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Cluster and meta‐analyses of genetic parameters for feed intake traits in growing beef cattle
Author(s) -
Diaz I.D.P.S.,
Crews D.H.,
Enns R.M.
Publication year - 2014
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/jbg.12063
Subject(s) - heritability , restricted maximum likelihood , statistics , genetic correlation , variance components , random effects model , beef cattle , biology , meta analysis , trait , mathematics , zoology , correlation , microbiology and biotechnology , genetic variation , maximum likelihood , genetics , medicine , gene , computer science , programming language , geometry
Summary A data set based on 50 studies including feed intake and utilization traits was used to perform a meta‐analysis to obtain pooled estimates using the variance between studies of genetic parameters for average daily gain ( ADG ); residual feed intake ( RFI ); metabolic body weight ( MBW ); feed conversion ratio ( FCR ); and daily dry matter intake ( DMI ) in beef cattle. The total data set included 128 heritability and 122 genetic correlation estimates published in the literature from 1961 to 2012. The meta‐analysis was performed using a random effects model where the restricted maximum likelihood estimator was used to evaluate variances among clusters. Also, a meta‐analysis using the method of cluster analysis was used to group the heritability estimates. Two clusters were obtained for each trait by different variables. It was observed, for all traits, that the heterogeneity of variance was significant between clusters and studies for genetic correlation estimates. The pooled estimates, adding the variance between clusters, for direct heritability estimates for ADG , DMI , RFI , MBW and FCR were 0.32 ± 0.04, 0.39 ± 0.03, 0.31 ± 0.02, 0.31 ± 0.03 and 0.26 ± 0.03, respectively. Pooled genetic correlation estimates ranged from −0.15 to 0.67 among ADG , DMI , RFI , MBW and FCR . These pooled estimates of genetic parameters could be used to solve genetic prediction equations in populations where data is insufficient for variance component estimation. Cluster analysis is recommended as a statistical procedure to combine results from different studies to account for heterogeneity.