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Open Access

Peer-reviewed

Research Article

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

Meida Wang,

Roles Formal analysis, Writing – original draft, Writing – review & editing

Affiliation Mathematical Sciences, Michigan Technological University, Houghton, MI, United States of America
⨯
Shuanglin Zhang,

Roles Conceptualization, Methodology, Resources, Supervision, Validation, Writing – review & editing

Affiliation Mathematical Sciences, Michigan Technological University, Houghton, MI, United States of America

https://orcid.org/0000-0002-9478-1199

⨯
Qiuying Sha

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

* E-mail: qsha@mtu.edu

Affiliation Mathematical Sciences, Michigan Technological University, Houghton, MI, United States of America

https://orcid.org/0000-0002-9342-3269

⨯

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

Meida Wang,
Shuanglin Zhang,
Qiuying Sha

x

Published: April 28, 2022
https://doi.org/10.1371/journal.pone.0260911
See the preprint

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