Polygenic diseases
DOI:
https://doi.org/10.12775/JEHS.2025.77.58225Keywords
Polygenic diseases, Gene polymorphism, Single nucleotide polymorphisms, Type 2 diabetes, Preeclampsia, Breast cancer, Athletic genetics, Nutrigenetics, Sports genomics, Personalized medicine, Genetic markers, DNA methylation, Epigenetics, Exercise genetics, Sports injuriesAbstract
Background: Recent advances in molecular genetics have revolutionized our understanding of hereditary diseases, particularly polygenic (multifactorial) conditions. This comprehensive review examines the genetic basis of common polygenic diseases, including diabetes, preeclampsia, breast cancer, and factors influencing athletic performance.
Material and Methods: Analysis of current literature covering genetic aspects of polygenic diseases, focusing on gene polymorphisms, single nucleotide polymorphisms (SNPs), and their role in disease development. The review encompasses nutrigenetics, sports genomics, and genetic markers associated with athletic performance and injury risk.
Results: Gene polymorphisms significantly impact disease susceptibility and progression. For type 2 diabetes, over 600 candidate genes have been identified. In preeclampsia, multiple genes influence maternal-fetal interactions and vascular function. Breast cancer involves complex interactions between high and moderate penetrance genes, with over 300 SNPs identified. Athletic performance is influenced by both genetic factors (approximately 66%) and environmental conditions, with specific genetic markers associated with endurance, strength, and injury risk.
Conclusions: Polygenic diseases result from complex interactions between multiple genes and environmental factors. Understanding these interactions enables more effective personalized approaches to prevention, diagnosis, and treatment. Integration of genetic testing into clinical practice offers opportunities for improved patient outcomes through targeted interventions and personalized medicine strategies.
References
1. Genetic Passport as the Basis of Individual and Predictive Medicine. Edited by V. S. Baranov. St. Petersburg: N-L Publishing, 2009. 528 pages.
2. Glotov O.S., Chernov A.N., Glotov A.S. Human Exome Sequencing and Prospects for Predictive Medicine: Analysis of International Data and Own Experience. Journal of Personalized Medicine, 2023; 13(8): 1236. doi: 10.3390/jpm13081236
3. Guerri G., Maniscalchi T., Barati S., et al. Non-syndromic Monogenic Male Infertility. Acta Biomedica, 2019; 90(Suppl 10): 62–67. doi: 10.23750/abm.v90i10-S.8762
4. Laan M., Kasak L., Punab M. Translational Aspects of Novel Findings in Genetics of Male Infertility—Status Quo 2021. British Medical Bulletin, 2021; 140(1): 5–22. doi: 10.1093/bmb/ldab025
5. Colaco S., Modi D. Genetics of the Human Y Chromosome and Its Association with Male Infertility. Reproductive Biology and Endocrinology, 2018; 16: 14. doi: 10.1186/s12958-018-0330-5
6. Harton G.L., Tempest H.G. Chromosomal Disorders and Male Infertility. Asian Journal of Andrology, 2012; 14(1): 32–39. doi: 10.1038/aja.2011.66
7. Kuroda S., Usui K., Sanjo H., et al. Genetic Disorders and Male Infertility. Reproductive Medicine and Biology, 2020; 19(4): 314–322. doi: 10.1002/rmb2.12336
8. Massart A., et al. Genetic Causes of Spermatogenic Failure. Asian Journal of Andrology, 2012; 14(1): 40–48. doi: 10.1038/aja.2011.67
9. Cerván-Martín M., et al. Genetic Landscape of Nonobstructive Azoospermia and New Perspectives for the Clinic. Journal of Clinical Medicine, 2020; 9(2): 300. doi: 10.3390/jcm9020300
