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1. Pleiotropic QTL on Chromosome 12q23-q24 Influences Triglyceride and High-Density Lipoprotein Cholesterol Levels: The HERITAGE Family Study

Author: Feitosa, Mary F, Rice, Treva, Borecki, Ingrid B, Rankinen, Tuomo, Leon, Arthur S, Skinner, James S., Després, Jean-Pierre, Blangero, John, Bouchard, Claude, and Rao, D. C. (Dabeeru C.)
Published: 2006
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2. Whole Genome Sequencing Identifies CRISPLD2 as a Lung Function Gene in Children With Asthma

Author: Kachroo, Priyadarshini, Hecker, Julian, Chawes, Bo L, Ahluwalia, Tarunveer S, Cho, Michael H, Qiao, Dandi, Kelly, Rachel S, Chu, Su H, Virkud, Yamini V, Huang, Mengna, Barnes, Kathleen C, Burchard, Esteban G, Eng, Celeste, Hu, Donglei, Celedón, Juan C, Daya, Michelle, Levin, Albert M, Gui, Hongsheng, Williams, L Keoki, Forno, Erick, Mak, Angel CY, Avila, Lydiana, Soto-Quiros, Manuel E, Cloutier, Michelle M, Acosta-Pérez, Edna, Canino, Glorisa, Bønnelykke, Klaus, Bisgaard, Hans, Raby, Benjamin A, Lange, Christoph, Weiss, Scott T, Lasky-Su, Jessica A, National Heart, Lung, Abe, Namiko, Abecasis, Goncalo, Albert, Christine, Allred, Nicholette Palmer, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Arking, Dan, Arnett, Donna K, Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Barnard, John, Barnes, Kathleen, Barr, R Graham, Barron-Casella, Emily, Beaty, Terri, Becker, Diane, Becker, Lewis, Beer, Rebecca, Begum, Ferdouse, Beitelshees, Amber, Benjamin, Emelia, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Boerwinkle, Eric, Borecki, Ingrid, Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Bunting, Karen, Burchard, Esteban, Cardwell, Jonathan, Carty, Cara, Casaburi, Richard, Casella, James, Chaffin, Mark, Chang, Christy, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Chen, Yii-Der Ida, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, and Das, Sayantan
Subjects: Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Clinical Research, Asthma, Genetics, Pediatric, Biotechnology, Human Genome, Lung, 2.1 Biological and endogenous factors, Respiratory, Adolescent, Adult, Cell Adhesion Molecules, Child, Child, Preschool, Costa Rica, Female, Forced Expiratory Volume, Humans, Interferon Regulatory Factors, Male, Middle Aged, Respiratory Physiological Phenomena, Vital Capacity, Whole Genome Sequencing, Young Adult, airway hyperresponsiveness, asthma, lung function, whole genome sequencing, National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Consortium, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract: BackgroundAsthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma.MethodsWGS data were generated for 1,053 individuals from trios and extended pedigrees participating in the family-based Genetic Epidemiology of Asthma in Costa Rica study. Asthma affection status was defined through a physician's diagnosis of asthma, and most participants with asthma also had airway hyperresponsiveness (AHR) to methacholine. Family-based association tests for single variants were performed to assess the associations with lung function phenotypes.ResultsA genome-wide significant association was identified between baseline FEV1/FVC ratio and a single-nucleotide polymorphism in the top hit cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) (rs12051168; P = 3.6 × 10-8 in the unadjusted model) that retained suggestive significance in the covariate-adjusted model (P = 5.6 × 10-6). Rs12051168 was also nominally associated with other related phenotypes: baseline FEV1 (P = 3.3 × 10-3), postbronchodilator (PB) FEV1 (7.3 × 10-3), and PB FEV1/FVC ratio (P = 2.7 × 10-3). The identified baseline FEV1/FVC ratio and rs12051168 association was meta-analyzed and replicated in three independent cohorts in which most participants with asthma also had confirmed AHR (combined weighted z-score P = .015) but not in cohorts without information about AHR.ConclusionsThese findings suggest that using specific asthma characteristics, such as AHR, can help identify more genetically homogeneous asthma subgroups with genotype-phenotype associations that may not be observed in all children with asthma. CRISPLD2 also may be important for baseline lung function in individuals with asthma who also may have AHR.
Published: 2019

3. Microbiome Signatures Associated With Steatohepatitis and Moderate to Severe Fibrosis in Children With Nonalcoholic Fatty Liver Disease

Author: Schwimmer, Jeffrey B, Johnson, Jethro S, Angeles, Jorge E, Behling, Cynthia, Belt, Patricia H, Borecki, Ingrid, Bross, Craig, Durelle, Janis, Goyal, Nidhi P, Hamilton, Gavin, Holtz, Mary L, Lavine, Joel E, Mitreva, Makedonka, Newton, Kimberly P, Pan, Amy, Simpson, Pippa M, Sirlin, Claude B, Sodergren, Erica, Tyagi, Rahul, Yates, Katherine P, Weinstock, George M, and Salzman, Nita H
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Nutrition and Dietetics, Clinical Research, Biotechnology, Genetics, Liver Disease, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Pediatric, Hepatitis, Aetiology, 2.1 Biological and endogenous factors, Oral and gastrointestinal, Adolescent, Bacteria, Case-Control Studies, Child, Cross-Sectional Studies, DNA, Bacterial, Dysbiosis, Feces, Female, Gastrointestinal Microbiome, Host-Pathogen Interactions, Humans, Intestines, Liver Cirrhosis, Male, Metagenome, Non-alcoholic Fatty Liver Disease, Prospective Studies, RNA, Ribosomal, 16S, Ribotyping, Severity of Illness Index, Intestinal Microbiota, Lipopolysaccharide, Flagellin, Neurosciences, Paediatrics and Reproductive Medicine, Gastroenterology & Hepatology, Clinical sciences, Nutrition and dietetics
Abstract: Background & aimsThe intestinal microbiome might affect the development and severity of nonalcoholic fatty liver disease (NAFLD). We analyzed microbiomes of children with and without NAFLD.MethodsWe performed a prospective, observational, cross-sectional study of 87 children (age range, 8-17 years) with biopsy-proven NAFLD and 37 children with obesity without NAFLD (controls). Fecal samples were collected and microbiome composition and functions were assessed using 16S ribosomal RNA amplicon sequencing and metagenomic shotgun sequencing. Microbial taxa were identified using zero-inflated negative binomial modeling. Genes contributing to bacterial pathways were identified using gene set enrichment analysis.ResultsFecal microbiomes of children with NAFLD had lower α-diversity than those of control children (3.32 vs 3.52, P = .016). Fecal microbiomes from children with nonalcoholic steatohepatitis (NASH) had the lowest α-diversity (control, 3.52; NAFLD, 3.36; borderline NASH, 3.37; NASH, 2.97; P = .001). High abundance of Prevotella copri was associated with more severe fibrosis (P = .036). Genes for lipopolysaccharide biosynthesis were enriched in microbiomes from children with NASH (P < .001). Classification and regression tree model with level of alanine aminotransferase and relative abundance of the lipopolysaccharide pathway gene encoding 3-deoxy-d-manno-octulosonate 8-phosphate-phosphatase identified patients with NASH with an area under the receiver operating characteristic curve value of 0.92. Genes involved in flagellar assembly were enriched in the fecal microbiomes of patients with moderate to severe fibrosis (P < .001). Classification and regression tree models based on level of alanine aminotransferase and abundance of genes encoding flagellar biosynthesis protein had good accuracy for identifying case children with moderate to severe fibrosis (area under the receiver operating characteristic curve, 0.87).ConclusionsIn an analysis of fecal microbiomes of children with NAFLD, we associated NAFLD and NASH with intestinal dysbiosis. NAFLD and its severity were associated with greater abundance of genes encoding inflammatory bacterial products. Alterations to the intestinal microbiome might contribute to the pathogenesis of NAFLD and be used as markers of disease or severity.
Published: 2019

4. Insulin Resistance Exacerbates Genetic Predisposition to Nonalcoholic Fatty Liver Disease in Individuals Without Diabetes

Author: Barata, Llilda, Feitosa, Mary F, Bielak, Lawrence F, Halligan, Brian, Baldridge, Abigail S, Guo, Xiuqing, Yerges‐Armstrong, Laura M, Smith, Albert V, Yao, Jie, Palmer, Nicholette D, VanWagner, Lisa B, Carr, J Jeffrey, Chen, Yii‐Der I, Allison, Matthew, Budoff, Matthew J, Handelman, Samuel K, Kardia, Sharon LR, Mosley, Thomas H, Ryan, Kathleen, Harris, Tamara B, Launer, Lenore J, Gudnason, Vilmundur, Rotter, Jerome I, Fornage, Myriam, Rasmussen‐Torvik, Laura J, Borecki, Ingrid B, O’Connell, Jeffrey R, Peyser, Patricia A, Speliotes, Elizabeth K, and Province, Michael A
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Nutrition, Diabetes, Prevention, Genetics, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Liver Disease, Obesity, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Oral and gastrointestinal, Clinical sciences
Abstract: The accumulation of excess fat in the liver (hepatic steatosis) in the absence of heavy alcohol consumption causes nonalcoholic fatty liver disease (NAFLD), which has become a global epidemic. Identifying metabolic risk factors that interact with the genetic risk of NAFLD is important for reducing disease burden. We tested whether serum glucose, insulin, insulin resistance, triglyceride (TG), low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, body mass index (BMI), and waist-to-hip ratio adjusted for BMI interact with genetic variants in or near the patatin-like phospholipase domain containing 3 (PNPLA3) gene, the glucokinase regulatory protein (GCKR) gene, the neurocan/transmembrane 6 superfamily member 2 (NCAN/TM6SF2) gene, and the lysophospholipase-like 1 (LYPLAL1) gene to exacerbate hepatic steatosis, estimated by liver attenuation. We performed association analyses in 10 population-based cohorts separately and then meta-analyzed results in up to 14,751 individuals (11,870 of European ancestry and 2,881 of African ancestry). We found that PNPLA3-rs738409 significantly interacted with insulin, insulin resistance, BMI, glucose, and TG to increase hepatic steatosis in nondiabetic individuals carrying the G allele. Additionally, GCKR-rs780094 significantly interacted with insulin, insulin resistance, and TG. Conditional analyses using the two largest European ancestry cohorts in the study showed that insulin levels accounted for most of the interaction of PNPLA3-rs738409 with BMI, glucose, and TG in nondiabetic individuals. Insulin, PNPLA3-rs738409, and their interaction accounted for at least 8% of the variance in hepatic steatosis in these two cohorts. Conclusion: Insulin resistance, either directly or through the resultant elevated insulin levels, more than other metabolic traits, appears to amplify the PNPLA3-rs738409-G genetic risk for hepatic steatosis. Improving insulin resistance in nondiabetic individuals carrying PNPLA3-rs738409-G may preferentially decrease hepatic steatosis.
Published: 2019

5. Disentangling the genetics of lean mass.

Author: Karasik, David, Zillikens, M, Hsu, Yi-Hsiang, Aghdassi, Ali, Akesson, Kristina, Amin, Najaf, Barroso, Inês, Bennett, David, Bertram, Lars, Bochud, Murielle, Borecki, Ingrid, Broer, Linda, Buchman, Aron, Byberg, Liisa, Campbell, Harry, Campos-Obando, Natalia, Cauley, Jane, Cawthon, Peggy, Chambers, John, Chen, Zhao, Cho, Nam, Choi, Hyung, Chou, Wen-Chi, Cummings, Steven, de Groot, Lisette, De Jager, Phillip, Demuth, Ilja, Diatchenko, Luda, Econs, Michael, Eiriksdottir, Gudny, Enneman, Anke, Eriksson, Joel, Eriksson, Johan, Estrada, Karol, Evans, Daniel, Feitosa, Mary, Fu, Mao, Gieger, Christian, Grallert, Harald, Gudnason, Vilmundur, Lenore, Launer, Hayward, Caroline, Hofman, Albert, Homuth, Georg, Huffman, Kim, Husted, Lise, Illig, Thomas, Ingelsson, Erik, Ittermann, Till, Jansson, John-Olov, Johnson, Toby, Biffar, Reiner, Jordan, Joanne, Jula, Antti, Karlsson, Magnus, Khaw, Kay-Tee, Kilpeläinen, Tuomas, Klopp, Norman, Kloth, Jacqueline, Koller, Daniel, Kooner, Jaspal, Kraus, William, Kritchevsky, Stephen, Kutalik, Zoltán, Kuulasmaa, Teemu, Kuusisto, Johanna, Laakso, Markku, Lahti, Jari, Langdahl, Bente, Lerch, Markus, Lewis, Joshua, Lill, Christina, Lind, Lars, Lindgren, Cecilia, Liu, Yongmei, Livshits, Gregory, Ljunggren, Östen, Loos, Ruth, Lorentzon, Mattias, Luan, Jianan, Luben, Robert, Malkin, Ida, McGuigan, Fiona, Medina-Gomez, Carolina, Meitinger, Thomas, Melhus, Håkan, Mellström, Dan, Michaëlsson, Karl, Mitchell, Braxton, Morris, Andrew, Mosekilde, Leif, Nethander, Maria, Newman, Anne, OConnell, Jeffery, Oostra, Ben, Orwoll, Eric, Palotie, Aarno, Peacock, Munro, Perola, Markus, and Peters, Annette
Subjects: ADAMTS Proteins, Absorptiometry, Photon, Adipose Tissue, Adolescent, Adult, Aged, Aged, 80 and over, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Body Composition, Body Fluid Compartments, Electric Impedance, Extracellular Matrix Proteins, Female, Genome-Wide Association Study, Humans, Male, Middle Aged, Muscle, Skeletal, Phenotype, Polymorphism, Single Nucleotide, RNA-Binding Proteins, Receptor, Melanocortin, Type 4, Versicans, White People, Young Adult
Abstract: BACKGROUND: Lean body mass (LM) plays an important role in mobility and metabolic function. We previously identified five loci associated with LM adjusted for fat mass in kilograms. Such an adjustment may reduce the power to identify genetic signals having an association with both lean mass and fat mass. OBJECTIVES: To determine the impact of different fat mass adjustments on genetic architecture of LM and identify additional LM loci. METHODS: We performed genome-wide association analyses for whole-body LM (20 cohorts of European ancestry with n = 38,292) measured using dual-energy X-ray absorptiometry) or bioelectrical impedance analysis, adjusted for sex, age, age2, and height with or without fat mass adjustments (Model 1 no fat adjustment; Model 2 adjustment for fat mass as a percentage of body mass; Model 3 adjustment for fat mass in kilograms). RESULTS: Seven single-nucleotide polymorphisms (SNPs) in separate loci, including one novel LM locus (TNRC6B), were successfully replicated in an additional 47,227 individuals from 29 cohorts. Based on the strengths of the associations in Model 1 vs Model 3, we divided the LM loci into those with an effect on both lean mass and fat mass in the same direction and refer to those as sumo wrestler loci (FTO and MC4R). In contrast, loci with an impact specifically on LM were termed body builder loci (VCAN and ADAMTSL3). Using existing available genome-wide association study databases, LM increasing alleles of SNPs in sumo wrestler loci were associated with an adverse metabolic profile, whereas LM increasing alleles of SNPs in body builder loci were associated with metabolic protection. CONCLUSIONS: In conclusion, we identified one novel LM locus (TNRC6B). Our results suggest that a genetically determined increase in lean mass might exert either harmful or protective effects on metabolic traits, depending on its relation to fat mass.
Published: 2019

6. Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies

Author: Chen, Han, Huffman, Jennifer E, Brody, Jennifer A, Wang, Chaolong, Lee, Seunggeun, Li, Zilin, Gogarten, Stephanie M, Sofer, Tamar, Bielak, Lawrence F, Bis, Joshua C, Blangero, John, Bowler, Russell P, Cade, Brian E, Cho, Michael H, Correa, Adolfo, Curran, Joanne E, de Vries, Paul S, Glahn, David C, Guo, Xiuqing, Johnson, Andrew D, Kardia, Sharon, Kooperberg, Charles, Lewis, Joshua P, Liu, Xiaoming, Mathias, Rasika A, Mitchell, Braxton D, O’Connell, Jeffrey R, Peyser, Patricia A, Post, Wendy S, Reiner, Alex P, Rich, Stephen S, Rotter, Jerome I, Silverman, Edwin K, Smith, Jennifer A, Vasan, Ramachandran S, Wilson, James G, Yanek, Lisa R, Consortium, NHLBI Trans-Omics for Precision Medicine, Group, TOPMed Hematology and Hemostasis Working, Redline, Susan, Smith, Nicholas L, Boerwinkle, Eric, Borecki, Ingrid B, Cupples, L Adrienne, Laurie, Cathy C, Morrison, Alanna C, Rice, Kenneth M, and Lin, Xihong
Subjects: Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Biotechnology, Aetiology, 2.5 Research design and methodologies (aetiology), Generic health relevance, Good Health and Well Being, Chromosomes, Human, Pair 4, Cloud Computing, Female, Fibrinogen, Genetic Association Studies, Genetics, Population, Humans, Male, Models, Genetic, National Heart, Lung, and Blood Institute (U.S.), Precision Medicine, Research Design, Time Factors, United States, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Hematology and Hemostasis Working Group, TOPMed, generalized linear mixed model, population structure, rare variants, relatedness, variant set association test, whole-genome sequencing, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract: With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-set mixed model association tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program. SMMATs share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be fit only once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMATs correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.
Published: 2019

7. Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes

Author: Gusarova, Viktoria, O’Dushlaine, Colm, Teslovich, Tanya M, Benotti, Peter N, Mirshahi, Tooraj, Gottesman, Omri, Van Hout, Cristopher V, Murray, Michael F, Mahajan, Anubha, Nielsen, Jonas B, Fritsche, Lars, Wulff, Anders Berg, Gudbjartsson, Daniel F, Sjögren, Marketa, Emdin, Connor A, Scott, Robert A, Lee, Wen-Jane, Small, Aeron, Kwee, Lydia C, Dwivedi, Om Prakash, Prasad, Rashmi B, Bruse, Shannon, Lopez, Alexander E, Penn, John, Marcketta, Anthony, Leader, Joseph B, Still, Christopher D, Kirchner, H Lester, Mirshahi, Uyenlinh L, Wardeh, Amr H, Hartle, Cassandra M, Habegger, Lukas, Fetterolf, Samantha N, Tusie-Luna, Teresa, Morris, Andrew P, Holm, Hilma, Steinthorsdottir, Valgerdur, Sulem, Patrick, Thorsteinsdottir, Unnur, Rotter, Jerome I, Chuang, Lee-Ming, Damrauer, Scott, Birtwell, David, Brummett, Chad M, Khera, Amit V, Natarajan, Pradeep, Orho-Melander, Marju, Flannick, Jason, Lotta, Luca A, Willer, Cristen J, Holmen, Oddgeir L, Ritchie, Marylyn D, Ledbetter, David H, Murphy, Andrew J, Borecki, Ingrid B, Reid, Jeffrey G, Overton, John D, Hansson, Ola, Groop, Leif, Shah, Svati H, Kraus, William E, Rader, Daniel J, Chen, Yii-Der I, Hveem, Kristian, Wareham, Nicholas J, Kathiresan, Sekar, Melander, Olle, Stefansson, Kari, Nordestgaard, Børge G, Tybjærg-Hansen, Anne, Abecasis, Goncalo R, Altshuler, David, Florez, Jose C, Boehnke, Michael, McCarthy, Mark I, Yancopoulos, George D, Carey, David J, Shuldiner, Alan R, Baras, Aris, Dewey, Frederick E, and Gromada, Jesper
Subjects: Biological Sciences, Biomedical and Clinical Sciences, Genetics, Diabetes, Atherosclerosis, Cardiovascular, Metabolic and endocrine, Amino Acid Substitution, Angiopoietin-Like Protein 4, Animals, Blood Glucose, Case-Control Studies, Diabetes Mellitus, Type 2, Female, Gene Silencing, Genetic Association Studies, Genetic Variation, Heterozygote, Homeostasis, Humans, Insulin Resistance, Lipoprotein Lipase, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, Risk Factors, Exome Sequencing
Abstract: Angiopoietin-like 4 (ANGPTL4) is an endogenous inhibitor of lipoprotein lipase that modulates lipid levels, coronary atherosclerosis risk, and nutrient partitioning. We hypothesize that loss of ANGPTL4 function might improve glucose homeostasis and decrease risk of type 2 diabetes (T2D). We investigate protein-altering variants in ANGPTL4 among 58,124 participants in the DiscovEHR human genetics study, with follow-up studies in 82,766 T2D cases and 498,761 controls. Carriers of p.E40K, a variant that abolishes ANGPTL4 ability to inhibit lipoprotein lipase, have lower odds of T2D (odds ratio 0.89, 95% confidence interval 0.85-0.92, p = 6.3 × 10-10), lower fasting glucose, and greater insulin sensitivity. Predicted loss-of-function variants are associated with lower odds of T2D among 32,015 cases and 84,006 controls (odds ratio 0.71, 95% confidence interval 0.49-0.99, p = 0.041). Functional studies in Angptl4-deficient mice confirm improved insulin sensitivity and glucose homeostasis. In conclusion, genetic inactivation of ANGPTL4 is associated with improved glucose homeostasis and reduced risk of T2D.
Published: 2018

8. Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries.

Author: Feitosa, Mary F, Kraja, Aldi T, Chasman, Daniel I, Sung, Yun J, Winkler, Thomas W, Ntalla, Ioanna, Guo, Xiuqing, Franceschini, Nora, Cheng, Ching-Yu, Sim, Xueling, Vojinovic, Dina, Marten, Jonathan, Musani, Solomon K, Li, Changwei, Bentley, Amy R, Brown, Michael R, Schwander, Karen, Richard, Melissa A, Noordam, Raymond, Aschard, Hugues, Bartz, Traci M, Bielak, Lawrence F, Dorajoo, Rajkumar, Fisher, Virginia, Hartwig, Fernando P, Horimoto, Andrea RVR, Lohman, Kurt K, Manning, Alisa K, Rankinen, Tuomo, Smith, Albert V, Tajuddin, Salman M, Wojczynski, Mary K, Alver, Maris, Boissel, Mathilde, Cai, Qiuyin, Campbell, Archie, Chai, Jin Fang, Chen, Xu, Divers, Jasmin, Gao, Chuan, Goel, Anuj, Hagemeijer, Yanick, Harris, Sarah E, He, Meian, Hsu, Fang-Chi, Jackson, Anne U, Kähönen, Mika, Kasturiratne, Anuradhani, Komulainen, Pirjo, Kühnel, Brigitte, Laguzzi, Federica, Luan, Jian'an, Matoba, Nana, Nolte, Ilja M, Padmanabhan, Sandosh, Riaz, Muhammad, Rueedi, Rico, Robino, Antonietta, Said, M Abdullah, Scott, Robert A, Sofer, Tamar, Stančáková, Alena, Takeuchi, Fumihiko, Tayo, Bamidele O, van der Most, Peter J, Varga, Tibor V, Vitart, Veronique, Wang, Yajuan, Ware, Erin B, Warren, Helen R, Weiss, Stefan, Wen, Wanqing, Yanek, Lisa R, Zhang, Weihua, Zhao, Jing Hua, Afaq, Saima, Amin, Najaf, Amini, Marzyeh, Arking, Dan E, Aung, Tin, Boerwinkle, Eric, Borecki, Ingrid, Broeckel, Ulrich, Brown, Morris, Brumat, Marco, Burke, Gregory L, Canouil, Mickaël, Chakravarti, Aravinda, Charumathi, Sabanayagam, Ida Chen, Yii-Der, Connell, John M, Correa, Adolfo, de Las Fuentes, Lisa, de Mutsert, Renée, de Silva, H Janaka, Deng, Xuan, Ding, Jingzhong, Duan, Qing, Eaton, Charles B, and Ehret, Georg
Subjects: InterAct Consortium, Humans, Hypertension, Genetic Predisposition to Disease, Cohort Studies, Pedigree, Alcohol Drinking, Blood Pressure, Polymorphism, Single Nucleotide, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Continental Population Groups, Female, Male, Genome-Wide Association Study, Young Adult, Gene-Environment Interaction, General Science & Technology
Abstract: Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.
Published: 2018

9. Erratum: Large meta-analysis of genome-wide association studies identifies five loci for lean body mass.

Author: Zillikens, M Carola, Demissie, Serkalem, Hsu, Yi-Hsiang, Yerges-Armstrong, Laura M, Chou, Wen-Chi, Stolk, Lisette, Livshits, Gregory, Broer, Linda, Johnson, Toby, Koller, Daniel L, Kutalik, Zoltán, Luan, Jian'an, Malkin, Ida, Ried, Janina S, Smith, Albert V, Thorleifsson, Gudmar, Vandenput, Liesbeth, Hua Zhao, Jing, Zhang, Weihua, Aghdassi, Ali, Åkesson, Kristina, Amin, Najaf, Baier, Leslie J, Barroso, Inês, Bennett, David A, Bertram, Lars, Biffar, Rainer, Bochud, Murielle, Boehnke, Michael, Borecki, Ingrid B, Buchman, Aron S, Byberg, Liisa, Campbell, Harry, Campos Obanda, Natalia, Cauley, Jane A, Cawthon, Peggy M, Cederberg, Henna, Chen, Zhao, Cho, Nam H, Jin Choi, Hyung, Claussnitzer, Melina, Collins, Francis, Cummings, Steven R, De Jager, Philip L, Demuth, Ilja, Dhonukshe-Rutten, Rosalie AM, Diatchenko, Luda, Eiriksdottir, Gudny, Enneman, Anke W, Erdos, Mike, Eriksson, Johan G, Eriksson, Joel, Estrada, Karol, Evans, Daniel S, Feitosa, Mary F, Fu, Mao, Garcia, Melissa, Gieger, Christian, Girke, Thomas, Glazer, Nicole L, Grallert, Harald, Grewal, Jagvir, Han, Bok-Ghee, Hanson, Robert L, Hayward, Caroline, Hofman, Albert, Hoffman, Eric P, Homuth, Georg, Hsueh, Wen-Chi, Hubal, Monica J, Hubbard, Alan, Huffman, Kim M, Husted, Lise B, Illig, Thomas, Ingelsson, Erik, Ittermann, Till, Jansson, John-Olov, Jordan, Joanne M, Jula, Antti, Karlsson, Magnus, Khaw, Kay-Tee, Kilpeläinen, Tuomas O, Klopp, Norman, Kloth, Jacqueline SL, Koistinen, Heikki A, Kraus, William E, Kritchevsky, Stephen, Kuulasmaa, Teemu, Kuusisto, Johanna, Laakso, Markku, Lahti, Jari, Lang, Thomas, Langdahl, Bente L, Launer, Lenore J, Lee, Jong-Young, Lerch, Markus M, Lewis, Joshua R, Lind, Lars, Lindgren, Cecilia, and Liu, Yongmei
Subjects: Epidemiology, Biological Sciences, Health Sciences, Genetics
Abstract: A correction to this article has been published and is linked from the HTML version of this article.
Published: 2017

10. Genetic and Pharmacologic Inactivation of ANGPTL3 and Cardiovascular Disease

Author: Dewey, Frederick E, Gusarova, Viktoria, Dunbar, Richard L, O'Dushlaine, Colm, Schurmann, Claudia, Gottesman, Omri, McCarthy, Shane, Van Hout, Cristopher V, Bruse, Shannon, Dansky, Hayes M, Leader, Joseph B, Murray, Michael F, Ritchie, Marylyn D, Kirchner, H Lester, Habegger, Lukas, Lopez, Alex, Penn, John, Zhao, An, Shao, Weiping, Stahl, Neil, Murphy, Andrew J, Hamon, Sara, Bouzelmat, Aurelie, Zhang, Rick, Shumel, Brad, Pordy, Robert, Gipe, Daniel, Herman, Gary A, Sheu, Wayne HH, Lee, I-Te, Liang, Kae-Woei, Guo, Xiuqing, Rotter, Jerome I, Chen, Yii-Der I, Kraus, William E, Shah, Svati H, Damrauer, Scott, Small, Aeron, Rader, Daniel J, Wulff, Anders Berg, Nordestgaard, Børge G, Tybjærg-Hansen, Anne, van den Hoek, Anita M, Princen, Hans MG, Ledbetter, David H, Carey, David J, Overton, John D, Reid, Jeffrey G, Sasiela, William J, Banerjee, Poulabi, Shuldiner, Alan R, Borecki, Ingrid B, Teslovich, Tanya M, Yancopoulos, George D, Mellis, Scott J, Gromada, Jesper, and Baras, Aris
Subjects: Genetics, Heart Disease, Clinical Research, Atherosclerosis, Heart Disease - Coronary Heart Disease, Cardiovascular, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Aged, Angiopoietin-Like Protein 3, Angiopoietin-like Proteins, Angiopoietins, Animals, Antibodies, Monoclonal, Cardiovascular Diseases, Coronary Artery Disease, Disease Models, Animal, Dose-Response Relationship, Drug, Double-Blind Method, Dyslipidemias, Female, Humans, Lipid Metabolism, Lipids, Male, Mice, Mice, Inbred Strains, Middle Aged, Mutation, Medical and Health Sciences, General & Internal Medicine
Abstract: BackgroundLoss-of-function variants in the angiopoietin-like 3 gene (ANGPTL3) have been associated with decreased plasma levels of triglycerides, low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol. It is not known whether such variants or therapeutic antagonism of ANGPTL3 are associated with a reduced risk of atherosclerotic cardiovascular disease.MethodsWe sequenced the exons of ANGPTL3 in 58,335 participants in the DiscovEHR human genetics study. We performed tests of association for loss-of-function variants in ANGPTL3 with lipid levels and with coronary artery disease in 13,102 case patients and 40,430 controls from the DiscovEHR study, with follow-up studies involving 23,317 case patients and 107,166 controls from four population studies. We also tested the effects of a human monoclonal antibody, evinacumab, against Angptl3 in dyslipidemic mice and against ANGPTL3 in healthy human volunteers with elevated levels of triglycerides or LDL cholesterol.ResultsIn the DiscovEHR study, participants with heterozygous loss-of-function variants in ANGPTL3 had significantly lower serum levels of triglycerides, HDL cholesterol, and LDL cholesterol than participants without these variants. Loss-of-function variants were found in 0.33% of case patients with coronary artery disease and in 0.45% of controls (adjusted odds ratio, 0.59; 95% confidence interval, 0.41 to 0.85; P=0.004). These results were confirmed in the follow-up studies. In dyslipidemic mice, inhibition of Angptl3 with evinacumab resulted in a greater decrease in atherosclerotic lesion area and necrotic content than a control antibody. In humans, evinacumab caused a dose-dependent placebo-adjusted reduction in fasting triglyceride levels of up to 76% and LDL cholesterol levels of up to 23%.ConclusionsGenetic and therapeutic antagonism of ANGPTL3 in humans and of Angptl3 in mice was associated with decreased levels of all three major lipid fractions and decreased odds of atherosclerotic cardiovascular disease. (Funded by Regeneron Pharmaceuticals and others; ClinicalTrials.gov number, NCT01749878 .).
Published: 2017

11. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass.

