نمره‌ی خطر پلی‌ژنیک: فرصت‌ها و چالش‌ها

نوع مقاله : مقاله مروری

نویسندگان

1 گروه بیوانفورماتیک، دانشکده‌ی فناوری‌های نوین پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران

2 گروه بیوانفورماتیک، دانشکده‌ی فناوری‌های نوین پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران. و واحد ژنتیک اپیدمیولوژی و بیوانفورماتیک، گروه اپیدمیولوژی، مرکز پزشکی دانشگاهی گرونینگن، دانشگاه گرونینگن، گرونینگن، هلند

3 گروه بیوانفورماتیک، دانشکده‌ی فناوری‌های نوین پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران و واحد ژنتیک اپیدمیولوژی و بیوانفورماتیک، گروه اپیدمیولوژی، مرکز پزشکی دانشگاهی گرونینگن، دانشگاه گرونینگن، گرونینگن، هلند

10.48305/jims.v43.i829.1076

چکیده

مقاله مروری




مقدمه: از انجام نخستین مطالعه‌ی همبستگی سراسر ژنومی در سال 2007 میلادی تاکنون، محتوای ژنتیکی چندین میلیون نمونه‌ی انسانی بررسی و هزاران واریانت ژنتیکی دخیل در بروز بیماری‌های گوناگون شناسایی شده است. ترکیب واریانت‌های ژنتیکی مرتبط با یک بیماری، تحت عنوان «نمره‌ی خطر پلی‌ژنیک»، می‌تواند بیانگر بخشی از آمادگی ژنتیکی افراد برای ابتلا به بیماری‌ها باشد.
روش‌ها: در این مقاله سعی داریم با بررسی منابع علمی موجود با ژنومیکس بیماری و توسعه‌ی نمره‌ی خطر پلی‌ژنیک از نتایج مطالعات ژنومی آغاز کرده و خلاصه‌ایی از روند توسعه نمره‌ی خطر پلی‌ژنیک، جایگاه کنونی آن در تحقیقات و فراتر از آن با ارائه‌ی مثال‌‌های واقعی، چا‌لش‌ها و چشم‌انداز آینده این فناوری درکاربرد‌های بالینی و سلامت عمومی ارائه دهیم.
یافته‌ها: پیشرفت‌های مداوم در توسعه‌ی نمره‌ی خطر پلی‌ژنیک، پتانسیل چشمگیری برای بهبود پیش‌بینی، غربالگری، تشخیص زودهنگام و پیش‌آگهی بیماری‌ها فراهم کرده و می‌تواند نقشی مؤثر در تحقق پزشکی شخص‌محور و بهینه‌سازی استراتژی‌های نظام سلامت ایفا کند. بااین‌حال، چالش‌هایی مانند نیاز به داده‌های ژنوتیپی بزرگتر و درک عمیق‌تر ابعاد اخلاقی و اجتماعی وجود دارند. با ادامه‌ی تحقیقات و رفع این موانع، آینده این فناوری در پزشکی امیدوارکننده به نظر می‌رسد.
نتیجه‌گیری: افزایش کمیت و کیفیت داده‌های ژنوتیپی و ارائه‌ی راه‌حل‌های نوآورانه می‌تواند به رفع چالش‌های فنی و اخلاقی- اجتماعی کمک کند. با رفع این موانع، نمره‌ی خطر پلی‌ژنیک پتانسیل تبدیل شدن به روشی قابل اعتماد برای پیش‌بینی و پیشگیری از بیماری‌ها را دارد. امید است با پیشرفت‌های مستمر و بیشتر، نمره‌ی خطر پلی‌ژنیک بتواند به ابزاری کارآمد و قابل اعتماد برای پیش‌بینی و در نتیجه پیشگیری از بیماری‌ها تبدیل شود.

