Our aim was to investigate the genetic correlations between CH production and body conformation, fertility, and health traits in dairy cows. Data were collected from 10 commercial Holstein herds in Denmark, including 5,758 cows with records for body conformation traits, 7,390 for fertility traits, 7,439 for health traits, and 1,397 with individual CH measurements. Methane production was measured during milking in automatic milking systems, using a sniffer approach. Correlations between CH and several different traits were estimated. These traits were interval between calving and first insemination, interval between first and last insemination, number of inseminations, udder diseases, other diseases, height, body depth, chest width, dairy character, top line, and body condition score. Bivariate linear models were used to estimate the genetic parameters within and between CH and the other traits. In general, the genetic correlations between CH and the traits investigated were low. The heritability of CH was 0.25, and ranged from 0.02 to 0.07 for fertility and health traits, and from 0.17 to 0.74 for body conformation traits. Further research with a larger data set should be performed to more accurately establish how CH relates to fertility, health, and body conformation traits in dairy cattle. This will be useful in the design of future breeding goals that consider the production of CH.

For a number of traits the phenotype considered to be the goal trait is a combination of 2 or more traits, like methane (CH) emission (CH/kg of milk). Direct selection on CH4 emission defined as a ratio is problematic, because it is uncertain whether the improvement comes from an improvement in milk yield, a decrease in CH emission or both. The goal was to test different strategies on selecting for 2 antagonistic traits- improving milk yield while decreasing methane emissions. The hypothesis was that to maximize genetic gain for a ratio trait, the best approach is to select directly for the component traits rather than using a ratio trait or a trait where 1 trait is corrected for the other as the selection criteria. Stochastic simulation was used to mimic a dairy cattle population. Three scenarios were tested, which differed in selection criteria but all selecting for increased milk yield: 1) selection based on a multitrait approach using the correlation structure between the 2 traits, 2) the ratio of methane to milk and 3) gross methane phenotypically corrected for milk. Four correlation sets were tested in all scenarios, to access robustness of the results. An average genetic gain of 66 kg of milk per yr was obtained in all scenarios, but scenario 1 had the best response for decreased methane emissions, with a genetic gain of 24.8 l/yr, while scenarios 2 and 3 had genetic gains of 27.1 and 27.3 kg/yr. The results found were persistent across correlation sets. These results confirm the hypothesis that to obtain the highest genetic gain a multitrait selection is a better approach than selecting for the ratio directly. The results are exemplified for a methane and milk scenario but can be generalized to other situations where combined traits need to be improved.

The main environmental factor that affects the regulation of reproductive seasonality is photoperiod through its effects on melatonin secretion. The melatonin receptor MTRN1A appears to be involved in regulating the reproductive seasonality and milk production in the period. The aim of this study was to identify polymorphisms in the MTRN1A gene and their possible associations with milk, fat and protein productions, fat and protein percentages, age at first calving, and first calving interval in buffaloes. Three genotypes (CC, CT, and TT) were identified by PCR-RFLP, and there was a significant association with protein percentage (P < 0.0001). Further studies are necessary to better understand the influence of melatonin gene and their receptors in the productive functions of buffaloes.

In beef cattle farming, growth and carcass traits are important for genetic breeding programs. Molecular markers can be used to assist selection and increase genetic gain. The ADIPOQ, OLR1 and PPARGC1A genes are involved in lipid synthesis and fat accumulation in adipose tissue. The objective of this study was to identify polymorphisms in these genes and to assess the association with growth and carcass traits in Nelore cattle. A total of 639 animals were genotyped by PCR-RFLP for rs208549452, rs109019599 and rs109163366 in ADIPOQ, OLR1 and PPARGC1A gene, respectively. We analyzed the association of SNPs identified with birth weight, weaning weight, female yearling weight, female hip height, male yearling weight, male hip height, loin eye area, rump fat thickness, and backfat thickness. The OLR1 marker was associated with rump fat thickness and weaning weight (P < 0.05) and the PPARGC1 marker was associated with female yearling weight.

As long as large-scale recording of expensive-to-measure and labor-consuming traits, such as dry matter intake (DMI) and CH 4 production (CH 4 P), continues to be challenging in practical conditions, alternative traits that are already routinely recorded in dairy herds should be investigated. An ideal indicator trait must, in addition to expressing genetic variation, have a strong correlation with the trait of interest. Our aim was to estimate individual level and phenotypic correlations between rumination time (RT), CH 4 P, and DMI to determine if RT could be used as an indicator trait for CH 4 P and DMI. Data from 343 Danish Holstein cows were collected at the Danish Cattle Research Centre for a period of approximately 3 yr. The data set consisted of 14,890 records for DMI, 15,835 for RT, and 6,693 for CH 4 P. Data were divided in primiparous cows only (PC) and all cows (MC), and then divided in lactation stage (early, mid, late, and whole lactation) to analyze the changes over lactation. Linear mixed models, including an animal effect but no pedigree, were used to estimate the correlations among traits. Phenotypic and individual level correlations between RT and both CH 4 P and DMI were close to zero, regardless of lactation stage and data set (PC or MC). However, CH 4 P and DMI were highly correlated, both across lactation stages and data sets. In conclusion, RT is unsuitable to be used as an indicator trait for either CH 4 P or DMI. Our study failed to validate RT as a useful indicator trait for both CH 4 P and DMI, but more studies with novel phenotypes can offer different approaches to select and incorporate important yet difficult to record traits into breeding goals and selection indexes.