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Animals : an open access journal from MDPI2025; 15(10); 1458; doi: 10.3390/ani15101458

Whole-Genome Resequencing Analysis of Copy Number Variations Associated with Athletic Performance in Grassland-Thoroughbred.

Abstract: Copy number variation (CNV) is an important source of genetic variation. However, studies utilizing whole-genome sequencing to investigate CNVs in horse populations and their effects on traits remain relatively limited. This study aims to address the lack of research on the impact of copy number variation (CNV) on racing performance in horse populations, providing new insights for locally bred racing breeds. We analyzed 60 offspring derived from the crossbreeding of Thoroughbred horses and Xilingol horses. These horses were temporarily named "Grassland-Thoroughbred" and were divided into two groups: 30 racing horses and 30 non-racing horses. A total of 89,527 CNVs were identified. After merging overlapping CNVs, 982 copy number variation regions (CNVRs) were recognized, among which the racing horse group (RH) had 29 unique CNVRs, while the non-racing horse group (NR) had 4 unique CNVRs. In addition, a total of 195 genes overlapping with CNVRs were identified. Transcriptomic analysis revealed 120 differentially expressed genes, with expressed in both CNVR-overlapping genes and mRNA. Both CNVR-overlapping genes and differentially expressed genes were enriched in the MAPK signaling pathway; CNV may affect gene expression through gene dosage effects or regulatory mechanisms. Using Vst statistical analysis, we further screened candidate CNVRs in autosomes that exceeded the 95% differentiation threshold between the RH and NR populations. Several key genes associated with energy metabolism and muscle function were identified, including , , , , , and . These findings provide new insights into the genetic structural variation in racing performance and adaptability, fill the gap in CNV studies in the genomics of Grassland-Thoroughbred horses, and offer valuable genomic data for optimizing breeding strategies in native racing horse populations.
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Publication Date: 2025-05-18 PubMed ID: 40427335PubMed Central: PMC12108297DOI: 10.3390/ani15101458Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research article discloses a comprehensive analysis of genetic variations, specifically copy number variations (CNVs), present in a unique group of horses, referred to as “Grassland-Thoroughbred,” and their potential impact on racing performance.

Copy Number Variations and Their Impact

  • The researchers structured their investigation around a phenomenon known as copy number variation (CNV). CNVs are segments of the genome where variations in the number of copies of DNA sections occur. These variations can significantly influence genetic diversity and contribute to different traits or characteristics within a species.
  • Though wide-ranging in potential, studies focusing on the impact of CNVs in horse populations, specifically relating to traits like racing performance, have been relatively minimal. This study is designed to bridge that gap.

Study Design and Findings

  • The study examined 60 offspring that resulted from crossbreeding Thoroughbred horses with Xilingol horses. The resultant group, temporarily named “Grassland-Thoroughbred,” was split into two sub-groups: a racing group of 30 horses and a non-racing group, also consisting of 30 horses.
  • Through their investigation, the researchers identified 89,527 CNVs. These were consolidated into 982 unique copy number variation regions (CNVRs), of which 29 were unique to the racing horse group, and four were unique to the non-racing group.
  • Further analysis identified a total of 195 genes overlapping with the CNVRs and 120 differentially expressed genes. Interestingly, these genes were found to mostly enrich in the MAPK signaling pathway – a critical biochemical pathway involved in many cellular processes including proliferation, differentiation, and migration.

Implication and Application of Findings

  • The study hypothesizes that CNVs might influence gene expressions either through gene dosage effects or through certain regulatory mechanisms, which could possibly affect a horse’s racing performance.
  • Moreover, some genes connected with energy metabolism and muscle function were recognized. Thus, findings from this study may be harnessed towards optimizing breeding strategies to improve the physical performance of native racing horse populations.
  • The discovered genetic variations and their associations with racing performance effectively enrich the understanding of genomics in Grassland-Thoroughbred horses and could help pave the way for targeted genetic enhancements in the future.

