Gert Pedersen Aamand

Genomic prediction for Nordic Red Cattle using one-step and selection index blending

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.

Comparison of genomic predictions using medium-density (∼54,000) and high-density (∼777,000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations

This study investigated genomic prediction using medium-density (∼54,000; 54K) and high-density marker panels (∼777,000; 777K), 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) 54K data set with missing genotypes, (2) 54K data set where missing genotypes were imputed, and (3) imputed high-density (HD) marker data set created by imputing the 54K data to the HD data based on 557 bulls genotyped using a 777K 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 54K 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 54K marker data did not improve genomic predictions for most of the traits.

A 660-Kb Deletion with Antagonistic Effects on Fertility and Milk Production Segregates at High Frequency in Nordic Red Cattle: Additional Evidence for the Common Occurrence of Balancing Selection in Livestock

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.

Novel Harmful Recessive Haplotypes Identified for Fertility Traits in Nordic Holstein Cattle

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.

Single-step genomic evaluation using multitrait random regression model and test-day data

The objectives of this study were to evaluate the feasibility of use of the test-day (TD) single-step genomic BLUP (ssGBLUP) using phenotypic records of Nordic Red Dairy cows. The critical point in ssGBLUP is how genomically derived relationships (G) are integrated with population-based pedigree relationships (A) into a combined relationship matrix (H). Therefore, we also tested how different weights for genomic and pedigree relationships affect ssGBLUP, validation reliability, and validation regression coefficients. Deregressed proofs for 305-d milk, protein, and fat yields were used for a posteriori validation. The results showed that the use of phenotypic TD records in ssGBLUP is feasible. Moreover, the TD ssGBLUP model gave considerably higher validation reliabilities and validation regression coefficients than the TD model without genomic information. No significant differences were found in validation reliability between the different TD ssGBLUP models according to bootstrap confidence intervals. However, the degree of inflation in genomic enhanced breeding values is affected by the method used in construction of the H matrix. The results showed that ssGBLUP provides a good alternative to the currently used multi-step approach but there is a great need to find the best option to combine pedigree and genomic information in the genomic matrix.

Economic basis for the Nordic Total Merit Index.

Within a group of cooperating countries, all breeding animals are judged according to the same criteria if a joint breeding goal is applied in these countries. This makes it easier for dairy farmers to compare national and foreign elite bulls and may lead to more selection across borders. However, a joint breeding goal is only an advantage if the countries share the same production environment. In this study, we investigated whether the development of a joint breeding goal for each of the major dairy cattle breeds across Denmark, Finland, and Sweden would be an advantage compared with national breeding goals. For that purpose, economic values for all breeding goal traits in the 3 countries were derived, and estimated rank correlations between bulls selected for a national breeding goal and a joint breeding goal were compared. The economic values within country were derived by means of an objective bio-economic model, and the basic situation in each of the 3 production environments was based on an average dairy cattle herd with regard to production system, production level, and management strategy. The common Nordic economic values for each trait were calculated as the average of that specific trait in each of the 3 production environments. Balanced breeding goals were obtained in all situations because the derived economic values for traits related to health, fertility, milk production, and longevity were sizeable. For both Nordic Red Dairy Cattle and Nordic Holstein, the estimated rank correlations between bulls selected for a national breeding goal and a joint breeding goal were very high. Thus, a joint breeding goal within breed is feasible for Denmark, Finland, and Sweden.

Short communication: Improving accuracy of Jersey genomic evaluations in the United States and Denmark by sharing reference population bulls1

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.

Including different groups of genotyped females for genomic prediction in a Nordic Jersey population.

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.

Effect of cow reference group on validation reliability of genomic evaluation

We studied the effect of including genomic data for cows in the reference population of single-step evaluations. Deregressed individual cow genetic evaluations (DRP) from milk production evaluations of Nordic Red Dairy cattle were used to estimate the single-step breeding values. Validation reliability and bias of the evaluations were calculated with four data sets including different amount of DRP record information from genotyped cows in the reference population. The gain in reliability was from 2% to 4% units for the production traits, depending on the used DRP data and the amount of genomic data. Moreover, inclusion of genotyped bull dams and their genotyped daughters seemed to create some bias in the single-step evaluation. Still, genotyping cows and their inclusion in the reference population is advantageous and should be encouraged.

Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey.

Small reference populations limit the accuracy of genomic prediction in numerically small breeds, such like Danish Jersey. The objective of this study was to investigate two approaches to improve genomic prediction by increasing size of reference population in Danish Jersey. The first approach was to include North American Jersey bulls in Danish Jersey reference population. The second was to genotype cows and use them as reference animals. The validation of genomic prediction was carried out on bulls and cows, respectively. In validation on bulls, about 300 Danish bulls (depending on traits) born in 2005 and later were used as validation data, and the reference populations were: (1) about 1050 Danish bulls, (2) about 1050 Danish bulls and about 1150 US bulls. In validation on cows, about 3000 Danish cows from 87 young half-sib families were used as validation data, and the reference populations were: (1) about 1250 Danish bulls, (2) about 1250 Danish bulls and about 1150 US bulls, (3) about 1250 Danish bulls and about 4800 cows, (4) about 1250 Danish bulls, 1150 US bulls and 4800 Danish cows. Genomic best linear unbiased prediction model was used to predict breeding values. De-regressed proofs were used as response variables. In the validation on bulls for eight traits, the joint DK-US bull reference population led to higher reliability of genomic prediction than the DK bull reference population for six traits, but not for fertility and longevity. Averaged over the eight traits, the gain was 3 percentage points. In the validation on cows for six traits (fertility and longevity were not available), the gain from inclusion of US bull in reference population was 6.6 percentage points in average over the six traits, and the gain from inclusion of cows was 8.2 percentage points. However, the gains from cows and US bulls were not accumulative. The total gain of including both US bulls and Danish cows was 10.5 percentage points. The results indicate that sharing reference data and including cows in reference population are efficient approaches to increase reliability of genomic prediction. Therefore, genomic selection is promising for numerically small population.