We investigated the effects of continuous redox gradients (0-12% O2) on the metabolic profiles of high-amylose maize starch (HAMS) in an in vitro human fecal fermentation system. After 24 h fermentation, total short-chain fatty acid production remained stable under mild oxidation (2% O2). However, a clear metabolic shift occurred: acetate proportions increased from 25.70% to 32.55%, whereas butyrate declined from 16.19% to 9.88%. Under high oxidation (12% O2), total fermentation was markedly suppressed (from 52.69 to 15.25 mmol/L), with metabolism shifting drastically toward acetate (69.09%) over butyrate (2.18%). This shift resulted from the selective enrichment of facultative anaerobes and acetate producers (e.g., Klebsiella and Parabacteroides) and the depletion of strict anaerobic butyrate producers (e.g., Butyricicoccus). Functional predictions revealed that oxidative stress redirected microbial metabolism from anaerobic fermentation to aerobic stress-response pathways. Overall, redox status critically shapes the HAMS fermentation profile, offering a theoretical foundation for redox-targeted dietary interventions with HAMS.

Semen cryopreservation and artificial insemination have crucial and beneficial effects on cattle breeding. The freezability of sperm, as primarily reflected by post-thaw sperm motility (PTM), is essential for evaluating semen quality. Some studies have shown notable differences in sperm freezability among various bulls. Here, we compared protein profiles of sperm cells and seminal plasma extracellular vesicles (SPEVs) in the high sperm freezability group and the low sperm freezability group of Holstein bulls to identify the important proteins and their mechanisms that affect sperm freezability. As a result, 432 and 394 differentially expressed proteins (DEPs) were identified in sperm and SPEVs between high and low freezability groups. The results of weighted correlation network analysis (WGCNA) showed that the blue module was significantly (r = 0.89, P = 9 × 10− 6) associated with sperm freezability. In addition, the pathway analysis revealed that “Metabolic pathways” and “Oxidative phosphorylation” were the predominant biological processes represented. Furthermore, 17 DEPs were found located in the previously identified QTLs related to post-thaw sperm motility, indicating possible variation in their genes. Interestingly, the expression of 142 protein pairs in sperm were significantly (|r| >0.9, P < 0.05) correlated with their expression in SPEVs. Finally, we detected genetic variations in six important candidate genes (STK38, HSPA1A, HSP90B1, LPO, DNASE2 and CUTA), and found that a missense mutation (Chr23g. 23:27522566 A > G) in HSPA1A may affect sperm freezability by decreasing the expression of HSPA1A. Our study highlighted the different protein characteristics of sperm and SPEVs in samples with distinct sperm freezability. These proteins, together with relevant SNPs might be useful markers for selecting bulls with high sperm freezability.

Genetic screening of monogenic traits is important for improving genetic health and increasing favorable milk protein in dairy cattle. This study developed and applied an amplification refractory mutation system PCR (ARMS-PCR) assay to simultaneously screen 21 causal variants underlying 18 monogenic traits in Holstein cattle, including 13 recessive genetic defects, 2 milk protein loci, and 3 morphological loci. The assay was designed as a unified multiplex PCR-capillary electrophoresis workflow, enabling clear detection of allele-specific products distinguished by fragment size and fluorescent color. Sanger sequencing validation of newly incorporated loci supported the accuracy of the assay. A total of 1656 cows from 12 commercial farms were genotyped using the multiplex ARMS-PCR panel, and amplicons were analyzed by capillary electrophoresis. Carriers were detected for all genetic defects except DUMPS, with carrier frequencies ranging from 0.12% to 6.64%. The highest frequencies were observed for HH5 (6.64%) and MWS (6.58%), whereas HH3, HH1, and HCD showed intermediate frequencies of 1.81% to 3.08%; all the remaining defects were below 1%. Overall, 22.10% of sampled cows carried at least 1 defect allele, including 20.47% carrying 1 defect, 1.51% carrying 2, and 0.12% carrying 3. For milk protein loci, the desirable beta-casein A2A2 and kappa-casein BB genotypes occurred at frequencies of 45.83% and 14.13%, respectively, and the favorable A2A2/BB combination was present in 6.22% of sampled cows. Dominant red, recessive red, and polled alleles were rare. These results indicate that multiplex fluorescent ARMS-PCR can serve as a practical targeted screening tool for the simultaneous management of known deleterious alleles and selection of favorable monogenic variants in dairy cattle breeding.

