Genome-wide association study on milk production and somatic cell score for Thai dairy cattle using weighted single-step approach with random regression test-day model.

Genome-wide association studies are a powerful tool to identify genomic regions and variants associated with phenotypes. However, only limited mutual confirmation from different studies is available. The objectives of this study were to identify genomic regions as well as genes and pathways associated with the first-lactation milk, fat, protein, and total solid yields; fat, protein, and total solid percentage; and somatic cell score (SCS) in a Thai dairy cattle population. Effects of SNPs were estimated by a weighted single-step GWAS, which back-solved the genomic breeding values predicted using single-step genomic BLUP (ssGBLUP) fitting a single-trait random regression test-day model. Genomic regions that explained at least 0.5% of the total genetic variance were selected for further analyses of candidate genes. Despite the small number of genotyped animals, genomic predictions led to an improvement in the accuracy over the traditional BLUP. Genomic predictions using weighted ssGBLUP were slightly better than the ssGBLUP. The genomic regions associated with milk production traits contained 210 candidate genes on 19 chromosomes [Bos taurus autosome (BTA) 1 to 7, 9, 11 to 16, 20 to 21, 26 to 27 and 29], whereas 21 candidate genes on 3 chromosomes (BTA 11, 16, and 21) were associated with SCS. Many genomic regions explained a small fraction of the genetic variance, indicating polygenic inheritance of the studied traits. Several candidate genes coincided with previous reports for milk production traits in Holstein cattle, especially a large region of genes on BTA14. We identified 141 and 5 novel genes related to milk production and SCS, respectively. These novel genes were also found to be functionally related to heat tolerance (e.g., SLC45A2, IRAG1, and LOC101902172), longevity (e.g., SYT10 and LOC101903327), and fertility (e.g., PAG1). These findings may be attributed to indirect selection in our population. Identified biological networks including intracellular cell transportation and protein catabolism implicate milk production, whereas the immunological pathways such as lymphocyte activation are closely related to SCS. Further studies are required to validate our findings before exploiting them in genomic selection.

Genomic prediction of milk-production traits and somatic cell score using single-step genomic best linear unbiased predictor with random regression test-day model in Thai dairy cattle.

Cow genotypes are expected to improve the accuracy of genomic estimated breeding values (GEBV) for young bulls in relatively small populations such as Thai Holstein-Friesian crossbred dairy cattle in Thailand. The objective of this study was to investigate the effect of cow genotypes on the predictive ability and individual accuracies of GEBV for young dairy bulls in Thailand. Test-day data included milk yield (n = 170,666), milk component traits (fat yield, protein yield, total solids yield, fat percentage, protein percentage, and total solids percentage; n = 160,526), and somatic cell score (n = 82,378) from 23,201, 82,378, and 13,737 (for milk yield, milk component traits, and SCS, respectively) cows calving between 1993 and 2017, respectively. Pedigree information included 51,128; 48,834; and 32,743 animals for milk yield, milk component traits, and somatic cell score, respectively. Additionally, 876, 868, and 632 pedigreed animals (for milk yield, milk component traits, and SCS, respectively) were genotyped (152 bulls and 724 cows), respectively, using Illumina Bovine SNP50 BeadChip. We cut off the data in the last 6 yr, and the validation animals were defined as genotyped bulls with no daughters in the truncated set. We calculated GEBV using a single-step random regression test-day model (SS-RR-TDM), in comparison with estimated breed value (EBV) based on the pedigree-based model used as the official method in Thailand (RR-TDM). Individual accuracies of GEBV were obtained by inverting the coefficient matrix of the mixed model equations, whereas validation accuracies were measured by the Pearson correlation between deregressed EBV from the full data set and (G)EBV predicted with the reduced data set. When only bull genotypes were used, on average, SS-RR-TDM increased individual accuracies by 0.22 and validation accuracies by 0.07, compared with RR-TDM. With cow genotypes, the additional increase was 0.02 for individual accuracies and 0.06 for validation accuracies. The inflation of GEBV tended to be reduced using cow genotypes. Genomic evaluation by SS-RR-TDM is feasible to select young bulls for the longitudinal traits in Thai dairy cattle, and the accuracy of selection is expected to be increased with more genotypes. Genomic selection using the SS-RR-TDM should be implemented in the routine genetic evaluation of the Thai dairy cattle population. The genetic evaluation should consider including genotypes of both sires and cows.

Changes in genetic parameters for milk yield and heat tolerance in the Thai Holstein crossbred dairy population under different heat stress levels and over time.

