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Zhao Z, Niu Q, Wu J, Wu T, Xie X, Wang Z, Zhang L, Gao H, Gao X, Xu L, Zhu B, Li J. Integrating multi-layered biological priors to improve genomic prediction accuracy in beef cattle. Biol Direct 2024; 19:147. [PMID: 39741345 DOI: 10.1186/s13062-024-00574-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2024] [Accepted: 12/02/2024] [Indexed: 01/02/2025] Open
Abstract
BACKGROUND Integrating multi-layered information can enhance the accuracy of genomic prediction for complex traits. However, the improvement and application of effective strategies for genomic prediction (GP) using multi-omics data remains challenging. METHODS We generated 11 feature sets for sequencing variants from genomics, transcriptomics, metabolomics, and epigenetics data in beef cattle, then we assessed the contribution of functional variants using genomic restricted maximum likelihood (GREML). We next estimated and ranked variant scores for 43 economically important traits, and compared the prediction accuracy of the top and bottom sets using genomic best linear unbiased prediction (GBLUP) and BayesB model. In addition, we annotated the variants from GWAS with functional feature sets and performed enrichment analysis. RESULTS We observed significant enrichments for 32 functional categories in 11 feature sets. The evolutionary related sets (conservation regions and selection signatures) contributed significantly to heritability (31.78-fold and 14.48-fold enrichment), while metabolomics and transcriptomics showed low heritability enrichments. We observed a significant increase in prediction accuracy using the top feature set variants compared to whole-genome sequencing (WGS) data. The prediction accuracy based on the top 10% variant set showed an average increase of 11.6% and 7.54% using BayesB and GBLUP across traits, respectively. Notably, the greatest increase of 31.52% was obtained for spleen weight (SW) using BayesB. Also, we found that the top 10% of variants show strong enrichment with weight related QTLs based on the Cattle QTL database. CONCLUSIONS Our findings suggest that integrating biological prior information from multiple layers can enhance our understanding of the genetic architecture underlying complex traits and further improve genomic prediction in beef cattle.
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Affiliation(s)
- Zhida Zhao
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Qunhao Niu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jiayuan Wu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Tianyi Wu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Xueyuan Xie
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Zezhao Wang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lupei Zhang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Huijiang Gao
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Xue Gao
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lingyang Xu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
| | - Bo Zhu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
- Northern Agriculture and Livestock Husbandry Technology Innovation Center, Hohhot, 010010, China.
| | - Junya Li
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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Lindtke D, Lerch S, Morel I, Neuditschko M. Assessment of genome complementarity in three beef-on-dairy crossbreds reveals sire-specific effects on production traits with comparable rates of genomic inbreeding reduction. BMC Genomics 2024; 25:1118. [PMID: 39567870 PMCID: PMC11577664 DOI: 10.1186/s12864-024-11029-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Accepted: 11/11/2024] [Indexed: 11/22/2024] Open
Abstract
BACKGROUND Crossbreeding beef bulls with dairy cows can improve the economic value and fitness of calves not entering dairy production owing to increased meat yield and heterosis. However, outcrossing might reduce the dosage of alleles that confer local adaptation or result in a higher risk of dystocia due to increased calf size. Given the clear phenotypic differences between beef breeds, the varying phylogenetic distances between beef and dairy breeds, and the genomic variations within breeds, the attainable economic and fitness gains of calves will strongly depend on the selection of sires for crossing. Thus, the aim of this study was to assess genome complementarity between Angus (AAN), Limousin (LIM), or Simmental (SIM) beef bulls and Brown Swiss (BSW) dairy cows by quantifying genomic inbreeding reduction in F1 crosses and identifying genes potentially under BSW-specific selection that might be affected by outcrossing. RESULTS Low-pass sequencing data from 181 cows, 34 bulls, and 301 of their F1 progeny, and body weight and carcass composition measurements of 248 F1s were obtained. The high genomic inbreeding levels detected in the BSW cows were substantially reduced in the crossbreds, with only minor differences between the sire breeds. In the BSW cows, 585 candidate genes under selection were identified, overrepresenting genes associated with milk, meat and carcass, and production traits. Only a few genes were strongly differentiated at nonsynonymous variants between the BSW and beef breeds, including four tightly clustered genes (FAM184B, NCAPG, DCAF16, and LCORL) nearly fixed for alternate alleles in the BSW cows but mostly heterozygous or homozygous for the reference alleles in the AAN and LIM bulls. The alternate allele dosage at these genes significantly correlated with reduced carcass weight and protein mass in F1s. CONCLUSION Some of the few genes that were highly divergent between the BSW and beef breeds at nonsynonymous variants were likely under strong selection for reduced carcass weight in the BSW breed, potentially due to trade-offs between beef and dairy productions. As alleles with opposing effects still segregate in beef cattle, marker-assisted selection of mating pairs may be used to modulate the desired phenotypes and simultaneously decrease genomic inbreeding.