10. Papadimitriou S., Gravel B., Nachtegael C., et al. Toward Reporting Standards for the Pathogenicity of Variant Combinations Involved in Multilocus/Oligogenic Diseases. HGG Advances, 2023; 4(1): 100165. doi: 10.1016/j.xhgg.2022.100165
11.Kousi M., Katsanis N. Genetic Modifiers and Oligogenic Inheritance. Cold Spring Harbor Perspectives in Medicine, 2015; 5(6): a017145. doi: 10.1101/cshperspect.a017145
1. Guo Y., Jamison D. C. The distribution of SNPs in human gene regulatory regions. BMC Genomics, 2005, Vol. 6, p. 140. doi:10.1186/1471-2164-6-140
2. Kim B.-C., Kim W.-Y., Park D. SNP@Promoter: a database of human SNPs (Single Nucleotide Polymorphisms) within the putative promoter regions. BMC Bioinformatics, 2008, Vol. 9(Suppl 1), p. S2. doi:10.1186/1471-2105-9-S1-S2
3. Vignal A., Milan D., Magali SanCristobal, Eggen A. A review on SNP and other types of molecular markers and their use in animal genetics. Genet. Sel. Evol., 2002, Vol. 34, pp. 275–305. DOI: 10.1051/gse:2002009
4. Johnson A.D. SNP bioinformatics: a comprehensive review of resources. Circ Cardiovasc Genet., 2009, Vol. 2, No. 5, pp. 530–536. doi:10.1161/CIRCGENETICS.109.872010
5. Bin Alwi Z. The use of SNPs in pharmacogenomics studies. Malaysian Journal of Medical Sciences, 2005, Vol. 12, No. 2, pp. 4-12.
6. Chen C., Chang I.-S., Hsiung C.A., Wasserman W.W. On the identification of potential regulatory variants within genome-wide association candidate SNP sets. BMC Medical Genomics, 2014, Vol. 7, p. 34. http://www.biomedcentral.com/1755-8794/7/34
7. Xu H., Gregory S.G., Hauser E.R. et al. SNPselector: a web tool for selecting SNPs for genetic association studies. Bioinformatics, 2005, Vol. 21, N22, pp. 4181–4186. doi:10.1093/bioinformatics/bti682
8. Ghalandari H., Hosseini-Esfahani F., Mirmiran P. The Association of Polymorphisms in Leptin/Leptin Receptor Genes and Ghrelin/Ghrelin Receptor Genes with Overweight/Obesity and the Related Metabolic Disturbances: A Review. Int. J. Endocrinol. Metab., 2015, Vol. 13, No. 3, p. e19073. DOI: 10.5812/ijem.19073v2
9. Howlader M., Sultana I., Akter F., Hossain M. Adiponectin gene polymorphisms associated with diabetes mellitus: A descriptive review. Heliyon, 2021, Vol. 7, C8, p. e07851. doi: 10.1016/j.heliyon.2021.e07851
10. Staiger H., Machicao F., Fritsche A., Haring H.-U. Pathomechanisms of Type 2 Diabetes Genes. Endocrine Reviews, 2009, Vol. 30, No. 6, pp. 557–585. doi: 10.1210/er.2009-0017
11. Liu S., Liu Y., Zhang Q. et al. Systematic identification of regulatory variants associated with cancer risk. Genome Biology, 2017, Vol. 18, p. 194. DOI 10.1186/s13059-017-1322-z
12. Zheng H.-T., Peng Z.-H., Li S., He L. Loss of heterozygosity analyzed by single nucleotide polymorphism array in cancer. World Journal of Gastroenterology, 2005, Vol. 11, No. 43, pp. 6740-6744. http://www.wjgnet.com/1007-9327/11/6740.asp
13. Engle L.J., Simpson C.L., Landers J.E. Using high-throughput SNP technologies to study cancer. Oncogene, 2006, Vol. 25, pp. 1594–1601. doi:10.1038/sj.onc.1209368
14. Li Y.-C., Korol A.B., Fahima T. et al. Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review. Molecular Ecology, 2002, Vol. 11, pp. 2453–2465. DOI: 10.1046/j.1365-294x.2002.01643.x
15. Jasinska A., Krzyzosiak W.J. Repetitive sequences that shape the human transcriptome. FEBS Letters, 2004, Vol. 567, pp. 136–141. doi:10.1016/j.febslet.2004.03.109
16. Ciesiolka A., Jazurek M., Drazkowska K., Krzyzosiak W.J. Structural Characteristics of Simple RNA Repeats Associated with Disease and their Deleterious Protein Interactions. Front. Cell. Neurosci., 2017, Vol. 11, p. 97. doi: 10.3389/fncel.2017.00097
17.Panzer S., Kuhl D.P.A., Caskey C.T. Unstable Triplet Repeat Sequences: A Source of Cancer Mutations? STEM CELLS, 1995, Vol. 13, pp. 146-157.
