Nordic Cattle Genetic Evaluation

Århus, Denmark

Nordic Cattle Genetic Evaluation

Århus, Denmark

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Sahana G.,University of Aarhus | Nielsen U.S.,Knowledge Center for Agriculture | Aamand G.P.,Nordic Cattle Genetic Evaluation | Lund M.S.,University of Aarhus | Guldbrandtsen B.,University of Aarhus
PLoS ONE | Year: 2013

Using genomic data, lethal recessives may be discovered from haplotypes that are common in the population but never occur in the homozygote state in live animals. This approach only requires genotype data from phenotypically normal (i.e. live) individuals and not from the affected embryos that die. A total of 7,937 Nordic Holstein animals were genotyped with BovineSNP50 BeadChip and haplotypes including 25 consecutive markers were constructed and tested for absence of homozygotes states. We have identified 17 homozygote deficient haplotypes which could be loosely clustered into eight genomic regions harboring possible recessive lethal alleles. Effects of the identified haplotypes were estimated on two fertility traits: non-return rates and calving interval. Out of the eight identified genomic regions, six regions were confirmed as having an effect on fertility. The information can be used to avoid carrier-by-carrier mattings in practical animal breeding. Further, identification of causative genes/polymorphisms responsible for lethal effects will lead to accurate testing of the individuals carrying a lethal allele. © 2013 Sahana et al.


Su G.,University of Aarhus | Madsen P.,University of Aarhus | Nielsen U.S.,Danish Agricultural Advisory Service | Mantysaari E.A.,Mtt Agrifood Research Finland | And 3 more authors.
Journal of Dairy Science | Year: 2012

This study investigated the accuracy of direct genomic breeding values (DGV) using a genomic BLUP model, genomic enhanced breeding values (GEBV) using a one-step blending approach, and GEBV using a selection index blending approach for 15 traits of Nordic Red Cattle. The data comprised 6,631 bulls of which 4,408 bulls were genotyped using Illumina Bovine SNP50 BeadChip (Illumina, San Diego, CA). To validate reliability of genomic predictions, about 20% of the youngest genotyped bulls were taken as test data set. Deregressed proofs (DRP) were used as response variables for genomic predictions. Reliabilities of genomic predictions in the validation analyses were measured as squared correlations between DRP and genomic predictions corrected for reliability of DRP, based on the bulls in the test data sets. A set of weighting (scaling) factors was used to construct the combined relationship matrix among genotyped and nongenotyped bulls for one-step blending, and to scale DGV and its expected reliability in the selection index blending. Weighting (scaling) factors had a small influence on reliabilities of GEBV, but a large influence on the variation of GEBV. Based on the validation analyses, averaged over the 15 traits, the reliability of DGV for bulls without daughter records was 11.0 percentage points higher than the reliability of conventional pedigree index. Further gain of 0.9 percentage points was achieved by combining information from conventional pedigree index using the selection index blending, and gain of 1.3 percentage points was achieved by combining information of genotyped and nongenotyped bulls simultaneously applying the one-step blending. These results indicate that genomic selection can greatly improve the accuracy of preselection for young bulls in Nordic Red population, and the one-step blending approach is a good alternative to predict GEBV in practical genetic evaluation program. © 2012 American Dairy Science Association.


Su G.,University of Aarhus | Brondum R.F.,University of Aarhus | Ma P.,University of Aarhus | Ma P.,China Agricultural University | And 3 more authors.
Journal of Dairy Science | Year: 2012

