Dairy cattle breeding objectives combining production and non-production traits for pasture based systems in Ireland.

End of Project ReportThe objectives of this study were: 1) to estimate genetic (co) variances among body condition score, body weight, milk production, linear type traits and fertility, and 2) to investigate the presence of genotype by environment interactions for milk production, body condition score, and body weight, in Irish grass based seasonal calving herds. Genetic parameters were estimated from a potential 8928 primiparous and multiparous Holstein-Friesian cows over two years (1999 and 2000). Heritability estimates for body condition score (BCS) and body weight (BW) were found to be moderate to high; estimates ranged from 0.27 to 0.51 for BCS, and from 0.39 to 0.61 for BW. Heritability estimates for BCS change and BW change at different stages of lactation were all less than 0.11. Heritability for the linear type traits varied from 0.11 to 0.43. Phenotypic and genetic correlations between BCS and BW at the same stage of lactation were all close to 0.50 indicating that approximately 25% of the genetic and phenotypic variation in BW may be attributed to differences in BCS. Genetic correlations between BCS and milk yield tended to be negative (-0.14 to –0.51) and genetic correlations between BW and milk yield were close to zero (-0.07 to 0.09). However, the genetic correlations between BW adjusted for differences in BCS were positive (0.15 to 0.39). Genetic correlations between BCS and the fertility traits investigated were all favourable, indicating that cows with a superior genetic merit for BCS are on average likely to be served sooner, receive less services and have higher pregnancy rates. The genetic correlations between linear type traits and milk yield indicate that selection for milk production has resulted in taller, deeper cows that tend to be more angular and have less body condition. Genetically these cows are predisposed to inferior reproductive efficiency. Moderate genetic correlations were found between some of the linear type traits investigated and somatic cell count. A comparison of BCS, as recorded by Teagasc personnel (scale 1-5) and Holstein herd-book classifiers (scale 1-9) indicated consistency between the two sources. Phenotypic and genetic correlations of 0.54 and 0.86, respectively, were observed between the two measurement sources on the same animals. Genotype by environment interactions, were found for milk yield across different silage quality environments, and for BCS across different herd-year milk yield, concentrate, grazing severity and silage quality environments

Accuracy of predicting milk yield from alternative milk recording schemes

peer-reviewedThe effect of reducing the frequency of official milk recording and the number of recorded samples per test-day on the accuracy of predicting daily yield and cumulative 305-day yield was investigated. A control data set consisting of 58 210 primiparous cows with milk test-day records every 4 weeks was used to investigate the influence of reduced milk recording frequencies. The accuracy of prediction of daily yield with one milk sample per test-day was investigated using 41 874 testday records from 683 cows. Results show that five or more test-day records taken at 8-weekly intervals (A8) predicted 305-day yield with a high level of accuracy. Correlations between 305-day yield predicted from 4-weekly recording intervals (A4) and from 8-weekly intervals were 0.99, 0.98 and 0.98 for milk, fat and protein, respectively. The mean error in estimating 305-day yield from the A8 scheme was 6.8 kg (s.d. 191 kg) for milk yield, 0.3 kg (s.d. 10 kg) for fat yield, and −0.3 kg (s.d. 7 kg) for protein yield, compared with the A4 scheme. Milk yield and composition taken during either morning (AM) or evening (PM) milking predicted 24-h yield with a high degree of accuracy. Alternating between AM and PM sampling every 4 weeks predicted 305-day yield with a higher degree of accuracy than either all AM or all PM sampling. Alternate AM-PM recording every 4 weeks and AM + PM recording every 8 weeks produced very similar accuracies in predicting 305-day yield compared with the official AM + PM recording every 4 weeks

Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein–Friesian cattle

<p>Background: Whole-genome sequence data is expected to capture genetic variation more completely than common genotyping panels. Our objective was to compare the proportion of variance explained and the accuracy of genomic prediction by using imputed sequence data or preselected SNPs from a genome-wide association study (GWAS) with imputed whole-genome sequence data. Methods: Phenotypes were available for 5503 Holstein-Friesian bulls. Genotypes were imputed up to whole-genome sequence (13,789,029 segregating DNA variants) by using run 4 of the 1000 bull genomes project. The program GCTA was used to perform GWAS for protein yield (PY), somatic cell score (SCS) and interval from first to last insemination (IFL). From the GWAS, subsets of variants were selected and genomic relationship matrices (GRM) were used to estimate the variance explained in 2087 validation animals and to evaluate the genomic prediction ability. Finally, two GRM were fitted together in several models to evaluate the effect of selected variants that were in competition with all the other variants. Results: The GRM based on full sequence data explained only marginally more genetic variation than that based on common SNP panels: for PY, SCS and IFL, genomic heritability improved from 0.81 to 0.83, 0.83 to 0.87 and 0.69 to 0.72, respectively. Sequence data also helped to identify more variants linked to quantitative trait loci and resulted in clearer GWAS peaks across the genome. The proportion of total variance explained by the selected variants combined in a GRM was considerably smaller than that explained by all variants (less than 0.31 for all traits). When selected variants were used, accuracy of genomic predictions decreased and bias increased. Conclusions: Although 35 to 42 variants were detected that together explained 13 to 19% of the total variance (18 to 23% of the genetic variance) when fitted alone, there was no advantage in using dense sequence information for genomic prediction in the Holstein data used in our study. Detection and selection of variants within a single breed are difficult due to long-range linkage disequilibrium. Stringent selection of variants resulted in more biased genomic predictions, although this might be due to the training population being the same dataset from which the selected variants were identified.</p

Effects of data structure on the estimation of covariance functions to describe genotype by environment interactions in a reaction norm model

Covariance functions have been proposed to predict breeding values and genetic (co)variances as a function of phenotypic within herd-year averages (environmental parameters) to include genotype by environment interaction. The objective of this paper was to investigate the influence of definition of environmental parameters and non-random use of sires on expected breeding values and estimated genetic variances across environments. Breeding values were simulated as a linear function of simulated herd effects. The definition of environmental parameters hardly influenced the results. In situations with random use of sires, estimated genetic correlations between the trait expressed in different environments were 0.93, 0.93 and 0.97 while simulated at 0.89 and estimated genetic variances deviated up to 30% from the simulated values. Non random use of sires, poor genetic connectedness and small herd size had a large impact on the estimated covariance functions, expected breeding values and calculated environmental parameters. Estimated genetic correlations between a trait expressed in different environments were biased upwards and breeding values were more biased when genetic connectedness became poorer and herd composition more diverse. The best possible solution at this stage is to use environmental parameters combining large numbers of animals per herd, while losing some information on genotype by environment interaction in the data

Simultaneous QTL detection and genomic breeding value estimation using high density SNP chips

Background: The simulated dataset of the 13th QTL-MAS workshop was analysed to i) detect QTL and ii) predict breeding values for animals without phenotypic information. Several parameterisations considering all SNP simultaneously were applied using Gibbs sampling. Results: Fourteen QTL were detected at the different time points. Correlations between estimated breeding values were high between models, except when the model was used that assumed that all SNP effects came from one distribution. The model that used the selected 14 SNP found associated with QTL, gave close to unity correlations with the full parameterisations. Conclusions: Nine out of 18 QTL were detected, however the six QTL for inflection point were missed. Models for genomic selection were indicated to be fairly robust, e.g. with respect to accuracy of estimated breeding values. Still, it is worthwhile to investigate the number QTL underlying the quantitative traits, before choosing the model used for genomic selection

