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1. The Liability Threshold Model for Predicting the Risk of Cardiovascular Disease in Patients with Type 2 Diabetes: A Multi-Cohort Study of Korean Adults

Author

Eun Pyo Hong, Seong Gu Heo, and Ji Wan Park

Subjects
diabetic cardiovascular disease, functional gene network, genetic risk prediction, liability threshold model, polygenic risk score, population-based cohort study, Microbiology, QR1-502
Abstract

Personalized risk prediction for diabetic cardiovascular disease (DCVD) is at the core of precision medicine in type 2 diabetes (T2D). We first identified three marker sets consisting of 15, 47, and 231 tagging single nucleotide polymorphisms (tSNPs) associated with DCVD using a linear mixed model in 2378 T2D patients obtained from four population-based Korean cohorts. Using the genetic variants with even modest effects on phenotypic variance, we observed improved risk stratification accuracy beyond traditional risk factors (AUC, 0.63 to 0.97). With a cutoff point of 0.21, the discrete genetic liability threshold model consisting of 231 SNPs (GLT231) correctly classified 87.7% of 2378 T2D patients as high or low risk of DCVD. For the same set of SNP markers, the GLT and polygenic risk score (PRS) models showed similar predictive performance, and we observed consistency between the GLT and PRS models in that the model based on a larger number of SNP markers showed much-improved predictability. In silico gene expression analysis, additional information was provided on the functional role of the genes identified in this study. In particular, HDAC4, CDKN2B, CELSR2, and MRAS appear to be major hubs in the functional gene network for DCVD. The proposed risk prediction approach based on the liability threshold model may help identify T2D patients at high CVD risk in East Asian populations with further external validations.

Published
2020
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2. Risk prediction of pulmonary tuberculosis using genetic and conventional risk factors in adult Korean population.

Author

Eun Pyo Hong, Min Jin Go, Hyung-Lae Kim, and Ji Wan Park

Subjects
Medicine, Science
Abstract

A complex interplay among host, pathogen, and environmental factors is believed to contribute to the risk of developing pulmonary tuberculosis (PTB). The lack of replication of published genome-wide association study (GWAS) findings limits the clinical utility of reported single nucleotide polymorphisms (SNPs). We conducted a GWAS using 467 PTB cases and 1,313 healthy controls obtained from two community-based cohorts in Korea. We evaluated the performance of PTB risk models based on different combinations of genetic and nongenetic factors and validated the results in an independent Korean population comprised of 179 PTB cases and 500 healthy controls. We demonstrated the polygenic nature of PTB and nongenetic factors such as age, sex, and body mass index (BMI) were strongly associated with PTB risk. None of the SNPs achieved genome-wide significance; instead, we were able to replicate the associations between PTB and ten SNPs near or in the genes, CDCA7, GBE1, GADL1, SPATA16, C6orf118, KIAA1432, DMRT2, CTR9, CCDC67, and CDH13, which may play roles in the immune and inflammatory pathways. Among the replicated SNPs, an intergenic SNP, rs9365798, located downstream of the C6orf118 gene showed the most significant association under the dominant model (OR = 1.59, 95% CI 1.32-1.92, P = 2.1×10-6). The performance of a risk model combining the effects of ten replicated SNPs and six nongenetic factors (i.e., age, sex, BMI, cigarette smoking, systolic blood pressure, and hemoglobin) were validated in the replication set (AUC = 0.80, 95% CI 0.76-0.84). The strategy of combining genetic and nongenetic risk factors ultimately resulted in better risk prediction for PTB in the adult Korean population.

Published
2017
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3. Genetic Risk Prediction for Normal-Karyotype Acute Myeloid Leukemia Using Whole-Exome Sequencing

Author

Seong Gu Heo, Eun Pyo Hong, and Ji Wan Park

Subjects
acute myeloid leukemia, DNA sequence analysis, genetic variation, risk assessment, Genetics, QH426-470
Abstract

Normal-karyotype acute myeloid leukemia (NK-AML) is a highly malignant and cytogenetically heterogeneous hematologic cancer. We searched for somatic mutations from 10 pairs of tumor and normal cells by using a highly efficient and reliable analysis workflow for whole-exome sequencing data and performed association tests between the NK-AML and somatic mutations. We identified 21 nonsynonymous single nucleotide variants (SNVs) located in a coding region of 18 genes. Among them, the SNVs of three leukemia-related genes (MUC4, CNTNAP2, and GNAS) reported in previous studies were replicated in this study. We conducted stepwise genetic risk score (GRS) models composed of the NK-AML susceptible variants and evaluated the prediction accuracy of each GRS model by computing the area under the receiver operating characteristic curve (AUC). The GRS model that was composed of five SNVs (rs75156964, rs56213454, rs6604516, rs10888338, and rs2443878) showed 100% prediction accuracy, and the combined effect of the three reported genes was validated in the current study (AUC, 0.98; 95% confidence interval, 0.92 to 1.00). Further study with large sample sizes is warranted to validate the combined effect of these somatic point mutations, and the discovery of novel markers may provide an opportunity to develop novel diagnostic and therapeutic targets for NK-AML.

Published
2013
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4. Sample Size and Statistical Power Calculation in Genetic Association Studies

Author

Eun Pyo Hong and Ji Wan Park

Subjects
case-control studies, case-parent study, genetic association studies, sample size, statistical power, Genetics, QH426-470
Abstract

A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power with increasing numbers of markers analyzed and compared the sample sizes that were required in case-control studies and case-parent studies. We computed the effective sample size and statistical power using Genetic Power Calculator. An analysis using a larger number of markers requires a larger sample size. Testing a single-nucleotide polymorphism (SNP) marker requires 248 cases, while testing 500,000 SNPs and 1 million markers requires 1,206 cases and 1,255 cases, respectively, under the assumption of an odds ratio of 2, 5% disease prevalence, 5% minor allele frequency, complete linkage disequilibrium (LD), 1:1 case/control ratio, and a 5% error rate in an allelic test. Under a dominant model, a smaller sample size is required to achieve 80% power than other genetic models. We found that a much lower sample size was required with a strong effect size, common SNP, and increased LD. In addition, studying a common disease in a case-control study of a 1:4 case-control ratio is one way to achieve higher statistical power. We also found that case-parent studies require more samples than case-control studies. Although we have not covered all plausible cases in study design, the estimates of sample size and statistical power computed under various assumptions in this study may be useful to determine the sample size in designing a population-based genetic association study.

Published
2012
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5. Preliminary study of environmental risk and protective factors during pregnancy for cleft lip with or without palate in the Korean population.

Author

Min-Jeong Kang, Nang Paung Li, Hyunseung Hong, Hyo-Sang Park, Ji Wan Park, Tolarova, Marie M., Il-Hyung Yang, Mihee Hong, and Seung-Hak Baek

Subjects
PASSIVE smoking, BIRTH weight, CLEFT lip, CLEFT palate, FOLIC acid
Abstract

Objective: To investigate which types of environmental exposure during pregnancy are risk and protective factors for cleft lip with or without cleft palate (CL/P). Methods: This case-control study included 278 orthodontic patients with CL/P (CL/P group) and 51 without CL/P (non-CL/P group). Demographic and environmental exposure data were collected using questionnaires completed by the parents. Statistical analyses were performed to identify the potential risk and protective factors for CL/P. Results: The two groups did not show significant difference in (1) body weight at birth and number of previous births; (2) fathers’ ages at birth and occupation; (3) parents’ chronic diseases, alcohol consumption, and exposure to harmful substances; and (4) mothers’ smoking, secondhand smoking, and vitamin and calcium intake. Most patients with CL/ P were born at normal term (≥ 37 weeks, 93.2%) with normal body weight (2.9–3.7 kg, 63.7%) and as either the first or second child (90.3%). In the CL/ P group, the percentages of mothers who were very young or old (≤ 19 years, ≥ 40 years) and with physical labor in their occupation were low (1.8% and 2.2%, respectively). Compared with the non-CL/P group, the CL/P group showed a lower percentage of maternal folic acid intake (68.6% vs. 20.9%, odds ratio [OR] = 0.121; P < 0.001) and higher percentages of mothers’ drug intake and fathers’ smoking habits (3.9% vs. 16.2%, OR = 4.73, P < 0.05; 39.2% vs. 61.2%, OR = 2.44, P < 0.01). Conclusions: The findings of this study may explain the association between environmental factors and CL/P risk. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models

Author

Geon Kang, Seung-Hak Baek, Young Ho Kim, Dong-Hyun Kim, and Ji Wan Park

Subjects
neural networks ensemble, Organic Chemistry, General Medicine, genetic risk prediction, Catalysis, Computer Science Applications, Inorganic Chemistry, machine learning, single nucleotide polymorphism, polygenic risk score, genetic algorithm, Physical and Theoretical Chemistry, Molecular Biology, artificial neural network, nonsyndromic cleft lip with or without cleft palate, Spectroscopy
Abstract

Recent deep learning algorithms have further improved risk classification capabilities. However, an appropriate feature selection method is required to overcome dimensionality issues in population-based genetic studies. In this Korean case–control study of nonsyndromic cleft lip with or without cleft palate (NSCL/P), we compared the predictive performance of models that were developed by using the genetic-algorithm-optimized neural networks ensemble (GANNE) technique with those models that were generated by eight conventional risk classification methods, including polygenic risk score (PRS), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and deep-learning-based artificial neural network (ANN). GANNE, which is capable of automatic input SNP selection, exhibited the highest predictive power, especially in the 10-SNP model (AUC of 88.2%), thus improving the AUC by 23% and 17% compared to PRS and ANN, respectively. Genes mapped with input SNPs that were selected by using a genetic algorithm (GA) were functionally validated for risks of developing NSCL/P in gene ontology and protein–protein interaction (PPI) network analyses. The IRF6 gene, which is most frequently selected via GA, was also a major hub gene in the PPI network. Genes such as RUNX2, MTHFR, PVRL1, TGFB3, and TBX22 significantly contributed to predicting NSCL/P risk. GANNE is an efficient disease risk classification method using a minimum optimal set of SNPs; however, further validation studies are needed to ensure the clinical utility of the model for predicting NSCL/P risk.

Published
2023
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7. Adiponectin concentrations: a genome-wide association study

Author

Sun Ha Jee, Jae Woong Sull, Jong-Eun Lee, Chol Shin, Jongkeun Park, Heejin Kimm, Eun-Young Cho, Eun-Soon Shin, Ji Eun Yun, Ji Wan Park, Sang Yeun Kim, Sun Ju Lee, Eun Jung Jee, Inkyung Baik, Kao, Linda, Sungjoo Kim Yoon, Yangsoo Jang, and Beaty, Terri H.

Subjects
Human genome -- Research, Insulin resistance -- Genetic aspects, Koreans -- Genetic aspects, Obesity -- Genetic aspects, Obesity -- Demographic aspects, Single nucleotide polymorphisms -- Analysis, Biological sciences
Abstract

A genome-wide association study (GWAS) is conducted among a sample of Koreans to identify the gene variants which influence levels of adiponectin, which is associated with obesity and insulin resistance. Results reveal that rs3865188 SNP in CDH13 gene on chromosome 16 is strongly associated with adiponectin, thereby demonstrating that genetic variants in CDH13 influence adiponectin levels in the Korean adults examined.