1.Genetic Passport — The Basis of Individual and Predictive Medicine, edited by V. S. Baranov. — St. Petersburg: Publishing House N-L, 2009. — 528 pages.
2.Shi C. et al. Multifactorial Diseases of the Heart, Kidneys, Lungs, and Liver and Incident Cancer: Epidemiology and Shared Mechanisms. Cancers (Basel). 2023 Feb; 15(3): 729. doi: 10.3390/cancers15030729.
3.Zamorano J.L., Lancellotti P., Rodriguez Munoz D. et al. 2016 ESC Position Paper on cancer treatments and cardiovascular toxicity developed under the auspices of the ESC Committee for Practice Guidelines: The Task Force for cancer treatments and cardiovascular toxicity of the European Society of Cardiology (ESC). Eur. Heart J. 2016; 37:2768–2801. doi: 10.1093/eurheartj/ehw211.
4.Hasin T., Gerber Y., McNallan S.M. et al. Patients with heart failure have an increased risk of incident cancer. J. Am. Coll. Cardiol. 2013; 62:881–886. doi: 10.1016/j.jacc.2013.04.088.
5.Meijers W.C., Maglione M., Bakker S.J.L. et al. Heart Failure Stimulates Tumor Growth by Circulating Factors. Circulation. 2018; 138:678–691. doi: 10.1161/CIRCULATIONAHA.117.030816.
6.Wong G., Hayen A., Chapman J.R. et al. Association of CKD and cancer risk in older people. J. Am. Soc. Nephrol. 2009; 20:1341–1350. doi: 10.1681/ASN.2008090998.
7.Wong G., Staplin N., Emberson J. et al. Chronic kidney disease and the risk of cancer: An individual patient data meta-analysis of 32,057 participants from six prospective studies. BMC Cancer. 2016; 16:488. doi: 10.1186/s12885-016-2532-6.
8.Locatelli F., Canaud B., Eckardt K.U. et al. Oxidative stress in end-stage renal disease: An emerging threat to patient outcome. Nephrol. Dial. Transpl. 2003; 18:1272–1280. doi: 10.1093/ndt/gfg074.
9.Igo R.P. Jr., Kinzy T.G., Bailey J.N. Genetic Risk Scores. Curr Protoc Hum Genet. 2019; 104(1): e95. doi: 10.1002/cphg.95.
10.Palomaki G.E., Melillo S., Marrone M., Douglas M.P. Use of genomic panels to determine risk of developing type 2 diabetes in the general population: a targeted evidence-based review. Genet Med. 2013; 15(8): 600–611. doi: 10.1038/gim.2013.8.
11.Akhlaghipour I., Bina A.R., Mogharrabi M.R. Single-nucleotide polymorphisms as important risk factors of diabetes among the Middle East population. Hum Genomics. 2022; 16: 11. doi: 10.1186/s40246-022-00383-2.
12.Shojima N., Yamauchi T. Progress in genetics of type 2 diabetes and diabetic complications. J Diabetes Investig. 2023; 14(4): 503–515. doi: 10.1111/jdi.13970.
13.Witka B.Z, Oktaviani D.J., Marcellino M. et al. Type 2 Diabetes-Associated Genetic Polymorphisms as Potential Disease Predictors. Diabetes Metab Syndr Obes. 2019; 12: 2689–2706. doi: 10.2147/DMSO.S230061.
14.Younes S., Ibrahim A., Al-Jurf R., Zayed H. Genetic polymorphisms associated with obesity in the Arab world: a systematic review. Int J Obes (Lond). 2021; 45(9): 1899–1913. doi: 10.1038/s41366-021-00867-6.
15.Ausoni S., Azzarello G. Development of Cancer in Patients With Heart Failure: How Systemic Inflammation Can Lay the Groundwork. Front Cardiovasc Med. 2020; 7: 598384. doi: 10.3389/fcvm.2020.598384.
