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In Canada, reproductive disorders known to affect the profitability of dairy cattle herds have been recorded by producers on a voluntary basis since 2007. Previous studies have shown the feasibility of using producer-recorded health data for genetic evaluations. Despite low heritability estimates and limited availability of phenotypic information, sufficient genetic variation has been observed for those traits to indicate that genetic progress, although slow, can be achieved. Pedigree- and genomic-based analyses were performed on producer-recorded health data of reproductive disorders, including retained placenta (RETP), metritis (METR), and cystic ovaries (CYST) using traditional BLUP and single-step genomic BLUP. Genome-wide association studies and functional analyses were carried out to unravel significant genomic regions and biological pathways, and to better understand the genetic mechanisms underlying RETP, METR, and CYST. Heritability estimates (posterior standard deviation in parentheses) were 0.02 (0.003), 0.01 (0.004), and 0.02 (0.003) for CYST, METR, and RETP, respectively. A moderate to strong genetic correlation of 0.69 (0.102) was found between METR and RETP. Averaged over all traits, sire proof reliabilities increased by approximately 11 percentage points with the incorporation of genomic data using a multiple-trait linear model. Biological pathways and associated genes underlying the studied traits were identified and will contribute to a better understanding of the biology of these 3 health disorders in dairy cattle.

The effects of 2 deleterious recessive haplotypes on reproduction performance of Ayrshire cattle, Ayrshire Haplotype 1 (AH1) and Ayrshire Haplotype 2 (AH2), were investigated in Canadian Ayrshire cattle. We calculated their phenotypic effects on stillbirth (SB) rate and 56-d nonreturn rate (NRR) by estimating the interaction of service sire carrier status with maternal grandsire carrier status using the official Canadian evaluation models for those 2 traits. The interaction term included 9 subclasses for the 3 possible statuses of each bull: haplotype carrier, noncarrier, or not geno-typed. For AH1, 394 carriers and 1,433 noncarriers were available, whereas 313 carriers and 1,543 noncarriers were available for the AH2 haplotype. The number of matings considered for SB was 34,312 for heifers (first parity) and 115,935 for cows (later parities). For NRR, 49,479 matings for heifers and 160,528 for cows were used to estimate the haplotype effects. We observed a negative effect of AH1 on SB rates, which was 2.0% higher for matings of AH1-carrier sires to dams that had an AH1-carrier sire; this effect was found for both heifers and cows. However, AH1 had small, generally nonsignificant effects on NRR. The AH2 haplotype had a substantial negative effect on NRR, with 5.1% more heifers and 4.0% more cows returning to service, but the effects on SB rates were inconsistent and mostly small effects. Our results validate the harmful effects of AH1 and AH2 on reproduction traits in the Canadian Ayrshire population. This information will be of great interest for the dairy industry, allowing producers to make mating decisions that would reduce reproductive losses.

International audience; The objective of this study was to implement single-step genomic BLUP (ssGBLUP) for national Angus cattle evaluation in the US. National evaluations include a variety of models with several linear and categorical traits, maternal effects, multibreed data, and a large number of genotyped animals. For the initial investigation, we used a dataset from 2014 that comprised over 8 million animals, 6 million birth weight (BW) and weaning weight (WW) records, 3.4 million post-weaning gain (PWG) records, and genotypes for 52k animals. A dataset from 2017 was later used that included 335k genotyped animals. The ability to predict future performance of young animals was investigated when using regular BLUP and ssGBLUP. Because of the increasing number of genotyped animals and the high computing cost to invert the genomic relationship matrix (G), the algorithm for proven and young (APY) was used to approximate the inverse of G. The APY uses recursions on a small subset of genotyped animals, called core. We further tested the feasibility of having daily interim genomic predictions for newly-genotyped animals based on SNP effects derived from the previous official ssGBLUP evaluation. In addition, we extended all models used in traditional evaluations to ssGBLUP, and compared genetic trends from traditional BLUP, ssGBLUP, and a multistep method that was implemented for the American Angus genomic evaluation in 2009. A new algorithm to approximate accuracy of GEBV for large genomic data was also developed. On average, the increase in ability to predict future performance, for BW, WW, and PWG, with ssGBLUP was 25% in the 2014 data and 36% in the 2017 data, compared to the traditional BLUP. The ssGBLUP with APY was as accurate as the regular ssGBLUP when the number of core animals was at least 10,000, independently of which animals were in the core group. Interim predictions derived from ssGBLUP provided accurate genomic values for newly-genotyped animals. Genetic trends for ssGBLUP and BLUP were similar, revealing overestimation in multistep evaluations, especially for traits with less phenotypes. Single-step GBLUP became a reality for American Angus evaluation and its implementation process resulted in successful updates in methodology, making this approach mature for national beef cattle evaluation.

