Search Results (69)

This study investigates the genetic diversity, population structure, and adaptive differentiation of Yunnan native cattle (YNC) using whole-genome SNP data from 457 individuals, representing eight cattle populations and two closely related bovine species (Zhongdian yak and Dulong gayal). Genetic diversity analyses revealed a distinct latitudinal gradient from north to south, with the highest diversity observed in the northern Diqing (DQC) and Zhaotong (ZTC) populations. The observed population structure was largely consistent with geographic distribution, identifying distinct ancestral components and complex admixture patterns. Genome-wide selective sweep scans revealed several key candidate genes underlying local adaptation. Notably, GRIA4 and DUOXA2 were associated with cold tolerance in northern populations, and ST3GAL3 and MST1 were implicated in heat stress adaptation in southern populations. Genome-wide balancing selection analyses further detected significant loci, such as MGST1 and SLC36A1, where divergent haplotype frequencies reflected differential selective pressures on milk-related traits between northern and southern populations. Additionally, we detected signals of historical introgression from Zhongdian yak into DQC cattle, highlighting the introgressed gene SLIT3 as a potential candidate associated with high-altitude thermogenesis. Collectively, these results provide a comprehensive genomic framework for the management and conservation of indigenous bovine genetic resources in Southwest China. Full article

Background/Objectives: Genome-wide association studies (GWAS) based on single-step genomic BLUP (ssGBLUP) commonly assume equal single nucleotide polymorphism (SNP) variances, which may not reflect the biological architecture of complex traits. Alternative weighting strategies can increase detection power but may affect stability. This study evaluated how different SNP weighting approaches influence genomic region detection and biological interpretation of ribeye area (REA) and subcutaneous fat thickness (SFT) in Guzerá cattle. Methods: Phenotypic records from 2729 animals and genotypes from 1405 individuals (43,039 SNPs after quality control) were analyzed. Heritabilities were estimated using Restricted Maximum Likelihood (REML), and GWAS were conducted under five approaches: unweighted method (UM), quadratic method (QM), and three Non-Linear A strategies with weighting constants (1.125, 1.2, and 1.5). Genomic windows of 20 adjacent SNPs explaining ≥0.5% of the additive genetic variance (AGV) were considered significant. Recurrent regions were prioritized, and functional enrichment analyses (KEGG, GO, and MeSH) were performed. Results: Heritability estimates were moderate for REA (0.26 ± 0.05) and SFT (0.22 ± 0.04). Weighted approaches increased detection sensitivity. For REA, UM identified 10 windows, whereas QM and A_1.5 detected 24 and 31 windows. For SFT, UM identified 8 windows, while QM and A_1.5 detected 30 and 23 windows. Recurrent chromosomes included 2, 4, 6, 12, 16, 19, and 22 for REA, and 2, 3, 5, 7, 11, 17, and 22 for SFT. Key genes included AKT3, NOS2, and MSTN. Enrichment highlighted pathways related to muscle growth and lipid metabolism. Conclusions: SNP-weighted GWAS increased detection sensitivity but involved trade-offs between signal amplification and stability. Integrating weighting strategies improves biological interpretation and supports robust candidate gene identification for genomic selection. Full article

Reproductive performance is a key determinant of overall livestock productivity. In both beef and dairy systems, reproductive failure represents a leading cause of cow culling. Reproductive traits are complex in nature and present a low heritability in general. Additionally, the collection of such phenotypes usually relies on indirect measures of fertility, such as conception success. Therefore, further investigation into genetic and non-genetic factors of reproductive traits in cattle is necessary. The hosts’ microbiome plays a crucial role in vertebrate biology, including reproduction. We, therefore, hypothesize that microbiome indicators may serve as a biomarker of fertility. This study explored the relationship between vaginal microbiome profiles and pregnancy among three beef cattle genetic groups using field data. Vaginal swabs were collected from 74 cows at Fort Keogh, MT, including 23 Angus, 23 Hereford Line 1, and 28 crossbreds, and DNA was extracted and analyzed via 16S rRNA gene amplification. Significant differences in alpha diversity (p < 0.05) were found among Line 1 cows compared to Angus and crossbreds in many indicators of alpha diversity. Pregnancy status did not influence alpha diversity of samples significantly, but trends toward significance were observed. PERMANOVA analysis indicated that genetic groups and pregnancy status affected microbial composition (p < 0.05), but their interaction was not significant. Each genetic group showed unique compositions of operational taxonomic units (OTUs), with higher proportions of Ureaplasma and Mycoplasma families in Line 1. Additionally, variations in microbial communities were observed between pregnant and non-pregnant cows, with certain uncultured bacteria more prevalent in non-pregnant cows. While field data are useful for such studies and represent a real production system, better-designed experiments are necessary to validate findings and test hypotheses. These results suggest variation in vaginal microbiomes across breeds and pregnancy status, emphasizing the need for further research to identify factors affecting these changes. Full article

