A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle

Bolormaa, Sunduimijid, Pryce, Jennie E., Reverter, Antonio, Zhang, Yuandan, Barendse, William, Kemper, Kathryn, Tier, Bruce, Savin, Keith, Hayes, Ben J. and Goddard, Michael E. (2014) A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle. PLoS Genetics, 10 3: . doi:10.1371/journal.pgen.1004198


Author Bolormaa, Sunduimijid
Pryce, Jennie E.
Reverter, Antonio
Zhang, Yuandan
Barendse, William
Kemper, Kathryn
Tier, Bruce
Savin, Keith
Hayes, Ben J.
Goddard, Michael E.
Title A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle
Journal name PLoS Genetics   Check publisher's open access policy
ISSN 1553-7404
Publication date 2014-03
Sub-type Article (original research)
DOI 10.1371/journal.pgen.1004198
Open Access Status DOI
Volume 10
Issue 3
Total pages 23
Place of publication San Francisco, United States
Publisher Public Library of Science
Language eng
Formatted abstract
Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t'V-1t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Article number e1004198

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Alliance for Agriculture and Food Innovation
Institute for Molecular Bioscience - Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 24 times in Thomson Reuters Web of Science Article | Citations
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