International audience; Past studies suggested limited dimensionality of the genomic SNP information was related to effective population size (Ne). The objective of this study was to estimate that dimensionality with simulated and with livestock (Holstein, Jersey, Angus, pigs, and chicken) data sets. That dimensionality can be defined as the number of non-negligible singular values of gene content, or the number of non-negligible eigenvalues of genomic relationship matrix (GRM). In this study, eigenvalue analysis determined the numbers of largest eigenvalues corresponding to 90, 95, 98, and 99% of the variation of the GRM for each population. With many genotyped animals and SNP markers, the numbers corresponding to 90, 95, and 98% approached NeL, 2NeL and 4NeL, respectively, where L is genome length in Morgans. Realized accuracies were calculated for single-step GBLUP (ssGBLUP) with an algorithm for inversion of GRM that takes into account the limited dimensionality of the genomic information. Realized accuracies peaked with the dimensionality corresponding to 98 to 99% of variation depending on population, indicating that 1 to 2% of variation in the GRM was due to noise. However, the accuracies were only slightly reduced at half the optimum dimensionality. Subsequently, the dimensionality of the genomic information was estimated at about 14,000 for Holstein and Angus cattle, 12,000 for Jersey cattle, and 6000 for pigs and chickens, which corresponds approximately to 3NeL. Based on interpolation of simulated and real data with L of 30 Morgans, approximate Ne was 149 for Holsteins, 101 for Jerseys, 113 for Angus, and 44 for chickens; for pigs and L of 20 Morgans, approximate Ne was 48. Limited dimensionality of the genomic information has serious implications for genomic prediction and possibly GWAS.

International audience

The success and sustainability of a breeding program incorporating genomic information is largely dependent on the accuracy of predictions. For low heritability traits, large training populations are required to achieve high accuracies of genomic estimated breeding values (GEBV). By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (ssGBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. The aim of this study was to compare the accuracy and bias of genomic predictions for various traits in Canadian Holstein cattle using ssGBLUP and multi-step genomic BLUP (msGBLUP) under different strategies, such as (1) adding genomic information of cows in the analysis, (2) testing different adjustments of the genomic relationship matrix, and (3) using a blending approach to obtain GEBV from msGBLUP. The following genomic predictions were evaluated regarding accuracy and bias: (1) GEBV estimated by ssGBLUP; (2) direct genomic value estimated by msGBLUP with polygenic effects of 5 and 20%; and (3) GEBV calculated by a blending approach of direct genomic value with estimated breeding values using polygenic effects of 5 and 20%. The effect of adding genomic information of cows in the evaluation was also assessed for each approach. When genomic information was included in the analyses, the average improvement in observed reliability of predictions was observed to be 7 and 13 percentage points for reproductive and workability traits, respectively, compared with traditional BLUP. Absolute deviation from 1 of the regression coefficient of the linear regression of de-regressed estimated breeding values on genomic predictions went from 0.19 when using traditional BLUP to 0.22 when using the msGBLUP method, and to 0.14 when using the ssGBLUP method. The use of polygenic weight of 20% in the msGBLUP slightly improved the reliability of predictions, while reducing the bias. A similar trend was observed when a blending approach was used. Adding genomic information of cows increased reliabilities, while decreasing bias of genomic predictions when using the ssGBLUP method. Differences between using a training population with cows and bulls or with only bulls for the msGBLUP method were small, likely due to the small number of cows included in the analysis. Predictions for lowly heritable traits benefit greatly from genomic information, especially when all phenotypes, pedigrees, and genotypes are used in a single-step approach.

