Fine tuning genomic evaluations in dairy cattle through SNP pre-selection with the Elastic-Net algorithm

Croiseau, Pascal; Legarra, Andrés; Guillaume, François; Fritz, Sébastien; Baur, Aurélia; Colombani, Carine; Robert-Granié, Christèle; Boichard, Didier; Ducrocq, Vincent

doi:10.1017/s0016672311000358

Cited by 24 publications

(25 citation statements)

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“…First, several dozens of QTL per trait were detected after QTL fine-mapping using a linkage disequilibrium linkage analysis (LDLA), as described by Druet et al [10]. Then, hundreds of haplotypes were chosen using the Elastic Net algorithm [11]. Finally, between 327 and 726 QTL for milk yield and between 404 and 525 QTL for SCC were included in the model depending on the breed.…”

Section: Methodsmentioning

confidence: 99%

Inclusion of cow records in genomic evaluations and impact on bias due to preferential treatment

et al. 2012

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BackgroundToday, genomic evaluations are an essential feature of dairy cattle breeding. Initially, genomic evaluation targeted young bulls but recently, a rapidly increasing number of females (both heifers and cows) are being genotyped. A rising issue is whether and how own performance of genotyped cows should be included in genomic evaluations. The purpose of this study was to assess the impact of including yield deviations, i.e. own performance of cows, in genomic evaluations.MethodsTwo different genomic evaluations were performed: one including only reliable daughter yield deviations of proven bulls based on their non-genotyped daughters, and one including both daughter yield deviations for males and own yield deviations for genotyped females. Milk yield, the trait most prone to preferential treatment, and somatic cell count, for which such a bias is very unlikely, were studied. Data consisted of two groups of animals from the three main dairy breeds in France: 11 884 elite females genotyped by breeding companies and 7032 cows genotyped for a research project (and considered as randomly selected from the commercial population).ResultsFor several measures that could be related to preferential treatment bias, the elite group presented a different pattern of estimated breeding values for milk yield compared to the other combinations of trait and group: for instance, for milk yield, the average difference between estimated breeding values with or without own yield deviations was significantly different from 0 for this group. Correlations between estimated breeding values with or without yield deviations were lower for elite females than for randomly selected cows for milk yield but were very similar for somatic cell count.ConclusionsThis study demonstrated that including own milk performance of elite females leads to biased (over-estimated) genomic evaluations. Thus, milk production records of elite cows require specific treatment in genomic evaluation.

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Section: Methodsmentioning

confidence: 99%

Inclusion of cow records in genomic evaluations and impact on bias due to preferential treatment

et al. 2012

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“…LSR, BRR, BayesA, and BayesB, Meuwissen et al (2001); G-BLUP, VanRaden (2008); TA-BLUP, Zhang et al (2010); BayesC, Habier et al (2011); Bayes SSVS, Calus et al (2008); BL, de los Campos et al (2009); DHGLM, Shen et al (2011); LASSO, Usai et al (2009); PLS and SVR, Moser et al (2009); PCR, Solberg et al (2009); EN, c roiseau et al (2011); RKHS, g ianola et al (2006); Boosting, González-Recio et al (2010); RF, González-Recio and Forni (2011); and NN, Okut et al (2011).aBoosting as an estimation technique could be applied to any method, Bayesian or penalized, parametric or nonparametric.bNN could be implemented in a nonpenalized, penalized, or Bayesian framework.…”

Section: Lessons Learned From Simulation and Empirical Data Analysismentioning

confidence: 99%

Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

et al. 2013

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Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade.

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“…In theory, differential shrinkage methods proposed to estimate GEBVs should ensure that false-positive or uninformative effects are regressed towards zero. But in practice, the false-positive or uninformative effects are not strictly equal to zero, and pre-selecting SNPs could be crucial for improving the quality of genomic predictions (Croiseau et al, 2011). This might also be important for reducing the cost of GS implementation in breeding programmes.…”

Section: Predictive Ability and Accuracy Of Gs Modelsmentioning

confidence: 99%

Accuracy of genomic selection models in a large population of open-pollinated families in white spruce

et al. 2014

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Genomic selection (GS) is of interest in breeding because of its potential for predicting the genetic value of individuals and increasing genetic gains per unit of time. To date, very few studies have reported empirical results of GS potential in the context of large population sizes and long breeding cycles such as for boreal trees. In this study, we assessed the effectiveness of marker-aided selection in an undomesticated white spruce (Picea glauca (Moench) Voss) population of large effective size using a GS approach. A discovery population of 1694 trees representative of 214 open-pollinated families from 43 natural populations was phenotyped for 12 wood and growth traits and genotyped for 6385 single-nucleotide polymorphisms (SNPs) mined in 2660 gene sequences. GS models were built to predict estimated breeding values using all the available SNPs or SNP subsets of the largest absolute effects, and they were validated using various cross-validation schemes. The accuracy of genomic estimated breeding values (GEBVs) varied from 0.327 to 0.435 when the training and the validation data sets shared half-sibs that were on average 90% of the accuracies achieved through traditionally estimated breeding values. The trend was also the same for validation across sites. As expected, the accuracy of GEBVs obtained after cross-validation with individuals of unknown relatedness was lower with about half of the accuracy achieved when half-sibs were present. We showed that with the marker densities used in the current study, predictions with low to moderate accuracy could be obtained within a large undomesticated population of related individuals, potentially resulting in larger gains per unit of time with GS than with the traditional approach.

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Fine tuning genomic evaluations in dairy cattle through SNP pre-selection with the Elastic-Net algorithm

Cited by 24 publications

References 28 publications

Inclusion of cow records in genomic evaluations and impact on bias due to preferential treatment

Inclusion of cow records in genomic evaluations and impact on bias due to preferential treatment

Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

Accuracy of genomic selection models in a large population of open-pollinated families in white spruce

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