Author: Zillikens, M Carola, Demissie, Serkalem, Hsu, Yi-Hsiang, Yerges-Armstrong, Laura M, Chou, Wen-Chi, Stolk, Lisette, Livshits, Gregory, Broer, Linda, Johnson, Toby, Koller, Daniel L, Kutalik, Zoltán, Luan, Jian'an, Malkin, Ida, Ried, Janina S, Smith, Albert V, Thorleifsson, Gudmar, Vandenput, Liesbeth, Hua Zhao, Jing, Zhang, Weihua, Aghdassi, Ali, Åkesson, Kristina, Amin, Najaf, Baier, Leslie J, Barroso, Inês, Bennett, David A, Bertram, Lars, Biffar, Rainer, Bochud, Murielle, Boehnke, Michael, Borecki, Ingrid B, Buchman, Aron S, Byberg, Liisa, Campbell, Harry, Campos Obanda, Natalia, Cauley, Jane A, Cawthon, Peggy M, Cederberg, Henna, Chen, Zhao, Cho, Nam H, Jin Choi, Hyung, Claussnitzer, Melina, Collins, Francis, Cummings, Steven R, De Jager, Philip L, Demuth, Ilja, Dhonukshe-Rutten, Rosalie AM, Diatchenko, Luda, Eiriksdottir, Gudny, Enneman, Anke W, Erdos, Mike, Eriksson, Johan G, Eriksson, Joel, Estrada, Karol, Evans, Daniel S, Feitosa, Mary F, Fu, Mao, Garcia, Melissa, Gieger, Christian, Girke, Thomas, Glazer, Nicole L, Grallert, Harald, Grewal, Jagvir, Han, Bok-Ghee, Hanson, Robert L, Hayward, Caroline, Hofman, Albert, Hoffman, Eric P, Homuth, Georg, Hsueh, Wen-Chi, Hubal, Monica J, Hubbard, Alan, Huffman, Kim M, Husted, Lise B, Illig, Thomas, Ingelsson, Erik, Ittermann, Till, Jansson, John-Olov, Jordan, Joanne M, Jula, Antti, Karlsson, Magnus, Khaw, Kay-Tee, Kilpeläinen, Tuomas O, Klopp, Norman, Kloth, Jacqueline SL, Koistinen, Heikki A, Kraus, William E, Kritchevsky, Stephen, Kuulasmaa, Teemu, Kuusisto, Johanna, Laakso, Markku, Lahti, Jari, Lang, Thomas, Langdahl, Bente L, Launer, Lenore J, Lee, Jong-Young, Lerch, Markus M, Lewis, Joshua R, Lind, Lars, Lindgren, Cecilia, and Liu, Yongmei
Subjects: Humans, Thinness, 17-Hydroxysteroid Dehydrogenases, Aldehyde Oxidoreductases, Extracellular Matrix Proteins, Body Composition, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Regulatory Elements, Transcriptional, Versicans, Genome-Wide Association Study, Insulin Receptor Substrate Proteins, ADAMTS Proteins, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Human Genome, Genetics, 1.1 Normal biological development and functioning
Abstract: Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p
Published: 2017

12. Protein-Truncating Variants at the Cholesteryl Ester Transfer Protein Gene and Risk for Coronary Heart Disease

Author: Nomura, Akihiro, Won, Hong-Hee, Khera, Amit V, Takeuchi, Fumihiko, Ito, Kaoru, McCarthy, Shane, Emdin, Connor A, Klarin, Derek, Natarajan, Pradeep, Zekavat, Seyedeh M, Gupta, Namrata, Peloso, Gina M, Borecki, Ingrid B, Teslovich, Tanya M, Asselta, Rosanna, Duga, Stefano, Merlini, Piera A, Correa, Adolfo, Kessler, Thorsten, Wilson, James G, Bown, Matthew J, Hall, Alistair S, Braund, Peter S, Carey, David J, Murray, Michael F, Kirchner, H Lester, Leader, Joseph B, Lavage, Daniel R, Manus, J Neil, Hartze, Dustin N, Samani, Nilesh J, Schunkert, Heribert, Marrugat, Jaume, Elosua, Roberto, McPherson, Ruth, Farrall, Martin, Watkins, Hugh, Juang, Jyh-Ming J, Hsiung, Chao A, Lin, Shih-Yi, Wang, Jun-Sing, Tada, Hayato, Kawashiri, Masa-Aki, Inazu, Akihiro, Yamagishi, Masakazu, Katsuya, Tomohiro, Nakashima, Eitaro, Nakatochi, Masahiro, Yamamoto, Ken, Yokota, Mitsuhiro, Momozawa, Yukihide, Rotter, Jerome I, Lander, Eric S, Rader, Daniel J, Danesh, John, Ardissino, Diego, Gabriel, Stacey, Willer, Cristen J, Abecasis, Goncalo R, Saleheen, Danish, Kubo, Michiaki, Kato, Norihiro, Ida Chen, Yii-Der, Dewey, Frederick E, and Kathiresan, Sekar
Subjects: Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Atherosclerosis, Heart Disease - Coronary Heart Disease, Heart Disease, Cardiovascular, Genetics, Adult, Aged, Case-Control Studies, Cholesterol Ester Transfer Proteins, Coronary Disease, Female, Genetic Variation, Humans, Male, Middle Aged, Risk Factors, case-control studies, cholesteryl ester transfer protein, coronary disease, lipids, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract: RationaleTherapies that inhibit CETP (cholesteryl ester transfer protein) have failed to demonstrate a reduction in risk for coronary heart disease (CHD). Human DNA sequence variants that truncate the CETP gene may provide insight into the efficacy of CETP inhibition.ObjectiveTo test whether protein-truncating variants (PTVs) at the CETP gene were associated with plasma lipid levels and CHD.Methods and resultsWe sequenced the exons of the CETP gene in 58 469 participants from 12 case-control studies (18 817 CHD cases, 39 652 CHD-free controls). We defined PTV as those that lead to a premature stop, disrupt canonical splice sites, or lead to insertions/deletions that shift frame. We also genotyped 1 Japanese-specific PTV in 27561 participants from 3 case-control studies (14 286 CHD cases, 13 275 CHD-free controls). We tested association of CETP PTV carrier status with both plasma lipids and CHD. Among 58 469 participants with CETP gene-sequencing data available, average age was 51.5 years and 43% were women; 1 in 975 participants carried a PTV at the CETP gene. Compared with noncarriers, carriers of PTV at CETP had higher high-density lipoprotein cholesterol (effect size, 22.6 mg/dL; 95% confidence interval, 18-27; P
Published: 2017

13. Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms

Author: Rusu, Victor, Hoch, Eitan, Mercader, Josep M, Tenen, Danielle E, Gymrek, Melissa, Hartigan, Christina R, DeRan, Michael, von Grotthuss, Marcin, Fontanillas, Pierre, Spooner, Alexandra, Guzman, Gaelen, Deik, Amy A, Pierce, Kerry A, Dennis, Courtney, Clish, Clary B, Carr, Steven A, Wagner, Bridget K, Schenone, Monica, Ng, Maggie CY, Chen, Brian H, Consortium, MEDIA, Shriner, Daniel, Li, Jiang, Chen, Wei-Min, Guo, Xiuqing, Liu, Jiankang, Bielinski, Suzette J, Yanek, Lisa R, Nalls, Michael A, Comeau, Mary E, Rasmussen-Torvik, Laura J, Jensen, Richard A, Evans, Daniel S, Sun, Yan V, An, Ping, Patel, Sanjay R, Lu, Yingchang, Long, Jirong, Armstrong, Loren L, Wagenknecht, Lynne, Yang, Lingyao, Snively, Beverly M, Palmer, Nicholette D, Mudgal, Poorva, Langefeld, Carl D, Keene, Keith L, Freedman, Barry I, Mychaleckyj, Josyf C, Nayak, Uma, Raffel, Leslie J, Goodarzi, Mark O, Chen, Y-D Ida, Taylor, Herman A, Correa, Adolfo, Sims, Mario, Couper, David, Pankow, James S, Boerwinkle, Eric, Adeyemo, Adebowale, Doumatey, Ayo, Chen, Guanjie, Mathias, Rasika A, Vaidya, Dhananjay, Singleton, Andrew B, Zonderman, Alan B, Igo, Robert P, Sedor, John R, Consortium, the FIND, Kabagambe, Edmond K, Siscovick, David S, McKnight, Barbara, Rice, Kenneth, Liu, Yongmei, Hsueh, Wen-Chi, Zhao, Wei, Bielak, Lawrence F, Kraja, Aldi, Province, Michael A, Bottinger, Erwin P, Gottesman, Omri, Cai, Qiuyin, Zheng, Wei, Blot, William J, Lowe, William L, Pacheco, Jennifer A, Crawford, Dana C, Consortium, the eMERGE, Consortium, the DIAGRAM, Grundberg, Elin, Consortium, the MuTHER, Rich, Stephen S, Hayes, M Geoffrey, Shu, Xiao-Ou, Loos, Ruth JF, Borecki, Ingrid B, Peyser, Patricia A, Cummings, Steven R, and Psaty, Bruce M
Subjects: Biological Sciences, Genetics, Clinical Research, Diabetes, Digestive Diseases, Liver Disease, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Basigin, Cell Membrane, Chromosomes, Human, Pair 17, Diabetes Mellitus, Type 2, Gene Knockdown Techniques, Haplotypes, Hepatocytes, Heterozygote, Histone Code, Humans, Liver, Models, Molecular, Monocarboxylic Acid Transporters, MEDIA Consortium, SIGMA T2D Consortium, MCT11, SLC16A11, disease mechanism, fatty acid metabolism, genetics, lipid metabolism, monocarboxylates, precision medicine, solute carrier, type 2 diabetes, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract: Type 2 diabetes (T2D) affects Latinos at twice the rate seen in populations of European descent. We recently identified a risk haplotype spanning SLC16A11 that explains ∼20% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s). We show that variants on the T2D-associated haplotype have two distinct effects: (1) decreasing SLC16A11 expression in liver and (2) disrupting a key interaction with basigin, thereby reducing cell-surface localization. Both independent mechanisms reduce SLC16A11 function and suggest SLC16A11 is the causal gene at this locus. To gain insight into how SLC16A11 disruption impacts T2D risk, we demonstrate that SLC16A11 is a proton-coupled monocarboxylate transporter and that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism that are associated with increased T2D risk. Our findings suggest that increasing SLC16A11 function could be therapeutically beneficial for T2D. VIDEO ABSTRACT.
Published: 2017

14. Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium.

Author: Ng, Maggie CY, Graff, Mariaelisa, Lu, Yingchang, Justice, Anne E, Mudgal, Poorva, Liu, Ching-Ti, Young, Kristin, Yanek, Lisa R, Feitosa, Mary F, Wojczynski, Mary K, Rand, Kristin, Brody, Jennifer A, Cade, Brian E, Dimitrov, Latchezar, Duan, Qing, Guo, Xiuqing, Lange, Leslie A, Nalls, Michael A, Okut, Hayrettin, Tajuddin, Salman M, Tayo, Bamidele O, Vedantam, Sailaja, Bradfield, Jonathan P, Chen, Guanjie, Chen, Wei-Min, Chesi, Alessandra, Irvin, Marguerite R, Padhukasahasram, Badri, Smith, Jennifer A, Zheng, Wei, Allison, Matthew A, Ambrosone, Christine B, Bandera, Elisa V, Bartz, Traci M, Berndt, Sonja I, Bernstein, Leslie, Blot, William J, Bottinger, Erwin P, Carpten, John, Chanock, Stephen J, Chen, Yii-Der Ida, Conti, David V, Cooper, Richard S, Fornage, Myriam, Freedman, Barry I, Garcia, Melissa, Goodman, Phyllis J, Hsu, Yu-Han H, Hu, Jennifer, Huff, Chad D, Ingles, Sue A, John, Esther M, Kittles, Rick, Klein, Eric, Li, Jin, McKnight, Barbara, Nayak, Uma, Nemesure, Barbara, Ogunniyi, Adesola, Olshan, Andrew, Press, Michael F, Rohde, Rebecca, Rybicki, Benjamin A, Salako, Babatunde, Sanderson, Maureen, Shao, Yaming, Siscovick, David S, Stanford, Janet L, Stevens, Victoria L, Stram, Alex, Strom, Sara S, Vaidya, Dhananjay, Witte, John S, Yao, Jie, Zhu, Xiaofeng, Ziegler, Regina G, Zonderman, Alan B, Adeyemo, Adebowale, Ambs, Stefan, Cushman, Mary, Faul, Jessica D, Hakonarson, Hakon, Levin, Albert M, Nathanson, Katherine L, Ware, Erin B, Weir, David R, Zhao, Wei, Zhi, Degui, Bone Mineral Density in Childhood Study (BMDCS) Group, Arnett, Donna K, Grant, Struan FA, Kardia, Sharon LR, Oloapde, Olufunmilayo I, Rao, DC, Rotimi, Charles N, Sale, Michele M, Williams, L Keoki, Zemel, Babette S, Becker, Diane M, and Borecki, Ingrid B
Subjects: Bone Mineral Density in Childhood Study (BMDCS) Group, Humans, Obesity, Genetic Predisposition to Disease, Serine Endopeptidases, Anthropometry, Body Mass Index, Waist-Hip Ratio, Chromosome Mapping, Gene Frequency, Linkage Disequilibrium, Polymorphism, Single Nucleotide, African Continental Ancestry Group, European Continental Ancestry Group, Female, Male, Adiposity, Genome-Wide Association Study, Transcription Factor 7-Like 2 Protein, Genetics, Human Genome, 2.1 Biological and endogenous factors, Developmental Biology
Abstract: Genome-wide association studies (GWAS) have identified >300 loci associated with measures of adiposity including body mass index (BMI) and waist-to-hip ratio (adjusted for BMI, WHRadjBMI), but few have been identified through screening of the African ancestry genomes. We performed large scale meta-analyses and replications in up to 52,895 individuals for BMI and up to 23,095 individuals for WHRadjBMI from the African Ancestry Anthropometry Genetics Consortium (AAAGC) using 1000 Genomes phase 1 imputed GWAS to improve coverage of both common and low frequency variants in the low linkage disequilibrium African ancestry genomes. In the sex-combined analyses, we identified one novel locus (TCF7L2/HABP2) for WHRadjBMI and eight previously established loci at P < 5×10-8: seven for BMI, and one for WHRadjBMI in African ancestry individuals. An additional novel locus (SPRYD7/DLEU2) was identified for WHRadjBMI when combined with European GWAS. In the sex-stratified analyses, we identified three novel loci for BMI (INTS10/LPL and MLC1 in men, IRX4/IRX2 in women) and four for WHRadjBMI (SSX2IP, CASC8, PDE3B and ZDHHC1/HSD11B2 in women) in individuals of African ancestry or both African and European ancestry. For four of the novel variants, the minor allele frequency was low (
Published: 2017

15. Genome-wide association meta-analysis of fish and EPA+DHA consumption in 17 US and European cohorts

Author: Mozaffarian, Dariush, Dashti, Hassan S, Wojczynski, Mary K, Chu, Audrey Y, Nettleton, Jennifer A, Männistö, Satu, Kristiansson, Kati, Reedik, Mägi, Lahti, Jari, Houston, Denise K, Cornelis, Marilyn C, van Rooij, Frank JA, Dimitriou, Maria, Kanoni, Stavroula, Mikkilä, Vera, Steffen, Lyn M, de Oliveira Otto, Marcia C, Qi, Lu, Psaty, Bruce, Djousse, Luc, Rotter, Jerome I, Harald, Kennet, Perola, Markus, Rissanen, Harri, Jula, Antti, Krista, Fischer, Mihailov, Evelin, Feitosa, Mary F, Ngwa, Julius S, Xue, Luting, Jacques, Paul F, Perälä, Mia-Maria, Palotie, Aarno, Liu, Yongmei, Nalls, Nike A, Ferrucci, Luigi, Hernandez, Dena, Manichaikul, Ani, Tsai, Michael Y, Jong, Jessica C Kiefte-de, Hofman, Albert, Uitterlinden, André G, Rallidis, Loukianos, Ridker, Paul M, Rose, Lynda M, Buring, Julie E, Lehtimäki, Terho, Kähönen, Mika, Viikari, Jorma, Lemaitre, Rozenn, Salomaa, Veikko, Knekt, Paul, Metspalu, Andres, Borecki, Ingrid B, Cupples, L Adrienne, Eriksson, Johan G, Kritchevsky, Stephen B, Bandinelli, Stefania, Siscovick, David, Franco, Oscar H, Deloukas, Panos, Dedoussis, George, Chasman, Daniel I, Raitakari, Olli, and Tanaka, Toshiko
Subjects: Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Nutrition and Dietetics, Human Genome, Prevention, Complementary and Integrative Health, Nutrition, Cardiovascular, Oral and gastrointestinal, Cancer, Stroke, Adult, Aged, Cohort Studies, Docosahexaenoic Acids, Eicosapentaenoic Acid, Europe, Female, Genome-Wide Association Study, Humans, Male, Middle Aged, Seafood, United States, White People, General Science & Technology
Abstract: BackgroundRegular fish and omega-3 consumption may have several health benefits and are recommended by major dietary guidelines. Yet, their intakes remain remarkably variable both within and across populations, which could partly owe to genetic influences.ObjectiveTo identify common genetic variants that influence fish and dietary eicosapentaenoic acid plus docosahexaenoic acid (EPA+DHA) consumption.DesignWe conducted genome-wide association (GWA) meta-analysis of fish (n = 86,467) and EPA+DHA (n = 62,265) consumption in 17 cohorts of European descent from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium Nutrition Working Group. Results from cohort-specific GWA analyses (additive model) for fish and EPA+DHA consumption were adjusted for age, sex, energy intake, and population stratification, and meta-analyzed separately using fixed-effect meta-analysis with inverse variance weights (METAL software). Additionally, heritability was estimated in 2 cohorts.ResultsHeritability estimates for fish and EPA+DHA consumption ranged from 0.13-0.24 and 0.12-0.22, respectively. A significant GWA for fish intake was observed for rs9502823 on chromosome 6: each copy of the minor allele (FreqA = 0.015) was associated with 0.029 servings/day (~1 serving/month) lower fish consumption (P = 1.96x10-8). No significant association was observed for EPA+DHA, although rs7206790 in the obesity-associated FTO gene was among top hits (P = 8.18x10-7). Post-hoc calculations demonstrated 95% statistical power to detect a genetic variant associated with effect size of 0.05% for fish and 0.08% for EPA+DHA.ConclusionsThese novel findings suggest that non-genetic personal and environmental factors are principal determinants of the remarkable variation in fish consumption, representing modifiable targets for increasing intakes among all individuals. Genes underlying the signal at rs72838923 and mechanisms for the association warrant further investigation.
Published: 2017