The objectives of this study were to investigate changes in genetic parameters for milk yield (MY) and heat tolerance of the crossbred Thai Holstein Friesian population under different heat stress levels over time, and to investigate the threshold point of heat stress manifestation on milk production. Genetic parameters were estimated using single-step genomic REML (ssGREML) and traditional REML models. Data included 58,965 test-day MY records from 1999 to 2008 (old data) and 105,485 test-day MY records from 2009 to 2018 (recent data) from the first parity of 24,520 cows. The pedigree included 55,168 animals, of which 882 animals had genotypes. Variance components were estimated with the REMLF90 program using a repeatability model with random regressions on a function of temperature-humidity index (THI) for additive genetic and permanent environmental effects. Fixed effects included farm-calving season combination, breed group-months in milk combination, and age at first calving. Random effects included additive genetic (intercept and slope) effects, permanent environmental (intercept and slope) effects, and herd-month-year of test. The phenotypic mean for MY was 13.33 ± 4.39 kg/d in the old data, and 14.48 ± 4.40 kg/d in the recent data. Estimates over different THI levels for the intercept additive genetic variance using old data ranged from 2.61 to 2.77 and from 5.02 to 5.38 using recent data with the REML method. In ssGREML analyses (performed with recent data only) the estimates for the intercept additive genetic variance ranged from 4.71 to 5.05. Estimates for the slope additive genetic variance were close to zero in all cases, with the largest values (0.024-0.030) at the most extreme THI value (80). Using REML, the covariance between the intercept and the slope additive genetic effects (THI from 72 to 80) ranged from -0.001 to 0.019 with old data and from 0.027 to 0.060 with recent data. The same covariance ranged from 0.026 to 0.057 in ssGREML analyses. The covariance between the intercept and the slope permanent environmental effects ranged from -0.42 to -0.67 for all data and THI levels. Across THI levels, the genetic correlation between MY and heat tolerance varied from -0.06 to 0.13 with old data, from 0.16 to 0.30 with recent data in REML analyses, and from 0.15 to 0.30 in ssGREML analyses, suggesting that in the current population the top animals for MY are more resistant to heat stress. This was expected, because of the introduction of Bos indicus genes in the last years. Heritability estimates for MY ranged from 0.19 to 0.21 (old data) and from 0.33 to 0.40 (recent data) for REML analyses. Heritability estimates for MY using ssGREML ranged from 0.31 to 0.38. A decline in MY was found when the animals' breed composition had more than 97.3% of Holstein genetics, and it was greatest at THI 80. The heritability and genetic correlations observed in this study show that selection for MY is possible without a negative correlated response for heat tolerance. Although the inclusion of genomic information is expected to increase the accuracy of selection, more genotypes must be collected for successful application. Future research should address other production and fitness traits within the Thai Holstein population.

Short communication: Genetic analysis for fertility traits of heifers and cows from smallholder dairy farms in a tropical environment.

The objective of this study was to estimate genetic parameters for various fertility traits on Holstein upgraded dairy heifers and cows in a smallholder system under tropical conditions using data sets from the Thailand national recording scheme. The investigated traits were age at first service (AFS), age at first calving (AFC), days from calving to first service (DTFS), days between first and last service (DFLS), days open (DO), calving interval (CI), number of services per conception (NSPC), and conception at first service (FSC). The data consisted of 68,555, 34,401, and 54,004 records on heifers, primiparous, and multiparous cows, respectively, calving between 1996 and 2011. Gibbs sampling was employed to obtain (co)variance components using both univariate and bivariate analyses with linear and threshold animal models. Virgin heifers had better fertility performance than primiparous and multiparous cows. The reproductive performance in primiparous cows was inferior compared with multiparous cows. Cows with higher Holstein-Friesian blood showed lower reproductive efficiency. Estimated heritabilities for most of the fertility traits were 0.04 or less except for AFS (0.26) and AFC (0.25). The estimated genetic correlations among fertility traits within parity indicated that selection for cows with high conception rate could lead to shortened DO and CI, as well as DTFS. The FSC and NSPC could be used as the best indicators for heifer and cow fertility and could be complemented by other traits, which were genetically considered as different traits such as DTFS and DFLS in terms of a fertility index. This would enable efficient selection for better fertility. Genetic correlations for fertility traits in primiparous and multiparous cows were very high (>0.90), but those between heifers and cows were lower (0.03 to 0.83). The latter results indicated that fertility traits of heifers and cows should be considered as different traits.

Short communication: genetic effects of heat stress on days open for Thai Holstein crossbreds.

Seasonality of days open (DO) for Thai crossbred Holsteins was examined and genetic effects of heat stress on DO open were determined. Data included 18,413 records for first and second parities of 12,162 cows that calved between 1990 and 2006. Least squares means for DO were estimated using a model with fixed effects of herd-year of calving, breed group based on percentage of Holstein genetics, calving month, calving age, and parity. A reaction norm model and solutions for calving month were used to calculate a heat stress index. Variance components were estimated with a multitrait random regression model. Days open were greatest for cows calving in March (summer) and fewest in October (late rainy season) for all breed groups and parities. Estimates of additive genetic and residual variances and heritability varied by calving month. Residual variances increased and additive genetic variance decreased with percentage of Holstein genetics and parity. Heritability estimates for DO ranged from 7.1 to 8.4% for first-parity cows with <87.5% Holstein genetics, 5.9 to 8.0% for 87.5 to 93.6% Holstein genetics, and 5.8 to 7.8% for ≥93.7% Holstein genetics and from 6.3 to 7.9, 4.9 to 7.3, and 4.5 to 7.7% for the corresponding breed groups for second parity. Genetic correlations between additive genetic effects on DO with and without heat stress considered were 0.43 for first-parity cows with <87.5% Holstein genetics, 0.46 for those with 87.5 to 93.6% Holstein genetics, and 0.52 for those with ≥93.7% Holstein genetics; correlations were 0.46, 0.51, and 0.55 for the corresponding breed groups for second parity. Effect of heat stress on DO was greater for second than first parity and was particularly large for cows with ≥93.7% Holstein genetics. Fewer DO can be achieved in Thailand through selective breeding of cows with <87.5% Holstein genetics.