In this study, we estimated the genetic parameters for 6 composite traits and 27 body conformation traits of 1016 dual-purpose Simmental cattle reared in northwestern China from 2010 to 2019 using a linear animal mixed model. To integrate these traits, a variety of methods were used as follows: (1) genetic parameters estimates for composite and individual body conformation traits based on the pedigree relationship matrix (A) and combined genomic-pedigree relationship matrix (H); (2) factor analysis to explore the relationships among body conformation traits; and (3) genetic parameters of factor scores estimated using A and H, and the correlations of EBVs of the factor scores and EBVs of the composite traits. Heritability estimates of the composite traits using A and H were low to medium (0.07–0.47). The 24 common latent factors explained 96.13% of the total variance. Among factors with eigenvalues ≥ 1, F1 was mainly related to body frame, muscularity, and rump; F2 was related to feet and legs; F3, F4, F5, and F6 were related to teat placement, teat size, udder size, and udder conformation; and F7 was related to body frame. Single-trait analysis of factor scores yielded heritability estimates that were low to moderate (0.008–0.43 based on A and 0.04–0.43 based on H). Spearman and Pearson correlations, derived from the best linear unbiased prediction analysis of composite traits and factor scores, showed a similar pattern. Thus, incorporating factor analysis into the morphological evaluation to simplify the assessment of body conformation traits may improve the genetics of dual-purpose Simmental cattle.

The gastrointestinal microbiota greatly affects the health status and production performance of bovines. Presently, many studies have used high-throughput sequencing methods to investigate the gastrointestinal microbiome in bovines. However, the microbiome profile of crossbred cattle across the whole gastrointestinal tract (GIT) has not been thoroughly reported. In this study, the digesta at ten regions (including the rumen, reticulum, omasum, abomasum, duodenum, jejunum, ileum, cecum, colon, and rectum) of the GIT were collected in three Simmental × Holstein crossbred heifers aged 17 months, and microbial DNA was extracted and amplified for sequencing of the V3–V4 regions of the 16S rRNA gene. Functional orthologs of the microbiota genome were predicted and analyzed. We found that samples were categorized into three groups (the stomach, small intestine, and large intestine) by principal coordinate analysis (PCoA) based on Bray–Curtis dissimilarity in both the bacterial composition and functional profile. Samples from small intestine had the lowest alpha diversity of bacteria composition and highest alpha diversity of the functional composition. Three groups of GIT regions were characterized by several microbiome features. The stomach was characterized by Bacteroidetes and Fibrobacteres at the phylum level, and KEGG pathways related to the metabolism of cofactors and vitamins, glycan biosynthesis, and metabolism were enriched in the stomach. The small intestine was characterized by Actinobacteria and Patescibacteria at the phylum level, and KEGG pathways related to xenobiotics biodegradation and metabolism were enriched in the small intestine. The large intestine featured Ruminococcaceae, Rikenellaceae, and Bacteroidacea at the family level, and KEGG pathways, including steroid hormone biosynthesis, linoleic acid metabolism, and cysteine and methionine metabolism were enriched in the large intestine. The results of the current study revealed the spatial heterogeneity of microbiota across the GIT in Simmental × Holstein crossbreeds and identified microbial biomarkers of different regions. The results can provide useful information for the study of the gastrointestinal microbiome in bovines.

Milk production systems in several countries show considerable differences between seasons. For example, in the Netherlands, cows are kept inside and fed silage in winter, whereas they are on pasture in summer. The differences between seasons affect milk yield and composition and might influence the genetic background of milk production traits. The objective of this study was to estimate phenotypic and genetic effects of season on milk production traits. For this purpose, 19,286 test-day milk production records of 1,800 first-parity Dutch Holstein-Frisian cows were available, and these cows were genotyped using a 50K SNP panel. Phenotypic effects of season were significant for all milk production traits. Effects of season were large for milk fat yield, fat content, and protein content. Genetic correlations between milk production traits in different seasons showed that genotype by season interaction effects were relatively small for most milk production traits. The genetic background of protein content and lactose content seems to be sensitive to seasonal effects. Furthermore, the genetic correlations between spring and autumn differed significantly from unity for almost all milk production traits. A genome-wide association study for genotype by season interaction identified chromosomal regions on BTA3, BTA14, BTA20, and BTA25 that showed genotype by season interaction effects, including a region containing DGAT1, which showed interaction effects for fat content and protein content.

