Heterogeneity in genetic variation and energy sink relationships for residual feed intake across research stations and countries
Tempelman, R. ; Spurlock, D.M. ; Coffey, M.P. ; Veerkamp, R.F. ; Armentano, L. ; Weigel, K. ; Haas, Y. de; Staples, C.R. ; Connor, E.E. ; Hanigan, M.D. ; Lu, Y.F. ; Haar, M.J. van de - \ 2015
Journal of Dairy Science 98 (2015)3. - ISSN 0022-0302 - p. 2013 - 2026.
random regression-models - dairy-cattle - lactation performance - efficiency - cows - selection - supplementation - heritability - components - variance
Our long-term objective is to develop breeding strategies for improving feed efficiency in dairy cattle. In this study, phenotypic data were pooled across multiple research stations to facilitate investigation of the genetic and nongenetic components of feed efficiency in Holstein cattle. Specifically, the heritability of residual feed intake (RFI) was estimated and heterogeneous relationships between RFI and traits relating to energy utilization were characterized across research stations. Milk, fat, protein, and lactose production converted to megacalories (milk energy; MilkE), dry matter intakes (DMI), and body weights (BW) were collected on 6,824 lactations from 4,893 Holstein cows from research stations in Scotland, the Netherlands, and the United States. Weekly DMI, recorded between 50 to 200 d in milk, was fitted as a linear function of MilkE, BW0.75, and change in BW (¿BW), along with parity, a fifth-order polynomial on days in milk (DIM), and the interaction between this polynomial and parity in a first-stage model. The residuals from this analysis were considered to be a phenotypic measure of RFI. Estimated partial regression coefficients of DMI on MilkE and on BW0.75 ranged from 0.29 to 0.47 kg/Mcal for MilkE across research stations, whereas estimated partial regression coefficients on BW0.75 ranged from 0.06 to 0.16 kg/kg0.75. Estimated partial regression coefficients on ¿BW ranged from 0.06 to 0.39 across stations. Heritabilities for country-specific RFI were based on fitting second-stage random regression models and ranged from 0.06 to 0.24 depending on DIM. The overall heritability estimate across all research stations and all DIM was 0.15±0.02, whereas an alternative analysis based on combining the first- and second-stage model as 1 model led to an overall heritability estimate of 0.18±0.02. Hence future genomic selection programs on feed efficiency appear to be promising; nevertheless, care should be taken to allow for potentially heterogeneous variance components and partial relationships between DMI and other energy sink traits across environments when determining RFI.
Effect of protein provision via milk replacer or solid feed on protein metabolism in veal calves
Berends, H. ; Borne, J.J.G.C. van den; Røjen, B.A. ; Hendriks, W.H. ; Gerrits, W.J.J. - \ 2015
Journal of Dairy Science 98 (2015)2. - ISSN 0022-0302 - p. 1119 - 1126.
heavy preruminant calves - amino-acids - energy-metabolism - rumen development - dairy-cattle - nitrogen - urea - growth - performance - deposition
The current study evaluated the effects of protein provision to calves fed a combination of solid feed (SF) and milk replacer (MR) at equal total N intake on urea recycling and N retention. Nitrogen balance traits and [15N2]urea kinetics were measured in 30 calves (23 wk of age, 180 ± 3.7 kg of body weight), after being exposed to the following experimental treatments for 11 wk: a low level of SF with a low N content (SF providing 12% of total N intake), a high level of SF with a low N content (SF providing 22% of total N intake), or a high level of SF with a high N content (SF providing 36% of total N intake). The SF mixture consisted of 50% concentrates, 25% corn silage, and 25% straw on a dry matter basis. Total N intake was equalized to 1.8 g of N·kg of BW-0.75·d-1 by adjusting N intake via MR. All calves were housed individually on metabolic cages to allow for quantification of a N balance of calves for 5 d, and for the assessment of urea recycling from [15N2]urea kinetics. Increasing low-N SF intake at equal total N intake resulted in a shift from urinary to fecal N excretion but did not affect protein retention (0.71 g of N·kg of BW-0.75·d-1). Increasing low-N SF intake increased urea recycling but urea reused for anabolism remained unaffected. Total-tract neutral detergent fiber digestibility decreased (-9%) with increasing low-N SF intake, indicating reduced rumen fermentation. Increasing the N content of SF at equal total N intake resulted in decreased urea production, excretion, and return to ornithine cycle, and increased protein retention by 17%. This increase was likely related to an effect of energy availability on protein retention due to an increase in total-tract neutral detergent fiber digestion (>10%) and due to an increased energy supply via the MR. In conclusion, increasing low-N SF intake at the expense of N intake from MR, did not affect protein retention efficiency in calves. Increasing the N content of SF at equal total N intake decreased urea production, increased protein retention, and coincided with improved fiber degradation. Therefore, results suggest that low N availability in the rumen limits microbial growth and rumen fermentation in calves fed low-N SF (93 g of CP/kg of DM), and this effect cannot be compensated for by recycling of urea originating from MR.
