利用自然遗传变异预测基因调控相互作用。

Predicting Gene Regulatory Interactions Using Natural Genetic Variation.

机构信息

Technical University of Munich & Weihenstephan-Triesdorf University of Applied Sciences, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany.

Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany.

出版信息

Methods Mol Biol. 2023;2698:301-322. doi: 10.1007/978-1-0716-3354-0_18.

DOI:10.1007/978-1-0716-3354-0_18

PMID:37682482

Abstract

Genome-wide association studies (GWAS) are a powerful tool to elucidate the genotype-phenotype map. Although GWAS are usually used to assess simple univariate associations between genetic markers and traits of interest, it is also possible to infer the underlying genetic architecture and to predict gene regulatory interactions. In this chapter, we describe the latest methods and tools to perform GWAS by calculating permutation-based significance thresholds. For this purpose, we first provide guidelines on univariate GWAS analyses that are extended in the second part of this chapter to more complex models that enable the inference of gene regulatory networks and how these networks vary.

摘要

全基因组关联研究（GWAS）是阐明基因型-表型图谱的有力工具。尽管 GWAS 通常用于评估遗传标记与感兴趣的性状之间的简单单变量关联，但也可以推断潜在的遗传结构并预测基因调控相互作用。在本章中，我们描述了通过计算基于置换的显著性阈值来进行 GWAS 的最新方法和工具。为此，我们首先提供了关于单变量 GWAS 分析的指南，这些指南在本章的第二部分扩展到更复杂的模型，这些模型能够推断基因调控网络以及这些网络如何变化。