16. Multiethnic genome-wide meta-analysis of ectopic fat depots identifies loci associated with adipocyte development and differentiation

Author: Chu, Audrey Y, Deng, Xuan, Fisher, Virginia A, Drong, Alexander, Zhang, Yang, Feitosa, Mary F, Liu, Ching-Ti, Weeks, Olivia, Choh, Audrey C, Duan, Qing, Dyer, Thomas D, Eicher, John D, Guo, Xiuqing, Heard-Costa, Nancy L, Kacprowski, Tim, Kent, Jack W, Lange, Leslie A, Liu, Xinggang, Lohman, Kurt, Lu, Lingyi, Mahajan, Anubha, O'Connell, Jeffrey R, Parihar, Ankita, Peralta, Juan M, Smith, Albert V, Zhang, Yi, Homuth, Georg, Kissebah, Ahmed H, Kullberg, Joel, Laqua, René, Launer, Lenore J, Nauck, Matthias, Olivier, Michael, Peyser, Patricia A, Terry, James G, Wojczynski, Mary K, Yao, Jie, Bielak, Lawrence F, Blangero, John, Borecki, Ingrid B, Bowden, Donald W, Carr, John Jeffrey, Czerwinski, Stefan A, Ding, Jingzhong, Friedrich, Nele, Gudnason, Vilmunder, Harris, Tamara B, Ingelsson, Erik, Johnson, Andrew D, Kardia, Sharon LR, Langefeld, Carl D, Lind, Lars, Liu, Yongmei, Mitchell, Braxton D, Morris, Andrew P, Mosley, Thomas H, Rotter, Jerome I, Shuldiner, Alan R, Towne, Bradford, Völzke, Henry, Wallaschofski, Henri, Wilson, James G, Allison, Matthew, Lindgren, Cecilia M, Goessling, Wolfram, Cupples, L Adrienne, Steinhauser, Matthew L, and Fox, Caroline S
Subjects: Nutrition, Human Genome, Genetics, Adipocytes, Animals, Body Fat Distribution, Cell Differentiation, Cohort Studies, Ethnicity, Female, Genetic Loci, Genetic Markers, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Mice, Mice, Inbred C57BL, Obesity, Phenotype, Polymorphism, Single Nucleotide, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract: Variation in body fat distribution contributes to the metabolic sequelae of obesity. The genetic determinants of body fat distribution are poorly understood. The goal of this study was to gain new insights into the underlying genetics of body fat distribution by conducting sample-size-weighted fixed-effects genome-wide association meta-analyses in up to 9,594 women and 8,738 men of European, African, Hispanic and Chinese ancestry, with and without sex stratification, for six traits associated with ectopic fat (hereinafter referred to as ectopic-fat traits). In total, we identified seven new loci associated with ectopic-fat traits (ATXN1, UBE2E2, EBF1, RREB1, GSDMB, GRAMD3 and ENSA; P < 5 × 10-8; false discovery rate < 1%). Functional analysis of these genes showed that loss of function of either Atxn1 or Ube2e2 in primary mouse adipose progenitor cells impaired adipocyte differentiation, suggesting physiological roles for ATXN1 and UBE2E2 in adipogenesis. Future studies are necessary to further explore the mechanisms by which these genes affect adipocyte biology and how their perturbations contribute to systemic metabolic disease.
Published: 2017

17. Multiethnic Exome-Wide Association Study of Subclinical Atherosclerosis

Author: Natarajan, Pradeep, Bis, Joshua C, Bielak, Lawrence F, Cox, Amanda J, Dörr, Marcus, Feitosa, Mary F, Franceschini, Nora, Guo, Xiuqing, Hwang, Shih-Jen, Isaacs, Aaron, Jhun, Min A, Kavousi, Maryam, Li-Gao, Ruifang, Lyytikäinen, Leo-Pekka, Marioni, Riccardo E, Schminke, Ulf, Stitziel, Nathan O, Tada, Hayato, van Setten, Jessica, Smith, Albert V, Vojinovic, Dina, Yanek, Lisa R, Yao, Jie, Yerges-Armstrong, Laura M, Amin, Najaf, Baber, Usman, Borecki, Ingrid B, Carr, J Jeffrey, Chen, Yii-Der Ida, Cupples, L Adrienne, de Jong, Pim A, de Koning, Harry, de Vos, Bob D, Demirkan, Ayse, Fuster, Valentin, Franco, Oscar H, Goodarzi, Mark O, Harris, Tamara B, Heckbert, Susan R, Heiss, Gerardo, Hoffmann, Udo, Hofman, Albert, Išgum, Ivana, Jukema, J Wouter, Kähönen, Mika, Kardia, Sharon LR, Kral, Brian G, Launer, Lenore J, Massaro, Joe, Mehran, Roxana, Mitchell, Braxton D, Mosley, Thomas H, de Mutsert, Renée, Newman, Anne B, Nguyen, Khanh-Dung, North, Kari E, O'Connell, Jeffrey R, Oudkerk, Matthijs, Pankow, James S, Peloso, Gina M, Post, Wendy, Province, Michael A, Raffield, Laura M, Raitakari, Olli T, Reilly, Dermot F, Rivadeneira, Fernando, Rosendaal, Frits, Sartori, Samantha, Taylor, Kent D, Teumer, Alexander, Trompet, Stella, Turner, Stephen T, Uitterlinden, Andre G, Vaidya, Dhananjay, van der Lugt, Aad, Völker, Uwe, Wardlaw, Joanna M, Wassel, Christina L, Weiss, Stefan, Wojczynski, Mary K, Becker, Diane M, Becker, Lewis C, Boerwinkle, Eric, Bowden, Donald W, Deary, Ian J, Dehghan, Abbas, Felix, Stephan B, Gudnason, Vilmundur, Lehtimäki, Terho, Mathias, Rasika, Mook-Kanamori, Dennis O, Psaty, Bruce M, Rader, Daniel J, Rotter, Jerome I, Wilson, James G, van Duijn, Cornelia M, Völzke, Henry, Kathiresan, Sekar, Peyser, Patricia A, and O'Donnell, Christopher J
Subjects: Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Heart Disease - Coronary Heart Disease, Aging, Human Genome, Atherosclerosis, Cardiovascular, Heart Disease, Clinical Research, Genetics, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Apolipoprotein B-100, Apolipoprotein E2, Asymptomatic Diseases, Black People, Carotid Artery Diseases, Carotid Intima-Media Thickness, Cholesterol, LDL, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Exome, Gene Frequency, Genetic Markers, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Odds Ratio, Oligonucleotide Array Sequence Analysis, Phenotype, Prognosis, Risk Assessment, Risk Factors, Vascular Calcification, White People, carotid intima-media thickness, coronary artery calcification, exome, genome-wide association study, genomics, CHARGE Consortium, carotid intima–media thickness, Medical Biotechnology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract: BackgroundThe burden of subclinical atherosclerosis in asymptomatic individuals is heritable and associated with elevated risk of developing clinical coronary heart disease. We sought to identify genetic variants in protein-coding regions associated with subclinical atherosclerosis and the risk of subsequent coronary heart disease.Methods and resultsWe studied a total of 25 109 European ancestry and African ancestry participants with coronary artery calcification (CAC) measured by cardiac computed tomography and 52 869 participants with common carotid intima-media thickness measured by ultrasonography within the CHARGE Consortium (Cohorts for Heart and Aging Research in Genomic Epidemiology). Participants were genotyped for 247 870 DNA sequence variants (231 539 in exons) across the genome. A meta-analysis of exome-wide association studies was performed across cohorts for CAC and carotid intima-media thickness. APOB p.Arg3527Gln was associated with 4-fold excess CAC (P=3×10-10). The APOE ε2 allele (p.Arg176Cys) was associated with both 22.3% reduced CAC (P=1×10-12) and 1.4% reduced carotid intima-media thickness (P=4×10-14) in carriers compared with noncarriers. In secondary analyses conditioning on low-density lipoprotein cholesterol concentration, the ε2 protective association with CAC, although attenuated, remained strongly significant. Additionally, the presence of ε2 was associated with reduced risk for coronary heart disease (odds ratio 0.77; P=1×10-11).ConclusionsExome-wide association meta-analysis demonstrates that protein-coding variants in APOB and APOE associate with subclinical atherosclerosis. APOE ε2 represents the first significant association for multiple subclinical atherosclerosis traits across multiple ethnicities, as well as clinical coronary heart disease.
Published: 2016

18. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci

Author: Liu, Chunyu, Kraja, Aldi T, Smith, Jennifer A, Brody, Jennifer A, Franceschini, Nora, Bis, Joshua C, Rice, Kenneth, Morrison, Alanna C, Lu, Yingchang, Weiss, Stefan, Guo, Xiuqing, Palmas, Walter, Martin, Lisa W, Chen, Yii-Der Ida, Surendran, Praveen, Drenos, Fotios, Cook, James P, Auer, Paul L, Chu, Audrey Y, Giri, Ayush, Zhao, Wei, Jakobsdottir, Johanna, Lin, Li-An, Stafford, Jeanette M, Amin, Najaf, Mei, Hao, Yao, Jie, Voorman, Arend, Larson, Martin G, Grove, Megan L, Smith, Albert V, Hwang, Shih-Jen, Chen, Han, Huan, Tianxiao, Kosova, Gulum, Stitziel, Nathan O, Kathiresan, Sekar, Samani, Nilesh, Schunkert, Heribert, Deloukas, Panos, Li, Man, Fuchsberger, Christian, Pattaro, Cristian, Gorski, Mathias, Kooperberg, Charles, Papanicolaou, George J, Rossouw, Jacques E, Faul, Jessica D, Kardia, Sharon LR, Bouchard, Claude, Raffel, Leslie J, Uitterlinden, André G, Franco, Oscar H, Vasan, Ramachandran S, O'Donnell, Christopher J, Taylor, Kent D, Liu, Kiang, Bottinger, Erwin P, Gottesman, Omri, Daw, E Warwick, Giulianini, Franco, Ganesh, Santhi, Salfati, Elias, Harris, Tamara B, Launer, Lenore J, Dörr, Marcus, Felix, Stephan B, Rettig, Rainer, Völzke, Henry, Kim, Eric, Lee, Wen-Jane, Lee, I-Te, Sheu, Wayne H-H, Tsosie, Krystal S, Edwards, Digna R Velez, Liu, Yongmei, Correa, Adolfo, Weir, David R, Völker, Uwe, Ridker, Paul M, Boerwinkle, Eric, Gudnason, Vilmundur, Reiner, Alexander P, van Duijn, Cornelia M, Borecki, Ingrid B, Edwards, Todd L, Chakravarti, Aravinda, Rotter, Jerome I, Psaty, Bruce M, Loos, Ruth JF, Fornage, Myriam, Ehret, Georg B, Newton-Cheh, Christopher, Levy, Daniel, and Chasman, Daniel I
Subjects: Biological Sciences, Genetics, Cardiovascular, Hypertension, Prevention, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, Blood Pressure, Exome, Genetic Variation, Genome, Human, Genome-Wide Association Study, Genotype, Humans, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, CHD Exome+ Consortium, ExomeBP Consortium, GoT2DGenes Consortium, T2D-GENES Consortium, Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia, CKDGen Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract: Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein-protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure-associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk.
Published: 2016

19. Meta-analysis of rare and common exome chip variants identifies S1PR4 and other loci influencing blood cell traits

Author: Pankratz, Nathan, Schick, Ursula M, Zhou, Yi, Zhou, Wei, Ahluwalia, Tarunveer Singh, Allende, Maria Laura, Auer, Paul L, Bork-Jensen, Jette, Brody, Jennifer A, Chen, Ming-Huei, Clavo, Vinna, Eicher, John D, Grarup, Niels, Hagedorn, Elliott J, Hu, Bella, Hunker, Kristina, Johnson, Andrew D, Leusink, Maarten, Lu, Yingchang, Lyytikainen, Leo-Pekka, Manichaikul, Ani, Marioni, Riccardo E, Nalls, Mike A, Pazoki, Raha, Smith, Albert Vernon, van Rooij, Frank JA, Yang, Min-Lee, Zhang, Xiaoling, Zhang, Yan, Asselbergs, Folkert W, Boerwinkle, Eric, Borecki, Ingrid B, Bottinger, Erwin P, Cushman, Mary, de Bakker, Paul IW, Deary, Ian J, Dong, Liguang, Feitosa, Mary F, Floyd, James S, Franceschini, Nora, Franco, Oscar H, Garcia, Melissa E, Grove, Megan L, Gudnason, Vilmundur, Hansen, Torben, Harris, Tamara B, Hofman, Albert, Jackson, Rebecca D, Jia, Jia, Kahonen, Mika, Launer, Lenore J, Lehtimaki, Terho, Liewald, David C, Linneberg, Allan, Liu, Yongmei, Loos, Ruth JF, Nguyen, Vy M, Numans, Mattijs E, Pedersen, Oluf, Psaty, Bruce M, Raitakari, Olli T, Rich, Stephen S, Rivadeneira, Fernando, Di Sant, Amanda M Rosa, Rotter, Jerome I, Starr, John M, Taylor, Kent D, Thuesen, Betina Heinsbaek, Tracy, Russell P, Uitterlinden, Andre G, Wang, Jiansong, Wang, Judy, Dehghan, Abbas, Huo, Yong, Cupples, L Adrienne, Wilson, James G, Proia, Richard L, Zon, Leonard I, O'Donnell, Christopher J, Reiner, Alex P, and Ganesh, Santhi K
Subjects: Biological Sciences, Genetics, Prevention, Human Genome, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Cardiovascular, Animals, Erythrocyte Count, Erythrocytes, Ethnicity, Exome, Female, Genetic Loci, Genome-Wide Association Study, Hematocrit, Humans, Male, Mice, Quantitative Trait Loci, Receptors, Lysosphingolipid, Zebrafish, CHARGE Consortium Hematology Working Group, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract: Hematologic measures such as hematocrit and white blood cell (WBC) count are heritable and clinically relevant. We analyzed erythrocyte and WBC phenotypes in 52,531 individuals (37,775 of European ancestry, 11,589 African Americans, and 3,167 Hispanic Americans) from 16 population-based cohorts with Illumina HumanExome BeadChip genotypes. We then performed replication analyses of new discoveries in 18,018 European-American women and 5,261 Han Chinese. We identified and replicated four new erythrocyte trait-locus associations (CEP89, SHROOM3, FADS2, and APOE) and six new WBC loci for neutrophil count (S1PR4), monocyte count (BTBD8, NLRP12, and IL17RA), eosinophil count (IRF1), and total WBC count (MYB). The association of a rare missense variant in S1PR4 supports the role of sphingosine-1-phosphate signaling in leukocyte trafficking and circulating neutrophil counts. Loss-of-function experiments for S1pr4 in mouse and s1pr4 in zebrafish demonstrated phenotypes consistent with the association observed in humans and altered kinetics of neutrophil recruitment and resolution in response to tissue injury.
Published: 2016