تازه های تحقیق

مینا نجفی پور: Google Scholar

ضحی کمالی: PubMed ,Google Scholar

سید احمد واعظ: Google Scholar

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Polygenic Risk Score: Opportunities and Challenges

نویسندگان [English]

  • Mina Najafipour 1
  • Zoha Kamali 2
  • Ahmad Vaez 3
1 Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
2 Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran. AND, Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
3 Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran, AND Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
چکیده [English]

Background: Since the report of the first genome-wide association study in 2007, the genetic content of millions of human samples has been examined, leading to the identification of thousands of genetic variants associated with different diseases. The calculated combinatorial effects of these genetic variants, known as the Polygenic Risk Score (PRS), partially reflect an individual’s genetic susceptibility to diseases.
Methods: This article starts with disease genomics, summarizes the development of PRSs from genomic study results, their current role in research and beyond by giving real examples, the challenges they face, and their prospects in clinical and public health applications by reviewing existing scientific literature.
Findings: Continuous advances in the development of PRSs offer significant potential for improving prediction, screening, early diagnosis, and prognosis of diseases, and can play an effective role in realizing personalized medicine and optimizing health system strategies. However, challenges such as the need for larger genotypic data and a deeper understanding of ethical and social dimensions still hinder their widespread application in the clinical stage. As research continues and these obstacles are overcome, the future of this technology in medicine looks promising.
Conclusion: Enhancing the quantity and quality of genotypic data, along with the development of innovative solutions, could address these technical and ethical-social challenges. Once these obstacles are overcome, polygenic risk scoring has the potential to become a reliable method for disease prediction and prevention. With further advancements, PRSs hold promise as efficient and reliable tools for predicting and preventing diseases.

کلیدواژه‌ها [English]

  • Genetic predisposition to disease
  • Genome-wide association study
  • Genetic risk score
  • Precision medicine

