Milk production is an important trait that influences the economic value of camels. However, the genetic regulatory mechanisms underlying milk production in camels have not yet been elucidated. We aimed to identify candidate molecular markers that affect camel milk production. We classified Junggar Bactrian camels (9-10-year-old) as low-yield (<1.96 kg/d) or high-yield (>2.75 kg/d) based on milk production performance. Milk fat (5.16 ± 0.51 g/100 g) and milk protein (3.59 ± 0.22 g/100 g) concentrations were significantly lower in high-yielding camels than those in low-yielding camels (6.21 ± 0.59 g/100 g, and 3.93 ± 0.27 g/100 g, respectively) (p < 0.01). There were no apparent differences in gland tissue morphology between the low- and high-production groups. Whole-genome resequencing of 12 low- and 12 high-yield camels was performed. The results of selection mapping methods, performed using two methods (FST and θπ), showed that 264 single nucleotide polymorphism sites (SNPs) overlapped between the two methods, identifying 181 genes. These genes were mainly associated with the regulation of oxytocin, estrogen, ErbB, Wnt, mTOR, PI3K-Akt, growth hormone synthesis/secretion/action, and MAPK signaling pathways. A total of 123 SNPs were selected, based on significantly associated genomic regions and important pathways for SNP genotyping, for verification in 521 additional Bactrian camels. This analysis showed that 13 SNPs were significantly associated with camel milk production yield and 18 SNPs were significantly associated with camel milk composition percentages. Most of these SNPs were located in coding regions of the genome. However, five and two important mutation sites were found in the introns of CSN2 (β-casein) and CSN3 (κ-casein), respectively. Among the candidate genes, NR4A1, ADCY8, PPARG, CSN2, and CSN3 have previously been well studied in dairy livestock. These observations provide a basis for understanding the molecular mechanisms underlying milk production in camels as well as genetic markers for breeding programs aimed at improving milk production.

In this study, our primary aim was to explore the genomic landscape of Barka cattle, a breed recognized for high milk production in a semi-arid environment, by focusing on genes with known roles in milk production traits. We employed genome-wide analysis and three selective sweep detection methods (ZFST, θπ ratio, and ZHp) to identify candidate genes associated with milk production and composition traits. Notably, ACAA1, P4HTM, and SLC4A4 were consistently identified by all methods. Functional annotation highlighted their roles in crucial biological processes such as fatty acid metabolism, mammary gland development, and milk protein synthesis. These findings contribute to understanding the genetic basis of milk production in Barka cattle, presenting opportunities for enhancing dairy cattle production in tropical climates. Further validation through genome-wide association studies and transcriptomic analyses is essential to fully exploit these candidate genes for selective breeding and genetic improvement in tropical dairy cattle.

Buffaloes are vital contributors to the global dairy industry. Understanding the genetic basis of milk production traits in buffalo populations is essential for breeding programs and improving productivity. In this study, we conducted whole-genome resequencing on 387 buffalo genomes from 29 diverse Asian breeds, including 132 river buffaloes, 129 swamp buffaloes, and 126 crossbred buffaloes. We identified 36,548 copy number variants (CNV) spanning 133.29 Mb of the buffalo genome, resulting in 2,100 CNV regions (CNVR), with 1,993 shared CNVR being found within the studied buffalo types. Analyzing CNVR highlighted distinct genetic differentiation between river and swamp buffalo subspecies, verified by evolutionary tree and principal component analyses. Admixture analysis grouped buffaloes into river and swamp categories, with crossbred buffaloes displaying mixed ancestry. To identify candidate genes associated with milk production traits, we employed 3 approaches. First, we used Vst-based population differentiation, revealing 11 genes within CNVR that exhibited significant divergence between different buffalo breeds, including genes linked to milk production traits. Second, expression quantitative loci analysis revealed differentially expressed CNVR-derived genes (DECG) associated with milk production traits. Notably, known milk production-related genes were among these DECG, validating their relevance. Last, a GWAS identified 3 CNVR significantly linked to peak milk yield. Our study provides comprehensive genomic insights into buffalo populations and identifies candidate genes associated with milk production traits. These findings facilitate genetic breeding programs aimed at increasing milk yield and improving quality in this economically important livestock species.