Impact of Interbeef on national beef cattle evaluations

Submitted 2020-07-02 | Accepted 2020-08-22 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.144-155International evaluation models for beef cattle allow to compare animals’ estimated breeding values (EBV) across different countries, thanks to sires having offspring in more than one country. In this study we aimed to provide an up-to-date picture of the Interbeef international beef cattle evaluations from a national perspective, considering both large and small populations. Limousin age-adjusted weaning weight (AWW) phenotypes were available for 3,115,598 animals from 10 European countries, born between 1972 and 2017. EBV and reliabilities were obtained using a multi-trait animal model including maternal effects where AWW from different countries are modelled as different traits. We investigated the country of origin of the sires with internationally publishable EBV and, among them, the country of origin of the top 100 sires for each country scale. All countries had 20 to 28,557 domestic sires whose EBV were publishable, according to Interbeef’s rules, on the scale of other countries. All countries, except one, had domestic sires that ranked among the top 100 sires on other country scales. Across countries, inclusion of information from relatives recorded in other countries increased the reliability of EBV for domestic animals on average by 9.6 percentage points for direct EBV, and 8.3 percentage points for maternal EBV. In conclusion, international evaluations provide small countries access to a panel of elite foreign sires with EBV on their country scale and a more accurate estimation of EBV of domestic animals, while large countries obtain EBV for their sires on the scale of different countries which helps to better promote them.Keywords: international breeding values, genotype-by-environment interaction, Interbeef, reliabilities, weaning weightReferencesBonifazi, R., Vandenplas, J., Napel, J. ten, Matilainen, K., Veerkamp, R. F., & Calus, M. P. L. (2020). Impact of sub-setting the data of the main Limousin beef cattle population on the estimates of across-country genetic correlations. Genetics Selection Evolution, 52(1), 32. https://doi.org/10.1186/s12711-020-00551-9Bouquet, A., Venot, E., Laloë, D., Forabosco, F., Fogh, A., Pabiou, T., Coffey, M., Eriksson, J-A., Renand, G., & Phocas, F. (2009). Genetic Structure of the European Limousin Cattle Metapopulation Using Pedigree Analyses. Interbull Bullettin, 40, 98–103.Durr, J., & Philipsson, J. (2012). International cooperation: The pathway for cattle genomics. Animal Frontiers, 2(1), 16–21. https://doi.org/10.2527/af.2011-0026Fikse, W. F., & Philipsson, J. (2007). Development of international genetic evaluations of dairy cattle for sustainable breeding programs. Animal Genetic Resources, (41), 29–43. https://doi.org/10.1017/S1014233900002315Goddard, M. (1985). A method of comparing sires evaluated in different countries. Livestock Production Science, 13(4), 321–331. https://doi.org/10.1016/0301-6226(85)90024-7Interbeef. (2020). Interbeef Working Group, ICAR. Retrieved August 20, 2020, from https://www.icar.org/index.php/technical-bodies/working-groups/interbeef-working-group/Jorjani, H., Emanuelson, U., & Fikse, W. F. (2005). Data Subsetting Strategies for Estimation of Across-Country Genetic Correlations. Journal of Dairy Science, 88(3), 1214–1224. https://doi.org/10.3168/jds.S0022-0302(05)72788-0Journaux, L., Wickham, B., Venot, E., & Pabiou, T. (2006). Development of Routine International Genetic Evaluation Services for Beef Cattle as an Extension of Interbull ’s Services. Interbull Bulletin, 35(1), 146–152.MiX99 Development Team. (2017). MiX99: A software package for solving large mixed model equations. Release XI/2017.Moore, S. G., & Hasler, J. F. (2017). A 100-Year Review: Reproductive technologies in dairy science. Journal of Dairy Science, 100(12), 10314–10331. https://doi.org/10.3168/jds.2017-13138Mrode, R. A., & Thompson, R. (2005). Linear models for the prediction of animal breeding values: Second Edition. In Linear Models For the Prediction of Animal Breeding Values: Second Edition.Philipsson, J. (2011). Interbull Developments, Global Genetic Trends and Role in the Era of Genomics. Interbull Bulletin, 44, i–xiii.Phocas, F., Donoghue, K., & Graser, H. U. (2005). Investigation of three strategies for an international genetic evaluation of beef cattle weaning weight. Genetics Selection Evolution, 37(4), 361–380. https://doi.org/10.1051/gse:2005006Quintanilla, R., Laloë, D., & Renand, G. (2002a). Heterogeneity of variances across regions for weaning weight in Charolais breed. 7th World Congress on Genetics Applied to Livestock Production, 19–23. Montpellier, France.Quintanilla, R., Laloë, D., & Renand, G. (2002b). Heteroskedasticity and genotype by environment interaction across European countries for weaning weight in Charolais breed. Proceedings of the 33rd Biennial Session of ICAR, 147–150. Interlaken, Switzerland: EAAP publication N. 107, 2003.Renand, G., Laloë, D., Quintanilla, R., & Fouilloux, M. N. (2003). A first attempt of an international genetic evaluation of beef breeds in Europe. Interbull Bulletin, 31, 151–155.Robinson, G. K. (1986). That BLUP Is a Good Thing: The Estimation of Random Effects. Statistical Science, 6(1), 15–51.Schaeffer, L. R. (1994). Multiple-Country Comparison of Dairy Sires. Journal of Dairy Science, 77(9), 2671–2678. https://doi.org/10.3168/jds.S0022-0302(94)77209-XTier, B., & Meyer, K. (2004). Approximating prediction error covariances among additive genetic effects within animals in multiple-trait and random regression models. Journal of Animal Breeding and Genetics, 121(2), 77–89. https://doi.org/10.1111/j.1439-0388.2003.00444.xVenot, E., Fouilloux, M. N., Forabosco, F., Fogh, A., Pabiou, T., Moore, K., Eriksson, J-A., Renand, G., Laloë, D.(2009). Interbeef genetic evaluation of Charolais and Limousine weaning weights. Interbull Bulletin, 40, 61–67.Venot, E., Pabiou, T., Hjerpe, E., Nilforooshan, M. M. A., Launay, A., & Wickham, B. W. W. (2014). Benefits ofInterbeef international genetic evaluations for weaning weight. 10th World Congress of Genetics Applied to Livestock Production.Venot, E, Pabiou, T., Guerrier, J., Cromie, A., Journaux, L., Flynn, J., & Wickham, B. (2007). Interbeef in Practice: Example of a Joint Genetic Evaluation between France, Ireland and United Kingdom for Pure Bred Limousine Weaning Weights. Interbull Bulletin, 36, 41–47.Venot, E, Pabiou, T., Wickham, B., & Journaux, L. (2006). First Steps Towards a European Joint Genetic Evaluation of the Limousine Breed. Interbull Bulletin, 35, 141–145.Venot, Eric, Fouilloux, M. N., Sullivan, P., & Laloë, D. (2008). Level of Connectedness and Reliability in International Beef Evaluation. Interbull Bulletin, 38(June 2008), 3–7.Vishwanath, R. (2003). Artificial insemination: The state of the art. Theriogenology, 59(2), 571–584. https://doi.org/10.1016/S0093-691X(02)01241-4Wickham, B. W., & Durr, J. W. (2011). A new international infrastructure for beef cattle breeding. Animal Frontiers, 1(2), 53–59. https://doi.org/10.2527/af.2011-0019Wilmink, J. B. M., Meijering, A., & Engel, B. (1986). Conversion of breeding values for milk from foreign populations. Livestock Production Science, 14(3), 223–229. https://doi.org/10.1016/0301-6226(86)90081-3

