| Title: | GESLM algorithm for detecting causal SNPs in GWAS with multiple phenotypes |
| Journal: | Briefings in Bioinformatics |
| Published: | 29 Jul 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34323927/ |
| DOI: | https://doi.org/10.1093/bib/bbab276 |
| Citations: | 4 (2 in last 2 years) as of 8 Aug 2024 |
Publication 5084
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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.6 Authors
- Ruiqi Lyu
- Jianle Sun
- Dong Xu
- Qianxue Jiang
- Chaochun Wei
- Yue Zhang
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