Scans for Signatures of Selection in Genomes of Wagyu and Buryat Cattle Breeds Reveal Candidate Genes and Genetic Variants for Adaptive Phenotypes and Production Traits
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Data Preparation
2.2. Selection Signatures Scans
2.2.1. HapFLK
2.2.2. DCMS
2.2.3. FST
2.2.4. PBS
2.3. SNP Annotation, Search for Candidate Causative Variants, and Enrichment Analysis
3. Results
3.1. Statistics of Window-Based and Single-Point Scans for Selection Signatures
3.2. Candidate Genes and Variants
3.2.1. Cold Climate Adaptation
3.2.2. Growth and Development
3.2.3. Feed Efficiency Traits and Metabolism
3.2.4. Meat Quality Traits
3.2.5. Immunity and Resistance to Pathogens
3.2.6. Reproduction
3.2.7. Milk Production Traits
3.3. Functional Enrichment Analysis and Functional Annotation Clustering
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Saravanan, K.A.; Panigrahi, M.; Kumar, H.; Bhushan, B.; Dutt, T.; Mishra, B.P. Selection signatures in livestock genome: A review of concepts, approaches and applications. Livest. Sci. 2020, 241, 104257. [Google Scholar] [CrossRef]
- Yudin, N.S.; Yurchenko, A.A.; Larkin, D.M. Signatures of selection and candidate genes for adaptation to extreme environmental factors in the genomes of Turano-Mongolian cattle breeds. Vavilovskii Zhurnal Genet. I Sel. 2021, 25, 190–201. [Google Scholar] [CrossRef] [PubMed]
- Wang, S.; Raza, S.H.A.; Zhang, K.; Mei, C.; Alamoudi, M.O.; Aloufi, B.H.; Alshammari, A.M.; Zan, L. Selection signatures of Qinchuan cattle based on whole-genome sequences. Anim. Biotechnol. 2023, 34, 1483–1491. [Google Scholar] [CrossRef] [PubMed]
- Hu, M.; Shi, L.; Yi, W.; Li, F.; Yan, S. Identification of genomic diversity and selection signatures in Luxi cattle using whole-genome sequencing data. Anim. Biosci. 2024, 37, 461–470. [Google Scholar] [CrossRef] [PubMed]
- Shi, L.; Hu, M.; Lai, W.; Yi, W.; Liu, Z.; Sun, H.; Li, F.; Yan, S. Detection of genomic variations and selection signatures in Wagyu using whole-genome sequencing data. Anim. Genet. 2023, 54, 808–812. [Google Scholar] [CrossRef] [PubMed]
- Seo, D.; Lee, D.H.; Jin, S.; Won, J.I.; Lim, D.; Park, M.; Kim, T.H.; Lee, H.K.; Kim, S.; Choi, I.; et al. Long-term artificial selection of Hanwoo (Korean) cattle left genetic signatures for the breeding traits and has altered the genomic structure. Sci. Rep. 2022, 12, 6438. [Google Scholar] [CrossRef] [PubMed]
- Wang, L.; Gao, Y.; Wang, J.; Huang, N.; Jiang, Q.; Ju, Z.; Yang, C.; Wei, X.; Xiao, Y.; Zhang, Y.; et al. Selection Signature and CRISPR/Cas9-Mediated Gene Knockout Analyses Reveal ZC3H10 Involved in Cold Adaptation in Chinese Native Cattle. Genes 2022, 13, 1910. [Google Scholar] [CrossRef] [PubMed]
- Buggiotti, L.; Yurchenko, A.A.; Yudin, N.S.; Vander Jagt, C.J.; Vorobieva, N.V.; Kusliy, M.A.; Vasiliev, S.K.; Rodionov, A.N.; Boronetskaya, O.I.; Zinovieva, N.A.; et al. Demographic history, adaptation, and NRAP convergent evolution at amino acid residue 100 in the world northernmost cattle from Siberia. Mol. Biol. Evol. 2021, 38, 3093–3110. [Google Scholar] [CrossRef] [PubMed]
- Kayumov, F.G.; Chernomyrdin, V.N.; Maevskaya, L.A.; Surundaeva, L.G.; Pol’skih, S.S. The use of Kalmyk cattle on animal breeding farms in Russia. Izv. Orenbg. State Agrar. Univ. 2014, 5, 116–119. [Google Scholar]
- Yurchenko, A.; Yudin, N.; Aitnazarov, R.; Plyusnina, A.; Brukhin, V.; Soloshenko, V.; Lhasaranov, B.; Popov, R.; Paronyan, I.A.; Plemyashov, K.V.; et al. Genome-wide genotyping uncovers genetic profiles and history of the Russian cattle breeds. Heredity 2018, 120, 125–137. [Google Scholar] [CrossRef]
- Animals from Mongolia Will Revive the Buryat Cow. Available online: https://dairynews.today/news/buryatskuyu-korovu-vozrodyat-zhivotnye-iz-mongolii.html (accessed on 19 April 2024).
- Lazebnaya, I.V.; Perchun, A.V.; Lhasaranov, B.B.; Lazebny, O.E.; Stolpovskiy, Y.A. Analysis of GH1, GHR and PRL gene polymorphisms for estimation of the genetic diversity of Buryat and Altai cattle breeds. Vavilov J. Genet. Breed. 2018, 22, 734–741. [Google Scholar] [CrossRef]
- Lhasaranov, B. Pasture Animal Husbandry in Eastern Siberia. Biomed. J. Sci. Tech. Res. 2020, 31, 24160–24163. [Google Scholar] [CrossRef]
- Miratorg Will Increase Wagyu Beef Production by 15 Times. Available online: https://www.agroinvestor.ru/companies/news/38329-miratorg-uvelichit-proizvodstvo-govyadiny-vagyu-v-15-raz/ (accessed on 19 April 2024).
