This study assessed the accuracy and bias of genomic prediction (GP) in purebred Holstein (H) and Jersey (J) as well as crossbred (H and J) validation cows using different reference sets and prediction strategies. The reference sets were made up of different combinations of 36,695 H and J purebreds and crossbreds. Additionally, the effect of using different sets of marker genotypes on GP was studied (conventional panel: 50k, custom panel enriched with, or close to, causal mutations: XT_50k, and conventional high-density with a limited custom set: pruned HDnGBS). We also compared the use of genomic best linear unbiased prediction (GBLUP) and Bayesian (emBayesR) models, and the traits tested were milk, fat, and protein yields. On average, by including crossbred cows in the reference population, the prediction accuracies increased by 0.01–0.08 and were less biased (regression coefficient closer to 1 by 0.02–0.16), and the benefit was greater for crossbreds compared to purebreds. The accuracy of prediction increased by 0.02 using XT_50k compared to 50k genotypes without affecting the bias. Although using pruned HDnGBS instead of 50k also increased the prediction accuracy by about 0.02, it increased the bias for purebred predictions in emBayesR models. Generally, emBayesR outperformed GBLUP for prediction accuracy when using 50k or pruned HDnGBS genotypes, but the benefits diminished with XT_50k genotypes. Crossbred predictions derived from a joint pure H and J reference were similar in accuracy to crossbred predictions derived from the two separate purebred reference sets and combined proportional to breed composition. However, the latter approach was less biased by 0.13. Most interestingly, using an equalized breed reference instead of an H-dominated reference, on average, reduced the bias of prediction by 0.16–0.19 and increased the accuracy by 0.04 for crossbred and J cows, with a little change in the H accuracy. In conclusion, we observed improved genomic predictions for both crossbreds and purebreds by equalizing breed contributions in a mixed breed reference that included crossbred cows. Furthermore, we demonstrate, that compared to the conventional 50k or high-density panels, our customized set of 50k sequence markers improved or matched the prediction accuracy and reduced bias with both GBLUP and Bayesian models.

Weaning weight records of 24 758 Bonsmara calves produced by 503 sires in 30 herds, from 1980 to 1993, were used to examine the importance of the inclusion of herd-year-season by sire interaction in the estimation of genetic parameters. Three separate models were used in the DFREML analysis of the data. In the first, permanent maternal environment was included as an additional random factor, while in the second, herd-year-season by sire interaction was included, also as an additional random factor. Both these factors were included as additional random factors in the third model. The estimates of the (co)variance components and heritabilities for direct additive (a), maternal additive (m) and additional random factor (c) for the three models analysed, were as follows: Q'u, 138.04, 65.23, 67.46; (o'..nr 84.77,143.80, 66.8*3; (oa.., (o'p"r..nt 73.72, 71.01; (o'Hysxs:41.79,+1.39; (o'":251.31,274.78,265.97; (ozo: 490.37,497.35,490.74',hza'.0.28,0.13,0.14; h',n: 0.17,0.29,0.14. The results indicate the importance of both permanent maternal environment and herdyear-season by sire interaction in the estimation of the genetic parameters. The exclusion of herd-year-season by sire interaction could lead to a serious overestimation of the direct additive components. The same applies for permanent maternal environment. The exclusion of this factor could lead to a serious overestimation of the maternal components. Both these factors should therefore be included in the across-herd weaning weight analysis of Bonsmara cattle.Speengewig rekords van 24 758 Bonsmarakalwers, die nageslag van 503 bulle uit 30 kuddes gebore vanaf 1980 tot 1993, is gebruik om die invloed van die insluiting van kudde-jaar-seisoen by vaar interaksie op die beraming van die genetiese parameters te ondersoek. Drie aparte modelle is gebruik in die DFREML analise van die data. In die eerste is permanente maternale omgewingseffekte ingesluit as 'n addisionele toevallige effek. Permanente maternale omgewingseffekte is in die tweede model vervang met kudde-jaar-seisoen by vaar interaksie. In die derde model is beide die faktore as addisionele toevallige effekte ingeluit. Die beramings van die (ko)variansie komponente en oorerflikhede vir direk additief (a), maternaal additief (m) en addisionele toevallige effekte vir die drie modelle gebruik in die analise was soos vo^lg: (o2": 138.04,65.23, 67.46; (o2r: 84.77,143.8b, 66.83; (o2per ^: 73.72, 71 .01; (o2Hvsrsi 41 .79, 41 .39; (o2": 251 .31',"274.78, 265.97; (o'o: 490.37,497.35,490.74;h'^:0.28,0.13, 0.14;h'^:0.17,0.29,0.14. Die resultate dui op die belangrikheid van beide die twee addisionele toevallige effekte in die beraming van die genetiese parameters. Die uitsluiting van kudde-jaar-seisoen by vaar interaksie kan lei tot 'n ernstige oorberaming van die direk additiewe komponente. Dieselfde geld vir permanente maternale omgewing. Die uitsluiting van die effek kan weer op sy beurt lei tot 'n oorberaming van die maternaal additiewe komponente, Beide die effekte behoort dus ingesluit te word in die tussen-kudd...

