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유전지표를 활용한 사상체질 분류모델

Predictive Models for Sasang Constitution Types Using Genetic Factors

  • 반효정 (한국한의학연구원 지능화추진팀) ;
  • 이시우 (한국한의학연구원 미래의학부) ;
  • 진희정 (한국한의학연구원 지능화추진팀)
  • Ban, Hyo-Jeong (Intellectual Information Team, Korea Institute of Oriental Medicine) ;
  • Lee, Siwoo (Future Medicine Division, Korea Institute of Oriental Medicine) ;
  • Jin, Hee-Jeong (Intellectual Information Team, Korea Institute of Oriental Medicine)
  • 투고 : 2020.04.20
  • 심사 : 2020.04.27
  • 발행 : 2020.06.30

초록

Objectives Genome-wide association studies(GWAS) is a useful method to identify genetic associations for various phenotypes. The purpose of this study was to develop predictive models for Sasang constitution types using genetic factors. Methods The genotypes of the 1,999 subjects was performed using Axiom Precision Medicine Research Array (PMRA) by Life Technologies. All participants were prescribed Sasang Constitution-specific herbal remedies for the treatment, and showed improvement of original symptoms as confirmed by Korean medicine doctor. The genotypes were imputed by using the IMPUTE program. Association analysis was conducted using a logistic regression model to discover Single Nucleotide Polymorphism (SNP), adjusting for age, sex, and BMI. Results & Conclusions We developed models to predict Korean medicine constitution types using identified genectic factors and sex, age, BMI using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN). Each maximum Area Under the Curve (AUC) of Teaeum, Soeum, Soyang is 0.894, 0.868, 0.767, respectively. Each AUC of the models increased by 6~17% more than that of models except for genetic factors. By developing the predictive models, we confirmed usefulness of genetic factors related with types. It demonstrates a mechanism for more accurate prediction through genetic factors related with type.