Cattle are integral to global food security, yet the molecular architecture of their complex traits remains poorly understood. Here, we present the Cattle Genotype–Tissue Expression (CattleG-TEx) Phase 1 resource (https://cattlegtex.farmgtex.org/), a substantial expansion of the pilot study. By leveraging 12,422 RNA-seq profiles across 43 tissues and 82 breeds, we characterized 433,972 primary and 161,428 non-primary regulatory effects spanning seven molecular phenotypes. This high-resolution atlas resolves 75% of GWAS signals for 44 complex traits, significantly addressing the "missing regulation" in livestock. We propose a genetic regulatory model demonstrating how variants across multiple biological layers interact with specific biological contexts to shape pheno-typic variation. Furthermore, CattleGTEx elucidates mechanisms underlying adaptive evolution between Bos taurus and Bos indicus, as well as artificial selection in dairy and beef breeds. Finally, by mapping evolutionary constraints on these regulatory effects, we demonstrate the translational value of this resource for prioritizing causal variants in human complex diseases. Together, Phase 1 of CattleGTEx provides a transformative framework for functional genomics, precision breeding, and comparative genetics.

BACKGROUND/OBJECTIVES:The Y chromosome plays a crucial role in male fertility, sex determination, and spermatogenesis, yet it remains poorly characterized in Mediterranean river buffalo (Bubalus bubalis, 2n = 50) because of its high repeat content, extensive heterochromatin, and complex palindromic structures. Although a chromosome-level Y assembly is available for swamp buffalo (2n = 48), no equivalent reference exists for the river type. METHODS:To address this gap, Y chromosomes from 10 Mediterranean buffalo bulls were isolated by laser microdissection following peripheral blood culture and whole-chromosome amplification. Probe specificity was verified by FISH, and amplified Y chromosomes were sequenced using Illumina NovaSeq 6000 (Illumina, San Diego, CA, USA). Sequencing data were assembled and analysed through de novo assembly, repeat identification, sequence alignment, and variant detection. Comparative analyses included alignment to the swamp buffalo Y chromosome and annotation of Y-linked genes using the Bos taurus reference genome. RESULTS:FISH confirmed the specificity of the isolated material, showing strong signals on the Y chromosome and on X/Y PAR and heterochromatic regions. Sequencing generated over 240 million paired-end reads, and de novo assembly produced 566,815 contigs. Repeat analysis identified 3.91% repetitive elements, mainly SINEs, while variant calling detected more than 23,000 variants. Comparative analyses mapped several contigs to the swamp buffalo Y chromosome and Y-linked genes. Annotation against the B. taurus genome identified 26 unique genes, including homologs shared with the X chromosome, and revealed MSY gene duplications, including 10 copies of TSPY and 3 of HSFY. CONCLUSIONS:These findings show that laser microdissection with NGS enables effective access to the buffalo Y chromosome, representing a milestone in the characterization of the river type genome, and providing a basis for studies on buffalo male fertility and breeding programs.

Preimplantation embryo genomic selection (eGS) enables selection prior to implantation and could accelerate genetic gain in cattle. A major hurdle is the limited DNA from embryo biopsies, requiring efficient whole-genome amplification (WGA) for accurate genomic analyses. However, alternative WGA methods and genotyping strategies have not been systematically compared in cattle. This study evaluated different methods for WGA (multiple displacement amplification (MDA) or multiple annealing and looping-based amplification cycles (MALBAC)) and for genotyping (single nucleotide polymorphism array (SNP-array), genotyping by targeted sequencing (GBTS), or whole-genome sequencing (WGS)) using 3-, 6-, and 9-cell bovine samples. MDA consistently outperformed MALBAC across various performance metrics, including amplification length, call rates, genome coverage (93.43–94.40% vs. 53.01–67.08%), and genotyping concordance (0.89–0.98 vs. 0.75–0.92). GBTS achieved the highest call rates, while SNP-array and GBTS showed excellent concordance and low error rates. WGS provided genome-wide data for precise aneuploidy detection. We further validated the workflow in trophectoderm biopsies and arrested embryos, generating reliable data for genomic evaluation, sex determination, and aneuploidy screening. MDA from ≥6 cells combined with GBTS or SNP-array showed a favorable balance of efficiency and accuracy for bovine eGS. This framework may facilitate the application of eGS in cattle breeding by enhancing selection intensity and accelerating genetic improvement.

