Conclusions - Multiomics Analyses for Phenotype Prediction

be assessed as early as possible. This is because genetic variants are congenital and mostly static and 1

therefore can predict the risk of developing a disease in the future, regardless of the current clinical 2

state. Moreover, an early assessment of the genomic risk becomes more beneficial since it can give a 3

greater chance to delay or even prevent the occurrence of disease in individuals with the risk of 4

developing it.

To promote healthy aging, our resources should be reorganized and optimized to improve 6

omics markers discovery by combining multiomics markers. One current obstacle in omics-related 7

studies is the lack of data reusability. Generally, in omics studies, only clinical data that are related to 8

the target disease is collected and that limits the data reusability for other studies. This results in a 9

vicious cycle where the magnitude of funding decides the sample size in studies; to increase the sample 10

size, the researchers often restrict the kind and amount of clinical data that is being collected. For 11

instance, an omics study related to diabetes mellitus would have limited reusability for a study on 12

depression due to a lack of relevant clinical information. Therefore, the primary task of researchers 13

should be the initiation of a shared “omics analysis consortium” on a worldwide level where the omics 14

data is generated and clinical information is assessed for all individuals periodically from birth to death.

Ideally, this would facilitate studies on target diseases using multiomics and clinical data from millions 16

of samples, thus facilitating the progression to a future disease-free era.

17 18

References

1. Bhak, Y.; Jeong, H.-o.; Cho, Y. S.; Jeon, S.; Cho, J.; Gim, J.-A.; Jeon, Y.; Blazyte, 2

A.; Park, S. G.; Kim, H.-M. J. T. p., Depression and suicide risk prediction models using blood- 3

derived multi-omics data. 2019, 9 (1), 1-8.

2. World Health Organization (WHO), Depression;

http://www.who.int/mediacentre/factsheets/fs369/en/, (accessed 22 March 2018) 6

3. World Health Organization (WHO), Suicide;

http://www.who.int/mediacentre/factsheets/fs398/en/. (accessed 22 March 2018) 8

4. Organisation for Economic Co-operation and Development (OECD), Suicide rates (indicator);

https://data.oecd.org/healthstat/suicide-rates.html. (accessed 2019) 10

5. World Health Organization (WHO),, Preventing suicide: A global imperative. : 2014.

6. Vieta, E.; Colom, F.; Corbella, B.; Martínez‐Arán, A.; Reinares, M.; Benabarre, A.;

Gastó, C. J. B. D., Clinical correlates of psychiatric comorbidity in bipolar I patients. 2001, 3 (5), 253- 13

258.

7. Wenger, N.; Boden, W.; Carabello, B.; Carney, R.; Cerqueira, M.; Criqul, M., 15

Cardiovascular Disability: Updating the Social Security Listings. National Academy of Sciences 2010.

8. World Health Organization (WHO), , Source: Global Health Estimates 2016: Death by Cause, 17

Age, Sex, by Country and by Region, 2000–2016. Geneva. 2018.

9. Kim, M.-S.; Kang, S.-J.; Lee, C.-W.; Han, S.; Park, D.-W.; Lee, S.-W.; Kim, Y.-H.;

Park, S.-W.; Park, S.-J.; Kim, J.-J. J. J. o. a.; thrombosis, Prevalence of coronary atherosclerosis in 20

asymptomatic healthy subjects: an intravascular ultrasound study of donor hearts. 2013, 15545.

10. Berenson, G. S.; Srinivasan, S. R.; Bao, W.; Newman, W. P.; Tracy, R. E.; Wattigney, 22

W. A. J. N. E. j. o. m., Association between multiple cardiovascular risk factors and atherosclerosis in 23

children and young adults. 1998, 338 (23), 1650-1656.

11. Strimbu, K.; Tavel, J. A., What are biomarkers? Current Opinion in HIV and AIDS 2010, 5 (6), 25

463.

12. Welter, D.; MacArthur, J.; Morales, J.; Burdett, T.; Hall, P.; Junkins, H.; Klemm, 27

A.; Flicek, P.; Manolio, T.; Hindorff, L., The NHGRI GWAS Catalog, a curated resource of SNP- 28

trait associations. Nucleic acids research 2014, 42 (D1), D1001-D1006.