- Gotoh, T.; Joo, S.-T. Characteristics and Health Benefit of Highly Marbled Wagyu and Hanwoo Beef. Korean J. food Sci. Anim. Resour. 2016, 36, 709–718. [Google Scholar] [CrossRef]
- Yurchenko, A.A.; Daetwyler, H.D.; Yudin, N.; Schnabel, R.D.; Vander Jagt, C.J.; Soloshenko, V.; Lhasaranov, B.; Popov, R.; Taylor, J.F.; Larkin, D.M. Scans for signatures of selection in Russian cattle breed genomes reveal new candidate genes for environmental adaptation and acclimation. Sci. Rep. 2018, 8, 12984. [Google Scholar] [CrossRef]
- Ma, Y.; Ding, X.; Qanbari, S.; Weigend, S.; Zhang, Q.; Simianer, H. Properties of different selection signature statistics and a new strategy for combining them. Heredity 2015, 115, 426–436. [Google Scholar] [CrossRef]
- Schaid, D.J.; Chen, W.; Larson, N.B. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 2018, 19, 491–504. [Google Scholar] [CrossRef]
- Ruvinskiy, D.; Igoshin, A.; Yurchenko, A.; Ilina, A.V.; Larkin, D.M. Resequencing the Yaroslavl cattle genomes reveals signatures of selection and a rare haplotype on BTA28 likely to be related to breed phenotypes. Anim. Genet. 2022, 53, 680–684. [Google Scholar] [CrossRef]
- Yan, C.-L.; Lin, J.; Huang, Y.-Y.; Gao, Q.-S.; Piao, Z.-Y.; Yuan, S.-L.; Chen, L.; Ren, X.; Ye, R.-C.; Dong, M.; et al. Population genomics reveals that natural variation in PRDM16 contributes to cold tolerance in domestic cattle. Zool. Res. 2022, 43, 275–284. [Google Scholar] [CrossRef]
- Shen, J.; Hanif, Q.; Cao, Y.; Yu, Y.; Lei, C.; Zhang, G.; Zhao, Y. Whole Genome Scan and Selection Signatures for Climate Adaption in Yanbian Cattle. Front. Genet. 2020, 11, 94. [Google Scholar] [CrossRef]
- Wang, Z.; Ma, H.; Xu, L.; Zhu, B.; Liu, Y.; Bordbar, F.; Chen, Y.; Zhang, L.; Gao, X.; Gao, H.; et al. Genome-wide scan identifies selection signatures in chinese wagyu cattle using a high-density SNP array. Animals 2019, 9, 296. [Google Scholar] [CrossRef]
- 1000 Bull Genomes Project. 1000 Bulls GATK Fastq to GVCF Guidelines (GATKv3.8); 2019. Available online: http://www.1000bullgenomes.com/doco/1000bullsGATK3.8pipelineSpecifications_Run8_Revision_20191101.docx (accessed on 7 August 2021).
- Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
- Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv 2013, arXiv:1303.3997. [Google Scholar] [CrossRef]
- Picard Tools—By Broad Institute. Available online: http://broadinstitute.github.io/picard/ (accessed on 24 February 2021).
- McKenna, A.; Hanna, M.; Banks, E.; Sivachenko, A.; Cibulskis, K.; Kernytsky, A.; Garimella, K.; Altshuler, D.; Gabriel, S.; Daly, M.; et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010, 20, 1297–1303. [Google Scholar] [CrossRef]
- GATK. Hard-Filtering Germline Short Variants. Available online: https://gatk.broadinstitute.org/hc/en-us/articles/360035890471-Hard-filtering-germline-short-variants/ (accessed on 24 February 2021).
- Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.R.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.W.; Daly, M.J.; et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef]
- Danecek, P.; Bonfield, J.K.; Liddle, J.; Marshall, J.; Ohan, V.; Pollard, M.O.; Whitwham, A.; Keane, T.; McCarthy, S.A.; Davies, R.M.; et al. Twelve years of SAMtools and BCFtools. Gigascience 2021, 10, giab008. [Google Scholar] [CrossRef]
- Fariello, M.I.; Boitard, S.; Naya, H.; SanCristobal, M.; Servin, B. Detecting signatures of selection through haplotype differentiation among hierarchically structured populations. Genetics 2013, 193, 929–941. [Google Scholar] [CrossRef]
- Scheet, P.; Stephens, M. A fast and flexible statistical model for large-scale population genotype data: Applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 2006, 78, 629–644. [Google Scholar] [CrossRef]
- Ripley, B.; Venables, B.; Bates, D.M.; Hornik, K.; Gebhardt, A.; Firth, D. Package “MASS.” R Package Version 7.3-53.1. 2021. Available online: https://CRAN.R-project.org/package=MASS (accessed on 25 March 2021).
- Storey, J.D.; Bass, A.J.; Dabney, A.; Robinson, D. Qvalue: Q-Value Estimation for False Discovery Rate Control. R Package Version 2.24.0. 2021. Available online: https://github.com/StoreyLab/qvalue/ (accessed on 25 March 2021).
- Garud, N.R.; Rosenberg, N.A. Enhancing the mathematical properties of new haplotype homozygosity statistics for the detection of selective sweeps. Theor. Popul. Biol. 2015, 102, 94–101. [Google Scholar] [CrossRef]
- Akbari, A.; Vitti, J.J.; Iranmehr, A.; Bakhtiari, M.; Sabeti, P.C.; Mirarab, S.; Bafna, V. Identifying the favored mutation in a positive selective sweep. Nat. Methods 2018, 15, 279–282. [Google Scholar] [CrossRef]
- Szpiech, Z.A.; Novak, T.E.; Bailey, N.P.; Stevison, L.S. Application of a novel haplotype-based scan for local adaptation to study high-altitude adaptation in rhesus macaques. Evol. Lett. 2021, 5, 408–421. [Google Scholar] [CrossRef]
- Harris, A.M.; DeGiorgio, M. A Likelihood Approach for Uncovering Selective Sweep Signatures from Haplotype Data. Mol. Biol. Evol. 2020, 37, 3023–3046. [Google Scholar] [CrossRef]
- Delaneau, O.; Zagury, J.-F.; Robinson, M.R.; Marchini, J.L.; Dermitzakis, E.T. Accurate, scalable and integrative haplotype estimation. Nat. Commun. 2019, 10, 5436. [Google Scholar] [CrossRef]
- Qanbari, S.; Wittenburg, D. Male recombination map of the autosomal genome in German Holstein. Genet. Sel. Evol. 2020, 52, 73. [Google Scholar] [CrossRef]
- DeGiorgio, M.; Szpiech, Z.A. A spatially aware likelihood test to detect sweeps from haplotype distributions. PLoS Genet. 2022, 18, e1010134. [Google Scholar] [CrossRef]
- Quinlan, A.R.; Hall, I.M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 2010, 26, 841–842. [Google Scholar] [CrossRef]
- Todorov, V. rrcovNA: Scalable Robust Estimators with High Breakdown Point for Incomplete Data. R Package Version 0.4-15. 2020. Available online: https://cran.r-project.org/package=rrcovNA (accessed on 25 March 2021).