High-throughput and cost-effective genotyping technologies are essential for animal genomics and breeding research. In this study, we developed a 100 K single nucleotide polymorphism (SNP) panel for genomic screening of Asian water buffalo using genotyping by target sequencing (GBTS). The panel consists of 101 032 SNPs, selected to include highly polymorphic variants from an existing SNP array-the Axiom 90 K Genotyping Array (16.6%), moderate- to high-impact variants in protein-coding genes (20.3%), swamp- and river-specific variants (10.6%), variants in functional genes associated with economic traits (0.9%), variants located on the X chromosome (0.6%) as well as gap-filling variants (51.0%). To validate the panel, we genotyped 78 buffaloes using the panel as well as whole-genome sequencing (WGS). The panel achieved an average call rate of 99.77%, with a mean genotype concordance of 99.43% across five replicate samples. Population genomics analyses using the panel yielded results highly consistent with those obtained from WGS. When using WGS data as the reference panel, the 100 K SNP panel achieved high imputation accuracy across three buffalo populations-0.89 in dairy river, 0.84 in local river, and 0.81 in swamp buffalo. After applying a stringent quality-control filter (DR2 > 0.9), the retained variants exhibited mean imputation accuracy > 0.90 in all populations while preserving a substantial number of high-quality SNPs (10.40, 5.59, and 2.50 million SNPs, respectively). Overall, the newly designed 100 K SNP panel represents a robust, high-throughput tool for genome-wide genotyping, facilitating the conservation of genetic resources and enabling sustainable genetic improvement of water buffalo populations.

Hybrids between closely related but genetically incompatible species are often inviable or sterile. Cattle-yak, an interspecific hybrid of yak and cattle, exhibits male-specific sterility, which limits the fixation of its desired traits and prevents genetic improvement in yak through crossbreeding. Transcriptome profiles of testicular tissues have been generated in cattle, yak, and cattle-yak; however, the genetic variations underlying differential gene expression associated with hybrid sterility have yet to be elucidated. We detected differences in the cellular composition and gene expression of testes from yak and cattle-yak at 3 mo of age, 10 mo of age, and adulthood. Histological analysis revealed that the most advanced germ cells were gonocytes (prospermatogonia) at 3 mo and spermatocytes at 10 mo. Complete spermatogenesis occurred in the seminiferous tubules of adult yak, whereas only spermatogonia and a limited number of spermatocytes were detected in the testis of adult cattle-yak. Transcriptome analysis revealed 180, 6,310, and 6,112 differentially expressed genes (DEG) in yak and cattle-yak at each stage, respectively. Next, we examined the spermatogenic cell types in the backcross generation (BC1) and detected the appearance of round spermatids, indicating the partial recovery of spermatogenesis in these animals. Compared with those in cattle-yak, 272 DEG were identified in the testes of BC1 animals. Notably, we discovered that the expression of X chromosome-linked genes was upregulated in the testis of cattle-yak compared with yak, suggesting a possible abnormality in the process of meiotic sex chromosome inactivation in hybrid animals. We next screened DEG harboring structural variations (SV) and identified a list of SV genes associated with spermatogonial development, meiotic recombination, and double-strand break repair. Furthermore, we found that the SV genes ESCO2 (establishment of sister chromatid cohesion N-acetyltransferase 2) and BRDT (bromodomain testis associated) may be involved in meiotic arrest of cattle-yak spermatocytes. Overall, our research provides a valuable database for identifying structural variant loci that contribute to hybrid sterility.

