Evaluation and development of animal breeding in Ireland

End of project reportThe primary objectives of this study were: 1) to annually evaluate the pertinence of the Irish dairy cattle breeding index, the Economic Breeding Index (EBI) and where necessary modify, 2) to evaluate the potential of do-it-yourself milk recording as an alternative to current supervised methods of milk recording, and 3) to estimate the level and rate of accumulation of inbreeding in Irish dairy and beef cattle, to quantify its effects on traits of economic importance, and to develop remedial measures to minimise the future accumulation of inbreeding in Ireland

The association between herd- and cow-level factors and somatic cell count of Irish dairy cows

peer-reviewedSomatic cell count (SCC) is an indicator of both udder health and milk quality and is measured at an animal level through national milk recording schemes. The objective of this study was to assess the animal and herd factors contributing to elevated SCC (i.e. poorer milk quality). Test day records (n = 2,658,928) from 519,456 cow lactations obtained between 2007 and 2011 were included in the analyses. Herd factors tested included the geographical region of the herd and production system operated (spring calving or mixed calving system). Animal factors tested included breed, parity and age nested within parity. Four definitions of normalised SCC (i.e. SCS) were considered: 1) average test-day SCS within a 24 hour period (TD_SCS), 2) maximum SCS (peak_SCS), 3) minimum SCS (min_SCS), and 4) average SCS (avg_SCS) recorded across cow lactation; in addition, the proportion of test day records with an SCC count >200,000 (prop_200) or >250,000 (prop_250) within cow lactation were included. Following adjustment for fixed effects, average TD_SCS was 179,308 cells per mL while avg_SCS, and average min_SCS and peak_SCS were 119,481, 50,992 and 298,813 cells per mL, respectively. All animal and herd factors had a significant effect on SCC. Older animals, animals which were younger at calving than contemporaries and Holstein animals had higher SCC than younger alternative breed animals who calved at the median age. In addition, mixed calving production systems and herds in Connaught had higher SCC than spring calving herds in the other regions of Ireland.The authors gratefully acknowledge funding for this work from the Department of Agriculture, Fisheries and Food under the Joint FIRM / RSF Initiative (Project Number: 10/RD/AAQUALITYMILK/ TMFRC713)

Detection of selection signatures in dairy and beef cattle using high-density genomic information

peer-reviewedBackground Artificial selection for economically important traits in cattle is expected to have left distinctive selection signatures on the genome. Access to high-density genotypes facilitates the accurate identification of genomic regions that have undergone positive selection. These findings help to better elucidate the mechanisms of selection and to identify candidate genes of interest to breeding programs. Results Information on 705 243 autosomal single nucleotide polymorphisms (SNPs) in 3122 dairy and beef male animals from seven cattle breeds (Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental) were used to detect selection signatures by applying two complementary methods, integrated haplotype score (iHS) and global fixation index (FST). To control for false positive results, we used false discovery rate (FDR) adjustment to calculate adjusted iHS within each breed and the genome-wide significance level was about 0.003. Using the iHS method, 83, 92, 91, 101, 85, 101 and 86 significant genomic regions were detected for Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental cattle, respectively. None of these regions was common to all seven breeds. Using the FST approach, 704 individual SNPs were detected across breeds. Annotation of the regions of the genome that showed selection signatures revealed several interesting candidate genes i.e. DGAT1, ABCG2, MSTN, CAPN3, FABP3, CHCHD7, PLAG1, JAZF1, PRKG2, ACTC1, TBC1D1, GHR, BMP2, TSG1, LYN, KIT and MC1R that play a role in milk production, reproduction, body size, muscle formation or coat color. Fifty-seven common candidate genes were found by both the iHS and global FST methods across the seven breeds. Moreover, many novel genomic regions and genes were detected within the regions that showed selection signatures; for some candidate genes, signatures of positive selection exist in the human genome. Multilevel bioinformatic analyses of the detected candidate genes suggested that the PPAR pathway may have been subjected to positive selection. Conclusions This study provides a high-resolution bovine genomic map of positive selection signatures that are either specific to one breed or common to a subset of the seven breeds analyzed. Our results will contribute to the detection of functional candidate genes that have undergone positive selection in future studies.This study was financially supported by a grant from the Irish Department of Agriculture, Food and Marine Research Stimulus Fund (11/S/112), the Agricultural Science and Technology Innovation Program (No. ASTIP-IAS-TS-6) and the Natural Science Foundation of China (No. 31200927)