Published
2010

8. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Author

Bailey, Matthew H, Meyerson, William U, Dursi, Lewis Jonathan, Wang, Liang-Bo, Dong, Guanlan, Liang, Wen-Wei, Weerasinghe, Amila, Shantao, Li, Kelso, Sean, Saksena, Gordon, Ellrott, Kyle, Wendl, Michael C, Wheeler, David A, Getz, Gad, Simpson, Jared T, Gerstein, Mark B, Ding, Lirehan, Akbani, Pavana, Anur, Matthew, H Bailey, Alex, Buchanan, Kami, Chiotti, Kyle, Covington, Allison, Creason, Ding, Li, Kyle, Ellrott, Fan, Yu, Steven, Foltz, Gad, Getz, Walker, Hale, David, Haussler, Julian, M Hess, Carolyn, M Hutter, Cyriac, Kandoth, Katayoon, Kasaian, Melpomeni, Kasapi, Dave, Larson, Ignaty, Leshchiner, John, Letaw, Singer, Ma, Michael, D McLellan, Yifei, Men, Gordon, B Mills, Beifang, Niu, Myron, Peto, Amie, Radenbaugh, Sheila, M Reynolds, Gordon, Saksena, Heidi, Sofia, Chip, Stewart, Adam, J Struck, Joshua, M Stuart, Wenyi, Wang, John, N Weinstein, David, A Wheeler, Christopher, K Wong, Liu, Xi, Kai, Ye, Matthias, Bieg, Paul, C Boutros, Ivo, Buchhalter, Adam, P Butler, Ken, Chen, Zechen, Chong, Oliver, Drechsel, Lewis Jonathan Dursi, Roland, Eils, Shadrielle M, G Espiritu, Robert, S Fulton, Shengjie, Gao, Josep L, L Gelpi, Mark, B Gerstein, Santiago, Gonzalez, Ivo, G Gut, Faraz, Hach, Michael, C Heinold, Jonathan, Hinton, Taobo, Hu, Vincent, Huang, Huang, Yi, Barbara, Hutter, David, R Jones, Jongsun, Jung, Natalie, Jäger, Hyung-Lae, Kim, Kortine, Kleinheinz, Sushant, Kumar, Yogesh, Kumar, Christopher, M Lalansingh, Ivica, Letunic, Dimitri, Livitz, Eric, Z Ma, Yosef, E Maruvka, R Jay Mashl, Andrew, Menzies, Ana, Milovanovic, Morten Muhlig Nielsen, Stephan, Ossowski, Nagarajan, Paramasivam, Jakob Skou Pedersen, Marc, D Perry, Montserrat, Puiggròs, Keiran, M Raine, Esther, Rheinbay, Romina, Royo, S Cenk Sahinalp, Iman, Sarrafi, Matthias, Schlesner, Jared, T Simpson, Lucy, Stebbings, Miranda, D Stobbe, Jon, W Teague, Grace, Tiao, David, Torrents, Jeremiah, A Wala, Jiayin, Wang, Sebastian, M Waszak, Joachim, Weischenfeldt, Michael, C Wendl, Johannes, Werner, Zhenggang, Wu, Hong, Xue, Sergei, Yakneen, Takafumi, N Yamaguchi, Venkata, D Yellapantula, Christina, K Yung, Junjun, Zhang, Lauri, A Aaltonen, Federico, Abascal, Adam, Abeshouse, Hiroyuki, Aburatani, David, J Adams, Nishant, Agrawal, Keun Soo Ahn, Sung-Min, Ahn, Hiroshi, Aikata, Rehan, Akbani, Kadir, C Akdemir, Hikmat, Al-Ahmadie, Sultan, T Al-Sedairy, Fatima, Al-Shahrour, Malik, Alawi, Monique, Albert, Kenneth, Aldape, Ludmil, B Alexandrov, Adrian, Ally, Kathryn, Alsop, Eva, G Alvarez, Fernanda, Amary, Samirkumar, B Amin, Brice, Aminou, Ole, Ammerpohl, Matthew, J Anderson, Yeng, Ang, Davide, Antonello, Samuel, Aparicio, Elizabeth, L Appelbaum, Yasuhito, Arai, Axel, Aretz, Koji, Arihiro, Shun-Ichi, Ariizumi, Joshua, Armenia, Laurent, Arnould, Sylvia, Asa, Yassen, Assenov, Gurnit, Atwal, Sietse, Aukema, J Todd Auman, Miriam, R Aure, Philip, Awadalla, Marta, Aymerich, Gary, D Bader, Adrian, Baez-Ortega, Peter, J Bailey, Miruna, Balasundaram, Saianand, Balu, Pratiti, Bandopadhayay, Rosamonde, E Banks, Stefano, Barbi, Andrew, P Barbour, Jonathan, Barenboim, Jill, Barnholtz-Sloan, Hugh, Barr, Elisabet, Barrera, John, Bartlett, Javier, Bartolome, Bassi, Claudio, Oliver, F Bathe, Daniel, Baumhoer, Prashant, Bavi, Stephen, B Baylin, Wojciech, Bazant, Duncan, Beardsmore, Timothy, A Beck, Sam, Behjati, Andreas, Behren, Cindy, Bell, Sergi, Beltran, Christopher, Benz, Andrew, Berchuck, Anke, K Bergmann, Erik, N Bergstrom, Benjamin, P Berman, Daniel, M Berney, Stephan, H Bernhart, Rameen, Beroukhim, Mario, Berrios, Samantha, Bersani, Johanna, Bertl, Miguel, Betancourt, Vinayak, Bhandari, Shriram, G Bhosle, Andrew, V Biankin, Darell, Bigner, Hans, Binder, Ewan, Birney, Michael, Birrer, Nidhan, K Biswas, Bodil, Bjerkehagen, Tom, Bodenheimer, Lori, Boice, Giada, Bonizzato, Johann, S De Bono, Arnoud, Boot, Moiz, S Bootwalla, Ake, Borg, Arndt, Borkhardt, Keith, A Boroevich, Ivan, Borozan, Christoph, Borst, Marcus, Bosenberg, Mattia, Bosio, Jacqueline, Boultwood, Guillaume, Bourque, G Steven Bova, David, T Bowen, Reanne, Bowlby, David D, L Bowtell, Sandrine, Boyault, Rich, Boyce, Jeffrey, Boyd, Alvis, Brazma, Paul, Brennan, Daniel, S Brewer, Arie, B Brinkman, Robert, G Bristow, Russell, R Broaddus, Jane, E Brock, Malcolm, Brock, Annegien, Broeks, Angela, N Brooks, Denise, Brooks, Benedikt, Brors, Søren, Brunak, Timothy J, C Bruxner, Alicia, L Bruzos, Christiane, Buchholz, Susan, Bullman, Hazel, Burke, Birgit, Burkhardt, Kathleen, H Burns, John, Busanovich, Carlos, D Bustamante, Atul, J Butte, Niall, J Byrne, Anne-Lise, Børresen-Dale, Samantha, J Caesar-Johnson, Andy, Cafferkey, Declan, Cahill, Claudia, Calabrese, Carlos, Caldas, Fabien, Calvo, Niedzica, Camacho, Peter, J Campbell, Elias, Campo, Cinzia, Cantù, Shaolong, Cao, Thomas, E Carey, Joana, Carlevaro-Fita, Rebecca, Carlsen, Ivana, Cataldo, Mario, Cazzola, Jonathan, Cebon, Robert, Cerfolio, Dianne, E Chadwick, Dimple, Chakravarty, Don, Chalmers, Calvin Wing Yiu Chan, Kin, Chan, Michelle, Chan-Seng-Yue, Vishal, S Chandan, David, K Chang, Stephen, J Chanock, Lorraine, A Chantrill, Aurélien, Chateigner, Nilanjan, Chatterjee, Kazuaki, Chayama, Hsiao-Wei, Chen, Jieming, Chen, Yiwen, Chen, Zhaohong, Chen, Andrew, D Cherniack, Jeremy, Chien, Yoke-Eng, Chiew, Suet-Feung, Chin, Juok, Cho, Sunghoon, Cho, Jung Kyoon Choi, Wan, Choi, Christine, Chomienne, Su Pin Choo, Angela, Chou, Angelika, N Christ, Elizabeth, L Christie, Eric, Chuah, Carrie, Cibulskis, Kristian, Cibulskis, Sara, Cingarlini, Peter, Clapham, Alexander, Claviez, Sean, Cleary, Nicole, Cloonan, Marek, Cmero, Colin, C Collins, Ashton, A Connor, Susanna, L Cooke, Colin, S Cooper, Leslie, Cope, Corbo, Vincenzo, Matthew, G Cordes, Stephen, M Cordner, Isidro, Cortés-Ciriano, Prue, A Cowin, Brian, Craft, David, Craft, Chad, J Creighton, Yupeng, Cun, Erin, Curley, Ioana, Cutcutache, Karolina, Czajka, Bogdan, Czerniak, Rebecca, A Dagg, Ludmila, Danilova, Maria Vittoria Davi, Natalie, R Davidson, Helen, Davies, Ian, J Davis, Brandi, N Davis-Dusenbery, Kevin, J Dawson, Francisco, M De La Vega, Ricardo De Paoli-Iseppi, Timothy, Defreitas, Angelo, P Dei Tos, Olivier, Delaneau, John, A Demchok, Jonas, Demeulemeester, German, M Demidov, Deniz, Demircioğlu, Nening, M Dennis, Robert, E Denroche, Stefan, C Dentro, Nikita, Desai, Vikram, Deshpande, Amit, G Deshwar, Christine, Desmedt, Jordi, Deu-Pons, Noreen, Dhalla, Neesha, C Dhani, Priyanka, Dhingra, Rajiv, Dhir, Anthony, Dibiase, Klev, Diamanti, Shuai, Ding, Huy, Q Dinh, Luc, Dirix, Harshavardhan, Doddapaneni, Nilgun, Donmez, Michelle, T Dow, Ronny, Drapkin, Ruben, M Drews, Serge, Serge, Tim, Dudderidge, Ana, Dueso-Barroso, Andrew, J Dunford, Michael, Dunn, Fraser, R Duthie, Ken, Dutton-Regester, Jenna, Eagles, Douglas, F Easton, Stuart, Edmonds, Paul, A Edwards, Sandra, E Edwards, Rosalind, A Eeles, Anna, Ehinger, Juergen, Eils, Adel, El-Naggar, Matthew, Eldridge, Serap, Erkek, Georgia, Escaramis, Xavier, Estivill, Dariush, Etemadmoghadam, Jorunn, E Eyfjord, Bishoy, M Faltas, Daiming, Fan, William, C Faquin, Claudiu, Farcas, Matteo, Fassan, Aquila, Fatima, Francesco, Favero, Nodirjon, Fayzullaev, Ina, Felau, Sian, Fereday, Martin, L Ferguson, Vincent, Ferretti, Lars, Feuerbach, Matthew, A Field, J Lynn Fink, Gaetano, Finocchiaro, Cyril, Fisher, Matthew, W Fittall, Anna, Fitzgerald, Rebecca, C Fitzgerald, Adrienne, M Flanagan, Neil, E Fleshner, Paul, Flicek, John, A Foekens, Kwun, M Fong, Nuno, A Fonseca, Christopher, S Foster, Natalie, S Fox, Michael, Fraser, Scott, Frazer, Milana, Frenkel-Morgenstern, William, Friedman, Joan, Frigola, Catrina, C Fronick, Akihiro, Fujimoto, Masashi, Fujita, Masashi, Fukayama, Lucinda, A Fulton, Mayuko, Furuta, P Andrew Futreal, Anja, Füllgrabe, Stacey, B Gabriel, Steven, Gallinger, Carlo, Gambacorti-Passerini, Jianjiong, Gao, Levi, Garraway, Øystein, Garred, Erik, Garrison, Dale, W Garsed, Nils, Gehlenborg, Joshy, George, Daniela, S Gerhard, Clarissa, Gerhauser, Jeffrey, E Gershenwald, Moritz, Gerstung, Mohammed, Ghori, Ronald, Ghossein, Nasra, H Giama, Richard, A Gibbs, Anthony, J Gill, Pelvender, Gill, Dilip, D Giri, Dominik, Glodzik, Vincent, J Gnanapragasam, Maria Elisabeth Goebler, Mary, J Goldman, Carmen, Gomez, Abel, Gonzalez-Perez, Dmitry, A Gordenin, James, Gossage, Kunihito, Gotoh, Ramaswamy, Govindan, Dorthe, Grabau, Janet, S Graham, Robert, C Grant, Anthony, R Green, Eric, Green, Liliana, Greger, Nicola, Grehan, Sonia, Grimaldi, Sean, M Grimmond, Robert, L Grossman, Adam, Grundhoff, Gunes, Gundem, Qianyun, Guo, Manaswi, Gupta, Shailja, Gupta, Marta, Gut, Jonathan, Göke, Gavin, Ha, Andrea, Haake, David, Haan, Siegfried, Haas, Kerstin, Haase, James, E Haber, Nina, Habermann, Syed, Haider, Natsuko, Hama, Freddie, C Hamdy, Anne, Hamilton, Mark, P Hamilton, Leng, Han, George, B Hanna, Martin, Hansmann, Nicholas, J Haradhvala, Olivier, Harismendy, Ivon, Harliwong, Arif, O Harmanci, Eoghan, Harrington, Takanori, Hasegawa, Steve, Hawkins, Shinya, Hayami, Shuto, Hayashi, D Neil Hayes, Stephen, J Hayes, Nicholas, K Hayward, Steven, Hazell, Yao, He, Allison, P Heath, Simon, C Heath, David, Hedley, Apurva, M Hegde, David, I Heiman, Zachary, Heins, Lawrence, E Heisler, Eva, Hellstrom-Lindberg, Mohamed, Helmy, Seong Gu Heo, Austin, J Hepperla, José María Heredia-Genestar, Carl, Herrmann, Peter, Hersey, Holmfridur, Hilmarsdottir, Satoshi, Hirano, Nobuyoshi, Hiraoka, Katherine, A Hoadley, Asger, Hobolth, Ermin, Hodzic, Jessica, I Hoell, Steve, Hoffmann, Oliver, Hofmann, Andrea, Holbrook, Aliaksei, Z Holik, Michael, A Hollingsworth, Oliver, Holmes, Robert, A Holt, Chen, Hong, Eun Pyo Hong, Jongwhi, H Hong, Gerrit, K Hooijer, Henrik, Hornshøj, Fumie, Hosoda, Yong, Hou, Volker, Hovestadt, William, Howat, Alan, P Hoyle, Ralph, H Hruban, Jianhong, Hu, Xing, Hua, Kuan-Lin, Huang, Mei, Huang, Mi Ni Huang, Wolfgang, Huber, Thomas, J Hudson, Michael, Hummel, Jillian, A Hung, David, Huntsman, Ted, R Hupp, Jason, Huse, Matthew, R Huska, Daniel, Hübschmann, Christine, A Iacobuzio-Donahue, Charles David Imbusch, Marcin, Imielinski, Seiya, Imoto, William, B Isaacs, Keren, Isaev, Shumpei, Ishikawa, Murat, Iskar, M Ashiqul Islam, S, Michael, Ittmann, Sinisa, Ivkovic, Jose M, G Izarzugaza, Jocelyne, Jacquemier, Valerie, Jakrot, Nigel, B Jamieson, Gun Ho Jang, Se Jin Jang, Joy, C Jayaseelan, Reyka, Jayasinghe, Stuart, R Jefferys, Karine, Jegalian, Jennifer, L Jennings, Seung-Hyup, Jeon, Lara, Jerman, Yuan, Ji, Wei, Jiao, Peter, A Johansson, Amber, L Johns, Jeremy, Johns, Rory, Johnson, Todd, A Johnson, Clemency, Jolly, Yann, Joly, Jon, G Jonasson, Corbin, D Jones, David T, W Jones, Nic, Jones, Steven J, M Jones, Jos, Jonkers, Young Seok Ju, Hartmut, Juhl, Malene, Juul, Randi Istrup Juul, Sissel, Juul, Rolf, Kabbe, Andre, Kahles, Abdullah, Kahraman, Vera, B Kaiser, Hojabr, Kakavand, Sangeetha, Kalimuthu, Christof von Kalle, Koo Jeong Kang, Katalin, Karaszi, Beth, Karlan, Rosa, Karlić, Dennis, Karsch, Karin, S Kassahn, Hitoshi, Katai, Mamoru, Kato, Hiroto, Katoh, Yoshiiku, Kawakami, Jonathan, D Kay, Stephen, H Kazakoff, Marat, D Kazanov, Maria, Keays, Electron, Kebebew, Richard, F Kefford, Manolis, Kellis, James, G Kench, Catherine, J Kennedy, Jules N, A Kerssemakers, David, Khoo, Vincent, Khoo, Narong, Khuntikeo, Ekta, Khurana, Helena, Kilpinen, Hark Kyun Kim, Hyung-Yong, Kim, Hyunghwan, Kim, Jaegil, Kim, Jihoon, Kim, Jong, K Kim, Youngwook, Kim, Tari, A King, Wolfram, Klapper, Leszek, J Klimczak, Stian, Knappskog, Michael, Kneba, Bartha, M Knoppers, Youngil, Koh, Jan, Komorowski, Daisuke, Komura, Mitsuhiro, Komura, Kong, Gu, Marcel, Kool, Jan, O Korbel, Viktoriya, Korchina, Andrey, Korshunov, Michael, Koscher, Roelof, Koster, Zsofia, Kote-Jarai, Antonios, Koures, Milena, Kovacevic, Barbara, Kremeyer, Helene, Kretzmer, Markus, Kreuz, Savitri, Krishnamurthy, Dieter, Kube, Kiran, Kumar, Pardeep, Kumar, Ritika, Kundra, Kirsten, Kübler, Ralf, Küppers, Jesper, Lagergren, Phillip, H Lai, Peter, W Laird, Sunil, R Lakhani, Emilie, Lalonde, Fabien, C Lamaze, Adam, Lambert, Eric, Lander, Pablo, Landgraf, Landoni, Luca, Anita, Langerød, Andrés, Lanzós, Denis, Larsimont, Erik, Larsson, Mark, Lathrop, Loretta M, S Lau, Chris, Lawerenz, Rita, T Lawlor, Michael, S Lawrence, Alexander, J Lazar, Xuan, Le, Darlene, Lee, Donghoon, Lee, Eunjung Alice Lee, Hee Jin Lee, Jake June-Koo Lee, Jeong-Yeon, Lee, Juhee, Lee, Ming Ta Michael Lee, Henry, Lee-Six, Kjong-Van, Lehmann, Hans, Lehrach, Dido, Lenze, Conrad, R Leonard, Daniel, A Leongamornlert, Louis, Letourneau, Douglas, A Levine, Lora, Lewis, Tim, Ley, Chang, Li, Constance, H Li, Haiyan Irene Li, Jun, Li, Lin, Li, Siliang, Li, Xiaobo, Li, Xiaotong, Li, Xinyue, Li, Yilong, Li, Han, Liang, Sheng-Ben, Liang, Peter, Lichter, Pei, Lin, Ziao, Lin, M Linehan, W, Ole Christian Lingjærde, Dongbing, Liu, Eric Minwei Liu, Fei-Fei, Liu, Fenglin, Liu, Jia, Liu, Xingmin, Liu, Julie, Livingstone, Naomi, Livni, Lucas, Lochovsky, Markus, Loeffler, Georgina, V Long, Armando, Lopez-Guillermo, Shaoke, 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T, Nakano, K, Nandi, T, Nangalia, J, Nastic, M, Navarro, A, Navarro, F, Neal, D, Nettekoven, G, Newell, F, Newhouse, S, Newton, Y, Ng, A, Nicholson, J, Nicol, D, Nie, Y, Nielsen, G, Nik-Zainal, S, Noble, M, Nones, K, Northcott, P, Notta, F, O'Connor, B, O'Donnell, P, O'Donovan, M, O'Meara, S, O'Neill, B, O'Neill, J, Ocana, D, Ochoa, A, Oesper, L, Ogden, C, Ohdan, H, Ohi, K, Ohno-Machado, L, Oien, K, Ojesina, A, Ojima, H, Okusaka, T, Omberg, L, Ong, C, Ott, G, Ouellette, B, P'Ng, C, Paczkowska, M, Paiella, S, Pairojkul, C, Pajic, M, Pan-Hammarstrom, Q, Papaemmanuil, E, Papatheodorou, I, Park, J, Park, K, Park, P, Parker, J, Parsons, S, Pass, H, Pasternack, D, Pastore, A, Patch, A, Pauporte, I, Pea, A, Pearson, J, Pedamallu, C, Pederzoli, P, Peifer, M, Pennell, N, Perou, C, Petersen, G, Petrelli, N, Petryszak, R, Pfister, S, Phillips, M, Pich, O, Pickett, H, Pihl, T, Pillay, N, Pinder, S, Pinese, M, Pinho, A, Pitkanen, E, Pivot, X, Pineiro-Yanez, E, Planko, L, Plass, C, Polak, P, Pons, T, Popescu, I, Potapova, O, Prasad, A, Preston, S, Prinz, M, Pritchard, A, Prokopec, S, Provenzano, E, Puente, X, Puig, S, Pulido-Tamayo, S, Pupo, G, Purdie, C, Quinn, M, Rabionet, R, Rader, J, Radlwimmer, B, Radovic, P, Raeder, B, Ramakrishna, M, Ramakrishnan, K, Ramalingam, S, Raphael, B, Rathmell, W, Rausch, T, Reifenberger, G, Reimand, J, Reis-Filho, J, Reuter, V, Reyes-Salazar, I, Reyna, M, Riazalhosseini, Y, Richardson, A, Richter, J, Ringel, M, Ringner, M, Rino, Y, Rippe, K, Roach, J, Roberts, L, Roberts, N, Roberts, S, Robertson, A, Rodriguez, J, Rodriguez-Martin, B, Rodriguez-Gonzalez, F, Roehrl, M, Rohde, M, Rokutan, H, Romieu, G, Rooman, I, Roques, T, Rosebrock, D, Rosenberg, M, Rosenstiel, P, Rosenwald, A, Rowe, E, Rozen, S, Rubanova, Y, Rubin, M, Rubio-Perez, C, Rudneva, V, Rusev, B, Ruzzenente, A, Ratsch, G, Sabarinathan, R, Sabelnykova, V, Sadeghi, S, Saini, N, Saito-Adachi, M, Salcedo, A, Salgado, R, Salichos, L, Sallari, R, Saller, C, Salvia, R, Sam, M, Samra, J, Sanchez-Vega, F, Sander, C, Sanders, G, Sarin, R, Sasaki-Oku, A, Sauer, T, Sauter, G, Saw, R, Scardoni, M, Scarlett, C, Scarpa, A, Scelo, G, Schadendorf, D, Schein, J, Schilhabel, M, Schlomm, T, Schmidt, H, Schramm, S, Schreiber, S, Schultz, N, Schumacher, S, Schwarz, R, Scolyer, R, Scott, D, Scully, R, Seethala, R, Segre, A, Selander, I, Semple, C, Senbabaoglu, Y, Sengupta, S, Sereni, E, Serra, S, Sgroi, D, Shackleton, M, Shah, N, Shahabi, S, Shang, C, Shang, P, Shapira, O, Shelton, T, Shen, C, Shen, H, Shepherd, R, Shi, R, Shi, Y, Shiah, Y, Shibata, T, Shih, J, Shimizu, E, Shimizu, K, Shin, S, Shiraishi, Y, Shmaya, T, Shmulevich, I, Shorser, S, Short, C, Shrestha, R, Shringarpure, S, Shriver, C, Shuai, S, Sidiropoulos, N, Siebert, R, Sieuwerts, A, Sieverling, L, Signoretti, S, Sikora, K, Simbolo, M, Simon, R, Simons, J, Simpson, P, Singer, S, Sinnott-Armstrong, N, Sipahimalani, P, Skelly, T, Smid, M, Smith, J, Smith-McCune, K, Socci, N, Soloway, M, Song, L, Sood, A, Sothi, S, 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Yamamoto, S, Yamaue, H, Yang, F, Yang, H, Yang, J, Yang, L, Yang, S, Yang, T, Yang, Y, Yao, X, Yaspo, M, Yates, L, Yau, C, Ye, C, Yoon, C, Yoon, S, Yousif, F, Yu, J, Yu, K, Yu, W, Yu, Y, Yuan, K, Yuan, Y, Yuen, D, Zaikova, O, Zamora, J, Zapatka, M, Zenklusen, J, Zenz, T, Zeps, N, Zhang, C, Zhang, F, Zhang, H, Zhang, X, Zhang, Y, Zhang, Z, Zhao, Z, Zheng, L, Zheng, X, Zhou, W, Zhou, Y, Bin, Z, Zhu, H, Zhu, J, Zhu, S, Zou, L, Zou, X, Defazio, A, van As, N, van Deurzen, C, van de Vijver, M, van't Veer, L, von Mering, C, Heilbrigðisvísindasvið (HÍ), School of Health Sciences (UI), Háskóli Íslands, University of Iceland, Tampere University, BioMediTech, TAYS Cancer Centre, University of St Andrews. Sir James Mackenzie Institute for Early Diagnosis, University of St Andrews. Cellular Medicine Division, University of St Andrews. Statistics, University of St Andrews. School of Medicine, University of Zurich, Gerstein, Mark B, Ding, Li, Bailey, Matthew H [0000-0003-4526-9727], Wheeler, David A [0000-0002-9056-6299], Gerstein, Mark B [0000-0002-9746-3719], Faculty of Economic and Social Sciences and Solvay Business School, Lauri Antti Aaltonen / Principal Investigator, Genome-Scale Biology (GSB) Research Program, Department of Medical and Clinical Genetics, Organismal and Evolutionary Biology Research Programme, Helsinki Institute for Information Technology, Institute of Biotechnology, Bioinformatics, Department of Computer Science, Faculty of Medicine, and HUS Helsinki and Uusimaa Hospital District

Subjects
VARIANTS, 0302 clinical medicine, 706/648/697/129/2043, Databases, Genetic, Cancer genomics, SOMATIC POINT MUTATIONS, Càncer, lcsh:Science, Exome, Exome sequencing, Cancer, Base Composition, Neoplasms -- genetics, 1184 Genetics, developmental biology, physiology, 3100 General Physics and Astronomy, 3. Good health, 030220 oncology & carcinogenesis, Science & Technology - Other Topics, Transformació genètica, Genetic databases, Erfðarannsóknir, Human, GENES, Science, 1600 General Chemistry, General Biochemistry, Genetics and Molecular Biology, RC0254, 03 medical and health sciences, Genetic, SDG 3 - Good Health and Well-being, 1300 General Biochemistry, Genetics and Molecular Biology, Exome Sequencing, Genetics, Humans, Author Correction, Retrospective Studies, Whole genome sequencing, Comparative genomics, Science & Technology, RC0254 Neoplasms. Tumors. Oncology (including Cancer), INSERTIONS, DNA, PERFORMANCE, Human genetics, Communication and replication, Cancérologie, 692/4028/67/69, Genòmica, 030104 developmental biology, Mutation, Genome mutation, Human genome, lcsh:Q, COMPREHENSIVE CHARACTERIZATION, Genètica, 0301 basic medicine, Medizin, General Physics and Astronomy, Genome, Whole Exome Sequencing, Genetic transformation, International Cancer Genome Consortium, Neoplasms, 631/114/2399, Genamengi, Medicine and Health Sciences, Medicine(all), Women's cancers Radboud Institute for Molecular Life Sciences [Radboudumc 17], Multidisciplinary, 318 Medical biotechnology, Exome -- genetics, article, Exons, Women's cancers Radboud Institute for Health Sciences [Radboudumc 17], Multidisciplinary Sciences, CAPTURE, 1181 Ecology, evolutionary biology, oncology, DNA, Intergenic, 139, Medical Genetics, Biotechnology, ICGC/TCGA Pan-Cancer Analysis, 3122 Cancers, 610 Medicine & health, 45/23, QH426 Genetics, Biology, MC3 Working Group, Databases, Germline mutation, PCAWG novel somatic mutation calling methods working group, Krabbameinsrannsóknir, Cancer Genome Atlas, Genome, Human -- genetics, ddc:610, QH426, Medicinsk genetik, Krabbamein, Intergenic, Whole Genome Sequencing, Genome, Human, Human Genome, PCAWG Consortium, DAS, General Chemistry, DELETIONS, Good Health and Well Being, 10032 Clinic for Oncology and Hematology, 3111 Biomedicine, 631/1647/2217/748
Abstract

