Single-step genome-wide association for longitudinal traits of Canadian Ayrshire,Holstein, and Jersey dairy cattle

Estimating single nucleotide polymorphism (SNP) effects over time is essential to identify and validate candidate genes (or quantitative trait loci) associated with time-dependent variation of economically important traits and to better understand the underlying mechanisms of lactation biology. Therefore, in this study, we aimed to estimate time-dependent effects of SNP and identifying candidate genes associated with milk (MY), fat (FY), and protein (PY) yields, and somatic cell score (SCS) in the first 3 lactations of Canadian Ayrshire, Holstein, and Jersey breeds, as well as suggest their potential pattern of phenotypic effect over time. Random regression coefficients for the additive direct genetic effect were estimated for each animal using single-step genomic BLUP, based on 2 random regression models: one considering MY, FY, and PY in the first 3 lactations and the other considering SCS in the first 3 lactations. Thereafter, SNP solutions were obtained for random regression coefficients, which were used to estimate the SNP effects over time (from 5 to 305 d in lactation). The top 1% of SNP that showed a high magnitude of SNP effect in at least 1 d in lactation were selected as relevant SNP for further analyses of candidate genes, and clustered according to the trajectory of their SNP effects over time. The majority of SNP selected for MY, FY, and PY increased the magnitude of their effects over time, for all breeds. In contrast, for SCS, most selected SNP decreased the magnitude of their effects over time, especially for the Holstein and Jersey breeds. In general, we identified a different set of candidate genes for each breed, and similar genes were found across different lactations for the same trait in the same breed. For some of the candidate genes, the suggested pattern of phenotypic effect changed among lactations. Among the lactations, candidate genes (and their suggested phenotypic effect over time) identified for the second and third lactations were more similar to each other than for the first lactation. Well-known candidate genes with major effects on milk production traits presented different suggested patterns of phenotypic effect across breeds, traits, and lactations in which they were identified. The candidate genes identified in this study can be used as target genes in studies of gene expression.     

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from ?0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.     

Marker selection and genomic prediction of economically important traits using imputed high-density genotypes for 5 breeds of dairy cattle

Marker sets used in US dairy genomic predictions were previously expanded by including high-density (HD) or sequence markers with the largest effects for Holstein breed only. Other non-Holstein breeds lacked enough HD genotyped animals to be used as a reference population at that time, and thus were not included in the genomic prediction. Recently, numbers of non-Holstein breeds genotyped using HD panels reached an acceptable level for imputation and marker selection, allowing HD genomic prediction and HD marker selection for Holstein plus 4 other breeds. Genotypes for 351,461 Holsteins, 347,570 Jerseys, 42,346 Brown Swiss, 9,364 Ayrshires (including Red dairy cattle), and 4,599 Guernseys were imputed to the HD marker list that included 643,059 SNP. The separate HD reference populations included Illumina BovineHD (San Diego, CA) genotypes for 4,012 Holsteins, 407 Jerseys, 181 Brown Swiss, 527 Ayrshires, and 147 Guernseys. The 643,059 variants included the HD SNP and all 79,254 (80K) genetic markers and QTL used in routine national genomic evaluations. Before imputation, approximately 91 to 97% of genotypes were unknown for each breed; after imputation, 1.1% of Holstein, 3.2% of Jersey, 6.7% of Brown Swiss, 4.8% of Ayrshire, and 4.2% of Guernsey alleles remained unknown due to lower density haplotypes that had no matching HD haplotype. The higher remaining missing rates in non-Holstein breeds are mainly due to fewer HD genotyped animals in the imputation reference populations. Allele effects for up to 39 traits were estimated separately within each breed using phenotypic reference populations that included up to 6,157 Jersey males and 110,130 Jersey females. Correlations of HD with 80K genomic predictions for young animals averaged 0.986, 0.989, 0.985, 0.992, and 0.978 for Jersey, Ayrshire, Brown Swiss, Guernsey, and Holstein breeds, respectively. Correlations were highest for yield traits (about 0.991) and lowest for foot angle and rear legs–side view (0.981and 0.982, respectively). Some HD effects were more than twice as large as the largest 80K SNP effect, and HD markers had larger effects than nearby 80K markers for many breed-trait combinations. Previous studies selected and included markers with large effects for Holstein traits; the newly selected HD markers should also improve non-Holstein and crossbred genomic predictions and were added to official US genomic predictions in April 2020.     

