Multiple-breed genomic evaluation by principal component analysis in small size populations

BackgroundBreed-specific effects are observed when the same allele of a given genetic marker has a different effect depending on its breed origin, which results in different allele substitution effects across breeds. In such a case, single-breed breeding values may not be the most accurate predictors of crossbred performance. Our aim was to estimate the contribution of alleles from each parental breed to the genetic variance of traits that are measured in crossbred offspring, and to compare the prediction accuracies of estimated direct genomic values (DGV) from a traditional genomic selection model (GS) that are trained on purebred or crossbred data, with accuracies of DGV from a model that accounts for breed-specific effects (BS), trained on purebred or crossbred data. The final dataset was composed of 924 Large White, 924 Landrace and 924 two-way cross (F1) genotyped and phenotyped animals. The traits evaluated were litter size (LS) and gestation length (GL) in pigs.ResultsThe genetic correlation between purebred and crossbred performance was higher than 0.88 for both LS and GL. For both traits, the additive genetic variance was larger for alleles inherited from the Large White breed compared to alleles inherited from the Landrace breed (0.74 and 0.56 for LS, and 0.42 and 0.40 for GL, respectively). The highest prediction accuracies of crossbred performance were obtained when training was done on crossbred data. For LS, prediction accuracies were the same for GS and BS DGV (0.23), while for GL, prediction accuracy for BS DGV was similar to the accuracy of GS DGV (0.53 and 0.52, respectively).ConclusionsIn this study, training on crossbred data resulted in higher prediction accuracy than training on purebred data and evidence of breed-specific effects for LS and GL was demonstrated. However, when training was done on crossbred data, both GS and BS models resulted in similar prediction accuracies. In future studies, traits with a lower genetic correlation between purebred and crossbred performance should be included to further assess the value of the BS model in genomic predictions.

Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle

Visualization of the transmission of direct genomic values for paternal and maternal chromosomes for 15 traits in US Brown Swiss, Holstein, and Jersey cattle

BackgroundIn dairy cattle populations in which crossbreeding has been used, animals show some level of diversity in their origins. In rotational crossbreeding, for instance, crossbred dams are mated with purebred sires from different pure breeds, and the genetic composition of crossbred animals is an admixture of the breeds included in the rotation. How to use the data of such individuals in genomic evaluations is still an open question. In this study, we aimed at providing methodologies for the use of data from crossbred individuals with an admixed genetic background together with data from multiple pure breeds, for the purpose of genomic evaluations for both purebred and crossbred animals. A three-breed rotational crossbreeding system was mimicked using simulations based on animals genotyped with the 50 K single nucleotide polymorphism (SNP) chip.ResultsFor purebred populations, within-breed genomic predictions generally led to higher accuracies than those from multi-breed predictions using combined data of pure breeds. Adding admixed population’s (MIX) data to the combined pure breed data considering MIX as a different breed led to higher accuracies. When prediction models were able to account for breed origin of alleles, accuracies were generally higher than those from combining all available data, depending on the correlation of quantitative trait loci (QTL) effects between the breeds. Accuracies varied when using SNP effects from any of the pure breeds to predict the breeding values of MIX. Using those breed-specific SNP effects that were estimated separately in each pure breed, while accounting for breed origin of alleles for the selection candidates of MIX, generally improved the accuracies. Models that are able to accommodate MIX data with the breed origin of alleles approach generally led to higher accuracies than models without breed origin of alleles, depending on the correlation of QTL effects between the breeds.ConclusionsCombining all available data, pure breeds’ and admixed population’s data, in a multi-breed reference population is beneficial for the estimation of breeding values for pure breeds with a small reference population. For MIX, such an approach can lead to higher accuracies than considering breed origin of alleles for the selection candidates, and using breed-specific SNP effects estimated separately in each pure breed. Including MIX data in the reference population of multiple breeds by considering the breed origin of alleles, accuracies can be further improved. Our findings are relevant for breeding programs in which crossbreeding is systematically applied, and also for populations that involve different subpopulations and between which exchange of genetic material is routine practice.

