤其适用于野生或非模式生物的研究。相较于传统方法，随机森林可同时高效分析数千个基因座并考量非加性互作效应，因此在生态与群体遗传学领域的应用日益广泛。然而，明晰随机森林的优势与局限，对其正确实施及结果解读至关重要。为此，本文针对该算法在分子标记与表型关联识别中的应用，提供了兼具实践性的入门介绍，涵盖数据局限性、算法启动与优化，以及结果解读等主题。此外，本文还附带简短的R语言教程作为实操示例，旨在为该算法的实施提供实操指南。本文所探讨的内容，可为有志于运用随机森林在基因组数据集中识别性状关联的分子生态学家提供入门指引。