- Verity, R.; Collins, C.; Card, D.C.; Schaal, S.M.; Wang, L.; Lotterhos, K.E. minotaur: A platform for the analysis and visualization of multivariate results from genome scans with R Shiny. Mol. Ecol. Resour. 2017, 17, 33–43. [Google Scholar] [CrossRef]
- Danecek, P.; Auton, A.; Abecasis, G.; Albers, C.A.; Banks, E.; DePristo, M.A.; Handsaker, R.E.; Lunter, G.; Marth, G.T.; Sherry, S.T.; et al. The variant call format and VCFtools. Bioinformatics 2011, 27, 2156–2158. [Google Scholar] [CrossRef]
- Jiang, X.; Assis, R. Population-Specific Genetic and Expression Differentiation in Europeans. Genome Biol. Evol. 2020, 12, 358–369. [Google Scholar] [CrossRef]
- Yi, X.; Liang, Y.; Huerta-Sanchez, E.; Jin, X.; Cuo, Z.X.P.; Pool, J.E.; Xu, X.; Jiang, H.; Vinckenbosch, N.; Korneliussen, T.S.; et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science 2010, 329, 75–78. [Google Scholar] [CrossRef]
- Wang, G.-D.; Zhang, B.-L.; Zhou, W.-W.; Li, Y.-X.; Jin, J.-Q.; Shao, Y.; Yang, H.-C.; Liu, Y.-H.; Yan, F.; Chen, H.-M.; et al. Selection and environmental adaptation along a path to speciation in the Tibetan frog Nanorana parkeri. Proc. Natl. Acad. Sci. USA 2018, 115, E5056–E5065. [Google Scholar] [CrossRef]
- Cingolani, P. Variant Annotation and Functional Prediction: SnpEff. Methods Mol. Biol. 2022, 2493, 289–314. [Google Scholar] [CrossRef] [PubMed]
- Johnson, M.; Zaretskaya, I.; Raytselis, Y.; Merezhuk, Y.; McGinnis, S.; Madden, T.L. NCBI BLAST: A better web interface. Nucleic Acids Res. 2008, 36, W5–W9. [Google Scholar] [CrossRef] [PubMed]
- Jensen, J.D. On the unfounded enthusiasm for soft selective sweeps. Nat. Commun. 2014, 5, 5281. [Google Scholar] [CrossRef] [PubMed]
- Sherman, B.T.; Hao, M.; Qiu, J.; Jiao, X.; Baseler, M.W.; Lane, H.C.; Imamichi, T.; Chang, W. DAVID: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022, 50, W216–W221. [Google Scholar] [CrossRef]
- Smedley, D.; Haider, S.; Ballester, B.; Holland, R.; London, D.; Thorisson, G.; Kasprzyk, A. BioMart–biological queries made easy. BMC Genom. 2009, 10, 22. [Google Scholar] [CrossRef] [PubMed]
- Llavanera, M.; Delgado-Bermúdez, A.; Fernandez-Fuertes, B.; Recuero, S.; Mateo, Y.; Bonet, S.; Barranco, I.; Yeste, M. GSTM3, but not IZUMO1, is a cryotolerance marker of boar sperm. J. Anim. Sci. Biotechnol. 2019, 10, 61. [Google Scholar] [CrossRef] [PubMed]
- Liu, X.; Sha, Y.; Lv, W.; Cao, G.; Guo, X.; Pu, X.; Wang, J.; Li, S.; Hu, J.; Luo, Y. Multi-Omics Reveals That the Rumen Transcriptome, Microbiome, and Its Metabolome Co-regulate Cold Season Adaptability of Tibetan Sheep. Front. Microbiol. 2022, 13, 859601. [Google Scholar] [CrossRef] [PubMed]
- Krapf, S.; Schjølberg, T.; Asoawe, L.; Honkanen, S.K.; Kase, E.T.; Thoresen, G.H.; Haugen, F. Novel methods for cold exposure of skeletal muscle in vivo and in vitro show temperature-dependent myokine production. J. Therm. Biol. 2021, 98, 102930. [Google Scholar] [CrossRef]
- Lai, Y.; Zhao, A.; Tan, M.; Yang, M.; Lin, Y.; Li, S.; Song, J.; Zheng, H.; Zhu, Z.; Liu, D.; et al. DOCK5 regulates energy balance and hepatic insulin sensitivity by targeting mTORC1 signaling. EMBO Rep. 2020, 21, e49473. [Google Scholar] [CrossRef]
- Chen, Q.; Shi, P.; Wang, D.; Liu, Q.; Li, X.; Wang, Y.; Zou, D.; Huang, Z.; Gao, X.; Lin, Z. Epidermis-Activated Gasdermin-A3 Enhances Thermogenesis of Brown Adipose Tissue through IL-6/Stat3 Signaling. Am. J. Pathol. 2019, 189, 1041–1052. [Google Scholar] [CrossRef]
- Weyrich, A.; Benz, S.; Karl, S.; Jeschek, M.; Jewgenow, K.; Fickel, J. Paternal heat exposure causes DNA methylation and gene expression changes of Stat3 in Wild guinea pig sons. Ecol. Evol. 2016, 6, 2657–2666. [Google Scholar] [CrossRef] [PubMed]
- Yoshida, A.; Furube, E.; Mannari, T.; Takayama, Y.; Kittaka, H.; Tominaga, M.; Miyata, S. TRPV1 is crucial for proinflammatory STAT3 signaling and thermoregulation-associated pathways in the brain during inflammation. Sci. Rep. 2016, 6, 26088. [Google Scholar] [CrossRef] [PubMed]
- Gao, Q.; Wolfgang, M.J.; Neschen, S.; Morino, K.; Horvath, T.L.; Shulman, G.I.; Fu, X.-Y. Disruption of neural signal transducer and activator of transcription 3 causes obesity, diabetes, infertility, and thermal dysregulation. Proc. Natl. Acad. Sci. USA 2004, 101, 4661–4666. [Google Scholar] [CrossRef] [PubMed]
- Reynés, B.; van Schothorst, E.M.; Keijer, J.; Palou, A.; Oliver, P. Effects of cold exposure revealed by global transcriptomic analysis in ferret peripheral blood mononuclear cells. Sci. Rep. 2019, 9, 19985. [Google Scholar] [CrossRef]
- Jedema, H.P.; Gold, S.J.; Gonzalez-Burgos, G.; Sved, A.F.; Tobe, B.J.; Wensel, T.; Grace, A.A. Chronic cold exposure increases RGS7 expression and decreases alpha(2)-autoreceptor-mediated inhibition of noradrenergic locus coeruleus neurons. Eur. J. Neurosci. 2008, 27, 2433–2443. [Google Scholar] [CrossRef] [PubMed]
- Yudin, N.S.; Larkin, D.M. Candidate genes for domestication and resistance to cold climate according to whole genome sequencing data of Russian cattle and sheep breeds. Vavilovskii Zhurnal Genet. Selektsii 2023, 27, 463–470. [Google Scholar] [CrossRef] [PubMed]
- Trotta, R.J.; Harmon, D.L.; Matthews, J.C.; Swanson, K.C. Nutritional and Physiological Constraints Contributing to Limitations in Small Intestinal Starch Digestion and Glucose Absorption in Ruminants. Ruminants 2022, 2, 1. [Google Scholar] [CrossRef]
- Vigors, S.; Sweeney, T.; O’Shea, C.J.; Kelly, A.K.; O’Doherty, J.V. Pigs that are divergent in feed efficiency, differ in intestinal enzyme and nutrient transporter gene expression, nutrient digestibility and microbial activity. Animal 2016, 10, 1848–1855. [Google Scholar] [CrossRef]
