Search Results - "Sung, Yun J"

Deriving stratified effects from joint models investigating gene-environment interactions by Laville, Vincent, Majarian, Timothy, de Vries, Paul S, Bentley, Amy R, Feitosa, Mary F, Sung, Yun J, Rao, D C, Manning, Alisa, Aschard, Hugues

“…Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE)…”
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Genetic architecture of plasma Alzheimer disease biomarkers by Bradley, Joseph, Gorijala, Priyanka, Schindler, Suzanne E, Sung, Yun J, Ances, Beau, Fernandez, Maria V, Cruchaga, Carlos

“…Abstract Genome-wide association studies (GWAS) of cerebrospinal fluid (CSF) Alzheimer’s Disease (AD) biomarker levels have identified novel genes implicated…”
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Gene–Smoking Interactions Identify Several Novel Blood Pressure Loci in the Framingham Heart Study by Sung, Yun J., de las Fuentes, Lisa, Schwander, Karen L., Simino, Jeannette, Rao, Dabeeru C.

“…BACKGROUND Cardiovascular diseases are among the most significant health problems in the United States. Blood pressure (BP) variability has a genetic…”
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Gene-lifestyle interactions in the genomics of human complex traits by Laville, Vincent, Majarian, Timothy, Sung, Yun J, Schwander, Karen, Feitosa, Mary F, Chasman, Daniel I, Bentley, Amy R, Rotimi, Charles N, Cupples, L Adrienne, de Vries, Paul S, Brown, Michael R, Morrison, Alanna C, Kraja, Aldi T, Province, Mike, Gu, C Charles, Gauderman, W James, Rao, D C, Manning, Alisa K, Aschard, Hugues

“…The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the…”
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Multiancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts: Design and Rationale by Rao, D C, Sung, Yun J, Winkler, Thomas W, Schwander, Karen, Borecki, Ingrid, Cupples, L Adrienne, Gauderman, W James, Rice, Kenneth, Munroe, Patricia B, Psaty, Bruce M

“…Several consortia have pursued genome-wide association studies for identifying novel genetic loci for blood pressure, lipids, hypertension, etc. They…”
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Rare coding variants associated with blood pressure variation in 15 914 individuals of African ancestry by Nandakumar, Priyanka, Lee, Dongwon, Richard, Melissa A, Tekola-Ayele, Fasil, Tayo, Bamidele O, Ware, Erin, Sung, Yun J, Salako, Babatunde, Ogunniyi, Adesola, Gu, C Charles, Grove, Megan L, Fornage, Myriam, Kardia, Sharon, Rotima, Charles, Cooper, Richard S, Morrison, Alanna C, Ehret, Georg, Chakravarti, Aravinda

“…OBJECTIVES:Hypertension is a major risk factor for all cardiovascular diseases, especially among African Americans. This study focuses on identifying specific…”
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Multiancestry Study of Gene–Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts by Rao, D C, Sung, Yun J, Winkler, Thomas W, Schwander, Karen, Borecki Ingrid, Adrienne, Cupples L, James, Gauderman W, Rice, Kenneth, Munroe, Patricia B, Psaty, Bruce M

“…Background—Several consortia have pursued genome-wide association studies for identifying novel genetic loci for blood pressure, lipids, hypertension, etc…”
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Genotype Imputation for African Americans Using Data From HapMap Phase II Versus 1000 Genomes Projects by Sung, Yun J., Gu, C. Charles, Tiwari, Hemant K., Arnett, Donna K., Broeckel, Ulrich, Rao, Dabeeru C.