13. Li, M.; Zou, D.; Li, Z.; Gao, R.; Sang, J.; Zhang, Y.; Li, R.; Xia, L.; Zhang, T.;

Niu, G., EWAS Atlas: a curated knowledgebase of epigenome-wide association studies. Nucleic acids 31

research 2019, 47 (D1), D983-D988.

14. Papatheodorou, I.; Moreno, P.; Manning, J.; Fuentes, A. M.-P.; George, N.; Fexova, 33

S.; Fonseca, N. A.; Füllgrabe, A.; Green, M.; Huang, N., Expression Atlas update: from tissues to 34

single cells. Nucleic acids research 2020, 48 (D1), D77-D83.

15. Le-Niculescu, H.; Levey, D.; Ayalew, M.; Palmer, L.; Gavrin, L.; Jain, N.; Winiger, 1

E.; Bhosrekar, S.; Shankar, G.; Radel, M. J. M. p., Discovery and validation of blood biomarkers 2

for suicidality. 2013, 18 (12), 1249.

16. Davies, M. N.; Krause, L.; Bell, J. T.; Gao, F.; Ward, K. J.; Wu, H.; Lu, H.; Liu, 4

Y.; Tsai, P.-C.; Collier, D. A. J. G. b., Hypermethylation in the ZBTB20 gene is associated with major 5

depressive disorder. 2014, 15 (4), R56.

17. Guintivano, J.; Brown, T.; Newcomer, A.; Jones, M.; Cox, O.; Maher, B. S.; Eaton, 7

W. W.; Payne, J. L.; Wilcox, H. C.; Kaminsky, Z. A. J. A. j. o. p., Identification and replication of a 8

combined epigenetic and genetic biomarker predicting suicide and suicidal behaviors. 2014, 171 (12), 9

1287-1296.

18. Spijker, S.; Van Zanten, J. S.; De Jong, S.; Penninx, B. W.; van Dyck, R.; Zitman, F.

G.; Smit, J. H.; Ylstra, B.; Smit, A. B.; Hoogendijk, W. J. J. B. p., Stimulated gene expression 12

profiles as a blood marker of major depressive disorder. 2010, 68 (2), 179-186.

19. Khera, A. V.; Kathiresan, S. J. N. R. G., Genetics of coronary artery disease: discovery, biology 14

and clinical translation. 2017, 18 (6), 331.

20. MacArthur, J.; Bowler, E.; Cerezo, M.; Gil, L.; Hall, P.; Hastings, E.; Junkins, H.;

McMahon, A.; Milano, A.; Morales, J. J. N. a. r., The new NHGRI-EBI Catalog of published genome- 17

wide association studies (GWAS Catalog). 2017, 45 (D1), D896-D901.

21. Leinonen, R.; Sugawara, H.; Shumway, M.; research, I. N. S. D. C. J. N. a., The sequence 19

read archive. 2010, 39 (suppl_1), D19-D21.

22. Kodama, Y.; Shumway, M.; Leinonen, R. J. N. a. r., The Sequence Read Archive: explosive 21

growth of sequencing data. 2012, 40 (D1), D54-D56.

23. Galfalvy, H.; Haghighi, F.; Hodgkinson, C.; Goldman, D.; Oquendo, M. A.; Burke, 23

A.; Huang, Y. y.; Giegling, I.; Rujescu, D.; Bureau, A. J. A. J. o. M. G. P. B. N. G., A genome‐

wide association study of suicidal behavior. 2015, 168 (7), 557-563.

24. Kimbrel, N. A.; Garrett, M. E.; Dennis, M. F.; Hauser, M. A.; Ashley-Koch, A. E.;

Beckham, J. C. J. P. r., A genome-wide association study of suicide attempts and suicidal ideation in US 27

military veterans. 2018, 269, 64-69.

25. Mullins, N.; Hodgson, K.; Tansey, K. E.; Perroud, N.; Maier, W.; Mors, O.;

Rietschel, M.; Hauser, J.; Henigsberg, N.; Souery, D. J. T. p., Investigation of blood mRNA 30

biomarkers for suicidality in an independent sample. 2014, 4 (10), e474-e474.

26. Khera, A. V.; Chaffin, M.; Zekavat, S. M.; Collins, R. L.; Roselli, C.; Natarajan, P.;

Lichtman, J. H.; D’Onofrio, G.; Mattera, J.; Dreyer, R. J. C., Whole-genome sequencing to 33

characterize monogenic and polygenic contributions in patients hospitalized with early-onset 34

myocardial infarction. 2019, 139 (13), 1593-1602.