16.Romejko K, Markowska M., Niemczyk S. The Review of Current Knowledge on Neutrophil Gelatinase-Associated Lipocalin (NGAL). Int J Mol Sci. 2023; 24(13): 10470. doi: 10.3390/ijms241310470.
17.del Bosque-Plata L., Martínez-Martínez E., Espinoza-Camacho M.Á., Gragnoli C. The Role of TCF7L2 in Type 2 Diabetes. Diabetes. 2021; 70(6): 1220–1228. doi: 10.2337/db20-0573.
18.Lorenz K., Voight B.F. Dissecting an adiposity locus with an arsenal of genomics. Genome Biol. 2018; 19: 74. Published online 2018 Jun 7. doi: 10.1186/s13059-018-1460-y.
19.Akash M.S.H., Rasheed S., Rehman K. et al. Biochemical Activation and Regulatory Functions of Trans-Regulatory KLF14 and Its Association with Genetic Polymorphisms. Metabolites. 2023; 13(2): 199. doi: 10.3390/metabo13020199.
20.Yu X., Liao M., Zeng Y. et al. Associations of KCNQ1 Polymorphisms with the Risk of Type 2 Diabetes Mellitus: An Updated Meta-Analysis with Trial Sequential Analysis. J Diabetes Res. 2020; 2020: 7145139. doi: 10.1155/2020/7145139.
21.Popović A.-M., Huđek Turković A., Žuna K. et al. FTO Gene Polymorphisms at the Crossroads of Metabolic Pathways of Obesity and Epigenetic Influences. Food Technol Biotechnol. 2023; 61(1): 14–26. doi: 10.17113/ftb.61.01.23.7594.
22.Radi S.H. et al. HNF4α isoforms: the fraternal twin master regulators of liver function. Front Endocrinol (Lausanne). 2023; 14: 1226173. doi: 10.3389/fendo.2023.1226173.
23.Zhang P., Wu W., Ma C. et al. RNA-Binding Proteins in the Regulation of Adipogenesis and Adipose Function. Cells. 2022; 11(15): 2357. doi: 10.3390/cells11152357.
24.Davidson H.W., Wenzlau J.M., O’Brien R.M. Zinc transporter 8 (znt8) and beta cell function. Trends Endocrinol Metab. 2014; 25(8): 415–424. doi: 10.1016/j.tem.2014.03.008.
25.Yi B., Huang G., Zhou Z. Different role of zinc transporter 8 between type 1 diabetes mellitus and type 2 diabetes mellitus. J Diabetes Investig. 2016; 7(4): 459–465. doi: 10.1111/jdi.12441.
26.Hamidi Y. et al. A Meta-analysis of ADIPOQ rs2241766 polymorphism association with type 2 diabetes. J Diabetes Metab Disord. 2022; 21(2): 1895–1901. doi: 10.1007/s40200-022-01086-0.
27.Baldelli S. et al. The Role of Adipose Tissue and Nutrition in the Regulation of Adiponectin. Nutrients. 2024; 16(15): 2436. doi: 10.3390/nu16152436.
1. Genetic Passport: The Foundation of Individual and Predictive Medicine / Edited by V. S. Baranov. — Saint Petersburg: N-L Publishing, 2009. — 528 pages.
2. Glotov A. S., Vashukova E. S., Glotov O. S., et al. Study of Population Frequencies of Gene Polymorphisms Associated with Gestosis. Ecological Genetics. 2013; Volume XI, No. 1: 91-100.