Genetic parameters for milk mineral content and acidity predicted by mid-infrared spectroscopy in Holstein–Friesian cows

peer-reviewedThis article was first published in animal, Volume 9, Issue 05, May 2015, pp 775-780 © The Animal Consortium 2015The aim of the present study was to estimate genetic parameters for calcium (Ca), phosphorus (P) and titratable acidity (TA) in bovine milk predicted by mid-IR spectroscopy (MIRS). Data consisted of 2458 Italian Holstein−Friesian cows sampled once in 220 farms. Information per sample on protein and fat percentage, pH and somatic cell count, as well as test-day milk yield, was also available. (Co)variance components were estimated using univariate and bivariate animal linear mixed models. Fixed effects considered in the analyses were herd of sampling, parity, lactation stage and a two-way interaction between parity and lactation stage; an additive genetic and residual term were included in the models as random effects. Estimates of heritability for Ca, P and TA were 0.10, 0.12 and 0.26, respectively. Positive moderate to strong phenotypic correlations (0.33 to 0.82) existed between Ca, P and TA, whereas phenotypic weak to moderate correlations (0.00 to 0.45) existed between these traits with both milk quality and yield. Moderate to strong genetic correlations (0.28 to 0.92) existed between Ca, P and TA, and between these predicted traits with both fat and protein percentage (0.35 to 0.91). The existence of heritable genetic variation for Ca, P and TA, coupled with the potential to predict these components for routine cow milk testing, imply that genetic gain in these traits is indeed possible

The distribution of runs of homozygosity and selection signatures in six commercial meat sheep breeds

peer-reviewedDomestication and the subsequent selection of animals for either economic or morphological features can leave a variety of imprints on the genome of a population. Genomic regions subjected to high selective pressures often show reduced genetic diversity and frequent runs of homozygosity (ROH). Therefore, the objective of the present study was to use 42,182 autosomal SNPs to identify genomic regions in 3,191 sheep from six commercial breeds subjected to selection pressure and to quantify the genetic diversity within each breed using ROH. In addition, the historical effective population size of each breed was also estimated and, in conjunction with ROH, was used to elucidate the demographic history of the six breeds. ROH were common in the autosomes of animals in the present study, but the observed breed differences in patterns of ROH length and burden suggested differences in breed effective population size and recent management. ROH provided a sufficient predictor of the pedigree inbreeding coefficient, with an estimated correlation between both measures of 0.62. Genomic regions under putative selection were identified using two complementary algorithms; the fixation index and hapFLK. The identified regions under putative selection included candidate genes associated with skin pigmentation, body size and muscle formation; such characteristics are often sought after in modern-day breeding programs. These regions of selection frequently overlapped with high ROH regions both within and across breeds. Multiple yet uncharacterised genes also resided within putative regions of selection. This further substantiates the need for a more comprehensive annotation of the sheep genome as these uncharacterised genes may contribute to traits of interest in the animal sciences. Despite this, the regions identified as under putative selection in the current study provide an insight into the mechanisms leading to breed differentiation and genetic variation in meat production.This work was supported by MultiGS Research Stimulus Fund (11/S/112) and OviGen project (14/S/849) which are funded by the Department of Agriculture, Food and Marine, Ireland

Prediction of 24-hour milk yield and composition in dairy cows from a single part-day yield and sample

peer-reviewedTeagasc PublicationIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 Prediction of 24-hour milk yield and composition in dairy cows from a single part-day yield and sample S. McParlandemail , B. Coughlan , B. Enright , M. O’Keeffe , R. O’Connor , L. Feeney and D.P. Berry DOI: https://doi.org/10.2478/ijafr-2019-0007 | Published online: 09 Aug 2019 PDF Abstract Article PDF References Recommendations Abstract The objective was to evaluate the accuracy of predicting 24-hour milk yield and composition from a single morning (AM) or evening (PM) milk weight and composition. A calibration dataset of 37,481 test-day records with both AM and PM yields and composition was used to generate the prediction equations; equations were validated using 4,644 test-day records. Prediction models were developed within stage of lactation and parity while accounting for the inter-milking time interval. The mean correlation between the predicted 24-hour yields and composition of milk, fat and protein and the respective actual values was 0.97 when based on just an AM milk yield and composition with a mean correlation of 0.95 when based on just a PM milk yield and composition. The regression of predicted 24-hour yield and composition on the respective actual values varied from 0.97 to 1.01 with the exception of 24-hour fat percentage predicted from a PM sample (1.06). A single AM sample is useful to predict 24-hour milk yield and composition when the milking interval is known