مراجع

  1. Wray NR, Goddard ME, Visscher PM. Prediction of individual genetic risk of complex disease. Curr Opin Genet Dev 2008; 18(3): 257–63.
  2. GWAS to the people. Nat Med 2018; 24(10): 1483.
  3. Manna EDF, Serrano D, Cazzaniga L, Mannucci S, Zanzottera C, Fava F, et al. Hereditary Breast Cancer: Comprehensive Risk Assessment and Prevention Strategies. Genes (Basel) 2025; 16(1): 82.
  4. Lilyquist J, Ruddy KJ, Vachon CM, Couch FJ. Common Genetic Variation and Breast Cancer Risk—Past, Present, and Future. Cancer Epidemiol Biomarkers Prev 2018; 27(4): 380–94.
  5. Kamali Z, Keaton JM, Haghjooy Javanmard S, International Consortium of Blood Pressure, Million Veteran Program, eQTLGen Consortium, et al. Large-Scale Multi-Omics Studies Provide New Insights into Blood Pressure Regulation. Int J Mol Sci 2022; 23(14): 7557.
  6. Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores. Nat Rev Genet 2018; 19(9): 581–90.
  7. Vaez A, Jansen R, Prins BP, Hottenga JJ, de Geus EJC, Boomsma DI, et al. In Silico Post Genome-Wide Association Studies Analysis of C-Reactive Protein Loci Suggests an Important Role for Interferons. Circ Cardiovasc Genet 2015; 8(3): 487–97.
  8. Warren M. Health predictions based on the make-up of the human genome have taken a great leap forward. But polygenic risk scores are still highly controversial.
  9. Yengo L, Vedantam S, Marouli E, Sidorenko J, Bartell E, Sakaue S, et al. A saturated map of common genetic variants associated with human height. Nature 2022; 610(7933): 704–12.
  10. Mathieson I, Day FR, Barban N, Tropf FC, Brazel DM, eQTLGen Consortium, et al. Genome-wide analysis identifies genetic effects on reproductive success and ongoing natural selection at the FADS locus. Nat Hum Behav 2023; 7(5): 790-801.
  11. Keaton JM, Kamali Z, Xie T, Vaez A, Williams A, Goleva SB, et al. Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits. Nat Genet 2024; 56(5): 778-91.
  12. Vaez A, van der Most PJ, Prins BP, Snieder H, van den Heuvel E, Alizadeh BZ, et al. lodGWAS: a software package for genome-wide association analysis of biomarkers with a limit of detection. Bioinformatics 2016; 32(10): 1552–4.
  13. Abdellaoui A, Yengo L, Verweij KJH, Visscher PM. 15 years of GWAS discovery: Realizing the promise. Am J Hum Genet 2023; 110(2): 179–94.
  14. Babb De Villiers C, Kroese M, Moorthie S. Understanding polygenic models, their development and the potential application of polygenic scores in healthcare. J Med Genet 2020; 57(11): 725–32.
  15. Allegrini AG, Baldwin JR, Barkhuizen W, Pingault J. Research Review: A guide to computing and implementing polygenic scores in developmental research. J Child Psychol Psychiatry 2022; 63(10): 1111–24.
  16. Slunecka JL, van der Zee MD, Beck JJ, Johnson BN, Finnicum CT, Pool R, et al. Implementation and implications for polygenic risk scores in healthcare. Hum Genomics 2021; 15(1): 46.
  17. Pokorny MR, de Rooij M, Duncan E, Schröder FH, Parkinson R, Barentsz JO, et al. Prospective study of diagnostic accuracy comparing prostate cancer detection by transrectal ultrasound-guided biopsy versus magnetic resonance (MR) imaging with subsequent MR-guided biopsy in men without previous prostate biopsies. Eur Urol 2014; 66(1): 22–9.
  18. Al Olama AA, Eeles RA, Kote-Jarai Z, Easton DF. Risk Analysis of Prostate Cancer in PRACTICAL Consortium--Response. Cancer Epidemiol Biomarkers Prev 2016; 25(1): 223.
  19. Wray NR, Lin T, Austin J, McGrath JJ, Hickie IB, Murray GK, et al. From Basic Science to Clinical Application of Polygenic Risk Scores: A Primer. JAMA Psychiatry 2021; 78(1): 101-9.
  20. US Preventive Services Task Force, Mangione CM, Barry MJ, Nicholson WK, Cabana M, Chelmow D, et al. Statin Use for the Primary Prevention of Cardiovascular Disease in Adults: US Preventive Services Task Force Recommendation Statement. JAMA 2022; 328(8): 746-53.
  21. Reith C, Preiss D, Blackwell L, Emberson J, Spata E, Davies K, et al. Effects of statin therapy on diagnoses of new-onset diabetes and worsening glycaemia in large-scale randomised blinded statin trials: an individual participant data meta-analysis. Lancet Diabetes Endocrinol 2024; 12(5): 306–19.
  22. Aragam KG, Jiang T, Goel A, Kanoni S, Wolford BN, Atri DS, et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat Genet 2022; 54(12): 1803–15.
  23. First WHO report details devastating impact of hypertension and ways to stop it [Internet]. [cited 2024 May 4]. Available from: https://www.who.int/news/item/19-09-2023-first-who-report-details-devastating-impact-of-hypertension-and-ways-to-stop-it
  24. Ganji-Arjenaki M, Kamali Z, International Consortium of Blood Pressure, Sardari S, de Borst M, Snieder H, et al. Prioritization of Kidney Cell Types Highlights Myofibroblast Cells in Regulating Human Blood Pressure. Kidney Int Rep 2024; 9(6): 1849–59.