This study investigated genomic prediction using medium-density (∼54,000; 54. K) and high-density marker panels (∼777,000; 777. K), based on data from Nordic Holstein and Red Dairy Cattle (RDC). The Holstein data comprised 4,539 progeny-tested bulls, and the RDC data 4,403 progeny-tested bulls. The data were divided into reference data and test data using October 1, 2001, as a cut-off date (birth date of the bulls). This resulted in about 25% genotyped bulls in the Holstein test data and 20% in the RDC test data. For each breed, 3 data sets of markers were used to predict breeding values: (1) 54. K data set with missing genotypes, (2) 54. K data set where missing genotypes were imputed, and (3) imputed high-density (HD) marker data set created by imputing the 54. K data to the HD data based on 557 bulls genotyped using a 777. K single nucleotide polymorphism chip in Holstein, and 706 bulls in RDC. Based on the 3 marker data sets, direct genomic breeding values (DGV) for protein, fertility, and udder health were predicted using a genomic BLUP model (GBLUP) and a Bayesian mixture model with 2 normal distributions. Reliability of DGV was measured as squared correlations between deregressed proofs (DRP) and DGV corrected for reliability of DRP. Unbiasedness was assessed by regression of DRP on DGV, based on the bulls in the test data sets. Averaged over the 3 traits, reliability of DGV based on the HD markers was 0.5% higher than that based on the 54. K data in Holstein, and 1.0% higher than that in RDC. In addition, the HD markers led to an improvement of unbiasedness of DGV. The Bayesian mixture model led to 0.5% higher reliability than the GBLUP model in Holstein, but not in RDC. Imputing missing genotypes in the 54. K marker data did not improve genomic predictions for most of the traits. © 2012 American Dairy Science Association.


Su G.,University of Aarhus | Guldbrandtsen B.,University of Aarhus | Aamand G.P.,Nordic Cattle Genetic Evaluation | Stranden I.,Mtt Agrifood Research Finland | Lund M.S.,University of Aarhus
Genetics Selection Evolution | Year: 2014

Background: Although the X chromosome is the second largest bovine chromosome, markers on the X chromosome are not used for genomic prediction in some countries and populations. In this study, we presented a method for computing genomic relationships using X chromosome markers, investigated the accuracy of imputation from a low density (7K) to the 54K SNP (single nucleotide polymorphism) panel, and compared the accuracy of genomic prediction with and without using X chromosome markers. Methods. The impact of considering X chromosome markers on prediction accuracy was assessed using data from Nordic Holstein bulls and different sets of SNPs: (a) the 54K SNPs for reference and test animals, (b) SNPs imputed from the 7K to the 54K SNP panel for test animals, (c) SNPs imputed from the 7K to the 54K panel for half of the reference animals, and (d) the 7K SNP panel for all animals. Beagle and Findhap were used for imputation. GBLUP (genomic best linear unbiased prediction) models with or without X chromosome markers and with or without a residual polygenic effect were used to predict genomic breeding values for 15 traits. Results: Averaged over the two imputation datasets, correlation coefficients between imputed and true genotypes for autosomal markers, pseudo-autosomal markers, and X-specific markers were 0.971, 0.831 and 0.935 when using Findhap, and 0.983, 0.856 and 0.937 when using Beagle. Estimated reliabilities of genomic predictions based on the imputed datasets using Findhap or Beagle were very close to those using the real 54K data. Genomic prediction using all markers gave slightly higher reliabilities than predictions without X chromosome markers. Based on our data which included only bulls, using a G matrix that accounted for sex-linked relationships did not improve prediction, compared with a G matrix that did not account for sex-linked relationships. A model that included a polygenic effect did not recover the loss of prediction accuracy from exclusion of X chromosome markers. Conclusions: The results from this study suggest that markers on the X chromosome contribute to accuracy of genomic predictions and should be used for routine genomic evaluation. © 2014 Su et al.; licensee BioMed Central Ltd.


Kadri N.K.,University of Aarhus | Sahana G.,University of Aarhus | Charlier C.,University of Liège | Iso-Touru T.,Mtt Agrifood Research Finland | And 10 more authors.
PLoS Genetics | Year: 2014

In dairy cattle, the widespread use of artificial insemination has resulted in increased selection intensity, which has led to spectacular increase in productivity. However, cow fertility has concomitantly severely declined. It is generally assumed that this reduction is primarily due to the negative energy balance of high-producing cows at the peak of lactation. We herein describe the fine-mapping of a major fertility QTL in Nordic Red cattle, and identify a 660-kb deletion encompassing four genes as the causative variant. We show that the deletion is a recessive embryonically lethal mutation. This probably results from the loss of RNASEH2B, which is known to cause embryonic death in mice. Despite its dramatic effect on fertility, 13%, 23% and 32% of the animals carry the deletion in Danish, Swedish and Finnish Red Cattle, respectively. To explain this, we searched for favorable effects on other traits and found that the deletion has strong positive effects on milk yield. This study demonstrates that embryonic lethal mutations account for a non-negligible fraction of the decline in fertility of domestic cattle, and that associated positive effects on milk yield may account for part of the negative genetic correlation. Our study adds to the evidence that structural variants contribute to animal phenotypic variation, and that balancing selection might be more common in livestock species than previously appreciated. © 2014 Kadri et al.