3. Tsakhilova S. G., Akulenko L. V., Kuznetsov V. M., et al. Genetic Predictors of Preeclampsia. Problems of Reproduction. 2017; No. 1: 110-114. doi: 10.17116/repro2017231110-114
4. Kaptilnyy V. A., Reyshtat D. Y. Preeclampsia: Definition, Advances in Pathogenesis, Guidelines, Treatment, and Prevention. V. F. Snegirev Archives of Obstetrics and Gynecology, Russian Journal. 2020; 7(1): 19-30. (in Russian). DOI: http://dx.doi.org/10.18821/2313-8726-2020-7-1-19-30
5. Williams P. J., Broughton Pipkin F. The Genetics of Pre-eclampsia and other Hypertensive Disorders of Pregnancy. Best Pract Res Clin Obstet Gynaecol. 2011; 25(4): 405–417. doi: 10.1016/j.bpobgyn.2011.02.007
6. Kleinrouweler C. E., van Uitert M., Moerland P. D., et al. Differentially Expressed Genes in the Pre-Eclamptic Placenta: A Systematic Review and Meta-Analysis. PLoS One. 2013; 8(7): e68991. doi: 10.1371/journal.pone.0068991
7. Parada-Niño L., Castillo-León L. F., Morel A. Preeclampsia, Natural History, Genes, and miRNAs Associated with the Syndrome. J Pregnancy. 2022; 2022: 3851225. doi: 10.1155/2022/3851225
8. Duan W., Xia C., Wang K., et al. A Meta-Analysis of the Vascular Endothelial Growth Factor Polymorphisms Associated with the Risk of Pre-eclampsia. Biosci Rep. 2020; 40(5): BSR20190209. doi: 10.1042/BSR20190209
9. Rokoт́yanskaya E. A., Panova I. A., Malyshkina A. I., et al. Technologies for Prediction of Preeclampsia. Sovrem Tekhnologii Med. 2020; 12(5): 78–84. doi: 10.17691/stm2020.12.5.09
10. Sandrim V. C., Rizzatti Luizon M., Pilan E. Interaction Between NOS3 and HMOX1 on Antihypertensive Drug Responsiveness in Preeclampsia. Rev Bras Ginecol Obstet. 2020; 42(8): 460–467. doi: 10.1055/s-0040-1712484
11. Lykke J. A., Bare L. A., Olsen J., et al. Vascular Associated Gene Variants in Patients with Preeclampsia: Results from the Danish National Birth Cohort. Acta Obstet Gynecol Scand. 2012; 91(9): 1053–1060. doi: 10.1111/j.1600-0412.2012.01479.x
12. Liu L., Zhang X., Qin K., et al. Characteristics of Serum Lipid Metabolism among Women Complicated with Hypertensive Disorders in Pregnancy: A Retrospective Cohort Study in Mainland China. Obstet Gynecol Int. 2024; 2024: 9070748. doi: 10.1155/2024/9070748
13. Hajagos-Tóth J., Ducza E., Samavati R., et al. Obesity in Pregnancy: A Novel Concept on the Roles of Adipokines in Uterine Contractility. Croat Med J. 2017; 58(2): 96–104. doi: 10.3325/cmj.2017.58.96
14. Howlader M., Sultana I., Akter F., Hossain M. Adiponectin Gene Polymorphisms Associated with Diabetes Mellitus: A Descriptive Review. Heliyon. 2021; 7(8): e07851. doi: 10.1016/j.heliyon.2021.e07851
15. Zhang G., Zhao J., Yi J. Association Between Gene Polymorphisms on Chromosome 1 and Susceptibility to Pre-Eclampsia: An Updated Meta-Analysis. Med Sci Monit. 2016; 22: 2202–2214. doi: 10.12659/MSM.896552
16. Boulanger H., Bounan S., Mahdhi A., et al. Immunologic Aspects of Preeclampsia. AJOG Glob Rep. 2024; 4(1): 100321. doi: 10.1016/j.xagr.2024.100321
17. Xu X., Zhou Y., Wei H. Roles of HLA-G in the Maternal-Fetal Immune Microenvironment. Front Immunol. 2020; 11: 592010. doi: 10.3389/fimmu.2020.592010
18. Zhuang B., Shang J., Yao Y. HLA-G: An Important Mediator of Maternal-Fetal Immune-Tolerance. Front Immunol. 2021; 12: 744324. doi: 10.3389/fimmu.2021.744324
19. Wang X., Zhu H., Lei L., et al. Integrated Analysis of Key Genes and Pathways Involved in Fetal Growth Restriction and Their Associations with the Dysregulation of the Maternal Immune System. Front Genet. 2020; 11: 581789. doi: 10.3389/fgene.2020.581789
20. Mora-Palazuelos C., Bermúdez M., Aguilar-Medina M., et al. Cytokine-Polymorphisms Associated with Preeclampsia: A Review. Medicine (Baltimore). 2022; 101(39): e30870. doi: 10.1097/MD.0000000000030870
21. Chen A., Zhao H., Wang J., et al. Haplotype Analysis of Candidate Genes Involved in Inflammation and Oxidative Stress and the Susceptibility to Preeclampsia. J Immunol Res. 2020; 2020: 4683798. doi: 10.1155/2020/4683798
1. Zolotykh M.A., Bilyalov A.I., Nesterova A.I., et al. Breast Cancer: Genetic Factors of Personal Risk. // Modern Oncology. 2023; 25(2): 190–198.
