Imputation accuracy from low- to medium-density SNP chips for US crossbred dairy cattle.

This study aimed at evaluating the quality of imputation accuracy (IA) by marker (IAm) and by individual (IAi) in US crossbred dairy cattle. Holstein × Jersey crossbreds were used to evaluate IA from a low- (7K) to a medium-density (50K) SNP chip. Crossbred animals, as well as their sires (53), dams (77), and maternal grandsires (63), were all genotyped with a 78K SNP chip. Seven different scenarios of reference populations were tested, in which some scenarios used different family relationships and others added random unrelated purebred and crossbred individuals to those different family relationship scenarios. The same scenarios were tested on Holstein and Jersey purebred animals to compare these outcomes against those attained in crossbred animals. The genotype imputation was performed with findhap (version 4) software (VanRaden, 2015). There were no significant differences in IA results depending on whether the sire of imputed individuals was Holstein and the dam was Jersey, or vice versa. The IA increased significantly with the addition of related individuals in the reference population, from 86.70 ± 0.06% when only sires or dams were included in the reference population to 90.09 ± 0.06% when sire (S), dam (D), and maternal grandsire genomic data were combined in the reference population. In all scenarios including related individuals in the reference population, IAm and IAi were significantly superior in purebred Jersey and Holstein animals than in crossbreds, ranging from 90.75 ± 0.06 to 94.02 ± 0.06%, and from 90.88 ± 0.11 to 94.04 ± 0.10%, respectively. Additionally, a scenario called SPB+DLD(where PB indicates purebread and LD indicates low density), similar to the genomic evaluations performed on US crossbred dairy, was tested. In this scenario, the information from the 5 evaluated breeds (Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey) genotyped with a 50K SNP chip and genomic information from the dams genotyped with a 7K SNP chip were combined in the reference population, and the IAm and IAi were 80.87 ± 0.06% and 80.85 ± 0.08%, respectively. Adding randomly nonrelated genotyped individuals in the reference population reduced IA for both purebred and crossbred cows, except for scenario SPB+DLD, where adding crossbreds to the reference population increased IA values. Our findings demonstrate that IA for US Holstein × Jersey crossbred ranged from 85 to 90%, and emphasize the significance of designing and defining the reference population for improved IA.

Inbreeding depression for producer-recorded udder, metabolic, and reproductive diseases in US dairy cattle.

This study leveraged a growing dataset of producer-recorded phenotypes for mastitis, reproductive diseases (metritis and retained placenta), and metabolic diseases (ketosis, milk fever, and displaced abomasum) to investigate the potential presence of inbreeding depression for these disease traits. Phenotypic, pedigree, and genomic information were obtained for 354,043 and 68,292 US Holstein and Jersey cows, respectively. Total inbreeding coefficients were calculated using both pedigree and genomic information; the latter included inbreeding estimates obtained using a genomic relationship matrix and runs of homozygosity. We also generated inbreeding coefficients based on the generational inbreeding for recent and old pedigree inbreeding, for different run-of-homozygosity length classes, and for recent and old homozygous-by-descent segment-based inbreeding. Estimates on the liability scale revealed significant evidence of inbreeding depression for reproductive-disease traits, with an increase in total pedigree and genomic inbreeding showing a notable effect for recent inbreeding. However, we found inconsistent evidence for inbreeding depression for mastitis or any metabolic diseases. Notably, in Holsteins, the probability of developing displaced abomasum decreased with inbreeding, particularly for older inbreeding. Estimates of disease probability for cows with low, average, and high inbreeding levels did not significantly differ across any inbreeding coefficient and trait combination, indicating that although inbreeding may affect disease incidence, it likely plays a smaller role compared with management and environmental factors.

Genomic characterization of autozygosity and recent inbreeding trends in all major breeds of US dairy cattle.

Maintaining a genetically diverse dairy cattle population is critical to preserving adaptability to future breeding goals and avoiding declines in fitness. This study characterized the genomic landscape of autozygosity and assessed trends in genetic diversity in 5 breeds of US dairy cattle. We analyzed a sizable genomic data set containing 4,173,679 pedigreed and genotyped animals of the Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey breeds. Runs of homozygosity (ROH) of 2 Mb or longer in length were identified in each animal. The within-breed means for number and the combined length of ROH were highest in Jerseys (62.66 ± 8.29 ROH and 426.24 ± 83.40 Mb, respectively; mean ± SD) and lowest in Ayrshires (37.24 ± 8.27 ROH and 265.05 ± 85.00 Mb, respectively). Short ROH were the most abundant, but moderate to large ROH made up the largest proportion of genome autozygosity in all breeds. In addition, we identified ROH islands in each breed. This revealed selection patterns for milk production, productive life, health, and reproduction in most breeds and evidence for parallel selective pressure for loci on chromosome 6 between Ayrshire and Brown Swiss and for loci on chromosome 20 between Holstein and Jersey. We calculated inbreeding coefficients using 3 different approaches, pedigree-based (FPED), marker-based using a genomic relationship matrix (FGRM), and segment-based using ROH (FROH). The average inbreeding coefficient ranged from 0.06 in Ayrshires and Brown Swiss to 0.08 in Jerseys and Holsteins using FPED, from 0.22 in Holsteins to 0.29 in Guernsey and Jerseys using FGRM, and from 0.11 in Ayrshires to 0.17 in Jerseys using FROH. In addition, the effective population size at past generations (5-100 generations ago), the yearly rate of inbreeding, and the effective population size in 3 recent periods (2000-2009, 2010-2014, and 2015-2018) were determined in each breed to ascertain current and historical trends of genetic diversity. We found a historical trend of decreasing effective population size in the last 100 generations in all breeds and breed differences in the effect of the recent implementation of genomic selection on inbreeding accumulation.