20. A genome-wide study of lipid response to fenofibrate in Caucasians

Author: Irvin, Marguerite R, Rotroff, Daniel M, Aslibekyan, Stella, Zhi, Degui, Hidalgo, Bertha, Motsinger-Reif, Alison, Marvel, Skylar, Srinivasasainagendra, Vinodh, Claas, Steven A, Buse, John B, Straka, Robert J, Ordovas, Jose M, Borecki, Ingrid B, Guo, Xiuqing, Chen, Ida YD, Rotter, Jerome I, Wagner, Michael J, and Arnett, Donna K
Subjects: Biological Sciences, Biomedical and Clinical Sciences, Genetics, Atherosclerosis, Human Genome, Cardiovascular, Clinical Trials as Topic, Female, Fenofibrate, Genetic Markers, Genome-Wide Association Study, Genotype, Humans, Hypertriglyceridemia, Hypolipidemic Agents, Lipid Metabolism, Lipids, Male, Meta-Analysis as Topic, Middle Aged, Outcome Assessment, Health Care, White People, cholesterol, dyslipidemia, fenofibrate, genome-wide association study, lipid, lipoprotein, triglyceride, Pharmacology and Pharmaceutical Sciences, Pharmacology & Pharmacy, Pharmacology and pharmaceutical sciences
Abstract: BackgroundFibrates are commonly prescribed for hypertriglyceridemia, but they also lower LDL cholesterol and increase HDL cholesterol. Large interindividual variations in lipid response suggest that some patients may benefit more than others and genetic studies could help identify such patients.MethodsWe carried out the first genome-wide association study of lipid response to fenofibrate using data from two well-characterized clinical trials: the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Study and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Study. Genome-wide association study data from both studies were imputed to the 1000 Genomes CEU reference panel (phase 1). Lipid response was modeled as the log ratio of the post-treatment lipid level to the pretreatment level. Linear mixed models (GOLDN, N=813 from 173 families) and linear regression models (ACCORD, N=781) adjusted for pretreatment lipid level, demographic variables, clinical covariates, and ancestry were used to evaluate the association of genetic markers with lipid response. Among Caucasians, the results were combined using inverse-variance weighted fixed-effects meta-analyses. The main findings from the meta-analyses were examined in other ethnic groups from the HyperTG study (N=267 Hispanics) and ACCORD (N=83 Hispanics, 138 African Americans).ResultsA known lipid locus harboring the pre-B-cell leukemia homeobox 4 (PBX4) gene on chromosome 19 is important for LDL cholesterol response to fenofibrate (smallest P=1.5×10). The main results replicated with nominal statistical significance in Hispanics from ACCORD (P
Published: 2016

21. An Empirical Comparison of Joint and Stratified Frameworks for Studying G × E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene‐Lifestyle Interactions Working Group

Author: Sung, Yun Ju, Winkler, Thomas W, Manning, Alisa K, Aschard, Hugues, Gudnason, Vilmundur, Harris, Tamara B, Smith, Albert V, Boerwinkle, Eric, Brown, Michael R, Morrison, Alanna C, Fornage, Myriam, Lin, Li-An, Richard, Melissa, Bartz, Traci M, Psaty, Bruce M, Hayward, Caroline, Polasek, Ozren, Marten, Jonathan, Rudan, Igor, Feitosa, Mary F, Kraja, Aldi T, Province, Michael A, Deng, Xuan, Fisher, Virginia A, Zhou, Yanhua, Bielak, Lawrence F, Smith, Jennifer, Huffman, Jennifer E, Padmanabhan, Sandosh, Smith, Blair H, Ding, Jingzhong, Liu, Yongmei, Lohman, Kurt, Bouchard, Claude, Rankinen, Tuomo, Rice, Treva K, Arnett, Donna, Schwander, Karen, Guo, Xiuqing, Palmas, Walter, Rotter, Jerome I, Alfred, Tamuno, Bottinger, Erwin P, Loos, Ruth JF, Amin, Najaf, Franco, Oscar H, van Duijn, Cornelia M, Vojinovic, Dina, Chasman, Daniel I, Ridker, Paul M, Rose, Lynda M, Kardia, Sharon, Zhu, Xiaofeng, Rice, Kenneth, Borecki, Ingrid B, Rao, Dabeeru C, Gauderman, W James, and Cupples, L Adrienne
Subjects: Epidemiology, Biological Sciences, Health Sciences, Genetics, Clinical Research, Human Genome, Blood Pressure, Cohort Studies, Databases, Factual, Family, Gene Frequency, Gene-Environment Interaction, Genome-Wide Association Study, Genotype, Humans, Phenotype, Smoking, gene-environment interaction, meta-analysis, low-frequency variants, Public Health and Health Services
Abstract: Studying gene-environment (G × E) interactions is important, as they extend our knowledge of the genetic architecture of complex traits and may help to identify novel variants not detected via analysis of main effects alone. The main statistical framework for studying G × E interactions uses a single regression model that includes both the genetic main and G × E interaction effects (the "joint" framework). The alternative "stratified" framework combines results from genetic main-effect analyses carried out separately within the exposed and unexposed groups. Although there have been several investigations using theory and simulation, an empirical comparison of the two frameworks is lacking. Here, we compare the two frameworks using results from genome-wide association studies of systolic blood pressure for 3.2 million low frequency and 6.5 million common variants across 20 cohorts of European ancestry, comprising 79,731 individuals. Our cohorts have sample sizes ranging from 456 to 22,983 and include both family-based and population-based samples. In cohort-specific analyses, the two frameworks provided similar inference for population-based cohorts. The agreement was reduced for family-based cohorts. In meta-analyses, agreement between the two frameworks was less than that observed in cohort-specific analyses, despite the increased sample size. In meta-analyses, agreement depended on (1) the minor allele frequency, (2) inclusion of family-based cohorts in meta-analysis, and (3) filtering scheme. The stratified framework appears to approximate the joint framework well only for common variants in population-based cohorts. We conclude that the joint framework is the preferred approach and should be used to control false positives when dealing with low-frequency variants and/or family-based cohorts.
Published: 2016

22. Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels

Author: van Leeuwen, Elisabeth M, Sabo, Aniko, Bis, Joshua C, Huffman, Jennifer E, Manichaikul, Ani, Smith, Albert V, Feitosa, Mary F, Demissie, Serkalem, Joshi, Peter K, Duan, Qing, Marten, Jonathan, van Klinken, Jan B, Surakka, Ida, Nolte, Ilja M, Zhang, Weihua, Mbarek, Hamdi, Li-Gao, Ruifang, Trompet, Stella, Verweij, Niek, Evangelou, Evangelos, Lyytikäinen, Leo-Pekka, Tayo, Bamidele O, Deelen, Joris, van der Most, Peter J, van der Laan, Sander W, Arking, Dan E, Morrison, Alanna, Dehghan, Abbas, Franco, Oscar H, Hofman, Albert, Rivadeneira, Fernando, Sijbrands, Eric J, Uitterlinden, Andre G, Mychaleckyj, Josyf C, Campbell, Archie, Hocking, Lynne J, Padmanabhan, Sandosh, Brody, Jennifer A, Rice, Kenneth M, White, Charles C, Harris, Tamara, Isaacs, Aaron, Campbell, Harry, Lange, Leslie A, Rudan, Igor, Kolcic, Ivana, Navarro, Pau, Zemunik, Tatijana, Salomaa, Veikko, Study, The LifeLines Cohort, Kooner, Angad S, Kooner, Jaspal S, Lehne, Benjamin, Scott, William R, Tan, Sian-Tsung, de Geus, Eco J, Milaneschi, Yuri, Penninx, Brenda WJH, Willemsen, Gonneke, de Mutsert, Renée, Ford, Ian, Gansevoort, Ron T, Segura-Lepe, Marcelo P, Raitakari, Olli T, Viikari, Jorma S, Nikus, Kjell, Forrester, Terrence, McKenzie, Colin A, de Craen, Anton JM, de Ruijter, Hester M, Group, CHARGE Lipids Working, Pasterkamp, Gerard, Snieder, Harold, Oldehinkel, Albertine J, Slagboom, P Eline, Cooper, Richard S, Kähönen, Mika, Lehtimäki, Terho, Elliott, Paul, van der Harst, Pim, Jukema, J Wouter, Mook-Kanamori, Dennis O, Boomsma, Dorret I, Chambers, John C, Swertz, Morris, Ripatti, Samuli, van Dijk, Ko Willems, Vitart, Veronique, Polasek, Ozren, Hayward, Caroline, Wilson, James G, Wilson, James F, Gudnason, Vilmundur, Rich, Stephen S, Psaty, Bruce M, Borecki, Ingrid B, Boerwinkle, Eric, Rotter, Jerome I, Cupples, L Adrienne, and van Duijn, Cornelia M
Subjects: Biological Sciences, Genetics, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Angiopoietin-Like Protein 4, Angiopoietins, Exons, Fasting, Female, Genome, Human, Genome-Wide Association Study, Genotype, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, LifeLines Cohort Study, CHARGE Lipids Working Group, Complex traits, Epidemiology, Genome-wide, circulating lipid levels, Medical and Health Sciences, Genetics & Heredity, Clinical sciences
Abstract: BackgroundSo far, more than 170 loci have been associated with circulating lipid levels through genome-wide association studies (GWAS). These associations are largely driven by common variants, their function is often not known, and many are likely to be markers for the causal variants. In this study we aimed to identify more new rare and low-frequency functional variants associated with circulating lipid levels.MethodsWe used the 1000 Genomes Project as a reference panel for the imputations of GWAS data from ∼60 000 individuals in the discovery stage and ∼90 000 samples in the replication stage.ResultsOur study resulted in the identification of five new associations with circulating lipid levels at four loci. All four loci are within genes that can be linked biologically to lipid metabolism. One of the variants, rs116843064, is a damaging missense variant within the ANGPTL4 gene.ConclusionsThis study illustrates that GWAS with high-scale imputation may still help us unravel the biological mechanism behind circulating lipid levels.
Published: 2016

23. A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease

Author: Scott, Robert A, Freitag, Daniel F, Li, Li, Chu, Audrey Y, Surendran, Praveen, Young, Robin, Grarup, Niels, Stancáková, Alena, Chen, Yuning, Varga, Tibor V, Yaghootkar, Hanieh, Luan, Jian’an, Zhao, Jing Hua, Willems, Sara M, Wessel, Jennifer, Wang, Shuai, Maruthur, Nisa, Michailidou, Kyriaki, Pirie, Ailith, van der Lee, Sven J, Gillson, Christopher, Al Olama, Ali Amin, Amouyel, Philippe, Arriola, Larraitz, Arveiler, Dominique, Aviles-Olmos, Iciar, Balkau, Beverley, Barricarte, Aurelio, Barroso, Inês, Garcia, Sara Benlloch, Bis, Joshua C, Blankenberg, Stefan, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Borecki, Ingrid B, Bork-Jensen, Jette, Bowden, Sarah, Caldas, Carlos, Caslake, Muriel, consortium, The CVD50, Cupples, L Adrienne, Cruchaga, Carlos, Czajkowski, Jacek, Hoed, Marcel den, Dunn, Janet A, Earl, Helena M, Ehret, Georg B, Ferrannini, Ele, Ferrieres, Jean, Foltynie, Thomas, Ford, Ian, Forouhi, Nita G, Gianfagna, Francesco, Gonzalez, Carlos, Grioni, Sara, Hiller, Louise, Jansson, Jan-Håkan, Jørgensen, Marit E, Jukema, J Wouter, Kaaks, Rudolf, Kee, Frank, Kerrison, Nicola D, Key, Timothy J, Kontto, Jukka, Kote-Jarai, Zsofia, Kraja, Aldi T, Kuulasmaa, Kari, Kuusisto, Johanna, Linneberg, Allan, Liu, Chunyu, Marenne, Gaëlle, Mohlke, Karen L, Morris, Andrew P, Muir, Kenneth, Müller-Nurasyid, Martina, Munroe, Patricia B, Navarro, Carmen, Nielsen, Sune F, Nilsson, Peter M, Nordestgaard, Børge G, Packard, Chris J, Palli, Domenico, Panico, Salvatore, Peloso, Gina M, Perola, Markus, Peters, Annette, Poole, Christopher J, Quirós, J Ramón, Rolandsson, Olov, Sacerdote, Carlotta, Salomaa, Veikko, Sánchez, María-José, Sattar, Naveed, Sharp, Stephen J, Sims, Rebecca, Slimani, Nadia, Smith, Jennifer A, Thompson, Deborah J, and Trompet, Stella
Subjects: Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Clinical Research, Diabetes, Human Genome, Cardiovascular, Prevention, Obesity, Genetics, Heart Disease, Heart Disease - Coronary Heart Disease, Clinical Trials and Supportive Activities, Aetiology, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, 2.1 Biological and endogenous factors, Metabolic and endocrine, Good Health and Well Being, Alleles, Coronary Disease, Diabetes Mellitus, Type 2, Dipeptidyl Peptidase 4, Genotype, Glucagon-Like Peptide-1 Receptor, Humans, Receptor, Cannabinoid, CB2, Receptor, Serotonin, 5-HT2C, Receptors, Somatostatin, Sodium-Glucose Transporter 1, CVD50 consortium, GERAD_EC Consortium, Neurology Working Group of the Cohorts for Heart, Aging Research in Genomic Epidemiology, Alzheimer’s Disease Genetics Consortium, Pancreatic Cancer Cohort Consortium, European Prospective Investigation into Cancer and Nutrition–Cardiovascular Disease, EPIC-InterAct, CHARGE consortium, CHD Exome+ Consortium, CARDIOGRAM Exome Consortium, Biological Sciences, Medical and Health Sciences, Medical biotechnology, Biomedical engineering
Abstract: Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico follow-up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.
Published: 2016

24. General Framework for Meta‐Analysis of Haplotype Association Tests

Author: Wang, Shuai, Zhao, Jing Hua, An, Ping, Guo, Xiuqing, Jensen, Richard A, Marten, Jonathan, Huffman, Jennifer E, Meidtner, Karina, Boeing, Heiner, Campbell, Archie, Rice, Kenneth M, Scott, Robert A, Yao, Jie, Schulze, Matthias B, Wareham, Nicholas J, Borecki, Ingrid B, Province, Michael A, Rotter, Jerome I, Hayward, Caroline, Goodarzi, Mark O, Meigs, James B, and Dupuis, Josée
Subjects: Epidemiology, Biological Sciences, Health Sciences, Genetics, Aging, Human Genome, Co-Repressor Proteins, Cohort Studies, DNA-Binding Proteins, Fasting, Female, Genetic Association Studies, Genetic Variation, Glucose, Glucose-6-Phosphatase, Haplotypes, Heart, Humans, Least-Squares Analysis, Male, Meta-Analysis as Topic, Models, Genetic, Molecular Epidemiology, Multivariate Analysis, Neoplasm Proteins, Phenotype, Reproducibility of Results, Research Design, meta-analysis, haplotype association tests, family samples, linear mixed effects model, Public Health and Health Services
Abstract: For complex traits, most associated single nucleotide variants (SNV) discovered to date have a small effect, and detection of association is only possible with large sample sizes. Because of patient confidentiality concerns, it is often not possible to pool genetic data from multiple cohorts, and meta-analysis has emerged as the method of choice to combine results from multiple studies. Many meta-analysis methods are available for single SNV analyses. As new approaches allow the capture of low frequency and rare genetic variation, it is of interest to jointly consider multiple variants to improve power. However, for the analysis of haplotypes formed by multiple SNVs, meta-analysis remains a challenge, because different haplotypes may be observed across studies. We propose a two-stage meta-analysis approach to combine haplotype analysis results. In the first stage, each cohort estimate haplotype effect sizes in a regression framework, accounting for relatedness among observations if appropriate. For the second stage, we use a multivariate generalized least square meta-analysis approach to combine haplotype effect estimates from multiple cohorts. Haplotype-specific association tests and a global test of independence between haplotypes and traits are obtained within our framework. We demonstrate through simulation studies that we control the type-I error rate, and our approach is more powerful than inverse variance weighted meta-analysis of single SNV analysis when haplotype effects are present. We replicate a published haplotype association between fasting glucose-associated locus (G6PC2) and fasting glucose in seven studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and we provide more precise haplotype effect estimates.
Published: 2016

25. Consumption of meat is associated with higher fasting glucose and insulin concentrations regardless of glucose and insulin genetic risk scores: a meta-analysis of 50,345 Caucasians 1 , 2