A Genome-Wide Association Study Reveals Dominance Effects on Number of Teats in Pigs
Lopes, M.S. ; Bastiaansen, J.W.M. ; Harlizius, B. ; Knol, E.F. ; Bovenhuis, H. - \ 2014
PLoS ONE 9 (2014)8. - ISSN 1932-6203 - 8 p.
quantitative trait loci - milk fatty-acids - resource population - affecting reproduction - carcass composition - genetic-basis - dairy-cattle - meat quality - qtl analysis - meishan
Dominance has been suggested as one of the genetic mechanisms explaining heterosis. However, using traditional quantitative genetic methods it is difficult to obtain accurate estimates of dominance effects. With the availability of dense SNP (Single Nucleotide Polymorphism) panels, we now have new opportunities for the detection and use of dominance at individual loci. Thus, the aim of this study was to detect additive and dominance effects on number of teats (NT), specifically to investigate the importance of dominance in a Landrace-based population of pigs. In total, 1,550 animals, genotyped for 32,911 SNPs, were used in single SNP analysis. SNPs with a significant genetic effect were tested for their mode of gene action being additive, dominant or a combination. In total, 21 SNPs were associated with NT, located in three regions with additive (SSC6, 7 and 12) and one region with dominant effects (SSC4). Estimates of additive effects ranged from 0.24 to 0.29 teats. The dominance effect of the QTL located on SSC4 was negative (-0.26 teats). The additive variance of the four QTLs together explained 7.37% of the total phenotypic variance. The dominance variance of the four QTLs together explained 1.82% of the total phenotypic variance, which corresponds to one-fourth of the variance explained by additive effects. The results suggest that dominance effects play a relevant role in the genetic architecture of NT. The QTL region on SSC7 contains the most promising candidate gene: VRTN. This gene has been suggested to be related to the number of vertebrae, a trait correlated with NT.
A comparison of principal component regression and genomic REML for genomic prediction across populations
Dadousis, C. ; Veerkamp, R.F. ; Heringstad, B. ; Pszczola, M.J. ; Calus, M.P.L. - \ 2014
Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X - 14 p.
breeding values - multi-breed - short communication - variable selection - wide association - genetic value - dairy-cattle - data sets - accuracy - information
Background Genomic prediction faces two main statistical problems: multicollinearity and n¿«¿p (many fewer observations than predictor variables). Principal component (PC) analysis is a multivariate statistical method that is often used to address these problems. The objective of this study was to compare the performance of PC regression (PCR) for genomic prediction with that of a commonly used REML model with a genomic relationship matrix (GREML) and to investigate the full potential of PCR for genomic prediction. Methods The PCR model used either a common or a semi-supervised approach, where PC were selected based either on their eigenvalues (i.e. proportion of variance explained by SNP (single nucleotide polymorphism) genotypes) or on their association with phenotypic variance in the reference population (i.e. the regression sum of squares contribution). Cross-validation within the reference population was used to select the optimum PCR model that minimizes mean squared error. Pre-corrected average daily milk, fat and protein yields of 1609 first lactation Holstein heifers, from Ireland, UK, the Netherlands and Sweden, which were genotyped with 50 k SNPs, were analysed. Each testing subset included animals from only one country, or from only one selection line for the UK. Results In general, accuracies of GREML and PCR were similar but GREML slightly outperformed PCR. Inclusion of genotyping information of validation animals into model training (semi-supervised PCR), did not result in more accurate genomic predictions. The highest achievable PCR accuracies were obtained across a wide range of numbers of PC fitted in the regression (from one to more than 1000), across test populations and traits. Using cross-validation within the reference population to derive the number of PC, yielded substantially lower accuracies than the highest achievable accuracies obtained across all possible numbers of PC. Conclusions On average, PCR performed only slightly less well than GREML. When the optimal number of PC was determined based on realized accuracy in the testing population, PCR showed a higher potential in terms of achievable accuracy that was not capitalized when PC selection was based on cross-validation. A standard approach for selecting the optimal set of PC in PCR remains a challenge.
A quantitative trait locus on Bos taurus autosome 17 explains a large proportion of the genetic variation in de novo synthesized milk fatty acids
Duchemin, S.I. ; Visker, M.H.P.W. ; Arendonk, J.A.M. van; Bovenhuis, H. - \ 2014
Journal of Dairy Science 97 (2014)11. - ISSN 0022-0302 - p. 7276 - 7285.
selection signatures - genotype imputation - dairy-cattle - protein - dgat1 - association - population - parameters - lactation - summer
A genomic region associated with milk fatty acid (FA) composition has been detected on Bos taurus autosome (BTA)17 based on 50,000 (50K) single nucleotide polymorphism (SNP) genotypes. The aim of our study was to fine-map BTA17 with imputed 777,000 (777K) SNP genotypes to identify candidate genes associated with milk FA composition. Phenotypes consisted of gas chromatography measurements of 14 FA based on winter and summer milk samples. Phenotypes and genotypes were available on 1,640 animals in winter milk, and on 1,581 animals in summer milk samples. Single-SNP analyses showed that several SNP in a region located between 29.0 and 34.0 Mbp were in strong association with C6:0, C8:0, and C10:0. This region was further characterized based on haplotypes. In summer milk samples, for example, these haplotypes explained almost 10% of the genetic variance in C6:0, 9% in C8:0, 3.5% in C10:0, 1.8% in C12:0, and 0.9% in C14:0. Two groups of haplotypes with distinct predicted effects could be defined, suggesting the presence of one causal variant. Predicted haplotype effects tended to increase from C6:0 to C14:0; however, the proportion of genetic variance explained by the haplotypes tended to decrease from C6:0 to C14:0. This is an indication that the quantitative trait locus (QTL) region is involved either in the elongation process or in early termination of de novo synthesized FA. Although many genes are present in this QTL region, most of these genes on BTA17 have not been characterized yet. The strongest association was found close to the progesterone receptor membrane component 2 (PGRMC2) gene, which has not yet been associated with milk FA composition. Therefore, no clear candidate gene associated with milk FA composition could be identified for this QTL.