Cite This Article

APA
Ding W, Gong W, Bou T, Shi L, Lin Y, Shi X, Li Z, Wu H, Dugarjaviin M, Bai D. (2025). Whole-Genome Resequencing Analysis of Copy Number Variations Associated with Athletic Performance in Grassland-Thoroughbred. Animals (Basel), 15(10), 1458. https://doi.org/10.3390/ani15101458

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 15
Issue: 10
PII: 1458

Researcher Affiliations

Ding, Wenqi
  • Key Laboratory of Equus Germplasm Innovation (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hohhot 010018, China.
  • Inner Mongolia Key Laboratory of Equine Science Research and Technology Innovation, Inner Mongolia Agricultural University, Hohhot 010018, China.
  • Equus Research Center, College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.
Gong, Wendian
  • Key Laboratory of Equus Germplasm Innovation (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hohhot 010018, China.
  • Inner Mongolia Key Laboratory of Equine Science Research and Technology Innovation, Inner Mongolia Agricultural University, Hohhot 010018, China.
  • Equus Research Center, College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.
Bou, Tugeqin
  • Key Laboratory of Equus Germplasm Innovation (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hohhot 010018, China.
  • Inner Mongolia Key Laboratory of Equine Science Research and Technology Innovation, Inner Mongolia Agricultural University, Hohhot 010018, China.
  • Equus Research Center, College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.
Shi, Lin
  • Key Laboratory of Equus Germplasm Innovation (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hohhot 010018, China.
  • Inner Mongolia Key Laboratory of Equine Science Research and Technology Innovation, Inner Mongolia Agricultural University, Hohhot 010018, China.
  • Equus Research Center, College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.
Lin, Yanan
  • Key Laboratory of Equus Germplasm Innovation (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hohhot 010018, China.
  • Inner Mongolia Key Laboratory of Equine Science Research and Technology Innovation, Inner Mongolia Agricultural University, Hohhot 010018, China.
  • Equus Research Center, College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.
Shi, Xiaoyuan
  • Key Laboratory of Equus Germplasm Innovation (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hohhot 010018, China.
  • Inner Mongolia Key Laboratory of Equine Science Research and Technology Innovation, Inner Mongolia Agricultural University, Hohhot 010018, China.
  • Equus Research Center, College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.
Li, Zheng
  • Key Laboratory of Equus Germplasm Innovation (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hohhot 010018, China.
  • Inner Mongolia Key Laboratory of Equine Science Research and Technology Innovation, Inner Mongolia Agricultural University, Hohhot 010018, China.
  • Equus Research Center, College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.
Wu, Huize
  • Key Laboratory of Equus Germplasm Innovation (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hohhot 010018, China.
  • Inner Mongolia Key Laboratory of Equine Science Research and Technology Innovation, Inner Mongolia Agricultural University, Hohhot 010018, China.
  • Equus Research Center, College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.
Dugarjaviin, Manglai
  • Key Laboratory of Equus Germplasm Innovation (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hohhot 010018, China.
  • Inner Mongolia Key Laboratory of Equine Science Research and Technology Innovation, Inner Mongolia Agricultural University, Hohhot 010018, China.
  • Equus Research Center, College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.
Bai, Dongyi
  • Key Laboratory of Equus Germplasm Innovation (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hohhot 010018, China.
  • Inner Mongolia Key Laboratory of Equine Science Research and Technology Innovation, Inner Mongolia Agricultural University, Hohhot 010018, China.
  • Equus Research Center, College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.

Grant Funding

  • U23A20224 / the National Natural Science Foundation of China
  • BR22-11-03 / the Basic Research Operating Expenses of Colleges and Universities Project of the Department of Education of the Inner Mongolia Autonomous Region
  • 2020ZD0004 / the construction projects of the Inner Mongolia Science and Technology Department
  • RK2400002235 / the Agricultural and Animal Husbandry Characteristic Seed Industry Project

Conflict of Interest Statement

The authors declare no conflicts of interest.