Genetic heterogeneity of feed intake,energy-corrected milk,and body weight across lactation in primiparous Holstein,Nordic Red,and Jersey cows

In this study, we aimed to estimate and compare the genetic parameters of dry matter intake (DMI), energy-corrected milk (ECM), and body weight (BW) as 3 feed efficiency–related traits across lactation in 3 dairy cattle breeds (Holstein, Nordic Red, and Jersey). The analyses were based on weekly records of DMI, ECM, and BW per cow across lactation for 842 primiparous Holstein cows, 746 primiparous Nordic Red cows, and 378 primiparous Jersey cows. A random regression model was applied to estimate variance components and genetic parameters for DMI, ECM, and BW in each lactation week within each breed. Phenotypic means of DMI, ECM, and BW observations across lactation showed to be in very similar patterns between breeds, whereas breed differences lay in the average level of DMI, ECM, and BW. Generally, for all studied breeds, the heritability for DMI ranged from 0.2 to 0.4 across lactation and was in a range similar to the heritability for ECM. The heritability for BW ranged from 0.4 to 0.6 across lactation, higher than the heritability for DMI or ECM. Among the studied breeds, the heritability estimates for DMI shared a very similar range between breeds, whereas the heritability estimates for ECM tended to be different between breeds. For BW, the heritability estimates also tended to follow a similar range between breeds. Among the studied traits, the genetic variance and heritability for DMI varied across lactation, and the genetic correlations between DMI at different lactation stages were less than unity, indicating a genetic heterogeneity of feed intake across lactation in dairy cattle. In contrast, BW was the most genetically consistent trait across lactation, where BW among all lactation weeks was highly correlated. Genetic correlations between DMI, ECM, and BW changed across lactation, especially in early lactation. Energy-corrected milk had a low genetic correlation with both DMI and BW at the beginning of lactation, whereas ECM was highly correlated with DMI in mid and late lactation. Based on our results, genetic heterogeneity of DMI, ECM, and BW across lactation generally was observed in all studied dairy breeds, especially for DMI, which should be carefully considered for the recording strategy of these traits. The genetic correlations between DMI, ECM, and BW changed across lactation and followed similar patterns between breeds.     

A validated genome-wide association study in 2 dairy cattle breeds for milk production and fertility traits using variable length haplotypes

Genome-wide association studies (GWAS) were used to discover genomic regions explaining variation in dairy production and fertility traits. Associations were detected with either single nucleotide polymorphism (SNP) markers or haplotypes of SNP alleles. An across-breed validation strategy was used to narrow the genomic interval containing causative mutations. There were 39,048 SNP tested in a discovery population of 780 Holstein sires and validated in 386 Holsteins and 364 Jersey sires. Previously identified mutations affecting milk production traits were confirmed. In addition, several novel regions were identified, including a putative quantitative trait loci for fertility on chromosome 18 that was detected only using haplotypes greater than 3 SNP long. It was found that the precision of quantitative trait loci mapping increased with haplotype length as did the number of validated haplotypes discovered, especially across breed. Promising candidate genes have been identified in several of the validated regions.     

Growth and reproduction characteristics of purebred and crossbred dairy cattle in first lactation

Breed additive, maternal, and heterozygotic effects on 11 monthly postpartum body weights, average size, weight gain, age at first calving, and days open were estimated by linear regression analyses from records of 680 purebred and crossbred animals. Calving year, age, and milk yield were significant environmental effects. Positive regressions of age on body weights indicated late calving cows were heaviest postpartum and had most days open. Negative regressions of milk yield on weight gain and postpartum weight showed greatest losses of weight associated with highest yields. There were breed differences in growth; purebreds highest to lowest were Brown Swiss, Holstein, Red Dane, Jersey, and Ayrshire and for weight gain, Brown Swiss, Ayrshire, and for weight gain, Brown Swiss, Ayrshire, Jersey, Holsteins, and Red Dane. Crosses were superior to purebreds in these traits. Additive effects of Holstein were greater than Ayrshire, Jersey, and Red Dane for postpartum body weights and average size. All constants for heterozygotic effect combinations were significant for postpartum weights and average size. Heterozygosity effects increased in magnitude with advancing lactation. Generally, age at first calving and postpartum interval to conception reflected little heterozygotic or maternal effects. There was some indication of breed differences in mean and additive effects for age at first calving.     