PubMed | University of Aarhus, Knowledge Center for Agriculture, U.S. Department of Agriculture and Nordic Cattle Genetic Evaluation
Type: Journal Article | Journal: Journal of dairy science | Year: 2015

The effect on prediction accuracy for Jersey genomic evaluations of Danish and US bulls from using a larger reference population was assessed. Each country contributed genotypes from 1,157 Jersey bulls to the reference population of the other. Data were separated into reference (US only, Danish only, and combined US-Danish) and validation (US only and Danish only) populations. Depending on trait (milk, fat, and protein yields and component percentages; productive life; somatic cell score; daughter pregnancy rate; 14 conformation traits; and net merit), the US reference population included 2,720 to 4,772 bulls and cows with traditional evaluations as of August 2009; the Danish reference population included 635 to 996 bulls. The US validation population included 442 to 712 bulls that gained a traditional evaluation between August 2009 and December 2013; the Danish validation population included 105 to 196 bulls with multitrait across-country evaluations on the US scale by December 2013. Genomic predicted transmitting abilities (GPTA) were calculated on the US scale using a selection index that combined direct genomic predictions with either traditional predicted transmitting ability for the reference population or traditional parent averages (PA) for the validation population and a traditional evaluation based only on genotyped animals. Reliability for GPTA was estimated from the reference population and August 2009 traditional PA and PA reliability. For prediction of December 2013 deregressed daughter deviations on the US scale, mean August 2009 GPTA reliability for Danish validation bulls was 0.10 higher when based on the combined US-Danish reference population than when the reference population included only Danish bulls; for US validation bulls, mean reliability increased by 0.02 when Danish bulls were added to the US reference population. Exchanging genotype data to increase the size of the reference population is an efficient approach to increasing the accuracy of genomic prediction when the reference population is small.


PubMed | University of Aarhus, Seges and Nordic Cattle Genetic Evaluation
Type: Journal Article | Journal: Journal of dairy science | Year: 2015

Including genotyped females in a reference population (RP) is an obvious way to increase the RP in genomic selection, especially for dairy breeds of limited population size. However, the incorporation of these females must be conducted cautiously because of the potential preferential treatment of the genotyped cows and lower reliabilities of phenotypes compared with the proven pseudo-phenotypes of bulls. Breeding organizations in Denmark, Finland, and Sweden have implemented a female-genotyping project with the possibility of genotyping entire herds using the low-density (LD) chip. In the present study, 5 scenarios for building an RP were investigated in the Nordic Jersey population: (1) bulls only, (2) bulls with females from the LD project, (3) bulls with females from the LD project plus non-LD project females genotyped before their first calving, (4) bulls with females from the LD project plus non-LD project females genotyped after their first calving, and (5) bulls with all genotyped females. The genomically enhanced breeding value (GEBV) was predicted for 8 traits in the Nordic total merit index through a genomic BLUP model using deregressed proof (DRP) as the response variable in all scenarios. In addition, (daughter) yield deviation and raw phenotypic data were studied as response variables for comparison with the DRP, using stature as a model trait. The validation population was formed using a cut-off birth year of 2005 based on the genotyped Nordic Jersey bulls with DRP. The average increment in reliability of the GEBV across the 8 traits investigated was 1.9 to 4.5 percentage points compared with using only bulls in the RP (scenario 1). The addition of all the genotyped females to the RP resulted in the highest gain in reliability (scenario 5), followed by scenario 3, scenario 2, and scenario 4. All scenarios led to inflated GEBV because the regression coefficients are less than 1. However, scenario 2 and scenario 3 led to less bias of genomic predictions than scenario 5, with regression coefficients showing less deviation from scenario 1. For the study on stature, the daughter yield deviation/daughter yield deviation performed slightly better than the DRP as the response variable in the genomic BLUP (GBLUP) model. Therefore, adding unselected females in the RP could significantly improve the reliabilities and tended to reduce the prediction bias compared with adding selectively genotyped females. Although the DRP has performed robustly so far, the use of raw data is recommended with a single-step model as an optimal solution for future genomic evaluations.