References

This article includes 71 references
  1. Chien P.M.. Tourism and Hospitality in the Contemporary World: Trends, Changes & Complexity. Proceedings of the 24th Annual Conference CAUTHE 2014; Proceedings of the CAUTHE Conference 2014; Brisbane, Australia. 10–13 February 2014.
  2. Worthington A.C.. National exuberance: A note on the Melbourne Cup effect in Australian stock returns. Econ. Pap. A J. Appl. Econ. Policy 2007;26:170–179.
    doi: 10.1111/j.1759-3441.2007.tb01014.xgoogle scholar: lookup
  3. Narayan P.K., Smyth R.. The race that stops a nation: The demand for the Melbourne Cup. Econ. Rec. 2004;80:193–207.
    doi: 10.1111/j.1475-4932.2004.00172.xgoogle scholar: lookup
  4. Mostafavi A., Fozi M.A., Koshkooieh A.E., Mohammadabadi M., Babenko O.I., Klopenko N.I.. Effect of LCORL gene polymorphism on body size traits in horse populations. Acta Scientiarum. Anim. Sci. 2019;42:e47483.
    doi: 10.4025/actascianimsci.v42i1.47483google scholar: lookup
  5. Davis M.. When Things Get Dark: A Mongolian Winter’s Tale. Macmillan; Tuggerah, Australia: 2010.
  6. Qi B.. Xilin Gol League Animal Husbandry Chronicle. Inner Mongolia People’s Publishing House; Hohhot, China: 2002.
  7. Zhang F., Gu W., Hurles M.E., Lupski J.R.. Copy number variation in human health, disease, and evolution. Annu. Rev. Genom. Hum. Genet. 2009;10:451–481.
    doi: 10.1146/annurev.genom.9.081307.164217pmc: PMC4472309pubmed: 19715442google scholar: lookup
  8. Stranger B.E., Forrest M.S., Dunning M., Ingle C.E., Beazley C., Thorne N., Redon R., Bird C.P., De Grassi A., Lee C.. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 2007;315:848–853.
    doi: 10.1126/science.1136678pmc: PMC2665772pubmed: 17289997google scholar: lookup
  9. Conrad D.F., Pinto D., Redon R., Feuk L., Gokcumen O., Zhang Y., Aerts J., Andrews T.D., Barnes C., Campbell P.. Origins and functional impact of copy number variation in the human genome. Nature 2010;464:704–712.
    doi: 10.1038/nature08516pmc: PMC3330748pubmed: 19812545google scholar: lookup
  10. Scherer S.W., Lee C., Birney E., Altshuler D.M., Eichler E.E., Carter N.P., Hurles M.E., Feuk L.. Challenges and standards in integrating surveys of structural variation. Nat. Genet. 2007;39:S7–S15.
    doi: 10.1038/ng2093pmc: PMC2698291pubmed: 17597783google scholar: lookup
  11. Freeman J.L., Perry G.H., Feuk L., Redon R., McCarroll S.A., Altshuler D.M., Aburatani H., Jones K.W., Tyler-Smith C., Hurles M.E.. Copy number variation: New insights in genome diversity. Genome Res. 2006;16:949–961.
    doi: 10.1101/gr.3677206pubmed: 16809666google scholar: lookup
  12. Sebat J., Lakshmi B., Troge J., Alexander J., Young J., Lundin P., Manér S., Massa H., Walker M., Chi M.. Large-scale copy number polymorphism in the human genome. Science 2004;305:525–528.
    doi: 10.1126/science.1098918pubmed: 15273396google scholar: lookup
  13. Clop A., Vidal O., Amills M.. Copy number variation in the genomes of domestic animals. Anim. Genet. 2012;43:503–517.
    doi: 10.1111/j.1365-2052.2012.02317.xpubmed: 22497594google scholar: lookup
  14. Conrad D.F., Bird C., Blackburne B., Lindsay S., Mamanova L., Lee C., Turner D.J., Hurles M.E.. Mutation spectrum revealed by breakpoint sequencing of human germline CNVs. Nat. Genet. 2010;42:385–391.