Identification of functional candidate variants and genes for feed efficiency in Holstein and Jersey cattle breeds using RNA-sequencing

The identification of functional genetic variants and associated candidate genes linked to feed efficiency may help improve selection for feed efficiency in dairy cattle, providing economic and environmental benefits for the dairy industry. This study used RNA-sequencing data obtained from liver tissue from 9 Holstein cows [n = 5 low residual feed intake (RFI), n = 4 high RFI] and 10 Jersey cows (n = 5 low RFI, n = 5 high RFI), which were selected from a single population of 200 animals. Using RNA-sequencing, 3 analyses were performed to identify: (1) variants within low or high RFI Holstein cattle; (2) variants within low or high RFI Jersey cattle; and (3) variants within low or high RFI groups, which are common across both Holstein and Jersey cattle breeds. From each analysis, all variants were filtered for moderate, modifier, or high functional effect, and co-localized quantitative trait loci (QTL) classes, enriched biological processes, and co-localized genes related to these variants, were identified. The overlapping of the resulting genes co-localized with functional SNP from each analysis in both breeds for low or high RFI groups were compared. For the first two analyses, the total number of candidate genes associated with moderate, modifier, or high functional effect variants fixed within low or high RFI groups were 2,810 and 3,390 for Holstein and Jersey breeds, respectively. The major QTL classes co-localized with these variants included milk and reproduction QTL for the Holstein breed, and milk, production, and reproduction QTL for the Jersey breed. For the third analysis, the common variants across both Holstein and Jersey breeds, uniquely fixed within low or high RFI groups were identified, revealing a total of 86,209 and 111,126 functional variants in low and high RFI groups, respectively. Across all 3 analyses for low and high RFI cattle, 12 and 31 co-localized genes were overlapping, respectively. Among the overlapping genes across breeds, 9 were commonly detected in both the low and high RFI groups (INSRR, CSK, DYNC1H1, GAB1, KAT2B, RXRA, SHC1, TRRAP, PIK3CB), which are known to play a key role in the regulation of biological processes that have high metabolic demand and are related to cell growth and regeneration, metabolism, and immune function. The genes identified and their associated functional variants may serve as candidate genetic markers and can be implemented into breeding programs to help improve the selection for feed efficiency in dairy cattle.     

Genome-wide association study for selected cheese-making properties in Dual-Purpose Belgian Blue cows

This study aimed to estimate genetic parameters and identify genomic region(s) associated with selected cheese-making properties (CMP) in Dual-Purpose Belgian Blue (DPBB) cows. Edited data were 46,301 test-day records of milk yield, fat percentage, protein percentage, casein percentage, milk calcium content (CC), coagulation time (CT), curd firmness after 30 min from rennet addition (a30), and milk titratable acidity (MTA) collected from 2014 to 2020 on 4,077 first-parity (26,027 test-day records), and 3,258 second-parity DPBB cows (20,274 test-day records) distributed in 124 herds in the Walloon Region of Belgium. Data of 28,266 SNP, located on 29 Bos taurus autosomes (BTA) of 1,699 animals were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 25 consecutive SNPs (with an average size of ～2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Heritability estimates for the included CMP ranged from 0.19 (CC) to 0.50 (MTA), and 0.24 (CC) to 0.41 (MTA) in the first and second parity, respectively. The genetic correlation estimated between CT and a30 varied from ?0.61 to ?0.41 and from ?0.55 to ?0.38 in the first and second lactations, respectively. Negative genetic correlations were found between CT and milk yield and composition, while those estimated between curd firmness and milk composition were positive. Genome-wide association analyses results identified 4 genomic regions (BTA1, BTA3, BTA7, and BTA11) associated with the considered CMP. The identified genomic regions showed contrasting results between parities and among the different stages of each parity. It suggests that different sets of candidate genes underlie the phenotypic expression of the considered CMP between parities and lactation stages of each parity. The findings of this study can be used for future implementation and use of genomic evaluation to improve the cheese-making traits in DPBB cows.     