Identifying key causal genes is critical for unraveling the genetic basis of complex economic traits, yet it remains a formidable challenge. The advent of large-scale sequencing data and computational algorithms, such as transcriptome-wide association studies (TWASs), offers a promising avenue for identifying potential causal genes. In this study, we harnessed the power of TWAS to identify genes potentially responsible for milk production traits, including daily milk yield (MY), fat percentage (FP), and protein percentage (PP), within a cohort of 100 buffaloes. Our approach began by generating the genotype and expression profiles for these 100 buffaloes through whole-genome resequencing and RNA sequencing, respectively. Through comprehensive genome-wide association studies (GWAS), we pinpointed a total of seven and four single nucleotide polymorphisms (SNPs) significantly associated with MY and FP traits, respectively. By using TWAS, we identified 55, 71, and 101 genes as significant signals for MY, FP, and PP traits, respectively. To delve deeper, we conducted protein-protein interaction (PPI) analysis, revealing the categorization of these genes into distinct PPI networks. Interestingly, several TWAS-identified genes within the PPI network played a vital role in milk performance. These findings open new avenues for identifying potentially causal genes underlying important traits, thereby offering invaluable insights for genomics and breeding in buffalo populations.

Milk production traits are the most important quantitative economic traits in dairy cow production; improving the yield and quality of milk is an important way to ensure the production efficiency of the dairy industry. This study carried out a series of in-depth statistical genetics studies and molecular analyses on the Chinese Holstein cows in the Jiangsu Province, such as descriptive statistics and copy number variation analysis. A genetic correlation, phenotypic correlation, and descriptive statistical analysis of five milk production traits (milk yield, milk fat percentage, milk fat yield, milk protein percentage, and milk protein yield) of the dairy cows were analyzed using the SPSS and DMU software. Through quality control, 4173 cows and their genomes were used for genomic study. Then, SNPs were detected using DNA chips, and a copy number variation (CNV) analysis was carried out to locate the quantitative trait loci (QTL) of the milk production traits by Perl program software Penn CNV and hidden Markov model (HMM). The phenotypic means of the milk yield, milk fat percentage, milk fat mass, milk protein percentage, and milk protein mass at the first trimester were lower than those at the other trimesters by 8.821%, 1.031%, 0.930%, 0.003%, and 0.826%, respectively. The five milk production traits showed a significant phenotypic positive correlation (p < 0.01) and a high genetic positive correlation among the three parities. Based on the GGPBovine 100 K SNP data, QTL-detecting research on the fist-parity milk performance of dairy cows was carried out via the CNV. We identified 1731 CNVs and 236 CNVRs in the 29 autosomes of 984 Holstein dairy cows, and 19 CNVRs were significantly associated with the milk production traits (p < 0.05). These CNVRs were analyzed via a bioinformatics analysis; a total of 13 gene ontology (GO) terms and 20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched (p < 0.05), and these terms and pathways are mainly related to lipid metabolism, amino acid metabolism, and cellular catabolic processes. This study provided a theoretical basis for the molecular-marker-assisted selection of dairy cows by developing descriptive statistics on the milk production traits of dairy cows and by locating the QTL and functional genes that affect the milk production traits of first-born dairy cows. The results describe the basic status of the milk production traits of the Chinese Holstein cows in Jiangsu and locate the QTL and functional genes that affect the milk production traits of the first-born cows, providing a theoretical basis for the molecular-marker-assisted selection of dairy cows.

