Genotyping strategies for genomic selection in small dairy cattle populations

Jiménez-Montero, J. A.; González-Recio, Óscar; Alenda, R.

doi:10.1017/s1751731112000341

Cited by 45 publications

(45 citation statements)

References 32 publications

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“…In fact, our results suggest that when resources are limited to compose a training dataset, the best approach may be to genotype only the animals with high EBV. Some simulation studies show that selecting only the top animals may lead to substantially biased [31] and inaccurate predictions [32]. In our study, reliabilities for four out of six traits were higher for the TOP50 scenario than for the RAN50 scenario.…”

Section: Discussionmentioning

confidence: 46%

Genomic prediction of breeding values using previously estimated SNP variances

2014

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BackgroundGenomic prediction requires estimation of variances of effects of single nucleotide polymorphisms (SNPs), which is computationally demanding, and uses these variances for prediction. We have developed models with separate estimation of SNP variances, which can be applied infrequently, and genomic prediction, which can be applied routinely.MethodsSNP variances were estimated with Bayes Stochastic Search Variable Selection (BSSVS) and BayesC. Genome-enhanced breeding values (GEBV) were estimated with RR-BLUP (ridge regression best linear unbiased prediction), using either variances obtained from BSSVS (BLUP-SSVS) or BayesC (BLUP-C), or assuming equal variances for each SNP. Datasets used to estimate SNP variances comprised (1) all animals, (2) 50% random animals (RAN50), (3) 50% best animals (TOP50), or (4) 50% worst animals (BOT50). Traits analysed were protein yield, udder depth, somatic cell score, interval between first and last insemination, direct longevity, and longevity including information from predictors.ResultsBLUP-SSVS and BLUP-C yielded similar GEBV as the equivalent Bayesian models that simultaneously estimated SNP variances. Reliabilities of these GEBV were consistently higher than from RR-BLUP, although only significantly for direct longevity. Across scenarios that used data subsets to estimate GEBV, observed reliabilities were generally higher for TOP50 than for RAN50, and much higher than for BOT50. Reliabilities of TOP50 were higher because the training data contained more ancestors of selection candidates. Using estimated SNP variances based on random or non-random subsets of the data, while using all data to estimate GEBV, did not affect reliabilities of the BLUP models. A convergence criterion of 10−8 instead of 10−10 for BLUP models yielded similar GEBV, while the required number of iterations decreased by 71 to 90%. Including a separate polygenic effect consistently improved reliabilities of the GEBV, but also substantially increased the required number of iterations to reach convergence with RR-BLUP. SNP variances converged faster for BayesC than for BSSVS.ConclusionsCombining Bayesian variable selection models to re-estimate SNP variances and BLUP models that use those SNP variances, yields GEBV that are similar to those from full Bayesian models. Moreover, these combined models yield predictions with higher reliability and less bias than the commonly used RR-BLUP model.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-014-0052-x) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 46%

Genomic prediction of breeding values using previously estimated SNP variances

2014

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“…The limited number of phenotypic records for feed intake will, however, result in a relatively low accuracy of genomic selection for feed intake. One solution to partly overcome this effect of the limited size of the reference population is to optimize its design (Jimé nez-Montero et al, 2012, Pszczola et al, 2012a. Another solution, commonly used in traditional breeding programmes, is to use predictor traits.…”

Section: Introductionmentioning

confidence: 99%

Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population

Pszczoła

Veerkamp

Haas

et al. 2013

Animal

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The genomic breeding value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the breeding value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic breeding value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n 5 869) and the predictor traits were fat-protein-corrected milk (FPCM, n 5 1520) and live weight (LW, n 5 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and breeding values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n 5 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV-and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI breeding value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI breeding value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) breeding values of scarcely recorded traits such as feed intake.

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“…With ~10,000 bulls in the training population, accuracies of the GEBV of young bulls without daughter records approach those of EBV obtained with progeny tests with ~50 daughters per sire, (Lund et al, 2011;Wiggans et al, 2011), whereas accuracies of GEBV for young bulls derived from reference populations of ~1,000 genotyped bulls are generally no higher than traditional BLUP based only on pedigree information (Thomasen et al, 2012;Van Grevenhof et al, 2012). Jiménez-Montero et al (2012) suggested that more accurate GEBV could be obtained with training populations consisting chiefly of genotyped cows with EBV computed based on their own records. The potential number of genotyped cows with records is much larger than the number of bulls with progeny tests.…”

Section: Introductionmentioning

confidence: 89%

Genomic evaluation of a relatively small dairy cattle population by combination with a larger population

Weller

Stoop

Eding

et al. 2015

Journal of Dairy Science

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The objectives were to investigate the accuracy of genomic evaluations obtained for a small dairy cattle population (Israeli Holsteins) via joint evaluation with a larger population (Dutch Holsteins), and to evaluate the use of pedigree data from foreign bulls computed by Interbull without daughter records in Israel. The training population included 4,010 Dutch bulls and 713 Israeli bulls. The validation population included 185 Israeli bulls with daughter records for milk production traits and slightly fewer bulls for the nonproduction traits. Milk, fat, and protein yields, somatic cell score, longevity, female fertility, direct and maternal calving ease, direct and maternal stillbirth, and the Israeli breeding index were analyzed. The genomic prediction model was based on the Bayesian multi-QTL model of Meuwissen and Goddard, where the effects of dense single nucleotide polymorphisms across the whole genome are fitted directly, without the use of haplotypes or identical-by-descent probabilities. Correlations of May 2014 estimated breeding values (EBV14) with genomic EBV (GEBV) were higher than the correlations of EBV14 with parent averages (PA) computed from the June 2009 evaluation for all traits. For the Israel selection index, the difference between EBV14 and GEBV correlation on the one hand and EBV14 and PA computed using Interbull data on the other hand was 15 percentage points. For protein, the difference between the corresponding correlations was 14 percentage points. Generally, correlations of EBV14 with PA based on Israeli EBV only were similar to correlations of EBV14 with PA including Interbull evaluations. Relative to EBV14, milk production traits were biased upwards for both GEBV and PA, but the bias was greater for PA. The Y-intercepts of regressions of EBV14 were significantly different from zero for regression on GEBV for all 3 milk production traits and the Israeli selection index. This was not the case for regression of EBV14 on PA. The regression line intersected with the line of unbiased estimation near the EBV of the bulls with highest values. Because only bulls with high evaluations are of interest for selection, GEBV for these bulls were less biased compared with that of bulls with lower evaluations. The difference in mean EBV14 between bulls born during 2007-2008 selected by GEBV and PA was 65 units. If half of all inseminations are by young bulls, then the annual genetic gain obtained by implementation of genomic evaluation will be 8 units per year (65/8). Because annual gain is currently 107 units, this is a gain of 7%.

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Genotyping strategies for genomic selection in small dairy cattle populations

Cited by 45 publications

References 32 publications

Genomic prediction of breeding values using previously estimated SNP variances

Genomic prediction of breeding values using previously estimated SNP variances

Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population

Genomic evaluation of a relatively small dairy cattle population by combination with a larger population

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