27. Otsuka, I.; Akiyama, M.; Shirakawa, O.; Okazaki, S.; Momozawa, Y.; Kamatani, Y.;

Izumi, T.; Numata, S.; Takahashi, M.; Boku, S. J. N., Genome-wide association studies identify 1

polygenic effects for completed suicide in the Japanese population. 2019, 44 (12), 2119-2124.

28. Mitchell, T., Machine Learning. 1997. ISBN: 0070428077. McGraw-Hill.

29. Zhang, L.; Lv, C.; Jin, Y.; Cheng, G.; Fu, Y.; Yuan, D.; Tao, Y.; Guo, Y.; Ni, X.;

Shi, T. J. F. i. g., Deep learning-based multi-omics data integration reveals two prognostic subtypes in 5

high-risk neuroblastoma. 2018, 9, 477.

30. Chaudhary, K.; Poirion, O. B.; Lu, L.; Garmire, L. X. J. C. C. R., Deep learning–based 7

multi-omics integration robustly predicts survival in liver cancer. 2018, 24 (6), 1248-1259.

31. Chung, N. C.; Mirza, B.; Choi, H.; Wang, J.; Wang, D.; Ping, P.; Wang, W. J. M., 9

Unsupervised classification of multi-omics data during cardiac remodeling using deep learning. 2019, 10

166, 66-73.

32. Khera, A. V.; Chaffin, M.; Aragam, K. G.; Haas, M. E.; Roselli, C.; Choi, S. H.;

Natarajan, P.; Lander, E. S.; Lubitz, S. A.; Ellinor, P. T. J. N. g., Genome-wide polygenic scores for 13

common diseases identify individuals with risk equivalent to monogenic mutations. 2018, 50 (9), 1219- 14

1224.

33. Gibson, G., Rare and common variants: twenty arguments. Nature Reviews Genetics 2012, 13 16

(2), 135-145.

34. Golan, D.; Lander, E. S.; Rosset, S., Measuring missing heritability: inferring the 18

contribution of common variants. Proceedings of the National Academy of Sciences 2014, 111 (49), 19

E5272-E5281.

35. Fuchsberger, C.; Flannick, J.; Teslovich, T. M.; Mahajan, A.; Agarwala, V.; Gaulton, 21

K. J.; Ma, C.; Fontanillas, P.; Moutsianas, L.; McCarthy, D. J., The genetic architecture of type 2 22

diabetes. Nature 2016, 536 (7614), 41-47.

36. Hawton, K.; van Heeringen, K., Suicide. Lancet 2009, 373 (9672), 1372-81.

37. Just, M. A.; Pan, L.; Cherkassky, V. L.; McMakin, D. L.; Cha, C.; Nock, M. K.; Brent, 25

D., Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth.

Nature human behaviour 2017, 1 (12), 911-919.

38. Walsh, C. G.; Ribeiro, J. D.; Franklin, J. C., Predicting risk of suicide attempts over time 28

through machine learning. Clinical Psychological Science 2017, 5 (3), 457-469.

39. Nagy, C.; Suderman, M.; Yang, J.; Szyf, M.; Mechawar, N.; Ernst, C.; Turecki, G. J.

M. p., Astrocytic abnormalities and global DNA methylation patterns in depression and suicide. 2015, 31

20 (3), 320-328.

40. Witt, S. H.; Sommer, W. H.; Hansson, A. C.; Sticht, C.; Rietschel, M.; Witt, C. C. J. I.

s. p., Comparison of gene expression profiles in the blood, hippocampus and prefrontal cortex of rats.

2013, 1 (1), 15.

41. Walton, E.; Hass, J.; Liu, J.; Roffman, J. L.; Bernardoni, F.; Roessner, V.; Kirsch, 36

M.; Schackert, G.; Calhoun, V.; Ehrlich, S. J. S. b., Correspondence of DNA methylation between 1

blood and brain tissue and its application to schizophrenia research. 2016, 42 (2), 406-414.

42. Sullivan, P. F.; Fan, C.; Perou, C. M. J. A. J. o. M. G. P. B. N. G., Evaluating the 3

comparability of gene expression in blood and brain. 2006, 141 (3), 261-268.

43. Hamilton, M., The Hamilton rating scale for depression. In Assessment of depression, Springer:

1986; pp 143-152.

