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|Title:||A Multi-Trait, Meta-analysis for Detecting Pleiotropic Polymorphisms for Stature, Fatness and Reproduction in Beef Cattle||Contributor(s):||Bolormaa, Sunduimijid (author); Pryce, Jennie E (author); Reverter, Antonio (author); Zhang, Yuandan (author); Barendse, William (author); Kemper, Kathryn Elizabeth (author); Tier, Bruce (author); Savin, Keith (author); Hayes, Ben J (author); Goddard, Michael E (author)||Publication Date:||2014||Open Access:||Yes||DOI:||10.1371/journal.pgen.1004198||Handle Link:||https://hdl.handle.net/1959.11/17212||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 metaanalysis 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.||Publication Type:||Journal Article||Source of Publication:||PLoS Genetics, 10(3), p. 1-23||Publisher:||Public Library of Science (PLoS)||Place of Publication:||San Francisco, United States of America||ISSN:||1553-7404||Field of Research (FOR):||070201 Animal Breeding
|Socio-Economic Outcome Codes:||830301 Beef Cattle||Peer Reviewed:||Yes||HERDC Category Description:||C1 Refereed Article in a Scholarly Journal||Statistics to Oct 2018:||Visitors: 617
|Appears in Collections:||Animal Genetics and Breeding Unit (AGBU)|
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