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Affiliation(s)
| | - Sylvain Lerch
- Ruminant Nutrition and Emissions, 1725 Posieux, Agroscope, Switzerland
| | - Isabelle Morel
- Ruminant Nutrition and Emissions, 1725 Posieux, Agroscope, Switzerland
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Somenzi E, Partel E, Barbato M, Chero Osorio AM, Colli L, Franceschi N, Mantovani R, Pilla F, Komjanc M, Achilli A, Hauffe HC, Ajmone Marsan P. Genetic legacy and adaptive signatures: investigating the history, diversity, and selection signatures in Rendena cattle resilient to eighteenth century rinderpest epidemics. Genet Sel Evol 2024; 56:32. [PMID: 38698323 PMCID: PMC11064358 DOI: 10.1186/s12711-024-00900-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 04/05/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Rendena is a dual-purpose cattle breed, which is primarily found in the Italian Alps and the eastern areas of the Po valley, and recognized for its longevity, fertility, disease resistance and adaptability to steep Alpine pastures. It is categorized as 'vulnerable to extinction' with only 6057 registered animals in 2022, yet no comprehensive analyses of its molecular diversity have been performed to date. The aim of this study was to analyse the origin, genetic diversity, and genomic signatures of selection in Rendena cattle using data from samples collected in 2000 and 2018, and shed light on the breed's evolution and conservation needs. RESULTS Genetic analysis revealed that the Rendena breed shares genetic components with various Alpine and Po valley breeds, with a marked genetic proximity to the Original Braunvieh breed, reflecting historical restocking efforts across the region. The breed shows signatures of selection related to both milk and meat production, environmental adaptation and immune response, the latter being possibly the result of multiple rinderpest epidemics that swept across the Alps in the eighteenth century. An analysis of the Rendena cattle population spanning 18 years showed an increase in the mean level of inbreeding over time, which is confirmed by the mean number of runs of homozygosity per individual, which was larger in the 2018 sample. CONCLUSIONS The Rendena breed, while sharing a common origin with Brown Swiss, has developed distinct traits that enable it to thrive in the Alpine environment and make it highly valued by local farmers. Preserving these adaptive features is essential, not only for maintaining genetic diversity and enhancing the ability of this traditional animal husbandry to adapt to changing environments, but also for guaranteeing the resilience and sustainability of both this livestock system and the livelihoods within the Rendena valley.
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Affiliation(s)
- Elisa Somenzi
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del Sacro Cuore, Piacenza, Italy.
| | - Erika Partel
- Unità risorse foraggere e produzioni zootecniche, Centro Trasferimento Tecnologico, Fondazione Edmund Mach, S. Michele all'Adige, Trento, Italy
| | - Mario Barbato
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del Sacro Cuore, Piacenza, Italy
- Department of Veterinary Science, Università degli Studi di Messina, Messina, Italy
| | - Ana María Chero Osorio
- Dipartimento di Biologia e Biotecnologie "L. Spallanzani", University of Pavia, Pavia, Italy
| | - Licia Colli
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del Sacro Cuore, Piacenza, Italy
- Centro di Ricerca Sulla Biodiversità e sul DNA Antico, BioDNA, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Niccolò Franceschi
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Roberto Mantovani
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padua, Padua, Italy
| | - Fabio Pilla
- Department of Agriculture Environment and Food Science, University of Molise, Campobasso, Italy
| | - Matteo Komjanc
- Unità risorse foraggere e produzioni zootecniche, Centro Trasferimento Tecnologico, Fondazione Edmund Mach, S. Michele all'Adige, Trento, Italy
| | - Alessandro Achilli
- Dipartimento di Biologia e Biotecnologie "L. Spallanzani", University of Pavia, Pavia, Italy
| | - Heidi Christine Hauffe
- Conservation Genomics Research Unit, Research and Innovation Centre, Fondazione Edmund Mach, S. Michele all'Adige, Trento, Italy
| | - Paolo Ajmone Marsan
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del Sacro Cuore, Piacenza, Italy
- Centro di Ricerca Nutrigenomica e Proteomica-PRONUTRIGEN, Università Cattolica del Sacro Cuore, Piacenza, Italy
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Colombi D, Rovelli G, Luigi-Sierra MG, Ceccobelli S, Guan D, Perini F, Sbarra F, Quaglia A, Sarti FM, Pasquini M, Amills M, Lasagna E. Population structure and identification of genomic regions associated with productive traits in five Italian beef cattle breeds. Sci Rep 2024; 14:8529. [PMID: 38609445 PMCID: PMC11014930 DOI: 10.1038/s41598-024-59269-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/09/2024] [Indexed: 04/14/2024] Open
Abstract
Italy has a long history in beef production, with local breeds such as Marchigiana, Chianina, Romagnola, Maremmana, and Podolica which produce high-quality meat. Selection has improved meat production, precocity, growth ability and muscle development, but the genetic determinism of such traits is mostly unknown. Using 33K SNPs-data from young bulls (N = 4064) belonging to these five Italian breeds, we demonstrated that the Maremmana and Podolica rustic breeds are closely related, while the specialised Marchigiana, Chianina, and Romagnola breeds are more differentiated. A genome-wide association study for growth and muscle development traits (average daily gain during the performance test, weight at 1 year old, muscularity) was conducted in the five Italian breeds. Results indicated a region on chromosome 2, containing the myostatin gene (MSTN), which displayed significant genome-wide associations with muscularity in Marchigiana cattle, a breed in which the muscle hypertrophy phenotype is segregating. Moreover, a significant SNP on chromosome 14 was associated, in the Chianina breed, to muscularity. The identification of diverse genomic regions associated with conformation traits might increase our knowledge about the genomic basis of such traits in Italian beef cattle and, eventually, such information could be used to implement marker-assisted selection of young bulls tested in the performance test.