The relationship between the direct-maternal genetic (co)variance σam and sire by year (SY) interactions for weaning weight in Merino sheep was examined through simulation and real data analyses. Weaning weight was simulated using models containing interaction and σam = 0 (S1), interaction and σam < 0 (S2), interaction and σam >�0 (S3), and without interaction and σam < 0 (S4). When S1 data were analysed ignoring interaction, a negative (co)variance was observed and direct and maternal variances were inflated. Analysis of S2 data ignoring σam resulted in deflated direct and maternal variances, inflated residual and interaction variances, and no change for the permanent environmental component. Ignoring the interaction effect in S3 data resulted again in a negative (co)variance component and highly biased genetic parameters. On application to weaning weight of Merino sheep, the model ignoring SY resulted in a direct-maternal genetic correlation of –0.43. The model using both (co)variance and interaction effects fit the data better (P < 0.001). The interaction variance represented 9.2% of the phenotypic variance but explained 86% of the (co)variance between direct and maternal genetic effects estimated ignoring SY.�A small (–0.096) but still negative estimate of the genetic correlation was obtained. The implication of these findings in the context of Central Test Sire Evaluation and Maternal Sire Central Progeny Test Schemes is discussed.

The objectives of this study were (1) to evaluate the computational feasibility of the multitrait test-day single-step SNP-BLUP (ssSNP-BLUP) model using phenotypic records of genotyped and nongenotyped animals, and (2) to compare accuracies (coefficient of determination; R 2 ) and bias of genomic estimated breeding values (GEBV) and de-regressed proofs as response variables in 3 Australian dairy cattle breeds (i.e., Holstein, Jersey, and Red breeds). Additive genomic random regression coefficients for milk, fat, protein yield and somatic cell score were predicted in the first, second, and third lactation. The predicted coefficients were used to derive 305-d GEBV and were compared with the traditional parent averages obtained from a BLUP model without genomic information. Cow fertility traits were evaluated from the 5-trait repeatability model (i.e., calving interval, days from calving to first service, pregnancy diagnosis, first service nonreturn rate, and lactation length). The de-regressed proofs were only for calving interval. Our results showed that ssSNP-BLUP using multitrait test-day model increased reliability and reduced bias of breeding values of young animals when compared with parent average from traditional BLUP in Australian Holsten, Jersey, and Red breeds. The use of a custom selection of approximately 46,000 SNP (custom XT SNP list) increased the reliability of GEBV compared with the results obtained using the commercial Illumina 50K chip (Illumina, San Diego, CA). The use of the second preconditioner substantially improved the convergence rate of the preconditioned conjugate gradient method, but further work is needed to improve the efficiency of the computation of the Kronecker matrix product by vector. Application of ssSNP-BLUP to multitrait random regression models is computationally feasible.