MC3 Working Group: Rehan Akbani21, Pavana Anur22, Matthew H. Bailey1,2,3, Alex Buchanan9, Kami Chiotti9, Kyle Covington12,23, Allison Creason9, Li Ding1,2,3,20, Kyle Ellrott9, Yu Fan21, Steven Foltz1,2, Gad Getz8,14,15,16, Walker Hale12, David Haussler24,25, Julian M. Hess8,26, Carolyn M. Hutter27, Cyriac Kandoth28, Katayoon Kasaian29,30, Melpomeni Kasapi27, Dave Larson1 , Ignaty Leshchiner8, John Letaw31, Singer Ma32, Michael D. McLellan1,3,20, Yifei Men32, Gordon B. Mills33,34, Beifang Niu35, Myron Peto22, Amie Radenbaugh24, Sheila M. Reynolds36, Gordon Saksena8, Heidi Sofia27, Chip Stewart8, Adam J. Struck31, Joshua M. Stuart24,37, Wenyi Wang21, John N. Weinstein38, David A. Wheeler12,13, Christopher K. Wong24,39, Liu Xi12 & Kai Ye40,41 21Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 22Molecular and Medical Genetics, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 23Castle Biosciences Inc, Friendswood, TX 77546, USA. 24UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 25Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 26Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02114, USA. 27National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA. 28Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 29Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. 30Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada. 31Computational Biology Program, School of Medicine, Oregon Health and Science University, Portland, OR 97239, USA. 32DNAnexus Inc, Mountain View, CA 94040, USA. 33Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. 34Precision Oncology, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 35Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. 36Institute for Systems Biology, Seattle, WA 98109, USA. 37Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 38Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 39Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 40School of Elect, PCAWG novel somatic mutation calling methods working group: Matthew H. Bailey1,2,3, Beifang Niu35, Matthias Bieg42,43, Paul C. Boutros6,44,45,46, Ivo Buchhalter43,47,48, Adam P. Butler49, Ken Chen50, Zechen Chong51, Li Ding1,2,3,20, Oliver Drechsel52,53, Lewis Jonathan Dursi6,7, Roland Eils47,48,54,55, Kyle Ellrott9, Shadrielle M. G. Espiritu6, Yu Fan21, Robert S. Fulton1,3,20, Shengjie Gao56, Josep L. l. Gelpi57,58, Mark B. Gerstein5,18,19, Gad Getz8,14,15,16, Santiago Gonzalez59,60, Ivo G. Gut52,61, Faraz Hach62,63, Michael C. Heinold47,48, Julian M. Hess8,26, Jonathan Hinton49, Taobo Hu64, Vincent Huang6, Yi Huang65,66, Barbara Hutter43,67,68, David R. Jones49, Jongsun Jung69, Natalie Jäger47, Hyung-Lae Kim70, Kortine Kleinheinz47,48, Sushant Kumar5,19, Yogesh Kumar64, Christopher M. Lalansingh6, Ignaty Leshchiner8, Ivica Letunic71, Dimitri Livitz8, Eric Z. Ma64, Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Michael D. McLellan1,3,20, Andrew Menzies49, Ana Milovanovic57, Morten Muhlig Nielsen73, Stephan Ossowski52,53,74, Nagarajan Paramasivam43,47, Jakob Skou Pedersen73,75, Marc D. Perry76,77, Montserrat Puiggròs57, Keiran M. Raine49, Esther Rheinbay8,14,72, Romina Royo57, S. Cenk Sahinalp62,78,79, Gordon Saksena8, Iman Sarrafi62,78, Matthias Schlesner47,80, Jared T. Simpson6,17, Lucy Stebbings49, Chip Stewart8, Miranda D. Stobbe52,61, Jon W. Teague49, Grace Tiao8, David Torrents57,81, Jeremiah A. Wala8,14,82, Jiayin Wang1,40,66, Wenyi Wang21, Sebastian M. Waszak60, Joachim Weischenfeldt60,83,84, Michael C. Wendl1,10,11, Johannes Werner47,85, Zhenggang Wu64, Hong Xue64, Sergei Yakneen60, Takafumi N. Yamaguchi6, Kai Ye40,41, Venkata D. Yellapantula20,86, Christina K. Yung76 & Junjun Zhang76, PCAWG Consortium: Lauri A. Aaltonen87, Federico Abascal49, Adam Abeshouse88, Hiroyuki Aburatani89, David J. Adams49, Nishant Agrawal90, Keun Soo Ahn91, Sung-Min Ahn92, Hiroshi Aikata93, Rehan Akbani21, Kadir C. Akdemir50, Hikmat Al-Ahmadie88, Sultan T. Al-Sedairy94, Fatima Al-Shahrour95, Malik Alawi96,97, Monique Albert98, Kenneth Aldape99,100, Ludmil B. Alexandrov49,101,102, Adrian Ally30, Kathryn Alsop103, Eva G. Alvarez104,105,106, Fernanda Amary107, Samirkumar B. Amin108,109,110, Brice Aminou76, Ole Ammerpohl111,112, Matthew J. Anderson113, Yeng Ang114, Davide Antonello115, Pavana Anur22, Samuel Aparicio116, Elizabeth L. Appelbaum1,117, Yasuhito Arai118, Axel Aretz119, Koji Arihiro93, Shun-ichi Ariizumi120, Joshua Armenia121, Laurent Arnould122, Sylvia Asa123,124, Yassen Assenov125, Gurnit Atwal6,126,127, Sietse Aukema112,128, J. Todd Auman129, Miriam R. Aure130, Philip Awadalla6,126, Marta Aymerich131, Gary D. Bader126, Adrian Baez-Ortega132, Matthew H. Bailey1,2,3, Peter J. Bailey133, Miruna Balasundaram30, Saianand Balu134, Pratiti Bandopadhayay8,135,136, Rosamonde E. Banks137, Stefano Barbi138, Andrew P. Barbour139,140, Jonathan Barenboim6, Jill Barnholtz-Sloan141,142, Hugh Barr143, Elisabet Barrera59, John Bartlett98,144, Javier Bartolome57, Claudio Bassi115, Oliver F. Bathe145,146, Daniel Baumhoer147, Prashant Bavi148, Stephen B. Baylin149,150, Wojciech Bazant59, Duncan Beardsmore151, Timothy A. Beck152,153, Sam Behjati49, Andreas Behren154, Beifang Niu35, Cindy Bell155, Sergi Beltran52,61, Christopher Benz156, Andrew Berchuck157, Anke K. Bergmann158, Erik N. Bergstrom101,102, Benjamin P. Berman159,160,161, Daniel M. Berney162, Stephan H. Bernhart163,164,165, Rameen Beroukhim8,14,82, Mario Berrios166, Samantha Bersani167, Johanna Bertl73,168, Miguel Betancourt169, Vinayak Bhandari6,44, Shriram G. Bhosle49, Andrew V. Biankin133,170,171,172, Matthias Bieg42,43, Darell Bigner173, Hans Binder163,164, Ewan Birney59, Michael Birrer72, Nidhan K. Biswas174, Bodil Bjerkehagen147,175, Tom Bodenheimer134, Lori Boice176, Giada Bonizzato177, Johann S. De Bono178, Arnoud Boot179,180, Moiz S. Bootwalla166, Ake Borg181, Arndt Borkhardt182, Keith A. Boroevich183,184, Ivan Borozan6, Christoph Borst185, Marcus Bosenberg186, Mattia Bosio52,53,57, Jacqueline Boultwood187, Guillaume Bourque188,189, Paul C. Boutros6,44,45,46, G. Steven Bova190, David T. Bowen49,191, Reanne Bowlby30, David D. L. Bowtell103, Sandrine Boyault192, Rich Boyce59, Jeffrey Boyd193, Alvis Brazma59, Paul Brennan194, Daniel S. Brewer195,196, Arie B. Brinkman197, Robert G. Bristow44,198,199,200,201, Russell R. Broaddus99, Jane E. Brock202, Malcolm Brock203, Annegien Broeks204, Angela N. Brooks8,24,37,82, Denise Brooks30, Benedikt Brors67,205,206, Søren Brunak207,208, Timothy J. C. Bruxner113,209, Alicia L. Bruzos104,105,106, Alex Buchanan9, Ivo Buchhalter43,47,48, Christiane Buchholz210, Susan Bullman8,82, Hazel Burke211, Birgit Burkhardt212, Kathleen H. Burns213,214, John Busanovich8,215, Carlos D. Bustamante216,217, Adam P. Butler49, Atul J. Butte218, Niall J. Byrne76, Anne-Lise Børresen-Dale130,219, Samantha J. Caesar-Johnson220, Andy Cafferkey59, Declan Cahill221, Claudia Calabrese59,60, Carlos Caldas222,223, Fabien Calvo224, Niedzica Camacho178, Peter J. Campbell49,225, Elias Campo226,227, Cinzia Cantù177, Shaolong Cao21, Thomas E. Carey228, Joana Carlevaro-Fita229,230,231, Rebecca Carlsen30, Ivana Cataldo167,177, Mario Cazzola232, Jonathan Cebon154, Robert Cerfolio233, Dianne E. Chadwick234, Dimple Chakravarty235, Don Chalmers236, Calvin Wing Yiu Chan47,237, Kin Chan238, Michelle Chan-Seng-Yue148, Vishal S. Chandan239, David K. Chang133,170, Stephen J. Chanock240, Lorraine A. Chantrill170,241, Aurélien Chateigner76,242, Nilanjan Chatterjee149,243, Kazuaki Chayama93, Hsiao-Wei Chen114,121, Jieming Chen218, Ken Chen50, Yiwen Chen21, Zhaohong Chen244, Andrew D. Cherniack8,82, Jeremy Chien245, Yoke-Eng Chiew246,247, Suet-Feung Chin222,223, Juok Cho8, Sunghoon Cho248, Jung Kyoon Choi249, Wan Choi250, Christine Chomienne251, Zechen Chong51, Su Pin Choo252, Angela Chou170,246, Angelika N. Christ113, Elizabeth L. Christie103, Eric Chuah30, Carrie Cibulskis8, Kristian Cibulskis8, Sara Cingarlini253, Peter Clapham49, Alexander Claviez254, Sean Cleary148,255, Nicole Cloonan256, Marek Cmero257,258,259, Colin C. Collins62, Ashton A. Connor255,260, Susanna L. Cooke133, Colin S. Cooper178,196,261, Leslie Cope149, Vincenzo Corbo138,177, Matthew G. Cordes1,262, Stephen M. Cordner263, Isidro Cortés-Ciriano264,265,266, Kyle Covington12,23, Prue A. Cowin267, Brian Craft24, David Craft8,268, Chad J. Creighton269, Yupeng Cun270, Erin Curley271, Ioana Cutcutache179,180, Karolina Czajka272, Bogdan Czerniak99,273, Rebecca A. Dagg274, Ludmila Danilova149, Maria Vittoria Davi275, Natalie R. Davidson276,277,278,279,280, Helen Davies49,281,282, Ian J. Davis283, Brandi N. Davis-Dusenbery284, Kevin J. Dawson49, Francisco M. De La Vega216,217,285, Ricardo De Paoli-Iseppi211, Timothy Defreitas8, Angelo P. Dei Tos286, Olivier Delaneau287,288,289, John A. Demchok220, Jonas Demeulemeester290,291, German M. Demidov52,53,74, Deniz Demircioğlu292,293, Nening M. Dennis221, Robert E. Denroche148, Stefan C. Dentro49,290,294, Nikita Desai76, Vikram Deshpande72, Amit G. Deshwar295, Christine Desmedt296,297, Jordi Deu-Pons298,299, Noreen Dhalla30, Neesha C. Dhani300, Priyanka Dhingra301,302, Rajiv Dhir303, Anthony DiBiase304, Klev Diamanti305, Li Ding1,2,3,20, Shuai Ding306, Huy Q. Dinh159, Luc Dirix307, HarshaVardhan Doddapaneni12, Nilgun Donmez62,78, Michelle T. Dow244, Ronny Drapkin308, Oliver Drechsel52,53, Ruben M. Drews223, Serge Serge49, Tim Dudderidge150,221, Ana Dueso-Barroso57, Andrew J. Dunford8, Michael Dunn309, Lewis Jonathan Dursi6,7, Fraser R. Duthie133,310, Ken Dutton-Regester311, Jenna Eagles272, Douglas F. Easton312,313, Stuart Edmonds314, Paul A. Edwards223,315, Sandra E. Edwards178, Rosalind A. Eeles178,221, Anna Ehinger316, Juergen Eils54,55, Roland Eils47,48,54,55, Adel El-Naggar99,273, Matthew Eldridge223, Kyle Ellrott9, Serap Erkek60, Georgia Escaramis53,317,318, Shadrielle M. G. Espiritu6, Xavier Estivill53,319, Dariush Etemadmoghadam103, Jorunn E. Eyfjord320, Bishoy M. Faltas280, Daiming Fan321, Yu Fan21, William C. Faquin72, Claudiu Farcas244, Matteo Fassan322, Aquila Fatima323, Francesco Favero324, Nodirjon Fayzullaev76, Ina Felau220, Sian Fereday103, Martin L. Ferguson325, Vincent Ferretti76,326, Lars Feuerbach205, Matthew A. Field327, J. Lynn Fink57,113, Gaetano Finocchiaro328, Cyril Fisher221, Matthew W. Fittall290, Anna Fitzgerald329, Rebecca C. Fitzgerald282, Adrienne M. Flanagan330, Neil E. Fleshner331, Paul Flicek59, John A. Foekens332, Kwun M. Fong333, Nuno A. Fonseca59,334, Christopher S. Foster335,336, Natalie S. Fox6, Michael Fraser6, Scott Frazer8, Milana Frenkel-Morgenstern337, William Friedman338, Joan Frigola298, Catrina C. Fronick1,262, Akihiro Fujimoto184, Masashi Fujita184, Masashi Fukayama339, Lucinda A. Fulton1 , Robert S. Fulton1,3,20, Mayuko Furuta184, P. Andrew Futreal340, Anja Füllgrabe59, Stacey B. Gabriel8, Steven Gallinger148,255,260, Carlo Gambacorti-Passerini341, Jianjiong Gao121, Shengjie Gao56, Levi Garraway82, Øystein Garred342, Erik Garrison49, Dale W. Garsed103, Nils Gehlenborg8,343, Josep L. l. Gelpi57,58, Joshy George110, Daniela S. Gerhard344, Clarissa Gerhauser345, Jeffrey E. Gershenwald346,347, Mark B. Gerstein5,18,19, Moritz Gerstung59,60, Gad Getz8,14,15,16, Mohammed Ghori49, Ronald Ghossein348, Nasra H. Giama349, Richard A. Gibbs12, Anthony J. Gill170,350, Pelvender Gill351, Dilip D. Giri348, Dominik Glodzik49, Vincent J. Gnanapragasam352,353, Maria Elisabeth Goebler354, Mary J. Goldman24, Carmen Gomez355, Santiago Gonzalez59,60, Abel Gonzalez-Perez298,299,356, Dmitry A. Gordenin357, James Gossage358, Kunihito Gotoh359, Ramaswamy Govindan3, Dorthe Grabau360, Janet S. Graham133,361, Robert C. Grant148,260, Anthony R. Green315, Eric Green27, Liliana Greger59, Nicola Grehan282, Sonia Grimaldi177, Sean M. Grimmond362, Robert L. Grossman363, Adam Grundhoff97,364, Gunes Gundem88, Qianyun Guo75, Manaswi Gupta8, Shailja Gupta365, Ivo G. Gut52,61, Marta Gut52,61, Jonathan Göke292,366, Gavin Ha8, Andrea Haake111, David Haan37, Siegfried Haas185, Kerstin Haase290, James E. Haber367, Nina Habermann60, Faraz Hach62,63, Syed Haider6, Natsuko Hama118, Freddie C. Hamdy351, Anne Hamilton267, Mark P. Hamilton368, Leng Han369, George B. Hanna370, Martin Hansmann371, Nicholas J. Haradhvala8,72, Olivier Harismendy102,372, Ivon Harliwong113, Arif O. Harmanci5,373, Eoghan Harrington374, Takanori Hasegawa375, David Haussler24,25, Steve Hawkins223, Shinya Hayami376, Shuto Hayashi375, D. Neil Hayes134,377,378, Stephen J. Hayes379,380, Nicholas K. Hayward211,311, Steven Hazell221, Yao He381, Allison P. Heath382, Simon C. Heath52,61, David Hedley300, Apurva M. Hegde38, David I. Heiman8, Michael C. Heinold47,48, Zachary Heins88, Lawrence E. Heisler152, Eva Hellstrom-Lindberg383, Mohamed Helmy384, Seong Gu Heo385, Austin J. Hepperla134, José María Heredia-Genestar386, Carl Herrmann47,48,387, Peter Hersey211, Julian M. Hess8,26, Holmfridur Hilmarsdottir320, Jonathan Hinton49, Satoshi Hirano388, Nobuyoshi Hiraoka389, Katherine A. Hoadley134,390, Asger Hobolth75,168, Ermin Hodzic78, Jessica I. Hoell182, Steve Hoffmann163,164,165,391, Oliver Hofmann392, Andrea Holbrook166, Aliaksei Z. Holik53, Michael A. Hollingsworth393, Oliver Holmes209,311, Robert A. Holt30, Chen Hong205,237, Eun Pyo Hong385, Jongwhi H. Hong394, Gerrit K. Hooijer395, Henrik Hornshøj73, Fumie Hosoda118, Yong Hou56,396, Volker Hovestadt397, William Howat352, Alan P. Hoyle134, Ralph H. Hruban149, Jianhong Hu12, Taobo Hu64, Xing Hua240, Kuan-lin Huang1,398, Mei Huang176, Mi Ni Huang179,180, Vincent Huang6, Yi Huang65,66, Wolfgang Huber60, Thomas J. Hudson272,399, Michael Hummel400, Jillian A. Hung246,247, David Huntsman401, Ted R. Hupp402, Jason Huse88, Matthew R. Huska403, Barbara Hutter43,67,68, Carolyn M. Hutter27, Daniel Hübschmann48,54,404,405,406, Christine A. Iacobuzio-Donahue348, Charles David Imbusch205, Marcin Imielinski407,408, Seiya Imoto375, William B. Isaacs409, Keren Isaev6,44, Shumpei Ishikawa410, Murat Iskar397, S. M. Ashiqul Islam244, Michael Ittmann411,412,413, Sinisa Ivkovic284, Jose M. G. Izarzugaza414, Jocelyne Jacquemier415, Valerie Jakrot211, Nigel B. Jamieson133,172,416, Gun Ho Jang148, Se Jin Jang417, Joy C. Jayaseelan12, Reyka Jayasinghe1 , Stuart R. Jefferys134, Karine Jegalian418, Jennifer L. Jennings419, Seung-Hyup Jeon250, Lara Jerman60,420, Yuan Ji421,422, Wei Jiao6, Peter A. Johansson311, Amber L. Johns170, Jeremy Johns272, Rory Johnson230,423, Todd A. Johnson183, Clemency Jolly290, Yann Joly424, Jon G. Jonasson320, Corbin D. Jones425, David R. Jones49, David T. W. Jones426,427, Nic Jones428, Steven J. M. Jones30, Jos Jonkers204, Young Seok Ju49,249, Hartmut Juhl429, Jongsun Jung69, Malene Juul73, Randi Istrup Juul73, Sissel Juul374, Natalie Jäger47, Rolf Kabbe47, Andre Kahles276,277,278,279,430, Abdullah Kahraman431,432,433, Vera B. Kaiser434, Hojabr Kakavand211, Sangeetha Kalimuthu148, Christof von Kalle405, Koo Jeong Kang91, Katalin Karaszi351, Beth Karlan435, Rosa Karlić436, Dennis Karsch437, Katayoon Kasaian29,30, Karin S. Kassahn113,438, Hitoshi Katai439, Mamoru Kato440, Hiroto Katoh410, Yoshiiku Kawakami93, Jonathan D. Kay117, Stephen H. Kazakoff209,311, Marat D. Kazanov441,442,443, Maria Keays59, Electron Kebebew444,445, Richard F. Kefford446, Manolis Kellis8,447, James G. Kench170,350,448, Catherine J. Kennedy246,247, Jules N. A. Kerssemakers47, David Khoo273, Vincent Khoo221, Narong Khuntikeo115,449, Ekta Khurana301,302,450,451, Helena Kilpinen117, Hark Kyun Kim452, Hyung-Lae Kim70, Hyung-Yong Kim415, Hyunghwan Kim250, Jaegil Kim8, Jihoon Kim453, Jong K. Kim454, Youngwook Kim455,456, Tari A. King457,458,459, Wolfram Klapper128, Kortine Kleinheinz47,48, Leszek J. Klimczak460, Stian