    doi: 10.1038/ng.564pmc: PMC3428939pubmed: 20364136google scholar: lookup
  15. Stankiewicz P., Lupski J.R.. Structural variation in the human genome and its role in disease. Annu. Rev. Med. 2010;61:437–455.
    doi: 10.1146/annurev-med-100708-204735pubmed: 20059347google scholar: lookup
  16. Mazuet C., Legeay C., Sautereau J., Bouchier C., Criscuolo A., Bouvet P., Trehard H., Jourdan Da Silva N., Popoff M.. Characterization of Clostridium Baratii Type F Strains Responsible for an Outbreak of Botulism Linked to Beef Meat Consumption in France. PLoS Curr. 2017;9.
    pmc: PMC5959735pubmed: 29862134
  17. Chen S., Zhou Y., Chen Y., Gu J.. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018;34:i884–i890.
    doi: 10.1093/bioinformatics/bty560pmc: PMC6129281pubmed: 30423086google scholar: lookup
  18. Wingett S.W., Andrews S.. FastQ Screen: A tool for multi-genome mapping and quality control. F1000Res. 2018;7:1338.
    doi: 10.12688/f1000research.15931.1pmc: PMC6124377pubmed: 30254741google scholar: lookup
  19. Li H., Durbin R.. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754–1760.
    doi: 10.1093/bioinformatics/btp324pmc: PMC2705234pubmed: 19451168google scholar: lookup
  20. Li H., Handsaker B., Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R., Subgroup G.P.D.P.. The sequence alignment/map format and SAMtools. Bioinformatics 2009;25:2078–2079.
    doi: 10.1093/bioinformatics/btp352pmc: PMC2723002pubmed: 19505943google scholar: lookup
  21. Bathke J., Lühken G.. OVarFlow: A resource optimized GATK 4 based Open source Variant calling workFlow. BMC Bioinform. 2021;22:402.
    doi: 10.1186/s12859-021-04317-ypmc: PMC8361789pubmed: 34388963google scholar: lookup
  22. Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M.A., Bender D., Maller J., Sklar P., De Bakker P.I., Daly M.J.. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007;81:559–575.
    doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
  23. Abyzov A., Urban A.E., Snyder M., Gerstein M.. CNVnator: An approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 2011;21:974–984.
    doi: 10.1101/gr.114876.110pmc: PMC3106330pubmed: 21324876google scholar: lookup
  24. Pierce M.D., Dzama K., Muchadeyi F.C.. Genetic diversity of seven cattle breeds inferred using copy number variations. Front. Genet. 2018;9:163.
    doi: 10.3389/fgene.2018.00163pmc: PMC5962699pubmed: 29868114google scholar: lookup
  25. Kim D., Langmead B., Salzberg S.L.. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 2015;12:357–360.
    doi: 10.1038/nmeth.3317pmc: PMC4655817pubmed: 25751142google scholar: lookup
  26. Kovaka S., Zimin A.V., Pertea G.M., Razaghi R., Salzberg S.L., Pertea M.. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol. 2019;20:278.
    doi: 10.1186/s13059-019-1910-1pmc: PMC6912988pubmed: 31842956google scholar: lookup
  27. Pertea M., Kim D., Pertea G.M., Leek J.T., Salzberg S.L.. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 2016;11:1650–1667.
    doi: 10.1038/nprot.2016.095pmc: PMC5032908pubmed: 27560171google scholar: lookup
  28. Love M.I., Huber W., Anders S.. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