Genomic prediction of breeding values using previously estimated SNP variances
Calus, M.P.L. ; Schrooten, C. ; Veerkamp, R.F. - \ 2014
Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X - 13 p.
preconditioned conjugate-gradient - dairy-cattle - genotyping strategies - genetic evaluations - complex traits - selection - accuracy - information - preselection - population
Background Genomic prediction requires estimation of variances of effects of single nucleotide polymorphisms (SNPs), which is computationally demanding, and uses these variances for prediction. We have developed models with separate estimation of SNP variances, which can be applied infrequently, and genomic prediction, which can be applied routinely. Methods SNP variances were estimated with Bayes Stochastic Search Variable Selection (BSSVS) and BayesC. Genome-enhanced breeding values (GEBV) were estimated with RR-BLUP (ridge regression best linear unbiased prediction), using either variances obtained from BSSVS (BLUP-SSVS) or BayesC (BLUP-C), or assuming equal variances for each SNP. Datasets used to estimate SNP variances comprised (1) all animals, (2) 50% random animals (RAN50), (3) 50% best animals (TOP50), or (4) 50% worst animals (BOT50). Traits analysed were protein yield, udder depth, somatic cell score, interval between first and last insemination, direct longevity, and longevity including information from predictors. Results BLUP-SSVS and BLUP-C yielded similar GEBV as the equivalent Bayesian models that simultaneously estimated SNP variances. Reliabilities of these GEBV were consistently higher than from RR-BLUP, although only significantly for direct longevity. Across scenarios that used data subsets to estimate GEBV, observed reliabilities were generally higher for TOP50 than for RAN50, and much higher than for BOT50. Reliabilities of TOP50 were higher because the training data contained more ancestors of selection candidates. Using estimated SNP variances based on random or non-random subsets of the data, while using all data to estimate GEBV, did not affect reliabilities of the BLUP models. A convergence criterion of 10-8 instead of 10-10 for BLUP models yielded similar GEBV, while the required number of iterations decreased by 71 to 90%. Including a separate polygenic effect consistently improved reliabilities of the GEBV, but also substantially increased the required number of iterations to reach convergence with RR-BLUP. SNP variances converged faster for BayesC than for BSSVS. Conclusions Combining Bayesian variable selection models to re-estimate SNP variances and BLUP models that use those SNP variances, yields GEBV that are similar to those from full Bayesian models. Moreover, these combined models yield predictions with higher reliability and less bias than the commonly used RR-BLUP model.
Phosphorylation of as1-casein is regulated by different genes
Bijl, E. ; Valenberg, H.J.F. van; Huppertz, T. ; Hooijdonk, A.C.M. van; Bovenhuis, H. - \ 2014
Journal of Dairy Science 97 (2014)11. - ISSN 0022-0302 - p. 7240 - 7246.
milk protein-composition - mammary-gland - dairy-cattle - casein kinase - bovine-milk - association - dgat1 - polymorphism - specificity - cows
Casein phosphorylation is a posttranslational modification catalyzed by kinase enzymes that attach phosphate groups to specific AA in the protein sequence. This modification is one of the key factors responsible for the stabilization of calcium phosphate nanoclusters in casein micelles and for the internal structure of the casein micelles. aS1-Casein (as1-CN) is of special interest because it constitutes up to 40% of the total casein fraction in milk, and it has 2 common phosphorylation states, with 8 (aS1-CN-8P) and 9 (aS1-CN-9P) phosphorylated serine residues. Factors affecting this variation in the degree of phosphorylation are not currently known. The objective of this research was to determine the genetic background of aS1-CN-8P and aS1-CN-9P. The genetic and phenotypic correlation between aS1-CN-8P and aS1-CN-9P was low (0.18 and 0.19, respectively). This low genetic correlation suggests a different genetic background. These differences were further investigated by means of a genome-wide association study, which showed that both aS1-CN-8P and aS1-CN-9P were affected by a region on Bos taurus autosome (BTA) 6, but only aS1-CN-8P was affected by a region on BTA11 that contains the gene that encodes for ß-lactoglobulin (ß-LG), and only aS1-CN-9P was affected by a region on BTA14 that contains the diacylglycerol acyltransferase 1 (DGAT1) gene. Estimated effects of ß-LG protein genotypes showed that only aS1-CN-8P was associated with the ß-LG A/B polymorphism (g.1772G>A and g.3054C>T); the AA genotype of ß-LG was associated with a lower concentration of aS1-CN-8P (-0.32% wt/wt) than the BB genotype (+0.41% wt/wt). Estimated effects of DGAT1 K232A genotypes showed that only aS1-CN-9P was associated with the DGAT1 gene polymorphism; DGAT1 AA genotype was associated with a higher aS1-CN-9P concentration (+0.53% wt/wt) than the DGAT1 KK genotype (-0.44% wt/wt). The results give insight in phosphorylation of aS1-CN-8P and aS1-CN-9P, which seem to be regulated by a different set of genes.
