Literature DB >> 9814895

Heterogeneity of (co)variance among herds for backfat measures of swine.

M T See1.   

Abstract

Within-herd variation for the trait of backfat was examined in swine. Backfat depth data (n=21,877) from 11 herds that had each recorded over 1,000 measures were evaluated using univariate animal model procedures in analyses for individual herds and all herds together. Variance components were estimated by a derivative-free REML algorithm, and significance tests for variance components were carried out as likelihood ratio tests. Adjustment for heterogeneous variance by correction for within-herd phenotypic SD was also evaluated. Additive genetic and residual variances were heterogeneous across herds and ranged from 1.39 to 9.78 mm2 and from .88 to 7.05 mm2, respectively. Eleven parameter estimates from individual herd analyses were significantly different (P < .05) from the analysis for all herds together. However, only small differences were observed between estimates of heritability, which ranged from .37 to .73. Scaling data to a constant within-herd SD resulted in more homogeneous variance. For additive genetic variance, only one herd differed significantly (P < .01) from the analysis for all herds together. However, five estimates of residual variance were found to be significantly different (P < .05) from the analysis for all herds together. The rank correlation between EPD predicted from the analyses with all herds together, homogeneous variance, and scaled to a constant within-herd SD was .97 (P < .01). Effects of heterogeneous variance may need to be accounted for in genetic evaluation procedures for swine.

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Year:  1998        PMID: 9814895     DOI: 10.2527/1998.76102568x

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  2 in total

1.  The Statistical Scale Effect as a Source of Positive Genetic Correlation Between Mean and Variability: A Simulation Study.

Authors:  Adile Tatliyer; Isabel Cervantes; Nora Formoso-Rafferty; Juan Pablo Gutiérrez
Journal:  G3 (Bethesda)       Date:  2019-09-04       Impact factor: 3.154

2.  Genomic Prediction Accounting for Residual Heteroskedasticity.

Authors:  Zhining Ou; Robert J Tempelman; Juan P Steibel; Catherine W Ernst; Ronald O Bates; Nora M Bello
Journal:  G3 (Bethesda)       Date:  2015-11-12       Impact factor: 3.154

  2 in total

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