Knappskog49,461, Michael Kneba437, Bartha M. Knoppers424, Youngil Koh462,463, Jan Komorowski305,464, Daisuke Komura410, Mitsuhiro Komura375, Gu Kong415, Marcel Kool426,465, Jan O. Korbel59,60, Viktoriya Korchina12, Andrey Korshunov465, Michael Koscher465, Roelof Koster466, Zsofia Kote-Jarai178, Antonios Koures244, Milena Kovacevic284, Barbara Kremeyer49, Helene Kretzmer164,165, Markus Kreuz467, Savitri Krishnamurthy99,468, Dieter Kube469, Kiran Kumar8, Pardeep Kumar221, Sushant Kumar5,19, Yogesh Kumar64, Ritika Kundra114,121, Kirsten Kübler8,14,72, Ralf Küppers470, Jesper Lagergren383,471, Phillip H. Lai166, Peter W. Laird472, Sunil R. Lakhani473, Christopher M. Lalansingh6, Emilie Lalonde6, Fabien C. Lamaze6, Adam Lambert351, Eric Lander8, Pablo Landgraf474,475, Luca Landoni115, Anita Langerød130, Andrés Lanzós230,231,423, Denis Larsimont476, Erik Larsson477, Mark Lathrop189, Loretta M. S. Lau478, Chris Lawerenz55, Rita T. Lawlor177, Michael S. Lawrence8,72,183, Alexander J. Lazar99,108, Xuan Le479, Darlene Lee30, Donghoon Lee5, Eunjung Alice Lee480, Hee Jin Lee417, Jake June-Koo Lee264,266, Jeong-Yeon Lee481, Juhee Lee482, Ming Ta Michael Lee340, Henry Lee-Six49, Kjong-Van Lehmann276,277,278,279,430, Hans Lehrach483, Dido Lenze400, Conrad R. Leonard209,311, Daniel A. Leongamornlert49,178, Ignaty Leshchiner8, Louis Letourneau484, Ivica Letunic71, Douglas A. Levine88,485, Lora Lewis12, Tim Ley486, Chang Li56,396, Constance H. Li6,44, Haiyan Irene Li30, Jun Li21, Lin Li56, Shantao Li5, Siliang Li56,396, Xiaobo Li56,396, Xiaotong Li5, Xinyue Li56, Yilong Li49, Han Liang21, Sheng-Ben Liang234, Peter Lichter68,397, Pei Lin8, Ziao Lin8,487, W. M. Linehan488, Ole Christian Lingjærde489, Dongbing Liu56,396, Eric Minwei Liu88,301,302, Fei-Fei Liu201,490, Fenglin Liu381,491, Jia Liu492, Xingmin Liu56,396, Julie Livingstone6, Dimitri Livitz8, Naomi Livni221, Lucas Lochovsky5,19,110, Markus Loeffler467, Georgina V. Long211, Armando Lopez-Guillermo493, Shaoke Lou5,19, David N. Louis72, Laurence B. Lovat117, Yiling Lu38, Yong-Jie Lu162,494, Youyong Lu495,496,497, Claudio Luchini167, Ilinca Lungu144,148, Xuemei Luo152, Hayley J. Luxton117, Andy G. Lynch223,315,498, Lisa Lype36, Cristina López111,112, Carlos López-Otín499, Eric Z. Ma64, Yussanne Ma30, Gaetan MacGrogan500, Shona MacRae501, Geoff Macintyre223, Tobias Madsen73, Kazuhiro Maejima184, Andrea Mafficini177, Dennis T. Maglinte166,502, Arindam Maitra174, Partha P. Majumder174, Luca Malcovati232, Salem Malikic62,78, Giuseppe Malleo115, Graham J. Mann211,246,503, Luisa Mantovani-Löffler504, Kathleen Marchal505,506, Giovanni Marchegiani115, Elaine R. Mardis1,193,507, Adam A. Margolin31, Maximillian G. Marin37, Florian Markowetz223,315, Julia Markowski403, Jeffrey Marks508, Tomas Marques-Bonet61,81,386,509, Marco A. Marra30, Luke Marsden351, John W. M. Martens332, Sancha Martin49,510, Jose I. Martin-Subero81,511, Iñigo Martincorena49, Alexander Martinez-Fundichely301,302,451 Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Charlie E. Massie223, Thomas J. Matthew37, Lucy Matthews178, Erik Mayer221,512, Simon Mayes513, Michael Mayo30, Faridah Mbabaali272, Karen McCune514, Ultan McDermott49, Patrick D. McGillivray19, Michael D. McLellan1,3,20, John D. McPherson148,272,515, John R. McPherson179,180, Treasa A. McPherson260, Samuel R. Meier8, Alice Meng516, Shaowu Meng134, Andrew Menzies49, Neil D. Merrett115,517, Sue Merson178, Matthew Meyerson8,14,82, William U. Meyerson4,5, Piotr A. Mieczkowski518, George L. Mihaiescu76, Sanja Mijalkovic284, Ana Mijalkovic Mijalkovic-Lazic284, Tom Mikkelsen519, Michele Milella253, Linda Mileshkin103, Christopher A. Miller1 , David K. Miller113,170, Jessica K. Miller272, Gordon B. Mills33,34, Ana Milovanovic57, Sarah Minner520, Marco Miotto115, Gisela Mir Arnau267, Lisa Mirabello240, Chris Mitchell103, Thomas J. Mitchell49,315,352, Satoru Miyano375, Naoki Miyoshi375, Shinichi Mizuno521, Fruzsina Molnár-Gábor522, Malcolm J. Moore300, Richard A. Moore30, Sandro Morganella49, Quaid D. Morris127,490, Carl Morrison523,524, Lisle E. Mose134, Catherine D. Moser349, Ferran Muiños298,299, Loris Mularoni298,299, Andrew J. Mungall30, Karen Mungall30, Elizabeth A. Musgrove133, Ville Mustonen525,526,527, David Mutch528, Francesc Muyas52,53,74, Donna M. Muzny12, Alfonso Muñoz59, Jerome Myers529, Ola Myklebost461, Peter Möller530, Genta Nagae89, Adnan M. Nagrial170, Hardeep K. Nahal-Bose76, Hitoshi Nakagama531, Hidewaki Nakagawa184, Hiromi Nakamura118, Toru Nakamura388, Kaoru Nakano184, Tannistha Nandi532, Jyoti Nangalia49, Mia Nastic284, Arcadi Navarro61,81,386, Fabio C. P. Navarro19, David E. Neal223,352, Gerd Nettekoven533, Felicity Newell209,311, Steven J. Newhouse59, Yulia Newton37, Alvin Wei Tian Ng534, Anthony Ng535, Jonathan Nicholson49, David Nicol221, Yongzhan Nie321,536, G. Petur Nielsen72, Morten Muhlig Nielsen73, Serena Nik-Zainal49,281,282,537, Michael S. Noble8, Katia Nones209,311, Paul A. Northcott538, Faiyaz Notta148,539, Brian D. O’Connor76,540, Peter O’Donnell541, Maria O’Donovan282, Sarah O’Meara49, Brian Patrick O’Neill542, J. Robert O’Neill543, David Ocana59, Angelica Ochoa88, Layla Oesper544, Christopher Ogden221, Hideki Ohdan93, Kazuhiro Ohi375, Lucila Ohno-Machado244, Karin A. Oien523,545, Akinyemi I. Ojesina546,547,548, Hidenori Ojima549, Takuji Okusaka550, Larsson Omberg551, Choon Kiat Ong552, Stephan Ossowski52,53,74, German Ott553, B. F. Francis Ouellette76,554, Christine P’ng6, Marta Paczkowska6, Salvatore Paiella115, Chawalit Pairojkul523, Marina Pajic170, Qiang Pan-Hammarström56,555, Elli Papaemmanuil49, Irene Papatheodorou59, Nagarajan Paramasivam43,47, Ji Wan Park385, Joong-Won Park556, Keunchil Park557,558, Kiejung Park559, Peter J. Park264,266, Joel S. Parker518, Simon L. Parsons124, Harvey Pass560, Danielle Pasternack272, Alessandro Pastore276, Ann-Marie Patch209,311, Iris Pauporté251, Antonio Pea115, John V. Pearson209,311, Chandra Sekhar Pedamallu8,14,82, Jakob Skou Pedersen73,75, Paolo Pederzoli115, Martin Peifer270, Nathan A. Pennell561, Charles M. Perou129,518, Marc D. Perry76,77, Gloria M. Petersen562, Myron Peto22, Nicholas Petrelli563, Robert Petryszak59, Stefan M. Pfister426,465,564, Mark Phillips424, Oriol Pich298,299, Hilda A. Pickett478, Todd D. Pihl565, Nischalan Pillay566, Sarah Pinder567, Mark Pinese170, Andreia V. Pinho568, Esa Pitkänen60, Xavier Pivot569, Elena Piñeiro-Yáñez95, Laura Planko533, Christoph Plass345, Paz Polak8,14,15, Tirso Pons570, Irinel Popescu571, Olga Potapova572, Aparna Prasad52, Shaun R. Preston573, Manuel Prinz47, Antonia L. Pritchard311, Stephenie D. Prokopec6, Elena Provenzano574, Xose S. Puente499, Sonia Puig176, Montserrat Puiggròs57, Sergio Pulido-Tamayo505,506, Gulietta M. Pupo246, Colin A. Purdie575, Michael C. Quinn209,311, Raquel Rabionet52,53,576, Janet S. Rader577, Bernhard Radlwimmer397, Petar Radovic284, Benjamin Raeder60, Keiran M. Raine49, Manasa Ramakrishna49, Kamna Ramakrishnan49, Suresh Ramalingam578, Benjamin J. Raphael579, W. Kimryn Rathmell580, Tobias Rausch60, Guido Reifenberger475, Jüri Reimand6,44, Jorge Reis-Filho348, Victor Reuter348, Iker Reyes-Salazar298, Matthew A. Reyna579, Sheila M. Reynolds36, Esther Rheinbay8,14,72, Yasser Riazalhosseini189, Andrea L. Richardson323, Julia Richter111,128, Matthew Ringel581, Markus Ringnér181, Yasushi Rino582, Karsten Rippe405, Jeffrey Roach583, Lewis R. Roberts349, Nicola D. Roberts49, Steven A. Roberts584, A. Gordon Robertson30, Alan J. Robertson113, Javier Bartolomé Rodriguez57, Bernardo Rodriguez-Martin104,105,106, F. Germán Rodríguez-González83,332, Michael H. A. Roehrl44,123,148,234,585,586, Marius Rohde587, Hirofumi Rokutan440, Gilles Romieu588, Ilse Rooman170, Tom Roques262, Daniel Rosebrock8, Mara Rosenberg8,72, Philip C. Rosenstiel589, Andreas Rosenwald590, Edward W. Rowe221,591, Romina Royo57, Steven G. Rozen179,180,592, Yulia Rubanova17,127, Mark A. Rubin423,593,594,595,596, Carlota Rubio-Perez298,299,597, Vasilisa A. Rudneva60, Borislav C. Rusev177, Andrea Ruzzenente598, Gunnar Rätsch276,277,278,279,280,430, Radhakrishnan Sabarinathan298,299,599, Veronica Y. Sabelnykova6, Sara Sadeghi30, S. Cenk Sahinalp62,78,79, Natalie Saini357, Mihoko Saito-Adachi440, Gordon Saksena8, Adriana Salcedo6, Roberto Salgado600, Leonidas Salichos5,19, Richard Sallari8, Charles Saller601, Roberto Salvia115, Michelle Sam272, Jaswinder S. Samra115,602, Francisco Sanchez-Vega114,121, Chris Sander276,603,604, Grant Sanders134, Rajiv Sarin605, Iman Sarrafi62,78, Aya Sasaki-Oku184, Torill Sauer489, Guido Sauter520, Robyn P. M. Saw211, Maria Scardoni167, Christopher J. Scarlett170,606, Aldo Scarpa177, Ghislaine Scelo194, Dirk Schadendorf68,607, Jacqueline E. Schein30, Markus B. Schilhabel589, Matthias Schlesner47,80, Thorsten Schlomm84,608, Heather K. Schmidt1 , Sarah-Jane Schramm246, Stefan Schreiber609, Nikolaus Schultz121, Steven E. Schumacher8,323, Roland F. Schwarz59,403,405,610, Richard A. Scolyer211,448,602, David Scott428, Ralph Scully611, Raja Seethala612, Ayellet V. Segre8,613, Iris Selander260, Colin A. Semple434, Yasin Senbabaoglu276, Subhajit Sengupta614, Elisabetta Sereni115, Stefano Serra585, Dennis C. Sgroi72, Mark Shackleton103, Nimish C. Shah352, Sagedeh Shahabi234, Catherine A. Shang329, Ping Shang211, Ofer Shapira8,323, Troy Shelton271, Ciyue Shen603,604, Hui Shen615, Rebecca Shepherd49, Ruian Shi490, Yan Shi134, Yu-Jia Shiah6, Tatsuhiro Shibata118,616, Juliann Shih8,82, Eigo Shimizu375, Kiyo Shimizu617, Seung Jun Shin618, Yuichi Shiraishi375, Tal Shmaya285, Ilya Shmulevich36, Solomon I. Shorser6, Charles Short59, Raunak Shrestha62, Suyash S. Shringarpure217, Craig Shriver619, Shimin Shuai6,126, Nikos Sidiropoulos83, Reiner Siebert112,620, Anieta M. Sieuwerts332, Lina Sieverling205,237, Sabina Signoretti202,621, Katarzyna O. Sikora177, Michele Simbolo138, Ronald Simon520, Janae V. Simons134, Jared T. Simpson6,17, Peter T. Simpson473, Samuel Singer115,458, Nasa Sinnott-Armstrong8,217, Payal Sipahimalani30, Tara J. Skelly390, Marcel Smid332, Jaclyn Smith622, Karen Smith-McCune514, Nicholas D. Socci276, Heidi J. Sofia27, Matthew G. Soloway134, Lei Song240, Anil K. Sood623,624,625, Sharmila Sothi626, Christos Sotiriou244, Cameron M. Soulette37, Paul N. Span627, Paul T. Spellman22, Nicola Sperandio177, Andrew J. Spillane211, Oliver Spiro8, Jonathan Spring628, Johan Staaf181, Peter F. Stadler163,164,165, Peter Staib629, Stefan G. Stark277,279,618,630, Lucy Stebbings49, Ólafur Andri Stefánsson631, Oliver Stegle59,60,632, Lincoln D. Stein6,126, Alasdair Stenhouse633, Chip Stewart8, Stephan Stilgenbauer634, Miranda D. Stobbe52,61, Michael R. Stratton49, Jonathan R. Stretch211, Adam J. Struck31, Joshua M. Stuart24,37, Henk G. Stunnenberg396,635, Hong Su56,396, Xiaoping Su99, Ren X. Sun6, Stephanie Sungalee60, Hana Susak52,53, Akihiro Suzuki89,636, Fred Sweep637, Monika Szczepanowski128, Holger Sültmann67,638, Takashi Yugawa617, Angela Tam30, David Tamborero298,299, Benita Kiat Tee Tan639, Donghui Tan518, Patrick Tan180,532,592,640, Hiroko Tanaka375, Hirokazu Taniguchi616, Tomas J. Tanskanen641, Maxime Tarabichi49,290, Roy Tarnuzzer220, Patrick Tarpey642, Morgan L. Taschuk152, Kenji Tatsuno89, Simon Tavaré223,643, Darrin F. Taylor113, Amaro Taylor-Weiner8, Jon W. Teague49, Bin Tean Teh180,592,640,644,645, Varsha Tembe246, Javier Temes104,105, Kevin Thai76, Sarah P. Thayer393, Nina Thiessen30, Gilles Thomas646, Sarah Thomas221, Alan Thompson221, Alastair M. Thompson633, John F. Thompson211, R. Houston Thompson647, Heather Thorne103, Leigh B. Thorne176, Adrian Thorogood424, Grace Tiao8, Nebojsa Tijanic284, Lee E. Timms272, Roberto Tirabosco648, Marta Tojo106, Stefania Tommasi649, Christopher W. Toon170, Umut H. Toprak48,650, David Torrents57,81, Giampaolo Tortora651,652, Jörg Tost653, Yasushi Totoki118, David Townend654, Nadia Traficante103, Isabelle Treilleux655,656, Jean-Rémi Trotta61, Lorenz H. P. Trümper469, Ming Tsao124,539, Tatsuhiko Tsunoda183,657,658,659, Jose M. C. Tubio104,105,106, Olga Tucker660, Richard Turkington661, Daniel J. Turner513, Andrew Tutt323, Masaki Ueno376, Naoto T. Ueno662, Christopher Umbricht151,213,663, Husen M. Umer305,664, Timothy J. Underwood665, Lara Urban59,60, Tomoko Urushidate616, Tetsuo Ushiku339, Liis Uusküla-Reimand666,667, Alfonso Valencia57,81, David J. Van Den Berg166, Steven Van Laere307, Peter Van Loo290,291, Erwin G. Van Meir668, Gert G. Van den Eynden307, Theodorus Van der Kwast123, Naveen Vasudev137, Miguel Vazquez57,669, Ravikiran Vedururu267, Umadevi Veluvolu518, Shankar Vembu490,670, Lieven P. C. Verbeke506,671, Peter Vermeulen307, Clare Verrill351,672, Alain Viari177, David Vicente57, Caterina Vicentini177, K. Vijay Raghavan365, Juris Viksna673, Ricardo E. Vilain674, Izar Villasante57, Anne Vincent-Salomon635, Tapio Visakorpi190, Douglas Voet8, Paresh Vyas311,351, Ignacio Vázquez-García49,86,675,676, Nick M. Waddell209, Nicola Waddell209,311, Claes Wadelius677, Lina Wadi6, Rabea Wagener111,112, Jeremiah A. Wala8,14,82, Jian Wang56, Jiayin Wang1,40,66, Linghua Wang12, Qi Wang465, Wenyi Wang21, Yumeng Wang21, Zhining Wang220, Paul M. Waring523, Hans-Jörg Warnatz483, Jonathan Warrell5,19, Anne Y. Warren352,678, Sebastian M. Waszak60, David C. Wedge49,294,679, Dieter Weichenhan345, Paul Weinberger680, John N. Weinstein38, Joachim Weischenfeldt60,83,84, Daniel J. Weisenberger166, Ian Welch681, Michael C. Wendl1,10,11, Johannes Werner47,85, Justin P. Whalley61,682, David A. Wheeler12,13, Hayley C. Whitaker117, Dennis Wigle683, Matthew D. Wilkerson518, Ashley Williams244, James S. Wilmott211, Gavin W. Wilson6,148, Julie M. Wilson148, Richard K. Wilson1,684, Boris Winterhoff685, Jeffrey A. Wintersinger17,127,384, Maciej Wiznerowicz686,687, Stephan Wolf688, Bernice H. Wong689, Tina Wong1,30, Winghing Wong690, Youngchoon Woo250, Scott Wood209,311, Bradly G. Wouters44, Adam J. Wright6, Derek W. Wright133,691, Mark H. Wright217, Chin-Lee Wu72, Dai-Ying Wu285, Guanming Wu692, Jianmin Wu170, Kui Wu56,396, Yang Wu179,180, Zhenggang Wu64, Liu Xi12, Tian Xia693, Qian Xiang76, Xiao Xiao66, Rui Xing497, Heng Xiong56,396, Qinying Xu209,311, Yanxun Xu694, Hong Xue64, Shinichi Yachida118,695, Sergei Yakneen60, Rui Yamaguchi375, Takafumi N. Yamaguchi6, Masakazu Yamamoto120, Shogo Yamamoto89, Hiroki Yamaue376, Fan Yang490, Huanming Yang56, Jean Y. Yang696, Liming Yang220, Lixing Yang697, Shanlin Yang306, Tsun-Po Yang270, Yang Yang369, Xiaotong Yao408,698, Marie-Laure Yaspo483, Lucy Yates49, Christina Yau156, Chen Ye56,396, Kai Ye40,41, Venkata D. Yellapantula20,86, Christopher J. Yoon249, Sung-Soo Yoon463, Fouad Yousif6, Jun Yu699, Kaixian Yu700, Willie Yu701, Yingyan Yu702, Ke Yuan223,510,703, Yuan Yuan21, Denis Yuen6, Takashi Yugawa617, Christina K. Yung76, Olga Zaikova704, Jorge Zamora49,104,105,106, Marc Zapatka397, Jean C. Zenklusen220, Thorsten Zenz67, Nikolajs Zeps705,706, Cheng-Zhong Zhang8,707, Fan Zhang381, Hailei Zhang8, Hongwei Zhang494, Hongxin Zhang121, Jiashan Zhang220, Jing Zhang5, Junjun Zhang76, Xiuqing Zhang56, Xuanping Zhang66,369, Yan Zhang5,708,709, Zemin Zhang381,710, Zhongming Zhao711, Liangtao Zheng381, Xiuqing Zheng381, Wanding Zhou615, Yong Zhou56, Bin Zhu240, Hongtu Zhu700,712, Jingchun Zhu24, Shida Zhu56,396, Lihua Zou713, Xueqing Zou49, Anna deFazio246,247,714, Nicholas van As221, Carolien H. M. van Deurzen715, Marc J. van de Vijver523, L. van’t Veer716 & Christian von Mering433,717, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Published
2020
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9. Novel Genetic Variants Associated with Lumbar Spondylosis in Koreans : A Genome-Wide Association Study

