Predicting Gene Regulatory Interactions Using Natural Genetic Variation

Maura John; Dominik Grimm; Arthur Korte

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

Predicting Gene Regulatory Interactions Using Natural Genetic Variation

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

Authors

Maura John¹, Dominik Grimm¹, Arthur Korte²

Affiliations

¹ Technical University of Munich & Weihenstephan-Triesdorf University of Applied Sciences, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany.
² Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany. arthur.korte@uni-wuerzburg.de.

PMID: 37682482
DOI: 10.1007/978-1-0716-3354-0_18

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.

Keywords: GWAS; Gene regulatory networks; TWAS.

MeSH terms

Epistasis, Genetic*
Gene Regulatory Networks
Genetic Variation
Genome-Wide Association Study*
Phenotype