44. Beck, A. T.; Kovacs, M.; Weissman, A. J. J. o. c.; psychology, c., Assessment of suicidal 7

intention: the Scale for Suicide Ideation. 1979, 47 (2), 343.

45. Patel, R. K.; Jain, M. J. P. o., NGS QC Toolkit: a toolkit for quality control of next generation 9

sequencing data. 2012, 7 (2).

46. Krueger, F.; Andrews, S. R. J. b., Bismark: a flexible aligner and methylation caller for 11

Bisulfite-Seq applications. 2011, 27 (11), 1571-1572.

47. Barturen, G.; Rueda, A.; Oliver, J. L.; Hackenberg, M. J. F., MethylExtract: high-quality 13

methylation maps and SNV calling from whole genome bisulfite sequencing data. 2013, 2.

48. Leek, J. T.; Johnson, W. E.; Parker, H. S.; Jaffe, A. E.; Storey, J. D. J. B., The sva package 15

for removing batch effects and other unwanted variation in high-throughput experiments. 2012, 28 (6), 16

882-883.

49. Akalin, A.; Kormaksson, M.; Li, S.; Garrett-Bakelman, F. E.; Figueroa, M. E.;

Melnick, A.; Mason, C. E. J. G. b., methylKit: a comprehensive R package for the analysis of genome- 19

wide DNA methylation profiles. 2012, 13 (10), R87.

50. Wang, K.; Singh, D.; Zeng, Z.; Coleman, S. J.; Huang, Y.; Savich, G. L.; He, X.;

Mieczkowski, P.; Grimm, S. A.; Perou, C. M. J. N. a. r., MapSplice: accurate mapping of RNA-seq 22

reads for splice junction discovery. 2010, 38 (18), e178-e178.

51. Li, B.; Dewey, C. N. J. B. b., RSEM: accurate transcript quantification from RNA-Seq data 24

with or without a reference genome. 2011, 12 (1), 323.

52. Love, M. I.; Huber, W.; Anders, S. J. G. b., Moderated estimation of fold change and 26

dispersion for RNA-seq data with DESeq2. 2014, 15 (12), 550.

53. Scikit, S.-l. D. J. J. o. M. L. R., Scikit-learn: Machine learning in python. 2011, 12, 2825-2830.

54. Sherman, B. T.; Lempicki, R. A. J. N. p., Systematic and integrative analysis of large gene lists 29

using DAVID bioinformatics resources. 2009, 4 (1), 44.

55. Beghi, M.; Rosenbaum, J. F.; Cerri, C.; Cornaggia, C. M. J. N. d.; treatment, Risk factors 31

for fatal and nonfatal repetition of suicide attempts: a literature review. 2013, 9, 1725.

56. El Khouli, R. H.; Macura, K. J.; Barker, P. B.; Habba, M. R.; Jacobs, M. A.; Bluemke, 33

D. A. J. J. o. M. R. I. A. O. J. o. t. I. S. f. M. R. i. M., Relationship of temporal resolution to diagnostic 34

performance for dynamic contrast enhanced MRI of the breast. 2009, 30 (5), 999-1004.

57. Yang, S.; Berdine, G. J. T. S. R.; Chronicles, C. C., The receiver operating characteristic 36

(ROC) curve. 2017, 5 (19), 34-36.

58. Furukawa, R.; Hachiya, T.; Ohmomo, H.; Shiwa, Y.; Ono, K.; Suzuki, S.; Satoh, 2

M.; Hitomi, J.; Sobue, K.; Shimizu, A. J. S. r., Intraindividual dynamics of transcriptome and 3

genome-wide stability of DNA methylation. 2016, 6, 26424.

59. El Hajj, N.; Dittrich, M.; Haaf, T. In Epigenetic dysregulation of protocadherins in human 5

disease, Seminars in cell & developmental biology, Elsevier: 2017; pp 172-182.

60. Clive, M. L.; Boks, M. P.; Vinkers, C. H.; Osborne, L. M.; Payne, J. L.; Ressler, K.

J.; Smith, A. K.; Wilcox, H. C.; Kaminsky, Z. J. C. e., Discovery and replication of a peripheral 8

tissue DNA methylation biosignature to augment a suicide prediction model. 2016, 8 (1), 113.