Sire evaluation for total number born in pigs using a genomic reaction norms approach
Silva, F.F. ; Mulder, H.A. ; Knol, E.F. ; Lopes, M.S. ; Guimaraes, S.E.F. ; Lopes, P.S. ; Mathur, P.K. ; Viana, J.M.S. ; Bastiaansen, J.W.M. - \ 2014
Journal of Animal Science 92 (2014)9. - ISSN 0021-8812 - p. 3825 - 3834.
environment interaction - dairy-cattle - genetic-parameters - production traits - genotype - models - pedigree - matrices - merit - milk
In the era of genome-wide selection (GWS), genotype-by-environment (G×E) interactions can be studied using genomic information, thus enabling the estimation of SNP marker effects and the prediction of genomic estimated breeding values (GEBVs) for young candidates for selection in different environments. Although G×E studies in pigs are scarce, the use of artificial insemination has enabled the distribution of genetic material from sires across multiple environments. Given the relevance of reproductive traits such as the total number born (TNB) and the variation in environmental conditions encountered by commercial dams, understanding G×E interactions can be essential to choose the best sires for different environments. The present work proposes a two-step reaction norm approach for G×E analysis using genomic information. The first step provided estimates of environmental effects (herd-year-season - HYS), and the second step provided estimates of the intercept and slope for the TNB across different HYS levels, obtained from the first step, using a random regression model. In both steps, pedigree (A) and genomic (G) relationship matrices were considered. The genetic parameters (variance components, h2 and genetic correlations) were very similar when estimated using the A and G relationship matrices. The reaction norm graphs showed considerable differences in environmental sensitivity between sires, indicating a reranking of sires in terms of genetic merit across the HYS levels. Based on the G matrix analysis, SNP by environment interactions were observed. For some SNPs, the effects increased at increasing HYS levels, while for others, the effects decreased at increasing HYS levels or showed no changes between HYS levels. Cross-validation analysis demonstrated better performance of the genomic approach with respect to traditional pedigrees for both the G×E and standard models. The genomic reaction norm model resulted in an accuracy of GEBVs for “juvenile” boars varying from 0.14 to 0.44 across different HYS levels, while the accuracy of the standard genomic prediction model, without reaction norms, varied from 0.09 to 0.28. These results show that it is important and feasible to consider G×E interactions in evaluations of sires using genomic prediction models and that genomic information can increase the accuracy of selection across environments.
Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle
Daetwyler, H.D. ; Capitan, A. ; Pausch, H. ; Stothard, P. ; Binsbergen, R. van; Brondum, R.F. ; Liao, X. ; Djari, A. ; Rodriguez, S.C. ; Grohs, C. ; Esquerré, D. ; Bouchez, O. ; Rossignol, M.N. ; Klopp, C. ; Rocha, D. ; Fritz, S. ; Eggen, A. ; Bowman, P.J. ; Coote, D. ; Chamberlain, A.J. ; Anderson, C.L. ; Tassel, C.P. ; Hulsegge, B. ; Goddard, M.E. ; Guldbrandsten, B. ; Lund, M.S. ; Veerkamp, R.F. ; Boichard, D.A. ; Fries, R. ; Hayes, B.J. - \ 2014
Nature Genetics 46 (2014). - ISSN 1061-4036 - p. 858 - 865.
boophilus-microplus resistance - mitotic chromosomes - genotype imputation - holstein calves - dairy-cattle - milk-yield - bos-taurus - condensin - mutations - gene
The 1000 bull genomes project supports the goal of accelerating the rates of genetic gain in domestic cattle while at the same time considering animal health and welfare by providing the annotated sequence variants and genotypes of key ancestor bulls. In the first phase of the 1000 bull genomes project, we sequenced the whole genomes of 234 cattle to an average of 8.3-fold coverage. This sequencing includes data for 129 individuals from the global Holstein-Friesian population, 43 individuals from the Fleckvieh breed and 15 individuals from the Jersey breed. We identified a total of 28.3 million variants, with an average of 1.44 heterozygous sites per kilobase for each individual. We demonstrate the use of this database in identifying a recessive mutation underlying embryonic death and a dominant mutation underlying lethal chrondrodysplasia. We also performed genome-wide association studies for milk production and curly coat, using imputed sequence variants, and identified variants associated with these traits in cattle.
Genetic parameters across lactation for feed intake, fat and protein corrected milk, and live weight in first parity Holstein cattle
Manzanilla Pech, C.I.V. ; Veerkamp, R.F. ; Calus, M.P.L. ; Zom, R.L.G. ; Knegsel, A. van; Pryce, J.E. ; Haas, Y. de - \ 2014
Journal of Dairy Science 97 (2014)9. - ISSN 0022-0302 - p. 5851 - 5862.
