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.

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.

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.