61. McGowan, P. O.; Suderman, M.; Sasaki, A.; Huang, T. C.; Hallett, M.; Meaney, M.

J.; Szyf, M. J. P. o., Broad epigenetic signature of maternal care in the brain of adult rats. 2011, 6 (2).

62. Leung, L. C.; Urbančič, V.; Baudet, M.-L.; Dwivedy, A.; Bayley, T. G.; Lee, A. C.;

Harris, W. A.; Holt, C. E. J. N. n., Coupling of NF-protocadherin signaling to axon guidance by cue- 13

induced translation. 2013, 16 (2), 166.

63. Kim, S.; Mo, J.; Han, S.; Choi, S.; Han, S.; Moon, B.; Rhyu, I.; Sun, W.; Kim, H.

J. N., The expression of non-clustered protocadherins in adult rat hippocampal formation and the 16

connecting brain regions. 2010, 170 (1), 189-199.

64. Anitha, A.; Thanseem, I.; Nakamura, K.; Yamada, K.; Iwayama, Y.; Toyota, T.;

Iwata, Y.; Suzuki, K.; Sugiyama, T.; Tsujii, M. J. J. o. p.; JPN, n., Protocadherin α (PCDHA) as a 19

novel susceptibility gene for autism. 2013, 38 (3), 192.

65. Córdova-Palomera, A.; Fatjó-Vilas, M.; Gastó, C.; Navarro, V.; Krebs, M.; Fañanás, L.

J. T. p., Genome-wide methylation study on depression: differential methylation and variable 22

methylation in monozygotic twins. 2015, 5 (4), e557-e557.

66. Breitfeld, J.; Scholl, C.; Steffens, M.; Laje, G.; Stingl, J. J. T. p., Gene expression and 24

proliferation biomarkers for antidepressant treatment resistance. 2017, 7 (3), e1061-e1061.

67. Shi, Y.; Yuan, Y.; Xu, Z.; Pu, M.; Wang, C.; Zhang, Y.; Liu, Z.; Wang, C.; Li, 26

L.; Zhang, Z. J. J. o. a. d., Genetic variation in the calcium/calmodulin-dependent protein kinase (CaMK) 27

pathway is associated with antidepressant response in females. 2012, 136 (3), 558-566.

68. Flegal, R.; Furie, K.; Go, A.; Greenlund, K.; Haase, N.; Hailpern, S., Heart disease and 29

stroke statistics–2008 update: a report from the American Heart Association statistics committee and 30

stroke statistics subcommittee. Circulation 2008, 117, e25-e146.

69. Lloyd-Jones, D. M.; Nam, B.-H.; D'Agostino Sr, R. B.; Levy, D.; Murabito, J. M.;

Wang, T. J.; Wilson, P. W.; O'Donnell, C. J., Parental cardiovascular disease as a risk factor for 33

cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. Jama 2004, 34

291 (18), 2204-2211.

70. Control, C. f. D.; Prevention, Prevalence of heart disease--United States, 2005. MMWR.

Morbidity and mortality weekly report 2007, 56 (6), 113.

71. Nora, J.; Lortscher, R.; Spangler, R.; Nora, A.; Kimberling, W., Genetic--epidemiologic 2

study of early-onset ischemic heart disease. Circulation 1980, 61 (3), 503-508.

72. Elliott, J.; Bodinier, B.; Bond, T. A.; Chadeau-Hyam, M.; Evangelou, E.; Moons, K.

G.; Dehghan, A.; Muller, D. C.; Elliott, P.; Tzoulaki, I. J. J., Predictive accuracy of a polygenic 5

risk score–enhanced prediction model vs a clinical risk score for coronary artery disease. 2020, 323 (7), 6

636-645.

73. Levin, M. G.; Kember, R. L.; Judy, R.; Birtwell, D.; Williams, H.; Arany, Z.; Giri, 8

J.; Guerraty, M.; Cappola, T.; Genomic, R. G. C. J. C.; Medicine, P., Genomic risk stratification 9

predicts all-cause mortality after cardiac catheterization. 2018, 11 (11), e002352.

74. Cutlip, D. E.; Windecker, S.; Mehran, R.; Boam, A.; Cohen, D. J.; van Es, G.-A.;

Gabriel Steg, P.; Morel, M.-a. l.; Mauri, L.; Vranckx, P. J. C., Clinical end points in coronary stent 12

trials: a case for standardized definitions. 2007, 115 (17), 2344-2351.