body condition score - dry-matter intake - random regression-models - negative-energy balance - linear type traits - dairy-cattle - live weight - covariance functions - cows - efficiency
Breeding values for dry matter intake (DMI) are important to optimize dairy cattle breeding goals for feed efficiency. However, generally, only small data sets are available for feed intake, due to the cost and difficulty of measuring DMI, which makes understanding the genetic associations between traits across lactation difficult, let alone the possibility for selection of breeding animals. However, estimating national breeding values through cheaper and more easily measured correlated traits, such as milk yield and liveweight (LW), could be a first step to predict DMI. Combining DMI data across historical nutritional experiments might help to expand the data sets. Therefore, the objective was to estimate genetic parameters for DMI, fat- and protein-corrected milk (FPCM) yield, and LW across the entire first lactation using a relatively large data set combining experimental data across the Netherlands. A total of 30,483 weekly records for DMI, 49,977 for FPCM yield, and 31,956 for LW were available from 2,283 Dutch Holstein-Friesian first-parity cows between 1990 and 2011. Heritabilities, covariance components, and genetic correlations were estimated using a multivariate random regression model. The model included an effect for year-season of calving, and polynomials for age of cow at calving and days in milk (DIM). The random effects were experimental treatment, year-month of measurement, and the additive genetic, permanent environmental, and residual term. Additive genetic and permanent environmental effects were modeled using a third-order orthogonal polynomial. Estimated heritabilities ranged from 0.21 to 0.40 for DMI, from 0.20 to 0.43 for FPCM yield, and from 0.25 to 0.48 for LW across DIM. Genetic correlations between DMI at different DIM were relatively low during early and late lactation, compared with mid lactation. The genetic correlations between DMI and FPCM yield varied across DIM. This correlation was negative (up to -0.5) between FPCM yield in early lactation and DMI across the entire lactation, but highly positive (above 0.8) when both traits were in mid lactation. The correlation between DMI and LW was 0.6 during early lactation, but decreased to 0.4 during mid lactation. The highest correlations between FPCM yield and LW (0.3–0.5) were estimated during mid lactation. However, the genetic correlations between DMI and either FPCM yield or LW were not symmetric across DIM, and differed depending on which trait was measured first. The results of our study are useful to understand the genetic relationship of DMI, FPCM yield, and LW on specific days across lactation.
Evaluation of measures of correctness of genotype imputation in the context of genomic prediction: a review of livestock applications
Calus, M.P.L. ; Bouwman, A.C. ; Hickey, J.M. ; Veerkamp, R.F. ; Mulder, H.A. - \ 2014
Animal 8 (2014)11. - ISSN 1751-7311 - p. 1743 - 1753.
density marker panels - nucleotide polymorphism genotypes - dutch holstein cattle - dairy-cattle - snp genotypes - jersey cattle - beef-cattle - accuracy - populations - selection
In livestock, many studies have reported the results of imputation to 50k single nucleotide polymorphism (SNP) genotypes for animals that are genotyped with low-density SNP panels. The objective of this paper is to review different measures of correctness of imputation, and to evaluate their utility depending on the purpose of the imputed genotypes. Across studies, imputation accuracy, computed as the correlation between true and imputed genotypes, and imputation error rates, that counts the number of incorrectly imputed alleles, are commonly used measures of imputation correctness. Based on the nature of both measures and results reported in the literature, imputation accuracy appears to be a more useful measure of the correctness of imputation than imputation error rates, because imputation accuracy does not depend on minor allele frequency (MAF), whereas imputation error rate depends on MAF. Therefore imputation accuracy can be better compared across loci with different MAF. Imputation accuracy depends on the ability of identifying the correct haplotype of a SNP, but many other factors have been identified as well, including the number of genotyped immediate ancestors, the number of animals with genotypes at the high-density panel, the SNP density on the low- and high-density panel, the MAF of the imputed SNP and whether imputed SNP are located at the end of a chromosome or not. Some of these factors directly contribute to the linkage disequilibrium between imputed SNP and SNP on the low-density panel. When imputation accuracy is assessed as a predictor for the accuracy of subsequent genomic prediction, we recommend that: (1) individual-specific imputation accuracies should be used that are computed after centring and scaling both true and imputed genotypes; and (2) imputation of gene dosage is preferred over imputation of the most likely genotype, as this increases accuracy and reduces bias of the imputed genotypes and the subsequent genomic predictions.
Short communication : Validation of genomic breeding value predictions for feed intake and feed efficiency traits
Pryce, J.E. ; Wales, W.J. ; Haas, Y. de; Veerkamp, R.F. ; Hayes, B.J. ; Coffey, M.P. ; Marett, L.C. ; Bornhill, J.B. ; Gonzalez-Recio, O. - \ 2014
Journal of Dairy Science 97 (2014)1. - ISSN 0022-0302 - p. 537 - 542.
dairy-cattle - data sets - accuracy
Validating genomic prediction equations in independent populations is an important part of evaluating genomic selection. Published genomic predictions from 2 studies on (1) residual feed intake and (2) dry matter intake (DMI) were validated in a cohort of 78 multiparous Holsteins from Australia. The mean realized accuracy of genomic prediction for residual feed intake was 0.27 when the reference population included phenotypes from 939 New Zealand and 843 Australian growing heifers (aged 5–8 mo) genotyped on high density (770k) single nucleotide polymorphism chips. The 90% bootstrapped confidence interval of this estimate was between 0.16 and 0.36. The mean realized accuracy was slightly lower (0.25) when the reference population comprised only Australian growing heifers. Higher realized accuracies were achieved for DMI in the same validation population and using a multicountry model that included 958 lactating cows from the Netherlands and United Kingdom in addition to 843 growing heifers from Australia. The multicountry analysis for DMI generated 3 sets of genomic predictions for validation animals, one on each country scale. The highest mean accuracy (0.72) was obtained when the genomic breeding values were expressed on the Dutch scale. Although the validation population used in this study was small (n=78), the results illustrate that genomic selection for DMI and residual feed intake is feasible. Multicountry collaboration in the area of dairy cow feed efficiency is the evident pathway to achieving reasonable genomic prediction accuracies for these valuable traits.