Imputation of ungenotyped parental genotypes in dairy and beef cattle from progeny genotypes

peer-reviewedThe objective of this study was to quantify the accuracy of imputing the genotype of parents using information on the genotype of their progeny and a family-based and population-based imputation algorithm. Two separate data sets were used, one containing both dairy and beef animals (n = 3122) with high-density genotypes (735 151 single nucleotide polymorphisms (SNPs)) and the other containing just dairy animals (n = 5489) with medium-density genotypes (51 602 SNPs). Imputation accuracy of three different genotype density panels were evaluated representing low (i.e. 6501 SNPs), medium and high density. The full genotypes of sires with genotyped half-sib progeny were masked and subsequently imputed. Genotyped half-sib progeny group sizes were altered from 4 up to 12 and the impact on imputation accuracy was quantified. Up to 157 and 258 sires were used to test the accuracy of imputation in the dairy plus beef data set and the dairy-only data set, respectively. The efficiency and accuracy of imputation was quantified as the proportion of genotypes that could not be imputed, and as both the genotype concordance rate and allele concordance rate. The median proportion of genotypes per animal that could not be imputed in the imputation process decreased as the number of genotyped half-sib progeny increased; values for the medium-density panel ranged from a median of 0.015 with a half-sib progeny group size of 4 to a median of 0.0014 to 0.0015 with a half-sib progeny group size of 8. The accuracy of imputation across different paternal half-sib progeny group sizes was similar in both data sets. Concordance rates increased considerably as the number of genotyped half-sib progeny increased from four (mean animal allele concordance rate of 0.94 in both data sets for the medium-density genotype panel) to five (mean animal allele concordance rate of 0.96 in both data sets for the medium-density genotype panel) after which it was relatively stable up to a half-sib progeny group size of eight. In the data set with dairy-only animals, sufficient sires with paternal half-sib progeny groups up to 12 were available and the withinanimal mean genotype concordance rates continued to increase up to this group size. The accuracy of imputation was worst for the low-density genotypes, especially with smaller half-sib progeny group sizes but the difference in imputation accuracy between density panels diminished as progeny group size increased; the difference between high and medium-density genotype panels was relatively small across all half-sib progeny group sizes. Where biological material or genotypes are not available on individual animals, at least five progeny can be genotyped (on either a medium or high-density genotyping platform) and the parental alleles imputed with, on average, ⩾96% accuracy

Milk mid-infrared spectral data as a tool to predict feed intake in lactating Norwegian Red dairy cows

peer-reviewedMid-infrared (MIR) spectroscopy of milk was used to predict dry matter intake (DMI) and net energy intake (NEI) in 160 lactating Norwegian Red dairy cows. A total of 857 observations were used in leave-one-out cross-validation and external validation to develop and validate prediction equations using 5 different models. Predictions were performed using (multiple) linear regression, partial least squares (PLS) regression, or best linear unbiased prediction (BLUP) methods. Linear regression was implemented using just milk yield (MY) or fat, protein, and lactose concentration in milk (Mcont) or using MY together with body weight (BW) as predictors of intake. The PLS and BLUP methods were implemented using just the MIR spectral information or using the MIR together with Mcont, MY, BW, or NEI from concentrate (NEIconc). When using BLUP, the MIR spectral wavelengths were always treated as random effects, whereas Mcont, MY, BW, and NEIconc were considered to be fixed effects. Accuracy of prediction (R) was defined as the correlation between the predicted and observed feed intake test-day records. When using the linear regression method, the greatest R of predicting DMI (0.54) and NEI (0.60) in the external validation was achieved when the model included both MY and BW. When using PLS, the greatest R of predicting DMI (0.54) and NEI (0.65) in the external validation data set was achieved when using both BW and MY as predictors in combination with the MIR spectra. When using BLUP, the greatest R of predicting DMI (0.54) in the external validation was when using MY together with the MIR spectra. The greatest R of predicting NEI (0.65) in the external validation using BLUP was achieved when the model included both BW and MY in combination with the MIR spectra or when the model included both NEIconc and MY in combination with MIR spectra. However, although the linear regression coefficients of actual on predicted values for DMI and NEI were not different from unity when using PLS, they were less than unity for some of the models developed using BLUP. This study shows that MIR spectral data can be used to predict NEI as a measure of feed intake in Norwegian Red dairy cattle and that the accuracy is augmented if additional, often available data are also included in the prediction model