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Affiliation(s)
- Daniele Colombi
- Department of Agricultural, Food and Environmental Sciences (DSA3), University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy
| | - Giacomo Rovelli
- Department of Agricultural, Food and Environmental Sciences (DSA3), University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus Universitat Autonòma de Barcelona, Carrer de la Vall Moronta, 08193, Bellaterra de Cerdanyola del Vallés, Spain
| | - Maria Gracia Luigi-Sierra
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus Universitat Autonòma de Barcelona, Carrer de la Vall Moronta, 08193, Bellaterra de Cerdanyola del Vallés, Spain
| | - Simone Ceccobelli
- Department of Agricultural, Food and Environmental Sciences (D3A), Università Politecnica delle Marche, 60131, Ancona, Italy
| | - Dailu Guan
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus Universitat Autonòma de Barcelona, Carrer de la Vall Moronta, 08193, Bellaterra de Cerdanyola del Vallés, Spain
- Department of Animal Science, University of California, Davis, CA, 2251, USA
| | - Francesco Perini
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, 35020, Legnaro, Italy
| | - Fiorella Sbarra
- National Association of Italian Beef-Cattle Breeders (ANABIC), 06132, San Martino in Colle, Perugia, Italy
| | - Andrea Quaglia
- National Association of Italian Beef-Cattle Breeders (ANABIC), 06132, San Martino in Colle, Perugia, Italy
| | - Francesca Maria Sarti
- Department of Agricultural, Food and Environmental Sciences (DSA3), University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy
| | - Marina Pasquini
- Department of Agricultural, Food and Environmental Sciences (D3A), Università Politecnica delle Marche, 60131, Ancona, Italy
| | - Marcel Amills
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus Universitat Autonòma de Barcelona, Carrer de la Vall Moronta, 08193, Bellaterra de Cerdanyola del Vallés, Spain.
- Department of Animal and Food Science, Universitat Autònoma de Barcelona, 08193, Bellaterra, Spain.
| | - Emiliano Lasagna
- Department of Agricultural, Food and Environmental Sciences (DSA3), University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy.
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5
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Rajawat D, Ghildiyal K, Sonejita Nayak S, Sharma A, Parida S, Kumar S, Ghosh AK, Singh U, Sivalingam J, Bhushan B, Dutt T, Panigrahi M. Genome-wide mining of diversity and evolutionary signatures revealed selective hotspots in Indian Sahiwal cattle. Gene 2024; 901:148178. [PMID: 38242377 DOI: 10.1016/j.gene.2024.148178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/10/2024] [Accepted: 01/16/2024] [Indexed: 01/21/2024]
Abstract
The Sahiwal cattle breed is the best indigenous dairy cattle breed, and it plays a pivotal role in the Indian dairy industry. This is due to its exceptional milk-producing potential, adaptability to local tropical conditions, and its resilience to ticks and diseases. The study aimed to identify selective sweeps and estimate intrapopulation genetic diversity parameters in Sahiwal cattle using ddRAD sequencing-based genotyping data from 82 individuals. After applying filtering criteria, 78,193 high-quality SNPs remained for further analysis. The population exhibited an average minor allele frequency of 0.221 ± 0.119. Genetic diversity metrics, including observed (0.597 ± 0.196) and expected heterozygosity (0.433 ± 0.096), nucleotide diversity (0.327 ± 0.114), the proportion of polymorphic SNPs (0.726), and allelic richness (1.323 ± 0.134), indicated ample genomic diversity within the breed. Furthermore, an effective population size of 74 was observed in the most recent generation. The overall mean linkage disequilibrium (r2) for pairwise SNPs was 0.269 ± 0.057. Moreover, a greater proportion of short Runs of Homozygosity (ROH) segments were observed suggesting that there may be low levels of recent inbreeding in this population. The genomic inbreeding coefficients, computed using different inbreeding estimates (FHOM, FUNI, FROH, and FGROM), ranged from -0.0289 to 0.0725. Subsequently, we found 146 regions undergoing selective sweeps using five distinct statistical tests: Tajima's D, CLR, |iHS|, |iHH12|, and ROH. These regions, located in non-overlapping 500 kb windows, were mapped and revealed various protein-coding genes associated with enhanced immune systems and disease resistance (IFNL3, IRF8, BLK), as well as production traits (NRXN1, PLCE1, GHR). Notably, we identified interleukin 2 (IL2) on Chr17: 35217075-35223276 as a gene linked to tick resistance and uncovered a cluster of genes (HSPA8, UBASH3B, ADAMTS18, CRTAM) associated with heat stress. These findings indicate the evolutionary impact of natural and artificial selection on the environmental adaptation of the Sahiwal cattle population.