Domestic cattle represent one of the most significant evolutionary successes in the history of human–animal mutualism. This review synthesizes evidence from paleogenomics and modern population genetics, particularly recent pangenome analyses, to reconstruct a comprehensive evolutionary trajectory of cattle. We outline the two domestication events: the emergence of taurine cattle (Bos taurus) in the Fertile Crescent (~10,500 years ago) and zebu cattle (Bos indicus) in the Indus Valley (~8000 years ago). Following domestication, cattle dispersed globally alongside human migration, resulting in a complex genetic mosaic shaped by introgression with wild relatives and extensive admixture between lineages. By integrating data from mitochondrial DNA, Y-chromosome haplotypes, and whole-genome sequencing of modern, ancient, and wild samples, we reconstruct the detailed global dispersal of cattle. Furthermore, we dissect the molecular mechanisms underlying phenotypic diversity, emphasizing how natural selection has driven environmental adaptation, how artificial selection has optimized production traits, and how the emerging bovine pangenome is unveiling “hidden” genetic variations critical for climate resilience and disease resistance. Ultimately, this review summarizes the origin, dispersal, and genomic diversity of cattle, offering vital insights for the conservation of indigenous genetic resources and the advancement of molecular breeding strategies in the face of a changing global climate. Full article

The bovine tongue is a complex and very important muscular and gustatory organ, yet a comprehensive understanding of its gustatory apparatus across diverse genetic resources remains elusive. In this study, we conducted a multidimensional analysis of the lingual morphology and taste bud (TB) distribution in 40 specimens from 12 representative bovine breeds and species across China, encompassing Bos taurus taurus (Taurine cattle), Bos taurus indicus (Zebu cattle), Bubalus bubalis (water buffalo), and Bos grunniens (domestic yak). Morphometric measurements and histological quantifications were integrated to evaluate the influence of species, sex, age, and geographical factors. Given the relatively limited sample size per breed, these findings are presented as exploratory research. Our results revealed that yak and water buffalo showed the most distinct morphological patterns of mechanical papillae compared to the other populations. Taurine and Zebu cattle displayed more similar lingual morphology traits. Although high phenotypic correlations were observed between lingual morphometric parameters and quantitative papillae indicators, factors such as age, altitude, and feeding methods showed minimal influence on lingual phenotypic variation within this cohort (p > 0.05). Furthermore, we constructed a topological atlas of TB distribution, revealing that TB distribution patterns are decoupled from macro-anatomical dimensions, highlighting the complexity of the bovine gustatory system. These findings provide a quantitative baseline for ruminant comparative anatomy and offer structural insights into the evolutionary adaptation and nutrient regulation mechanisms of diverse bovine species in varying environments. Full article