Conjugated linoleic acid, a long-chain fatty acid, reduces milk fat content in dairy cows, which may alleviate the negative energy balance during the early lactation period by reducing lactation energy output and thereby decreasing the occurrence of ketosis and fatty liver due to a negative energy balance. Although research has revealed the milk fat inhibition mechanism, the mechanisms underlying the metabolic interactions among the liver, subcutaneous fat, and mammary gland tissues during this period remain unclear. In this study, the molecular mechanisms underlying CLA-induced milk fat depression in the liver, subcutaneous white adipose tissue, and mammary gland tissues of dairy cows were investigated. Chinese Holstein dairy cows at 30 d postpartum were fed a control diet (6 cows) or a CLA-supplemented diet (6 cows) for 7 d, and all cows were fed the control diet only for another 7 d. Tissue samples from the liver, subcutaneous white adipose tissue, and mammary gland were collected on d 0 (before CLA supplementation) and d 7 (after supplementation) for RNA sequencing analysis. In addition, blood samples were collected from control and CLA-treated cows on d 0, 4, 7, and 14 (post-treatment) to measure the concentrations of nonesterified fatty acids (NEFA) and β-hydroxybutyric acid (BHBA). Furthermore, from d 0 to 14, milk composition and milk yield were measured. The results of lactation performance indicated that supplementation with CLA led to a significant reduction in milk fat percentage, decreasing from 4.0% to 3.5%, and no other changes in the lactation performance of the CLA group were observed. With respect to the indicators of excessive fat mobilization and circulating ketone bodies, the CLA group presented significantly lower serum NEFA concentrations than did the control group, with levels decreasing from 0.70 to 0.55 mmol/L between one day before CLA supplementation (d 0) and d 7 of CLA supplementation (d 7), whereas the BHBA concentration tended to decrease over this period. Transcriptomic analysis of mammary tissue identified 674 differentially expressed genes (DEG). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that the downregulated DEG in the CLA group were involved mainly in lipid biosynthetic processes, fatty acid biosynthesis, the p53 signaling pathway, and steroid biosynthesis. Transcriptomic analysis of subcutaneous white adipose tissue revealed 614 DEG between the CLA and control groups. These DEG were significantly enriched in pathways related to fatty acid metabolism, fatty acid transport, fatty acid β-oxidation, lipid metabolism, the peroxisome proliferator-activated receptor (PPAR) signaling pathway, and AMP-activated protein kinase (AMPK) signaling pathway. Transcriptomic analysis of liver tissue identified 479 DEG, which were enriched in pathways related to fatty acid metabolism, the PPAR signaling pathway, lipid transport activity, the AMPK signaling pathway, the phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway, the insulin signaling pathway, and the insulin resistance pathway. Collectively, these findings indicate that CLA successfully induced milk fat depression in postpartum dairy cows. Based on the up- and downregulation of genes within fatty acid metabolism, fatty acid β-oxidation, and lipid metabolism pathways in adipose tissue, CLA may promote lipid synthesis while inhibiting fatty acid oxidation and lipolysis in subcutaneous white adipose tissue. Additionally, gene expression patterns related to lipid transport activity, fatty acid metabolism, PPAR, and AMPK pathways in liver tissue suggest that CLA may increase lipid transport capacity while reducing hepatic lipid accumulation. These findings provide theoretical evidence in support of the regulation of energy balance in early-lactating cows through the inhibition of milk fat synthesis by CLA, which may be used as a strategy for preventing type I ketosis.

Genetic mutation and drift, coupled with natural and human-mediated selection and migration, have produced a wide variety of genotypes and phenotypes in farmed animals. We here introduce the Farm Animal Genotype-Tissue Expression (FarmGTEx) Project, which aims to elucidate the genetic determinants of gene expression across 16 terrestrial and aquatic domestic species under diverse biological and environmental contexts. For each species, we aim to collect multiomics data, particularly genomics and transcriptomics, from 50 tissues of 1,000 healthy adults and 200 additional animals representing a specific context. This Perspective provides an overview of the priorities of FarmGTEx and advocates for coordinated strategies of data analysis and resource-sharing initiatives. FarmGTEx aims to serve as a platform for investigating context-specific regulatory effects, which will deepen our understanding of molecular mechanisms underlying complex phenotypes. The knowledge and insights provided by FarmGTEx will contribute to improving sustainable agriculture-based food systems, comparative biology and eventual human biomedicine.