“…Genotype imputation provides imputation of untyped single nucleotide polymorphisms (SNPs) that are present on a reference panel such as those from the HapMap…”
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Cardiorespiratory Fitness Level Alters Blood Pressure Responses Induced By Histamine H1- And H2-receptor Blockade During Dynamic Exercise: 163 by Lira, Albert Adrian, Lirazan, Dustin J., Meneses, Ronaldo U., Yun, Sung J., Kim, Jong-Kyung

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Cardiorespiratory Fitness Level Alters Blood Pressure Responses Induced By Histamine H1- And H2-receptor Blockade During Dynamic Exercise: 163 by Lira, Albert Adrian, Lirazan, Dustin J., Meneses, Ronaldo U., Yun, Sung J., Kim, Jong-Kyung

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Sex‐specific molecular profiling to understand pathology and identify causal genes and drug targets for Alzheimer’s disease by Sung, Yun J, Do, Anh, Timsina, Jigyasha, Wang, Lihua, Wang, Fengxian, Cruchaga, Carlos

“…Background Alzheimer’s disease (AD) is a highly heterogeneous multifactorial disease. Genetic influences on AD are strong as shown by several pathogenic genes…”
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Multi‐tissue proteomic profiling for genetically defined Alzheimer disease cases by Sung, Yun J., Yang, Chengran, Suhy, Adam J., Rhinn, Herve, Norton, Joanne, Wang, Fengxian, Bradley, Joseph, Farias, Fabiana H.G., Benitez, Bruno A., Harari, Oscar, Cruchaga, Carlos

“…Background Alzheimer disease (AD) is a complex and heterogeneous disease. Most AD cases present sporadically, while 1‐2% of the cases have autosomal dominant…”
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Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium by Merino, Jordi, Dashti, Hassan S., Li, Sherly X., Sarnowski, Chloé, Justice, Anne E., Graff, Misa, Papoutsakis, Constantina, Smith, Caren E., Dedoussis, George V., Lemaitre, Rozenn N., Wojczynski, Mary K., Männistö, Satu, Ngwa, Julius S., Kho, Minjung, Ahluwalia, Tarunveer S., Pervjakova, Natalia, Houston, Denise K., Bouchard, Claude, Huang, Tao, Orho-Melander, Marju, Frazier-Wood, Alexis C., Mook-Kanamori, Dennis O., Pérusse, Louis, Pennell, Craig E., de Vries, Paul S., Voortman, Trudy, Li, Olivia, Kanoni, Stavroula, Rose, Lynda M., Lehtimäki, Terho, Zhao, Jing Hua, Feitosa, Mary F., Luan, Jian’an, McKeown, Nicola M., Smith, Jennifer A., Hansen, Torben, Eklund, Niina, Nalls, Mike A., Rankinen, Tuomo, Huang, Jinyan, Hernandez, Dena G., Schulz, Christina-Alexandra, Manichaikul, Ani, Li-Gao, Ruifang, Vohl, Marie-Claude, Wang, Carol A., van Rooij, Frank J. A., Kalafati, Ioanna P., Day, Felix, Ridker, Paul M., Kähönen, Mika, Siscovick, David S., Langenberg, Claudia, Zhao, Wei, Astrup, Arne, Knekt, Paul, Garcia, Melissa, Rao, D. C., Qi, Qibin, Ferrucci, Luigi, Ericson, Ulrika, Hofman, Albert, Pausova, Zdenka, Wareham, Nick J., Kardia, Sharon L. R, Pedersen, Oluf, Jula, Antti, Curran, Joanne E., Zillikens, M. Carola, Viikari, Jorma S., Forouhi, Nita G., Ordovás, José M., Lieske, John C., Rissanen, Harri, Uitterlinden, André G., Raitakari, Olli T., Dupuis, Josée, Rotter, Jerome I., North, Kari E., Scott, Robert A., Province, Michael A., Cupples, L. Adrienne, Turner, Stephen T., Sørensen, Thorkild I. A., Salomaa, Veikko, Liu, Yongmei, Sung, Yun J., Qi, Lu, Bandinelli, Stefania, Rich, Stephen S., de Mutsert, Renée, Oddy, Wendy H., Franco, Oscar H., Paus, Tomas, Florez, Jose C., Deloukas, Panos, Lyytikäinen, Leo-Pekka, Chasman, Daniel I., Chu, Audrey Y., Tanaka, Toshiko

“…Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with…”
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Abstract P127: Gene-Lifestyle Interactions Detect Novel Blood Pressure Loci by Osazuwa-Peters, Oyomoare L, Schwander, Karen L, Sung, Yun J, de las Fuentes, Lisa, Rao, Dabeeru C

“…Abstract only Introduction: Gene-lifestyle interaction effects (GxE more generally) on blood pressure (BP), a promising approach for novel loci discovery, is…”
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A Multi-Ancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610,475 Individuals from 124 Cohorts: Design and Rationale by Rao, D. C., Sung, Yun J., Winkler, Thomas W., Schwander, Karen, Borecki, Ingrid, Cupples, L. Adrienne, Gauderman, W. James, Rice, Kenneth, Munroe, Patricia B., Psaty, Bruce M.