Our previous research identified the Kruppel like factor 6 (KLF6) gene as a prospective candidate for milk production traits in dairy cattle. The expression of KLF6 in the livers of Holstein cows during the peak of lactation was significantly higher than that during the dry and early lactation periods. Notably, it plays an essential role in activating peroxisome proliferator-activated receptor α (PPARα) signaling pathways. The primary aim of this study was to further substantiate whether the KLF6 gene has significant genetic effects on milk traits in dairy cattle.
Through direct sequencing of PCR products with pooled DNA, we totally identified 12 single nucleotide polymorphisms (SNPs) within the KLF6 gene. The set of SNPs encompasses 7 located in 5\' flanking region, 2 located in exon 2 and 3 located in 3\' untranslated region (UTR). Of these, the g.44601035G > A is a missense mutation that resulting in the replacement of arginine (CGG) with glutamine (CAG), consequently leading to alterations in the secondary structure of the KLF6 protein, as predicted by SOPMA. The remaining 7 regulatory SNPs significantly impacted the transcriptional activity of KLF6 following mutation (P < 0.005), manifesting as changes in transcription factor binding sites. Additionally, 4 SNPs located in both the UTR and exons were predicted to influence the secondary structure of KLF6 mRNA using the RNAfold web server. Furthermore, we performed the genotype-phenotype association analysis using SAS 9.2 which found all the 12 SNPs were significantly correlated to milk yield, fat yield, fat percentage, protein yield and protein percentage within both the first and second lactations (P < 0.0001 ~ 0.0441). Also, with Haploview 4.2 software, we found the 12 SNPs linked closely and formed a haplotype block, which was strongly associated with five milk traits (P < 0.0001 ~ 0.0203).
In summary, our study represented the KLF6 gene has significant impacts on milk yield and composition traits in dairy cattle. Among the identified SNPs, 7 were implicated in modulating milk traits by impacting transcriptional activity, 4 by altering mRNA secondary structure, and 1 by affecting the protein secondary structure of KLF6. These findings provided valuable molecular insights for genomic selection program of dairy cattle.

BACKGROUND: Milk production traits are complex traits with vital economic importance in the camel industry. However, the genetic mechanisms regulating milk production traits in camels remain poorly understood. Therefore, we aimed to identify candidate genes and metabolic pathways that affect milk production traits in Bactrian camels.
METHODS: We classified camels (fourth parity) as low- or high-yield, examined pregnant camels using B-mode ultrasonography, observed the microscopic changes in the mammary gland using hematoxylin and eosin (HE) staining, and used RNA sequencing to identify differentially expressed genes (DEGs) and pathways.
RESULTS: The average standard milk yield over the 300 days during parity was recorded as 470.18 ± 9.75 and 978.34 ± 3.80 kg in low- and high-performance camels, respectively. Nine female Junggar Bactrian camels were subjected to transcriptome sequencing, and 609 and 393 DEGs were identified in the low-yield vs. high-yield (WDL vs. WGH) and pregnancy versus colostrum period (RSQ vs. CRQ) comparison groups, respectively. The DEGs were compared with genes associated with milk production traits in the Animal Quantitative Trait Loci database and in Alashan Bactrian camels, and 65 and 46 overlapping candidate genes were obtained, respectively. Functional enrichment and protein-protein interaction network analyses of the DEGs and candidate genes were conducted. After comparing our results with those of other livestock studies, we identified 16 signaling pathways and 27 core candidate genes associated with maternal parturition, estrogen regulation, initiation of lactation, and milk production traits. The pathways suggest that emerged milk production involves the regulation of multiple complex metabolic and cellular developmental processes in camels. Finally, the RNA sequencing results were validated using quantitative real-time PCR; the 15 selected genes exhibited consistent expression changes.
CONCLUSIONS: This study identified DEGs and metabolic pathways affecting maternal parturition and milk production traits. The results provides a theoretical foundation for further research on the molecular mechanism of genes related to milk production traits in camels. Furthermore, these findings will help improve breeding strategies to achieve the desired milk yield in camels.