Fine mapping of a quantitative trait locus for bovine milk fat composition on Bos taurus autosome 19
Bouwman, A.C. ; Visker, M.H.P.W. ; Arendonk, J.A.M. van; Bovenhuis, H. - \ 2014
Journal of Dairy Science 97 (2014)2. - ISSN 0022-0302 - p. 1139 - 1149.
genome-wide association - stearoyl-coa desaturase - dairy-cattle - acid-composition - genotype imputation - genetic-parameters - missense mutation - fasn gene - polymorphisms - yield
A major quantitative trait locus (QTL) for milk fat content and fatty acids in both milk and adipose tissue has been detected on Bos taurus autosome 19 (BTA19) in several cattle breeds. The objective of this study was to refine the location of the QTL on BTA19 for bovine milk fat composition using a denser set of markers. Opportunities for fine mapping were provided by imputation from 50,000 genotyped single nucleotide polymorphisms (SNP) toward a high-density SNP panel with up to 777,000 SNP. The QTL region was narrowed down to a linkage disequilibrium block formed by 22 SNP covering 85,007 bp, from 51,303,322 to 51,388,329 bp on BTA19. This linkage disequilibrium block contained 2 genes: coiled-coil domain containing 57 (CCDC57) and fatty acid synthase (FASN). The gene CCDC57 is minimally characterized and has not been associated with bovine milk fat previously, but is expressed in the mammary gland. The gene FASN has been associated with bovine milk fat and fat in adipose tissue before. This gene is a likely candidate for the QTL on BTA19 because of its involvement in de novo fat synthesis. Future studies using sequence data of both CCDC57 and FASN, and eventually functional studies, will have to be pursued to assign the causal variant(s).
Technical note: Evaluation of an ear-attached movement sensor to record cow feeding behavior and activity
Bikker, J.P. ; Laar, H. van; Rump, P. ; Doorenbos, J. ; Meurs, K. van; Griffioen, G.M. ; Dijkstra, J. - \ 2014
Journal of Dairy Science 97 (2014)5. - ISSN 0022-0302 - p. 2974 - 2979.
dairy-cattle - monitoring rumination - automatic system - coefficient - agreement - ovulation - time
The ability to monitor dairy cow feeding behavior and activity could improve dairy herd management. A 3-dimensional accelerometer (SensOor; Agis Automatisering BV, Harmelen, the Netherlands) has been developed that can be attached to ear identification tags. Based on the principle that behavior can be identified by ear movements, a proprietary model classifies sensor data as “ruminating,” “eating,” “resting,” or “active.” The objective of the study was to evaluate this sensor on accuracy and precision. First, a pilot evaluation of agreement between 2 independent observers, recording behavior from 3 cows for a period of approximately 9 h each, was performed. Second, to evaluate the sensor, the behavior of 15 cows was monitored both visually (VIS) and with the sensor (SENS), with approximately 20 h of registration per cow, evenly distributed over a 24-h period, excluding milking. Cows were chosen from groups of animals in different lactation stages and parities. Each minute of SENS and VIS data was classified into 1 of 9 categories (8 behaviors and 1 transition behavior) and summarized into 4 behavioral groups, namely ruminating, eating, resting, or active, which were analyzed by calculating kappa (¿) values. For the pilot evaluation, a high level of agreement between observers was obtained, with ¿ values of =0.96 for all behavioral categories, indicating that visual observation provides a good standard. For the second trial, relationships between SENS and VIS were studied by ¿ values on a minute basis and Pearson correlation and concordance correlation coefficient analysis on behavior expressed as percentage of total registration time. Times spent ruminating, eating, resting, and active were 42.6, 15.9, 31.6, and 9.9% (SENS) respectively, and 42.1, 13.0, 30.0, and 14.9% (VIS), respectively. Overall ¿ for the comparison of SENS and VIS was substantial (0.78), with ¿ values of 0.85, 0.77, 0.86, and 0.47 for “ruminating,” “eating,” “resting,” and “active,” respectively. Pearson correlation and concordance correlation coefficients between SENS and VIS for “ruminating,” “eating,” “resting,” and “active” were 0.93, 0.88, 0.98, and 0.73, and 0.93, 0.75, 0.97, and 0.35, respectively. In conclusion, the results provide strong evidence that the present ear sensor technology can be used to monitor ruminating and resting behavior of freestall-housed dairy cattle. Our results also suggest that this technology shows promise for monitoring eating behavior, whereas more work is needed to determine its suitability to monitor activity of dairy cattle.
Automatic lameness detection based on consecutive 3D-video recordings
Hertem, T. van; Viazzi, S. ; Steensels, M. ; Maltz, E. ; Antler, A. ; Alchanatis, V. ; Schlageter-Tello, A. ; Lokhorst, C. ; Romanini, C.E.B. ; Bahr, C. ; Berckmans, D. ; Halachmi, I. - \ 2014
Biosystems Engineering 119 (2014). - ISSN 1537-5110 - p. 108 - 116.
dairy-cattle - risk-factors - milk-yield - clinical lameness - gait assessment - scoring system - back posture - cows - locomotion - herds
Manual locomotion scoring for lameness detection is a time-consuming and subjective procedure. Therefore, the objective of this study is to optimise the classification output of a computer vision based algorithm for automated lameness scoring. Cow gait recordings were made during four consecutive night-time milking sessions on an Israeli dairy farm, using a 3D-camera. A live on-the-spot assessed 5-point locomotion score was the reference for the automatic lameness score evaluation. A dataset of 186 cows with four automatic lameness scores and four live locomotion score repetitions was used for testing three different classification methods. The analysis of the automatic scores as independent observations led to a correct classification rate of 53.0% on a 5-point level scale. A multinomial logistic regression model based on four individual consecutive measures obtained a correct classification rate of 60.2%. When allowing a 1 unit error on the 5-point level scale, a correct classification rate of 90.9% was obtained. Strict binary classification to Lame vs. Not-Lame categories reached 81.2% correct classification rate. The use of cow individual consecutive measurements improved the correct classification rate of an automatic lameness detection system.
