| Title: | Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer network |
| Journal: | Bioinformatics |
| Published: | 22 Nov 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37991852/ |
| DOI: | https://doi.org/10.1093/bioinformatics/btad707 |
| URL: | https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad707/53686138/btad707.pdf |
Publication 8276
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Abstract
MOTIVATION: Genome-wide association studies is an essential tool for analyzing associations between phenotypes and single nucleotide polymorphisms (SNPs). Most of binary phenotypes in large biobanks are extremely unbalanced, which leads to inflated type I error rates for many widely used association tests for joint analysis of multiple phenotypes. In this article, we first propose a novel method to construct a Multi-Layer Network (MLN) using individuals with at least one case status among all phenotypes. Then, we introduce a computationally efficient community detection method to group phenotypes into disjoint clusters based on the MLN. Finally, we propose a novel approach, MLN with Omnibus (MLN-O), to jointly analyse the association between phenotypes and a SNP. MLN-O uses the score test to test the association of each merged phenotype in a cluster and a SNP, then uses the Omnibus test to obtain an overall test statistic to test the association between all phenotypes and a SNP.</p>
RESULTS: We conduct extensive simulation studies to reveal that the proposed approach can control type I error rates and is more powerful than some existing methods. Meanwhile, we apply the proposed method to a real data set in the UK Biobank. Using phenotypes in Chapter XIII (Diseases of the musculoskeletal system and connective tissue) in the UK Biobank, we find that MLN-O identifies more significant SNPs than other methods we compare with.</p>
AVAILABILITY AND IMPLEMENTATION: https://github.com/Hongjing-Xie/Multi-Layer-Network-with-Omnibus-MLN-O.</p>
4 Authors
- Hongjing Xie
- Xuewei Cao
- Shuanglin Zhang
- Qiuying Sha
Enabling scientific discoveries that improve human health