PubMed | University of Aarhus, Danish Agricultural Advisory Service, Nordic Cattle Genetic Evaluation, Mtt Agrifood Research Finland and University of Liège
Type: Journal Article | Journal: PLoS genetics | Year: 2014

In dairy cattle, the widespread use of artificial insemination has resulted in increased selection intensity, which has led to spectacular increase in productivity. However, cow fertility has concomitantly severely declined. It is generally assumed that this reduction is primarily due to the negative energy balance of high-producing cows at the peak of lactation. We herein describe the fine-mapping of a major fertility QTL in Nordic Red cattle, and identify a 660-kb deletion encompassing four genes as the causative variant. We show that the deletion is a recessive embryonically lethal mutation. This probably results from the loss of RNASEH2B, which is known to cause embryonic death in mice. Despite its dramatic effect on fertility, 13%, 23% and 32% of the animals carry the deletion in Danish, Swedish and Finnish Red Cattle, respectively. To explain this, we searched for favorable effects on other traits and found that the deletion has strong positive effects on milk yield. This study demonstrates that embryonic lethal mutations account for a non-negligible fraction of the decline in fertility of domestic cattle, and that associated positive effects on milk yield may account for part of the negative genetic correlation. Our study adds to the evidence that structural variants contribute to animal phenotypic variation, and that balancing selection might be more common in livestock species than previously appreciated.


PubMed | University of Aarhus, Knowledge Center for Agriculture and Nordic Cattle Genetic Evaluation
Type: Journal Article | Journal: PloS one | Year: 2013

Using genomic data, lethal recessives may be discovered from haplotypes that are common in the population but never occur in the homozygote state in live animals. This approach only requires genotype data from phenotypically normal (i.e. live) individuals and not from the affected embryos that die. A total of 7,937 Nordic Holstein animals were genotyped with BovineSNP50 BeadChip and haplotypes including 25 consecutive markers were constructed and tested for absence of homozygotes states. We have identified 17 homozygote deficient haplotypes which could be loosely clustered into eight genomic regions harboring possible recessive lethal alleles. Effects of the identified haplotypes were estimated on two fertility traits: non-return rates and calving interval. Out of the eight identified genomic regions, six regions were confirmed as having an effect on fertility. The information can be used to avoid carrier-by-carrier mattings in practical animal breeding. Further, identification of causative genes/polymorphisms responsible for lethal effects will lead to accurate testing of the individuals carrying a lethal allele.


PubMed | University of Aarhus and Nordic Cattle Genetic Evaluation
Type: Journal Article | Journal: Journal of dairy science | Year: 2016

Dairy cows milked in automatic milking systems (AMS) with more than 1 milking box may, as individuals, have a preference for specific milking boxes if allowed free choice. Estimates of quantitative genetic variation in behavioral traits of farmed animals have previously been reported, with estimates of heritability ranging widely. However, for the consistency of choice in dairy cows, almost no published estimates of heritability exist. The hypothesis for this study was that choice consistency is partly under additive genetic control and partly controlled by permanent environmental (animal) effects. The aims of this study were to obtain estimates of genetic and phenotypic parameters for choice consistency in dairy cows milked in AMS herds. Data were obtained from 5 commercial Danish herds (I-V) with 2 AMS milking boxes (A, B). Milking data were only from milkings where both the present and the previous milkings were coded as completed. This filter was used to fulfill a criterion of free-choice situation (713,772 milkings, 1,231 cows). The lactation was divided into 20 segments covering 15d each, from 5 to 305d in milk. Choice consistency scores were obtained as the fraction of milkings without change of box [i.e., 1.0 - (box change)] for each segment. Data were analyzed for one part of lactation at a time using a linear mixed model for first-parity cows alone and for all parities jointly. Choice consistency was found to be only weakly heritable (heritability=0.02 to 0.14) in first as well as in later parities, and having intermediate repeatability (repeatability coefficients=0.27 to 0.56). Heritability was especially low at early and late lactation states. These results indicate that consistency, which is itself an indication of repeated similar choices, is also repeatable as a trait observed over longer time periods. However, the genetic background seems to play a smaller role compared with that of the permanent animal effects, indicating that consistency could also be a learned behavior. We concluded that consistency in choices are quantifiable, but only under weak genetic control.

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