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Affiliation(s)
- Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Kanika Ghildiyal
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Anurodh Sharma
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Subhashree Parida
- Pharmacology & Toxicology Division, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Shive Kumar
- Department of Animal Genetics and Breeding, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
| | - A K Ghosh
- Department of Animal Genetics and Breeding, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
| | - Umesh Singh
- ICAR Central Institute for Research on Cattle, Meerut, UP, India
| | | | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India.
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Yang X, Wang J, Cheng J, Zhang D, Huang K, Zhang Y, Li X, Zhao Y, Zhao L, Xu D, Ma Z, Liu J, Huang Z, Li C, Tian H, Weng X, Wang W, Zhang X. Relationship between sheep feces scores and gastrointestinal microorganisms and their effects on growth traits and blood indicators. Front Microbiol 2024; 15:1348873. [PMID: 38419634 PMCID: PMC10899443 DOI: 10.3389/fmicb.2024.1348873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
Fecal scores are crucial for assessing the digestive and gastrointestinal status of animals. The Bristol fecal scoring system is a commonly used method for the subjective evaluation of host feces, there is limited research on fecal scoring standards for fattening Hu sheep. In this study, Hu sheep were collected for rumen, rectum, and colon contents for 16S rDNA sequencing. 514 Hu sheep feces were scored based on the Bristol fecal scoring system, and production performance at each stage was measured. Finally, we developed the scoring standard of the manure of Hu sheep in the fattening period (a total of five grades). The result shows that moisture content significantly increased with higher grades (p < 0.05). We analyzed the relationship between fecal scores and production traits, blood indices, muscle nutrients, and digestive tract microorganisms. The growth traits (body weight, body height, body length, average daily gain (ADG), and average daily feed intake (ADFI) during 80-180 days), body composition traits of the F3 group, and the carcass traits were found to be significantly higher (p < 0.05) than those of the F1 and F2 groups. There was no significant difference in gastrointestinal microflora diversity among all groups (p > 0.05). Significant differences were observed in Aspartate aminotransferase, Glucose, Total bilirubin, and Red Blood Cell Count between groups (p < 0.05). The mutton moisture content in group F4 was significantly higher than in the other groups, and the protein content was also the lowest (p < 0.05). The results of the correlation analysis demonstrated that Actinobacteria, Peptostreptococcaceae, Acidaminococcales, Gammaproteobacteria, and Proteobacteria were the significant bacteria affecting fecal scores. In addition, Muribaculaceae and Oscillospiraceae were identified as the noteworthy flora affecting growth performance and immunity. This study highlights the differences in production traits and blood indicators between fecal assessment groups and the complex relationship between intestinal microbiota and fecal characteristics in Hu sheep, suggesting potential impacts on animal performance and health, which suggest strategies for improved management.
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Affiliation(s)
- Xiaobin Yang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Jianghui Wang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Jiangbo Cheng
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Deyin Zhang
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Kai Huang
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Yukun Zhang
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Xiaolong Li
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Yuan Zhao
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Liming Zhao
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Dan Xu
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Zongwu Ma
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Jia Liu
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Zhiqiang Huang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Chong Li
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Huibin Tian
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Xiuxiu Weng
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Weimin Wang
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Xiaoxue Zhang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
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Lukic B, Curik I, Drzaic I, Galić V, Shihabi M, Vostry L, Cubric-Curik V. Genomic signatures of selection, local adaptation and production type characterisation of East Adriatic sheep breeds. J Anim Sci Biotechnol 2023; 14:142. [PMID: 37932811 PMCID: PMC10626677 DOI: 10.1186/s40104-023-00936-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/04/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations. Sheep production system is extensive and generally carried out in traditional systems without intensive systematic breeding programmes for high uniform trait production (carcass, wool and milk yield). Therefore, eight indigenous Croatian sheep breeds from eastern Adriatic treated here as metapopulation (EAS), are generally considered as multipurpose breeds (milk, meat and wool), not specialised for a particular type of production, but known for their robustness and resistance to certain environmental conditions. Our objective was to identify genomic regions and genes that exhibit patterns of positive selection signatures, decipher their biological and productive functionality, and provide a "genomic" characterization of EAS adaptation and determine its production type. RESULTS We identified positive selection signatures in EAS using several methods based on reduced local variation, linkage disequilibrium and site frequency spectrum (eROHi, iHS, nSL and CLR). Our analyses identified numerous genomic regions and genes (e.g., desmosomal cadherin and desmoglein gene families) associated with environmental adaptation and economically important traits. Most candidate genes were related to meat/production and health/immune response traits, while some of the candidate genes discovered were important for domestication and evolutionary processes (e.g., HOXa gene family and FSIP2). These results were also confirmed by GO and QTL enrichment analysis. CONCLUSIONS Our results contribute to a better understanding of the unique adaptive genetic architecture of EAS and define its productive type, ultimately providing a new opportunity for future breeding programmes. At the same time, the numerous genes identified will improve our understanding of ruminant (sheep) robustness and resistance in the harsh and specific Mediterranean environment.