Background/Objectives: Accurate estimates of variance components are essential in breeding programs. In this context, the main objective of this study was to estimate variance components for growth traits in the Montana Composite^® beef population, which was developed in Brazil by crossing various taurine and indicine breeds. After 30 years of selection, the impact of recombination, heterosis, and inbreeding may have influenced the genetic background of the population. Methods: We analyzed data of birth weight, weaning weight, post-weaning weight gain, and yearling weight using 124,255 phenotypic records, 193,129 pedigree records, and 3911 genotyped individuals. Ten single-trait animal models (M1–M10) were compared, differing in the relationship matrix (pedigree- or genome-based relationships) and the inclusion of direct/maternal breed composition, heterosis, and recombination effects. Results: Models incorporating genomic information consistently yielded better fit and lower residual variances than pedigree-based models, highlighting the advantage of genomic information in capturing Mendelian sampling and realized genetic relationships. The inclusion of heterosis effects improved model fit and led to a partial reallocation of genetic variance from additive to non-additive components. In contrast, the inclusion of recombination effects in the models minimally influenced variance component estimates. Nevertheless, more complex models affected animal rankings and altered the breed composition of top-ranked selection candidates, with selection overlap between pedigree- and genomic-based evaluations ranging from moderate to high. Conclusions: Overall, genome-based models accounting for breed composition, heterosis, and recombination provided the most robust variance component estimates and the best support for long-term selection goals in the studied tropical composite beef cattle population. Full article

Cattle breeds are optimized either for milk or meat production and secrete consumed nutrients in the form of milk or accrete nutrients as skeletal muscle tissue, respectively. Surplus energy is usually stored in the form of fat in adipose tissues. To gain more insight into the physiological and genetic background of nutrient accretion as either protein or fat, an experimental F₂ population was generated crossing Charolais (CH) bulls and German Holstein (GH) cows. Mutations in two genes with known, profound effects on growth were segregating in this population: the I442M mutation in the non-SMC condensin I complex, subunit G (NCAPG) gene, and the Q204X mutation in the myostatin (MSTN) gene. The major aim of this study was to close the gap between the described effects of the NCAPG/LCORL region and MSTN SNPs on carcass and meat quality traits, as well as on the structure and composition of the underlying tissues. Whole carcass data, meat quality traits, composition of major cuts and their dominating muscles, including muscle and fat cell structure, were analyzed as well as chemical and fatty acid composition. Mutant alleles of both loci were associated with higher weights, increased muscularity, and reduced fatness, e.g., each explaining about 15% of the observed variance. However, both loci apparently affect traits in a specific manner, influencing either dimensional traits or mass accretion. Full article

Multi-omics studies have multiplied associations, but many still lack causal resolution and a clear path to application. We present a practical roadmap built on four sequential steps: first, identify signals from genome-wide association studies; second, confirm these signals through regulatory colocalization and transcriptome-wide association analyses; third, integrate the evidence using network analyses and causal inference; and, fourth, test shortlisted candidates through functional and phenotypic validation. The roadmap is supported by three safeguards that make results reliable and reusable: containerized workflows that ensure end-to-end reproducibility, harmonization across batches with concise minimum-information records, and consistent identifier mapping with quality control across data layers. Across four classes of traits—growth and development, carcass and meat quality, reproduction, and environmental adaptation and resilience—we prioritize signals that remain robust across ancestries and environments, highlight modules with explicit regulatory support, and advance candidates that have already progressed to functional testing. Two application tracks follow from this process: integrating stable candidates into selection indices with context-dependent weighting, and recording and targeting mechanistic nodes for nutritional and management interventions. Taken together, this roadmap improves causal interpretability, strengthens cross-population robustness, and shortens the path from statistical association to genetic evaluation and industry uptake. Full article

Understanding the dispersal capacity of Triatoma infestans, the main vector of Chagas disease in South America, is vital for vector control and managing recolonization after insecticide use. This study compares the seasonal frequency of melanic and non-melanic T. infestans morphs in Northwestern Córdoba Province, Argentina, and examines their association with environmental variables, morphometric traits, nutritional status, and flight capacity. Insects were collected at the beginning and end of the warm season. Dorsal coloration, morphometric traits, nutritional status, flight-related indices, climatic variables, and vegetation cover were recorded. Chromatic morph frequencies were analyzed using chi-square tests. Biological predictors were identified through multi-model inference, and environmental associations explored with Canonical Correspondence Analysis. Melanic individuals decreased from early to late warm season, especially males. Wing loading correlated strongly with morphotype, being higher in non-melanic forms. Pronotum size were also a significant predictor. Nutritional status had no clear effect. Cattle pasture cover and rainfall influenced morph frequency, mainly in males. These results reveal a complex interaction between phenotypic and environmental factors shaping color morph variation, highlighting the importance of understanding these dynamics to optimize vector surveillance and control in areas prone to reinfestation. Full article