75. Sungwon Jeon, Y. B., Yeonsong Choi, Yeonsu Jeon, Seunghoon Kim, Jaeyoung Jang, Jinho 14

Jang, Asta Blazyte, Changjae Kim, Yeonkyung Kim et al, Korean Genome Project: 1,094 Korean 15

personal genomes with clinical information. Science Advances 2020, In press.

76. Martin, M. J. E. j., Cutadapt removes adapter sequences from high-throughput sequencing 17

reads. 2011, 17 (1), 10-12.

77. Poplin, R.; Ruano-Rubio, V.; DePristo, M. A.; Fennell, T. J.; Carneiro, M. O.; Van 19

der Auwera, G. A.; Kling, D. E.; Gauthier, L. D.; Levy-Moonshine, A.; Roazen, D. J. B., Scaling 20

accurate genetic variant discovery to tens of thousands of samples. 2018, 201178.

78. Nature, G. P. C. J., A global reference for human genetic variation. 2015, 526 (7571), 68-74.

79. Consortium, W. T. C. C., Genome-wide association study of 14,000 cases of seven common 23

diseases and 3,000 shared controls. Nature 2007, 447 (7145), 661.

80. Pearson, T. A.; Manolio, T. A. J. J., How to interpret a genome-wide association study. 2008, 25

299 (11), 1335-1344.

81. Vilhjálmsson, B. J.; Yang, J.; Finucane, H. K.; Gusev, A.; Lindström, S.; Ripke, S.;

Genovese, G.; Loh, P.-R.; Bhatia, G.; Do, R. J. T. a. j. o. h. g., Modeling linkage disequilibrium 28

increases accuracy of polygenic risk scores. 2015, 97 (4), 576-592.

82. Nikpay, M.; Goel, A.; Won, H.-H.; Hall, L. M.; Willenborg, C.; Kanoni, S.;

Saleheen, D.; Kyriakou, T.; Nelson, C. P.; Hopewell, J. C. J. N. g., A comprehensive 1000 31

Genomes–based genome-wide association meta-analysis of coronary artery disease. 2015, 47 (10), 1121.

83. 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. J. T. A. j. o. h. g., PLINK: a tool set for whole- 34

genome association and population-based linkage analyses. 2007, 81 (3), 559-575.

84. Zhao, H.; Sun, Z.; Wang, J.; Huang, H.; Kocher, J.-P.; Wang, L. J. B., CrossMap: a 36

versatile tool for coordinate conversion between genome assemblies. 2014, 30 (7), 1006-1007.

85. Team, R. C., R: A language and environment for statistical computing. 2013.

86. Robin, X.; Turck, N.; Hainard, A.; Tiberti, N.; Lisacek, F.; Sanchez, J.-C.; Müller, M.

J. B. b., pROC: an open-source package for R and S+ to analyze and compare ROC curves. 2011, 12 4

(1), 77.

87. Therneau, T.; Grambsch, P. J. N. S. C., Modeling Survival Data: Extending the Cox Model 6

2000 New York.

88. Hong, E. P.; Park, J. W. J. G.; informatics, Sample size and statistical power calculation in 8

genetic association studies. 2012, 10 (2), 117.

89. Spencer, C. C.; Su, Z.; Donnelly, P.; Marchini, J. J. P. g., Designing genome-wide 10

association studies: sample size, power, imputation, and the choice of genotyping chip. 2009, 5 (5).

90. Gunter, R.; Szeto, E.; Jeong, S.-H.; Suh, S. A.; Waters, A. J. J. K. J. o. F. M., Cigarette 12

Smoking in South Korea: A Narrative Review. 2020, 41 (1), 3.

91. Khera, A. V.; Emdin, C. A.; Drake, I.; Natarajan, P.; Bick, A. G.; Cook, N. R.;

Chasman, D. I.; Baber, U.; Mehran, R.; Rader, D. J. J. N. E. J. o. M., Genetic risk, adherence to a 15

healthy lifestyle, and coronary disease. 2016, 375 (24), 2349-2358.

92. Shrestha, R.; Copenhaver, M. J. C. m. r. i. v. h., Long-term effects of childhood risk factors on 17

cardiovascular health during adulthood. 2015, 7, 1.

93. Reisberg, S.; Iljasenko, T.; Läll, K.; Fischer, K.; Vilo, J. J. P. o., Comparing distributions 19

of polygenic risk scores of type 2 diabetes and coronary heart disease within different populations. 2017, 20

12 (7).