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Affiliation(s)
- Boris Lukic
- Faculty of Agrobiotechnical Sciences Osijek, J.J, Strossmayer University of Osijek, Vladimira Preloga 1, 31000, Osijek, Croatia.
| | - Ino Curik
- Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000, Zagreb, Croatia.
| | - Ivana Drzaic
- Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000, Zagreb, Croatia
| | - Vlatko Galić
- Department of Maize Breeding and Genetics, Agricultural Institute Osijek, Južno predgrađe 17, 31000, Osijek, Croatia
| | - Mario Shihabi
- Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000, Zagreb, Croatia
| | - Luboš Vostry
- Czech University of Life Sciences Prague, Kamýcká 129, 165 00, Praque, Czech Republic
| | - Vlatka Cubric-Curik
- Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000, Zagreb, Croatia
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Bovine HOXA11 Gene Identified from RNA-Seq: mRNA Profile Analysis and Genetic Variation Detection Using ME Method and Their Associations with Carcass Traits. Cells 2023; 12:cells12040539. [PMID: 36831206 PMCID: PMC9953915 DOI: 10.3390/cells12040539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/21/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023] Open
Abstract
The Homeobox A11 (HOXA11) gene regulates limb skeletal development and muscle growth, thus, it was selected as a candidate gene for bovine carcass traits. In this study, we analyzed the mRNA expression level of HOXA11 in various tissues and cells, and determined the genetic variations in the HOXA11 gene, which might be used as molecular markers for cattle breeding. The mRNA expression profiles of HOXA11 in bovine different tissues showed that HOXA11 was highly expressed in both fat and muscle. The gene expression trend of HOXA11 in myoblasts and adipocytes indicated that HOXA11 might be involved in the differentiation of bovine myoblasts and adipocytes. The data in the Ensembl database showed that there are two putative insertion/deletion (InDel) polymorphisms in the bovine HOXA11 gene. The insertion site (rs515880802) was located in the upstream region (NC_037331.1: g. 68853364-68853365) and named as P1-Ins-4-bp, and the deletion site (rs517582703) was located in the intronic region (NC_037331.1: g. 68859510-68859517) and named as P2-Del-8-bp. These polymorphisms within the HOXA11 gene were identified and genotyped by PCR amplification, agarose gel electrophoresis and DNA sequencing in the 640 Shandong Black Cattle Genetic Resource (SDBCGR) population. Moreover, the mutation frequency was very low after detection, so the mathematical expectation (ME) method was used for detection. Statistical analysis demonstrated that P1-Ins-4-bp was significantly correlated with the beef shoulder (p = 0.012) and tongue root (p = 0.004). Meanwhile, P2-Del-8-bp displayed a significant correlation with the back tendon (p = 0.008), money tendon (p = 2.84 × 10-4), thick flank (p = 0.034), beef shin (p = 9.09 × 10-7), triangle thick flank (p = 0.04), triangle flank (p = 1.00 × 10-6), rump (p = 0.018) and small tenderloin (p = 0.043) in the female SDBCGR population. In summary, these outcomes may provide a new perspective for accelerating the molecular breeding of cattle through marker-assisted selection (MAS) strategies.
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Wang H, Wang X, Li M, Sun H, Chen Q, Yan D, Dong X, Pan Y, Lu S. Genome-wide association study reveals genetic loci and candidate genes for meat quality traits in a four-way crossbred pig population. Front Genet 2023; 14:1001352. [PMID: 36814900 PMCID: PMC9939654 DOI: 10.3389/fgene.2023.1001352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 01/24/2023] [Indexed: 02/08/2023] Open
Abstract
Meat quality traits (MQTs) have gained more attention from breeders due to their increasing economic value in the commercial pig industry. In this genome-wide association study (GWAS), 223 four-way intercross pigs were genotyped using the specific-locus amplified fragment sequencing (SLAF-seq) and phenotyped for PH at 45 min post mortem (PH45), meat color score (MC), marbling score (MA), water loss rate (WL), drip loss (DL) in the longissimus muscle, and cooking loss (CL) in the psoas major muscle. A total of 227, 921 filtered single nucleotide polymorphisms (SNPs) evenly distributed across the entire genome were detected to perform GWAS. A total of 64 SNPs were identified for six meat quality traits using the mixed linear model (MLM), of which 24 SNPs were located in previously reported QTL regions. The phenotypic variation explained (PVE) by the significant SNPs was from 2.43% to 16.32%. The genomic heritability estimates based on SNP for six meat-quality traits were low to moderate (0.07-0.47) being the lowest for CL and the highest for DL. A total of 30 genes located within 10 kb upstream or downstream of these significant SNPs were found. Furthermore, several candidate genes for MQTs were detected, including pH45 (GRM8), MC (ANKRD6), MA (MACROD2 and ABCG1), WL (TMEM50A), CL (PIP4K2A) and DL (CDYL2, CHL1, ABCA4, ZAG and SLC1A2). This study provided substantial new evidence for several candidate genes to participate in different pork quality traits. The identification of these SNPs and candidate genes provided a basis for molecular marker-assisted breeding and improvement of pork quality traits.