The convergence of genomic selection and artificial intelligence (AI) is redefining precision breeding in dairy cattle, enabling earlier, more accurate, and multi-trait selection for health, fertility, climate resilience, and economic efficiency. This review critically examines how advanced genomic tools—such as genome-wide association studies (GWAS), genomic breeding values (GEBVs), machine learning (ML), and deep learning (DL) models to accelerate genetic gain for complex, low heritability traits. Key applications include improved resistance to mastitis and metabolic diseases, enhanced thermotolerance, reduced enteric methane emissions, and increased milk yield. We discuss emerging computational frameworks that combine sensor-derived phenotypes, omics datasets, and environmental data to support data-driven selection decisions. Furthermore, we address implementation challenges related to data integration, model interpretability, ethical considerations, and access in low-resource settings. By synthesizing interdisciplinary advances, this review provides a roadmap for developing AI-augmented genomic selection pipelines that support sustainable, climate-smart, and economically viable dairy systems. Full article

Background: Chinese Red Steppe cattle (CRS) combine indigenous environmental resilience with moderate dairy performance, whereas Holstein cattle (HOL), despite their high milk yield, suffer reduced genetic diversity and compromised adaptation. A comparative analysis of their population genetic architecture and selection signatures can reveal valuable targets for CRS dairy improvement. Methods: We genotyped 61 CRS and 392 HOL individuals using the Illumina GGP Bovine 100K SNP array and performed stringent quality control. Population structure was assessed via principal component analysis, neighbor-joining trees, and sparse nonnegative matrix factorization. Historical effective population size (Ne) and divergence time were inferred with SMC++. Genome-wide selection scans combined Fixation Index (FST) and Cross-Population Composite Likelihood Ratio test (XP-CLR); overlapping high-confidence regions were annotated and subjected to GO and KEGG enrichment analyses. Results: CRS and HOL were clearly separated along PC1 (explaining 57.48% of variance), with CRS exhibiting high internal homogeneity and weak substructure, versus greater diversity and complex substructure in HOL. SMC++ indicated a split approximately 3500 years ago (700 generations) and a pronounced recent decline in Ne for both breeds. Joint selection mapping identified 767 candidate genes; notably, the ACSM1/2B/3/4 cluster on chromosome 25—key to butanoate metabolism—showed the strongest signal. Enrichment analyses highlighted roles for proteasome function, endoplasmic reticulum stress response, ion homeostasis, and RNA processing in regulating milk fat synthesis and protein secretion. Conclusion: This study delineates the genetic divergence and demographic history of CRS and HOL, and pinpoints core genes and pathways—particularly those governing butanoate metabolism and protein quality control—underlying dairy traits. These findings furnish molecular markers and theoretical guidance for precision breeding and sustainable utilization of Chinese Red Steppe cattle. Full article