- Fonseca, P.A.S.; Lam, S.; Chen, Y.; Waters, S.M.; Guan, L.L.; Cánovas, A. Multi-breed host rumen epithelium transcriptome and microbiome associations and their relationship with beef cattle feed efficiency. Sci. Rep. 2023, 13, 16209. [Google Scholar] [CrossRef]
- De Las Heras-Saldana, S.; Clark, S.A.; Duijvesteijn, N.; Gondro, C.; van der Werf, J.H.J.; Chen, Y. Combining information from genome-wide association and multi-tissue gene expression studies to elucidate factors underlying genetic variation for residual feed intake in Australian Angus cattle. BMC Genom. 2019, 20, 939. [Google Scholar] [CrossRef]
- Zhang, F.; Wang, Y.; Mukiibi, R.; Chen, L.; Vinsky, M.; Plastow, G.; Basarab, J.; Stothard, P.; Li, C. Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: I: Feed efficiency and component traits. BMC Genom. 2020, 21, 36. [Google Scholar] [CrossRef] [PubMed]
- Vickerman, L.; Neufeld, S.; Cobb, J. Shox2 function couples neural, muscular and skeletal development in the proximal forelimb. Dev. Biol. 2011, 350, 323–336. [Google Scholar] [CrossRef] [PubMed]
- Yu, L.; Liu, H.; Yan, M.; Yang, J.; Long, F.; Muneoka, K.; Chen, Y. Shox2 is required for chondrocyte proliferation and maturation in proximal limb skeleton. Dev. Biol. 2007, 306, 549–559. [Google Scholar] [CrossRef] [PubMed]
- Liu, R.; Han, M.; Liu, X.; Yu, K.; Bai, X.; Dong, Y. Genome-Wide Identification and Characterization of Long Non-Coding RNAs in Longissimus dorsi Skeletal Muscle of Shandong Black Cattle and Luxi Cattle. Front. Genet. 2022, 13, 849399. [Google Scholar] [CrossRef]
- Zhao, Y.; Zhang, X.; Li, F.; Zhang, D.; Zhang, Y.; Li, X.; Song, Q.; Zhou, B.; Zhao, L.; Wang, J.; et al. Whole Genome Sequencing Analysis to Identify Candidate Genes Associated With the rib eye Muscle Area in Hu Sheep. Front. Genet. 2022, 13, 824742. [Google Scholar] [CrossRef]
- Smith, J.L.; Wilson, M.L.; Nilson, S.M.; Rowan, T.N.; Schnabel, R.D.; Decker, J.E.; Seabury, C.M. Genome-wide association and genotype by environment interactions for growth traits in U.S. Red Angus cattle. BMC Genom. 2022, 23, 517. [Google Scholar] [CrossRef]
- Woolley, S.A.; Eager, K.L.M.; Häfliger, I.M.; Bauer, A.; Drögemüller, C.; Leeb, T.; O’Rourke, B.A.; Tammen, I. An ABCA12 missense variant in a Shorthorn calf with ichthyosis fetalis. Anim. Genet. 2019, 50, 749–752. [Google Scholar] [CrossRef] [PubMed]
- Eager, K.L.M.; Conyers, L.E.; Woolley, S.A.; Tammen, I.; O’Rourke, B.A. A novel ABCA12 frameshift mutation segregates with ichthyosis fetalis in a Polled Hereford calf. Anim. Genet. 2020, 51, 837–838. [Google Scholar] [CrossRef]
- Takahashi, K.; Sakurai, N.; Emura, N.; Hashizume, T.; Sawai, K. Effects of downregulating GLIS1 transcript on preimplantation development and gene expression of bovine embryos. J. Reprod. Dev. 2015, 61, 369–374. [Google Scholar] [CrossRef]
- Silva, P.G.C.; Moura, M.T.; Silva, R.L.O.; Nascimento, P.S.; Silva, J.B.; Ferreira-Silva, J.C.; Cantanhêde, L.F.; Chaves, M.S.; Benko-Iseppon, A.M.; Oliveira, M.A.L. Temporal expression of pluripotency-associated transcription factors in sheep and cattle preimplantation embryos. Zygote 2018, 26, 270–278. [Google Scholar] [CrossRef]
- Bordbar, F.; Mohammadabadi, M.; Jensen, J.; Xu, L.; Li, J.; Zhang, L. Identification of Candidate Genes Regulating Carcass Depth and Hind Leg Circumference in Simmental Beef Cattle Using Illumina Bovine Beadchip and Next-Generation Sequencing Analyses. Animals 2022, 12, 1103. [Google Scholar] [CrossRef] [PubMed]
- Salilew-Wondim, D.; Tesfaye, D.; Rings, F.; Held-Hoelker, E.; Miskel, D.; Sirard, M.-A.; Tholen, E.; Schellander, K.; Hoelker, M. The global gene expression outline of the bovine blastocyst: Reflector of environmental conditions and predictor of developmental capacity. BMC Genom. 2021, 22, 408. [Google Scholar] [CrossRef] [PubMed]
- Jiang, H.; Chai, Z.-X.; Cao, H.-W.; Zhang, C.-F.; Zhu, Y.; Zhang, Q.; Xin, J.-W. Genome-wide identification of SNPs associated with body weight in yak. BMC Genom. 2022, 23, 833. [Google Scholar] [CrossRef] [PubMed]
- Zou, Y.; Donkervoort, S.; Salo, A.M.; Foley, A.R.; Barnes, A.M.; Hu, Y.; Makareeva, E.; Leach, M.E.; Mohassel, P.; Dastgir, J.; et al. P4HA1 mutations cause a unique congenital disorder of connective tissue involving tendon, bone, muscle and the eye. Hum. Mol. Genet. 2017, 26, 2207–2217. [Google Scholar] [CrossRef] [PubMed]
- Reyes, R.A.; Clarke, K.; Gonzales, S.J.; Cantwell, A.M.; Garza, R.; Catano, G.; Tragus, R.E.; Patterson, T.F.; Bol, S.; Bunnik, E.M. SARS-CoV-2 spike-specific memory B cells express higher levels of T-bet and FcRL5 after non-severe COVID-19 as compared to severe disease. PLoS ONE 2021, 16, e0261656. [Google Scholar] [CrossRef]
- Kim, C.C.; Baccarella, A.M.; Bayat, A.; Pepper, M.; Fontana, M.F. FCRL5(+) Memory B Cells Exhibit Robust Recall Responses. Cell Rep. 2019, 27, 1446–1460.e4. [Google Scholar] [CrossRef]
- Bisutti, V.; Mach, N.; Giannuzzi, D.; Vanzin, A.; Capra, E.; Negrini, R.; Gelain, M.E.; Cecchinato, A.; Ajmone-Marsan, P.; Pegolo, S. Transcriptome-wide mapping of milk somatic cells upon subclinical mastitis infection in dairy cattle. J. Anim. Sci. Biotechnol. 2023, 14, 93. [Google Scholar] [CrossRef] [PubMed]
- Sollero, B.P.; Junqueira, V.S.; Gomes, C.C.G.; Caetano, A.R.; Cardoso, F.F. Tag SNP selection for prediction of tick resistance in Brazilian Braford and Hereford cattle breeds using Bayesian methods. Genet. Sel. Evol. 2017, 49, 49. [Google Scholar] [CrossRef] [PubMed]