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Affiliation(s)
- Huiyu Wang
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China,Faculty of Animal Science, Xichang University, Xichang, Sichuan, China
| | - Xiaoyi Wang
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Mingli Li
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Hao Sun
- Faculty of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Qiang Chen
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Dawei Yan
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Xinxing Dong
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Yuchun Pan
- Faculty of Animal Science, Zhejiang University, Hangzhou, Zhejiang, China,*Correspondence: Yuchun Pan, ; Shaoxiong Lu,
| | - Shaoxiong Lu
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China,*Correspondence: Yuchun Pan, ; Shaoxiong Lu,
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Jiménez JM, Morales RM, Menéndez-Buxadera A, Demyda-Peyrás S, Laseca N, Molina A. Estimation of the Genetic Components of (Co)variance and Preliminary Genome-Wide Association Study for Reproductive Efficiency in Retinta Beef Cattle. Animals (Basel) 2023; 13:ani13030501. [PMID: 36766391 PMCID: PMC9913610 DOI: 10.3390/ani13030501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/19/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023] Open
Abstract
In this study, we analyzed the variation of reproductive efficiency, estimated as the deviation between the optimal and real parity number of females at each stage of the cow's life, in 12,554 cows belonging to the Retinta Spanish cattle breed, using classical repeatability and random regression models. The results of the analyses using repeatability model and the random regression model suggest that reproductive efficiency is not homogeneous throughout the cow's life. The h2 estimate for this model was 0.30, while for the random regression model it increased across the parities, from 0.24 at the first calving to 0.51 at calving number 9. Additionally, we performed a preliminary genome-wide association study for this trait in a population of 252 Retinta cows genotyped using the Axiom Bovine Genotyping v3 Array. The results showed 5 SNPs significantly associated with reproductive efficiency, located in two genomic regions (BTA4 and BTA28). The functional analysis revealed the presence of 5 candidate genes located within these regions, which were previously involved in different aspects related to fertility in cattle and mice models. This new information could give us a better understanding of the genetic architecture of reproductive traits in this species, as well as allow us to accurately select more fertile cows.
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Affiliation(s)
| | - Rosa María Morales
- Department of Genetics, Veterinary School, Campus de Rabanales, University of Córdoba, Edificio Gregor Mendel, Ctra. Madrid-Cádiz, km 396, 14014 Córdoba, Spain
- Correspondence: ; Tel.: +34-957-21-10-70
| | - Alberto Menéndez-Buxadera
- Department of Genetics, Veterinary School, Campus de Rabanales, University of Córdoba, Edificio Gregor Mendel, Ctra. Madrid-Cádiz, km 396, 14014 Córdoba, Spain
| | - Sebastián Demyda-Peyrás
- Departamento de Producción Animal, Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, La Plata 1900, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), La Plata 1900, Argentina
| | - Nora Laseca
- Department of Genetics, Veterinary School, Campus de Rabanales, University of Córdoba, Edificio Gregor Mendel, Ctra. Madrid-Cádiz, km 396, 14014 Córdoba, Spain
| | - Antonio Molina
- Department of Genetics, Veterinary School, Campus de Rabanales, University of Córdoba, Edificio Gregor Mendel, Ctra. Madrid-Cádiz, km 396, 14014 Córdoba, Spain
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Wang X, Bao J, Bi Y, Hu W, Zhang L. Polymorphism, Expression, and Structure Analysis of a Key Gene ARNT in Sheep ( Ovis aries). BIOLOGY 2022; 11:biology11121795. [PMID: 36552304 PMCID: PMC9774921 DOI: 10.3390/biology11121795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 11/28/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022]
Abstract