Genome-wide association study for lactation persistency,female fertility,longevity, and lifetime profit index traits in Holstein dairy cattle

Female fertility in Holstein cattle can decline when intense genetic selection is placed on milk production. One approach to improving fertility is to identify the genomic regions and variants affecting fertility traits and then incorporate this knowledge into selection decisions. The objectives of this study were to identify or refine the positions of the genomic regions associated with lactation persistency, female fertility traits (age at first service, cow first service to conception, heifer and cow nonreturn rates), longevity traits (herd life, indirect herd life, and direct herd life), and lifetime profit index in the North American Holstein dairy cattle population. A genome-wide association study was performed for each trait, using a single SNP (single nucleotide polymorphism) regression mixed linear model and imputed high-density panel (777k) genotypes. No associations were identified for fertility traits. Several peak regions were detected for lifetime profit index, lactation persistency, and longevity. The results overlap with previous findings and identify some novel regions for lactation persistency. Previously proposed causative and candidate genes supported by this work include DGAT1, GRINA, and CPSF1, whereas new candidate genes are SLC2A4RG and THRB. Thus, the chromosomal regions identified in this study not only confirm several previous findings but also highlight new regions that may contribute to genetic variation in lactation persistency and longevity-associated traits in dairy cattle.     

Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels

Achieving accurate genomic estimated breeding values for dairy cattle requires a very large reference population of genotyped and phenotyped individuals. Assembling such reference populations has been achieved for breeds such as Holstein, but is challenging for breeds with fewer individuals. An alternative is to use a multi-breed reference population, such that smaller breeds gain some advantage in accuracy of genomic estimated breeding values (GEBV) from information from larger breeds. However, this requires that marker-quantitative trait loci associations persist across breeds. Here, we assessed the gain in accuracy of GEBV in Jersey cattle as a result of using a combined Holstein and Jersey reference population, with either 39,745 or 624,213 single nucleotide polymorphism (SNP) markers. The surrogate used for accuracy was the correlation of GEBV with daughter trait deviations in a validation population. Two methods were used to predict breeding values, either a genomic BLUP (GBLUP_mod), or a new method, BayesR, which used a mixture of normal distributions as the prior for SNP effects, including one distribution that set SNP effects to zero. The GBLUP_mod method scaled both the genomic relationship matrix and the additive relationship matrix to a base at the time the breeds diverged, and regressed the genomic relationship matrix to account for sampling errors in estimating relationship coefficients due to a finite number of markers, before combining the 2 matrices. Although these modifications did result in less biased breeding values for Jerseys compared with an unmodified genomic relationship matrix, BayesR gave the highest accuracies of GEBV for the 3 traits investigated (milk yield, fat yield, and protein yield), with an average increase in accuracy compared with GBLUP_mod across the 3 traits of 0.05 for both Jerseys and Holsteins. The advantage was limited for either Jerseys or Holsteins in using 624,213 SNP rather than 39,745 SNP (0.01 for Holsteins and 0.03 for Jerseys, averaged across traits). Even this limited and nonsignificant advantage was only observed when BayesR was used. An alternative panel, which extracted the SNP in the transcribed part of the bovine genome from the 624,213 SNP panel (to give 58,532 SNP), performed better, with an increase in accuracy of 0.03 for Jerseys across traits. This panel captures much of the increased genomic content of the 624,213 SNP panel, with the advantage of a greatly reduced number of SNP effects to estimate. Taken together, using this panel, a combined breed reference and using BayesR rather than GBLUP_mod increased the accuracy of GEBV in Jerseys from 0.43 to 0.52, averaged across the 3 traits.     

Differences in modified contemporary comparison sire evaluations from first and later lactations by breed

Sire evaluations from three sets of daughter records, first records only (first), later records after firsts (later), and all records (all) by Modified Contemporary Comparison procedures were used to examine differences in first and later lactation evaluations by breed. January 1984 evaluations for milk for 767 Ayrshires, 3,175 Guernsey, 29,498 Holstein, 3,530 Jersey, and 984 Brown Swiss bulls with 10 or more daughters in each set were used. Average differences between evaluations (later minus first) were 36, 0, 6, 35, and 35 kg milk for Ayrshire, Guernsey, Holstein, Jersey, and Brown Swiss bulls. Standard deviations of the difference were 114, 94, 142, 91, and 134 kg, showing considerable sire-to-sire variation in difference. Correlations between evaluations based on first and later records were .84 to .86 for all breeds except .92 for Jerseys. Percent of first lactations culled was correlated .20, .18, .16, .16, and .19 with difference for Ayrshire, Guernsey, Holstein, Jersey, and Brown Swiss, indicating that culling produced larger differences between evaluations for first and later lactations in favor of later evaluations. Prediction of sire evaluation from later records was enhanced by knowledge of sire's age in addition to first evaluation for Guernsey, Holstein and Jersey sires. In these breeds, for a constant first evaluation, and percent culled in first lactation, younger bulls had higher evaluations from later records. This study showed important differences between evaluations from first and later records for all breeds.     

Multibreed genomic evaluations using purebred Holsteins, Jerseys, and Brown Swiss

Multibreed models are currently used in traditional US Department of Agriculture (USDA) dairy cattle genetic evaluations of yield and health traits, but within-breed models are used in genomic evaluations. Multibreed genomic models were developed and tested using the 19,686 genotyped bulls and cows included in the official August 2009 USDA genomic evaluation. The data were divided into training and validation sets. The training data set comprised bulls that were daughter proven and cows that had records as of November 2004, totaling 5,331Holstein, 1,361 Jersey, and 506 Brown Swiss. The validation data set had 2,508Holstein, 413 Jersey, and 185 Brown Swiss bulls that were unproven (no daughter information) in November 2004 and proven by August 2009. A common set of 43,385 single nucleotide polymorphisms (SNP) was used for all breeds. Three methods of multibreed evaluation were investigated. Method 1 estimated SNP effects separately within breed and then applied those breed-specific SNP estimates to the other breeds. Method 2 estimated a common set of SNP effects from combined genotypes and phenotypes of all breeds. Method 3 solved for correlated SNP effects within each breed estimated jointly using a multitrait model where breeds were treated as different traits. Across-breed genomic predicted transmitting ability (GPTA) and within-breed GPTA were compared using regressions to predict the deregressed validation data. Method 1 worked poorly, and coefficients of determination (R(2)) were much lower using training data from a different breed to estimate SNP effects. Correlations between direct genomic values computed using training data from different breeds were less than 30% and sometimes negative. Across-breed GPTA from method 2had higher R(2) values than parent average alone but typically produced lower R(2) values than the within-breed GPTA. The across-breed R(2) exceeded the within-breed R(2) for a few traits in the Brown Swiss breed, probably because information from the other breeds compensated for the small numbers of Brown Swiss training animals. Correlations between within-breed GPTA and across-breed GPTA ranged from 0.91 to 0.93. The multibreed GPTA from method 3 were significantly better than the current within-breed GPTA, and adjusted R(2) for protein yield (the only trait tested for method 3) were highest of all methods for all breeds. However, method 3 increased the adjusted R(2) by only 0.01 for Holsteins, ≤0.01 for Jerseys, and 0.01 for Brown Swiss compared with within-breed predictions.     

Genetic markers for lactation persistency in primiparous Australian dairy cows

Good performance in extended lactations of dairy cattle may have a beneficial effect on food costs, health, and fertility. Because data for extended lactation performance is scarce, lactation persistency has been suggested as a suitable selection criterion. Persistency phenotypes were calculated in several ways: P1 was yield relative to an approximate peak, P2 was the slope after peak production, and P3 was a measure derived to be phenotypically uncorrelated to yield and calculated as a function of linear regressions on test-day deviations of days in milk. Phenotypes P1, P2, and P3 were calculated for sires as solutions estimated from a random regression model fitted to milk yield. Because total milk yield, calculated as the sum of daily sire solutions, was correlated to P1 and P2 (r = 0.30 and 0.35 for P1 and P2, respectively), P1 and P2 were also adjusted for milk yield (P1adj and P2adj, respectively). To find genomic regions associated with the persistency phenotypes, we used a discovery population of 743 Holstein bulls proven before 2005 and 2 validation data sets of 357 Holstein bulls proven after 2005 and 294 Jersey sires. Two strategies were used to search for genomic regions associated with persistency: 1) persistency solutions were regressed on each single nucleotide polymorphism (SNP) in turn and 2) a genomic selection method (BayesA) was used where all SNP were fitted simultaneously. False discovery rates in the validation data were high (>66% in Holsteins and >77% in Jerseys). However, there were 2 genomic regions on chromosome 6 that validated in both breeds, including a cluster of 6 SNP at around 13.5 to 23.7 Mbp and another cluster of 5 SNP (70.4 to 75.6 Mbp). A third cluster validated in both breeds on chromosome 26 (0.33 to 1.46 Mbp). Validating SNP effects across 2 breeds is unlikely to happen by chance even when false discovery rates within each breed are high. However, marker-assisted selection on these selected SNP may not be the best way to improve this trait because the average variation explained by validated SNP was only 1 to 2%. Genomic selection could be a better alternative. Correlations between genomic breeding values predicted using all SNP simultaneously and estimated breeding values based on progeny test were twice as high as the equivalent correlations between estimated breeding values and parent average. Persistency is a good candidate for genomic selection because the trait is expressed late in lactation.     

Breed and parity effects on energy balance profiles through lactation: evidence of genetically driven body energy change

The aim of this study was to characterize patterns of energy balance through lactation of cows kept under constant feeding conditions. Danish Holstein, Danish Red, and Jersey cows were studied during consecutive lactations and remained on the same dietary treatment throughout. They were fed a normal (13.55 MJ of digestible energy/kg of dry matter) or a lower energy diet (12.88 MJ of digestible energy/kg of dry matter) ad libitum throughout lactation. Energy balance was calculated using the effective energy (EE) system in such a way that energy balance equated to body energy reserve change. In the EE system the energy values assigned to feeds are directly equivalent to the energy requirements of the animal; 1 MJ of EE supply has the same energy value as 1 MJ of lipid loss from the body. The resulting body energy change data were analyzed using a linear spline model. There was no evidence to suggest that different combinations of breed and parity required different knot placements. The Holstein mobilized significantly more body energy in early lactation than the Danish Red and Jersey breeds. Parity 1 cows mobilized significantly less than parity 2 and 3 cows. There was a significant interaction between breed and parity in the first half of lactation due to parity 1 Jersey cows having a greater mobilization than would be expected of the difference between parities in the other breeds. As lactation progressed, the differences between parities and between breeds decreased. Cows on the higher energy diet had a more positive energy balance. Within breed and parity, the following possible predictors of individual differences in body energy change were examined: fatness-corrected live weight, condition score at calving, and genotype. There was no difference in the predicted cow effect or residual energy balance profile when grouped according to quartiles of corrected live weight or according to condition score at calving. During the period of most negative energy balance (d 14) there was no significant relationship between live weight and intake, suggesting that, within diet type, the systematic patterns of body energy change through lactation in cows that were kept under stable and sufficient nutritional conditions cannot be accounted for by environmental factors such as constrained intake or condition score at calving. Thus, these patterns appear to have a genetic basis. The proportion of the phenotypic variation (remaining after accounting for fixed effects) accounted for by additive genetic effects varied through lactation from 4.2 to 13.0%. Genetic correlations between early and late lactation energy balances were low and close to zero, suggesting that body energy changes in early and late lactation are genetically independent traits.     

Genome-wide association study with imputed whole-genome sequence variants including large deletions for female fertility in 3 Nordic dairy cattle breeds

Fertility is an economically important trait in livestock. Poor fertility in dairy cattle can be due to loss-of-function variants affecting any essential gene that causes early embryonic mortality in homozygotes. To identify fertility-associated quantitative trait loci, we performed single-marker association analyses for 8 fertility traits in Holstein, Jersey, and Nordic Red Dairy cattle using imputed whole-genome sequence variants including SNPs, indels, and large deletion. We then performed stepwise selection of independent markers from GWAS loci using conditional and joint association analyses. From single-marker analyses for fertility traits, we reported genome-wide significant associations of 30,384 SNPs, 178 indels, and 3 deletions in Holstein; 23,481 SNPs, 189 indels, and 13 deletions in Nordic Red; and 17 SNPs in Jersey cattle. Conditional and joint association analyses identified 37 and 23 independent associations in Holstein and Nordic Red Dairy cattle, respectively. Fertility-associated GWAS loci were enriched for developmental and cellular processes (Gene Ontology enrichment, false discovery rate < 0.05). For these quantitative trait loci regions (top marker and 500 kb of surrounding regions), we proposed several candidate genes with functional annotations corresponding to embryonic lethality and various fertility-related phenotypes in mouse and cattle. The inclusion of these top markers in future releases of the custom SNP chip used for genomic evaluations will enable their validation in independent populations and improve the accuracy of genomic predictions.     

Relationships between reproduction traits, age and body weight at calving, and days dry in first lactation Ayrshires and Holsteins

Individual cow test day records collected between December 1979 and June 1986 were used to calculate measures of reproductive performance, age and weight at calving, and days dry for 7824 Ayrshire and 79,755 Holstein cows in first lactation. Separate analyses by breed were carried out according to a multiple-trait mixed model. Sixty-two Ayrshire and 369 Holstein sires were treated as random in the analyses. Ayrshires were, on average, older and lighter at calving than Holsteins, but the breeds differed little in reproduction measures and days dry. Heritabilities of fertility traits, days to first breeding, days open, and services per conception were all less than .015 in the multi-trait analyses. With the exception of body weight, heritability estimates for the other traits were less than .05. Phenotypic correlations between traits were almost identical for the two breeds, and genetic correlations tended to be similar. Exceptions involved the trait days to first breeding and services per conception, but heritabilities of these traits were close to zero (p less than .008) in Ayrshires. Fertility traits were positively correlated genetically. Genetic correlations between days open and both age and body weight at calving were small. The genetic correlation between age and weight at calving was -.90 and -.68 in Ayrshires and Holsteins, respectively. Genetic correlations between days dry and all traits except body weight were moderate and positive.     

Short communication: Replication of genome-wide association studies for milk production traits in Chinese Holstein by an efficient rotated linear mixed model

Milk is regarded as an important nutrient for humans, and Chinese Holstein cows provide high-quality milk for billions of Chinese people. Therefore, detecting quantitative trait nucleotides (QTN) or candidate genes for milk production traits in Chinese Holstein is important. In this study, we performed genome-wide association studies (GWAS) in a Chinese Holstein population of 6,675 cows and 71,633 SNP using deregressed proofs (DRP) as phenotypes to replicate our previous study in a population of 1,815 cows and 39,163 SNP using estimated breeding values (EBV) as phenotypes. The associations between 3 milk production traits—milk yield (MY), fat percentage (FP), and protein percentage (PP)—and the SNP were determined by using an efficient rotated linear mixed model, which benefits from linear transformations of genomic estimated values and Eigen decomposition of the genomic relationship matrix algorithm. In total, we detected 94 SNP that were significantly associated with one or more milk production traits, including 7 SNP for MY, 76 for FP, and 36 for PP; 87% of these SNP were distributed across Bos taurus autosomes 14 and 20. In total, 83 SNP were found to be located within the reported quantitative trait loci (QTL) regions, and one novel segment (between 1.41 and 1.49 Mb) on chromosome 14 was significantly associated with FP, which could be an important candidate QTL region. In addition, the detected intervals were narrowed down from the reported regions harboring causal variants. The top significant SNP for the 3 traits was ARS-BFGL-NGS-4939, which is located within the DGAT1 gene. Five detected genes (CYHR1, FOXH1, OPLAH, PLEC, VPS28) have effects on all 3 traits. Our study provides a suite of QTN, candidate genes, and a novel QTL associated with milk production traits, and thus forms a solid basis for genomic selection and molecular breeding for milk production traits in Chinese Holstein.     

Analysis of the relationship between workability traits and functional longevity in Canadian dairy breeds

The aim of this study was to assess the effect of workability traits like milking speed and temperament on functional longevity of Canadian dairy cattle using a Weibull proportional hazards model. First-lactation data consisted of the following: 1,728,289 and 2,426,123 Holstein cows for milking temperament and milking speed, respectively, from 18,401 herds and sired by 8,248 sires; 39,618 and 60,121 Jersey cows for milking temperament and milking speed, respectively, from 1,845 herds and sired by 2,413 sires; and 54,391 and 94,847 Ayrshire cows for milking temperament and milking speed, respectively, from 1,316 herds and sired by 2,779 sires. Functional longevity was defined as the number of days from the first calving to culling, death, or censoring adjusted for production. Milking temperament and milking speed were recorded on a 1- to 5-point scale from very nervous to very calm and from very slow to very fast, respectively. The statistical model included the effects of stage of lactation; season of production; the annual change in herd size; type of milk recording supervision; age at first calving; effects of milk, fat, and protein yields calculated as within herd-year-parity deviations; herd-year-season of calving; sire; and milking temperament or milking speed class. The relative culling rate was calculated for animals in each milking temperament or milking speed class after accounting for the above-mentioned effects. The study showed that there was a statistically significant association between workability traits and functional longevity. Very nervous cows were 26, 23, and 46% more likely to be culled than very calm cows in Holstein, Ayrshire, and Jersey breeds, respectively. Similarly, very slow milkers were 36, 33, and 28% more likely to be culled than average milkers in Holstein, Ayrshire, and Jersey breeds, respectively. Additionally, very fast milkers were 11, 13, and 15% more likely to be culled than average milkers in Holstein, Ayrshire, and Jersey breeds, respectively. Producers might want to avoid consequences associated with the fast milkers such as udder health problems.     

Single-step genome-wide association for selected milk fatty acids in Dual-Purpose Belgian Blue cows

The aim of this study was to estimate genetic parameters and identify genomic regions associated with selected individual and groups of milk fatty acids (FA) predicted by milk mid-infrared spectrometry in Dual-Purpose Belgian Blue cows. The used data were 69,349 test-day records of milk yield, fat percentage, and protein percentage along with selected individual and groups FA of milk (g/dL milk) collected from 2007 to 2020 on 7,392 first-parity (40,903 test-day records), and 5,185 second-parity (28,446 test-day records) cows distributed in 104 herds in the Walloon Region of Belgium. Data of 28,466 SNPs, located on 29 Bos taurus autosomes (BTA), of 1,699 animals (639 males and 1,060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by each 25-SNP sliding window (with an average size of ~2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Average daily heritability estimated for the included milk FA traits ranged from 0.01 (C4:0) to 0.48 (C12:0) and 0.01 (C4:0) to 0.42 (C12:0) in the first and second parities, respectively. Genetic correlations found between milk yield and the studied individual milk FA, except for C18:0, C18:1 trans, C18:1 cis-9, were positive. The results showed that fat percentage and protein percentage were positively genetically correlated with all studied individual milk FA. Genome-wide association analyses identified 11 genomic regions distributed over 8 chromosomes [BTA1, BTA4, BTA10, BTA14 (4 regions), BTA19, BTA22, BTA24, and BTA26] associated with the studied FA traits, though those found on BTA14 partly overlapped. The genomic regions identified differed between parities and lactation stages. Although these differences in genomic regions detected may be due to the power of quantitative trait locus detection, it also suggests that candidate genes underlie the phenotypic expression of the studied traits may vary between parities and lactation stages. These findings increase our understanding about the genetic background of milk FA and can be used for the future implementation of genomic evaluation to improve milk FA profile in Dual-Purpose Belgian Blue cows.