94. Duncan, L.; Shen, H.; Gelaye, B.; Meijsen, J.; Ressler, K.; Feldman, M.; Peterson, 22

R.; Domingue, B. J. N. c., Analysis of polygenic risk score usage and performance in diverse human 23

populations. 2019, 10 (1), 1-9.

95. Darst, B. F.; Koscik, R. L.; Racine, A. M.; Oh, J. M.; Krause, R. A.; Carlsson, C. M.;

Zetterberg, H.; Blennow, K.; Christian, B. T.; Bendlin, B. B. J. J. o. A. s. D., Pathway-specific 26

polygenic risk scores as predictors of amyloid-β deposition and cognitive function in a sample at 27

increased risk for Alzheimer’s disease. 2017, 55 (2), 473-484.

96. Martin, A. R.; Kanai, M.; Kamatani, Y.; Okada, Y.; Neale, B. M.; Daly, M. J. J. N. g., 29

Clinical use of current polygenic risk scores may exacerbate health disparities. 2019, 51 (4), 584.

97. Martin, A. R.; Gignoux, C. R.; Walters, R. K.; Wojcik, G. L.; Neale, B. M.; Gravel, 31

S.; Daly, M. J.; Bustamante, C. D.; Kenny, E. E. J. T. A. J. o. H. G., Human demographic history 32

impacts genetic risk prediction across diverse populations. 2017, 100 (4), 635-649.

98. Bibikova, M.; Barnes, B.; Tsan, C.; Ho, V.; Klotzle, B.; Le, J. M.; Delano, D.;

Zhang, L.; Schroth, G. P.; Gunderson, K. L. J. G., High density DNA methylation array with single 35

CpG site resolution. 2011, 98 (4), 288-295.

99. Nazarenko, M. S.; Markov, A. V.; Lebedev, I. N.; Freidin, M. B.; Sleptcov, A. A.;

Koroleva, I. A.; Frolov, A. V.; Popov, V. A.; Barbarash, O. L.; Puzyrev, V. P. J. P. O., A comparison 2

of genome-wide DNA methylation patterns between different vascular tissues from patients with 3

coronary heart disease. 2015, 10 (4).

4 5

Acknowledgments

For the stressomics study, I thank Prof. Yoon-Kyung Cho for supporting this project. I also thank Korea 2

University Anam Hospital members for helping in blood sampling and information collection of the 3

participants. Further, the Korea Institute of Science and Technology Information (KISTI) provided us 4

with the Korea Research Environment Open NETwork (KREONET). This work was supported by the 5

Civil-Military Dual-Use Technology Development Program (14-BR-SS-03) through the Agency for 6

Defense Development; U-K BRAND Research Fund (1.190007.01) of UNIST; Research Project 7

Funded by Ulsan City Research Fund (1.190033.01) of UNIST; and the Next-Generation Information 8

Computing Development Program through the National Research Foundation of Korea funded by the 9

Ministry of Science and ICT (NRF-2016M3C4A7952635).

For the cardiomics study, I appreciate all participants and Ulsan citizens in supporting the Genome 11

Korea in Ulsan project, which provided the Korea10K genome information. The biospecimens for this 12

study were provided by Ulsan Medical Center and the Biobanks of Chungbuk National University 13

Hospital (18-27, 20-04), Kyung Hee University Hospital (2018-4, 2019-4, 2019-6), Ulsan University 14

Hospital (60SA2017002-005), and the members of the National Biobank of Korea; this is supported by 15

the Ministry of Health, Welfare and Family Affairs. All samples derived from the National Biobank of 16

Korea were obtained with informed consent under the institutional review board-approved protocols.

For all my studies, including this dissertation, I thank my family and colleagues, sincerely.

Appendix

The stressomics publication 2

The Korea1K genome publication 1

2 3 4

The bat genome publication 1

2 3 4

The leopard genome publication 1

2 3

The Neolithic East Asian genome publication 1

2 3 4

The Indian Gujarati genome publication 1

2 3

The Egyptian genome publication 1

2 3

The cow genome publication 1

2 3

The jellyfish genome publication 1

2 3

The coral genome publication 1

2 3

The Kazakh genome publication 1

2 3

Dalam dokumen Multiomics Analyses for Phenotype Prediction (Halaman 40-63)