- Czarnik, U.; Barcewicz, M.; Sachajko, M.; Żukowski, K.; Pareek, C.S. Association of bovine CXCL8 polymorphism with clinical mastitis and fertility trait in Polish HF cattle. Transl. Res. Vet. Sci. 2020, 3, 9–17. [Google Scholar]
- Hillmer, E.J.; Zhang, H.; Li, H.S.; Watowich, S.S. STAT3 signaling in immunity. Cytokine Growth Factor Rev. 2016, 31, 1–15. [Google Scholar] [CrossRef]
- Cronin, J.G.; Kanamarlapudi, V.; Thornton, C.A.; Sheldon, I.M. Signal transducer and activator of transcription-3 licenses Toll-like receptor 4-dependent interleukin (IL)-6 and IL-8 production via IL-6 receptor-positive feedback in endometrial cells. Mucosal Immunol. 2016, 9, 1125–1136. [Google Scholar] [CrossRef]
- Chen, C.; Herzig, C.T.A.; Telfer, J.C.; Baldwin, C.L. Antigenic basis of diversity in the gammadelta T cell co-receptor WC1 family. Mol. Immunol. 2009, 46, 2565–2575. [Google Scholar] [CrossRef]
- Jiminez, J.; Timsit, E.; Orsel, K.; van der Meer, F.; Guan, L.L.; Plastow, G. Whole-Blood Transcriptome Analysis of Feedlot Cattle With and Without Bovine Respiratory Disease. Front. Genet. 2021, 12, 627623. [Google Scholar] [CrossRef]
- Correia, C.N.; McHugo, G.P.; Browne, J.A.; McLoughlin, K.E.; Nalpas, N.C.; Magee, D.A.; Whelan, A.O.; Villarreal-Ramos, B.; Vordermeier, H.M.; Gormley, E.; et al. High-resolution transcriptomics of bovine purified protein derivative-stimulated peripheral blood from cattle infected with Mycobacterium bovis across an experimental time course. Tuberculosis 2022, 136, 102235. [Google Scholar] [CrossRef] [PubMed]
- Mallikarjunappa, S.; Brito, L.F.; Pant, S.D.; Schenkel, F.S.; Meade, K.G.; Karrow, N.A. Johne’s Disease in Dairy Cattle: An Immunogenetic Perspective. Front. Vet. Sci. 2021, 8, 718987. [Google Scholar] [CrossRef] [PubMed]
- Yang, J.; Vrettou, C.; Connelley, T.; Morrison, W.I. Identification and annotation of bovine granzyme genes reveals a novel granzyme encoded within the trypsin-like locus. Immunogenetics 2018, 70, 585–597. [Google Scholar] [CrossRef]
- Malvisi, M.; Palazzo, F.; Morandi, N.; Lazzari, B.; Williams, J.L.; Pagnacco, G.; Minozzi, G. Responses of Bovine Innate Immunity to Mycobacterium avium subsp. paratuberculosis Infection Revealed by Changes in Gene Expression and Levels of MicroRNA. PLoS ONE 2016, 11, e0164461. [Google Scholar] [CrossRef]
- Baldassini, W.; Gagaoua, M.; Santiago, B.; Rocha, L.; Torrecilhas, J.; Torres, R.; Curi, R.; Neto, O.M.; Padilha, P.; Santos, F.; et al. Meat Quality and Muscle Tissue Proteome of Crossbred Bulls Finished under Feedlot Using Wet Distiller Grains By-Product. Foods 2022, 11, 3233. [Google Scholar] [CrossRef]
- Won, K.; Kim, D.; Hwang, I.; Lee, H.-K.; Oh, J.-D. Genome-wide association studies on collagen contents trait for meat quality in Hanwoo. J. Anim. Sci. Technol. 2023, 65, 311–323. [Google Scholar] [CrossRef] [PubMed]
- Oliveira, P.S.N.; Cesar, A.S.M.; Oliveira, G.B.; Tizioto, P.C.; Poleti, M.D.; Diniz, W.J.S.; Lima, A.O.D.; Reecy, J.M.; Coutinho, L.L.; Regitano, L.C.A. 0341 miRNAs related to fatty acids composition in Nellore cattle. J. Anim. Sci. 2016, 94, 164. [Google Scholar] [CrossRef]
- Marín-Garzón, N.A.; Magalhães, A.F.B.; Mota, L.F.M.; Fonseca, L.F.S.; Chardulo, L.A.L.; Albuquerque, L.G. Genome-wide association study identified genomic regions and putative candidate genes affecting meat color traits in Nellore cattle. Meat Sci. 2021, 171, 108288. [Google Scholar] [CrossRef] [PubMed]
- Li, Y.; Yang, M.; Lou, A.; Yun, J.; Ren, C.; Li, X.; Xia, G.; Nam, K.; Yoon, D.; Jin, H.; et al. Integrated analysis of expression profiles with meat quality traits in cattle. Sci. Rep. 2022, 12, 5926. [Google Scholar] [CrossRef] [PubMed]
- Jiang, J.; Ma, L.; Prakapenka, D.; VanRaden, P.M.; Cole, J.B.; Da, Y. A Large-Scale Genome-Wide Association Study in U.S. Holstein Cattle. Front. Genet. 2019, 10, 412. [Google Scholar] [CrossRef] [PubMed]
- Garriga, F.; Llavanera, M.; Viñolas-Vergés, E.; Recuero, S.; Tamargo, C.; Delgado-Bermúdez, A.; Yeste, M. Glutathione S-transferase Mu 3 is associated to in vivo fertility, but not sperm quality, in bovine. Animal 2022, 16, 100609. [Google Scholar] [CrossRef]
- Oliver, K.F.; Wahl, A.M.; Dick, M.; Toenges, J.A.; Kiser, J.N.; Galliou, J.M.; Moraes, J.G.N.; Burns, G.W.; Dalton, J.; Spencer, T.E.; et al. Genomic Analysis of Spontaneous Abortion in Holstein Heifers and Primiparous Cows. Genes 2019, 10, 954. [Google Scholar] [CrossRef]
- Martins, T.; Sponchiado, M.; Silva, F.A.C.C.; Estrada-Cortés, E.; Hansen, P.J.; Peñagaricano, F.; Binelli, M. Progesterone-dependent and progesterone-independent modulation of luminal epithelial transcription to support pregnancy in cattle. Physiol. Genom. 2022, 54, 71–85. [Google Scholar] [CrossRef]
- Grigoletto, L.; Santana, M.H.A.; Bressan, F.F.; Eler, J.P.; Nogueira, M.F.G.; Kadarmideen, H.N.; Baruselli, P.S.; Ferraz, J.B.S.; Brito, L.F. Genetic Parameters and Genome-Wide Association Studies for Anti-Müllerian Hormone Levels and Antral Follicle Populations Measured After Estrus Synchronization in Nellore Cattle. Animals 2020, 10, 1185. [Google Scholar] [CrossRef]
- Yang, F.; Gracia Gervasi, M.; Orta, G.; Tourzani, D.A.; la Vega-Beltrán, J.L.; Ruthel, G.; Darszon, A.; Visconti, P.E.; Wang, P.J. C2CD6 regulates targeting and organization of the CatSper calcium channel complex in sperm flagella. Development 2022, 149, dev199988. [Google Scholar] [CrossRef]