Characterizing the cell type-specific transcriptome is crucial for understanding the cellular and molecular regulatory mechanisms underlying adaptive evolution and complex phenotypes. Here, single-cell/nucleus RNA sequencing (sc/snRNA-seq) is used to construct a cell transcriptomic atlas of 397,011 cells, representing 57 cell types, from 12 tissues in river and swamp buffalo, which exhibit significant divergence in milk production. Differential expression analyses identify metabolic and secretory tissues (i.e., liver, mammary gland, and pituitary) and cell types (e.g., hepatocytes, luminal cells, somatotropes, and lactotropes) that mediate the divergence of milk production. Lactotrope-specific downregulation of TRHDE in river buffalo is associated with high milk production. Integrative analyses of sc/snRNA-seq data with genomic data in buffalo and cattle reveal key cell types (e.g., luminal cells and excitatory neurons) and genes (e.g., RPL13 and LALBA) associated with milk production. Ultimately, the Buffalo Cell Atlas (http://bovomicshub.com) will serve as a valuable resource for advancing buffalo genetics and genomics research, enabling cross-species comparative transcriptome studies and providing deeper insights into the regulation of milk synthesis and secretion.

BACKGROUND:Whole-genome sequencing (WGS) data contain a large proportion of genetic information for farm animals, while machine learning algorithms offer a powerful approach for discovering population-informative genetic markers for population assignment. These methods can enhance population classification, supporting genetic resource conservation and optimizing breeding strategies. METHODS:WGS data from 187 individuals representing eight water buffalo breeds (four river type breeds, i.e., Mediterranean, Murrah, Nili Ravi, and Binglangjiang, and four swamp type breeds, i.e., Diandongnan, Dehong, Guizhoubai, and Shanghai) were analyzed. Multiple single nucleotide polymorphism (SNP) selection methods including the population genetic differentiation index (FST), the machine learning-based Minimum Redundancy Maximum Relevance (mRMR) and Relief-F algorithm were investigated to pinpoint the highly informative SNPs for population assignment. Four machine learning classifiers-Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), and Random Forest (RF)-were applied for population assignment of the eight water buffalo breeds. Nine different SNP set sizes (100, 250, 500, 800, 1000, 2000, 3000, 5000, 10000) were examined. The impact of SNP selection methods, classifiers and SNP set size on assignment accuracy was evaluated. RESULTS:Population genetic analysis revealed hierarchical structures among the eight water buffalo breeds, with two divergent groups (swamp type buffaloes and river type buffaloes) and relatively closely related breeds within each group. Among the classifiers, SVM and NB outperformed RF and KNN in terms of assignment accuracy. Accuracy improved with an increasing number of SNPs, reaching over 85% for all classifiers with 10,000 SNPs. The mRMR method provided the highest accuracy, achieving 97.6% with 2000 SNPs when paired with the SVM classifier, followed by Relief-F (94%) and FST (90.4%). A panel of 768 mRMR-selected SNPs achieved 98.8% assignment accuracy using SVM classifiers, indicating it is a robust tool for reliable population assignment. CONCLUSION:By integrating machine learning-based feature selection with machine learning classifier, this study provides an effective framework for mining highly informative genetic markers for population assignment from whole-genome sequencing data.

Broilers grown in a high-density (HD) stocking environment may experience intense competition that may adversely affect their growth relative to animals reared at a normal density (ND). The growth performance of HD broilers is increased by aspirin eugenol ester (AEE), although the mechanism by which this compound modulates hypothalamus-regulated feeding behavior is unclear. The aims of this study were to determine the effects of including AEE in the basal diet on the hypothalamic transcriptome and to examine in parallel the impact of these modifications on broiler production performance in HD conditions. Three hundred sixty one-day-old male Arbor Acres broilers were randomly divided into four groups: an ND group (14 broilers/m2), HD group (22 broilers/m2), ND-AEE group, and HD-AEE group. Each treatment group had 10 replicates, with 7 broilers per replicate in the ND and ND-AEE groups and 11 broilers per replicate in the HD and HD-AEE groups. Broiler growth performance was monitored, and hypothalamus samples were collected for transcriptome analysis on day 28. The HD group exhibited a reduced body weight (p < 0.01) at this timepoint compared to the ND group. However, the addition of AEE significantly improved average daily feed intake, average daily gain, and feed conversion ratio in the HD group from days 22 to 28 compared to the HD group without AEE (p < 0.05). The transcriptome results showed that 20 signaling pathways were commonly enriched among the groups (ND vs. HD, HD vs. HD-AEE). Several potential candidate genes were identified as involved in chicken central nervous system development and regulation of feed intake. Thus, the current study provides new insights into hypothalamic transcription patterns that are associated with the ameliorative effects of AEE in HD broilers.