Functional and population genetic features of copy number variations in two dairy cattle populations

Background: Copy Number Variations (CNVs) are gain or loss of DNA segments that are known to play a role in shaping a wide range of phenotypes. In this study, we used two dairy cattle populations, Holstein Friesian and Jersey, to discover CNVs using the Illumina BovineHD Genotyping BeadChip aligned to the ARS-UCD1.2 assembly. The discovered CNVs were investigated for their functional impact and their population genetics features. Results: We discovered 14,272 autosomal CNVs, which were aggregated into 1755 CNV regions (CNVR) from 451 animals. These CNVRs together cover 2.8% of the bovine autosomes. The assessment of the functional impact of CNVRs showed that rare CNVRs (MAF 2 = ~ 0.1 at 10 kb distance) than the rest. Nevertheless, this LD is still lower than SNP-SNP LD (r 2 = ~ 0.5 at 10 kb distance). Conclusions: Our analyses showed that CNVRs detected using BovineHD BeadChip arrays are likely to be functional. This finding indicates that CNVs can potentially disrupt the function of genes and thus might alter phenotypes. Also, the population differentiation index revealed two candidate genes, MGAM and ADAMTS17, which hint at adaptive evolution between the two populations. Lastly, low CNVR-SNP LD implies that genetic variation from CNVs might not be fully captured in routine animal genetic evaluation, which relies solely on SNP markers.</p