- Nikitkina, E.; Dementieva, N.; Shcherbakov, Y.; Musidray, A.; Krutikova, A.; Bogdanova, S.; Plemyashov, K. Search for genetic associations with semen morphology after cryopreservation in bulls. Anim. Reprod. Sci. 2022, 247, 107117. [Google Scholar] [CrossRef]
- Singh, R.; Deb, R.; Sengar, G.S.; Raja, T.V.; Kumar, S.; Singh, U.; Das, A.K.; Alex, R.; Kumar, A.; Tyagi, S.; et al. Differentially expressed microRNAs in biochemically characterized Frieswal(TM) crossbred bull semen. Anim. Biotechnol. 2023, 34, 25–38. [Google Scholar] [CrossRef]
- Zhang, Y.; Labrecque, R.; Tremblay, P.; Plessis, C.; Dufour, P.; Martin, H.; Sirard, M.A. Sperm-borne tsRNAs and miRNAs analysis in relation to dairy cattle fertility. Theriogenology 2024, 215, 241–248. [Google Scholar] [CrossRef]
- Alves, M.B.R.; de Arruda, R.P.; De Bem, T.H.C.; Florez-Rodriguez, S.A.; de Sá Filho, M.F.; Belleannée, C.; Meirelles, F.V.; da Silveira, J.C.; Perecin, F.; Celeghini, E.C.C. Sperm-borne miR-216b modulates cell proliferation during early embryo development via K-RAS. Sci. Rep. 2019, 9, 10358. [Google Scholar] [CrossRef]
- Conboy, J.G. Developmental regulation of RNA processing by Rbfox proteins. Wiley Interdiscip. Rev. RNA 2017, 8, e1398. [Google Scholar] [CrossRef] [PubMed]
- Singh, R.K.; Kolonin, A.M.; Fiorotto, M.L.; Cooper, T.A. Rbfox-Splicing Factors Maintain Skeletal Muscle Mass by Regulating Calpain3 and Proteostasis. Cell Rep. 2018, 24, 197–208. [Google Scholar] [CrossRef] [PubMed]
- Júnior, G.A.F.; Costa, R.B.; de Camargo, G.M.F.; Carvalheiro, R.; Rosa, G.J.M.; Baldi, F.; Garcia, D.A.; Gordo, D.G.M.; Espigolan, R.; Takada, L.; et al. Genome scan for postmortem carcass traits in Nellore cattle. J. Anim. Sci. 2016, 94, 4087–4095. [Google Scholar] [CrossRef] [PubMed]
- Tong, B.; Li, G.P.; Sasaki, S.; Muramatsu, Y.; Ohta, T.; Kose, H.; Yamada, T. Association of the expression levels in the skeletal muscle and a SNP in the CDC10 gene with growth-related traits in Japanese Black beef cattle. Anim. Genet. 2015, 46, 200–204. [Google Scholar] [CrossRef] [PubMed]
- Higgins, W.J.; Grehan, G.T.; Wynne, K.J.; Worrall, D.M. SerpinI2 (pancpin) is an inhibitory serpin targeting pancreatic elastase and chymotrypsin. Biochim. Biophys. Acta Proteins Proteom. 2017, 1865, 195–200. [Google Scholar] [CrossRef]
- Mukiibi, R.; Vinsky, M.; Keogh, K.; Fitzsimmons, C.; Stothard, P.; Waters, S.M.; Li, C. Liver transcriptome profiling of beef steers with divergent growth rate, feed intake, or metabolic body weight phenotypes1. J. Anim. Sci. 2019, 97, 4386–4404. [Google Scholar] [CrossRef]
- Al-Husseini, W.; Gondro, C.; Quinn, K.; Herd, R.M.; Gibson, J.P.; Chen, Y. Expression of candidate genes for residual feed intake in Angus cattle. Anim. Genet. 2014, 45, 12–19. [Google Scholar] [CrossRef]
- Chitraju, C.; Mejhert, N.; Haas, J.T.; Diaz-Ramirez, L.G.; Grueter, C.A.; Imbriglio, J.E.; Pinto, S.; Koliwad, S.K.; Walther, T.C.; Farese, R.V.J. Triglyceride Synthesis by DGAT1 Protects Adipocytes from Lipid-Induced ER Stress during Lipolysis. Cell Metab. 2017, 26, 407–418. [Google Scholar] [CrossRef]
- Li, X.; Ekerljung, M.; Lundström, K.; Lundén, A. Association of polymorphisms at DGAT1, leptin, SCD1, CAPN1 and CAST genes with color, marbling and water holding capacity in meat from beef cattle populations in Sweden. Meat Sci. 2013, 94, 153–158. [Google Scholar] [CrossRef] [PubMed]
- Kong, H.S.; Oh, J.D.; Lee, J.H.; Yoon, D.H.; Choi, Y.H.; Cho, B.W.; Lee, H.K.; Jeon, G.J. Association of Sequence Variations in DGAT 1 Gene with Economic Traits in Hanwoo (Korea Cattle). Asian-Australas. J. Anim. Sci. 2007, 20, 817–820. [Google Scholar] [CrossRef]
- Thaller, G.; Kühn, C.; Winter, A.; Ewald, G.; Bellmann, O.; Wegner, J.; Zühlke, H.; Fries, R. DGAT1, a new positional and functional candidate gene for intramuscular fat deposition in cattle. Anim. Genet. 2003, 34, 354–357. [Google Scholar] [CrossRef]
- Chiariello, C.S.; LaComb, J.F.; Bahou, W.F.; Schmidt, V.A. Ablation of Iqgap2 protects from diet-induced hepatic steatosis due to impaired fatty acid uptake. Regul. Pept. 2012, 173, 36–46. [Google Scholar] [CrossRef] [PubMed]
- Sen, A.; Youssef, S.; Wendt, K.; Anakk, S. Depletion of IQ motif-containing GTPase activating protein 2 (IQGAP2) reduces hepatic glycogen and impairs insulin signaling. J. Biol. Chem. 2023, 299, 105322. [Google Scholar] [CrossRef] [PubMed]
- Giotto, F.M. MicroRNAs as Biomarkers for Meat Quality and Evidence of Absorption of Beef-Derived microRNAs in the Mammalian Digestive System; University of Nevada: Reno, NV, USA, 2022. [Google Scholar]
- Nonneman, D.J.; Shackelford, S.D.; King, D.A.; Wheeler, T.L.; Wiedmann, R.T.; Snelling, W.M.; Rohrer, G.A. Genome-wide association of meat quality traits and tenderness in swine. J. Anim. Sci. 2013, 91, 4043–4050. [Google Scholar] [CrossRef] [PubMed]
- Fonseca, P.A.S.; Suárez-Vega, A.; Cánovas, A. Weighted Gene Correlation Network Meta-Analysis Reveals Functional Candidate Genes Associated with High- and Sub-Fertile Reproductive Performance in Beef Cattle. Genes 2020, 11, 543. [Google Scholar] [CrossRef] [PubMed]
- Chen, S.-Y.; Schenkel, F.S.; Melo, A.L.P.; Oliveira, H.R.; Pedrosa, V.B.; Araujo, A.C.; Melka, M.G.; Brito, L.F. Identifying pleiotropic variants and candidate genes for fertility and reproduction traits in Holstein cattle via association studies based on imputed whole-genome sequence genotypes. BMC Genom. 2022, 23, 331. [Google Scholar] [CrossRef] [PubMed]
- About the Advantages of Breeding Buryat Cows. Available online: https://www.infpol.ru/97007-o-porodnykh-kachestvakh-i-preimushchestvakh-buryatskoy-korovy/ (accessed on 12 April 2024).
