Novel Resampling Improves Statistical Power for Multiple-Tra…
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- DOI 10.1534/g3.116.037531
- PMID https://pubmed.ncbi.nlm.nih.gov/28064191
- PMCID https://www.ncbi.nlm.nih.gov/pmc/articles/5345711
- OpenAlex ID W2588739546
- Type Article
- Language EN
- Published 01 Mar 2017
- Volume 7 (3)
- Pages 813–822
- Source G3 Genes Genomes Genetics
- Publisher Genetics Society of America
- FWCI 0.91
- Countries 2
- Institutions 3
- Added 24 Feb 2017
- Updated 28 Oct 2026
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Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping
Article 2017 EN Gold
Abstract Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a small proportion of the traits. Excluding QTL effects that contribute little to the test statistic can improve statistical power. In this art…
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3 authors| # | Author | Institutions | ORCID |
|---|---|---|---|
| 1 | Riyan Cheng Corresponding | Australian National University (AU) ARC Centre of Excellence in Pl… (AU) | |
| 2 | Rebecca W. Doerge | Carnegie Mellon University (US) | — |
| 3 | Justin Borevitz | Australian National University (AU) ARC Centre of Excellence in Pl… (AU) |
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