Background/Objectives: Buffalo populations exhibit distinct genetic variations influenced by domestication history, geographic distribution, and selection pressures. This study investigates the genetic structure and differentiation of 11 buffalo populations, focusing on five loci related to milk protein (CSN1S1 and CSN3) and fat metabolism (LPL, DGAT1 and SCD). The aim is to assess genetic variation between river, swamp, and wild-type buffaloes and identify key loci contributing to population differentiation. Methods: Genetic diversity was analyzed through allele frequency distribution, the Hardy−Weinberg equilibrium testing, and observed (Ho) and expected heterozygosity (He) calculations. Population structure was assessed using principal component analysis (PCA), FST statistics, and phylogenetic clustering (k-means and UPGMA tree). The silhouette score (SS) and the Davies−Bouldin index (DBI) were applied to determine optimal population clustering. Results: Significant genetic differentiation was observed between river and swamp buffaloes (p < 0.001). DGAT1 and CSN3 emerged as key markers distinguishing buffalo types. The Italian Mediterranean buffalo exhibited the highest genetic diversity (Ho = 0.464; He = 0.454), while the Indonesian, Chinese, and Vietnamese populations showed low heterozygosity, likely due to selection pressures and geographic isolation. The global FST (0.2143; p = 0.001) confirmed moderate differentiation, with closely related populations (e.g., Nepal and Pakistan) exhibiting minimal genetic divergence, while distant populations (e.g., Egypt and Indonesia) showed marked differences, and the Romanian population showed a unique genetic position. Conclusions: These findings contribute to a deeper understanding of buffalo genetic diversity and provide a valuable basis for exploiting the potential of this species in the light of future breeding and conservation strategies specific for each buffalo type.

Lactophorin is a major whey protein in camelid milk that prevents fat globule aggregation and inhibits spontaneous lipolysis. It has also been proposed to play immunological functions, including the prevention of mastitis in lactating animals and the suppression of pathogen replication in the respiratory and gastrointestinal tracts of suckling offspring. In this study, we explore the genetic variation of the glycosylation-dependent cell adhesion molecule-1 (GlyCam-1) gene in camelids, which encodes lactophorin, and examine the functional implications of the identified polymorphisms. The regulatory regions and the complete gene were sequenced and analysed in the Old World Camelids (OWC, dromedary and bactrian) and New World Camelids (NWC, llama and alpaca) using an integrated approach of molecular techniques and bioinformatics. The GlyCam-1 gene spans 2,567 bp in OWC and 2,504 bp in NWC and consists of 4 exons and 3 introns, highlighting its conserved structure. Sequencing results revealed inter- and intraspecies genetic variation, with 18 polymorphic sites in dromedaries, 7 in bactrian camels, 32 in alpacas, and 40 in llamas. Significant exonic polymorphisms were observed in NWC, potentially affecting gene expression and protein structure. Notably, the p.Glu46Lys and p.Ser134Pro variants in NWC were predicted to have a deleterious effect on protein function. Regulatory region analysis identified SNPs predicted to alter transcription factor binding sites (TFBS). In dromedaries, the g.563C > T substitution was predicted to affect the NF-κB binding, whereas in NWC, g.163A > G was predicted to modify a MZF1 binding site. The first SNP was associated with increased gene expression, and the latter was linked to significantly lower gene expression as a result of dual-luciferase reporter gene assay. Five haplotypes were observed in both NWC, with AGG and AGGG being the most prevalent in alpacas (0.707) and llamas (0.803), respectively; whereas a deviation from Hardy-Weinberg equilibrium was found for the SNP g.163A > G in alpacas. This study underscores the evolutionary conservation of GlyCam-1, highlights the importance of the found genetic variants and their potential use as candidate markers for future association studies with immune regulation and dairy traits.

