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Title : Bioinformatic Analysis in Genetic Epidemiology

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Abstract : Classic epidemiology has mainly focused on the determination of environmental risk factors. However, the human genome project made a turning point it to examine genetic risk factors governing diseases. Many classic epidemiology cohorts therefore have been transformed into the genetic epidemiology cohorts that aim to determine both the environmental and genetic factors as well as the interaction of both factors. The transition makes it better to understand the architecture of complex diseases and provide the strategies of disease prevention and treatment.

During the last decade we could identify many genetic factors affecting complex diseases by genome-wide association study (GWAS) using the genetic epidemiology cohorts including nation-wide biobanks. So far close to 10 thousand genetic variants have been reported to influence the human phenotypes and several databases such as dbGAP and GWAS Catalog have been constructed to archive the results. However, the outcome falls short of our expectation for disease prediction as well as diagnosis and prevention. The explanation for the disease development, based on the genetic factors identified by the GWAS, was not sufficient, lower than 20% in most complex diseases, which is called ¡®missing heritability¡¯. There are several arguments explaining the ¡®missing heritability¡¯, including the role of rare allele, gene-environment interaction and epigenetic modification.

The gene-environment interaction is a tempting possibility to explain the missing heritability since some genetic factors could interact with environment factors to affect diseases. The genetic factors would not be identified by GWAS if the gene-environment interaction was not considered for the analysis. Traditionally the gene-environment interaction has been analyzed by the Cox proportionally hazard model using the follow-up cohort study. When we compared the results between the follow-up and cross-sectional investigations, the cross-sectional study showed the better results in the KoGES cohort study of Korea National Institute of Health (KNIH). Therefore, we concentrated the environmental interaction on the baseline data of KoGES for the type 2 diabetes, hypertension and obesity.

In this talk, I would like to introduce the general features of genetic epidemiology and explain the analysis procedure of GWAS and gene-environment interaction. Additionally the results of both studies will be presented and discussed.

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1978. 03. 01 - 1982. 02. 26 ¼­¿ï´ëÇб³ »ç¹ü´ëÇÐ »ý¹°°ú (ÀÌÇлç)

1982. 03. 01 - 1984. 02. 25 ¼­¿ï´ëÇб³ °úÇб³À°°ú »ý¹°Àü°ø (±³À°Çм®»ç)

1986. 09. 01 - 1992. 05. 21 ¹Ì±¹ Rutgers University; The State University of New Jersey (ÀÌÇйڻç)

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1992. 02 - 1993. 05 ¹Ì±¹ The Wistar Institute ¹Ú»çÈÄ¿¬±¸¿ø(Postdoctoral Fellow)

1993. 05 - 1993. 10 µ¶ÀÏ Max-Planck Institute in Freiburg ¹Ú»çÈÄ¿¬±¸¿ø (Postdoctoral Fellow)

1993. 10 - 1997. 06 ¹Ì±¹ The Jackson Laboratory ¹Ú»çÈÄ¿¬±¸¿ø (Postdoctoral Associate)

1997. 06 - 1998. 10 ¹Ì±¹ The Jackson Laboratory ¼±ÀÓ¿¬±¸¿ø (Research Associate)

1998. 10 - 1999. 12 ±¹¸³º¸°Ç¿ø, Ư¼öÁúȯºÎ, À¯ÀüÁúȯ°ú Brain Pool

1999. 12 - 2002. 03 ±¹¸³º¸°Ç¿ø, Ư¼öÁúȯºÎ, À¯ÀüÁúȯ°ú º¸°Ç¿¬±¸°ü

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Selected Publications

Hong, K-W., Shin, Y-B., Kim, K., Oh, B. 2011 Differentially expressed genes by inhibition of C-terminal src kinase by siRNA in human vascular smooth muscle cells and their association with blood pressure. Genomics & Informatics 9: 102-113.

Hong, K-W., Shin, D-J., Lee, S-H., Son, N-H., Go, M-J., Lim, J-E., Shin, C., Jang, Y., Oh, B. 2012 Common variants in RYR1 are associated with left ventricular hypertrophy assessed by ECG. European Heart J 33: 1250-1256.

Hong, K-W., Oh, B. 2012 Recapitulation of genome-wide association studies on body mass index in the Korean population. Int J Obesity 36: 1127-1130.

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Hong, K-W., Lim, J-E., Go, M.J., Cho, Y.S., Ahn, Y., Han, B-G. and Oh, B. 2012 Recapitulation of the Association of the Val66Met Polymorphism of BDNF Gene With BMI in Koreans. Obesity 20: 1871-1875.

Kim, J.W., Hong, K-W., Go, M.J., Kim, S.S., Tabara, Y., Kita, Y., Tanigawa, T., Cho, Y.S., Han, B-G. and Oh, B. 2012 A Common Variant in SLC8A1 Is Associated with the Duration of the Electrocardiographic QT Interval. 2012 Am. J. Hum. Genet. 91: 180-184

Ji, S-M., Shin, Y-B., Park, S-Y., Lee, H-J. and Oh, B. 2012 Decreases in Casz1 mRNA by an siRNA complex do not alter blood pressure in mice. Genomics & Informatics 10: 40-43.

Lim, J-E., Hong, K-W., Jin, H-S., and Oh, B. 2012 Association analysis of reactive oxygen species-hypertension genes discovered by literature mining. Genomics & Informatics 10: 244-248.

Lim, J-E., Shin, Y-A., Hong, K-W., Jin, H-S., Koh, I.S., and Oh, B. 2013 Characterization of functional variants in 33 blood pressure loci using 1000 genomes project data. Genes & Genomics Epub ahead of print DOI 10.1007

Jin, H-S., Kim, B-Y., Kim, J., Hong, K-W., Jung, S-Y., Lee, Y-S., Huh, D., Oh, B., Chung, Y-S., and Jeong, S-Y. 2013 Association between the SPRY1 gene polymorphism and obesity-related traits and osteoporosis in Korean women. Molecular Genetics and Metabolism 108: 95101.

Shin, Y-B., Lim, J-E., Ji, S-M., Lee, H-J., Park, S-Y., Hong, K-W., Lim, M. L., McCarthy, M.I., Lee, Y-H., Oh, B. 2013 Silencing of Atp2b1 increases blood pressure through vasoconstriction. J Hypertension 31: 1575-1583.

Park, S-Y., Lee, H-J., Ji, S-M., Kim, M-E., Jigden, B, Lim, J-E., Oh, B. 2014 ANTXR2 is a potential causative gene in the genome-wide association study of the blood pressure locus 4q21.Hypertens Res. 37:811-7.

Hong, K-W., Lim, J-E., Kim, J.W., Tabara, Y., Ueshima, H., Miki, T., Matsuda, F., Cho, Y.S., Kim, Y., Oh, B. 2014 Identification of three novel genetic variations associated with electrocardiographic traits (QRS duration and PR interval) in East Asians. Human Molecular Genetics 23:6659-67.

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