    doi: 10.1186/s13059-014-0550-8pmc: PMC4302049pubmed: 25516281google scholar: lookup
  29. Huang D.W., Sherman B.T., Lempicki R.A.. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009;4:44–57.
    doi: 10.1038/nprot.2008.211pubmed: 19131956google scholar: lookup
  30. Quinlan A.R., Hall I.M.. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 2010;26:841–842.
    doi: 10.1093/bioinformatics/btq033pmc: PMC2832824pubmed: 20110278google scholar: lookup
  31. Aleman M., Nieto J.E.. Gene expression of proteolytic systems and growth regulators of skeletal muscle in horses with myopathy associated with pituitary pars intermedia dysfunction. Am. J. Vet. Res. 2010;71:664–670.
    doi: 10.2460/ajvr.71.6.664pubmed: 20513182google scholar: lookup
  32. Doan R., Cohen N., Harrington J., Veazy K., Juras R., Cothran G., McCue M.E., Skow L., Dindot S.V.. Identification of copy number variants in horses. Genome Res. 2012;22:899–907.
    doi: 10.1101/gr.128991.111pmc: PMC3337435pubmed: 22383489google scholar: lookup
  33. Ghosh S., Das P., McQueen C., Gerber V., Swiderski C., Lavoie J.P., Chowdhary B., Raudsepp T.. Analysis of genomic copy number variation in equine recurrent airway obstruction (heaves). Anim. Genet. 2016;47:334–344.
    doi: 10.1111/age.12426pubmed: 26932307google scholar: lookup
  34. Wang W., Wang S., Hou C., Xing Y., Cao J., Wu K., Liu C., Zhang D., Zhang L., Zhang Y.. Genome-wide detection of copy number variations among diverse horse breeds by array CGH. PLoS ONE 2014;9:e86860.
    doi: 10.1371/journal.pone.0086860pmc: PMC3907382pubmed: 24497987google scholar: lookup
  35. Solé M., Ablondi M., Binzer-Panchal A., Velie B.D., Hollfelder N., Buys N., Ducro B.J., François L., Janssens S., Schurink A.. Inter-and intra-breed genome-wide copy number diversity in a large cohort of European equine breeds. BMC Genom. 2019;20:1–12.
    doi: 10.1186/s12864-019-6141-zpmc: PMC6805398pubmed: 31640551google scholar: lookup
  36. Tang X., Zhu B., Ren R., Chen B., Li S., Gu J.. Genome-wide copy number variation detection in a large cohort of diverse horse breeds by whole-genome sequencing. Front. Vet. Sci. 2023;10:1296213.
    doi: 10.3389/fvets.2023.1296213pmc: PMC10710158pubmed: 38076560google scholar: lookup
  37. Schurink A., da Silva V.H., Velie B.D., Dibbits B.W., Crooijmans R.P., Franҫois L., Janssens S., Stinckens A., Blott S., Buys N.. Copy number variations in Friesian horses and genetic risk factors for insect bite hypersensitivity. BMC Genet. 2018;19:49.
    doi: 10.1186/s12863-018-0657-0pmc: PMC6065148pubmed: 30060732google scholar: lookup
  38. Dupuis M.-C., Zhang Z., Durkin K., Charlier C., Lekeux P., Georges M.. Detection of copy number variants in the horse genome and examination of their association with recurrent laryngeal neuropathy. Anim. Genet. 2013;44:206–208.
    doi: 10.1111/j.1365-2052.2012.02373.xpubmed: 22582820google scholar: lookup
  39. Pawlina-Tyszko K., Gurgul A., Szmatoła T., Ropka-Molik K., Semik-Gurgul E., Klukowska-Rötzler J., Koch C., Mählmann K., Bugno-Poniewierska M.. Genomic landscape of copy number variation and copy neutral loss of heterozygosity events in equine sarcoids reveals increased instability of the sarcoid genome. Biochimie 2017;140:122–132.