Variation among sows in response to porcine reproductive and respiratory syndrome
Rashidi, H. ; Mulder, H.A. ; Mathur, P.K. ; Knol, E.F. ; Arendonk, J.A.M. van - \ 2014
Journal of Animal Science 92 (2014)1. - ISSN 0021-8812 - p. 95 - 105.
syndrome virus - syndrome prrs - dairy-cattle - tolerance - resistance - performance - infection - selection - genotype - models
Porcine reproductive and respiratory syndrome (PRRS) is a viral disease with negative impacts on reproduction of sows. Genetic selection to improve the response of sows to PRRS could be an approach to control the disease. Determining sow response to PRRS requires knowing pathogen burden and sow performance. In practice, though, records of pathogen burden are unavailable. We develop a statistical method to distinguish healthy and disease phases and to develop a method to quantify sows’ responses to PRRS without having individual pathogen burden. We analyzed 10,910 sows with 57,135 repeated records of reproduction performance. Disease phases were recognized as strong deviation of herd-year-week estimates for reproduction traits using two methods: Method 1 used raw weekly averages of the herd; Method 2 used a linear model with fixed effects for seasonality, parity, and year, and random effects for herd-year-week and sow. The variation of sows in response to PRRS was quantified using 2 models on the traits number of piglets born alive (NBA) and number of piglets born dead (LOSS): 1) bivariate model considering the trait in healthy and disease phases as different traits, and 2) reaction norm model modeling the response of sows as a linear regression of the trait on herd-year-week estimates of NBA. The linear model for NBA had the highest sensitivity (78%) for disease phases. Residual variances of both were more than doubled in the disease phase compared with the healthy phase. Trait correlations between healthy and disease phases deviated from unity (0.57 ± 0.13 – 0.87 ± 0.18). In the bivariate model, repeatabilities were lower in disease phase compared with healthy phase (0.07 ± 0.027 and 0.16 ± 0.005 for NBA; 0.07 ± 0.027 and 0.09 ± 0.004 for LOSS). The reaction norm model fitted the data better than the bivariate model based on Akaike’s information criterion, and had also higher predictive ability in disease phase based on cross validation. Our results show that the linear model is a practical method to distinguish between healthy and disease phases in farm data. We showed that there is variation among sows in response to PRRS, implying possibilities for selection, and the reaction norm model is a good model to study the response of animals toward diseases.
Cystic ovaries in intermittently-suckled sows: follicle growth and endocrine profiles
Gerritsen, R. ; Laurenssen, B.F.A. ; Hazeleger, W. ; Langendijk, P. ; Kemp, B. ; Soede, N.M. - \ 2014
Reproduction Fertility and Development 26 (2014). - ISSN 1031-3613 - p. 462 - 468.
luteinizing-hormone - reproductive-performance - estrous behavior - dairy-cattle - lactation - estrus - pigs - estradiol - ovulation - cortisol
This paper presents follicle development and hormone profiles for sows with normal ovulation or cystic follicles during an intermittent-suckling (IS) regime that started at Day 14 of lactation. Sows were subjected to separation from their piglets during blocks of 6 h or 12 h. In total, 8 out of 52 sows developed cystic follicles; either full cystic ovaries (n = 6) or partial ovulation (n = 2). Increase in follicle size of these sows was similar to that of normal ovulating sows until pre-ovulatory size at Day 5 after the start of separation, but from then on became larger (P <0.05). LH surge was smaller or absent in sows that developed (partially) cystic ovaries (0.4 ± 0.1 vs 3.6 ± 0.3 ng mL–1; P <0.01). Peak levels of oestradiol (E2) were similar but high E2 levels persisted in sows that developed (partly) cystic ovaries and duration of oestrus tended to be longer. The risk of developing (partly) cystic ovaries was higher when IS occurred in blocks of 6 h versus 12 h (33 vs 10%). In conclusion, the appearance of cystic ovaries at approximately Day 20 of ongoing lactation was related to an insufficient LH surge, as is also the case in non-lactating sows.
Genotype-by-environment interaction of growth traits in rainbow trout (Oncorhynchus mykiss): A continental scale study.
Sae-Lim, P. ; Kause, A. ; Mulder, H.A. ; Martin, K.E. ; Barfoot, A.J. ; Arendonk, J.A.M. van; Komen, J. - \ 2013
Journal of Animal Science 91 (2013)12. - ISSN 0021-8812 - p. 5572 - 5581.