Effect of using internal teat sealant with or without antibiotic therapy at dry-off on subsequent somatic cell count and milk production

peer-reviewedThe objective of this study was to assess the effect of treating cows with teat sealant only compared with antibiotic plus teat sealant at drying off on weekly somatic cell count, potential intramammary infection, and milk production across the entire subsequent lactation. In 3 research herds in the south of Ireland, cows with SCC that did not exceed 200,000 cells/mL in the previous lactation (LowSCC) were randomly assigned to 1 of 2 treatments at drying off: internal teat sealant alone (ITS) or antibiotic plus teat sealant (AB+ITS). Cows with SCC that exceeded 200,000 cells/mL in the previous lactation were treated with AB+ITS and included in the analyses as a separate group (HighSCC). Weekly individual animal composite SCC records were available for 654 cow lactations and were transformed to somatic cell scores (SCS) for the purpose of analysis. Data were divided into 3 data sets to represent records obtained (1) up to 35 DIM, (2) up to 120 DIM, and (3) across the lactation. Foremilk secretions were taken from all quarters at drying off, at calving, 2 wk after calving, and in mid-lactation and were cultured to detect the presence of bacteria. The LowSCC cows treated with ITS alone had higher daily milk yield (0.67 kg/d) across lactation compared with LowSCC cows treated with AB+ITS. The LowSCC cows treated with ITS alone had higher SCS in early, up to mid, and across lactation compared with LowSCC cows treated with AB+ITS. We detected no difference in weekly SCS of LowSCC cows treated with ITS alone and SCS of HighSCC cows. The least squares means back-transformed SCC across lactation of the LowSCC cows treated with ITS alone, LowSCC cows treated with AB+ITS, and HighSCC cows were 41,523, 34,001, and 38,939 cells/mL respectively. The odds of LowSCC cows treated with ITS alone having bacteria present in their foremilk across lactation was 2.7 (95% confidence interval: 1.91 to 3.85) and 1.6 (1.22 to 2.03) times the odds of LowSCC cows treated with AB+ITS and of HighSCC cows treated with AB+ITS, respectively. In this study, Staphylococcus aureus was the most prevalent pathogen isolated from the population. Recategorizing the threshold for LowSCC cows as ≤150,000 cells/mL or ≤100,000 cells/mL in the previous lactation had no effect on the results. The results indicate that herds with good mastitis control programs may use ITS alone at dry-off in cows with SCC <200,000 cells/mL across lactation with only a small effect on herd SCC

How herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic merit

peer-reviewedSustainable dairy cow performance relies on coevolution in the development of breeding and management strategies. Tailoring breeding programs to herd performance metrics facilitates improved responses to breeding decisions. Although herd-level raw metrics on performance are useful, implicitly included within such statistics is the mean herd genetic merit. The objective of the present study was to quantify the expected response from selection decisions on additive and nonadditive merit by herd performance metrics independent of herd mean genetic merit. Performance traits considered in the present study were age at first calving, milk yield, calving to first service, number of services, calving interval, and survival. Herd-level best linear unbiased estimates (BLUE) for each performance trait were available on a maximum of 1,059 herds, stratified as best, average, and worst for each performance trait separately. The analyses performed included (1) the estimation of (co)variance for each trait in the 3 BLUE environments and (2) the regression of cow-level phenotypic performance on either the respective estimated breeding value (EBV) or the heterosis coefficient of the cow. A fundamental assumption of genetic evaluations is that 1 unit change in EBV equates to a 1 unit change in the respective phenotype; results from the present study, however, suggest that the realization of the change in phenotypic performance is largely dependent on the herd BLUE for that trait. Herds achieving more yield, on average, than expected from their mean genetic merit, had a 20% greater response to changes in EBV as well as 43% greater genetic standard deviation relative to herds within the worst BLUE for milk yield. Conversely, phenotypic performance in fertility traits (with the exception of calving to first service) tended to have a greater response to selection as well as a greater additive genetic standard deviation within the respective worst herd BLUE environments; this is suggested to be due to animals performing under more challenging environments leading to larger achievable gains. The attempts to exploit nonadditive genetic effects such as heterosis are often the basis of promoting cross-breeding, yet the results from the present study suggest that improvements in phenotypic performance is largely dependent on the environment. The largest gains due to heterotic effects tended to be within the most stressful (i.e., worst) BLUE environment for all traits, thus suggesting the heterosis effects can be beneficial in mitigating against poorer environments.This publication emanated from research supported in part by a research grant from Science Foundation Ireland (Dublin, Ireland) and the Department of Agriculture, Food and Marine on behalf of the Government of Ireland (Dublin, Ireland) under the Grant 16/RC/3835 (VistaMilk) as well as funding from the Irish Department of Agriculture, Food and the Marine STIMULUS research grant MultiRepro (Dublin, Ireland)