Cystic echinococcosis is a zoonosis caused by the cestode Echinococcus granulosus sensu stricto. Population genetic studies and phylogeographic patterns are essential to understanding the transmission dynamics of this parasite under varying environmental conditions. In this study, the genetic diversity of E. granulosus s.s. was evaluated using 46 hydatid cyst samples obtained from sheep, goats, cattle, and humans across three regions of Chile: Coquimbo, La Araucanía, and Magallanes. Mitochondrial cox1 gene sequences were analyzed and compared with reference sequences reported from South America, Europe, Africa, Asia, and Oceania. In Chile, the EG01 haplotype was the predominant haplotype. A total of four haplotypes were identified, with low haplotype diversity (Hd = 0.461 ± 0.00637) and low nucleotide diversity (π = 0.00181 ± 0.00036). The haplotype network displayed a star-like configuration, with the EG01 genotype at the center, suggesting a potentially ancestral or widely distributed lineage. In Coquimbo (Tajima’s D = −0.93302, p = 0.061; Fu’s Fs = −0.003, p = 0.502) and Magallanes (Tajima’s D = −0.17406, p = 0.386; Fu’s Fs = −0.121, p = 0.414), both neutrality tests were non-significant, indicating no strong evidence for recent population expansion or selection. Star-like haplotype network patterns were also observed in populations from Europe, the Middle East, Asia, Africa, and Oceania, with the EG01 genotype occupying the central position. The population genetic structure of Echinococcus granulosus s.s. in Chile demonstrates considerable complexity, with EG01 as the predominant haplotype. Further comprehensive studies are required to assess the intraspecific genetic variability of E. granulosus s.s. throughout Chile and to determine whether this variability influences the key biological traits of the parasite. This structure may prove even more complex when longer fragments are analyzed, which could allow for the detection of finer-scale microdiversity among isolates from different hosts. We recommended that future cystic echinococcosis control programs take into account the genetic variability of E. granulosus s.s. strains circulating in each endemic region, to better understand their epidemiological, immunological, and possibly pathological differences. Full article

A unique line of Holstein cattle has been maintained without selection in Minnesota since 1964. After many generations, unselected cattle produce less milk, but have better reproductive performance and health traits when compared with contemporary cows. Comparisons between this line of unselected Holstein and those under selection provide useful insights that connect selection and complex traits in cattle. Utilizing these unique resources and sequence data, we sought to identify genome changes due to selection. We sequenced 30 unselected and 54 selected Holstein cattle and compared their sequence variants to identify selection signatures. After many years, the two populations showed completely different patterns in their genome-level population structures and linkage disequilibrium. By integrating signals from five different detection methods, we detected consensus selection signatures from at least four methods covering 14,533 SNPs and 155 protein-coding genes. An integrated analysis of selection signatures with gene annotation, pathways, and the cattle QTL database demonstrated that the genomic regions under selection are related to milk productivity, health, and reproductive efficiency. The polygenic nature of these complex traits is evident from hundreds of selection signatures and candidate genes, suggesting that long-term artificial selection has acted on the whole genome rather than a few major genes. In summary, our study identified candidate selection signatures underlying phenotypic differences between unselected and selected Holstein cows and revealed insights into the genetic basis of complex traits in cattle. Full article

Herbivorous livestock, such as cattle, sheep, goats, horses, and donkeys, play a crucial role in agricultural production and possess remarkable resilience to extreme environmental conditions, driven by complex genetic mechanisms. Recent advancements in high-throughput sequencing, genome assembly, and environmental data integration have enabled a deeper understanding of the genetic basis of their environmental adaptation. This review identifies key genes associated with high-altitude, heat, cold, and drought adaptation, providing insights into the molecular mechanisms underlying these traits. By elucidating these genetic adaptations, our study aims to support conservation efforts, inform selective breeding programs, and enhance agricultural productivity, ultimately contributing to sustainable livestock farming and economic benefits for farmers. Full article

Nonparametric models have recently been receiving increased attention due to their effectiveness in genomic prediction for complex traits. However, regular nonparametric models cannot effectively differentiate the relative importance of various SNPs, which significantly impedes the further application of these methods for genomic prediction. To enhance the fitting ability of nonparametric models and improve genomic prediction accuracy, a weighted kernel ridge regression model (WKRR) was proposed in this study. For this new method, different weights were assigned to different SNPs according to the p-values from GWAS, and then a KRR model based on these weighted SNPs was constructed for genomic prediction. Cross-validation was further adopted to choose appropriate hyper-parameters during the weighting and prediction process for generalization. We compared the predictive accuracy of WKRR with the genomic best linear unbiased prediction (GBLUP), BayesR, and unweighted KRR using both simulated and real datasets. The results showed that WKRR outperformed unweighted KRR in all simulated scenarios. Additionally, WKRR achieved an average improvement of 1.70% in accuracies across all traits in a mice dataset and 2.17% for three lactation-related traits in a cattle dataset compared to GBLUP, and yielded competitive results compared to BayesR. These findings demonstrated the great potential of weighted nonparametric models for genomic prediction. Full article