Author

Young Ok Jung, Hyun Ah Kim, Ji Wan Park, and Seong Gu Heo

Subjects
0301 basic medicine, medicine.medical_specialty, Genome-wide association study, Single-nucleotide polymorphism, Osteoarthritis, Gastroenterology, 03 medical and health sciences, Internal medicine, medicine, Cutoff, Lumbar spondylosis, Clinical Article, business.industry, General Neuroscience, Genetic variants, Odds ratio, Stepwise regression, medicine.disease, Single nucleotide polymorphism, 030104 developmental biology, Surgery, Neurology (clinical), Spondylosis, business
Abstract

Objective The aim of this study was to identify the susceptibility genes responsible for lumbar spondylosis (LS) in Korean patients. Methods Data from 1427 subjects were made available for radiographic grading and genome wide association studies (GWAS) analysis. Lateral lumbar spine radiographs were obtained and the various degrees of degenerative change were semi-quantitatively scored. A pilot GWAS was performed using the AffymetrixGenome-Wide Human single-nucleotide polymorphisms (SNPs), 500K array. A total of 352228 SNPs were analyzed and the association between the SNPs and case-control status was analyzed by stepwise logistic regression analyses. Results The top 100 SNPs with a cutoff p-value of less than 3.7×10-4 were selected for joint space narrowing, while a cutoff p-value of 6.0×10-4 was applied to osteophytes and the Kellgren-Lawrence (K-L) osteoarthritis grade. The SNPs with the strongest effect on disc space narrowing, osteophytes, and K-L grade were serine incorporator 1 (rs155467, odds ratio [OR]=17.58, p=1.6×10-4), stromal interaction molecule 2 (STIM1, rs210781, OR=5.53, p=5×10-4), and transient receptor potential cation channel, subfamily C (rs11224760, OR=3.99, p=4.8×10-4), respectively. Leucine-rich repeat-containing G protein-coupled receptor 4 was significantly associated with both disc space narrowing and osteophytes (rs1979400, OR=2.01, p=1.1×10-4 for disc space narrowing, OR=1.79, p=3×10-4 for osteophytes), while zinc finger and BTB domain containing 7C was significantly and negatively associated with both osteophytes and a K-L grade >2 (rs12457004,OR=0.25, p=5.8×10-4 and OR=0.27, p=5.3×10-4, respectively). Conclusion We identified SNPs that potentially contribute to the pathogenesis of LS. This is the first report of a GWAS in an Asian population.

Published
2017

10. The Liability Threshold Model for Predicting the Risk of Cardiovascular Disease in Patients with Type 2 Diabetes: A Multi-Cohort Study of Korean Adults

Author

Seong Gu Heo, Eun Pyo Hong, and Ji Wan Park

Subjects
liability threshold model, 0301 basic medicine, Mixed model, Oncology, medicine.medical_specialty, Endocrinology, Diabetes and Metabolism, Population, lcsh:QR1-502, Single-nucleotide polymorphism, Disease, Type 2 diabetes, 030204 cardiovascular system & hematology, Biochemistry, lcsh:Microbiology, Article, population-based cohort study, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, medicine, education, Molecular Biology, education.field_of_study, functional gene network, business.industry, genetic risk prediction, Precision medicine, medicine.disease, 030104 developmental biology, polygenic risk score, Threshold model, business, diabetic cardiovascular disease, Cohort study
Abstract

Personalized risk prediction for diabetic cardiovascular disease (DCVD) is at the core of precision medicine in type 2 diabetes (T2D). We first identified three marker sets consisting of 15, 47, and 231 tagging single nucleotide polymorphisms (tSNPs) associated with DCVD using a linear mixed model in 2378 T2D patients obtained from four population-based Korean cohorts. Using the genetic variants with even modest effects on phenotypic variance, we observed improved risk stratification accuracy beyond traditional risk factors (AUC, 0.63 to 0.97). With a cutoff point of 0.21, the discrete genetic liability threshold model consisting of 231 SNPs (GLT231) correctly classified 87.7% of 2378 T2D patients as high or low risk of DCVD. For the same set of SNP markers, the GLT and polygenic risk score (PRS) models showed similar predictive performance, and we observed consistency between the GLT and PRS models in that the model based on a larger number of SNP markers showed much-improved predictability. In silico gene expression analysis, additional information was provided on the functional role of the genes identified in this study. In particular, HDAC4, CDKN2B, CELSR2, and MRAS appear to be major hubs in the functional gene network for DCVD. The proposed risk prediction approach based on the liability threshold model may help identify T2D patients at high CVD risk in East Asian populations with further external validations.

Published
2020
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11. Male-specific genetic effect on hypertension and metabolic disorders

Author

Seong Gu Heo, Saangyong Uhmn, Jong-Young Lee, Burmseok Oh, Ji Wan Park, Joo-Yeon Hwang, and Min Jin Go

Subjects
Adult, Blood Glucose, Male, medicine.medical_specialty, Genotype, Population, Blood Pressure, Genome-wide association study, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Body Mass Index, Sex Factors, Meta-Analysis as Topic, Metabolic Diseases, Risk Factors, Internal medicine, Republic of Korea, Genetic model, Genetics, medicine, Humans, Genetic Predisposition to Disease, education, Triglycerides, Genetics (clinical), Aged, Genetic association, education.field_of_study, Cholesterol, HDL, Confounding, Case-control study, Cholesterol, LDL, Middle Aged, Logistic Models, Endocrinology, Case-Control Studies, Hypertension, Multivariate Analysis, Female, Body mass index, Genome-Wide Association Study
Abstract

Genetic risk factors for hypertension may have age or gender specificity and pleiotropic effects. This study aims to measure the risk of genetic and non-genetic factors in the occurrence of hypertension and related diseases, with consideration of potential confounding factors and age-gender stratification. A discovery set of 352,228 genotyped plus 1.8 million imputed single-nucleotide polymorphisms were analyzed for 2,886 hypertensive cases and 3,440 healthy controls obtained from two community-based cohorts in Korea, and selected gene variants were replicated in the Health Examinee cohort (665 cases and 1,285 controls). Genome-wide association analyses were conducted in 12 groups stratified by age and gender after adjusting for potential covariates under three genetic models. Age, rural area residence, body mass index, family history of hypertension, male gender, current alcohol drinking status, and current smoking status were significantly associated with hypertension (P = 4 × 10(-151) to 0.011). Five gene variants, rs11066280 (C12orf51), rs12229654 and rs3782889 (MYL2), rs2072134 (OAS3), rs2093395 (TREML2), and rs17249754 (ATP2B1), were found to be associated with hypertension mostly in men (P = 4.76 × 10(-14) to 4.46 × 10(-7) in the joint analysis); three SNPs (rs11066280, rs12229654, and rs3782889) remained significant after Bonferroni correction in an independent population. Three gene variants, rs12229654, rs17249754, and rs11066280, were significantly associated with metabolic disorders such as hyperlipidemia and diabetes (P = 0.00071 to 0.0097, respectively). Careful consideration of the potential confounding effects in future genome-wide association studies is necessary to uncover the genetic underpinnings of complex diseases.

Published
2013
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12. Associations between the risk of tooth agenesis and single-nucleotide polymorphisms of MSX1 and PAX9 genes in nonsyndromic cleft patients

Author

Ji Wan Park, Yu-Jin Seo, Young-Ho Kim, and Seung-Hak Baek

Subjects
Male, Risk, Genotype, Cleft Lip, Orthodontics, Single-nucleotide polymorphism, Polymorphism, Single Nucleotide, Odds Ratio, Humans, Medicine, SNP, Genetic Predisposition to Disease, Anodontia, Genetic association, MSX1 Transcription Factor, Genetics, business.industry, Original Articles, Odds ratio, medicine.disease, Cleft Palate, stomatognathic diseases, Exact test, Agenesis, Regression Analysis, Female, PAX9 Transcription Factor, business, PAX9, Genome-Wide Association Study
Abstract

Objective: To investigate the association between the risk of tooth agenesis and single-nucleotide polymorphisms (SNPs) of MSX1 and PAX9 genes in nonsyndromic cleft patients. Materials and Methods: The subjects were 126 Korean nonsyndromic cleft patients. Tooth agenesis type (TAT) was classified as none (0); cleft area (1); cleft area + other area (2); and other area (3) based on agenesis of the maxillary lateral incisor (MXLI) and another tooth within or outside the cleft area. TAT was further grouped into two subcategories (0 and 1) and four subcategories (0, 1, 2, and 3). Three SNPs of MSX1 and 10 SNPs of PAX9 were investigated using Fisher's exact test and logistic regression analysis. Results: Although the association between genotype distribution of PAX9-rs7142363 and TAT was significant (P < .05 in four subcategories), genotypic odds ratios (GORs) of SNPs in each TAT were not meaningful. However, for MSX1-rs12532 and PAX9-rs2073247, associations between genotypic distribution and TAT were significant (P < .01 in four subcategories and P < .05 in two subcategories; P < .01 in two subcategories, respectively). In cleft area, GORs of MXLI agenesis in genotypes GA of MSX1-rs12532 and CT of PAX9-rs2073247 were increased by 3.14-fold and 4.15-fold compared with genotype GG of MSX1-rs12532 and CC of PAX9-rs2073247, respectively (P Conclusion: Genetic disturbances of MSX1 and PAX9 genes are associated with tooth agenesis within and outside the cleft area.

Published
2013
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13. Initial growth pattern of children with cleft before alveolar bone graft stage according to cleft type

Author

Seung-Hak Baek, Yu-Jin Seo, Young-Ho Kim, and Ji Wan Park

Subjects
medicine.medical_specialty, Primary palate, business.industry, Mandible, Dentistry, Orthodontics, Alveolar bone graft, Maxilla, Orthopedic surgery, Medicine, Gonial angle, Stage (cooking), Secondary palate, business
Abstract

Objective: To test the null hypothesis that there is no significant difference in the initial growth pattern among three cleft types before alveolar bone graft (ABG) according to cleft type (unilateral cleft lip and alveolus [UCLA], unilateral cleft lip and palate [UCLP], and cleft palate [CP]). Materials and Methods: Samples consisted of the UCLA group, the UCLP group, and the CP group. Individuals were treated with the identical surgical technique by the same surgeon and had no history of orthodontic/orthopedic treatment. Lateral cephalograms taken 1 month before ABG were analyzed using 29 variables. One-way analysis of variance (ANOVA) testing and bivariate and logistic regression analyses were performed. Results: An increasing tendency for Class III relationships in the order of UCLA, UCLP, and CP was noted (ANB, AB-to-facial plane angle, AB-to-mandibular plane angle; P , .001, respectively). UCLP and CP groups demonstrated more posterior positioning of the maxilla (SNA, A-to-N-perp; P , .001, respectively) and a hyperdivergent pattern (gonial angle, SN-GoMe angle, FMA; P , .001, respectively) compared with the UCLA group. Because no differences in palatal plane angle and SN-to-occlusal plane angle were noted among the three groups, the hyperdivergent pattern in the UCLP and CP groups might be due to an innate growth pattern and eventual adaptation of the mandible to maxillary growth. UCLP and CP groups showed more Class III relationships (ANB: P , .05, P , .001, respectively) and a more hyperdivergent pattern (FMA: P , .05, P , .01, respectively) than the UCLA group. Conclusion: When the degree of cleft involvement increases from the primary palate to the secondary palate, the predominance of the Class III relationship and the hyperdivergent pattern increases also. (Angle Orthod 2011;81:1103–1110.)

Published
2011
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14. Adiponectin Concentrations: A Genome-wide Association Study

Author

Jae Woong Sull, Eun Soon Shin, Yangsoo Jang, Sang Yeun Kim, Inkyung Baik, Sun Ju Lee, Chol Shin, Linda Kao, Jongkeun Park, Sungjoo Kim Yoon, Terri H. Beaty, Sun Ha Jee, Ji Eun Yun, Eun Jung Jee, Ji Wan Park, Jong Eun Lee, Eunyoung Cho, and Heejin Kimm

Subjects
Adult, Male, Linkage disequilibrium, medicine.medical_specialty, Genotype, Blood Pressure, Genome-wide association study, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Linkage Disequilibrium, Body Mass Index, Cell Line, Insulin resistance, Asian People, Polymorphism (computer science), Report, Internal medicine, Genetics, medicine, Humans, SNP, Genetics(clinical), Allele, Promoter Regions, Genetic, Genetics (clinical), DNA Primers, Adiponectin, Sequence Analysis, DNA, Middle Aged, Cadherins, medicine.disease, Cholesterol, Endocrinology, Female, Genome-Wide Association Study
Abstract

Adiponectin is associated with obesity and insulin resistance. To date, there has been no genome-wide association study (GWAS) of adiponectin levels in Asians. Here we present a GWAS of a cohort of Korean volunteers. A total of 4,001 subjects were genotyped by using a genome-wide marker panel in a two-stage design (979 subjects initially and 3,022 in a second stage). Another 2,304 subjects were used for follow-up replication studies with selected markers. In the discovery phase, the top SNP associated with mean log adiponectin was rs3865188 in CDH13 on chromosome 16 (p = 1.69 × 10(-15) in the initial sample, p = 6.58 × 10(-39) in the second genome-wide sample, and p = 2.12 × 10(-32) in the replication sample). The meta-analysis p value for rs3865188 in all 6,305 individuals was 2.82 × 10(-83). The association of rs3865188 with high-molecular-weight adiponectin (p = 7.36 × 10(-58)) was even stronger in the third sample. A reporter assay that evaluated the effects of a CDH13 promoter SNP in complete linkage disequilibrium with rs3865188 revealed that the major allele increased expression 2.2-fold. This study clearly shows that genetic variants in CDH13 influence adiponectin levels in Korean adults.

Published
2010
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15. Genome-Wide Association Analyses on Blood Pressure Using Three Different Phenotype Definitions

Author

Jong-Young Lee, Yoon Shin Cho, Saanyong Uhmm, Chol Shin, Nam H. Cho, and Ji Wan Park

Subjects
Candidate gene, Case-control study, Physiology, Health Informatics, Genome-wide association study, Single-nucleotide polymorphism, Biology, Bioinformatics, Blood pressure, Genetics, SNP, Risk factor, ATP2B1, Ecology, Evolution, Behavior and Systematics, circulatory and respiratory physiology
Abstract

Hypertension is the most prevalent disease worldwide and is itself a risk factor for cerebral, cardiac, and renal diseases. The inconsistency of candidate genes suggested by previous genomewide association studies (GWASs) may be due to not only differences in study design and genetic or environmental background but also the difference in the power of analysis between continuous traits and discrete traits. We analyzed 352,228 single nucleotide polymorphisms (SNPs) in 8842 unrelated Koreans obtained from Ansan and Ansung cohorts. We performed a series of GWA analyses using three different phenotype models; young hypertensive cases (278 subjects) versus elderly normotensive controls (680 subjects); the upper 25% (2211 hypertensive cases) versus the lower 25% of the SBP distribution (2211 hypotensive controls); and finally SBP and DBP as continuous traits (8842 subjects). The numbers of young hypertensive cases and elderly normotensive controls were not large enough to achieve genomewide significance. The model comparing the upper 25% subjects to the lower 25% of subjects showed a power that was approximate to that of QTL analysis. Two neighboring SNPs of the ATP2B1 gene, rs17249754 (SBP, p=; DBP, p=) and rs7136259 (SBP, p=; DBP, p=), were associated with both SBP and DBP. Interestingly, a SNP of the RPL6 gene, rs11066280, revealed a significant genomewide association with SBP in men only (p=), and four SNPs located near the MAN2A1 gene showed a strong association with DBP only in elderly men aged 60-70 years (e.g., rs6421827, p=). However, we did not observe any gene variant attaining genomewide significance consistently in the three phenotype models except for the ATP2B1 gene variants. In general, the association signal with blood pressure was stronger in women than in men. Genes identified in GWASs are expected to open the way for prevention, early diagnosis, and personalized treatment of hypertension.