Milk production traits as the most important economic traits of dairy cows, they directly reflect the benefits of breeding and the economic benefits of pasture. In this study, A disintegrin and metalloproteinase-12 (ADAM12), Parkinson\'s disease gene 2 (PRKN) and dipeptidyl peptidase-like protein subtype 6 (DPP6) polymorphism in 384 Chinese Holstein cows were detected by time-of-flight mass spectrometry and through statistical analysis using software such as Popgene 32, SAS 9.4 and Origin 2022, the relationship between single nucleotide polymorphisms (SNPs) of three genes with four milk production traits such as daily milk yield (DMY), milk fat percentage (MFP), milk protein percentage (MPP) and somatic cell score (SCS) was verified at molecular level. The results showed that four polymorphic loci (116,467,133, 116,604,487, 116,618,268 and 116,835,111) of DPP6 gene, two polymorphic loci (97,665,052 and 97,159,837) of PRKN gene and two polymorphic loci (45,542,714 and 45,553,888) of ADAM12 gene were detected. PRKN-97665052, DPP6-116467133, ADAM12-45553888, DPP6-116604487 and DPP6-116835111 were all in Hardy-Weinberg equilibrium state (p > .05). ADAM12-45542714, PRKN-97159837 and PRKN-97665052 were moderately polymorphic (0.25 ≤ PIC <0.50) in Holstein. It is evident that the selection potential and genetic variation of these five loci are relatively large, and the genetic richness is relatively high. The correlation analysis of different genotypes between these eight loci and milk production traits of Holstein showed that ADAM12-45542714 and DPP6-116835111 (p < .01) had an extremely significant effects on the DMY of Chinese Holstein in Ningxia, while PRKN-97665052 had an extremely significant effect on MFP (p < .01). The effect of PRKN-97665052 and DPP6-116467133 on MPP of Holstein were extremely significant (p < .01). DPP6-116618268 had an extremely significant effect on the SCS of Holstein in Ningxia (p < .01), and AA genotype individuals showed a higher SCS than GG genotype individuals; the other two loci (ADAM12-45553888 and DPP6-116604487) had no significant effects on milk production traits of Holstein (p > .05). In addition, through the joint analysis of DPP6, PRKN and ADAM12 gene loci, it was found that the interaction effect between the three gene loci could significantly affect the DMY, SCS (p < .01) and MPP (p < .05). In conclusion, several different loci of DPP6, PRKN and ADAM12 genes can affect the milk production traits of Holstein to different degrees. PRKN, DPP6 and ADAM12 genes can be used as potential candidate genes for milk production traits of Holstein for marker-assisted selection, providing theoretical basis for breeding of Holstein.

Longitudinal traits, such as milk production traits in dairy cattle, are featured by having phenotypic values at multiple time points, which change dynamically over time. In this study, we first imputed SNP chip (50-100K) data to whole-genome sequence (WGS) data in a Chinese Holstein population consisting of 6,470 cows. The imputation accuracies were 0.88 to 0.97 on average after quality control. We then performed longitudinal GWAS in this population based on a random regression test-day model using the imputed WGS data. The longitudinal GWAS revealed 16, 39, and 75 quantitative trait locus regions associated with milk yield, fat percentage, and protein percentage, respectively. We estimated the 95% confidence intervals (CI) for these quantitative trait locus regions using the logP drop method and identified 581 genes involved in these CI. Further, we focused on the CI that covered or overlapped with only 1 gene or the CI that contained an extremely significant top SNP. Twenty-eight candidate genes were identified in these CI. Most of them have been reported in the literature to be associated with milk production traits, such as DGAT1, HSF1, MGST1, GHR, ABCG2, ADCK5, and CSN1S1. Among the unreported novel genes, some also showed good potential as candidate genes, such as CCSER1, CUX2, SNTB1, RGS7, OSR2, and STK3, and are worth being further investigated. Our study provided not only new insights into the candidate genes for milk production traits, but also a general framework for longitudinal GWAS based on random regression test-day model using WGS data.

Cattle and buffalo served as the first and second largest dairy animals, respectively, providing 96% milk products worldwide. Understanding the mechanisms underlying milk synthesis is critical to develop the technique to improve milk production. Thiolases, also known as acetyl-coenzyme A acetyltransferases (ACAT), are an enzyme family that plays vital roles in lipid metabolism, including ACAT1, ACAT2, ACAA1, ACAA2, and HADHB. Our present study showed that these five members were orthologous in six livestock species including buffalo and cattle. Transcriptomic data analyses derived from different lactations stages showed that ACAA1 displayed different expression patterns between buffalo and cattle. Immunohistochemistry staining revealed that ACAA1 were dominantly located in the mammary epithelial cells of these two dairy animals. Knockdown of ACAA1 inhibited mammary epithelial cell proliferation and triglyceride and β-casein secretion by regulating related gene expressions in cattle and buffalo. In contrast, ACAA1 overexpression promoted cell proliferation and triglyceride secretion. Finally, three novel SNPs (g.-681A>T, g.-23117C>T, and g.-24348G>T) were detected and showed significant association with milk production traits of Mediterranean buffaloes. In addition, g.-681A>T mutation located in the promoter region changed transcriptional activity significantly. Our findings suggested that ACAA1 play a key role in regulating buffalo and cattle milk synthesis and provided basic information to further understand the dairy animal lactation physiology.