Genome-wide association mapping of milk production traits in Braunvieh cattle

BackgroundCurrently, genome-wide evaluation of cattle populations is based on SNP-genotyping using ~ 54 000 SNP. Increasing the number of markers might improve genomic predictions and power of genome-wide association studies. Imputation of genotypes makes it possible to extrapolate genotypes from lower to higher density arrays based on a representative reference sample for which genotypes are obtained at higher density.MethodsGenotypes using 639 214 SNP were available for 797 bulls of the Fleckvieh cattle breed. The data set was divided into a reference and a validation population. Genotypes for all SNP except those included in the BovineSNP50 Bead chip were masked and subsequently imputed for animals of the validation population. Imputation of genotypes was performed with Beagle, findhap.f90, MaCH and Minimac. The accuracy of the imputed genotypes was assessed for four different scenarios including 50, 100, 200 and 400 animals as reference population. The reference animals were selected to account for 78.03%, 89.21%, 97.47% and > 99% of the gene pool of the genotyped population, respectively.ResultsImputation accuracy increased as the number of animals and relatives in the reference population increased. Population-based algorithms provided highly reliable imputation of genotypes, even for scenarios with 50 and 100 reference animals only. Using MaCH and Minimac, the correlation between true and imputed genotypes was > 0.975 with 100 reference animals only. Pre-phasing the genotypes of both the reference and validation populations not only provided highly accurate imputed genotypes but was also computationally efficient. Genome-wide analysis of imputation accuracy led to the identification of many misplaced SNP.ConclusionsGenotyping key animals at high density and subsequent population-based genotype imputation yield high imputation accuracy. Pre-phasing the genotypes of the reference and validation populations is computationally efficient and results in high imputation accuracy, even when the reference population is small.

Annual rhythms of milk and milk fat and protein production in dairy cattle in the United States

Genomic predictions for crossbred dairy cattle

The purpose of this study was to determine the impact of genomic selection on A2A2 ß-casein genotype in Brown Swiss cattle on important dairy production traits – traits included in total merit index (TMI). A total of 190 Slovenian Brown Swiss cattle, 83 females and 107 males, with known direct genomic values (DGV) from InterGenomics project and known genotype for ß-casein were used for estimation of the genotype effect. The determined genotype frequencies in population were 2.63 % for A1A1, 35.26 % for A1A2 and 62.11 % for A2A2 genotype. For DGV data analysis a single–trait model was used, considering genotype for ß-casein as fixed effect. Analysis showed significant differences between genotypes for ß-casein for three dairy production traits –TMI for dual purpose, pasterns and rear teat position. Our research indicates that selection on A2A2 ß-casein genotype would not have implications for traits important for dairy production.

Age and Month of Calving Adjustments of Holstein Protein, Milk, and Fat Lactation Yields

Previous studies have demonstrated that the p.Lys232Ala substitution in the acylCoA: diacylglycerol acyltransferase (DGAT1) gene and the p.Phe279Tyr mutation in the growth hormone receptor (GHR) gene are the causative quantitative trait loci underlying milk yield and composition on BTA14 and BTA20 respectively. To examine their applications in the genetic improvement of Chinese dairy cattle productivity, we herein investigated the effects of the DGAT1 p.Lys232Ala and GHR p.Phe279Tyr mutations on milk, fat and protein yield, as well as fat and protein percentage in the milk of 1222 Holstein cows. Genotyping was performed using PCR-RFLP for DGAT1 or primer-introduced restriction analysis (PCR-PIRA) for GHR. With a mixed animal model, the significant associations of the DGAT1 p.Lys232Ala substitution with 305-day milk, fat and protein yield were identified (P = 0.0001). The DGAT1 allele that encode lysine at position 232 was associated with increased 305-day milk fat yield, but with decreased 305-day milk and protein yield, whereas the GHR p.Phe279Tyr mutation was found to be significantly associated with protein percentage (P = 0.0014). The allele substitution effect of p.279Phe by p.279Tyr may lead to a significant increase in protein percentage. Our findings indicate that DGAT1 p.232Ala and GHR p.279Phe could be used to increase milk yield and protein yield of Chinese Holstein cows.