    doi: 10.1016/j.biochi.2017.07.006pubmed: 28743673google scholar: lookup
  40. Kader A., Liu X., Dong K., Song S., Pan J., Yang M., Chen X., He X., Jiang L., Ma Y.. Identification of copy number variations in three Chinese horse breeds using 70K single nucleotide polymorphism BeadChip array. Anim. Genet. 2016;47:560–569.
    doi: 10.1111/age.12451pubmed: 27440410google scholar: lookup
  41. Ghosh S., Qu Z., Das P.J., Fang E., Juras R., Cothran E.G., McDonell S., Kenney D.G., Lear T.L., Adelson D.L.. Copy number variation in the horse genome. PLoS Genet. 2014;10:e1004712.
    doi: 10.1371/journal.pgen.1004712pmc: PMC4207638pubmed: 25340504google scholar: lookup
  42. Wang M., Liu Y., Bi X., Ma H., Zeng G., Guo J., Guo M., Ling Y., Zhao C.. Genome-wide detection of copy number variants in Chinese indigenous horse breeds and verification of CNV-overlapped genes related to heat adaptation of the Jinjiang horse. Genes 2022;13:603.
    doi: 10.3390/genes13040603pmc: PMC9033042pubmed: 35456409google scholar: lookup
  43. Metzger J., Philipp U., Lopes M.S., da Camara Machado A., Felicetti M., Silvestrelli M., Distl O.. Analysis of copy number variants by three detection algorithms and their association with body size in horses. BMC Genom. 2013;14:487.
    doi: 10.1186/1471-2164-14-487pmc: PMC3720552pubmed: 23865711google scholar: lookup
  44. Shiraishi S., Nakamura Y.-N., Iwamoto H., Haruno A., Sato Y., Mori S., Ikeuchi Y., Chikushi J., Hayashi T., Sato M.. S-myotrophin promotes the hypertrophy of skeletal muscle of mice in vivo. Int. J. Biochem. Cell Biol. 2006;38:1114–1122.
    doi: 10.1016/j.biocel.2005.11.014pubmed: 16531094google scholar: lookup
  45. Anderson K.M., Berrebi-Bertrand I., Kirkpatrick R.B., McQueney M.S., Underwood D.C., Rouanet S., Chabot-Fletcher M.. cDNA sequence and characterization of the gene that encodes human myotrophin/V-1 protein, a mediator of cardiac hypertrophy. J. Mol. Cell. Cardiol. 1999;31:705–719.
    doi: 10.1006/jmcc.1998.0903pubmed: 10329199google scholar: lookup
  46. Bordbar F., Jensen J., Du M., Abied A., Guo W., Xu L., Gao H., Zhang L., Li J.. Identification and validation of a novel candidate gene regulating net meat weight in Simmental beef cattle based on imputed next-generation sequencing. Cell Prolif. 2020;53:e12870.
    doi: 10.1111/cpr.12870pmc: PMC7507581pubmed: 32722873google scholar: lookup
  47. De Mello Costa M., Anderson G., Davies H., El-Hage C., Slocombe R.. Circulating angiotensin converting enzyme in endurance horses: Effect of exercise on blood levels and its value in predicting performance. Equine Vet. J. 2010;42:152–154.
    doi: 10.1111/j.2042-3306.2010.00171.xpubmed: 21058998google scholar: lookup
  48. Nader G.A., Esser K.A.. Intracellular signaling specificity in skeletal muscle in response to different modes of exercise. J. Appl. Physiol. 2001;90:1936–1942.
    doi: 10.1152/jappl.2001.90.5.1936pubmed: 11299288google scholar: lookup
  49. Widegren U., Jiang X.J., Krook A., Chibalin A.V., Björnholm M., Tally M., Roth R.A., Henriksson J., Wallberg-Henriksson H., Zierath J.R.. Divergent effects of exercise on metabolic and mitogenic signaling pathways in human skeletal muscle. FASEB J. 1998;12:1379–1389.