2. Smirnova N.V., Elmuradov A.U. Prospects for Using the Polygenic Risk Score (PRS) for Assessing Breast Cancer Risk. // Bulletin of Surgut University. Medicine. 2019, 42(4): 74-78.
3. Mavaddat N., Michailidou K., Dennis J., et al. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. Am J Hum Genet. 2019; 104(1): 21–34. doi: 10.1016/j.ajhg.2018.11.002
4. Graffeo R., Rana H.Q., Conforti F., et al. Moderate Penetrance Genes Complicate Genetic Testing for Breast Cancer Diagnosis: ATM, CHEK2, BARD1 and RAD51D. Breast. 2022; 65: 32–40. doi: 10.1016/j.breast.2022.06.003
5. Roberts E., Howell S., Evans D.G. Polygenic Risk Scores and Breast Cancer Risk Prediction. Breast. 2023; 67: 71–77. doi: 10.1016/j.breast.2023.01.003
6. Rebello Alves L.N., Meira D.D., Merigueti L.P., et al. Biomarkers in Breast Cancer: An Old Story with a New End. Genes (Basel). 2023; 14(7): 1364. doi: 10.3390/genes14071364
7. Karami F., Mehdipour P. A Comprehensive Focus on Global Spectrum of BRCA1 and BRCA2 Mutations in Breast Cancer. Biomed Res Int. 2013; 2013: 928562. doi: 10.1155/2013/928562
8. Polyak K. Breast Cancer: Origins and Evolution. J Clin Invest. 2007; 117(11): 3155–3163. doi: 10.1172/JCI33295
9. Sun Y.-S., Zhao Z., Yang Z., et al. Risk Factors and Preventions of Breast Cancer. Int J Biol Sci. 2017; 13(11): 1387–1397. doi: 10.7150/ijbs.21635
10. Schon K., Tischkowitz M. Clinical Implications of Germline Mutations in Breast Cancer: TP53. Breast Cancer Res Treat. 2018; 167(2): 417–423. doi: 10.1007/s10549-017-4531-y
11. Waarts M.R., et al. Targeting Mutations in Cancer. J Clin Invest. 2022; 132(8): e154943. doi: 10.1172/JCI154943
12. Brett J.O., et al. ESR1 Mutation as an Emerging Clinical Biomarker in Metastatic Hormone Receptor-Positive Breast Cancer. Breast Cancer Res. 2021; 23: 85. doi: 10.1186/s13058-021-01462-3
13. Dustin D., Gu G., Fuqua S.A.W. ESR1 Mutations in Breast Cancer. Cancer. 2019; 125(21): 3714–3728. doi: 10.1002/cncr.32345
14. Silwal-Pandit L., Langerød A., Børresen-Dale A.-L. TP53 Mutations in Breast and Ovarian Cancer. Cold Spring Harb Perspect Med. 2017; 7(1): a026252. doi: 10.1101/cshperspect.a026252
15. Kuusisto K.M., Bebel A., Vihinen M., et al. Screening for BRCA1, BRCA2, CHEK2, PALB2, BRIP1, RAD50, and CDH1 Mutations in High-risk Finnish BRCA1/2-founder Mutation-Negative Breast and/or Ovarian Cancer Individuals. Breast Cancer Res. 2011; 13(1): R20. doi: 10.1186/bcr2832
16. Yoshimura A., Imoto I., Iwata H. Functions of Breast Cancer Predisposition Genes: Implications for Clinical Management. Int J Mol Sci. 2022; 23(13): 7481. doi: 10.3390/ijms23137481
17. Long G., Hu K., Zhang X., et al. Spectrum of BRCA1 Interacting Helicase 1 Aberrations and Potential Prognostic and Therapeutic Implications: A Pan Cancer Analysis. Sci Rep. 2023; 13: 4435. doi: 10.1038/s41598-023-31109-6
18. Mahdi K.M., Nassiri M.R., Nasiri K. Hereditary Genes and SNPs Associated with Breast Cancer. Asian Pac J Cancer Prev. 2013; 14(6): 3403-409. doi: 10.7314/apjcp.2013.14.6.3403.