Genomic selection increases accuracy and decreases generation interval, accelerating genetic changes in populations. Assumptions of genetic improvement must be addressed to quantify the magnitude and direction of change. Genetic trends of US dairy cattle breeds were examined to determine the genetic gain since the implementation of genomic evaluations in 2009. Inbreeding levels and generation intervals were also investigated. Breeds included Ayrshire, Brown Swiss, Guernsey, Holstein (HO), and Jersey (JE), which were characterized by the evaluation breed the animal received. Mean genomic predicted breeding values (PBV¯) were analyzed per year to calculate genetic trends for bulls and cows. The data set contained 154,008 bulls and 33,022,242 cows born since 1975. Breakpoints were estimated using linear regression, and nonlinear regression was used to fit the piecewise model for the small sample number in some years. Generation intervals and inbreeding levels were also investigated since 1975. Milk, fat, and protein yields, somatic cell score, productive life, daughter pregnancy rate, and livability PBV¯ were documented. In 2017, 100% of bulls in this data set were genotyped. The percentage of genotyped cows has increased 23 percentage points since 2010. Overall, production traits have increased steadily over time, as expected. The HO and JE breeds have benefited most from genomics, with up to 192% increase in genetic gain since 2009. Due to the low number of observations, trends for Ayrshire, Brown Swiss, and Guernsey are difficult to infer from. Trends in fertility are most substantial; particularly, most breeds are trending downwards and daughter pregnancy rate for JE has been decreasing steadily since 1975 for bulls and cows. Levels of genomic inbreeding are increasing in HO bulls and cows. In 2017, genomic inbreeding levels were 12.7% for bulls and 7.9% for cows. A suggestion to control this is to include the genomic inbreeding coefficient with a negative weight to the selection index of bulls with high future genomic inbreeding levels. For sires of bulls, the current generation intervals are 2.2 yr in HO, 3.2 in JE, 4.4 in Brown Swiss, 5.1 in Ayrshire, and 4.3 in Guernsey. The number of colored breed bulls in the United States is currently at an extremely low level, and this number will only increase with a market incentive or additional breed association involvement. Increased education and extension could be beneficial to increase knowledge about inbreeding levels, use of genomics and genetic improvement, and genetic diversity in the genomic selection era.

Genomic tools can help in the selection of animals genetically resistant to heat stress, especially the genome-wide association studies (GWAS). The objective of this study was to compare the variance explained by SNPs and direct genomic breeding values (DGVs) at two levels of a temperature and humidity index (THI). Records of milk yield (MY), somatic cell score (SCS), and percentages of casein (CAS), saturated fatty acids (SFA), and unsaturated fatty acids (UFA) in milk from 1157 Holstein cows were used. Traditional breeding values (EBV) were determined in a previous study and used as pseudo-phenotypes. Two levels of THI (heat comfort zone and heat stress zone) were used as environments and were treated as “traits” in a bi-trait model. The GWAS was performed using the genomic best linear unbiased prediction (GBLUP) method. Considering the top 50 SNPs, a total of 36 SNPs were not common between environments, eight of which were located in gene regions related to the evaluated traits. Even for those SNPs that had differences in their explained variances between the two environments, the differences were very small. The animals showed virtually no rank order, with rank correlation values of 0.90, 0.88, 1.00, 0.88, and 0.97 for MY, CAS, SCS, SFA, and UFA, respectively. The small difference between the environments studied can be attributed to the small difference in the pseudo-phenotypes used between the environments, on-farm acclimation, the polygenic nature of the traits, and the THI values studied near the threshold between comfort and heat stress. It is recommended that future studies be conducted with a larger number of animals and at more extreme THI levels.

Short communication: Interactions of milk, fat, and protein yield genotypes with herd feeding characteristics

Associations of breed and feeding management with milk production curves at herd level using a random regression test-day model