Published
2010
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16. BMI and Stroke Risk in Korean Women

Author

Sun Ha Jee, Heon Choe, Su Young Kim, Sang-Yi Lee, and Ji Wan Park

Subjects
Adult, medicine.medical_specialty, Endocrinology, Diabetes and Metabolism, Medicine (miscellaneous), Body Mass Index, Cohort Studies, Stroke risk, Endocrinology, Risk Factors, Internal medicine, Odds Ratio, medicine, Humans, Longitudinal Studies, Obesity, Prospective Studies, cardiovascular diseases, Prospective cohort study, Stroke, Aged, Proportional Hazards Models, Aged, 80 and over, Korea, Nutrition and Dietetics, business.industry, Proportional hazards model, Smoking, Age Factors, Odds ratio, Middle Aged, medicine.disease, Physical therapy, Female, business, Body mass index, Cohort study
Abstract

Objective: Obesity is known to be associated with diverse disease outcomes; however, the effect of body weight on the occurrence of stroke remains controversial and has not been studied sufficiently, particularly among Asian populations and among women. The purpose of this study was to investigate the effect of BMI on the risk of stroke among Korean women. Methods and Procedures: The overall risk of stroke and the risk of different subtypes of stroke were evaluated in relation to BMI using Cox's proportional hazard models among 439,582 Korean women, aged 30–95 years, in a 13-year prospective cohort study with enrollment from 1992–1995. Stratified analyses were performed for age groups and cigarette smoking status. Results: The average BMI was 23.2 kg/m2 at baseline. The overall risk of stroke was the lowest in the group with a BMI

Published
2008
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17. Analysis of candidate genes on chromosome 2 in oral cleft case-parent trios from three populations

Author

Ethylin Wang Jabs, Ji Wan Park, I. S. L. Ng, Kung-Yee Liang, Richard J. Redett, Roxann G. Ingersoll, Yah Huei Wu-Chou, Vincent Yeow, Craig A. Vanderkolk, Shangzhi Huang, Simeon A. Boyadjiev, Hong Wang, Philip Kuo-Ting Chen, Terri H. Beaty, Jae Woong Sull, M. D. Fallin, Felicia S.H. Cheah, Iain McIntosh, Y. F. Chiu, Jun Cheng, Alan F. Scott, Xiaoqian Ye, Jacqueline B. Hetmanski, and Samuel S. Chong

Subjects
Male, Candidate gene, Linkage disequilibrium, Genotype, Genetic Linkage, Cleft Lip, Population, Taiwan, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Linkage Disequilibrium, Nuclear Family, Gene Frequency, Genetic linkage, Genetic variation, Genetics, Humans, Genetic Predisposition to Disease, education, Genetics (clinical), Family Health, Singapore, education.field_of_study, Maryland, Haplotype, Chromosome Mapping, Cleft Palate, Haplotypes, Chromosomes, Human, Pair 2, Multivariate Analysis, Female, Allelic heterogeneity
Abstract

Isolated oral clefts, including cleft lip with/without cleft palate (CL/P) and cleft palate (CP), have a complex and heterogeneous etiology. Case-parent trios from three populations were used to study genes spanning chromosome 2, where single nucleotide polymorphic (SNP) markers were analyzed individually and as haplotypes. Case-parent trios from three populations (74 from Maryland, 64 from Singapore and 95 from Taiwan) were genotyped for 962 SNPs in 104 genes on chromosome 2, including two well-recognized candidate genes: TGFA and SATB2. Individual SNPs and haplotypes (in sliding windows of 2-5 SNPs) were used to test for linkage and disequilibrium separately in CL/P and CP trios. A novel candidate gene (ZNF533) showed consistent evidence of linkage and disequilibrium in all three populations for both CL/P and CP. SNPs in key regions of ZNF533 showed considerable variability in estimated genotypic odds ratios and their significance, suggesting allelic heterogeneity. Haplotype frequencies for regions of ZNF533 were estimated and used to partition genetic variance into among-and within-population components. Wright's fixation index, a measure of genetic diversity, showed little difference between Singapore and Taiwan compared with Maryland. The tensin-1 gene (TNS1) also showed evidence of linkage and disequilibrium among both CL/P and CP trios in all three populations, albeit at a lower level of significance. Additional genes (VAX2, GLI2, ZHFX1B on 2p; WNT6-WNT10A and COL4A3-COL4A4 on 2q) showed consistent evidence of linkage and disequilibrium only among CL/P trios in all three populations, and TGFA showed significant evidence in two of three populations.

Published
2006
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18. Comparing Whole-Genome Amplification Methods and Sources of Biological Samples for Single-Nucleotide Polymorphism Genotyping

Author

Alan F. Scott, Paul Boyce, Iain McIntosh, Ji Wan Park, and Terri H. Beaty

Subjects
Genotype, Clinical Biochemistry, Buccal swab, Loop-mediated isothermal amplification, Bacillus Phages, DNA-Directed DNA Polymerase, Biology, Molecular Inversion Probe, Polymerase Chain Reaction, Polymorphism, Single Nucleotide, Specimen Handling, Humans, Genotyping, Gene Library, Genetics, Whole Genome Amplification, Blood Specimen Collection, Genome, Human, Biochemistry (medical), Multiple displacement amplification, Reproducibility of Results, DNA, Φ29 DNA polymerase, Molecular biology, genomic DNA, Nucleic Acid Amplification Techniques
Abstract

High-throughput genotyping systems promise to be an efficient means of identifying susceptibility genes involved in the etiology of non-Mendelian disorders. Adequate amounts of high-quality DNA are essential, however, for large-scale genotyping studies (1). The supply of genomic DNA is frequently limited, and the quality of DNA obtained from oral buccal swabs or Guthrie cards has not been thoroughly evaluated for high-throughput single-nucleotide polymorphism (SNP) genotyping (2)(3). Whole-genome amplification (WGA) technologies offer the opportunity to expand DNA from depleted biological samples. The first generation of WGA strategies (i.e., PCR-based methods) (4)(5), however, was limited by substantial amplification bias and incomplete coverage of genetic markers (6)(7). Recently, new strategies for WGA, such as multiple displacement amplification (MDA) or OmniPlex® WGA technology (Rubicon Genomics) have been developed. MDA is an isothermal amplification with the bacteriophage φ29 DNA polymerase (6)(8), whereas OmniPlex uses in vitro libraries with fragmented DNA (∼1.5 kb) to amplify the entire genome by PCR (9). To apply WGA technology to BeadArray™ genotyping (Illumina), the utility of MDA and/or OmniPlex on DNA samples derived from lymphoblast cells has been evaluated (9)(10). In this study, we determined the genotyping success rate and reliability of 2 MDA variants (8)(11) and OmniPlex with and without 7-deaza-dGTP, using buccal swabs, whole blood, dried blood spots, and sheared genomic DNA on 1260- and 1228-SNP BeadArray panels. The 7-deaza-dGTP nucleotide analog was included in an attempt to ensure amplification of GC-rich DNA. After Institutional Review Board approval and informed consent were obtained, DNA samples were collected from participants in a study of oral clefts (12). Genomic DNA samples were prepared from peripheral blood by protein precipitation (13). Buccal cells were obtained by rubbing the inside of cheeks with a brush (Medical Packaging), and DNA …

Published
2005
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19. Phenotype Severity and Genetic Variation at the Disease Locus: An Investigation of Nail Dysplasia in the Nail Patella Syndrome

Author

J. A. Dunston, S. Lin, M. Malbroux, Iain McIntosh, and Ji Wan Park

Subjects
Male, Genotype, Genetic Linkage, DNA Mutational Analysis, LIM-Homeodomain Proteins, Nails, Malformed, Locus (genetics), Biology, Polymorphism, Single Nucleotide, symbols.namesake, Nail-Patella Syndrome, Genetic variation, Genetics, medicine, Humans, SNP, Allele, Genetics (clinical), Nail patella syndrome, Homeodomain Proteins, Haplotype, Genetic Variation, medicine.disease, Phenotype, Haplotypes, Mutation, Mendelian inheritance, symbols, Female, Transcription Factors
Abstract

Summary The genetic bases underlying the range and severity of phenotypes in Mendelian disorders is poorly understood; however, improvements in this area have the potential to facilitate analysis of oligogenic disorders. The nail dysplasia observed in Nail Patella Syndrome (NPS) was selected as a quantifiable variable within a Mendelian disorder, for which data could be readily obtained, to allow investigation of the genetic basis of variation. Analysis of SNP haplotypes across the LMX1B gene demonstrated association between the haplotype of the mutant allele and the variability in the nail score (p = 0.024). These results are in contrast to those obtained previously, which supported a modifying role for the wild-type allele. Since there is no evidence that particular mutations, or classes of mutation, are associated with the variation (p > 0.5), further work is required to identify the elements associated with the LMX1B gene that mediate phenotypic severity.

Published
2005
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20. Novel Genetic Variants Associated with Lumbar Spondylosis in Koreans : A Genome-Wide Association Study.

Author

Hyun Ah Kim, Seong Gu Heo, Ji Wan Park, and Young Ok Jung

Subjects
SPONDYLOSIS, SINGLE nucleotide polymorphisms, RADIOGRAPHY, G protein coupled receptors, KOREANS, DIAGNOSIS, DISEASES
Abstract

Objective : The aim of this study was to identify the susceptibility genes responsible for lumbar spondylosis (LS) in Korean patients. Methods : Data from 1427 subjects were made available for radiographic grading and genome wide association studies (GWAS) analysis. Lateral lumbar spine radiographs were obtained and the various degrees of degenerative change were semi-quantitatively scored. A pilot GWAS was performed using the AffymetrixGenome-Wide Human single-nucleotide polymorphisms (SNPs), 500K array. A total of 352228 SNPs were analyzed and the association between the SNPs and case-control status was analyzed by stepwise logistic regression analyses. Results : The top 100 SNPs with a cutoff p-value of less than 3.7×10-4 were selected for joint space narrowing, while a cutoff p-value of 6.0×10-4 was applied to osteophytes and the Kellgren-Lawrence (K-L) osteoarthritis grade. The SNPs with the strongest effect on disc space narrowing, osteophytes, and K-L grade were serine incorporator 1 (rs155467, odds ratio [OR]=17.58, p=1.6×10-4), stromal interaction molecule 2 (STIM1, rs210781, OR=5.53, p=5×10-4), and transient receptor potential cation channel, subfamily C (rs11224760, OR=3.99, p=4.8×10-4), respectively. Leucine-rich repeat-containing G protein-coupled receptor 4 was significantly associated with both disc space narrowing and osteophytes (rs1979400, OR=2.01, p=1.1×10-4 for disc space narrowing, OR=1.79, p=3×10-4 for osteophytes), while zinc finger and BTB domain containing 7C was significantly and negatively associated with both osteophytes and a K-L grade >2 (rs12457004,OR=0.25, p=5.8×10-4 and OR=0.27, p=5.3×10-4, respectively). Conclusion : We identified SNPs that potentially contribute to the pathogenesis of LS. This is the first report of a GWAS in an Asian population. [ABSTRACT FROM AUTHOR]

Published
2018
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21. Initial growth pattern of children with cleft before alveolar bone graft stage according to cleft type

Author

Yu-Jin, Seo, Ji-Wan, Park, Young Ho, Kim, and Seung-Hak, Baek

Subjects
Male, Analysis of Variance, Bone Transplantation, Cephalometry, Cleft Lip, Vertical Dimension, Original Articles, Cleft Palate, Logistic Models, Malocclusion, Angle Class III, Face, Alveolar Process, Humans, Female, Child, Maxillofacial Development, Retrospective Studies
Abstract

OBJECTIVE: To test the null hypothesis that there is no significant difference in the initial growth pattern among three cleft types before alveolar bone graft (ABG) according to cleft type (unilateral cleft lip and alveolus [UCLA], unilateral cleft lip and palate [UCLP], and cleft palate [CP]). MATERIALS AND METHODS: Samples consisted of the UCLA group, the UCLP group, and the CP group. Individuals were treated with the identical surgical technique by the same surgeon and had no history of orthodontic/orthopedic treatment. Lateral cephalograms taken 1 month before ABG were analyzed using 29 variables. One-way analysis of variance (ANOVA) testing and bivariate and logistic regression analyses were performed. RESULTS: An increasing tendency for Class III relationships in the order of UCLA, UCLP, and CP was noted (ANB, AB-to-facial plane angle, AB-to-mandibular plane angle; P < .001, respectively). UCLP and CP groups demonstrated more posterior positioning of the maxilla (SNA, A-to-N-perp; P < .001, respectively) and a hyperdivergent pattern (gonial angle, SN-GoMe angle, FMA; P < .001, respectively) compared with the UCLA group. Because no differences in palatal plane angle and SN-to-occlusal plane angle were noted among the three groups, the hyperdivergent pattern in the UCLP and CP groups might be due to an innate growth pattern and eventual adaptation of the mandible to maxillary growth. UCLP and CP groups showed more Class III relationships (ANB: P < .05, P < .001, respectively) and a more hyperdivergent pattern (FMA: P < .05, P < .01, respectively) than the UCLA group. CONCLUSION: When the degree of cleft involvement increases from the primary palate to the secondary palate, the predominance of the Class III relationship and the hyperdivergent pattern increases also.

Published
2011

22. Association between genes on chromosome 4p16 and non-syndromic oral clefts in four populations

Author

Tao Wu, Yah Huei Wu-Chou, Gerald V. Raymond, Jae Woong Sull, Sun Ha Jee, Samuel S. Chong, Terri H. Beaty, Vincent Yeow, Alan F. Scott, Ji Wan Park, Richard J. Redett, Jacqueline B. Hetmanski, Iain McIntosh, Xiaoqian Ye, Tanda Murray, Ethylin Wang Jabs, Roxann G. Ingersoll, Felicia S.H. Cheah, Shangzhi Huang, M. Daniele Fallin, Philip Kuo Ting Chen, and Hong Wang

Subjects
Male, Linkage disequilibrium, Candidate gene, Cleft Lip, Taiwan, Single-nucleotide polymorphism, Genome-wide association study, Biology, Protein Serine-Threonine Kinases, Polymorphism, Single Nucleotide, Article, Linkage Disequilibrium, Genetic variation, Genotype, Genetics, Humans, Genetic Predisposition to Disease, Allele, Genetics (clinical), MSX1 Transcription Factor, Singapore, Korea, Maryland, Haplotype, Membrane Proteins, Proteins, Cleft Palate, stomatognathic diseases, Genetics, Population, Genes, Intercellular Signaling Peptides and Proteins, Female, Chromosomes, Human, Pair 4, Genome-Wide Association Study
Abstract

Isolated cleft lip with or without cleft palate and cleft palate are among the most common human birth defects. Several candidate gene studies on MSX1 have shown significant association between markers in MSX1 and risk of oral clefts, and re-sequencing studies have identified multiple mutations in MSX1 in a small minority of cases, which may account for 1–2% of all isolated oral clefts cases. We explored the 2-Mb region around MSX1, using a marker map of 393 single nucleotide polymorphisms (SNPs) in 297 cleft lip, with or without cleft palate, case–parent trios and 84 cleft palate trios from Maryland, Taiwan, Singapore, and Korea. Both individual markers and haplotypes of two to five SNPs showed several regions yielding statistical evidence for linkage and disequilibrium. Two genes (STK32B and EVC) yielded consistent evidence from cleft lip, with or without cleft palate, trios in all four populations. These two genes plus EVC2 also yielded suggestive evidence for linkage and disequilibrium among cleft palate trios. This analysis suggests that several genes, not just MSX1, in this region may influence risk of oral clefts.

Published
2010

23. Candidate gene polymorphisms for diabetes mellitus, cardiovascular disease and cancer are associated with longevity in Koreans

Author

Yong Ick Ji, Joobae Park, Yoo Sook Joung, Duk-Hwan Kim, Byung Kyu Kang, Sang Chul Park, Eunhyun Jung, Se Young Cho, Yoon-Ho Choi, Hee-Youn Cho, Ji Wan Park, and Mi Yeon Kang

Subjects
Adult, Genetic Markers, Male, Candidate gene, media_common.quotation_subject, Clinical Biochemistry, Longevity, Genome-wide association study, Biology, Biochemistry, Diabetes mellitus genetics, Sex Factors, Asian People, Neoplasms, Genetic model, Genotype, Diabetes Mellitus, Humans, Paired Box Transcription Factors, Allele, Molecular Biology, Alleles, media_common, Aged, Genetics, Aged, 80 and over, Homeodomain Proteins, Korea, Polymorphism, Genetic, Aldehyde Dehydrogenase, Mitochondrial, Haplotype, Aldehyde Dehydrogenase, Middle Aged, ErbB Receptors, src-Family Kinases, Haplotypes, Proprotein Convertase 1, Cardiovascular Diseases, Molecular Medicine, Original Article, Female
Abstract

Long-lived people may have a unique genetic makeup that makes them more resistant than the general population to prevalent age-related diseases; however, not much is known about genes involved in the longevity. To identify susceptibility variants controlling longevity, we performed a high-throughput candidate gene study using 137 Koreans over 90 yr old and 213 young healthy Koreans. We evaluated 463 informative markers located in 176 candidate genes mostly for diabetes mellitus, cardiovascular disease and cancer under five genetic models. We estimated the odds ratios for each allele, genotype, haplotype, and gene-gene interaction using logistic regression analysis. Associations between 13 genes and longevity were detected at a P-value less than 0.01. Particularly, the rs671 (A) allele of the aldehyde dehydrogenase 2 family (mitochondrial) (ALDH2) gene was associated with longevity only in men (OR 2.11, P = 0.008). Four genes, proprotein convertase subtilisin/kexin type 1 (PCSK1, P = 0.008), epidermal growth factor receptor (EGFR, P = 0.003), paired box 4 (PAX4, P = 0.008), and V-yes-1 Yamaguchi sarcoma viral related oncogene homolog (LYN, P = 0.002) consistently yielded statistical evidence for association with longevity. The findings of the current study may provide a starting point for future studies to unravel genetic factors controlling longevity in Koreans.

Published
2009

24. Evidence that TGFA influences risk to cleft lip with/without cleft palate through unconventional genetic mechanisms

Author

Felicia S.H. Cheah, Ethylin Wang Jabs, Vincent Yeow, Ji Wan Park, Roxann G. Ingersoll, Tao Wu, Philip Kuo Ting Chen, Jacqueline B. Hetmanski, Samuel S. Chong, Alan F. Scott, Jae Woong Sull, Kung Yee Liang, Beyoung Yun Park, Richard J. Redett, Yah Huei Wu-Chou, Sun Ha Jee, Terri H. Beaty, and M. D. Fallin

Subjects
Male, Parents, Linkage disequilibrium, Genotype, Cleft Lip, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Article, Linkage Disequilibrium, Protein Interaction Mapping, Genetics, Humans, Imprinting (psychology), Genetics (clinical), Singapore, Maternal Transmission, Models, Genetic, Haplotype, Transmission disequilibrium test, Transforming Growth Factor alpha, Cleft Palate, Maternal Exposure, Interferon Regulatory Factors, IRF6, Female
Abstract

This study examined the association between markers in transforming growth factor alpha (TGFA) and isolated, non-syndromic cleft lip with/without palate (CL/P) using a case–parent trio design, considering parent-of-origin effects. We also tested for gene–environmental interaction with common maternal exposures, and for gene–gene interaction using markers in TGFA and another recognized causal gene, IRF6. CL/P case–parent trios from four populations (76 from Maryland, 146 from Taiwan, 35 from Singapore, and 40 from Korea) were genotyped for 17 single nucleotide polymorphisms (SNPs) in TGFA. The transmission disequilibrium test was used to test individual SNPs, and the parent-of-origin likelihood ratio test (PO-LRT) was used to assess parent-of-origin effects. We also screened for possible gene–environment interaction using PBAT, and tested for gene–gene interaction using conditional logistic regression models. When all trios were combined, four SNPs showed significant excess maternal transmission, two of which gave significant PO-LRT values [rs3821261: P = 0.004 and OR(imprinting) = 4.17; and rs3771475: P = 0.027 and OR(imprinting) = 2.44]. Haplotype analysis of these two SNPS also supported excess maternal transmission. We saw intriguing but suggestive evidence of G × E interaction for several SNPs in TGFA when either individual SNPs or haplotypes of adjacent SNPs were considered. Thus, TGFA appears to influence risk of CL/P through unconventional means with an apparent parent-of-origin effect (excess maternal transmission) and possible interaction with maternal exposures.