plant-based diets - body-weight - parental allocation - genetic-parameters - breeding programs - water temperature - sexual-maturity - dairy-cattle - selection - variance
Rainbow trout is a globally important fish species for aquaculture. However, fish for most farms worldwide are produced by only a few breeding companies. Selection based solely on fish performance recorded at a nucleus may lead to lower-than-expected genetic gains in other production environments when genotype-by-environment (G × E) interaction exists. The aim was to quantify the magnitude of G × E interaction of growth traits (tagging weight; BWT, harvest weight; BWH, and growth rate; TGC) measured across 4 environments, located in 3 different continents, by estimating genetic correlations between environments. A total of 100 families, of at least 25 in size, were produced from the mating 58 sires and 100 dams. In total, 13,806 offspring were reared at the nucleus (selection environment) in Washington State (NUC) and in 3 other environments: a recirculating aquaculture system in Freshwater Institute (FI), West Virginia; a high-altitude farm in Peru (PE), and a cold-water farm in Germany (GER). To account for selection bias due to selective mortality, a multitrait multienvironment animal mixed model was applied to analyze the performance data in different environments as different traits. Genetic correlation (rg) of a trait measured in different environments and rg of different traits measured in different environments were estimated. The results show that heterogeneity of additive genetic variances was mainly found for BWH measured in FI and PE. Additive genetic coefficient of variation for BWH in NUC, FI, PE, and GER were 7.63, 8.36, 8.64, and 9.75, respectively. Genetic correlations between the same trait in different environments were low, indicating strong reranking (BWT: rg = 0.15 to 0.37, BWH: rg = 0.19 to 0.48, TGC: rg = 0.31 to 0.36) across environments. The rg between BWT in NUC and BWH in both FI (0.31) and GER (0.36) were positive, which was also found between BWT in NUC and TGC in both FI (0.10) and GER (0.20). However, rg were negative between BWT in NUC and both BWH (–0.06) and TGC (–0.20) in PE. Correction for selection bias resulted in higher additive genetic variances. In conclusion, strong G × E interaction was found for BWT, BWH, and TGC. Accounting for G × E interaction in the breeding program, either by using sib information from testing stations or environment-specific breeding programs, would increase genetic gains for environments that differ significantly from NUC.
Integration of epidemiology into the genetic analysis of mastitis in Swedisch Holstein
Windig, J.J. ; Urioste, J.I. ; Strandberg, E. - \ 2013
Journal of Dairy Science 96 (2013)4. - ISSN 0022-0302 - p. 2617 - 2626.
somatic-cell count - 1st 3 lactations - random regression-model - clinical mastitis - dairy-cattle - environment interaction - canadian holsteins - traits - score - parameters
Heritability of mastitis (and diseases in general) tends to be low. One possible cause is that no clear distinction can be made between resistant and nonresistant animals, because healthy animals include animals that have not been exposed to pathogens and resistant animals. To account for this, we quantified the prevalence of clinical mastitis (CM) and subclinical mastitis (SCM) in 2,069 Swedish Holstein herds as a measure of exposure. Herd prevalence averaged 26.5% for SCM and 6.4% for CM; 61% of the first lactations of 177,309 cows were classified as having at least one case of SCM and 10% as having CM. In a reaction norm approach, heritability of (S)CM was quantified as a function of herd prevalence of (S)CM. The best-fitting model was a second-order polynomial of first-lactation cow SCM as a function of herd prevalence SCM, and a first-order (linear) polynomial of first-lactation cow CM as a function of CM herd prevalence. Heritability for SCM ranged from 0.069 to 0.105 and for CM from 0.016 to 0.032. For both, we found no clear effect of herd prevalence on their heritability. Genetic correlations within traits across herd prevalences were all greater than 0.92. Whether relationships among prevalence, exposure, disease, and genetics were as expected is a matter of discussion, but reaction norm analyses may be a valuable tool for epidemiological genetics.
Unraveling the genetic architecture of environmental variance of somatic cell score using high-density single nucleotide polymorphism and cow data from experimental farms
Mulder, H.A. ; Crump, R.E. ; Calus, M.P.L. ; Veerkamp, R.F. - \ 2013
Journal of Dairy Science 96 (2013)11. - ISSN 0022-0302 - p. 7306 - 7317.
quantitative trait loci - affecting clinical mastitis - generalized linear-models - wide association analysis - dairy-cattle - holstein cows - conformation traits - residual variance - breeding values - health traits
In recent years, it has been shown that not only is the phenotype under genetic control, but also the environmental variance. Very little, however, is known about the genetic architecture of environmental variance. The main objective of this study was to unravel the genetic architecture of the mean and environmental variance of somatic cell score (SCS) by identifying genome-wide associations for mean and environmental variance of SCS in dairy cows and by quantifying the accuracy of genome-wide breeding values. Somatic cell score was used because previous research has shown that the environmental variance of SCS is partly under genetic control and reduction of the variance of SCS by selection is desirable. In this study, we used 37,590 single nucleotide polymorphism (SNP) genotypes and 46,353 test-day records of 1,642 cows at experimental research farms in 4 countries in Europe. We used a genomic relationship matrix in a double hierarchical generalized linear model to estimate genome-wide breeding values and genetic parameters. The estimated mean and environmental variance per cow was used in a Bayesian multi-locus model to identify SNP associated with either the mean or the environmental variance of SCS. Based on the obtained accuracy of genome-wide breeding values, 985 and 541 independent chromosome segments affecting the mean and environmental variance of SCS, respectively, were identified. Using a genomic relationship matrix increased the accuracy of breeding values relative to using a pedigree relationship matrix. In total, 43 SNP were significantly associated with either the mean (22) or the environmental variance of SCS (21). The SNP with the highest Bayes factor was on chromosome 9 (Hapmap31053-BTA-111664) explaining approximately 3% of the genetic variance of the environmental variance of SCS. Other significant SNP explained less than 1% of the genetic variance. It can be concluded that fewer genomic regions affect the environmental variance of SCS than the mean of SCS, but genes with large effects seem to be absent for both traits.