Author

Meida Wang

Michigan Technological University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1

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Research Focus:benchmarking· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Approach:statistical model· 1

Frequent Co-Authors

Qiuying ShaiD· 1 shared Shuanglin ZhangiD· 1 shared

Activity by Year

2022-2022 · peak 1/yr

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2 top collaborators · 1 mutual co-author link

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1 shared paperBioinformatics

Qiuying ShaORCID

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1 paper

Recent·Most cited

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

Meida Wang, Shuanglin Zhang, Qiuying Sha

This paper introduces ceCLC, a computationally efficient method for joint GWAS analysis of multiple phenotypes, which outperforms existing methods in controlling type I error and maintaining statistical power.

PLoS ONE · Apr 28, 2022Peer-reviewedBioinformatics