Growth traits are influential factors that significantly affects the development of the sheep industry. A previous TMT proteomic analysis found that a key protein in the HIF signaling pathway, ARNT, may influence embryonic skeletal muscle growth and development in sheep. The purpose of this study was to better understand the association between the polymorphisms of ARNT and growth traits of sheep, and the potential function of ARNT. Real-time qPCR (qRT-PCR) of ARNT was carried out to compare its expression in different developmental stages of the muscle tissues and primary myoblasts in the Hu, Chinese merino, and Gangba sheep. The genetic variance of ARNT was detected using the Illumina Ovine SNP 50 K and 600 K BeadChip in the Hu and Ujimqin sheep populations, respectively. The CDS sequence of the ARNT gene was cloned in the Hu sheep using PCR technology. Finally, bioinformatic analytical methods were applied to characterize the genes and their hypothetical protein products. The qRT-PCR results showed that the ARNT gene was expressed significantly in the Chinese merino embryo after 85 gestation days (D85) (p < 0.05). Additionally, after the sheep were born, the expression of ARNT was significant at the weaning stage of the Hu sheep (p < 0.01). However, there was no difference in the Gangba sheep.In addition, six SNP loci were screened using 50 K and 600 K BeadChip. We found a significant association between rs413597480 A > G and the Hu sheep weight at weaning and backfat thickness in the 5-month-old sheep (p < 0.05), and four SNP loci (rs162298018 G > C, rs159644025 G > A, rs421351865 G > A, and rs401758103 A > G) were also associated with growth traits in the Ujimqin sheep (p < 0.05). Interestingly, we found that a G > C mutation at 1948 bp in the cloned ARNT CDS sequence of the Hu sheep was the same locus mutation as rs162298018 G > C identified using the 600 K BeadChip, which resulted in a nonconservative missense point mutation, leading to a change from proline to alanine and altering the number of DNA, protein-binding sites, and the α-helix of the ARNT protein. There was a strong linkage disequilibrium between rs162298018 G > C and rs159644025 G > A, and the ARNT protein was conserved among the goat, Hu sheep, and Texel sheep. And, we propose that a putative molecular marker for growth and development in sheep may be the G > C mutation at 1948 bp in the CDS region of the ARNT gene. Our study systematically analyzed the expression, structure, and function of the ARNT gene and its encoded proteins in sheep. This provides a basis for future studies of the regulatory mechanisms of the ARNT gene.
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Affiliation(s)
- Xinyue Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Jingjing Bao
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yazhen Bi
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
- College of Animal Science and Technology, Qingdao Agriculture University, Qingdao 266109, China
| | - Wenping Hu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Li Zhang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
- Correspondence: ; Tel.: +86-010-6281-6002
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12
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Cis-eQTL Analysis and Functional Validation of Candidate Genes for Carcass Yield Traits in Beef Cattle. Int J Mol Sci 2022; 23:ijms232315055. [PMID: 36499383 PMCID: PMC9736101 DOI: 10.3390/ijms232315055] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/19/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022] Open
Abstract
Carcass yield traits are of considerable economic importance for farm animals, which act as a major contributor to the world’s food supply. Genome-wide association studies (GWASs) have identified many genetic variants associated with carcass yield traits in beef cattle. However, their functions are not effectively illustrated. In this study, we performed an integrative analysis of gene-based GWAS with expression quantitative trait locus (eQTL) analysis to detect candidate genes for carcass yield traits and validate their effects on bovine skeletal muscle satellite cells (BSCs). The gene-based GWAS and cis-eQTL analysis revealed 1780 GWAS and 1538 cis-expression genes. Among them, we identified 153 shared genes that may play important roles in carcass yield traits. Notably, the identified cis-eQTLs of PON3 and PRIM2 were significantly (p < 0.001) enriched in previous GWAS loci for carcass traits. Furthermore, overexpression of PON3 and PRIM2 promoted the BSCs’ proliferation, increased the expression of MYOD and downregulated the expression of MYOG, which indicated that these genes may inhibit myogenic differentiation. In contrast, PON3 and PRIM2 were significantly downregulated during the differentiation of BSCs. These findings suggested that PON3 and PRIM2 may promote the proliferation of BSCs and inhibit them in the pre-differentiation stage. Our results further contribute to the understanding of the molecular mechanisms of carcass yield traits in beef cattle.