    doi: 10.1096/fasebj.12.13.1379pubmed: 9761781google scholar: lookup
  50. Yu M., Blomstrand E., Chibalin A.V., Krook A., Zierath J.R.. Marathon running increases ERK1/2 and p38 MAP kinase signalling to downstream targets in human skeletal muscle. J. Physiol. 2001;536:273–282.
    doi: 10.1111/j.1469-7793.2001.00273.xpmc: PMC2278852pubmed: 11579175google scholar: lookup
  51. Kramer H.F., Goodyear L.J.. Exercise, MAPK, and NF-κB signaling in skeletal muscle. J. Appl. Physiol. 2007;103:388–395.
    doi: 10.1152/japplphysiol.00085.2007pubmed: 17303713google scholar: lookup
  52. Rauramaa R., Kuhanen R., Lakka T.A., Väisänen S.B., Halonen P., Alén M., Rankinen T., Bouchard C.. Physical exercise and blood pressure with reference to the angiotensinogen M235T polymorphism. Physiol. Genom. 2002;10:71–77.
    doi: 10.1152/physiolgenomics.00050.2002pubmed: 12181364google scholar: lookup
  53. Corvol P., Jeunemaitre X.. Molecular genetics of human hypertension: Role of angiotensinogen. Endocr. Rev. 1997;18:662–677.
    doi: 10.1210/edrv.18.5.0312pubmed: 9331547google scholar: lookup
  54. Jones A., Woods D.R.. Skeletal muscle RAS and exercise performance. Int. J. Biochem. Cell Biol. 2003;35:855–866.
    doi: 10.1016/S1357-2725(02)00342-4pubmed: 12676172google scholar: lookup
  55. Ben-Zaken S., Eliakim A., Nemet D., Meckel Y.. Genetic Variability Among Power Athletes: The Stronger vs. the Faster. J. Strength. Cond. Res. 2019;33:1505–1511.
    doi: 10.1519/JSC.0000000000001356pubmed: 26840443google scholar: lookup
  56. Aleksandra Z., Zbigniew J., Waldemar M., Agata L.-D., Mariusz K., Marek S., Agnieszka M.-S., Piotr Ż., Krzysztof F., Grzegorz T.. The AGT gene M235T polymorphism and response of power-related variables to aerobic training. J. Sports Sci. Med. 2016;15:616.
    pmc: PMC5131215pubmed: 27928207
  57. Zarebska A., Sawczyn S., Kaczmarczyk M., Ficek K., Maciejewska-Karlowska A., Sawczuk M., LeoNska-Duniec A., Eider J., Grenda A., Cieszczyk P.. Association of rs699 (M235T) polymorphism in the AGT gene with power but not endurance athlete status. J. Strength. Cond. Res. 2013;27:2898–2903.
    doi: 10.1519/JSC.0b013e31828155b5pubmed: 23287839google scholar: lookup
  58. Perez K., Ciotlos S., McGirr J., Limbad C., Doi R., Nederveen J.P., Nilsson M.I., Winer D.A., Evans W., Tarnopolsky M.. Single nuclei profiling identifies cell specific markers of skeletal muscle aging, frailty, and senescence. Aging 2022;14:9393–9422.
    doi: 10.18632/aging.204435pmc: PMC9792217pubmed: 36516485google scholar: lookup
  59. Riedl I., Yoshioka M., Nishida Y., Tobina T., Paradis R., Shono N., Tanaka H., St-Amand J.. Regulation of skeletal muscle transcriptome in elderly men after 6 weeks of endurance training at lactate threshold intensity. Exp. Gerontol. 2010;45:896–903.
    doi: 10.1016/j.exger.2010.08.014pubmed: 20813182google scholar: lookup
  60. Li X., Baker J., Cracknell T., Haynes A.R., Blanco G.. IGFN1_v1 is required for myoblast fusion and differentiation. PLoS ONE 2017;12:e0180217.
    doi: 10.1371/journal.pone.0180217pmc: PMC5493368pubmed: 28665998google scholar: lookup
