Evidence map›Paper›PMID 35536304›Full record

ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2022

Canonical transformation for multivariate mixed model association analyses.

Li'ang Yang, Ying Zhang, Yuxin Song, Hengyu Zhang, Runqing Yang

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Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Li'ang Yang *College of Life Science and College of Animal Scientific and Technology, Northeast Agricultural University, Harbin, 150030, China.
Ying Zhang *College of Animal Science and Veterinary Medicine, Heilongjiang Bayi Agricultural University, Daqing, 163319, China.
Yuxin SongResearch Centre for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China.
Hengyu ZhangDepartment of Information and Computing Science, Heilongjiang Bayi Agricultural University, Daqing, 163319, China.
Runqing YangResearch Centre for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China. runqingyang@cafs.ac.cn.ORCID http://orcid.org/0000-0002-7236-5178

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

key messageIn extension of Single-RunKing to analyze multiple correlated traits, mvRunKing not only enlarged number of the analyzed phenotypes with canonical transformation, but also improved statistical power to detect pleiotropic QTNs through joint association analysis. Based on genomic variance-covariance matrices, we simplified multivariate mixed model association analysis to multiple univariate ones by using canonical transformation, and then individually implemented univariate association tests in the Single-RunKing. which enlarged number of the analyzed phenotypes. With canonical transformation back to the original scale, the association results would be biologically interpretable. Especially, we rapidly estimated genomic variance-covariance matrices with multivariate GEMMA and optimized separately the polygenic variances (or heritabilities) for only the markers that had large effects or higher significance levels in univariate mixed models, greatly improving computing efficiency for multiple univariate association tests. Beyond one test at once, joint association analysis for quantitative trait nucleotide (QTN) candidates can significantly increase statistical powers to detect QTNs. A user-friendly mvRunKing software was developed to efficiently implement multivariate mixed model association analyses.

Indexed as

Models, GeneticQuantitative Trait LociGenome-Wide Association StudyMultifactorial InheritanceMultivariate AnalysisPhenotypePolymorphism, Single Nucleotide

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.