Author: Fretts, Amanda M, Follis, Jack L, Nettleton, Jennifer A, Lemaitre, Rozenn N, Ngwa, Julius S, Wojczynski, Mary K, Kalafati, Ioanna Panagiota, Varga, Tibor V, Frazier-Wood, Alexis C, Houston, Denise K, Lahti, Jari, Ericson, Ulrika, van den Hooven, Edith H, Mikkilä, Vera, Kiefte-de Jong, Jessica C, Mozaffarian, Dariush, Rice, Kenneth, Renström, Frida, North, Kari E, McKeown, Nicola M, Feitosa, Mary F, Kanoni, Stavroula, Smith, Caren E, Garcia, Melissa E, Tiainen, Anna-Maija, Sonestedt, Emily, Manichaikul, Ani, van Rooij, Frank JA, Dimitriou, Maria, Raitakari, Olli, Pankow, James S, Djoussé, Luc, Province, Michael A, Hu, Frank B, Lai, Chao-Qiang, Keller, Margaux F, Perälä, Mia-Maria, Rotter, Jerome I, Hofman, Albert, Graff, Misa, Kähönen, Mika, Mukamal, Kenneth, Johansson, Ingegerd, Ordovas, Jose M, Liu, Yongmei, Männistö, Satu, Uitterlinden, André G, Deloukas, Panos, Seppälä, Ilkka, Psaty, Bruce M, Cupples, L Adrienne, Borecki, Ingrid B, Franks, Paul W, Arnett, Donna K, Nalls, Mike A, Eriksson, Johan G, Orho-Melander, Marju, Franco, Oscar H, Lehtimäki, Terho, Dedoussis, George V, Meigs, James B, and Siscovick, David S
Subjects: Biomedical and Clinical Sciences, Nutrition and Dietetics, Prevention, Diabetes, Genetics, Aging, Cardiovascular, Nutrition, Metabolic and endocrine, Blood Glucose, Cohort Studies, Genetic Association Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Hyperglycemia, Hyperinsulinism, Insulin, Insulin Resistance, Insulin Secretion, Insulin-Secreting Cells, Meat, Meat Products, Middle Aged, Polymorphism, Single Nucleotide, Risk Factors, diet, gene–diet interaction, glucose, insulin, meat intake, meta-analysis, Engineering, Medical and Health Sciences, Nutrition & Dietetics, Clinical sciences, Nutrition and dietetics
Abstract: BackgroundRecent studies suggest that meat intake is associated with diabetes-related phenotypes. However, whether the associations of meat intake and glucose and insulin homeostasis are modified by genes related to glucose and insulin is unknown.ObjectiveWe investigated the associations of meat intake and the interaction of meat with genotype on fasting glucose and insulin concentrations in Caucasians free of diabetes mellitus.DesignFourteen studies that are part of the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium participated in the analysis. Data were provided for up to 50,345 participants. Using linear regression within studies and a fixed-effects meta-analysis across studies, we examined 1) the associations of processed meat and unprocessed red meat intake with fasting glucose and insulin concentrations; and 2) the interactions of processed meat and unprocessed red meat with genetic risk score related to fasting glucose or insulin resistance on fasting glucose and insulin concentrations.ResultsProcessed meat was associated with higher fasting glucose, and unprocessed red meat was associated with both higher fasting glucose and fasting insulin concentrations after adjustment for potential confounders [not including body mass index (BMI)]. For every additional 50-g serving of processed meat per day, fasting glucose was 0.021 mmol/L (95% CI: 0.011, 0.030 mmol/L) higher. Every additional 100-g serving of unprocessed red meat per day was associated with a 0.037-mmol/L (95% CI: 0.023, 0.051-mmol/L) higher fasting glucose concentration and a 0.049-ln-pmol/L (95% CI: 0.035, 0.063-ln-pmol/L) higher fasting insulin concentration. After additional adjustment for BMI, observed associations were attenuated and no longer statistically significant. The association of processed meat and fasting insulin did not reach statistical significance after correction for multiple comparisons. Observed associations were not modified by genetic loci known to influence fasting glucose or insulin resistance.ConclusionThe association of higher fasting glucose and insulin concentrations with meat consumption was not modified by an index of glucose- and insulin-related single-nucleotide polymorphisms. Six of the participating studies are registered at clinicaltrials.gov as NCT0000513 (Atherosclerosis Risk in Communities), NCT00149435 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetics of Lipid Lowering Drugs and Diet Network), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).
Published: 2015

26. Parent-of-Origin Effects of the APOB Gene on Adiposity in Young Adults.

Author: Hochner, Hagit, Allard, Catherine, Granot-Hershkovitz, Einat, Chen, Jinbo, Sitlani, Colleen M, Sazdovska, Sandra, Lumley, Thomas, McKnight, Barbara, Rice, Kenneth, Enquobahrie, Daniel A, Meigs, James B, Kwok, Pui, Hivert, Marie-France, Borecki, Ingrid B, Gomez, Felicia, Wang, Ting, van Duijn, Cornelia, Amin, Najaf, Rotter, Jerome I, Stamatoyannopoulos, John, Meiner, Vardiella, Manor, Orly, Dupuis, Josée, Friedlander, Yechiel, and Siscovick, David S
Subjects: Humans, Obesity, Cholesterol, Insulin, Glucose, Body Mass Index, Waist-Hip Ratio, Genomic Imprinting, Polymorphism, Single Nucleotide, Adult, Female, Male, Adiposity, Apolipoprotein B-100, Waist Circumference, Genome-Wide Association Study, Young Adult, Developmental Biology, Genetics
Abstract: Loci identified in genome-wide association studies (GWAS) of cardio-metabolic traits account for a small proportion of the traits' heritability. To date, most association studies have not considered parent-of-origin effects (POEs). Here we report investigation of POEs on adiposity and glycemic traits in young adults. The Jerusalem Perinatal Family Follow-Up Study (JPS), comprising 1250 young adults and their mothers was used for discovery. Focusing on 18 genes identified by previous GWAS as associated with cardio-metabolic traits, we used linear regression to examine the associations of maternally- and paternally-derived offspring minor alleles with body mass index (BMI), waist circumference (WC), fasting glucose and insulin. We replicated and meta-analyzed JPS findings in individuals of European ancestry aged ≤50 belonging to pedigrees from the Framingham Heart Study, Family Heart Study and Erasmus Rucphen Family study (total N≅4800). We considered p0.6). Suggestive maternally-derived associations of rs1367117 were observed with fasting glucose (β = 0.9; 95%CI:0.3,1.5; p = 4.0x10-3) and insulin (ln-transformed, β = 0.06; 95%CI:0.03,0.1; p = 7.4x10-4). Bioinformatic annotation for rs1367117 revealed a variety of regulatory functions in this region in liver and adipose tissues and a 50% methylation pattern in liver only, consistent with allelic-specific methylation, which may indicate tissue-specific POE. Our findings demonstrate a maternal-specific association between a common APOB variant and adiposity, an association that was not previously detected in GWAS. These results provide evidence for the role of regulatory mechanisms, POEs specifically, in adiposity. In addition this study highlights the benefit of utilizing family studies for deciphering the genetic architecture of complex traits.
Published: 2015

27. Whole Genome Sequencing Identifies CRISPLD2 as a Lung Function Gene in Children With Asthma

Author: Abe, Namiko, Abecasis, Goncalo, Albert, Christine, Palmer Allred, Nicholette (Nichole), Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Arking, Dan, Arnett, Donna K., Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Beaty, Terri, Becker, Diane, Becker, Lewis, Beer, Rebecca, Begum, Ferdouse, Beitelshees, Amber, Benjamin, Emelia, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Boerwinkle, Eric, Borecki, Ingrid, Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Bunting, Karen, Burchard, Esteban, Cardwell, Jonathan, Carty, Cara, Casaburi, Richard, Casella, James, Chaffin, Mark, Chang, Christy, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Chen, Yii-Der Ida, Cho, Michael H., Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, Das, Sayantan, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Do, Ron, Duan, Qing, Duggirala, Ravi, Durda, Peter, Dutcher, Susan, Eaton, Charles, Ekunwe, Lynette, Ellinor, Patrick, Emery, Leslie, Farber, Charles, Farnam, Leanna, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Yan, Gass, Margery, Gelb, Bruce, Geng, Xiaoqi (Priscilla), Germer, Soren, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Haessler, Jeff, Hall, Michael, Harris, Daniel, Hawley, Nicola, He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hokanson, John, Holly, Kramer, Hong, Elliott, Hoth, Karin, (Agnes) Hsiung, Chao, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Jhun, Min A., Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kachroo, Priyadarshini, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kathiresan, Sekar, Kaufman, Laura, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Krauter, Stephanie, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Seunggeun Shawn, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Dan, Lewis, Joshua, Li, Yun, Lin, Honghuang, Lin, Keng Han, Liu, Simin, Liu, Yongmei, Loos, Ruth, Lubitz, Steven, Lunetta, Kathryn, Luo, James, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manson, JoAnn, Margolin, Lauren, Martin, Lisa, Mathai, Susan, Mathias, Rasika, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, Mei, Hao, Meyers, Deborah A., Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L., Mitchell, Braxton, Montasser, May E., Musani, Solomon, Mwasongwe, Stanford, Mychaleckyj, Josyf C., Nadkarni, Girish, Naik, Rakhi, Natarajan, Pradeep, Nekhai, Sergei, Nickerson, Deborah, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Pankow, James, Papanicolaou, George, Parker, Margaret, Parsa, Afshin, Penchev, Sara, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S., Phillips, Sam, Pollin, Toni, Post, Wendy, Becker, Julia Powers, Boorgula, Meher Preethi, Preuss, Michael, Prokopenko, Dmitry, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Ramachandran, Vasan, Rao, D.C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Regan, Elizabeth, Reiner, Alex, Rice, Ken, Rich, Stephen, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sakornsakolpat, Phuwanat, Salimi, Shabnam, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay, Scheller, Christopher, Schmidt, Ellen, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sheehan, Vivien, Shetty, Amol, Shetty, Aniket, Sheu, Wayne Hui-Heng, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne, Streeten, Elizabeth, Sung, Yun Ju, Su-Lasky, Jessica, Sylvia, Jody, Szpiro, Adam, Sztalryd, Carole, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Tinker, Lesley, Tirschwell, David, Tiwari, Hemant, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wan, Emily, Wang, Fei Fei, Watson, Karol, Weeks, Daniel E., Weir, Bruce, Weiss, Scott, Weng, Lu-Chen, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Wilson, Carla, Wilson, James, Wong, Quenna, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yang, Rongze, Zaghloul, Norann, Zekavat, Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zheng, Xiuwen, Zhi, Degui, Zhou, Xiang, Zody, Michael, Zoellner, Sebastian, Hecker, Julian, Chawes, Bo L., Ahluwalia, Tarunveer S., Kelly, Rachel S., Chu, Su H., Virkud, Yamini V., Huang, Mengna, Barnes, Kathleen C., Burchard, Esteban G., Eng, Celeste, Hu, Donglei, Celedón, Juan C., Levin, Albert M., Gui, Hongsheng, Forno, Erick, Mak, Angel C.Y., Avila, Lydiana, Soto-Quiros, Manuel E., Cloutier, Michelle M., Acosta-Pérez, Edna, Canino, Glorisa, Bønnelykke, Klaus, Bisgaard, Hans, Raby, Benjamin A., Weiss, Scott T., and Lasky-Su, Jessica A.
Published: 2019
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28. Genome of The Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels.

Author: van Leeuwen, Elisabeth M, Karssen, Lennart C, Deelen, Joris, Isaacs, Aaron, Medina-Gomez, Carolina, Mbarek, Hamdi, Kanterakis, Alexandros, Trompet, Stella, Postmus, Iris, Verweij, Niek, van Enckevort, David J, Huffman, Jennifer E, White, Charles C, Feitosa, Mary F, Bartz, Traci M, Manichaikul, Ani, Joshi, Peter K, Peloso, Gina M, Deelen, Patrick, van Dijk, Freerk, Willemsen, Gonneke, de Geus, Eco J, Milaneschi, Yuri, Penninx, Brenda WJH, Francioli, Laurent C, Menelaou, Androniki, Pulit, Sara L, Rivadeneira, Fernando, Hofman, Albert, Oostra, Ben A, Franco, Oscar H, Mateo Leach, Irene, Beekman, Marian, de Craen, Anton JM, Uh, Hae-Won, Trochet, Holly, Hocking, Lynne J, Porteous, David J, Sattar, Naveed, Packard, Chris J, Buckley, Brendan M, Brody, Jennifer A, Bis, Joshua C, Rotter, Jerome I, Mychaleckyj, Josyf C, Campbell, Harry, Duan, Qing, Lange, Leslie A, Wilson, James F, Hayward, Caroline, Polasek, Ozren, Vitart, Veronique, Rudan, Igor, Wright, Alan F, Rich, Stephen S, Psaty, Bruce M, Borecki, Ingrid B, Kearney, Patricia M, Stott, David J, Adrienne Cupples, L, Genome of The Netherlands Consortium, Jukema, J Wouter, van der Harst, Pim, Sijbrands, Eric J, Hottenga, Jouke-Jan, Uitterlinden, Andre G, Swertz, Morris A, van Ommen, Gert-Jan B, de Bakker, Paul IW, Eline Slagboom, P, Boomsma, Dorret I, Wijmenga, Cisca, and van Duijn, Cornelia M
Subjects: Genome of The Netherlands Consortium, Humans, Cholesterol, ATP-Binding Cassette Transporters, Gene Frequency, Mutation, Missense, Netherlands, Genetic Association Studies, Mutation, Missense
Abstract: Variants associated with blood lipid levels may be population-specific. To identify low-frequency variants associated with this phenotype, population-specific reference panels may be used. Here we impute nine large Dutch biobanks (~35,000 samples) with the population-specific reference panel created by the Genome of The Netherlands Project and perform association testing with blood lipid levels. We report the discovery of five novel associations at four loci (P value
Published: 2015

29. Fine mapping the CETP region reveals a common intronic insertion associated to HDL-C

Author: van Leeuwen, Elisabeth M, Huffman, Jennifer E, Bis, Joshua C, Isaacs, Aaron, Mulder, Monique, Sabo, Aniko, Smith, Albert V, Demissie, Serkalem, Manichaikul, Ani, Brody, Jennifer A, Feitosa, Mary F, Duan, Qing, Schraut, Katharina E, Navarro, Pau, van Vliet-Ostaptchouk, Jana V, Zhu, Gu, Mbarek, Hamdi, Trompet, Stella, Verweij, Niek, Lyytikäinen, Leo-Pekka, Deelen, Joris, Nolte, Ilja M, van der Laan, Sander W, Davies, Gail, Vermeij-Verdoold, Andrea JM, van Oosterhout, Andy ALJ, Vergeer-Drop, Jeannette M, Arking, Dan E, Trochet, Holly, Medina-Gomez, Carolina, Rivadeneira, Fernando, Uitterlinden, Andre G, Dehghan, Abbas, Franco, Oscar H, Sijbrands, Eric J, Hofman, Albert, White, Charles C, Mychaleckyj, Josyf C, Peloso, Gina M, Swertz, Morris A, Willemsen, Gonneke, de Geus, Eco J, Milaneschi, Yuri, Penninx, Brenda WJH, Ford, Ian, Buckley, Brendan M, de Craen, Anton JM, Starr, John M, Deary, Ian J, Pasterkamp, Gerard, Oldehinkel, Albertine J, Snieder, Harold, Slagboom, P Eline, Nikus, Kjell, Kähönen, Mika, Lehtimäki, Terho, Viikari, Jorma S, Raitakari, Olli T, van der Harst, Pim, Jukema, J Wouter, Hottenga, Jouke-Jan, Boomsma, Dorret I, Whitfield, John B, Montgomery, Grant, Martin, Nicholas G, Polasek, Ozren, Vitart, Veronique, Hayward, Caroline, Kolcic, Ivana, Wright, Alan F, Rudan, Igor, Joshi, Peter K, Wilson, James F, Lange, Leslie A, Wilson, James G, Gudnason, Vilmundur, Harris, Tamar B, Morrison, Alanna C, Borecki, Ingrid B, Rich, Stephen S, Padmanabhan, Sandosh, Psaty, Bruce M, Rotter, Jerome I, Smith, Blair H, Boerwinkle, Eric, Cupples, L Adrienne, and van Duijn, Cornelia
Subjects: Biological Sciences, Genetics, Aetiology, 2.1 Biological and endogenous factors, Generation Scotland, LifeLines Cohort Study, CHARGE Lipids Working Group, Biochemistry and Cell Biology, Medical Biotechnology, Clinical Sciences, Biochemistry and cell biology
Abstract: BackgroundIndividuals with exceptional longevity and their offspring have significantly larger high-density lipoprotein concentrations (HDL-C) particle sizes due to the increased homozygosity for the I405V variant in the cholesteryl ester transfer protein (CETP) gene. In this study, we investigate the association of CETP and HDL-C further to identify novel, independent CETP variants associated with HDL-C in humans.MethodsWe performed a meta-analysis of HDL-C within the CETP region using 59,432 individuals imputed with 1000 Genomes data. We performed replication in an independent sample of 47,866 individuals and validation was done by Sanger sequencing.ResultsThe meta-analysis of HDL-C within the CETP region identified five independent variants, including an exonic variant and a common intronic insertion. We replicated these 5 variants significantly in an independent sample of 47,866 individuals. Sanger sequencing of the insertion within a single family confirmed segregation of this variant. The strongest reported association between HDL-C and CETP variants, was rs3764261; however, after conditioning on the five novel variants we identified the support for rs3764261 was highly reduced (βunadjusted=3.179 mg/dl (P value=5.25×10-509), βadjusted=0.859 mg/dl (P value=9.51×10-25)), and this finding suggests that these five novel variants may partly explain the association of CETP with HDL-C. Indeed, three of the five novel variants (rs34065661, rs5817082, rs7499892) are independent of rs3764261.ConclusionsThe causal variants in CETP that account for the association with HDL-C remain unknown. We used studies imputed to the 1000 Genomes reference panel for fine mapping of the CETP region. We identified and validated five variants within this region that may partly account for the association of the known variant (rs3764261), as well as other sources of genetic contribution to HDL-C.
Published: 2015