Published
2009

25. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits

Author

Jong-Young Lee, Seung Hun Cha, Nam H. Cho, Young-Jin Kim, Dong Joon Kim, Dankyu Yoon, Yoon Shin Cho, Taesung Park, Bok Ghee Han, Ji Wan Park, Man Suk Park, Jun Woo Kim, Bermseok Oh, Hye Ree Han, Hye Yoon Jang, Miey Park, Jee Yeon Heo, Eunyoung Cho, Mark I. McCarthy, Lon R. Cardon, Haesook Min, Jong Koo Lee, Jong Eun Lee, Hyung Lae Kim, Ji Hee Oh, Geraldine M. Clarke, Younjhin Ahn, Jong-Keuk Lee, Chol Shin, Min Jin Go, Hyo Jeong Ban, and Mi-Hee Lee

Subjects
Adult, Genotype, Population, Quantitative Trait Loci, Population genetics, Genome-wide association study, Quantitative trait locus, Biology, Polymorphism, Single Nucleotide, Body Mass Index, Cohort Studies, Asian People, Bone Density, Genetics, Humans, Genetic Predisposition to Disease, education, Gene, Aged, education.field_of_study, Genome, Human, Waist-Hip Ratio, Chromosome, Middle Aged, Human genome, Genome-Wide Association Study
Abstract

To identify genetic factors influencing quantitative traits of biomedical importance, we conducted a genome-wide association study in 8,842 samples from population-based cohorts recruited in Korea. For height and body mass index, most variants detected overlapped those reported in European samples. For the other traits examined, replication of promising GWAS signals in 7,861 independent Korean samples identified six previously unknown loci. For pulse rate, signals reaching genome-wide significance mapped to chromosomes 1q32 (rs12731740, P = 2.9 x 10(-9)) and 6q22 (rs12110693, P = 1.6 x 10(-9)), with the latter approximately 400 kb from the coding sequence of GJA1. For systolic blood pressure, the most compelling association involved chromosome 12q21 and variants near the ATP2B1 gene (rs17249754, P = 1.3 x 10(-7)). For waist-hip ratio, variants on chromosome 12q24 (rs2074356, P = 7.8 x 10(-12)) showed convincing associations, although no regional transcript has strong biological candidacy. Finally, we identified two loci influencing bone mineral density at multiple sites. On chromosome 7q31, rs7776725 (within the FAM3C gene) was associated with bone density at the radius (P = 1.0 x 10(-11)), tibia (P = 1.6 x 10(-6)) and heel (P = 1.9 x 10(-10)). On chromosome 7p14, rs1721400 (mapping close to SFRP4, a frizzled protein gene) showed consistent associations at the same three sites (P = 2.2 x 10(-3), P = 1.4 x 10(-7) and P = 6.0 x 10(-4), respectively). This large-scale GWA analysis of well-characterized Korean population-based samples highlights previously unknown biological pathways.

Published
2009

26. Maternal transmission effects of the PAX genes among cleft case-parent trios from four populations

Author

Ethylin Wang Jabs, Terri H. Beaty, Ji Wan Park, Vincent Yeow, Richard J. Redett, Jae Woong Sull, Roxanne G. Ingersoll, Beyoung Yun Park, Sun Ha Jee, M. D. Fallin, Kung Yee Liang, Jacqueline B. Hetmanski, Alan F. Scott, Yah Huei Wu-Chou, Samuel S. Chong, Felicia S.H. Cheah, and Philip Kuo Ting Chen

Subjects
Male, Candidate gene, Linkage disequilibrium, Genotype, PAX6 Transcription Factor, Cleft Lip, Population, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Linkage Disequilibrium, Article, Genetics, Humans, Paired Box Transcription Factors, education, Eye Proteins, PAX3 Transcription Factor, Genetics (clinical), Homeodomain Proteins, education.field_of_study, Maternal Transmission, Maternal effect, Pax genes, PAX7 Transcription Factor, Transmission disequilibrium test, Cleft Palate, Repressor Proteins, Case-Control Studies, Female, PAX9 Transcription Factor
Abstract

Isolated cleft lip with or without cleft palate (CL/P) is among the most common human birth defects, with a prevalence of 1 in 700 live births. The paired box (PAX) genes have been suggested as candidate genes for CL/P based largely on mouse models; however, few human studies have focused on this gene family. This study tests for association between markers in four PAX genes and CL/P using a case-parent trio design considering parent-of-origin effects. Trios from four populations (76 from Maryland, 146 from Taiwan, 35 from Singapore, and 40 from Korea) were genotyped for 34 single nucleotide polymorphisms (SNPs) in the PAX3, PAX6, PAX7, and PAX9 genes. We performed the transmission disequilibrium test (TDT) on individual SNPs. Parent-of-origin effects were assessed using the transmission asymmetry test (TAT) and the parent-of-origin likelihood ratio test (PO-LRT). TDT analysis showed one SNP (rs766325) in PAX7 yielding evidence of linkage and association when parent-of-origin was not considered, with an OR(transmission)=1.62 (P=0.003), and five SNPs in PAX6 (including two pairs in near perfect linkage disequilibrium). TAT analysis of all trios revealed two SNPs in PAX7 and four SNPs in PAX3 showing significant excess maternal transmission. For these six SNPs, the maternal OR(transmission) ranged between 1.74 and 2.40, and PO-LRT was also significant (P-values=0.035–0.012). When this analysis was limited to trios with male cases, SNPs in PAX7 showed higher maternal OR(transmission) and greater significance. PAX genes may influence the risk of CL/P through maternal effects, possibly imprinting, which seems to be stronger among male cases.

Published
2009

27. Excess Maternal Transmission of Markers in TCOF1 Among Cleft Palate Case-Parent Trios From Three Populations

Author

Ethylin Wang Jabs, Yah-Huei Wu-Chou, Jae Woong Sull, Jacqueline B. Hetmanski, Beyoung Yun Park, Richard J. Redett, Kung-Yee Liang, Samuel S. Chong, Terri H. Beaty, Alan F. Scott, Vincent Yeow, Philip Kuo-Ting Chen, M. Daniele Fallin, Roxanne G. Ingersoll, Ji Wan Park, Sun Ha Jee, and Felicia S.H. Cheah

Subjects
Genetic Markers, Male, Candidate gene, Linkage disequilibrium, Population, Taiwan, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Article, Linkage Disequilibrium, Genomic Imprinting, Gene Frequency, Risk Factors, Genetics, Humans, education, Allele frequency, Genetics (clinical), education.field_of_study, Likelihood Functions, Singapore, Chi-Square Distribution, Maryland, Haplotype, Nuclear Proteins, Transmission disequilibrium test, Phosphoproteins, Minor allele frequency, Cleft Palate, Haplotypes, Female
Abstract

Isolated cleft palate is among the most common human birth defects. The TCOF1 gene has been suggested as a candidate gene for cleft palate based on animal models. This study tests for association between markers in TCOF1 and isolated, nonsyndromic cleft palate using a case-parent trio design considering parent-of-origin effects. Case-parent trios from three populations (comprising a total of 81 case-parent trios) were genotyped for single nucleotide polymorphisms (SNPs) in the TCOF1 gene. We used the transmission disequilibrium test and the transmission asymmetry test on individual SNPs. When all trios were combined, the odds ratio for transmission of the minor allele, OR(transmission), was significant for SNP rs15251 (OR = 2.88, P = 0.007), as well as rs2255796 and rs2569062 (OR = 2.08, P = 0.03; OR = 2.43, P = 0.041; respectively) when parent of origin was not considered. The transmission asymmetry test also revealed one SNP (rs15251) showing excess maternal transmission significant at the P = 0.005 level (OR = 6.50). Parent-of-origin effects were assessed using the parent-of-origin likelihood ratio test on both SNPs and haplotypes. While the parent-of-origin likelihood ratio test was only marginally significant for this SNP (P = 0.136), analysis of haplotypes of rs2255796 and rs15251 suggested excess maternal transmission. Therefore, these data suggest TCOF1 may influence risk of cleft palate through a parent-of-origin effect.

Published
2008

28. Differential Parental Transmission of Markers in RUNX2 Among Cleft Case-Parent Trios From Four Populations

Author

Felicia S.H. Cheah, Philip Kuo Ting Chen, Ingo Ruczinski, Alan F. Scott, Yah Huei Wu-Chou, Euiju Jung, Kung Yee Liang, M. D. Fallin, Ethylin Wang Jabs, Jae Woong Sull, Roxann G. Ingersoll, Terri H. Beaty, Beyoung Yun Park, Jacqueline B. Hetmanski, Samuel S. Chong, Richard J. Redett, Ji Wan Park, Vincent Yeow, and Sun Ha Jee

Subjects
Genetic Markers, Male, Candidate gene, Linkage disequilibrium, Genotype, Epidemiology, Cleft Lip, Parenteral transmission, Inheritance Patterns, Taiwan, Single-nucleotide polymorphism, Core Binding Factor Alpha 1 Subunit, Biology, Polymorphism, Single Nucleotide, Article, Linkage Disequilibrium, Genomic Imprinting, Humans, Genetic Predisposition to Disease, Genetics (clinical), Genetics, Likelihood Functions, Singapore, Maternal Transmission, Korea, Maryland, Transmission disequilibrium test, Odds ratio, Cleft Palate, Female
Abstract

Isolated cleft lip with or without cleft palate (CL/P) is among the most common human birth defects, with a prevalence around 1 in 700 live births. The Runt-related transcription factor 2 (RUNX2) gene has been suggested as a candidate gene for CL/P based largely on mouse models; however, no human studies have focused on RUNX2 as a risk factor for CL/P. This study examines the association between markers in RUNX2 and isolated, nonsyndromic CL/P using a case-parent trio design, while considering parent-of-origin effects. Case-parent trios from four populations (77 from Maryland, 146 from Taiwan, 35 from Singapore, and 40 from Korea) were genotyped for 24 single nucleotide polymorphisms (SNPs) in the RUNX2 gene. We performed the transmission disequilibrium test on individual SNPs. Parent-of-origin effects were assessed using the transmission asymmetry test and the parent-of-origin likelihood ratio test (PO-LRT). When all trios were combined, the transmission asymmetry test revealed a block of 11 SNPs showing excess maternal transmission significant at the P

Published
2008

29. Finding pathway regulators: gene set approach using peak identification algorithms

Author

Jung Hoon Woo, Eunjee Lee, Taesung Park, and Ji Wan Park

Subjects
Genetics, Messenger RNA, lcsh:R, Pair-rule gene, lcsh:Medicine, General Medicine, Biology, General Biochemistry, Genetics and Molecular Biology, Biological pathway, Proceedings, Genotype, Multiple comparisons problem, Gene expression, lcsh:Q, lcsh:Science, Set (psychology), Gene
Abstract

Recently, a number of different approaches have been used to examine variation in gene expression and to identify genes whose level of transcript differed greatly among unrelated individuals. Previous studies have commonly focused on identifying determinants that regulate gene expressions by targeting individual genes. However, it is difficult to detect true differences in the level of gene expression among genotypes from noise due to issues such as multiple testing and limited sample size. To increase the statistical power for detecting this difference, we consider a 'gene set' approach by focusing on subtle but coordinated changes in gene expression across multiple genes rather than individual genes. We defined a 'gene set' as a set of genes in the same biological pathway and focused on identifying common regulators based on an assumption that the genes within the same pathway are controlled by common regulators. We applied the gene set approach to the expression data of mRNA in Centre d'Etude du Polymorphisme Humain lymphoblast cells to identify regulators controlling the genes in a biological pathway. Our gene set approach successfully identified potent regulators controlling gene expression in an inflammatory response pathway.

Published
2007
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30. Association between IRF6 and nonsyndromic cleft lip with or without cleft palate in four populations

Author

Ethylin Wang Jabs, Ji Wan Park, Philip Kuo Ting Chen, Alan F. Scott, Craig A. Vander Kolk, Roxann G. Ingersoll, M. Daniele Fallin, Jacqueline B. Hetmanski, Samuel S. Chong, Yah Huei Wu-Chou, Terri H. Beaty, Vincent Yeow, Beyoung Yun Park, Sun Ha Jee, and Iain McIntosh

Subjects
Linkage disequilibrium, Genotype, Cleft Lip, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Linkage Disequilibrium, Article, Asian People, Population Groups, medicine, Humans, Van der Woude syndrome, Genetic Predisposition to Disease, Allele, Genetics (clinical), Genetics, Oral cleft, Haplotype, Infant, Newborn, medicine.disease, Cleft Palate, Haplotypes, Interferon Regulatory Factors, IRF6
Abstract

Nonsyndromic cleft lip with or without cleft palate (CL/P) is one of the most common birth defects with the birth prevalence being highest in Asian (2/1000 live births), intermediate in European (1/1000 live births), and lowest in African populations (0.4/1000 live births). CL/P is a complex disease with both genetic and environmental risk factors.1 Mutations in the interferon regulatory factor 6 gene (IRF6) located on chromosome 1q32.3-q41 are responsible for a majority of van der Woude syndrome (VWS) cases. VWS is an autosomal dominant syndrome that includes an oral cleft and pits on the lower lip in approximately 85% of cases. Fifteen percent of VWS cases have an isolated cleft with no lip pits and are clinically indistinguishable from nonsyndromic CL/P.2 The GATA124F08 marker located 1Mb from IRF6 has shown a significant heterogeneity LOD (1.15) with α = 0.45,3 and an anonymous marker (D1S205) in IRF6 has yielded significant evidence of linkage and linkage disequilibrium (LD) in 106 nonsyndromic CL/P trios.4 Recently, strong evidence of overtransmission of the G allele at the IRF6 c.820G>A marker (rs2235371) was found in CL/P case-parent trios from Asia and South America,5 and a significantly higher frequency of the GG genotype was observed among 192 Thai CL/P cases compared with controls (odds ratio = 1.67).6 This variant creates a valine→isoleucine substitution at amino acid 274 (p.V274I) in the protein-binding domain [the Smad-interferon regulatory factor binding domain (SMIR)] of IRF6, but the A allele is rare in white populations. Analysis of seven other single nucleotide polymorphisms (SNPs) in and around IRF6 has shown several distinct haplotypes demonstrating altered transmission in Io-wan and Danish trios.5 Confirmatory studies using Italian, European-American, and Belgian CL/P families, respectively, have strengthened the evidence that IRF6 is important in the etiology of nonsyndromic oral clefts.7–9 Risk of CL/P associated with particular variants in IRF6 may differ among ethnic groups, however. Here, we evaluated 13 SNPs in and around IRF6 to test for association with nonsyndromic CL/P in 77 European-American (including five incomplete trios), 146 (three incomplete trios) and 34 (11 incomplete trios) Han Chinese trios from Taiwan and Singapore, respectively, plus 40 (two incomplete trios) Korean CL/P trios. Expression of IRF6 in human craniofacial structures was also determined using publicly available data.

Published
2007

31. High throughput SNP and expression analyses of candidate genes for non‐syndromic oral clefts

Author

Michael Lovett, Tania Attié-Bitach, Michel Vekemans, Iain McIntosh, Alan F. Scott, M. D. Fallin, Terri H. Beaty, Juanliang Cai, Jacqueline B. Hetmanski, Roxann G. Ingersoll, Ethylin Wang Jabs, and Ji Wan Park

Subjects
Linkage disequilibrium, Candidate gene, Cleft Lip, ZNF202, Single-nucleotide polymorphism, Polymorphism, Single Nucleotide, Genetic determinism, Linkage Disequilibrium, Craniofacial Abnormalities, Reference Values, Genetics, SNP, Humans, Gene, Genotyping, Genetics (clinical), Oligonucleotide Array Sequence Analysis, biology, Maryland, Chromosome Mapping, DNA, Cleft Palate, biology.protein, Original Article, Mouth Abnormalities
Abstract

Background: Recent work suggests that multiple genes and several environmental risk factors influence risk for non-syndromic oral clefts, one of the most common birth defects in humans. Advances in high-throughput genotyping technology now make it possible to test multiple markers in many candidate genes simultaneously. Methods: We present findings from family based association tests of single nucleotide polymorphism (SNP) markers in 64 candidate genes genotyped using the BeadArray approach in 58 case-parent trios from Maryland (USA) to illustrate how multiple markers in multiple genes can be analysed. To assess whether these genes were expressed in human craniofacial structures relevant to palate and lip development, we also analysed data from the Craniofacial and Oral Gene Expression Network (COGENE) consortium, and searched public databases for expression profiles of these genes. Results: Thirteen candidate genes showed significant evidence of linkage in the presence of disequilibrium, and ten of these were found to be expressed in relevant embryonic tissues: SP100 , MLPH , HDAC4 , LEF1 , C6orf105 , CD44 , ALX4 , ZNF202 , CRHR1 , and MAPT . Three other genes showing statistical evidence ( ADH1C , SCN3B , and IMP5 ) were not expressed in the embryonic tissues examined here. Conclusions: This approach demonstrates how statistical evidence on large numbers of SNP markers typed in case-parent trios can be combined with expression data to identify candidate genes for complex disorders. Many of the genes reported here have not been previously studied as candidates for oral clefts and warrant further investigation.

Published
2006

32. Identification of somatic mutations using whole-exome sequencing in Korean patients with acute myeloid leukemia.

Author

Seong Gu Heo, Youngil Koh, Jong Kwang Kim, Jongsun Jung, Hyung-Lae Kim, Sung-Soo Yoon, and Ji Wan Park

Subjects
SOMATIC mutation, EXOMES, ACUTE myeloid leukemia, PUBLIC health, GENE ontology, PATIENTS, GENETICS
Abstract

Background: Acute myeloid leukemia (AML) is a biologically and clinically heterogeneous cancer of the bone marrow that is characterized by the rapid growth of abnormal myeloid cells. Methods: We performed a mutational analysis to identify AML somatic mutations using the whole-exome sequencing data of 36 tumor-normal sample pairs from Korean patients with de novo AML. We explored the functional impact of the genes identified in the mutational analyses through an integrated Gene Ontology (GO) and pathway analysis. Results: A total of 11 genes, including NEFH (p = 6.27 x 10-13 and q = 1.18 x 10-8) and TMPRSS13 (p = 1.40 x 10-10 and q = 1.32 x 10-6), also demonstrated q values less than 0.1 in 36 Korean AML patients. Five out of the 11 novel genes have previously been reported to be associated with other cancers. Two gene mutations, CEBPA (p = 5.22 x 10-5) and ATXN3 (p = 9.75 x 10-4), showed statistical significance exclusively in the M2 and M3 subtypes of the French-American-British classifications, respectively. A total of 501 genes harbored 478 missense, 22 nonsense, 93 frameshift indels, and/or three stop codon deletions and these gene mutations significantly enriched GO terms for signal transduction (GO:0007165, p = 1.77 x 10-3), plasma membrane (GO:0005886, p = 3.07 x 10-4), and scaffold protein binding (GO:0097110, p = 8.65 x 10-4). The mitogen-activated protein kinase (hsa04010, 7.67 x 10-4) was the most enriched Kyoto Encyclopedia of Genes and Genomes pathway. Conclusions: Morphological AML subtypes may in part reflect subtype specific patterns of genomic alterations. Following validation, future studies to evaluate the usefulness of these genes in genetic testing for the early diagnosis and prognostic prediction of AML patients would be worthwhile. [ABSTRACT FROM AUTHOR]

Published
2017
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33. The use of 'overall accuracy' to evaluate the validity of screening or diagnostic tests

Author

Brant W. Hager, Malcolm V. Brock, Ji Wan Park, Anthony J. Alberg, and Marie Diener-West

Subjects
Contingency table, Validation study, business.industry, Diagnostic test, Reproducibility of Results, Diagnostic accuracy, Test validity, Review, Clinical literature, Reference Standards, Sensitivity and Specificity, Statistics, Test efficiency, Internal Medicine, Medicine, Humans, Mass Screening, business, Mass screening, Diagnostic Techniques and Procedures
Abstract

Evaluations of screening or diagnostic tests sometimes incorporate measures of overall accuracy, diagnostic accuracy, or test efficiency. These terms refer to a single summary measurement calculated from 2 x 2 contingency tables that is the overall probability that a patient will be correctly classified by a screening or diagnostic test. We assessed the value of overall accuracy in studies of test validity, a topic that has not received adequate emphasis in the clinical literature.Guided by previous reports, we summarize the issues concerning the use of overall accuracy. To document its use in contemporary studies, a search was performed for test evaluation studies published in the clinical literature from 2000 to 2002 in which overall accuracy derived from a 2 x 2 contingency table was reported.Overall accuracy is the weighted average of a test's sensitivity and specificity, where sensitivity is weighted by prevalence and specificity is weighted by the complement of prevalence. Overall accuracy becomes particularly problematic as a measure of validity as 1) the difference between sensitivity and specificity increases and/or 2) the prevalence deviates away from 50%. Both situations lead to an increasing deviation between overall accuracy and either sensitivity or specificity. A summary of results from published studies (N = 25) illustrated that the prevalence-dependent nature of overall accuracy has potentially negative consequences that can lead to a distorted impression of the validity of a screening or diagnostic test.Despite the intuitive appeal of overall accuracy as a single measure of test validity, its dependence on prevalence renders it inferior to the careful and balanced consideration of sensitivity and specificity.