19. Hasson S.P., Menes T., Sonnenblick A. Comparison of Patient Susceptibility Genes Across Breast Cancer: Implications for Prognosis and Therapeutic Outcomes. Pharmgenomics Pers Med. 2020; 13: 227–238. doi: 10.2147/PGPM.S233485
20. Corso G., Figueiredo J., De Angelis S.P., et al. E-cadherin Deregulation in Breast Cancer. J Cell Mol Med. 2020; 24(11): 5930–5936. doi: 10.1111/jcmm.15140
1. Fenech M. et al. Nutrigenetics and Nutrigenomics: Viewpoints on the Current Status and Applications in Nutrition Research and Practice. J Nutrigenet Nutrigenomics. 2011; 4(2): 69–89. doi: 10.1159/000327772
2. Singar S. et al. Personalized Nutrition: Tailoring Dietary Recommendations through Genetic Insights. Nutrients. 2024; 16(16): 2673. doi: 10.3390/nu16162673
3. Suzuki M., Tomita M. Genetic Variations of Vitamin A-Absorption and Storage-Related Genes, and Their Potential Contribution to Vitamin A Deficiency Risks Among Different Ethnic Groups. Front Nutr. 2022; 9: 861619. doi: 10.3389/fnut.2022.861619
4. Kiani A.K. et al. Polymorphisms, diet, and nutrigenomics. J Prev Med Hyg 2022;63(suppl.3):E125-E141. https://doi.org/10.15167/2421-4248/jpmh2022.63.2S3.2754
5. Voruganti V.S. Precision Nutrition: Recent Advances in Obesity. Physiology (Bethesda). 2023; 38(1): 42–50. doi: 10.1152/physiol.00014.2022
6. Softic S. et al. Divergent effects of glucose and fructose on hepatic lipogenesis and insulin signaling. J Clin Invest. 2017; 127(11): 4059–4074. doi: 10.1172/JCI94585
7. Vesnina A. et al. Genes and Eating Preferences, Their Roles in Personalized Nutrition. Genes (Basel). 2020; 11(4): 357. doi: 10.3390/genes11040357
1. Genetic Passport: The Foundation of Individual and Predictive Medicine, edited by V. S. Baranov. St. Petersburg: N-L Publishing, 2009. — 528 pages.