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Integrative QTL mapping and selection signatures in Groningen White Headed cattle inferred from whole-genome sequences. PLoS One 2022; 17:e0276309. [PMID: 36288367 PMCID: PMC9605288 DOI: 10.1371/journal.pone.0276309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 10/04/2022] [Indexed: 11/04/2022] Open
Abstract
Here, we aimed to identify and characterize genomic regions that differ between Groningen White Headed (GWH) breed and other cattle, and in particular to identify candidate genes associated with coat color and/or eye-protective phenotypes. Firstly, whole genome sequences of 170 animals from eight breeds were used to evaluate the genetic structure of the GWH in relation to other cattle breeds by carrying out principal components and model-based clustering analyses. Secondly, the candidate genomic regions were identified by integrating the findings from: a) a genome-wide association study using GWH, other white headed breeds (Hereford and Simmental), and breeds with a non-white headed phenotype (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel, Dutch Belted, and Holstein Friesian); b) scans for specific signatures of selection in GWH cattle by comparison with four other Dutch traditional breeds (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel and Dutch Belted) and the commercial Holstein Friesian; and c) detection of candidate genes identified via these approaches. The alignment of the filtered reads to the reference genome (ARS-UCD1.2) resulted in a mean depth of coverage of 8.7X. After variant calling, the lowest number of breed-specific variants was detected in Holstein Friesian (148,213), and the largest in Deep Red (558,909). By integrating the results, we identified five genomic regions under selection on BTA4 (70.2-71.3 Mb), BTA5 (10.0-19.7 Mb), BTA20 (10.0-19.9 and 20.0-22.7 Mb), and BTA25 (0.5-9.2 Mb). These regions contain positional and functional candidate genes associated with retinal degeneration (e.g., CWC27 and CLUAP1), ultraviolet protection (e.g., ERCC8), and pigmentation (e.g. PDE4D) which are probably associated with the GWH specific pigmentation and/or eye-protective phenotypes, e.g. Ambilateral Circumocular Pigmentation (ACOP). Our results will assist in characterizing the molecular basis of GWH phenotypes and the biological implications of its adaptation.
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14
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Li Q, Wang Y, Hu X, Zhang Y, Li H, Zhang Q, Cai W, Wang Z, Zhu B, Xu L, Gao X, Chen Y, Gao H, Li J, Zhang L. Transcriptional states and chromatin accessibility during bovine myoblasts proliferation and myogenic differentiation. Cell Prolif 2022; 55:e13219. [PMID: 35362202 PMCID: PMC9136495 DOI: 10.1111/cpr.13219] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/17/2022] [Accepted: 02/25/2022] [Indexed: 12/14/2022] Open
Abstract
Objectives Although major advances have been made in bovine epigenome study, the epigenetic basis for fetal skeletal muscle development still remains poorly understood. The aim is to recapitulated the time course of fetal skeletal muscle development in vitro, and explore the dynamic changes of chromatin accessibility and gene expression during bovine myoblasts proliferation and differentiation. Methods PDGFR‐ cells were isolated from bovine fetal skeletal muscle, then cultured and induced myogenic differentiation in vitro in a time‐course study (P, D0, D2,and D4). The assay for transposase‐accessible chromatin sequencing (ATAC‐seq) and RNA sequencing (RNA‐seq) were performed. Results Among the enriched transcriptional factors with high variability, we determined the effects of MAFF, ZNF384, and KLF6 in myogenesis using RNA interference (RNAi). In addition, we identified both stage‐specific genes and chromatin accessibility regions to reveal the sequential order of gene expression, transcriptional regulatory, and signal pathways involved in bovine skeletal muscle development. Further investigation integrating chromatin accessibility and transcriptome data was conducted to explore cis‐regulatory regions in line with gene expression. Moreover, we combined bovine GWAS results of growth traits with regulatory regions defined by chromatin accessibility, providing a suggestive means for a more precise annotation of genetic variants of bovine growth traits. Conclusion Overall, these findings provide valuable information for understanding the stepwise regulatory mechanisms in skeletal muscle development and conducting beef cattle genetic improvement programs.
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Affiliation(s)
- Qian Li
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yahui Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Xin Hu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yapeng Zhang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Hongwei Li
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Qi Zhang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Wentao Cai
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zezhao Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Bo Zhu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Lingyang Xu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Xue Gao
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yan Chen
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Huijiang Gao
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Junya Li
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Lupei Zhang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
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Niu Q, Zhang T, Xu L, Wang T, Wang Z, Zhu B, Gao X, Chen Y, Zhang L, Gao H, Li J, Xu L. Identification of Candidate Variants Associated With Bone Weight Using Whole Genome Sequence in Beef Cattle. Front Genet 2021; 12:750746. [PMID: 34912371 PMCID: PMC8667311 DOI: 10.3389/fgene.2021.750746] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 10/18/2021] [Indexed: 11/13/2022] Open
Abstract
Bone weight is critical to affect body conformation and stature in cattle. In this study, we conducted a genome-wide association study for bone weight in Chinese Simmental beef cattle based on the imputed sequence variants. We identified 364 variants associated with bone weight, while 350 of them were not included in the Illumina BovineHD SNP array, and several candidate genes and GO terms were captured to be associated with bone weight. Remarkably, we identified four potential variants in a candidate region on BTA6 using Bayesian fine-mapping. Several important candidate genes were captured, including LAP3, MED28, NCAPG, LCORL, SLIT2, and IBSP, which have been previously reported to be associated with carcass traits, body measurements, and growth traits. Notably, we found that the transcription factors related to MED28 and LCORL showed high conservation across multiple species. Our findings provide some valuable information for understanding the genetic basis of body stature in beef cattle.
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Affiliation(s)
- Qunhao Niu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Tianliu Zhang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ling Xu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Tianzhen Wang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zezhao Wang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Bo Zhu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xue Gao
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yan Chen
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lupei Zhang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Huijiang Gao
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Junya Li
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lingyang Xu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
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