Sequence variants selected from a multi-breed GWAS can improve the reliability of genomic predictions in dairy cattle.

Sequence data can potentially increase the reliability of genomic predictions, because such data include causative mutations instead of relying on linkage disequilibrium (LD) between causative mutations and prediction variants. However, the location of the causative mutations is not known, and the presence of many variants that are in low LD with the causative mutations may reduce prediction reliability. Our objective was to investigate whether the use of variants at quantitative trait loci (QTL) that are identified in a multi-breed genome-wide association study (GWAS) for milk, fat and protein yield would increase the reliability of within- and multi-breed genomic predictions in Holstein, Jersey and Danish Red cattle. A wide range of scenarios that test different strategies to select prediction markers, for both within-breed and multi-breed prediction, were compared. For all breeds and traits, the use of variants selected from a multi-breed GWAS resulted in substantial increases in prediction reliabilities compared to within-breed prediction using a 50 K SNP array. Reliabilities depended highly on the choice of the prediction markers, and the scenario that led to the highest reliability varied between breeds and traits. While genomic correlations across breeds were low for genome-wide sequence variants, the effects of the QTL variants that yielded the highest reliabilities were highly correlated across breeds. Our results show that the use of sequence variants, which are located near peaks of QTL that are detected in a multi-breed GWAS, can increase reliability of genomic predictions.

van den Berg I ，Boichard D ，Lund MS 《-》

Use of meta-analyses and joint analyses to select variants in whole genome sequences for genomic evaluation: An application in milk production of French dairy cattle breeds.

As a result of the 1000 Bull Genome Project, it has become possible to impute millions of variants, with many of these potentially causative for traits of interest, for thousands of animals that have been genotyped with medium-density chips. This enormous source of data opens up very interesting possibilities for the inclusion of these variants in genomic evaluations. However, for computational reasons, it is not possible to include all variants in genomic evaluation procedures. One potential approach could be to select the most relevant variants based on the results of genome-wide association studies (GWAS); however, the identification of causative mutations is still difficult with this method, partly because of weak imputation accuracy for rare variants. To address this problem, this study assesses the ability of different approaches based on multi-breed GWAS (joint and meta-analyses) to identify single-nucleotide polymorphisms (SNP) for use in genomic evaluation in the 3 main French dairy cattle breeds. A total of 6,262 Holstein bulls, 2,434 Montbéliarde bulls, and 2,175 Normande bulls with daughter yield deviations for 5 milk production traits were imputed for 27 million variants. Within-breed and joint (including all 3 breeds) GWAS were performed and 3 models of meta-analysis were tested: fixed effect, random effect, and Z-score. Comparison of the results of within- and multi-breed GWAS showed that most of the quantitative trait loci identified using within-breed approaches were also found with multi-breed methods. However, the most significant variants identified in each region differed depending on the method used. To determine which approach highlighted the most predictive SNP for each trait, we used a marker-assisted best unbiased linear prediction model to evaluate lists of SNP generated by the different GWAS methods; each list contained between 25 and 2,000 candidate variants per trait, which were identified using a single within- or multi-breed GWAS approach. Among all the multi-breed methods tested in this study, variant selection based on meta-analysis (fixed effect) resulted in the most-accurate genomic evaluation (+1 to +3 points compared with other multi-breed approaches). However, the accuracies of genomic evaluation were always better when variants were selected using the results of within-breed GWAS. As has generally been found in studies of quantitative trait loci, these results suggest that part of the genetic variance of milk production traits is breed specific in Holstein, Montbéliarde, and Normande cattle.

Teissier M ，Sanchez MP ，Boussaha M ，Barbat A ，Hoze C ，Robert-Granie C ，Croiseau P ... -  《-》

Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions.

Genomic selection is increasingly widely practised, particularly in dairy cattle. However, the accuracy of current predictions using GBLUP (genomic best linear unbiased prediction) decays rapidly across generations, and also as selection candidates become less related to the reference population. This is likely caused by the effects of causative mutations being dispersed across many SNPs (single nucleotide polymorphisms) that span large genomic intervals. In this paper, we hypothesise that the use of a nonlinear method (BayesR), combined with a multi-breed (Holstein/Jersey) reference population will map causative mutations with more precision than GBLUP and this, in turn, will increase the accuracy of genomic predictions for selection candidates that are less related to the reference animals. BayesR improved the across-breed prediction accuracy for Australian Red dairy cattle for five milk yield and composition traits by an average of 7% over the GBLUP approach (Australian Red animals were not included in the reference population). Using the multi-breed reference population with BayesR improved accuracy of prediction in Australian Red cattle by 2 - 5% compared to using BayesR with a single breed reference population. Inclusion of 8478 Holstein and 3917 Jersey cows in the reference population improved accuracy of predictions for these breeds by 4 and 5%. However, predictions for Holstein and Jersey cattle were similar using within-breed and multi-breed reference populations. We propose that the improvement in across-breed prediction achieved by BayesR with the multi-breed reference population is due to more precise mapping of quantitative trait loci (QTL), which was demonstrated for several regions. New candidate genes with functional links to milk synthesis were identified using differential gene expression in the mammary gland. QTL detection and genomic prediction are usually considered independently but persistence of genomic prediction accuracies across breeds requires accurate estimation of QTL effects. We show that accuracy of across-breed genomic predictions was higher with BayesR than with GBLUP and that BayesR mapped QTL more precisely. Further improvements of across-breed accuracy of genomic predictions and QTL mapping could be achieved by increasing the size of the reference population, including more breeds, and possibly by exploiting pleiotropic effects to improve mapping efficiency for QTL with small effects.