BACKGROUND:Gene expression profiles hold potentially valuable information for the prediction of breeding values and phenotypes. However, in practical breeding programs, most reference population individuals typically have only genomic data, lacking transcriptomic data. Predicting gene expression based on genetic markers and integrating the genetically predicted gene expression data into genomic prediction may offer a potential solution. RESULTS:This study extends kernel ridge regression (KRR) to weighted multiple kernel ridge regression (WMKRR), which integrates genomic data and transcriptomic data predicted from genetic markers through a multiple kernel learning (MKL) approach. We evaluated the predictive ability of WMKRR compared to traditional genomic best linear unbiased prediction (GBLUP) and a combined genomic and transcriptomic best linear unbiased prediction (GTBLUP) in both genotype feature selection and non-feature selection scenarios in two datasets: (i) 3305 simulated data based on the Cattle Genotype-Tissue Expression (CattleGTEx) dataset, (ii) 5515 real dairy cattle data. Our results show that WMKRR yielded higher predictive abilities than GBLUP And GTBLUP in both simulated And real dairy cattle data. For the simulated data based on CattleGTEx, WMKRR achieved an average improvement in predictive ability of 1.12% And 1.13% over GBLUP And GTBLUP, respectively, under the non-feature selection scenario, And 3.17% And 3.23%, respectively, under the feature selection scenario. For the real dairy cattle data, in cross-validation, WMKRR improved over GBLUP And GTBLUP by An average of 5.56% And 7.23%, respectively, without feature selection, And by 5.66% And 6.40%, respectively, with feature selection. In forward validation, WMKRR improved over GBLUP And GTBLUP by An average of 5.68% And 8.41%, respectively, without feature selection, And by 4.66% And 7.06%, respectively, with feature selection. CONCLUSIONS:Our result demonstrates that the WMKRR model, which integrates genomic and genetically predicted transcriptomic data, achieves better prediction performance compared to traditional genomic prediction models. This study showed the potential of enhanced genomic breeding application using omics data with no further omics sequencing cost.

The common deleterious genetic defects in Holstein cattle include haplotypes 1-6 (HH1-HH6), haplotypes for cholesterol deficiency (HCD), bovine leukocyte adhesion deficiency (BLAD), complex vertebral malformation (CVM) and brachyspina syndrome (BS). Recessive inheritance patterns of these genetic defects permit the carriers to function normally, but homozygous recessive genotypes cause embryo loss or neonatal death. Therefore, rapid detection of the carriers is essential to manage these genetic defects. This study was conducted to develop a single-tube multiplex fluorescent amplification-refractory mutation system (mf-ARMS) PCR method for efficient genotyping of these 10 genetic defects and to compare its efficiency with the kompetitive allele specific PCR (KASP) genotyping assay. The mf-ARMS PCR method introduced 10 sets of tri-primers optimized with additional mismatches in the 3' end of wild and mutant-specific primers, size differentiation between wild and mutant-specific primers, fluorescent labeling of universal primers, adjustment of annealing temperatures and optimization of primer concentrations. The genotyping of 484 Holstein cows resulted in 16.12% carriers with at least one genetic defect, while no homozygous recessive genotype was detected. This study found carrier frequencies ranging from 0.0% (HH6) to 3.72% (HH3) for individual defects. The mf-ARMS PCR method demonstrated improved detection, time and cost efficiency compared with the KASP method for these defects. Therefore, the application of mf-ARMS PCR for genotyping Holstein cattle is anticipated to decrease the frequency of lethal alleles and limit the transmission of these genetic defects.

Abstract Background Biologically annotated neural networks (BANNs) are feedforward Bayesian neural network models that utilize partially connected architectures based on SNP-set annotations. As an interpretable neural network, BANNs model SNP and SNP-set effects in their input and hidden layers, respectively. Furthermore, the weights and connections of the network are regarded as random variables with prior distributions reflecting the manifestation of genetic effects at various genomic scales. However, its application in genomic prediction has yet to be explored. Results This study extended the BANNs framework to the area of genomic selection and explored the optimal SNP-set partitioning strategies by using dairy cattle datasets. The SNP-sets were partitioned based on two strategies–gene annotations and 100 kb windows, denoted as BANN_gene and BANN_100kb, respectively. The BANNs model was compared with GBLUP, random forest (RF), BayesB and BayesCπ through five replicates of five-fold cross-validation using genotypic and phenotypic data on milk production traits, type traits, and one health trait of 6,558, 6,210 and 5,962 Chinese Holsteins, respectively. Results showed that the BANNs framework achieves higher genomic prediction accuracy compared to GBLUP, RF and Bayesian methods. Specifically, the BANN_100kb demonstrated superior accuracy and the BANN_gene exhibited generally suboptimal accuracy compared to GBLUP, RF, BayesB and BayesCπ across all traits. The average accuracy improvements of BANN_100kb over GBLUP, RF, BayesB and BayesCπ were 4.86%, 3.95%, 3.84% and 1.92%, and the accuracy of BANN_gene was improved by 3.75%, 2.86%, 2.73% and 0.85% compared to GBLUP, RF, BayesB and BayesCπ, respectively across all seven traits. Meanwhile, both BANN_100kb and BANN_gene yielded lower overall mean square error values than GBLUP, RF and Bayesian methods. Conclusion Our findings demonstrated that the BANNs framework performed better than traditional genomic prediction methods in our tested scenarios, and might serve as a promising alternative approach for genomic prediction in dairy cattle.