Genetic analysis of egg production traits in turkeys (Meleagris gallopavo) using a single-step genomic random regression model.

BACKGROUND: Egg production traits are economically important in poultry breeding programs. Previous studies have shown that incorporating genomic data can increase the accuracy of genetic prediction of egg production. Our objective was to estimate the genetic and phenotypic parameters of such traits and compare the prediction accuracy of pedigree-based random regression best linear unbiased prediction (RR-PBLUP) and genomic single-step random regression BLUP (RR-ssGBLUP). Egg production was recorded on 7422 birds during 24 consecutive weeks from first egg laid. Hatch-week of birth by week of lay and week of lay by age at first egg were fitted as fixed effects and body weight as a covariate, while additive genetic and permanent environment effects were fitted as random effects, along with heterogeneous residual variances over 24 weeks of egg production. Predictions accuracies were compared based on two statistics: (1) the correlation between estimated breeding values and phenotypes divided by the square root of the trait heritability, and (2) the ratio of the variance of BLUP predictions of individual Mendelian sampling effects divided by one half of the estimate of the additive genetic variance. RESULTS: Heritability estimates along the production trajectory obtained with RR-PBLUP ranged from 0.09 to 0.22, with higher estimates for intermediate weeks. Estimates of phenotypic correlations between weekly egg production were lower than the corresponding genetic correlation estimates. Our results indicate that genetic correlations decreased over the laying period, with the highest estimate being between traits in later weeks and the lowest between early weeks and later ages. Prediction accuracies based on the correlation-based statistic ranged from 0.11 to 0.44 for RR-PBLUP and from 0.22 to 0.57 for RR-ssGBLUP using the correlation-based statistic. The ratios of the variances of BLUP predictions of Mendelian sampling effects and one half of the additive genetic variance ranged from 0.17 to 0.26 for RR-PBLUP and from 0.17 to 0.34 for RR-ssGBLUP. Although the improvement in accuracies from RR-ssGBLUP over those from RR-PBLUP was not uniform over time for either statistic, accuracies obtained with RR-ssGBLUP were generally equal to or higher than those with RR-PBLUP. CONCLUSIONS: Our findings show the potential advantage of incorporating genomic data in genetic evaluation of egg production traits using random regression models, which can contribute to the genetic improvement of egg production in turkey populations.

Trends in genetic diversity and the effect of inbreeding in American Angus cattle under genomic selection.

BACKGROUND: While the adoption of genomic evaluations in livestock has increased genetic gain rates, its effects on genetic diversity and accumulation of inbreeding have raised concerns in cattle populations. Increased inbreeding may affect fitness and decrease the mean performance for economically important traits, such as fertility and growth in beef cattle, with the age of inbreeding having a possible effect on the magnitude of inbreeding depression. The purpose of this study was to determine changes in genetic diversity as a result of the implementation of genomic selection in Angus cattle and quantify potential inbreeding depression effects of total pedigree and genomic inbreeding, and also to investigate the impact of recent and ancient inbreeding. RESULTS: We found that the yearly rate of inbreeding accumulation remained similar in sires and decreased significantly in dams since the implementation of genomic selection. Other measures such as effective population size and the effective number of chromosome segments show little evidence of a detrimental effect of using genomic selection strategies on the genetic diversity of beef cattle. We also quantified pedigree and genomic inbreeding depression for fertility and growth. While inbreeding did not affect fertility, an increase in pedigree or genomic inbreeding was associated with decreased birth weight, weaning weight, and post-weaning gain in both sexes. We also measured the impact of the age of inbreeding and found that recent inbreeding had a larger depressive effect on growth than ancient inbreeding. CONCLUSIONS: In this study, we sought to quantify and understand the possible consequences of genomic selection on the genetic diversity of American Angus cattle. In both sires and dams, we found that, generally, genomic selection resulted in decreased rates of pedigree and genomic inbreeding accumulation and increased or sustained effective population sizes and number of independently segregating chromosome segments. We also found significant depressive effects of inbreeding accumulation on economically important growth traits, particularly with genomic and recent inbreeding.

Evidence for recombination variability in purebred swine populations.

This study aimed to investigate interpopulation variation due to sex, breed and age, and the intrapopulation variation in the form of genetic variance for recombination in swine. Genome-wide recombination rate and recombination occurrences (RO) were traits studied in Landrace (LR) and Large White (LW) male and female populations. Differences were found for sex, breed, sex-breed interaction, and age effects for genome-wide recombination rate and RO at one or more chromosomes. Dams were found to have a higher genome-wide recombination rate and RO at all chromosomes than sires. LW animals had higher genome-wide recombination rate and RO at seven chromosomes but lower at two chromosomes than LR individuals. The sex-breed interaction effect did not show any pattern not already observable by sex. Recombination increased with increasing parity in females, while in males no effect of age was observed. We estimated heritabilities and repeatabilities for both investigated traits and obtained the genetic correlation between male and female genome-wide recombination rate within each of the two breeds studied. Estimates of heritability and repeatability were low (h2  = 0.01-0.26; r = 0.18-0.42) for both traits in all populations. Genetic correlations were high and positive, with estimates of 0.98 and 0.94 for the LR and LW breeds, respectively. We performed a GWAS for genome-wide recombination rate independently in the four sex/breed populations. The results of the GWAS were inconsistent across the four populations with different significant genomic regions identified. The results of this study provide evidence of variability for recombination in purebred swine populations.