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Abstract P530: Pleiotropic Effects on Blood Pressure Traits Using Genome-wide Analysis of Gene-alcohol Interactions by Kraja, Aldi T, Feitosa, Mary F, Chasman, Daniel, Sung, Yun J, Winkler, Thomas W, Ntalla, Ioanna, Aschard, Hugues, Rice, Kenneth, Manning, Alisa K, O’Connell, Jeffrey, Kardia, Sharon L, Munroe, Patricia, Cupples, L. Adrienne, Morrison, Alana, Gauderman, W. James, Levy, Daniel, Rao, D.C., Zhu, Xiaofeng, Province, Michael A, CHARGE Gene-Lifestyle Interactions, on behalf of the, the NHLBI grant HL 118305, This work is supported in part by

“…Abstract only We tested for pleiotropy in European ancestry subjects (N>90K) via GWAS of systolic and diastolic blood pressure (BP), mean arterial pressure,…”
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Genotype Imputation for A frican A mericans Using Data From H ap M ap Phase II Versus 1000 G enomes P rojects by Sung, Yun J., Gu, C. Charles, Tiwari, Hemant K., Arnett, Donna K., Broeckel, Ulrich, Rao, Dabeeru C.

“…Genotype imputation provides imputation of untyped single nucleotide polymorphisms ( SNP s) that are present on a reference panel such as those from the H ap M…”
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Comparison of marker types and map assumptions using Markov chain Monte Carlo-based linkage analysis of COGA data by Sieh, Weiva, Basu, Saonli, Fu, Audrey Q, Rothstein, Joseph H, Scheet, Paul A, Stewart, William C L, Sung, Yun J, Thompson, Elizabeth A, Wijsman, Ellen M

“…We performed multipoint linkage analysis of the electrophysiological trait ECB21 on chromosome 4 in the full pedigrees provided by the Collaborative Study on…”
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Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians by Kato, Norihiro, Takeuchi, Fumihiko, Tabara, Yasuharu, Kelly, Tanika N, Go, Min Jin, Sim, Xueling, Tay, Wan Ting, Chen, Chien-Hsiun, Zhang, Yi, Yamamoto, Ken, Katsuya, Tomohiro, Yokota, Mitsuhiro, Kim, Young Jin, Ong, Rick Twee Hee, Nabika, Toru, Gu, Dongfeng, Chang, Li-ching, Kokubo, Yoshihiro, Huang, Wei, Ohnaka, Keizo, Yamori, Yukio, Nakashima, Eitaro, Jaquish, Cashell E, Lee, Jong-Young, Seielstad, Mark, Isono, Masato, Hixson, James E, Chen, Yuan-Tsong, Miki, Tetsuro, Zhou, Xueya, Sugiyama, Takao, Jeon, Jae-Pil, Liu, Jian Jun, Takayanagi, Ryoichi, Kim, Sung Soo, Aung, Tin, Sung, Yun Ju, Zhang, Xuegong, Wong, Tien Yin, Han, Bok-Ghee, Kobayashi, Shotai, Ogihara, Toshio, Zhu, Dingliang, Iwai, Naoharu, Wu, Jer-Yuarn, Teo, Yik Ying, Tai, E Shyong, Cho, Yoon Shin, He, Jiang

“…Norihiro Kato and colleagues perform a meta-analysis of genome-wide association studies to identify common variants associated with blood pressure variation in…”
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Monte Carlo Likelihood Inference for Missing Data Models by Sung, Yun Ju, Geyer, Charles J.

“…We describe a Monte Carlo method to approximate the maximum likelihood estimate (MLE), when there are missing data and the observed data likelihood is not…”
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