Published
2004

34. Association betweenMSX1SNPs and Nonsyndromic Cleft Lip with or without Cleft Palate in the Korean Population

Author

Seung-Hak Baek, Young-Ho Kim, Ji Wan Park, and Nayoung Kim

Subjects
MSX1 SNP, Male, Linkage disequilibrium, Genotype, Cleft Lip, Korean, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Association Analysis, Linkage Disequilibrium, Asian People, Gene Frequency, Risk Factors, Human Genetics & Genomics, Republic of Korea, Odds Ratio, Humans, Nonsyndromic Cleft Lip with or without Palate, Allele frequency, Alleles, Genetic association, MSX1 Transcription Factor, Genetics, Haplotype, General Medicine, Transmission disequilibrium test, Odds ratio, Cleft Palate, Minor allele frequency, stomatognathic diseases, Logistic Models, Haplotypes, Original Article, Female, Software
Abstract

The purpose of this study was to investigate the contribution of MSX1 gene to the risk of nonsyndromic cleft lip with or without cleft palate (NS-CL ± P) in the Korean population. The samples consisted of 142 NS-CL ± P families (9 with cleft lip, 26 with cleft lip and alveolus, and 107 with cleft lip and palate; 76 trios and 66 dyads). Three single nucleotide polymorphisms (SNPs: rs3821949, rs12532, and rs4464513) were tested for association with NS-CL ± P case-parent trios using transmission disequilibrium test (TDT) and conditional logistic regression models (CLRMs). Minor allele frequency, heterozygosity, χ(2) test for Hardy-Weinberg equilibrium, and pairwise linkage disequilibrium (LD) at each SNP were computed. The family- and haplotype-based association test programs were used to perform allelic and genotypic TDTs for individual SNPs and to fabricate sliding windows of haplotypes. Genotypic odds ratios (GORs) were obtained from CLRMs using R software. Although the family-based TDT indicated a meaningful association for rs3821949 (P = 0.028), the haplotype analysis did not reveal any significant association with rs3821949, rs12532, or rs4464513. The A allele at rs3821949 had a significant increased risk of NS-CL ± P (GOR, 1.64; 95% confidence interval,1.03-2.63; P = 0.038, additive model). A positive association is suggested between MSX1 rs3821949 and NS-CL ± P in the Korean population.

Published
2013
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35. A hospital-based, case-control study of risk factors for hemorrhagic fever with renal syndrome in soldiers of the armed forces of the Republic of Korea

Author

Young Kee Cho, Ji Wan Park, Kenneth E. Dixon, Dong Hyun Kim, Joung Won Huh, Yeon Ja Hwang, and Roberto N. Nang

Subjects
Adult, Male, medicine.medical_specialty, Insecticides, Insecta, Korean hemorrhagic fever, Disease Vectors, Logistic regression, Interviews as Topic, Risk Factors, Virology, Epidemiology, Medicine, Animals, Humans, Risk factor, Korea, business.industry, Case-control study, Dust, Hospital based, Odds ratio, Surgery, Muridae, Infectious Diseases, Military Personnel, Case-Control Studies, Hemorrhagic Fever with Renal Syndrome, Insect Repellents, Housing, Parasitology, business, Demography
Abstract

A hospital-based, case-control study was conducted to measure risk factors for hemorrhagic fever with renal syndrome in Korean soldiers. Between October 1989 and January 1992, 196 cases and 295 controls were admitted into the study. Information was collected on behavioral and environmental variables, and the data were analyzed by multiple logistic regression. The strongest risk factors were seeing Apodemus in the area (odds ratio [OR] = 4.9), living in a primitive dwelling such as a lean-to (OR = 3.9), and being exposed to dust (OR = 3.7). The most protective factors were living in barracks (OR = 0.3-0.5) and self-reported use of any insecticide or insect repellent (OR = 0.5).

Published
1996

36. ADIPOQ Gene Variants Associated with Susceptibility to Obesity and Low Serum Adiponectin Levels in Healthy Koreans

Author

Jungyong Park, Ji Wan Park, and Sun Ha Jee

Subjects
medicine.medical_specialty, education.field_of_study, Adiponectin, business.industry, Population, Single-nucleotide polymorphism, General Medicine, Odds ratio, ADIPOQ Gene, medicine.disease, Bioinformatics, Obesity, Endocrinology, Blood serum, Internal medicine, medicine, Allele, business, education
Abstract

OBJECTIVES This study aimed to measure the association between the adiponectin, C1Q and collagen domain-containing (ADIPOQ) gene variants and obesity in Koreans. METHODS Three single nucleotide polymorphisms located in the ADIPOQ gene were genotyped in a population-based cross-sectional study of 986 healthy Koreans. Three different case-control groups (i.e. G1, G2, and G3) were defined according to body mass index (BMI) and serum adiponectin levels. Allelic and genotypic associations of this gene with obesity were measured using multivariate logistic regression analyses in each group. RESULTS The G allele of -11377C>G, a polymorphism located in the promoter region of the ADIPOQ gene (odds ratio (OR), 1.48; 95% confidence interval, 1.13-1.94) and most haplotypes including this allele significantly increased the risk for obesity. However, the OR decreased from 3.98 (G1 group) to 2.90 (G2 group) and 2.30 (G3 group) when a less strict definition of obesity was used. Most haplotypes, including this allele, significantly increased the risk of obesity. The statistical evidence from the GG genotype of -11377C>G (OR, 3.98) and the GT/GT diplotype composed of -11377G>C and +45T>G (OR, 5.20) confirmed the contribution of the G allele toward a predisposition for obesity. CONCLUSION These results suggest the contribution of the ADIPOQ gene toward susceptibility to obesity in healthy Koreans. The high-risk genotypes and haplotypes identified here may provide more information for identifying individuals who are at risk of obesity.

Published
2011
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37. Association Between IRF6 and Nonsyndromic Oral Clefts In 4 Populations

Author

Yah-Huei Wu-Chou, Ethylin Wang Jabs, Ji Wan Park, M. D. Fallin, Roxann G. Ingersoll, Terri H. Beaty, K T Chen, Sun Ha Jee, Alan F. Scott, Craig A. Vanderkolk, Iain McIntosh, Jacqueline B. Hetmanski, and Samuel S. Chong

Subjects
medicine.medical_specialty, Epidemiology, business.industry, Internal medicine, Association (object-oriented programming), Medicine, IRF6, business
Published
2006
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38. Associations between the risk of tooth agenesis and single-nucleotide polymorphisms of MSX1 and PAX9 genes in nonsyndromic cleft patients.

Author

Yu-Jin Seo, Ji Wan Park, Young Ho Kim, and Seung-Hak Baek

Subjects
HUMAN abnormalities, TEETH abnormalities, SINGLE nucleotide polymorphisms, CLEFT lip, TEETH abnormality genetics, GENETIC polymorphisms, PATIENTS
Abstract

Objective: To investigate the association between the risk of tooth agenesis and single-nucleotide polymorphisms (SNPs) of MSX1 and PAX9 genes in nonsyndromic cleft patients. Materials and Methods: The subjects were 126 Korean nonsyndromic cleft patients. Tooth agenesis type (TAT) was classified as none (0); cleft area (1); cleft area + other area (2); and other area (3) based on agenesis of the maxillary lateral incisor (MXLI) and another tooth within or outside the cleft area. TAT was further grouped into two subcategories (0 and 1) and four subcategories (0,1, 2, and 3). Three SNPs of MSX1 and 10 SNPs of PAX9 were investigated using Fisher's exact test and logistic regression analysis. Results: Although the association between genotype distribution of PAX9-rs7142363 and TAT was significant (P< .05 in four subcategories), genotypic odds ratios (GORs) of SNPs in each TAT were not meaningful. However, for /WSX7-rs12532 and PAX9-rs2073247, associations between genotypic distribution and TAT were significant (P < .01 in four subcategories and P < .05 in two subcategories; P < .01 in two subcategories, respectively). In cleft area, GORs of MXLI agenesis in genotypes GA of /WSX/-rs12532 and CT of PAX9-rs2073247 were increased by 3.14-fold and 4.15-fold compared with genotype GG of MSX1-rs12532 and CC of PAX9-rs2073247, respectively (P <. 01 ; P < .05). In cleft area + other area, the GOR of agenesis of MXLI and another tooth in genotype AA of MSX1-rs12532 was increased by fivefold compared with genotype GG (P < .05). Conclusion: Genetic disturbances of MSX1 and PAX9 genes are associated with tooth agenesis within and outside the cleft area. [ABSTRACT FROM AUTHOR]

Published
2013
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39. ADIPOQ Gene Variants Associated with Susceptibility to Obesity and Low Serum Adiponectin Levels in Healthy Koreans.

Author

Ji Wan Park, Jungyong Park, Sun Ha Jee, Park, Ji Wan, Park, Jungyong, and Jee, Sun Ha

Subjects
*ADIPONECTIN, *GENES, *METABOLIC disorders, *NUTRITION disorders, *OBESITY
Abstract

Objectives: This study aimed to measure the association between the adiponectin, C1Q and collagen domain-containing (ADIPOQ) gene variants and obesity in Koreans.Methods: Three single nucleotide polymorphisms located in the ADIPOQ gene were genotyped in a population-based cross-sectional study of 986 healthy Koreans. Three different case-control groups (i.e. G1, G2, and G3) were defined according to body mass index (BMI) and serum adiponectin levels. Allelic and genotypic associations of this gene with obesity were measured using multivariate logistic regression analyses in each group.Results: The G allele of -11377C>G, a polymorphism located in the promoter region of the ADIPOQ gene (odds ratio (OR), 1.48; 95% confidence interval, 1.13-1.94) and most haplotypes including this allele significantly increased the risk for obesity. However, the OR decreased from 3.98 (G1 group) to 2.90 (G2 group) and 2.30 (G3 group) when a less strict definition of obesity was used. Most haplotypes, including this allele, significantly increased the risk of obesity. The statistical evidence from the GG genotype of -11377C>G (OR, 3.98) and the GT/GT diplotype composed of -11377G>C and +45T>G (OR, 5.20) confirmed the contribution of the G allele toward a predisposition for obesity.Conclusion: These results suggest the contribution of the ADIPOQ gene toward susceptibility to obesity in healthy Koreans. The high-risk genotypes and haplotypes identified here may provide more information for identifying individuals who are at risk of obesity. [ABSTRACT FROM AUTHOR]

Published
2011
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40. Association between genes on chromosome 4p16 and non-syndromic oral clefts in four populations.

Author

Ingersoll, Roxann G., Hetmanski, Jacqueline, Ji-Wan Park, Fallin, M. Daniele, McIntosh, Iain, Yah-Huei Wu-Chou, Chen, Philip K., Yeow, Vincent, Chong, Samuel S., Cheah, Felicia, Jae Woong Sull, Sun Ha Jee, Hong Wang, Tao Wu, Murray, Tanda, Shangzhi Huang, Xiaoqian Ye, Jabs, Ethylin Wang, Redett, Richard, and Raymond, Gerald

Subjects
CLEFT lip, GENETIC mutation, GENETIC polymorphisms, GENES, HUMAN abnormalities
Abstract

Isolated cleft lip with or without cleft palate and cleft palate are among the most common human birth defects. Several candidate gene studies on MSX1 have shown significant association between markers in MSX1 and risk of oral clefts, and re-sequencing studies have identified multiple mutations in MSX1 in a small minority of cases, which may account for 1–2% of all isolated oral clefts cases. We explored the 2-Mb region around MSX1, using a marker map of 393 single nucleotide polymorphisms (SNPs) in 297 cleft lip, with or without cleft palate, case–parent trios and 84 cleft palate trios from Maryland, Taiwan, Singapore, and Korea. Both individual markers and haplotypes of two to five SNPs showed several regions yielding statistical evidence for linkage and disequilibrium. Two genes (STK32B and EVC) yielded consistent evidence from cleft lip, with or without cleft palate, trios in all four populations. These two genes plus EVC2 also yielded suggestive evidence for linkage and disequilibrium among cleft palate trios. This analysis suggests that several genes, not just MSX1, in this region may influence risk of oral clefts. [ABSTRACT FROM AUTHOR]

Published
2010
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41. Evidence that TGFA influences risk to cleft lip with/without cleft palate through unconventional genetic mechanisms.

Author

Jae Woong Sull, Kung-Yee Liang, Hetmanski, Jacqueline B., Tao Wu, Fallin, Margaret Daniele, Ingersoll, Roxann G., Ji Wan Park, Yah-Huei Wu-Chou, Chen, Philip K., Chong, Samuel S., Cheah, Felicia, Yeow, Vincent, Beyoung Yun Park, Sun Ha Jee, Jabs, Ethylin Wang, Redett, Richard, Scott, Alan F., and Beaty, Terri H.

Subjects
GENETIC polymorphisms, GROWTH factors, CYTOKINES, REGRESSION analysis, TRANSFORMING growth factors
Abstract

This study examined the association between markers in transforming growth factor alpha ( TGFA) and isolated, non-syndromic cleft lip with/without palate (CL/P) using a case–parent trio design, considering parent-of-origin effects. We also tested for gene–environmental interaction with common maternal exposures, and for gene–gene interaction using markers in TGFA and another recognized causal gene, IRF6. CL/P case–parent trios from four populations (76 from Maryland, 146 from Taiwan, 35 from Singapore, and 40 from Korea) were genotyped for 17 single nucleotide polymorphisms (SNPs) in TGFA. The transmission disequilibrium test was used to test individual SNPs, and the parent-of-origin likelihood ratio test (PO-LRT) was used to assess parent-of-origin effects. We also screened for possible gene–environment interaction using PBAT, and tested for gene–gene interaction using conditional logistic regression models. When all trios were combined, four SNPs showed significant excess maternal transmission, two of which gave significant PO-LRT values [rs3821261: P = 0.004 and OR(imprinting) = 4.17; and rs3771475: P = 0.027 and OR(imprinting) = 2.44]. Haplotype analysis of these two SNPS also supported excess maternal transmission. We saw intriguing but suggestive evidence of G × E interaction for several SNPs in TGFA when either individual SNPs or haplotypes of adjacent SNPs were considered. Thus, TGFA appears to influence risk of CL/P through unconventional means with an apparent parent-of-origin effect (excess maternal transmission) and possible interaction with maternal exposures. [ABSTRACT FROM AUTHOR]

Published
2009
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42. BMI and Stroke Risk in Korean Women.

Author

Ji Wan Park, Sang-Yi Lee, Su Young Kim, Heon Choe, and Sun Ha Jee

Subjects
MEDICAL research, BODY mass index, CEREBROVASCULAR disease risk factors, DISEASES in women, AGE factors in disease, SMOKING
Abstract

The article discusses a study which investigated the effect of body mass index (BMI) on the risk of stroke among Korean women. The study also assessed the association in relation to age and smoking as modifying factors. Findings suggest that the risk of ischemic stroke (IS) showed a strong and progressive linear relationship with an increase in the BMI, while the relationship was less linear for hemorrhagic stroke (HS).

Published
2008
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43. Identification of somatic mutations using whole-exome sequencing in Korean patients with acute myeloid leukemia

Author

Hyung Lae Kim, Jongsun Jung, Seong Gu Heo, Sung-Soo Yoon, Ji Wan Park, Jong Kwang Kim, and Youngil Koh

Subjects
0301 basic medicine, Adult, Male, Pathway analysis, DNA Mutational Analysis, Mutation, Missense, Biology, Gene mutation, Frameshift mutation, 03 medical and health sciences, 0302 clinical medicine, Germline mutation, Asian People, INDEL Mutation, Scaffold protein binding, CEBPA, Republic of Korea, Genetics, Missense mutation, Humans, Exome, Genetic Predisposition to Disease, Genetics(clinical), Frameshift Mutation, Genetics (clinical), Exome sequencing, Acute myeloid leukemia, Somatic mutation, Myeloid leukemia, Sequence Analysis, DNA, Middle Aged, Neoplasm Proteins, Leukemia, Myeloid, Acute, 030104 developmental biology, Codon, Nonsense, 030220 oncology & carcinogenesis, Whole-exome sequencing, Cancer research, Subtype-specific mutation, Female, Gene ontology, Research Article
Abstract

Background Acute myeloid leukemia (AML) is a biologically and clinically heterogeneous cancer of the bone marrow that is characterized by the rapid growth of abnormal myeloid cells. Methods We performed a mutational analysis to identify AML somatic mutations using the whole-exome sequencing data of 36 tumor-normal sample pairs from Korean patients with de novo AML. We explored the functional impact of the genes identified in the mutational analyses through an integrated Gene Ontology (GO) and pathway analysis. Results A total of 11 genes, including NEFH (p = 6.27 × 10−13 and q = 1.18 × 10−8) and TMPRSS13 (p = 1.40 × 10−10 and q = 1.32 × 10−6), also demonstrated q values less than 0.1 in 36 Korean AML patients. Five out of the 11 novel genes have previously been reported to be associated with other cancers. Two gene mutations, CEBPA (p = 5.22 × 10−5) and ATXN3 (p = 9.75 × 10−4), showed statistical significance exclusively in the M2 and M3 subtypes of the French-American-British classifications, respectively. A total of 501 genes harbored 478 missense, 22 nonsense, 93 frameshift indels, and/or three stop codon deletions and these gene mutations significantly enriched GO terms for signal transduction (GO:0007165, p = 1.77 × 10−3), plasma membrane (GO:0005886, p = 3.07 × 10−4), and scaffold protein binding (GO:0097110, p = 8.65 × 10−4). The mitogen-activated protein kinase (hsa04010, 7.67 × 10−4) was the most enriched Kyoto Encyclopedia of Genes and Genomes pathway. Conclusions Morphological AML subtypes may in part reflect subtype specific patterns of genomic alterations. Following validation, future studies to evaluate the usefulness of these genes in genetic testing for the early diagnosis and prognostic prediction of AML patients would be worthwhile. Electronic supplementary material The online version of this article (doi:10.1186/s12881-017-0382-y) contains supplementary material, which is available to authorized users.

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