Literature DB >> 23217224

Genotyping strategies for genomic selection in small dairy cattle populations.

J A Jiménez-Montero1, O González-Recio, R Alenda.   

Abstract

This study evaluated different female-selective genotyping strategies to increase the predictive accuracy of genomic breeding values (GBVs) in populations that have a limited number of sires with a large number of progeny. A simulated dairy population was utilized to address the aims of the study. The following selection strategies were used: random selection, two-tailed selection by yield deviations, two-tailed selection by breeding value, top yield deviation selection and top breeding value selection. For comparison, two other strategies, genotyping of sires and pedigree indexes from traditional genetic evaluation, were included in the analysis. Two scenarios were simulated, low heritability (h 2 = 0.10) and medium heritability (h 2 = 0.30). GBVs were estimated using the Bayesian Lasso. The accuracy of predicted GBVs using the two-tailed strategies was better than the accuracy obtained using other strategies (0.50 and 0.63 for the two-tailed selection by yield deviations strategy and 0.48 and 0.63 for the two-tailed selection by breeding values strategy in low- and medium-heritability scenarios, respectively, using 1000 genotyped cows). When 996 genotyped bulls were used as the training population, the sire' strategy led to accuracies of 0.48 and 0.55 for low- and medium-heritability traits, respectively. The Random strategies required larger training populations to outperform the accuracies of the pedigree index; however, selecting females from the top of the yield deviations or breeding values of the population did not improve accuracy relative to that of the pedigree index. Bias was found for all genotyping strategies considered, although the Top strategies produced the most biased predictions. Strategies that involve genotyping cows can be implemented in breeding programs that have a limited number of sires with a reliable progeny test. The results of this study showed that females that exhibited upper and lower extreme values within the distribution of yield deviations may be included as training population to increase reliability in small reference populations. The strategies that selected only the females that had high estimated breeding values or yield deviations produced suboptimal results.

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Year:  2012        PMID: 23217224     DOI: 10.1017/S1751731112000341

Source DB:  PubMed          Journal:  Animal        ISSN: 1751-7311            Impact factor:   3.240


  13 in total

1.  Genomic selection in a commercial winter wheat population.

Authors:  Sang He; Albert Wilhelm Schulthess; Vilson Mirdita; Yusheng Zhao; Viktor Korzun; Reiner Bothe; Erhard Ebmeyer; Jochen C Reif; Yong Jiang
Journal:  Theor Appl Genet       Date:  2016-01-08       Impact factor: 5.699

2.  The impact of clustering methods for cross-validation, choice of phenotypes, and genotyping strategies on the accuracy of genomic predictions.

Authors:  Johnna L Baller; Jeremy T Howard; Stephen D Kachman; Matthew L Spangler
Journal:  J Anim Sci       Date:  2019-04-03       Impact factor: 3.159

3.  Establishing gene Amelogenin as sex-specific marker in yak by genomic approach.

Authors:  P P Das; G Krishnan; J Doley; D Bhattacharya; S M Deb; P Chakravarty; P J Das
Journal:  J Genet       Date:  2019-03       Impact factor: 1.166

4.  Genomic prediction of breeding values using previously estimated SNP variances.

Authors:  Mario Pl Calus; Chris Schrooten; Roel F Veerkamp
Journal:  Genet Sel Evol       Date:  2014-09-25       Impact factor: 4.297

5.  Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches.

Authors:  Simon Rio; Alain Charcosset; Tristan Mary-Huard; Laurence Moreau; Renaud Rincent
Journal:  Methods Mol Biol       Date:  2022

Review 6.  Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits.

Authors:  C Egger-Danner; J B Cole; J E Pryce; N Gengler; B Heringstad; A Bradley; K F Stock
Journal:  Animal       Date:  2014-11-12       Impact factor: 3.240

7.  Training set optimization under population structure in genomic selection.

Authors:  Julio Isidro; Jean-Luc Jannink; Deniz Akdemir; Jesse Poland; Nicolas Heslot; Mark E Sorrells
Journal:  Theor Appl Genet       Date:  2014-11-01       Impact factor: 5.699

8.  Predictive ability of genome-assisted statistical models under various forms of gene action.

Authors:  Mehdi Momen; Ahmad Ayatollahi Mehrgardi; Ayyub Sheikhi; Andreas Kranis; Llibertat Tusell; Gota Morota; Guilherme J M Rosa; Daniel Gianola
Journal:  Sci Rep       Date:  2018-08-17       Impact factor: 4.379

9.  Selective genotyping and phenotypic data inclusion strategies of crossbred progeny for combined crossbred and purebred selection in swine breeding.

Authors:  Garrett M See; Benny E Mote; Matthew L Spangler
Journal:  J Anim Sci       Date:  2021-03-01       Impact factor: 3.159

10.  Systematic genotyping of groups of cows to improve genomic estimated breeding values of selection candidates.

Authors:  Laura Plieschke; Christian Edel; Eduardo C G Pimentel; Reiner Emmerling; Jörn Bennewitz; Kay-Uwe Götz
Journal:  Genet Sel Evol       Date:  2016-09-28       Impact factor: 4.297

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