Substantial evidence demonstrates that the genetic background of milk production traits changes during lactation. However, most GWAS for milk production traits assume that genetic effects are constant during lactation and therefore might miss those quantitative trait loci (QTL) whose effects change during lactation. The GWAS for genotype by lactation stage interaction are aimed at explicitly detecting the QTL whose effects change during lactation. The purpose of this study was to perform GWAS for genotype by lactation stage interaction for milk yield, lactose yield, lactose content, fat yield, fat content, protein yield, and somatic cell score to detect QTL with changing effects during lactation. For this study, 19,286 test-day records of 1,800 first-parity Dutch Holstein cows were available and cows were genotyped using a 50K SNP panel. A total of 7 genomic regions with effects that change during lactation were detected in the GWAS for genotype by lactation stage interaction. Two regions on Bos taurus autosome (BTA)14 and BTA19 were also significant based on a GWAS that assumed constant genetic effects during lactation. Five regions on BTA4, BTA10, BTA11, BTA16, and BTA23 were only significant in the GWAS for genotype by lactation stage interaction. The biological mechanisms that cause these changes in genetic effects are still unknown, but negative energy balance and effects of pregnancy may play a role. These findings increase our understanding of the genetic background of lactation and may contribute to the development of better management indicators based on milk composition.

Mastitis (MAS), endometritis (MET), and ketosis (KET) are prevalent diseases in dairy cows that result in substantial economic losses for the dairy farming industry. This study gathered 26,014 records of the health and sickness of dairy cows and 99,102 data of reproduction from 13 Holstein dairy farms in Central China; the milk protein and milk fat content from 56,640 milk samples, as well as the pedigree data of 37,836 dairy cows were obtained. The logistic regression method was used to analyze the variations in the prevalence rates of MAS, MET, and KET among various parities; the mixed linear model was used to examine the effects of the three diseases on milk production, milk quality, and reproductive traits. DMU software (version 5.2) utilized the DMUAI module in conjunction with the single-trait and two-trait animal model, as well as best linear unbiased prediction (BLUP), to estimate the genetic parameters for the three diseases, milk production, milk quality, and reproductive traits in dairy cows. The primary findings of the investigation comprised the following: (1) The prevalence rates of MAS, MET, and KET in dairy farms were 20.04%, 10.68%, and 7.33%, respectively. (2) MAS and MET had a substantial impact (p < 0.01) on milk production, resulting in significant decreases of 112 kg and 372 kg in 305-d Milk Yield (305-d MY), 4 kg and 12 kg in 305-d Protein Yield (305-d PY), and 6 kg and 16 kg in 305-d Fat Yield (305-d FY). As a result of their excessive 305-d MY, some cows were diagnosed with KET due to glucose metabolism disorder. The 305-d MY of cows with KET was significantly higher than that of healthy cows (205 kg, p < 0.01). (3) All three diseases resulted in an increase in the Interval from Calving to First Service (CTFS, 0.60–1.50 d), Interval from First Service to Conception (FSTC, 0.20–16.20 d), Calving Interval (CI, 4.00–7.00 d), and Number of Services (NUMS, 0.07–0.35). (4) The heritabilities of cows with MAS, MET, and KET were found to be low, with values of 0.09, 0.01, and 0.02, respectively. The genetic correlation between these traits ranged from 0.14 to 0.44. This study offers valuable insights on the prevention and control of the three diseases, as well as feeding management and genetic breeding.

Pregnancy is a prerequisite for the initiation of lactation and for maintaining the milk production cycle. Pregnancy affects milk production and therefore should be accounted for in the genetic evaluation. Furthermore, there might be genetic differences in pregnancy effects on milk composition. The objective of this study was to estimate phenotypic and genetic effects of pregnancy on milk production traits. For this purpose, test-day records and conception dates of 1,359 first-parity Holstein-Friesian cows were analyzed. Significant effects of pregnancy on all milk production traits were detected except somatic cell score (e.g., the cumulative effects of pregnancy on milk yield were -247 kg). The pregnancy effects on milk yield, lactose yield, protein yield, fat yield, and fat content were small during early gestation (<150 d) and substantially increased in late gestation. The effects of pregnancy on milk protein yield were relatively stronger than those on fat yield. The effects of pregnancy on milk production traits differed for DGAT1 genotypes. Milk yield, lactose yield, protein yield, and fat yield of DGAT1 AA cows were more affected by pregnancy than that of DGAT1 KK cows (e.g., the cumulative effects of pregnancy on milk yield were negligible for DGAT1 KK cows and were -443 kg for DGAT1 AA cows). These results suggest that DGAT1 KK cows may be more suitable for shortening or omitting the dry period than DGAT1 AA cows.