  61. Kilpinen S., Ojala K., Kallioniemi O.. Analysis of kinase gene expression patterns across 5681 human tissue samples reveals functional genomic taxonomy of the kinome. PLoS ONE 2010;5:e15068.
    doi: 10.1371/journal.pone.0015068pmc: PMC2997066pubmed: 21151926google scholar: lookup
  62. Lu B., Poirier C., Gaspar T., Gratzke C., Harrison W., Busija D., Matzuk M.M., Andersson K.-E., Overbeek P.A., Bishop C.E.. A mutation in the inner mitochondrial membrane peptidase 2-like gene (Immp2l) affects mitochondrial function and impairs fertility in mice. Biol. Reprod. 2008;78:601–610.
    doi: 10.1095/biolreprod.107.065987pubmed: 18094351google scholar: lookup
  63. Sako H., Yada K., Suzuki K.. Genome-Wide Analysis of Acute Endurance Exercise-Induced Translational Regulation in Mouse Skeletal Muscle. PLoS ONE 2016;11:e0148311.
    doi: 10.1371/journal.pone.0148311pmc: PMC4742069pubmed: 26845575google scholar: lookup
  64. Anunciado-Koza R.P., Zhang J., Ukropec J., Bajpeyi S., Koza R.A., Rogers R.C., Cefalu W.T., Mynatt R.L., Kozak L.P.. Inactivation of the mitochondrial carrier SLC25A25 (ATP-Mg2+/Pi transporter) reduces physical endurance and metabolic efficiency in mice. J. Biol. Chem. 2011;286:11659–11671.
    doi: 10.1074/jbc.M110.203000pmc: PMC3064218pubmed: 21296886google scholar: lookup
  65. Volpe S.L.. Magnesium in disease prevention and overall health. Adv. Nutr. 2013;4:378S–383S.
    doi: 10.3945/an.112.003483pmc: PMC3650510pubmed: 23674807google scholar: lookup
  66. Mastrototaro L., Smorodchenko A., Aschenbach J.R., Kolisek M., Sponder G.. Solute carrier 41A3 encodes for a mitochondrial Mg(2+) efflux system. Sci. Rep. 2016;6:27999.
    doi: 10.1038/srep27999pmc: PMC4908428pubmed: 27302215google scholar: lookup
  67. Williams G.S., Boyman L., Lederer W.J.. Mitochondrial calcium and the regulation of metabolism in the heart. J. Mol. Cell. Cardiol. 2015;78:35–45.
    doi: 10.1016/j.yjmcc.2014.10.019pmc: PMC6534814pubmed: 25450609google scholar: lookup
  68. Fleig A., Schweigel-Röntgen M., Kolisek M.. Solute carrier family SLC41: What do we really know about it?. Wiley Interdiscip. Rev. Membr. Transp. Signal. 2013;2:227–239.
    doi: 10.1002/wmts.95pmc: PMC3855994pubmed: 24340240google scholar: lookup
  69. Chiang Y.-F., Chen H.-Y., Lee I.-T., Chien L.-S., Huang J.-H., Kolisek M., Cheng F.-C., Tsai S.-W.. Magnesium-responsive genes are downregulated in diabetic patients after a three-month exercise program on a bicycle ergometer. J. Chin. Med. Assoc. 2019;82:495–499.
    doi: 10.1097/JCMA.0000000000000112pubmed: 31180948google scholar: lookup
  70. Terao M., Barzago M.M., Kurosaki M., Fratelli M., Bolis M., Borsotti A., Bigini P., Micotti E., Carli M., Invernizzi R.W.. Mouse aldehyde-oxidase-4 controls diurnal rhythms, fat deposition and locomotor activity. Sci. Rep. 2016;6:30343.
    doi: 10.1038/srep30343pmc: PMC4960552pubmed: 27456060google scholar: lookup
  71. Slocum N., Durrant J.R., Bailey D., Yoon L., Jordan H., Barton J., Brown R.H., Clifton L., Milliken T., Harrington W.. Responses of brown adipose tissue to diet-induced obesity, exercise, dietary restriction and ephedrine treatment. Exp. Toxicol. Pathol. 2013;65:549–557.
    doi: 10.1016/j.etp.2012.04.001pubmed: 22542811google scholar: lookup

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