30. Saturated Fat Intake Modulates the Association between an Obesity Genetic Risk Score and Body Mass Index in Two US Populations

Author: Casas-Agustench, Patricia, Arnett, Donna K, Smith, Caren E, Lai, Chao-Qiang, Parnell, Laurence D, Borecki, Ingrid B, Frazier-Wood, Alexis C, Allison, Matthew, Chen, Yii-Der Ida, Taylor, Kent D, Rich, Stephen S, Rotter, Jerome I, Lee, Yu-Chi, and Ordovás, José M
Subjects: Biomedical and Clinical Sciences, Nutrition and Dietetics, Health Sciences, Prevention, Cardiovascular, Nutrition, Genetics, Obesity, Aetiology, 2.3 Psychological, social and economic factors, 2.1 Biological and endogenous factors, Metabolic and endocrine, Oral and gastrointestinal, Cancer, Stroke, Adult, Aged, Body Mass Index, Cross-Sectional Studies, Diet, Dietary Fats, Energy Intake, Fatty Acids, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Genotype, Humans, Life Style, Linear Models, Male, Middle Aged, Motor Activity, Nutrition Assessment, Risk Factors, United States, White People, Body mass index, Genetic risk score, Saturated fat, Saturated fatty acids, Clinical Sciences, Anthropology, Biomedical and clinical sciences, Health sciences
Abstract: Combining multiple genetic variants related to obesity into a genetic risk score (GRS) might improve identification of individuals at risk of developing obesity. Moreover, characterizing gene-diet interactions is a research challenge to establish dietary recommendations to individuals with higher predisposition to obesity. Our objective was to analyze the association between an obesity GRS and body mass index (BMI) in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) population, focusing on gene-diet interactions with total fat and saturated fatty acid (SFA) intake, and to replicate findings in the Multi-Ethnic Study of Atherosclerosis (MESA) population. Cross-sectional analyses included 783 white US participants from GOLDN and 2,035 from MESA. Dietary intakes were estimated with validated food frequency questionnaires. Height and weight were measured. A weighted GRS was calculated on the basis of 63 obesity-associated variants. Multiple linear regression models adjusted by potential confounders were used to examine gene-diet interactions between dietary intake (total fat and SFA) and the obesity GRS in determining BMI. Significant interactions were found between total fat intake and the obesity GRS using these variables as continuous for BMI (P for interaction=0.010, 0.046, and 0.002 in GOLDN, MESA, and meta-analysis, respectively). These association terms were stronger when assessing interactions between SFA intake and GRS for BMI (P for interaction=0.005, 0.018, and
Published: 2014

31. Association of the PHACTR1/EDN1 Genetic Locus With Spontaneous Coronary Artery Dissection

Author: Motreff, Pascal, Belle, Loïc, Dupouy, Patrick, Barnay, Pierre, Meneveau, Nicolas, Gilard, Martine, Rioufol, Gilles, Range, Grégoire, Brunel, Philippe, Delarche, Nicolas, Filippi, Emmanuelle, Le Bivic, Louis, Harbaoui, Brahim, Benamer, Hakim, Cayla, Guillaume, Varenne, Olivier, Manzo-Silberman, Stephane Peggy, Silvain, Johanne, Spaulding, Christian, Caussin, Christophe, Gerbaud, Edouard, Valy, Yann, Koning, René, Lhermusier, Thibault, Champin, Stanislas, Salengro, Emmanuel, Fluttaz, Arnaud, Zabalawi, Amer, Cottin, Yves, Teiger, Emmanuel, Saint-Etienne, Christophe, Ducrocq, Grégory, Marliere, Stéphanie, Boiffard, Emmanuel, Aubry, Pierre, Georges, Jean Louis, Bresson, Didier, De Poli, Fabien, Karrillon, Gaëtan, Roule, Vincent, Bali, Laurent, Valla, Mathieu, Gerbay, Antoine, Houpe, David, Dubreuil, Olivier, Monnier, Arsène, Mayaud, Norbert, Manchuelle, Aurélie, Commeau, Philippe, Bedossa, Marc, Nikpay, Majid, Goel, Anuj, Won, Hong-Hee, Hall, Leanne M., Willenborg, Christina, Kanoni, Stavroula, Saleheen, Danish, Kyriakou, Theodosios, Nelson, Christopher P., Hopewell, Jemma C., Webb, Thomas R., Zeng, Lingyao, Dehghan, Abbas, Alver, Maris, Armasu, Sebastian M., Auro, Kirsi, Bjonnes, Andrew, Chasman, Daniel I., Chen, Shufeng, Ford, Ian, Franceschini, Nora, Gieger, Christian, Grace, Christopher, Gustafsson, Stefan, Huang, Jie, Hwang, Shih-Jen, Kim, Yun Kyoung, Kleber, Marcus E., Lau, King Wai, Lu, Xiangfeng, Lu, Yingchang, Lyytikäinen, Leo P., Mihailov, Evelin, Morrison, Alanna, Pervjakova, Natalia, Qu, Liming, Rose, Lynda M., Salfati, Elias, Saxena, Richa, Scholz, Markus, Smith, Albert V., Tikkanen, Emmi, Uitterlinden, Andre, Yang, Xueli, Zhang, Weihua, Zhao, Wei, de Andrade, Mariza, de Vries, Paul S., van Zuydam, Natalie R., Anand, Sonia S., Bertram, Lars, Beutner, Frank, Dedoussis, George, Frossard, Philippe, Gauguier, Dominique, Goodall, Alison H., Gottesman, Omri, Haber, Marc, Han, Bok-Ghee, Huang, Jianfeng, Jalilzadeh, Shapour, Kessler, Thorsten, König, Inke R., Lannfelt, Lars, Lieb, Wolfgang, Lind, Lars, Lindgren, Cecilia M., Lokki, Maisa, Magnusson, Patrik K., Mallick, Nadeem H., Mehra, Narinder, Meitinger, Thomas, Memon, Fazal-ur-Rehman, Morris, Andrew P., Nieminen, Markku S., Pedersen, Nancy L., Peters, Annette, Rallidis, Loukianos S., Rasheed, Asif, Samuel, Maria, Shah, Svati H., Sinisalo, Juha, Stirrups, Kathleen E., Trompet, Stella, Wang, Laiyuan, Zaman, Khan S., Ardissino, Diego, Boerwinkle, Eric, Borecki, Ingrid B., Bottinger, Erwin P., Buring, Julie E., Chambers, John C., Collins, Rory, Cupples, L Adrienne, Danesh, John, Demuth, Ilja, Elosua, Roberto, Epstein, Stephen E., Esko, Tõnu, Feitosa, Mary F., Franco, Oscar H., Franzosi, Maria Grazia, Granger, Christopher B., Gu, Dongfeng, Gudnason, Vilmundur, Hall, Alistair S., Hamsten, Anders, Harris, Tamara B., Hazen, Stanley L., Hengstenberg, Christian, Hofman, Albert, Ingelsson, Erik, Iribarren, Carlos, Jukema, J Wouter, Karhunen, Pekka J., Kim, Bong-Jo, Kooner, Jaspal S., Kullo, Iftikhar J., Lehtimäki, Terho, Loos, Ruth J., Melander, Olle, Metspalu, Andres, März, Winfried, Palmer, Colin N., Perola, Markus, Quertermous, Thomas, Rader, Daniel J., Ridker, Paul M., Ripatti, Samuli, Roberts, Robert, Salomaa, Veikko, Sanghera, Dharambir K., Schwartz, Stephen M., Seedorf, Udo, Stewart, Alexandre F., Stott, David J., Thiery, Joachim, Zalloua, Pierre A., O'Donnell, Christopher J., Reilly, Muredach P., Assimes, Themistocles L., Thompson, John R., Erdmann, Jeanette, Clarke, Robert, Watkins, Hugh, Kathiresan, Sekar, McPherson, Ruth, Deloukas, Panos, Schunkert, Heribert, Samani, Nilesh J., Farrall, Martin, Adlam, David, Olson, Timothy M., Combaret, Nicolas, Kovacic, Jason C., Iismaa, Siiri E., Al-Hussaini, Abtehale, O'Byrne, Megan M., Bouajila, Sara, Georges, Adrien, Mishra, Ketan, Braund, Peter S., d’Escamard, Valentina, Huang, Siying, Margaritis, Marios, Kadian-Dodov, Daniella, Welch, Catherine A., Mazurkiewicz, Stephani, Jeunemaitre, Xavier, Wong, Claire Mei Yi, Giannoulatou, Eleni, Sweeting, Michael, Muller, David, Wood, Alice, McGrath-Cadell, Lucy, Fatkin, Diane, Dunwoodie, Sally L., Harvey, Richard, Holloway, Cameron, Empana, Jean-Philippe, Jouven, Xavier, Olin, Jeffrey W., Gulati, Rajiv, Tweet, Marysia S., Hayes, Sharonne N., Graham, Robert M., and Bouatia-Naji, Nabila
Published: 2019
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32. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes.

Author: Ng, Maggie CY, Shriner, Daniel, Chen, Brian H, Li, Jiang, Chen, Wei-Min, Guo, Xiuqing, Liu, Jiankang, Bielinski, Suzette J, Yanek, Lisa R, Nalls, Michael A, Comeau, Mary E, Rasmussen-Torvik, Laura J, Jensen, Richard A, Evans, Daniel S, Sun, Yan V, An, Ping, Patel, Sanjay R, Lu, Yingchang, Long, Jirong, Armstrong, Loren L, Wagenknecht, Lynne, Yang, Lingyao, Snively, Beverly M, Palmer, Nicholette D, Mudgal, Poorva, Langefeld, Carl D, Keene, Keith L, Freedman, Barry I, Mychaleckyj, Josyf C, Nayak, Uma, Raffel, Leslie J, Goodarzi, Mark O, Chen, Y-D Ida, Taylor, Herman A, Correa, Adolfo, Sims, Mario, Couper, David, Pankow, James S, Boerwinkle, Eric, Adeyemo, Adebowale, Doumatey, Ayo, Chen, Guanjie, Mathias, Rasika A, Vaidya, Dhananjay, Singleton, Andrew B, Zonderman, Alan B, Igo, Robert P, Sedor, John R, FIND Consortium, Kabagambe, Edmond K, Siscovick, David S, McKnight, Barbara, Rice, Kenneth, Liu, Yongmei, Hsueh, Wen-Chi, Zhao, Wei, Bielak, Lawrence F, Kraja, Aldi, Province, Michael A, Bottinger, Erwin P, Gottesman, Omri, Cai, Qiuyin, Zheng, Wei, Blot, William J, Lowe, William L, Pacheco, Jennifer A, Crawford, Dana C, eMERGE Consortium, DIAGRAM Consortium, Grundberg, Elin, MuTHER Consortium, Rich, Stephen S, Hayes, M Geoffrey, Shu, Xiao-Ou, Loos, Ruth JF, Borecki, Ingrid B, Peyser, Patricia A, Cummings, Steven R, Psaty, Bruce M, Fornage, Myriam, Iyengar, Sudha K, Evans, Michele K, Becker, Diane M, Kao, WH Linda, Wilson, James G, Rotter, Jerome I, Sale, Michèle M, Liu, Simin, Rotimi, Charles N, Bowden, Donald W, and MEta-analysis of type 2 DIabetes in African Americans Consortium
Subjects: FIND Consortium, eMERGE Consortium, DIAGRAM Consortium, MuTHER Consortium, MEta-analysis of type 2 DIabetes in African Americans Consortium, Humans, Diabetes Mellitus, Type 2, HMGA2 Protein, HLA-B27 Antigen, Polymorphism, Single Nucleotide, African Americans, Mutant Chimeric Proteins, KCNQ1 Potassium Channel, Genome-Wide Association Study, Transcription Factor 7-Like 2 Protein, Human Genome, Diabetes, Genetics, 2.1 Biological and endogenous factors, Metabolic and endocrine, Developmental Biology
Abstract: Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94)
Published: 2014

33. Pleiotropic genes for metabolic syndrome and inflammation

Author: Kraja, Aldi T, Chasman, Daniel I, North, Kari E, Reiner, Alexander P, Yanek, Lisa R, Kilpeläinen, Tuomas O, Smith, Jennifer A, Dehghan, Abbas, Dupuis, Josée, Johnson, Andrew D, Feitosa, Mary F, Tekola-Ayele, Fasil, Chu, Audrey Y, Nolte, Ilja M, Dastani, Zari, Morris, Andrew, Pendergrass, Sarah A, Sun, Yan V, Ritchie, Marylyn D, Vaez, Ahmad, Lin, Honghuang, Ligthart, Symen, Marullo, Letizia, Rohde, Rebecca, Shao, Yaming, Ziegler, Mark A, Im, Hae Kyung, Group, Cross Consortia Pleiotropy, Heart and, the Cohorts for, Epidemiology, Aging Research in Genetic, Consortium, the Genetic Investigation of Anthropometric Traits, Consortium, the Global Lipids Genetics, the Meta-Analyses of Glucose, Consortium, Insulin-related traits, Consortium, the Global BPgen, Consortium, The ADIPOGen, Study, the Women's Genome Health, Study, the Howard University Family, Schnabel, Renate B, Jørgensen, Torben, Jørgensen, Marit E, Hansen, Torben, Pedersen, Oluf, Stolk, Ronald P, Snieder, Harold, Hofman, Albert, Uitterlinden, Andre G, Franco, Oscar H, Ikram, M Arfan, Richards, J Brent, Rotimi, Charles, Wilson, James G, Lange, Leslie, Ganesh, Santhi K, Nalls, Mike, Rasmussen-Torvik, Laura J, Pankow, James S, Coresh, Josef, Tang, Weihong, Kao, WH Linda, Boerwinkle, Eric, Morrison, Alanna C, Ridker, Paul M, Becker, Diane M, Rotter, Jerome I, Kardia, Sharon LR, Loos, Ruth JF, Larson, Martin G, Hsu, Yi-Hsiang, Province, Michael A, Tracy, Russell, Voight, Benjamin F, Vaidya, Dhananjay, O'Donnell, Christopher J, Benjamin, Emelia J, Alizadeh, Behrooz Z, Prokopenko, Inga, Meigs, James B, and Borecki, Ingrid B
Subjects: Biological Sciences, Biomedical and Clinical Sciences, Genetics, Diabetes, Prevention, Nutrition, Obesity, Clinical Research, Cardiovascular, Human Genome, 2.1 Biological and endogenous factors, Biomarkers, Computational Biology, Gene Regulatory Networks, Genetic Pleiotropy, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Inflammation, Meta-Analysis as Topic, Metabolic Syndrome, Phenotype, Quantitative Trait, Heritable, Metabolic syndrome, Inflammatory markers, Pleiotropic associations, Meta-analysis, Regulome, Cross Consortia Pleiotropy Group, Cohorts for Heart and, Aging Research in Genetic Epidemiology, Genetic Investigation of Anthropometric Traits Consortium, Global Lipids Genetics Consortium, Meta-Analyses of Glucose, Insulin-related traits Consortium, Global BPgen Consortium, ADIPOGen Consortium, Women's Genome Health Study, Howard University Family Study, Clinical Sciences, Genetics & Heredity, Clinical sciences
Abstract: Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factors, genetic variants exist that influence MetS and inflammatory markers forming a predisposing MetS genetic network. To test this hypothesis a staged approach was undertaken. (a) We analyzed 17 metabolic and inflammatory traits in more than 85,500 participants from 14 large epidemiological studies within the Cross Consortia Pleiotropy Group. Individuals classified with MetS (NCEP definition), versus those without, showed on average significantly different levels for most inflammatory markers studied. (b) Paired average correlations between 8 metabolic traits and 9 inflammatory markers from the same studies as above, estimated with two methods, and factor analyses on large simulated data, helped in identifying 8 combinations of traits for follow-up in meta-analyses, out of 130,305 possible combinations between metabolic traits and inflammatory markers studied. (c) We performed correlated meta-analyses for 8 metabolic traits and 6 inflammatory markers by using existing GWAS published genetic summary results, with about 2.5 million SNPs from twelve predominantly largest GWAS consortia. These analyses yielded 130 unique SNPs/genes with pleiotropic associations (a SNP/gene associating at least one metabolic trait and one inflammatory marker). Of them twenty-five variants (seven loci newly reported) are proposed as MetS candidates. They map to genes MACF1, KIAA0754, GCKR, GRB14, COBLL1, LOC646736-IRS1, SLC39A8, NELFE, SKIV2L, STK19, TFAP2B, BAZ1B, BCL7B, TBL2, MLXIPL, LPL, TRIB1, ATXN2, HECTD4, PTPN11, ZNF664, PDXDC1, FTO, MC4R and TOMM40. Based on large data evidence, we conclude that inflammation is a feature of MetS and several gene variants show pleiotropic genetic associations across phenotypes and might explain a part of MetS correlated genetic architecture. These findings warrant further functional investigation.
Published: 2014

