Literature DB >> 1880275

Additive and nonadditive genetic variance in female fertility of Holsteins.

I Hoeschele1.   

Abstract

Additive and nonadditive genetic variances were estimated for cow fertility of Holsteins. Measures of fertility were first lactation days open and service period as recorded and with upper bounds of 150 and 91 d, respectively. Six million inseminations from the Raleigh, North Carolina Processing Center were used to form fertility records of 379,009 cows. Data were analyzed with a model accounting for all additive, dominance, and additive by additive covariances traced through sires and maternal grandsires. Variance components were estimated by the tilde-hat approximation to REML. Heritability in the narrow sense was 2% for days open and .8% for service period. Dominance and additive by additive variance as a percentage of phenotypic variation strongly depended on imposition of upper bounds. Heritabilities in the broad sense ranged from 2.2 to 6.6% and were at least twice as large as heritabilities in the narrow sense. Effect of 25% inbreeding was only around an additional 3 d open. Specific combining abilities among bulls were estimated as sums of dominance and additive by additive interactions removing effect of inbreeding depression. Differences between maximum and minimum estimates were in the order of twice the estimated standard deviation, ranging from 1.5 to 6.7 d. Effects of inbreeding and specific combining ability could be jointly considered in mating programs following sire selection.

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Year:  1991        PMID: 1880275     DOI: 10.3168/jds.S0022-0302(91)78337-9

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  8 in total

1.  Gene regulatory networks generating the phenomena of additivity, dominance and epistasis.

Authors:  S W Omholt; E Plahte; L Oyehaug; K Xiang
Journal:  Genetics       Date:  2000-06       Impact factor: 4.562

2.  Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers.

Authors:  Guosheng Su; Ole F Christensen; Tage Ostersen; Mark Henryon; Mogens S Lund
Journal:  PLoS One       Date:  2012-09-13       Impact factor: 3.240

3.  Predicting genetic values: a kernel-based best linear unbiased prediction with genomic data.

Authors:  Ulrike Ober; Malena Erbe; Nanye Long; Emilio Porcu; Martin Schlather; Henner Simianer
Journal:  Genetics       Date:  2011-04-21       Impact factor: 4.562

4.  Validation of markers with non-additive effects on milk yield and fertility in Holstein and Jersey cows.

Authors:  Hassan Aliloo; Jennie E Pryce; Oscar González-Recio; Benjamin G Cocks; Ben J Hayes
Journal:  BMC Genet       Date:  2015-07-22       Impact factor: 2.797

5.  Kernel-based variance component estimation and whole-genome prediction of pre-corrected phenotypes and progeny tests for dairy cow health traits.

Authors:  Gota Morota; Prashanth Boddhireddy; Natascha Vukasinovic; Daniel Gianola; Sue Denise
Journal:  Front Genet       Date:  2014-03-24       Impact factor: 4.599

6.  Improvement of prediction ability for genomic selection of dairy cattle by including dominance effects.

Authors:  Chuanyu Sun; Paul M VanRaden; John B Cole; Jeffrey R O'Connell
Journal:  PLoS One       Date:  2014-08-01       Impact factor: 3.240

7.  Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits.

Authors:  Hassan Aliloo; Jennie E Pryce; Oscar González-Recio; Benjamin G Cocks; Ben J Hayes
Journal:  Genet Sel Evol       Date:  2016-02-01       Impact factor: 4.297

8.  Inbreeding depression across the genome of Dutch Holstein Friesian dairy cattle.

Authors:  Harmen P Doekes; Piter Bijma; Roel F Veerkamp; Gerben de Jong; Yvonne C J Wientjes; Jack J Windig
Journal:  Genet Sel Evol       Date:  2020-10-28       Impact factor: 4.297

  8 in total

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