Kemper KE ，Reich CM ，Bowman PJ ，Vander Jagt CJ ，Chamberlain AJ ，Mason BA ，Hayes BJ ，Goddard ME ... -  《-》

Reliability of genomic prediction for milk fatty acid composition by using a multi-population reference and incorporating GWAS results.

Large-scale phenotyping for detailed milk fatty acid (FA) composition is difficult due to expensive and time-consuming analytical techniques. Reliability of genomic prediction is often low for traits that are expensive/difficult to measure and for breeds with a small reference population size. An effective method to increase reference population size could be to combine datasets from different populations. Prediction models might also benefit from incorporation of information on the biological underpinnings of quantitative traits. Genome-wide association studies (GWAS) show that genomic regions on Bos taurus chromosomes (BTA) 14, 19 and 26 underlie substantial proportions of the genetic variation in milk FA traits. Genomic prediction models that incorporate such results could enable improved prediction accuracy in spite of limited reference population sizes. In this study, we combine gas chromatography quantified FA samples from the Chinese, Danish and Dutch Holstein populations and implement a genomic feature best linear unbiased prediction (GFBLUP) model that incorporates variants on BTA14, 19 and 26 as genomic features for which random genetic effects are estimated separately. Prediction reliabilities were compared to those estimated with traditional GBLUP models. Predictions using a multi-population reference and a traditional GBLUP model resulted in average gains in prediction reliability of 10% points in the Dutch, 8% points in the Danish and 1% point in the Chinese populations compared to predictions based on population-specific references. Compared to the traditional GBLUP, implementation of the GFBLUP model with a multi-population reference led to further increases in prediction reliability of up to 38% points in the Dutch, 23% points in the Danish and 13% points in the Chinese populations. Prediction reliabilities from the GFBLUP model were moderate to high across the FA traits analyzed. Our study shows that it is possible to predict genetic merits for milk FA traits with reasonable accuracy by combining related populations of a breed and using models that incorporate GWAS results. Our findings indicate that international collaborations that facilitate access to multi-population datasets could be highly beneficial to the implementation of genomic selection for detailed milk composition traits.

Gebreyesus G ，Bovenhuis H ，Lund MS ，Poulsen NA ，Sun D ，Buitenhuis B ... -  《-》

Using Sequence Variants in Linkage Disequilibrium with Causative Mutations to Improve Across-Breed Prediction in Dairy Cattle: A Simulation Study.

Sequence data are expected to increase the reliability of genomic prediction by containing causative mutations directly, especially in cases where low linkage disequilibrium between markers and causative mutations limits prediction reliability, such as across-breed prediction in dairy cattle. In practice, the causative mutations are unknown, and prediction with only variants in perfect linkage disequilibrium with the causative mutations is not realistic, leading to a reduced reliability compared to knowing the causative variants. Our objective was to use sequence data to investigate the potential benefits of sequence data for the prediction of genomic relationships, and consequently reliability of genomic breeding values. We used sequence data from five dairy cattle breeds, and a larger number of imputed sequences for two of the five breeds. We focused on the influence of linkage disequilibrium between markers and causative mutations, and assumed that a fraction of the causative mutations was shared across breeds and had the same effect across breeds. By comparing the loss in reliability of different scenarios, varying the distance between markers and causative mutations, using either all genome wide markers from commercial SNP chips, or only the markers closest to the causative mutations, we demonstrate the importance of using only variants very close to the causative mutations, especially for across-breed prediction. Rare variants improved prediction only if they were very close to rare causative mutations, and all causative mutations were rare. Our results show that sequence data can potentially improve genomic prediction, but careful selection of markers is essential.

van den Berg I ，Boichard D ，Guldbrandtsen B ，Lund MS ... -  《G3-Genes Genomes Genetics》