키워드

참고문헌

  1. Lee SH, Yoon YS, Kim HG, Kim JY. Clinical Study on the Distribution of Sasang Constitutions between Parents and their Offsprings. Journal of Physiology & Pathology in Korean Medicine. 2004;18(6):1904-7. (Korean)
  2. Lee SW, Hur YM, Park HY, Kim JY. A validation study on Sasang constitutions and genetic influences. FACT: Focus on Alternative and Complementary Therapies. 2007;2007(12). DOI: 10.1111/j.2042-7166.2007.tb05894.x. Korean.
  3. Lee MK, Jang ES, Sohn HY, Park JY, Koh BH, Sung J, et al. Investigation of Genetic Evidence for Sasang Constitution Types in South Korea. Genomics & Informatics. 2009;7(2):107-10. DOI: https://doi.org/10.5808/gi.2009.7.2.107.
  4. Hur YM, Lee SW, Jin HJ. Genetic and environmental overlaps among sasang constitution types: a multivariate twin study. Twin Research and Human Genetics. 2018; 21(6):518-26. DOI: 10.1017/thg.2018.56.
  5. Kim BY, Jin HJ, Kim JY. Genome-wide association analysis of Sasang constitution in the Korean population. The Journal of Alternative and Complementary Medicine. 2012;18(3):262-9. https://doi.org/10.1089/acm.2010.0764
  6. Cha SW, Yu HJ, Park AY, Oh SA, Kim JY. The obesity-risk variant of FTO is inversely related with the So-Eum constitutional type: genome-wide association and replication analyses. BMC complementary and alternative medicine. 2015;15(1):120. DOI: 10.1089/acm.2010.0764.
  7. Kim SH, Ko BH, Song IB. A study on the standardization of QSCC II (Questionnaire for the Sasang Constitution Classification II). The Journal of Korean Medicine. 1996;17(2):337-93. Korean.
  8. Lee SG, Kwak CK, Lee EJ, Koh BH, Song IB. The Study on the Upgrade of QSCC (II). J of Sasang Const Med. 2003;15(1):39-49. Korean.
  9. Baek YH, Jang ES, Park KH, Yoo JH, Jin HJ, Lee SW. Development and validation of brief KS-15 (Korea Sasang Constitutional Diagnostic Questionnaire) based on body shape, temperament and symptoms. Journal of Sasang Constitutional Medicine. 2015;27(2): 211-21. Korean. https://doi.org/10.7730/JSCM.2015.27.2.211
  10. Lee MS, Bae NY, Hwang MW, Chae H. Development and validation of the digestive function assessment instrument for traditional Korean medicine: Sasang digestive function inventory. Evidence-Based Complementary and Alternative Medicine. 2013;2013. DOI: 10.1155/2013/263752.
  11. So JH, Kim JW, Nam JH, Lee BJ, Kim YS, Kim JY, et al. The web application of constitution analysis system-SCAT (Sasang Constitution Analysis Tool). Journal of Sasang Constitutional Medicine. 2016;28(1):1-10. Korean. https://doi.org/10.7730/JSCM.2016.28.1.1
  12. Jin HJ, Baek YH, Kim HS, Ryu JH, Lee SW. Constitutional multicenter bank linked to Sasang constitutional phenotypic data. BMC complementary and alternative medicine. 2015;15(1):1. DOI: 10.1186/s12906-015-0553-3.
  13. Hyun MK, Baek YH, Lee SW. Association between digestive symptoms and sleep disturbance: a crosssectional community-based study. BMC gastroenterology. 2019;19(1):34. DOI: 10.1186/s12876-019-0945-9.
  14. Baek YH, Kim HS, Lee SW, Ryu JH, Kim YY, Jang ES. Study on the ordinary symptoms characteristics of gender difference according to Sasang constitution. 2009;23(1):251-8. Korean.
  15. Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic acids research. 2016;44(D1):D862-D8. DOI: 10.1093/nar/gkv1222.
  16. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic acids research. 2014;42(D1):D1001-D6.DOI:10.1093/nar/gkt1229.
  17. Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, et al. An integrated map of structural variation in 2,504 human genomes. Nature. 2015;526(7571):75-81.DIO:10.1038/nature15394.
  18. Consortium GP. A global reference for human genetic variation. Nature. 2015;526(7571):68-74.DOI:10.1038/nature15393.
  19. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR. Fast and accurate genotype imputation in genome-wide association studies through prephasing. Nature genetics. 2012;44(8):955-9. DOI:10.1038/ng.2354.
  20. Howie B, Marchini J, Stephens M. Genotype imputation with thousands of genomes. G3: Genes, Genomes, Genetics. 2011;1(6):457-70. DOI: 10.1534/g3.111.001198.
  21. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American journal of human genetics. 2007;81(3):559-75. DOI: 10.1086/519795.
  22. Team RC. R language definition. Vienna, Austria: R foundation for statistical computing. 2000.
  23. Kuhn M. Caret: classification and regression training. Astrophysics Source Code Library. 2015.
  24. Breiman L. Random forests. Machine learning. 2001; 45(1):5-32. https://doi.org/10.1023/A:1010933404324
  25. Karatzoglou A, Smola A, Hornik K, Zeileis A. kernlaban S4 package for kernel methods in R. Journal of statistical software. 2004;11(9):1-20.
  26. Venables WN, Ripley BD. Modern applied statistics with S-PLUS: Springer Science & Business Media; 2013.
  27. Kovacevic J, Vetterli M. The commutativity of up/ downsampling in two dimensions. IEEE transactions on information theory. 1991;37(3):695-8. DOI:10.1109/18.79936.
  28. Yoon HJ, Kim SH, Kim JH, Keum JS, Oh SI, Jo JI, et al. A Lesion-Based Convolutional Neural Network Improves Endoscopic Detection and Depth Prediction of Early Gastric Cancer. Journal of clinical medicine. 2019;8(9):1310. DOI: 10.3390/jcm8091310.
  29. Daetwyler HD, Villanueva B, Woolliams JA. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PloS one. 2008;3(10). DOI :10.1371/journal.pone.0003395.
  30. Chatterjee N, Shi J, García-Closas M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nature Reviews Genetics. 2016;17(7):392. DOI: 10.1038/nrg.2016.27.
  31. Janssens ACJ, Ioannidis JP, Bedrosian S, Boffetta P, Dolan SM, Dowling N, et al. Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration. European journal of epidemiology. 2011;26(4):313. DOI: 10.1111/j.1365-2362.2011.02493.x.
  32. Kraft P, Hunter DJ. Genetic risk prediction-are we there yet? New England Journal of Medicine. 2009; 360(17):1701-3.DOI:10.1093/jnci/djq413.
  33. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018; 562(7726):203-9. DOI: 10.1038/s41586-018-0579-z.
  34. Barbour V. UK Biobank: a project in search of a protocol? The Lancet. 2003;361(3970):1734-8. DOI: 10.1016/S0140-6736(03)13377-6.
  35. McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP, Larson EB, et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC medical genomics. 2011;4(1):13. DOI: 10.1186/1755-8794-4-13.
  36. Marquez-Luna C, Gazal S, Loh P-R, Furlotte N, Auton A, Price AL, et al. Modeling functional enrichment improves polygenic prediction accuracy in UK Biobank and 23andMe data sets. bioRxiv. 2018: 375337. DOI: 10.1101/375337.
  37. Lello L, Raben TG, Yong SY, Tellier LC, Hsu SD. Genomic prediction of 16 complex disease risks including heart attack, diabetes, breast and prostate cancer. Scientific reports. 2019;9(1):1-16. DOI: 10.1038/s41598-019-51258-x.
  38. Berliner JL, Brodke DJ, Chan V, SooHoo NF, Bozic KJ. John Charnley Award: preoperative patient-reported outcome measures predict clinically meaningful improvement in function after THA. Clinical Orthopaedics and Related Research${(R)}$. 2016;474(2): 321-9. DOI: 10.1007/s11999-015-4350-6.
  39. Keswani A, Tasi MC, Fields A, Lovy AJ, Moucha CS, Bozic KJ. Discharge destination after total joint arthroplasty: an analysis of postdischarge outcomes, placement risk factors, and recent trends. The Journal of arthroplasty. 2016;31(6):1155-62. DOI: 10.1016/j.arth.2015.11.044.
  40. Jones SE, Tyrrell J, Wood AR, Beaumont RN, Ruth KS, Tuke MA, et al. Genome-wide association analyses in 128,266 individuals identifies new morningness and sleep duration loci. PLoS genetics. 2016;12(8). DOI: 10.1371/journal.pgen.1006125.