Inbreeding depression due to recent and ancient inbreeding in Dutch Holstein–Friesian dairy cattle

International audienceBackground : Inbreeding decreases animal performance (inbreeding depression), but not all inbreeding is expected to be equally harmful. Recent inbreeding is expected to be more harmful than ancient inbreeding, because selection decreases the frequency of deleterious alleles over time. Selection efficiency is increased by inbreeding, a process called purging. Our objective was to investigate effects of recent and ancient inbreeding on yield, fertility and udder health traits in Dutch Holstein–Friesian cows.Methods : In total, 38,792 first-parity cows were included. Pedigree inbreeding ( FPED ) was computed and 75 k geno-type data were used to compute genomic inbreeding, among others based on regions of homozygosity (ROH) in the genome ( FROH).Results : Inbreeding depression was observed, e.g. a 1% increase in FROH was associated with a 36.3 kg (SE = 2.4) decrease in 305-day milk yield, a 0.48 day (SE =0.15) increase in calving interval and a 0.86 unit (SE =0.28) increase in somatic cell score for day 150 through to 400. These effects equalled −0.45, 0.12 and 0.05% of the trait means, respec-tively. When FPED was split into generation-based components, inbreeding on recent generations was more harmful than inbreeding on more distant generations for yield traits. When FPED was split into new and ancestral components, based on whether alleles were identical-by-descent for the first time or not, new inbreeding was more harmful than ancestral inbreeding, especially for yield traits. For example, a 1% increase in new inbreeding was associated with a 2.42 kg (SE =0.41) decrease in 305-day fat yield, compared to a 0.03 kg (SE =0.71) increase for ancestral inbreeding. There were no clear differences between effects of long ROH (recent inbreeding) and short ROH (ancient inbreeding).Conclusions : Inbreeding depression was observed for yield, fertility and udder health traits. For yield traits and based on pedigree, inbreeding on recent generations was more harmful than inbreeding on distant generations and there was evidence of purging. Across all traits, long and short ROH contributed to inbreeding depression. In future work, inbreeding depression and purging should be assessed in more detail at the genomic level, using higher density information and genomic time series

Regional Regulation of Transcription in the Bovine Genome

Eukaryotic genes are distributed along chromosomes as clusters of highly expressed genes termed RIDGEs (Regions of IncreaseD Gene Expression) and lowly expressed genes termed anti-RIDGEs, interspersed among genes expressed at intermediate levels or not expressed. Previous studies based on this observation suggested a dual mechanism of gene regulation, where, in addition to transcription factors, the chromosomal domain influences the expression level of their embedded genes. The objectives here were to provide evidence for the existence of chromosomal regional regulation of transcription in the bovine genome, to analyse the genomic features of genes located within RIDGEs versus anti-RIDGEs and tissue-specific genes versus housekeeping and to examine the genomic distribution of genes subject to positive selection in bovines. Gene expression analysis of four brain tissues and the anterior pituitary of 28 cows identified 70 RIDGEs and 41 anti-RIDGEs (harbouring 3735 and 1793 bovine genes respectively) across the bovine genome which are significantly higher than expected by chance. Housekeeping genes (defined here as genes expressed in all five tissues) were over-represented within RIDGEs but tissue-specific genes (genes expressed in only one of the five tissues) were not. Housekeeping genes and genes within RIDGEs had, in general, higher expression levels and GC content but shorter gene lengths and intron lengths than tissue-specific genes and genes within anti-RIDGES. Our findings suggest the existence of chromosomal regional regulation of transcription in the bovine genome. The genomic features observed for genes within RIDGEs and housekeeping genes in bovines agree with previous studies in several other species further strengthening the hypothesis of selective pressure to keep the highly and widely expressed genes short and compact for transcriptional efficiency. Further, positively selected genes were found non-randomly distributed on the genome with a preference for RIDGEs and regions of intermediate gene expression compared to anti-RIDGEs