34. Association of Low-Frequency and Rare Coding-Sequence Variants with Blood Lipids and Coronary Heart Disease in 56,000 Whites and Blacks

Author: Peloso, Gina M, Auer, Paul L, Bis, Joshua C, Voorman, Arend, Morrison, Alanna C, Stitziel, Nathan O, Brody, Jennifer A, Khetarpal, Sumeet A, Crosby, Jacy R, Fornage, Myriam, Isaacs, Aaron, Jakobsdottir, Johanna, Feitosa, Mary F, Davies, Gail, Huffman, Jennifer E, Manichaikul, Ani, Davis, Brian, Lohman, Kurt, Joon, Aron Y, Smith, Albert V, Grove, Megan L, Zanoni, Paolo, Redon, Valeska, Demissie, Serkalem, Lawson, Kim, Peters, Ulrike, Carlson, Christopher, Jackson, Rebecca D, Ryckman, Kelli K, Mackey, Rachel H, Robinson, Jennifer G, Siscovick, David S, Schreiner, Pamela J, Mychaleckyj, Josyf C, Pankow, James S, Hofman, Albert, Uitterlinden, Andre G, Harris, Tamara B, Taylor, Kent D, Stafford, Jeanette M, Reynolds, Lindsay M, Marioni, Riccardo E, Dehghan, Abbas, Franco, Oscar H, Patel, Aniruddh P, Lu, Yingchang, Hindy, George, Gottesman, Omri, Bottinger, Erwin P, Melander, Olle, Orho-Melander, Marju, Loos, Ruth JF, Duga, Stefano, Merlini, Piera Angelica, Farrall, Martin, Goel, Anuj, Asselta, Rosanna, Girelli, Domenico, Martinelli, Nicola, Shah, Svati H, Kraus, William E, Li, Mingyao, Rader, Daniel J, Reilly, Muredach P, McPherson, Ruth, Watkins, Hugh, Ardissino, Diego, Project, NHLBI GO Exome Sequencing, Zhang, Qunyuan, Wang, Judy, Tsai, Michael Y, Taylor, Herman A, Correa, Adolfo, Griswold, Michael E, Lange, Leslie A, Starr, John M, Rudan, Igor, Eiriksdottir, Gudny, Launer, Lenore J, Ordovas, Jose M, Levy, Daniel, Chen, Y-D Ida, Reiner, Alexander P, Hayward, Caroline, Polasek, Ozren, Deary, Ian J, Borecki, Ingrid B, Liu, Yongmei, Gudnason, Vilmundur, Wilson, James G, van Duijn, Cornelia M, Kooperberg, Charles, Rich, Stephen S, Psaty, Bruce M, Rotter, Jerome I, O’Donnell, Christopher J, Rice, Kenneth, Boerwinkle, Eric, Kathiresan, Sekar, and Cupples, L Adrienne
Subjects: Biological Sciences, Biomedical and Clinical Sciences, Genetics, Heart Disease - Coronary Heart Disease, Human Genome, Cardiovascular, Heart Disease, Atherosclerosis, Aetiology, 2.1 Biological and endogenous factors, 1-Alkyl-2-acetylglycerophosphocholine Esterase, Adult, Aged, Alleles, Animals, Black People, Cholesterol, HDL, Cholesterol, LDL, Cohort Studies, Coronary Disease, Female, Gene Frequency, Genetic Association Studies, Genetic Code, Genetic Variation, Humans, Linear Models, Male, Mice, Mice, Inbred C57BL, Microtubule-Associated Proteins, Middle Aged, Phenotype, Sequence Analysis, DNA, Subtilisins, Triglycerides, White People, NHLBI GO Exome Sequencing Project, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract: Low-frequency coding DNA sequence variants in the proprotein convertase subtilisin/kexin type 9 gene (PCSK9) lower plasma low-density lipoprotein cholesterol (LDL-C), protect against risk of coronary heart disease (CHD), and have prompted the development of a new class of therapeutics. It is uncertain whether the PCSK9 example represents a paradigm or an isolated exception. We used the "Exome Array" to genotype >200,000 low-frequency and rare coding sequence variants across the genome in 56,538 individuals (42,208 European ancestry [EA] and 14,330 African ancestry [AA]) and tested these variants for association with LDL-C, high-density lipoprotein cholesterol (HDL-C), and triglycerides. Although we did not identify new genes associated with LDL-C, we did identify four low-frequency (frequencies between 0.1% and 2%) variants (ANGPTL8 rs145464906 [c.361C>T; p.Gln121*], PAFAH1B2 rs186808413 [c.482C>T; p.Ser161Leu], COL18A1 rs114139997 [c.331G>A; p.Gly111Arg], and PCSK7 rs142953140 [c.1511G>A; p.Arg504His]) with large effects on HDL-C and/or triglycerides. None of these four variants was associated with risk for CHD, suggesting that examples of low-frequency coding variants with robust effects on both lipids and CHD will be limited.
Published: 2014

35. Whole-Exome Sequencing Identifies Rare and Low-Frequency Coding Variants Associated with LDL Cholesterol

Author: Lange, Leslie A, Hu, Youna, Zhang, He, Xue, Chenyi, Schmidt, Ellen M, Tang, Zheng-Zheng, Bizon, Chris, Lange, Ethan M, Smith, Joshua D, Turner, Emily H, Jun, Goo, Kang, Hyun Min, Peloso, Gina, Auer, Paul, Li, Kuo-ping, Flannick, Jason, Zhang, Ji, Fuchsberger, Christian, Gaulton, Kyle, Lindgren, Cecilia, Locke, Adam, Manning, Alisa, Sim, Xueling, Rivas, Manuel A, Holmen, Oddgeir L, Gottesman, Omri, Lu, Yingchang, Ruderfer, Douglas, Stahl, Eli A, Duan, Qing, Li, Yun, Durda, Peter, Jiao, Shuo, Isaacs, Aaron, Hofman, Albert, Bis, Joshua C, Correa, Adolfo, Griswold, Michael E, Jakobsdottir, Johanna, Smith, Albert V, Schreiner, Pamela J, Feitosa, Mary F, Zhang, Qunyuan, Huffman, Jennifer E, Crosby, Jacy, Wassel, Christina L, Do, Ron, Franceschini, Nora, Martin, Lisa W, Robinson, Jennifer G, Assimes, Themistocles L, Crosslin, David R, Rosenthal, Elisabeth A, Tsai, Michael, Rieder, Mark J, Farlow, Deborah N, Folsom, Aaron R, Lumley, Thomas, Fox, Ervin R, Carlson, Christopher S, Peters, Ulrike, Jackson, Rebecca D, van Duijn, Cornelia M, Uitterlinden, André G, Levy, Daniel, Rotter, Jerome I, Taylor, Herman A, Gudnason, Vilmundur, Siscovick, David S, Fornage, Myriam, Borecki, Ingrid B, Hayward, Caroline, Rudan, Igor, Chen, Y Eugene, Bottinger, Erwin P, Loos, Ruth JF, Sætrom, Pål, Hveem, Kristian, Boehnke, Michael, Groop, Leif, McCarthy, Mark, Meitinger, Thomas, Ballantyne, Christie M, Gabriel, Stacey B, O’Donnell, Christopher J, Post, Wendy S, North, Kari E, Reiner, Alexander P, Boerwinkle, Eric, Psaty, Bruce M, Altshuler, David, Kathiresan, Sekar, Lin, Dan-Yu, Jarvik, Gail P, Cupples, L Adrienne, Kooperberg, Charles, Wilson, James G, Nickerson, Deborah A, Abecasis, Goncalo R, and Rich, Stephen S
Subjects: Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Prevention, Clinical Research, Heart Disease, Cardiovascular, Atherosclerosis, Aetiology, 2.1 Biological and endogenous factors, Adult, Aged, Apolipoproteins E, Cholesterol, LDL, Cohort Studies, Dyslipidemias, Exome, Female, Follow-Up Studies, Gene Frequency, Genetic Code, Genome-Wide Association Study, Genotype, Humans, Lipase, Male, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Proprotein Convertase 9, Proprotein Convertases, Receptors, LDL, Sequence Analysis, DNA, Serine Endopeptidases, NHLBI Grand Opportunity Exome Sequencing Project, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract: Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or
Published: 2014

36. Genetics of Obesity: Etiologic Heterogeneity and Temporal Trends

Author: Borecki, Ingrid B., primary, Province, Michael A., additional, Bouchard, Claude, additional, and Rao, D. C., additional
Published: 2020
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37. Genetics of coronary artery calcification among African Americans, a meta-analysis

Author: Wojczynski, Mary K, Li, Mingyao, Bielak, Lawrence F, Kerr, Kathleen F, Reiner, Alex P, Wong, Nathan D, Yanek, Lisa R, Qu, Liming, White, Charles C, Lange, Leslie A, Ferguson, Jane F, He, Jing, Young, Taylor, Mosley, Thomas H, Smith, Jennifer A, Kral, Brian G, Guo, Xiuqing, Wong, Quenna, Ganesh, Santhi K, Heckbert, Susan R, Griswold, Michael E, O’Leary, Daniel H, Budoff, Matthew, Carr, J, Taylor,, Herman A, Bluemke, David A, Demissie, Serkalem, Hwang, Shih-Jen, Paltoo, Dina N, Polak, Joseph F, Psaty, Bruce M, Becker, Diane M, Province, Michael A, Post, Wendy S, O’Donnell, Christopher J, Wilson, James G, Harris, Tamara B, Kavousi, Maryam, Cupples, L, Rotter, Jerome I, Fornage, Myriam, Becker, Lewis C, Peyser, Patricia A, Borecki, Ingrid B, and Reilly, Muredach P
Abstract: Abstract Background Coronary heart disease (CHD) is the major cause of death in the United States. Coronary artery calcification (CAC) scores are independent predictors of CHD. African Americans (AA) have higher rates of CHD but are less well-studied in genomic studies. We assembled the largest AA data resource currently available with measured CAC to identify associated genetic variants. Methods We analyzed log transformed CAC quantity (ln(CAC + 1)), for association with ~2.5 million single nucleotide polymorphisms (SNPs) and performed an inverse-variance weighted meta-analysis on results for 5,823 AA from 8 studies. Heritability was calculated using family studies. The most significant SNPs among AAs were evaluated in European Ancestry (EA) CAC data; conversely, the significance of published SNPs for CAC/CHD in EA was queried within our AA meta-analysis. Results Heritability of CAC was lower in AA (~30%) than previously reported for EA (~50%). No SNP reached genome wide significance (p
Published: 2013

38. Genome-wide association of body fat distribution in African ancestry populations suggests new loci.

Author: Liu, Ching-Ti, Monda, Keri L, Taylor, Kira C, Lange, Leslie, Demerath, Ellen W, Palmas, Walter, Wojczynski, Mary K, Ellis, Jaclyn C, Vitolins, Mara Z, Liu, Simin, Papanicolaou, George J, Irvin, Marguerite R, Xue, Luting, Griffin, Paula J, Nalls, Michael A, Adeyemo, Adebowale, Liu, Jiankang, Li, Guo, Ruiz-Narvaez, Edward A, Chen, Wei-Min, Chen, Fang, Henderson, Brian E, Millikan, Robert C, Ambrosone, Christine B, Strom, Sara S, Guo, Xiuqing, Andrews, Jeanette S, Sun, Yan V, Mosley, Thomas H, Yanek, Lisa R, Shriner, Daniel, Haritunians, Talin, Rotter, Jerome I, Speliotes, Elizabeth K, Smith, Megan, Rosenberg, Lynn, Mychaleckyj, Josyf, Nayak, Uma, Spruill, Ida, Garvey, W Timothy, Pettaway, Curtis, Nyante, Sarah, Bandera, Elisa V, Britton, Angela F, Zonderman, Alan B, Rasmussen-Torvik, Laura J, Chen, Yii-Der Ida, Ding, Jingzhong, Lohman, Kurt, Kritchevsky, Stephen B, Zhao, Wei, Peyser, Patricia A, Kardia, Sharon LR, Kabagambe, Edmond, Broeckel, Ulrich, Chen, Guanjie, Zhou, Jie, Wassertheil-Smoller, Sylvia, Neuhouser, Marian L, Rampersaud, Evadnie, Psaty, Bruce, Kooperberg, Charles, Manson, Joann E, Kuller, Lewis H, Ochs-Balcom, Heather M, Johnson, Karen C, Sucheston, Lara, Ordovas, Jose M, Palmer, Julie R, Haiman, Christopher A, McKnight, Barbara, Howard, Barbara V, Becker, Diane M, Bielak, Lawrence F, Liu, Yongmei, Allison, Matthew A, Grant, Struan FA, Burke, Gregory L, Patel, Sanjay R, Schreiner, Pamela J, Borecki, Ingrid B, Evans, Michele K, Taylor, Herman, Sale, Michele M, Howard, Virginia, Carlson, Christopher S, Rotimi, Charles N, Cushman, Mary, Harris, Tamara B, Reiner, Alexander P, Cupples, L Adrienne, North, Kari E, and Fox, Caroline S
Subjects: Humans, Obesity, Waist-Hip Ratio, Polymorphism, Single Nucleotide, African Continental Ancestry Group, European Continental Ancestry Group, Female, Male, Adiposity, Body Fat Distribution, Genome-Wide Association Study, Genetic Loci, Polymorphism, Single Nucleotide, Developmental Biology, Genetics
Abstract: Central obesity, measured by waist circumference (WC) or waist-hip ratio (WHR), is a marker of body fat distribution. Although obesity disproportionately affects minority populations, few studies have conducted genome-wide association study (GWAS) of fat distribution among those of predominantly African ancestry (AA). We performed GWAS of WC and WHR, adjusted and unadjusted for BMI, in up to 33,591 and 27,350 AA individuals, respectively. We identified loci associated with fat distribution in AA individuals using meta-analyses of GWA results for WC and WHR (stage 1). Overall, 25 SNPs with single genomic control (GC)-corrected p-values
Published: 2013

39. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution

Author: Justice, Anne E., Karaderi, Tugce, Highland, Heather M., Young, Kristin L., Graff, Mariaelisa, Lu, Yingchang, Turcot, Valérie, Auer, Paul L., Fine, Rebecca S., Guo, Xiuqing, Schurmann, Claudia, Lempradl, Adelheid, Marouli, Eirini, Mahajan, Anubha, Winkler, Thomas W., Locke, Adam E., Medina-Gomez, Carolina, Esko, Tõnu, Vedantam, Sailaja, Giri, Ayush, Lo, Ken Sin, Alfred, Tamuno, Mudgal, Poorva, Ng, Maggie C. Y., Heard-Costa, Nancy L., Feitosa, Mary F., Manning, Alisa K., Willems, Sara M., Sivapalaratnam, Suthesh, Abecasis, Goncalo, Alam, Dewan S., Allison, Matthew, Amouyel, Philippe, Arzumanyan, Zorayr, Balkau, Beverley, Bastarache, Lisa, Bergmann, Sven, Bielak, Lawrence F., Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A., Bork-Jensen, Jette, Bottinger, Erwin P., Bowden, Donald W., