A confirmed association between DNA variants in CAPN9, OSM, and ITGAM candidate genes and the risk of umbilical hernia in pigs.

Umbilical hernia (UH) is one of the most prevalent defects of swine, affecting their welfare and causing considerable economic loss. The molecular mechanisms behind UH in pigs remain poorly understood. The aim of this study was to verify the association between UH and previously reported DNA variants in the CAPN9, OSM, ITGAM, and NUGGC genes. A case/control study design was applied in two different crossbred cohorts of commercial fatteners containing 412 and 171 pigs, respectively. SNPs within CAPN9, OSM, and ITGAM were analyzed using Sanger sequencing, and 10 SNPs in CAPN9, five in OSM, and two in ITGAM were identified. A structural variant in the NUGGC gene was studied by droplet-digital PCR, and an elevated copy number was detected in only a single individual. Significant differences in allele frequencies for four SNPs in CAPN9 were detected. The haplotype analysis showed the effect on the risk of UH for two genes. The CAGGA haplotype within OSM and AT haplotype in ITGAM reduced the relative risk of UH by 52% and 45%, respectively, confirming that variants in those genes are associated with the risk of UH in pigs. Moreover, the interaction between the CAPN9 haplotype and the sex of animals had also significant impact on UH risk.

Technical note: Interchangeability and comparison of methane measurements in dairy cows with 2 noninvasive infrared systems.

This study aimed to compare measurements of methane (CH4) and carbon dioxide (CO2) concentrations in the breath of dairy cows kept in commercial conditions using the Fourier-transform infrared spectroscopy (FTIR) and nondispersive infrared spectroscopy (NDIR) methods. The measurement systems were installed in an automated milking system. Measurements were carried out for 5 d using both systems during milkings. The measurements were averaged per milking, giving 467 observations of CH4 and CO2 concentrations of 44 Holstein Friesian cows. The Pearson correlation between observations from the 2 systems was 0.86 for CH4, 0.84 for CO2, and 0.88 for their ratio. The repeatability of FTIR (0.53 for CH4, 0.57 for CO2, and 0.28 for their ratio) was somewhat higher than that of NDIR (0.57 for CH4, 0.47 for CO2, and 0.25 for their ratio). The coefficient of individual agreement was 0.98 for CH4, 0.89 for CO2, and 0.89 for their ratio; the concordance correlation coefficient was 0.48 for both gases and 0.24 for their ratio. We showed that FTIR and NDIR give similar results in commercial farm conditions. They can therefore be used interchangeably to generate a larger data set, which could then be further used for genetic evaluation.

Short communication: Genetic correlations between methane and milk production, conformation, and functional traits.

Livestock produce CH4, contributing to the global warming effect. One of the currently investigated solutions to reduce CH4 production is selective breeding. The goal of this study was to estimate the genetic correlations between CH4 and milk production, conformation, and functional traits used in the selection index for Polish-Holstein cows. In total, 34,429 daily CH4 production observations collected from 483 cows were available, out of which 281 cows were genotyped. The CH4 was measured using a so-called sniffer device installed in an automated milking system. Breeding values for CH4 were estimated with the use of single-step genomic BLUP, and breeding values for remaining traits were obtained from the Polish national genomic evaluation. Genetic correlations between CH4 production and remaining traits were estimated using bivariate analyses. The estimated genetic correlations were in general low. The highest values were estimated for fat yield (0.21), milk yield (0.15), chest width (0.15), size (0.15), dairy strength (0.11), and somatic cell count (0.11). These estimates, as opposed to estimates for the remaining traits, were significantly different from zero.

Genome-wide association identifies methane production level relation to genetic control of digestive tract development in dairy cows.

The global temperatures are increasing. This increase is partly due to methane (CH4) production from ruminants, including dairy cattle. Recent studies on dairy cattle have revealed the existence of a heritable variation in CH4 production that enables mitigation strategies based on selective breeding. We have exploited the available heritable variation to study the genetic architecture of CH4 production and detected genomic regions affecting CH4 production. Although the detected regions explained only a small proportion of the heritable variance, we showed that potential QTL regions affecting CH4 production were located within QTLs related to feed efficiency, milk-related traits, body size and health status. Five candidate genes were found: CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25. These candidate genes were involved in a number of metabolic processes that are possibly related to CH4 production. One of the most promising candidate genes (PKD1) was related to the development of the digestive tract. The results indicate that CH4 production is a highly polygenic trait.

Heritability of methane emissions from dairy cows over a lactation measured on commercial farms.

Methane emission is currently an important trait in studies on ruminants due to its environmental and economic impact. Recent studies were based on short-time measurements on individual cows. As methane emission is a longitudinal trait, it is important to investigate its changes over a full lactation. In this study, we aimed to estimate the heritability of the estimated methane emissions from dairy cows using Fourier-transform infrared spectroscopy during milking in an automated milking system by implementing the random regression method. The methane measurements were taken on 485 Polish Holstein-Friesian cows at 2 commercial farms located in western Poland. The overall daily estimated methane emission was 279 g/d. Genetic variance fluctuated over the course of lactation around the average level of 1,509 (g/d), with the highest level, 1,866 (g/d), at the end of the lactation. The permanent environment variance values started at 2,865 (g/d) and then dropped to around 846 (g/d) at 100 d in milk (DIM) to reach the level of 2,444 (g/d) at the end of lactation. The residual variance was estimated at 2,620 (g/d). The average repeatability was 0.25. The heritability level fluctuated over the course of lactation, starting at 0.23 (SE 0.12) and then increasing to its maximum value of 0.3 (SE 0.08) at 212 DIM and ending at the level of 0.27 (SE 0.12). Average heritability was 0.27 (average SE 0.09). We have shown that estimated methane emission is a heritable trait and that the heritability level changes over the course of lactation. The observed changes and low genetic correlations between distant DIM suggest that it may be important to consider the period in which methane phenotypes are collected.

Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying.

Phenotypes have been reviewed to select for lower-emitting animals in order to decrease the environmental footprint of dairy cattle products. This includes direct selection for breath measurements, as well as indirect selection via indicator traits such as feed intake, milk spectral data, and rumen microbial communities. Many of these traits are expensive or difficult to record, or both, but with genomic selection, inclusion of methane emission as a breeding goal trait is feasible, even with a limited number of registrations. At present, methane emission is not included among breeding goals for dairy cattle worldwide. There is no incentive to include enteric methane in breeding goals, although global warming and the release of greenhouse gases is a much-debated political topic. However, if selection for reduced methane emission became a reality, there would be limited consensus as to which phenotype to select for: methane in liters per day or grams per day, methane in liters per kilogram of energy-corrected milk or dry matter intake, or a residual methane phenotype, where methane production is corrected for milk production and the weight of the cow. We have reviewed the advantages and disadvantages of these traits, and discuss the methods for selection and consequences for these phenotypes.

Prepubertal heifers versus cows-The differences in the follicular environment.

The oocyte quality is to a large extent influenced by the sexual maturity of the donor female. Although this phenomenon has already been broadly described in domestic animals, the underlying mechanisms are poorly understood. Published data focus on oocyte ultrastructure, fertilization abnormalities, and blastocyst developmental rate. The goal of the present experiment was to characterize the follicular environment (oocyte, cumulus [CC] and granulosa (GC) cells as well as follicular fluid [FF]) in ovarian follicles of prepubertal heifers and cows. Each experimental replicate included the following set of traits within individual follicles: lipid droplets (LDs) number in oocytes, expression of seven genes involved in energy metabolism (fatty acids [FAs] metabolism-ELOVL2, ELOVL5, SCD, FADS2, glucose transport-GLUT1, GLUT3, GLUT8) in CC and GC as well as FA composition and glucose concentration in FF. According to our results, cow oocytes were larger in diameter and contained more LD than those from prepubertal heifers, both before and after IVM. The LD number was also higher in cow oocytes after IVM, when compared to immature oocytes. The FF from cow follicles had elevated glucose content similarly to the majority of the analyzed FA. Transcript analysis revealed differences for five out of seven analyzed genes (ELOVL, FADS2, SCD, GLUT3, GLUT8) in CC and GC cells. However after considering the female category, the only difference was noticed for the mRNA of SCD gene, which was more abundant in cow GC. This finding may indicate distinct roles of CC and GC in follicular energy metabolism. In conclusions, we suggest that distinct properties of follicular environment in prepubertal heifers and cows may be responsible for differences in the quality of oocytes from the two categories of donors. We hypothesize that suboptimal environment in heifer follicles (glucose and FA lower content in FF) determines reduced quality of their oocytes (lower diameter and LD number) and limited maturation potential. Besides, energy demands of heifer oocytes may be restricted due to a low LD number, exerting a negative effect on the development of the future embryo. The advantages of cow gametes (e.g., higher LD number and diameter) attributed to oocytes of superior quality may support the statement that cows donate oocytes of better quality than heifers.

Updating the reference population to achieve constant genomic prediction reliability across generations.

The reliability of genomic breeding values (DGV) decays over generations. To keep the DGV reliability at a constant level, the reference population (RP) has to be continuously updated with animals from new generations. Updating RP may be challenging due to economic reasons, especially for novel traits involving expensive phenotyping. Therefore, the goal of this study was to investigate a minimal RP update size to keep the reliability at a constant level across generations. We used a simulated dataset resembling a dairy cattle population. The trait of interest was not included itself in the selection index, but it was affected by selection pressure by being correlated with an index trait that represented the overall breeding goal. The heritability of the index trait was assumed to be 0.25 and for the novel trait the heritability equalled 0.2. The genetic correlation between the two traits was 0.25. The initial RP (n=2000) was composed of cows only with a single observation per animal. Reliability of DGV using the initial RP was computed by evaluating contemporary animals. Thereafter, the RP was used to evaluate animals which were one generation younger from the reference individuals. The drop in the reliability when evaluating younger animals was then assessed and the RP was updated to re-gain the initial reliability. The update animals were contemporaries of evaluated animals (EVA). The RP was updated in batches of 100 animals/update. First, the animals most closely related to the EVA were chosen to update RP. The results showed that, approximately, 600 animals were needed every generation to maintain the DGV reliability at a constant level across generations. The sum of squared relationships between RP and EVA and the sum of off-diagonal coefficients of the inverse of the genomic relationship matrix for RP, separately explained 31% and 34%, respectively, of the variation in the reliability across generations. Combined, these parameters explained 53% of the variation in the reliability across generations. Thus, for an optimal RP update an algorithm considering both relationships between reference and evaluated animals, as well as relationships among reference animals, is required.

Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population.

The genomic breeding value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the breeding value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic breeding value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n = 869) and the predictor traits were fat-protein-corrected milk (FPCM, n = 1520) and live weight (LW, n = 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and breeding values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n = 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV- and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI breeding value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI breeding value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) breeding values of scarcely recorded traits such as feed intake.

Predicted accuracy of and response to genomic selection for new traits in dairy cattle.

Genomic selection relaxes the requirement of traditional selection tools to have phenotypic measurements on close relatives of all selection candidates. This opens up possibilities to select for traits that are difficult or expensive to measure. The objectives of this paper were to predict accuracy of and response to genomic selection for a new trait, considering that only a cow reference population of moderate size was available for the new trait, and that selection simultaneously targeted an index and this new trait. Accuracy for and response to selection were deterministically evaluated for three different breeding goals. Single trait selection for the new trait based only on a limited cow reference population of up to 10 000 cows, showed that maximum genetic responses of 0.20 and 0.28 genetic standard deviation (s.d.) per year can be achieved for traits with a heritability of 0.05 and 0.30, respectively. Adding information from the index based on a reference population of 5000 bulls, and assuming a genetic correlation of 0.5, increased genetic response for both heritability levels by up to 0.14 genetic s.d. per year. The scenario with simultaneous selection for the new trait and the index, yielded a substantially lower response for the new trait, especially when the genetic correlation with the index was negative. Despite the lower response for the index, whenever the new trait had considerable economic value, including the cow reference population considerably improved the genetic response for the new trait. For scenarios with a zero or negative genetic correlation with the index and equal economic value for the index and the new trait, a reference population of 2000 cows increased genetic response for the new trait with at least 0.10 and 0.20 genetic s.d. per year, for heritability levels of 0.05 and 0.30, respectively. We conclude that for new traits with a very small or positive genetic correlation with the index, and a high positive economic value, considerable genetic response can already be achieved based on a cow reference population with only 2000 records, even when the reliability of individual genomic breeding values is much lower than currently accepted in dairy cattle breeding programs. New traits may generally have a negative genetic correlation with the index and a small positive economic value. For such new traits, cow reference populations of at least 10 000 cows may be required to achieve acceptable levels of genetic response for the new trait and for the whole breeding goal.

The impact of genotyping different groups of animals on accuracy when moving from traditional to genomic selection.

Compared with traditional selection, the use of genomic information tends to increase the accuracy of estimated breeding values (EBV). The cause of this increase is, however, unknown. To explore this phenomenon, this study investigated whether the increase in accuracy when moving from traditional (AA) to genomic selection (GG) was mainly due to genotyping the reference population (GA) or the evaluated animals (AG). In it, a combined relationship matrix for simultaneous use of genotyped and ungenotyped animals was applied. A simulated data set reflected the dairy cattle population. Four differently designed (i.e., different average relationships within the reference population) small reference populations and 3 heritability levels were considered. The animals in the reference populations had high, moderate, low, and random (RND) relationships. The evaluated animals were juveniles. The small reference populations simulated difficult or expensive to measure traits (i.e., methane emission). The accuracy of selection was expressed as the reliability of (genomic) EBV and was predicted based on selection index theory using relationships. Connectedness between the reference populations and evaluated animals was calculated using the prediction error variance. Average (genomic) EBV reliabilities increased with heritability and with a decrease in the average relationship within the reference population. Reliabilities in AA and AG were lower than those in GG and were higher than those in GA (respectively, 0.039, 0.042, 0.052, and 0.048 for RND and a heritability of 0.01). Differences between AA and GA were small. Average connectedness with all animals in the reference population for all scenarios and reference populations ranged from 0.003 to 0.024; it was lowest when the animals were not genotyped (AA; e.g., 0.004 for RND) and highest when all the animals were genotyped (GG; e.g., 0.024 for RND). Differences present across designs of the reference populations were very small. Genomic relationships among animals in the reference population might be less important than those for the evaluated animals with no phenotypic observations. Thus, the main origin of the gain in accuracy when using genomic selection is due to genotyping the evaluated animals. However, genotyping only one group of animals will always yield less accurate estimates.

Reliability of direct genomic values for animals with different relationships within and to the reference population.

Accuracy of genomic selection depends on the accuracy of prediction of single nucleotide polymorphism effects and the proportion of genetic variance explained by markers. Design of the reference population with respect to its family structure may influence the accuracy of genomic selection. The objective of this study was to investigate the effect of various relationship levels within the reference population and different level of relationship of evaluated animals to the reference population on the reliability of direct genomic breeding values (DGV). The DGV reliabilities, expressed as squared correlation between estimated and true breeding value, were calculated for evaluated animals at 3 heritability levels. To emulate a trait that is difficult or expensive to measure, such as methane emission, reference populations were kept small and consisted of females with own performance records. A population reflecting a dairy cattle population structure was simulated. Four chosen reference populations consisted of all females available in the first genotyped generation. They consisted of highly (HR), moderately (MR), or lowly (LR) related animals, by selecting paternal half-sib families of decreasing size, or consisted of randomly chosen animals (RND). Of those 4 reference populations, RND had the lowest average relationship. Three sets of evaluated animals were chosen from 3 consecutive generations of genotyped animals, starting from the same generation as the reference population. Reliabilities of DGV predictions were calculated deterministically using selection index theory. The randomly chosen reference population had the lowest average relationship within the reference population. Average reliabilities increased when average relationship within the reference population decreased and the highest average reliabilities were achieved for RND (e.g., from 0.53 in HR to 0.61 in RND for a heritability of 0.30). A higher relationship to the reference population resulted in higher reliability values. At the average squared relationship of evaluated animals to the reference population of 0.005, reliabilities were, on average, 0.49 (HR) and 0.63 (RND) for a heritability of 0.30; 0.20 (HR) and 0.27 (RND) for a heritability of 0.05; and 0.07 (HR) and 0.09 (RND) for a heritability of 0.01. Substantial decrease in the reliability was observed when the number of generations to the reference population increased [e.g., for heritability of 0.30, the decrease from evaluated set I (chosen from the same generation as the reference population) to II (one generation younger than the reference population) was 0.04 for HR, and 0.07 for RND]. In this study, the importance of the design of a reference population consisting of cows was shown and optimal designs of the reference population for genomic prediction were suggested.

Effect of enlarging the reference population with (un)genotyped animals on the accuracy of genomic selection in dairy cattle.

Genomic selection (GS) permits accurate breeding values to be obtained for young animals, shortening the generation interval and accelerating the genetic gain, thereby leading to reduced costs for proven bulls. Genotyping a large number of animals using high-density single nucleotide polymorphism marker arrays is nevertheless expensive, and therefore, a method to reduce the costs of GS is desired. The aim of this study was to investigate an influence of enlarging the reference population, with either genotyped animals or individuals with predicted genotypes, on the accuracy of genomic estimated breeding values. A dairy cattle population was simulated in which proven bulls with 100 daughters were used as a reference population for GS. Phenotypic records were simulated for bulls with heritability equal to the reliability of daughter yield deviations based on 100 daughters. The simulated traits represented heritabilities at the level of individual daughter performance of 0.3, 0.05, and 0.01. Three scenarios were considered in which (1) the reference population consisted of 1,000 genotyped animals, (2) 1,000 ungenotyped animals were added to the reference population, and (3) the 1,000 animals added in scenario 2 were genotyped in addition to the 1,000 animals from scenario 1. Genotypes for ungenotyped animals were predicted with an average accuracy of 0.58. Additionally, an adjustment of the diagonal elements of the G matrix was proposed for animals with predicted genotypes. The accuracy of genomic estimated breeding values for juvenile animals was the highest for the scenario with 2,000 genotyped animals, being 0.90, 0.79, and 0.60 for the heritabilities of 0.3, 0.05, and 0.01, respectively. Accuracies did not differ significantly between the scenario with 1,000 genotyped animals only and the scenario in which 1,000 ungenotyped animals were added and the adjustment of the G matrix was applied. The absence of significant increase in the accuracy of genomic estimated breeding values was attributed to the low accuracy of predicted genotypes. Although the differences were not significant, the difference between scenario 1 and 2 increased with decreasing heritability. Without the adjustment of the diagonal elements of the G matrix, accuracy decreased. Results suggest that inclusion of ungenotyped animals is only expected to enhance the accuracy of GS when the unknown genotypes can be predicted with high accuracy.

Short communication: Trends for monthly changes in days open in Holsteins.

A reaction norm approach was used to estimate trends for days open (DO) with a model that indirectly accounted for heat stress. Data included 3.4 million first-parity records of DO of US Holsteins. A fixed effect model included herd-year, month of calving within region (MOC), age class, and regression on 305-d milk yield. An index calculated from the standardized solutions to MOC derived from the fixed effect model was treated as a proxy for an index on heat stress (SI). The lowest index for any region was set to zero. The highest index was 1.00 for the Southeast, 0.56 for the Northeast, 0.54 for the Midwest, 0.33 for the Northwest, and 0.42 for the Southwest. In all regions except the Northwest, the highest DO and the corresponding highest indices were in March-April. Compared with the fixed model, the reaction norm model also included the effect of an animal and a random regression on the SI; the 2 animal solutions are subsequently referred to as an intercept and a slope. Genetic trends were calculated for cows and sires separately. For cows, the trend for the intercept was -0.1 d/yr, whereas the trend for the slope was 1 d/yr. For sires, the same trends were -0.3 and 1.5, respectively. Official proofs were used to characterize the 100 top and 100 bottom bulls with at least 50 daughters for the intercept and the slope. Compared with the top bulls, the bottom bulls for the intercept gave 56 kg more milk and their type performance index was higher by 212 points. For the slope, the same numbers were -435 kg and -242 points, respectively. Trends for seasonal changes of days open are unfavorable.