The search for DNA polymorphisms useful for the genetic improvement of dairy farm animals has spanned more than 40 years, yielding relevant findings in cattle for milk traits, where the best combination of alleles for dairy processing has been found in casein genes and in DGAT1. Nowadays, similar results have not yet been reached in river buffaloes, despite the availability of advanced genomic technologies and accurate phenotype records. The aim of the present study was to investigate and validate the effect of four single nucleotide polymorphisms (SNP) in the CSN1S1, CSN3, SCD and LPL genes on seven milk traits in a larger buffalo population. These SNPs have previously been reported to be associated with, or affect, dairy traits in smaller populations often belonging to one farm. A total of 800 buffaloes were genotyped. The following traits were individually recorded, monthly, throughout each whole lactation period from 2010 to 2021: daily milk yield (dMY, kg), protein yield (dPY, kg) and fat yield (dFY, kg), fat and protein contents (dFP, % and dPP, %), somatic cell count (SCC, 103 cell/mL) and urea (mg/dL). A total of 15,742 individual milk test day records (2496 lactations) were available for 680 buffalo cows, with 3.6 ± 1.7 parities (from 1 to 13) and an average of 6.1 ± 1.2 test day records per lactation. Three out four SNPs in the CSN1S1, CSN3 and LPL genes were associated with at least one of analyzed traits. In particular, the CSN1S1 (AJ005430:c.578C>T) gave favorable associations with all yield traits (dMY, p = 0.022; dPY, p = 0.014; dFY, p = 0.029) and somatic cell score (SCS, p = 0.032). The CSN3 (HQ677596: c.536C>T) was positively associated with SCS (p = 0.005) and milk urea (p = 0.04). Favorable effects on daily milk yield (dMY, p = 0.028), fat (dFP, p = 0.027) and protein (dPP, p = 0.050) percentages were observed for the LPL. Conversely, the SCD did not show any association with milk traits. This is the first example of a confirmation study carried out in the Mediterranean river buffalo for genes of economic interest in the dairy field, and it represents a very important indication for the preselection of young bulls destined for breeding programs aimed at more sustainable dairy production.

Weaning weight is a key indicator of the early growth performance of cattle. An understanding of the genetic mechanisms underlying weaning weight will help increase the accuracy of selection of breeding animals. In order to identify candidate genes associated with weaning weight in Simmental-Holstein crossbred cattle, this study generated RNA-Sequencing (RNA-seq) data from 86 crossbred calves (37 males and 49 famales) and measured their weaning weight and body size traits (wither height, body length, chest girth, rump width, and rump length). Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were performed. A total of 498 differentially expressed genes (DEGs) were identified between the low weaning weight (LWW) group and the high weaning weight (HWW) group. Weaning weight was transcriptionally correlated (FDR < 0.05) with four of the eleven co-expression gene modules. By intersecting DEGs and hub genes of the four modules, we identified a final set of 37 candidate genes enriched in growth, development, or immune-related processes. In addition, one co-expression module was significantly correlated with all the five body size traits (P < 0.05), from which MX1 was identified as a key candidate gene through protein-protein interaction (PPI) analysis of hub genes. Further evidence from cattle transcriptome-wide association study analysis (TWAS) and human phenome-wide association study (PheWAS) validated significant associations of CACNA1S, SEMA7A, VCAN, CD101, CD19, and CSF2RB with growth and development traits (P < 0.05). Notably, CACNA1S and CD19 were also associated with typical immune traits such as B cell proliferation, differentiation, and activation. In conclusion, this study reveals new candidate genes significantly associated with weaning weight in Simmental-Holstein crossbred cattle, providing a basis for further exploration of the genetic mechanisms behind growth traits of cattle.