Dimensionality of genomic information and APY inverse of the genomic relationship matrix. 67. Annual Meeting of the European Federation of Animal Science (EAAP)

Most breeding companies evaluate economically important traits in males and females as a single trait, assuming genetic correlation of 1 between phenotypes measured in both sexes. This assumption may not be true because genes may be differently expressed in males and females. We estimated genetic correlations between males and females for growth and efficiency traits in broiler chickens, growth traits in American Angus beef cattle, and birth weight and preweaning mortality in purebred pigs; therefore, each trait was treated differently in males and females. Variance components were estimated in single- and multiple-trait models, jointly or separated into both sexes. Furthermore, we calculated traditional and genomic evaluations, and we correlated EBV or genomic EBV (GEBV) from joint and separate evaluations for males and females. For broiler chickens, genetic correlations ranged from 0.86 to 0.94. For Angus cattle, genetic correlations ranged from 0.86 to 0.98 for early growth traits and were less, ranging from 0.68 to 0.84, for postweaning gain. In pigs, genetic correlations ranged from 0.98 to 0.99 for birth weight and from 0.71 to 0.73 for preweaning mortality. For some models in all 3 animal species, the joint and separate analyses had different heritabilities. Despite differences in heritability, the correlations within the sex-specific trait EBV and between the sex-specific and the joint trait EBV were very strong, regardless of the model or inclusion of genomic information. Males and females differed for traits measured late in the animal’s life; however, strong traditional EBV correlations and also GEBV correlations indicate that considering the traits equal in males and females may have no negative impact on selection.

International audience

International audience

International audience

International audience; Predictive ability of genomic EBV when using single-step genomic BLUP (ssGBLUP) in Angus cattle was investigated. Over 6 million records were available on birth weight (BiW) and weaning weight (WW), almost 3.4 million on postweaning gain (PWG), and over 1.3 million on calving ease (CE). Genomic information was available on, at most, 51,883 animals, which included high and low EBV accuracy animals. Traditional EBV was computed by BLUP and genomic EBV by ssGBLUP and indirect prediction based on SNP effects was derived from ssGBLUP; SNP effects were calculated based on the following reference populations: ref_2k (contains top bulls and top cows that had an EBV accuracy for BiW ≥0.85), ref_8k (contains all parents that were genotyped), and ref_33k (contains all genotyped animals born up to 2012). Indirect prediction was obtained as direct genomic value (DGV) or as an index of DGV and parent average (PA). Additionally, runs with ssGBLUP used the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals (APY) that uses recursions on a small subset of reference animals. An extra reference subset included 3,872 genotyped parents of genotyped animals (ref_4k). Cross-validation was used to assess predictive ability on a validation population of 18,721 animals born in 2013. Computations for growth traits used multiple-trait linear model and, for CE, a bivariate CE-BiW threshold-linear model. With BLUP, predictivities were 0.29, 0.34, 0.23, and 0.12 for BiW, WW, PWG, and CE, respectively. With ssGBLUP and ref_2k, predictivities were 0.34, 0.35, 0.27, and 0.13 for BiW, WW, PWG, and CE, respectively, and with ssGBLUP and ref_33k, predictivities were 0.39, 0.38, 0.29, and 0.13 for BiW, WW, PWG, and CE, respectively. Low predictivity for CE was due to low incidence rate of difficult calving. Indirect predictions with ref_33k were as accurate as with full ssGBLUP. Using the APY and recursions on ref_4k gave 88% gains of full ssGBLUP and using the APY and recursions on ref_8k gave 97% gains of full ssGBLUP. Genomic evaluation in beef cattle with ssGBLUP is feasible while keeping the models (maternal, multiple trait, and threshold) already used in regular BLUP. Gains in predictivity are dependent on the composition of the reference population. Indirect predictions via SNP effects derived from ssGBLUP allow for accurate genomic predictions on young animals, with no advantage of including PA in the index if the reference population is large. With the APY conditioning on about 10,000 reference animals, ssGBLUP is potentially applicable to a large number of genotyped animals without compromising predictive ability.