A Dynamic Method and Program for Disease-Based Genetic Classification of Individuals

Abstract

Personalized medicine is gaining increasing importance. However, genetic-based diseases have different underlying genetic factors, requiring separate relative risk models for each disease. In addition to these difficulties, comparing individuals according to their genetic characteristics and determining a personalized treatment method based on this, is a separate problem which is very difficult to do manually. In this study, a dynamic classification method and program is proposed for disease-based classification of individuals according to their genetic characteristics. To the best of our knowledge, this is the first generic method which performs disease-based classification of individuals. In the developed program, relative risk models containing only genetic factors are an input of the program and a common format has been created for this purpose. Our generic classification method classifies people by using information from any relative risk model rearranged according to the common format. Thanks to this program, relative risk models can be managed from a single point, many people can be classified based on their genetic characteristics, and individuals, who are genetically most similar to any person, can be determined by experts using the outputs (relevant tables) of the program.

Keywords

• personalized medicine
• computational medicine
• genetic classification
• relative risk model
• genetic similarity
• genetic variation

References

1. J. Hardy, A. Singleton, “Genomewide association studies and human disease”, New England Journal of Medicine, 360(17), 1759–1768, 2009.
2. J. Krier, R. Barfield, R.C. Green, P. Kraft, “Reclassification of genetic-based risk predictions as GWAS data accumulate”, Genome medicine, 8(1), 1-11, 2016.
3. Internet: GWAS Catalog, https://www.ebi.ac.uk/gwas/, 30.07.2021.
4. T. A. Manolio, F. S. Collins, N. J. Cox, et. al., “Finding the missing heritability of complex diseases”, Nature, 461, 747–753, 2009.
5. T. Beck, T. Rowlands, T. Shorter, A. J. Brookes, GWAS Central: an expanding resource for finding and visualising genotype and phenotype data from genome-wide association studies, Nucleic Acids Research, Volume 51, Issue D1, 6 January 2023, Pages D986–D993, https://doi.org/10.1093/nar/gkac1017.
6. Hettiarachchi, G., & Komar, A. A. (2022). Genome Wide Association Studies (GWAS) to Identify SNPs Associated with Common Diseases and Individual Risk. In Single Nucleotide Polymorphisms: Human Variation and a Coming Revolution in Biology and Medicine (pp. 51-76). Cham: Springer International Publishing.
7. L. A. Hindorff, P. Sethupathy, H. A. Junkins, et. al., “Potential etiologic and functional implications of genome-wide association loci for human diseases and traits”, Proceedings of the National Academy of Sciences, 106(23), 9362-9367, 2009.
8. S. J. Schrodi, S. Mukherjee, Y. Shan, et. al., “Genetic-based prediction of disease traits: Prediction is very difficult, especially about the future”, Frontiers in genetics, 5, 162, 2014.

9. Lee, M. J., Lee, I., & Wang, K. (2022). Recent advances in RNA therapy and its carriers to treat the single-gene neurological disorders. Biomedicines, 10(1), 158.
10. M. M. Alves, Y. Sribudiani, R. W. W. Brouwer, et. al., “Contribution of rare and common variants determine complex diseases-Hirschsprung disease as a model”, Developmental biology, 382(1), 320-329, 2013.
11. J. Altmüller, L. J. Palmer, G. Fischer, et. al., “Genomewide scans of complex human diseases: True linkage is hard to find”, The American Journal of Human Genetics, 69(5), 936-950, 2001.
12. J. C. Barrett, S. Hansoul, D. L. Nicolae, et. al., “Genome-wide association defines more than 30 distinct susceptibility loci for Crohn’s disease”, Nature genetics, 40(8), 955-962, 2008.
13. J. Maller, S. George, S. Purcell, et. al., “Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration”, Nature genetics, 38(9), 1055-1059, 2006.
14. E. Zeggini, L. J. Scott, R. Saxena, et. al., “Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes”, Nature genetics, 40(5), 638-645, 2008.
15. K. Yasuda, K. Miyake, Y. Horikawa, et. al., “Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus”, Nature genetics, 40(9), 1092-1097, 2008.
16. S. Kathiresan, B. F. Voight, S. Purcell, et. al., “Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants”, Nature genetics, 41(3), 334, 2009.
17. Weeks, Elle M., et al. "Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases." Nature Genetics 55.8 (2023): 1267-1276.
18. Abdellaoui, A., Dolan, C. V., Verweij, K. J., & Nivard, M. G. (2022). Gene–environment correlations across geographic regions affect genome-wide association studies. Nature genetics, 54(9), 1345-1354.
19. C. Sabatti, S. K. Service, A. L. Hartikainen, et. al., “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population”, Nature genetics, 41(1), 35-46, 2009.
20. W. Zheng, J. Long, Y. T. Gao, et. al., “Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1”, Nature genetics, 41(3), 324-328, 2009.
21. C. Katsios, D. H. Roukos, “Individual genomes and personalized medicine: Life diversity and complexity”, Personalized Medicine, 7(4), 347-350, 2010.
22. M. A. Hamburg, F. S. Collins, “The path to personalized medicine”, New England Journal of Medicine, 363(4), 301-304, 2010.
23. G. S. Ginsburg, J. J. McCarthy, “Personalized medicine: Revolutionizing drug discovery and patient care”, TRENDS in Biotechnology, 19(12), 491-496, 2001.
24. N. J. Schork, “Personalized medicine: Time for one-person trials”, Nature News, 520(7549), 609, 2015.
25. Yamamoto, Y., Kanayama, N., Nakayama, Y., & Matsushima, N. (2022). Current status, issues and future prospects of personalized medicine for each disease. Journal of Personalized Medicine, 12(3), 444.
26. Hassan, M., et. al. (2022). Innovations in genomics and big data analytics for personalized medicine and health care: A review. International journal of molecular Sciences, 23(9), 4645.
27. The International Human Genome Sequencing Consortium, “Finishing the euchromatic sequence of the human genome”, Nature, 431(7011), 931-945, 2004.
28. S. Levy, G. Sutton, P. C. Ng, et. al., “The diploid genome sequence of an individual human”, PLoS biology, 5(10), 2113–2144, 2007.
29. International HapMap Consortium, “A second generation human haplotype map of over 3.1 million SNPs”, Nature, 449(7164), 851, 2007.
30. 1000 Genomes Project Consortium, “A map of human genome variation from population-scale sequencing”, Nature, 467(7319), 1061–1073, 2010.
31. 1000 Genomes Project Consortium, “An integrated map of genetic variation from 1,092 human genomes”, Nature, 491(7422), 56-65, 2012.
32. 1000 Genomes Project Consortium, “A global reference for human genetic variation”, Nature, 526(7571), 68-74, 2015.
33. 1000 Genomes Project Consortium, “An integrated map of structural variation in 2,504 human genomes”, Nature, 526(7571), 75-81, 2015.
34. Internet: 1000 Genomes Project Consortium, /vol1/ftp/release/20130502/ directory, ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/, 30.07.2021.
35. Internet: 1000 Genomes Project Consortium, /vol1/ftp/release/20130502/supporting/bcf_files directory, ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/bcf_files, 30.07.2021.