  25. Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet 2018; 50(10): 1412–25.
  26. Wain LV, Vaez A, Jansen R, Joehanes R, Van Der Most PJ, Erzurumluoglu AM, et al. Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets from Blood and the Kidney. Hypertension 2017; 70(3): e4-e19.
  27. Warren HR, Evangelou E, Cabrera CP, Gao H, Ren M, Mifsud B, et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat Genet 2017; 49(3): 403–15.
  28. Breast cancer [Internet]. [cited 2024 May 2]. Available from: https://www.who.int/news-room/fact-sheets/detail/breast-cancer
  29. Mavaddat N, Pharoah PDP, Michailidou K, Tyrer J, Brook MN, Bolla MK, et al. Prediction of Breast cancer risk based on profiling with common genetic variants. JNCI J Natl Cancer Inst 2015; 107(5): djv036.
  30. Tanigawa Y, Qian J, Venkataraman G, Justesen JM, Li R, Tibshirani R, et al. Significant sparse polygenic risk scores across 813 traits in UK Biobank. PLoS Genet 2022; 18(3): e1010105.
  31. Wang Y, Tsuo K, Kanai M, Neale BM, Martin AR. Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores. Annu Rev Biomed Data Sci 2022; 5: 293-320.
  32. Chapman CR. Ethical, legal, and social implications of genetic risk prediction for multifactorial disease: a narrative review identifying concerns about interpretation and use of polygenic scores. J Community Genet 2022; 14(5): 441–52.
  33. Silventoinen K, Sammalisto S, Perola M, Boomsma DI, Cornes BK, Davis C, et al. Heritability of adult body height: a comparative study of twin cohorts in eight countries. Twin Res 2003; 6(5): 399–408.
  34. Schousboe K, Willemsen G, Kyvik KO, Mortensen J, Boomsma DI, Cornes BK, et al. Sex differences in heritability of BMI: a comparative study of results from twin studies in eight countries. Twin Res 2003; 6(5): 409–21.
  35. Hyttinen V, Kaprio J, Kinnunen L, Koskenvuo M, Tuomilehto J. Genetic liability of type 1 diabetes and the onset age among 22,650 young Finnish twin pairs: a nationwide follow-up study. Diabetes 2003; 52(4): 1052–5.
  36. Kaprio J, Tuomilehto J, Koskenvuo M, Romanov K, Reunanen A, Eriksson J, et al. Concordance for type 1 (insulin-dependent) and type 2 (non-insulin-dependent) diabetes mellitus in a population-based cohort of twins in Finland. Diabetologia 1992; 35(11): 1060–7.
  37. Zdravkovic S, Wienke A, Pedersen NL, Marenberg ME, Yashin AI, De Faire U. Heritability of death from coronary heart disease: a 36-year follow-up of 20 966 Swedish twins. J Intern Med 2002; 252(3): 247–54.
  38. Zhang S, Liu X, Yu Y, Hong X, Christoffel KK, Wang B, et al. Genetic and environmental contributions to phenotypic components of metabolic syndrome: a population-based twin study. Obesity (Silver Spring) 2009; 17(8): 1581–7.
  39. Sas AA, Vaez A, Jamshidi Y, Nolte IM, Kamali Z, D Spector T, et al. Genetic and environmental influences on stability and change in baseline levels of C-reactive protein: A longitudinal twin study. Atherosclerosis 2017; 265: 172–8.
  40. Pedersen NL, Gatz M, Berg S, Johansson B. How heritable is Alzheimer’s disease late in life? Findings from Swedish twins. Ann Neurol 2004; 55(2): 180–5.
  41. Wirdefeldt K, Gatz M, Reynolds CA, Prescott CA, Pedersen NL. Heritability of Parkinson disease in Swedish twins: a longitudinal study. Neurobiol Aging 2011; 32(10): 1923.e1-8.
  42. Lundström S, Chang Z, Råstam M, Gillberg C, Larsson H, Anckarsäter H, et al. Autism spectrum disorders and autistic like traits: similar etiology in the extreme end and the normal variation. Arch Gen Psychiatry 2012; 69(1): 46–52.
  43. Sullivan PF, Neale MC, Kendler KS. Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry 2000; 157(10): 1552–62.
  44. Sullivan PF, Kendler KS, Neale MC. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry 2003; 60(12): 1187–92.
  45. Spector TD, MacGregor AJ. Risk factors for osteoarthritis: genetics. Osteoarthritis Cartilage 2004; 12 Suppl A: S39-44.
  46. MacGregor AJ, Snieder H, Rigby AS, Koskenvuo M, Kaprio J, Aho K, et al. Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from twins. Arthritis Rheum 2000; 43(1): 30–7.
  47. Thomsen SF, van der Sluis S, Kyvik KO, Skytthe A, Backer V. Estimates of asthma heritability in a large twin sample. Clin Exp Allergy J Br Soc Allergy Clin Immunol 2010; 40(7): 1054–61.
  48. Lichtenstein P, Holm NV, Verkasalo PK, Iliadou A, Kaprio J, Koskenvuo M, et al. Environmental and heritable factors in the causation of cancer--analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med. 2000; 343(2): 78–85.
مجله دانشکده پزشکی اصفهان
دوره 43، شماره 829
هفته 1، آبان
مهر و آبان 1404
صفحه 1076-1087

فایل ها

سابقه مقاله

  • تاریخ دریافت: 01 اسفند 1403
  • تاریخ پذیرش: 22 شهریور 1404

هم رسانی

ارجاع به این مقاله

آمار