- Scholz, N.; Langenhan, T.; Schöneberg, T. Revisiting the classification of adhesion GPCRs. Ann. N. Y. Acad. Sci. 2019, 1456, 80–95. [Google Scholar] [CrossRef]
- Vidal, O.M.; Vélez, J.I.; Arcos-Burgos, M. ADGRL3 genomic variation implicated in neurogenesis and ADHD links functional effects to the incretin polypeptide GIP. Sci. Rep. 2022, 12, 15922. [Google Scholar] [CrossRef]
- Ueda, S.; Hosoda, M.; Kasamatsu, K.; Horiuchi, M.; Nakabayashi, R.; Kang, B.; Shinohara, M.; Nakanishi, H.; Ohto-Nakanishi, T.; Yamanoue, M.; et al. Production of Hydroxy Fatty Acids, Precursors of γ-Hexalactone, Contributes to the Characteristic Sweet Aroma of Beef. Metabolites 2022, 12, 332. [Google Scholar] [CrossRef] [PubMed]
- Mashima, R.; Okuyama, T. The role of lipoxygenases in pathophysiology; new insights and future perspectives. Redox Biol. 2015, 6, 297–310. [Google Scholar] [CrossRef]
- Environmental Monitoring of Lake Baikal. Available online: https://baikalake.ru/en/monitoring/gosecomonit/hunting_resources/buryatiya/2020/ (accessed on 5 June 2024).
- Goulden, C.E.; Goulden, M.N. Adaptation to a Changing Climate in Northern Mongolia. In Climatic Change and Global Warming of Inland Waters; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2012; pp. 383–394. ISBN 9781118470596. [Google Scholar]
- Spasić, M.B.; Saicić, Z.S.; Buzadzić, B.; Korać, B.; Blagojević, D.; Petrović, V.M. Effect of long-term exposure to cold on the antioxidant defense system in the rat. Free Radic. Biol. Med. 1993, 15, 291–299. [Google Scholar] [CrossRef]
- Yuksel, S.; Asma, D.; Yesilada, O. Antioxidative and metabolic responses to extended cold exposure in rats. Acta Biol. Hung. 2008, 59, 57–66. [Google Scholar] [CrossRef]
- Zieger, M.A.J.; Gupta, M.P.; Wang, M. Proteomic analysis of endothelial cold-adaptation. BMC Genom. 2011, 12, 630. [Google Scholar] [CrossRef] [PubMed]
- Cao, H.; Fang, C.; Liu, L.-L.; Farnir, F.; Liu, W.-J. Identification of Susceptibility Genes Underlying Bovine Respiratory Disease in Xinjiang Brown Cattle Based on DNA Methylation. Int. J. Mol. Sci. 2024, 25, 4928. [Google Scholar] [CrossRef] [PubMed]
- Yudin, N.S.; Larkin, D.M.; Ignatieva, E.V. A compendium and functional characterization of mammalian genes involved in adaptation to Arctic or Antarctic environments. BMC Genet. 2017, 18, 111. [Google Scholar] [CrossRef] [PubMed]
- Hancock, A.M.; Witonsky, D.B.; Gordon, A.S.; Eshel, G.; Pritchard, J.K.; Coop, G.; Di Rienzo, A. Adaptations to climate in candidate genes for common metabolic disorders. PLoS Genet. 2008, 4, e32. [Google Scholar] [CrossRef]
- Xia, X.-T.; Achilli, A.; Lenstra, J.A.; Tong, B.; Ma, Y.; Huang, Y.-Z.; Han, J.-L.; Sun, Z.-Y.; Chen, H.; Lei, C.-Z.; et al. Mitochondrial genomes from modern and ancient Turano-Mongolian cattle reveal an ancient diversity of taurine maternal lineages in East Asia. Heredity 2021, 126, 1000–1008. [Google Scholar] [CrossRef]
- Igoshin, A.V.; Yudin, N.S.; Belonogova, N.M.; Larkin, D.M. Genome-wide association study for body weight in cattle populations from Siberia. Anim. Genet. 2019, 50, 250–253. [Google Scholar] [CrossRef]
- Reimer, C.; Rubin, C.-J.; Sharifi, A.R.; Ha, N.-T.; Weigend, S.; Waldmann, K.-H.; Distl, O.; Pant, S.D.; Fredholm, M.; Schlather, M.; et al. Analysis of porcine body size variation using re-sequencing data of miniature and large pigs. BMC Genom. 2018, 19, 687. [Google Scholar] [CrossRef] [PubMed]
- Mastrangelo, S.; Ben Jemaa, S.; Ciani, E.; Sottile, G.; Moscarelli, A.; Boussaha, M.; Montedoro, M.; Pilla, F.; Cassandro, M. Genome-wide detection of signatures of selection in three Valdostana cattle populations. J. Anim. Breed. Genet. 2020, 137, 609–621. [Google Scholar] [CrossRef] [PubMed]
- Ben-Jemaa, S.; Senczuk, G.; Ciani, E.; Ciampolini, R.; Catillo, G.; Boussaha, M.; Pilla, F.; Portolano, B.; Mastrangelo, S. Genome-Wide Analysis Reveals Selection Signatures Involved in Meat Traits and Local Adaptation in Semi-Feral Maremmana Cattle. Front. Genet. 2021, 12, 675569. [Google Scholar] [CrossRef]
- Porto-Neto, L.R.; Lee, S.H.; Lee, H.K.; Gondro, C. Detection of signatures of selection using Fst. Methods Mol. Biol. 2013, 1019, 423–436. [Google Scholar] [CrossRef]
Functional Category | Breed | Consensus Interval | Methods | Gene | Source |
---|---|---|---|---|---|