2. Ahmetov, I.I., Fedotovskaya, O.N. Current Progress in Sports Genomics. Advances in Clinical Chemistry. 2015;70:247-314. doi: 10.1016/bs.acc.2015.03.003
3. Ahmetov, I. et al. Chapter Five - Advances in Sports Genomics. Advances in Clinical Chemistry. 2022;107:215-263
4. Ginevičienė, V., Utkus, A., Pranckevičienė, E., et al. Perspectives in Sports Genomics. Biomedicines. 2022;10(2):298. doi: 10.3390/biomedicines10020298
5. Tanisawa, K., Wang, G., Seto, J., et al. Sport and Exercise Genomics: The FIMS 2019 Consensus Statement Update. British Journal of Sports Medicine. 2020;54(16):969–975. doi: 10.1136/bjsports-2019-101532
6. Hoffman, N.J. Omics and Exercise: Global Approaches for Mapping Exercise Biological Networks. Cold Spring Harbor Perspectives in Medicine. 2017;7(10):a029884. doi: 10.1101/cshperspect.a029884
7. Konopka, M.J. et al. Genetics and Athletic Performance: A Systematic SWOT Analysis of Non-Systematic Reviews. Frontiers in Genetics. 2023;14:1232987. doi: 10.3389/fgene.2023.1232987
8. Pitsiladis, Y.P., Tanaka, M., Eynon, N., et al. Athlome Project Consortium: A Concerted Effort to Discover Genomic and Other 'Omic' Markers of Athletic Performance. Physiological Genomics. 2016;48(3):183–190. doi: 10.1152/physiolgenomics.00105.2015
9. Semenova, E.A., Hall, E.C.R., Ahmetov, I.I. Genes and Athletic Performance: The 2023 Update. Genes (Basel). 2023;14(6):1235. doi: 10.3390/genes14061235
10. Varillas-Delgado, D., Del Coso, J., Gutiérrez-Hellín, J., et al. Genetics and Sports Performance: The Present and Future in the Identification of Talent for Sports Based on DNA Testing. European Journal of Applied Physiology. 2022;122(8):1811–1830. doi: 10.1007/s00421-022-04945-z
11. Kraemer, W.J., Ratamess, N.A., Hymer, W.C., et al. Growth Hormone(s), Testosterone, Insulin-Like Growth Factors, and Cortisol: Roles and Integration for Cellular Development and Growth With Exercise. Frontiers in Endocrinology (Lausanne). 2020;11:33. doi: 10.3389/fendo.2020.00033
12. Guest, N.S. et al. Sport Nutrigenomics: Personalized Nutrition for Athletic Performance. Frontiers in Nutrition. 2019;6:8. doi: 10.3389/fnut.2019.00008
13. Martín-Rodríguez, A. et al. Advances in Understanding the Interplay between Dietary Practices, Body Composition, and Sports Performance in Athletes. Nutrients. 2024;16(4):571. doi: 10.3390/nu16040571
14. Clemente-Suárez, V.J. et al. Personalizing Nutrition Strategies: Bridging Research and Public Health. Journal of Personalized Medicine. 2024;14(3):305. doi: 10.3390/jpm14030305
15. Pickering, C., Kiely, J., Grgic, J., et al. Can Genetic Testing Identify Talent for Sport? Genes (Basel). 2019;10(12):972. doi: 10.3390/genes10120972
16. Rajesh Kumar et al. The Role of Mitochondrial Genes in Neurodegenerative Disorders. Current Neuropharmacology. 2022;20(5):824–835. doi: 10.2174/1570159X19666210908163839
17. Sellam, M. et al. Molecular Big Data in Sports Sciences: State-of-Art and Future Prospects of OMICS-Based Sports Sciences. Frontiers in Molecular Biosciences. 2021;8:815410. doi: 10.3389/fmolb.2021.815410
18. Giraldo-Vallejo, J.E. et al. Nutritional Strategies in the Rehabilitation of Musculoskeletal Injuries in Athletes: A Systematic Integrative Review. Nutrients. 2023;15(4):819. doi: 10.3390/nu15040819
19. Goodlin, G.T., Roos, A.K., Roos, T.R., et al. Applying Personal Genetic Data to Injury Risk Assessment in Athletes. PLoS One. 2015;10(4):e0122676. doi: 10.1371/journal.pone.0122676
20. arcOGEN Consortium and arcOGEN Collaborators. Identification of New Susceptibility Loci for Osteoarthritis (arcOGEN): A Genome-Wide Association Study. Lancet. 2012;380(9844):815–823. doi: 10.1016/S0140-6736(12)60681-3
21. Maffulli, N. et al. The Genetics of Sports Injuries and Athletic Performance. Muscles, Ligaments and Tendons Journal. 2013;3(3):173–189.
22. Tarnowski, M. et al. Epigenetic Alterations in Sports-Related Injuries. Genes (Basel). 2022;13(8):1471. doi: 10.3390/genes13081471
23. Kumagai, H. et al. Novel Insights into Mitochondrial DNA: Mitochondrial Microproteins and mtDNA Variants Modulate Athletic Performance and Age-Related Diseases. Genes (Basel). 2023;14(2):286. doi: 10.3390/genes14020286
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