GESLM algorithm for detecting causal SNPs in GWAS with multiple phenotypes

Abstract

With the development of genome-wide association studies, how to gain information from a large scale of data has become an issue of common concern, since traditional methods are not fully developed to solve problems such as identifying loci-to-loci interactions (also known as epistasis). Previous epistatic studies mainly focused on local information with a single outcome (phenotype), while in this paper, we developed a two-stage global search algorithm, Greedy Equivalence Search with Local Modification (GESLM), to implement a global search of directed acyclic graph in order to identify genome-wide epistatic interactions with multiple outcome variables (phenotypes) in a case–control design. GESLM integrates the advantages of score-based methods and constraint-based methods to learn the phenotype-related Bayesian network and is powerful and robust to find the interaction structures that display both genetic associations with phenotypes and gene interactions. We compared GESLM with some common phenotype-related loci detecting methods in simulation studies. The results showed that our method improved the accuracy and efficiency compared with others, especially in an unbalanced case–control study. Besides, its application on the UK Biobank dataset suggested that our algorithm has great performance when handling genome-wide association data with more than one phenotype.

Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference

“…Causal diagram model provides an alternative way to represent the underlying causal relationships (Nogueira et al, 2022). With causal diagrams, machine learning techniques like the causal Bayesian network (BN) are currently applied to identify genetic interactions and causal variants (Lyu et al, 2021). They will also be a profitable complement to conventional MR (Howey et al, 2020;Amar et al, 2021).…”

STS-BN: An efficient Bayesian network method for detecting causal SNPs

“…Then in the late 1980s, Pearl (1988) and Neapolitan (1990) summarized the relevant properties of Bayesian network and made it a new research field. In recent years, the application of Bayesian network has become more popular with many successful examples, such as analyzing gene expression data, predicting protein-protein interactions, and so on ( Su et al, 2013 ; Lyu et al, 2021 ). Currently, several Bayesian network methods have been developed to detect epistatic interactions from GWAS data ( Han et al, 2010 ; Han and Chen, 2011 ; Peng et al, 2021 ).…”