Cold Climate Adaptation | Buryat | 3:33600001-33705000 | hapFLK, PBS | GSTM3 | [54,55] |
Buryat | 6:88788570-88838640 | hapFLK, PBS | CXCL8 | [56] | |
Buryat | 8:72603526-72650000 | hapFLK, FST | DOCK5 | [57] | |
Buryat | 19:42420001-42490000 | hapFLK, PBS, DCMS | STAT3 | [58,59,60,61] | |
Wagyu | 1:109350001-109425000 | hapFLK, FST | RSRC1 | [62] | |
Wagyu | 16:35455001-35525000 | PBS, FST | RGS7 | [63] | |
Wagyu | 16:63490001-63560000 | hapFLK, PBS | RGSL1 | [64] | |
Feed Efficiency Traits and Metabolism | Buryat | 1:102340001-102410000 | hapFLK, PBS | SI | [65,66] |
Wagyu | 1:109350001-109425000 | hapFLK, FST | SHOX2 | [67,68] | |
Wagyu | 2:103200001-103258854 | hapFLK, FST | SNORA70 | [69] | |
Growth and Development | Wagyu | 1:109350001-109425000 | hapFLK, FST | SHOX2 | [70,71,72] |
Wagyu | 2:90265001-90335000 | hapFLK, PBS | ALS2 | [73] | |
Wagyu | 2:93978974-94156953 | hapFLK, DCMS | NRP2 | [74] | |
Wagyu | 2:103200001-103258854 | hapFLK, FST | ABCA12 | [75,76] | |
Wagyu | 3:92470001-92610000 | hapFLK, PBS | GLIS1 | [77,78] | |
Wagyu | 5:29260001-29325368 | hapFLK, PBS | DIP2B | [79] | |
Wagyu | 6:115957434-116027698 | hapFLK, PBS | TNIP2 | [80,81] | |
Wagyu | 6:115957434-116027698 | hapFLK, PBS | FAM193A * | [81] | |
Wagyu | 28:29100001-29119970 | hapFLK, FST | P4HA1 | [82] | |
Immunity and Resistance to Pathogens | Buryat | 3:12800001-12845000 | PBS, FST | FCRL5 | [83,84,85] |
Buryat | 3:33600001-33705000 | hapFLK, PBS | EPS8L3 | [86] | |
Buryat | 6:88788570-88838640 | hapFLK, PBS | CXCL8 | [87] | |
Buryat | 19:42420001-42490000 | hapFLK, PBS, DCMS | STAT3 | [88,89] | |
Wagyu | 5:102242758-102367026 | hapFLK, PBS | WC1-12 | [90,91,92] | |
Wagyu | 20:23975001-24115000 | hapFLK, PBS | CDC20B | [93] | |
Wagyu | 20:23975001-24115000 | hapFLK, PBS | GZMA | [93,94] | |
Wagyu | 20:23975001-24115000 | hapFLK, PBS | GZMK | [93,94] | |
Wagyu | 20:23975001-24115000 | hapFLK, PBS | ESM1 | [93,95] | |
Meat Quality Traits | Buryat | 6:77455001-77560000 | hapFLK, PBS | ADGRL3 | [96] |
Wagyu | 1:109350001-109425000 | hapFLK, FST | RSRC1 | [97] | |
Wagyu | 5:29260001-29325368 | hapFLK, PBS | DIP2B | [98] | |
Wagyu | 5:102242758-102367026 | hapFLK, PBS | WC1-12 | [99] | |
Wagyu | 11:38600001-38675000 | PBS, FST | BTA-MIR-216B | [100] | |
Milk traits | Buryat | 6:77455001-77560000 | hapFLK, PBS | ADGRL3 | [101] |
Reproduction | Buryat | 3:33600001-33705000 | hapFLK, PBS | GSTM3 | [102] |
Buryat | 6:88788570-88838640 | hapFLK, PBS | CXCL8 | [87] | |
Buryat | 15:51651871-51696907 | hapFLK, PBS | NUMA1 | [103,104] | |
Wagyu | 1:109350001-109425000 | hapFLK, FST | RSRC1 | [105] | |
Wagyu | 2:90090001-90230000 | hapFLK, PBS | C2CD6 | [106] | |
Wagyu | 2:90265001-90335000 | hapFLK, PBS | ALS2 | [73] | |
Wagyu | 5:29260001-29325368 | hapFLK, PBS | DIP2B | [107] | |
Wagyu | 11:38600001-38675000 | PBS, FST | BTA-MIR-217 | [108] | |
Wagyu | 11:38600001-38675000 | PBS, FST | BTA-MIR-216A | [109] | |
Wagyu | 11:38600001-38675000 | PBS, FST | BTA-MIR-216B | [110] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Igoshin, A.V.; Romashov, G.A.; Yurchenko, A.A.; Yudin, N.S.; Larkin, D.M. Scans for Signatures of Selection in Genomes of Wagyu and Buryat Cattle Breeds Reveal Candidate Genes and Genetic Variants for Adaptive Phenotypes and Production Traits. Animals 2024, 14, 2059. https://doi.org/10.3390/ani14142059
Igoshin AV, Romashov GA, Yurchenko AA, Yudin NS, Larkin DM. Scans for Signatures of Selection in Genomes of Wagyu and Buryat Cattle Breeds Reveal Candidate Genes and Genetic Variants for Adaptive Phenotypes and Production Traits. Animals. 2024; 14(14):2059. https://doi.org/10.3390/ani14142059
Chicago/Turabian StyleIgoshin, Alexander V., Grigorii A. Romashov, Andrey A. Yurchenko, Nikolay S. Yudin, and Denis M. Larkin. 2024. "Scans for Signatures of Selection in Genomes of Wagyu and Buryat Cattle Breeds Reveal Candidate Genes and Genetic Variants for Adaptive Phenotypes and Production Traits" Animals 14, no. 14: 2059. https://doi.org/10.3390/ani14142059
APA StyleIgoshin, A. V., Romashov, G. A., Yurchenko, A. A., Yudin, N. S., & Larkin, D. M. (2024). Scans for Signatures of Selection in Genomes of Wagyu and Buryat Cattle Breeds Reveal Candidate Genes and Genetic Variants for Adaptive Phenotypes and Production Traits. Animals, 14(14), 2059. https://doi.org/10.3390/ani14142059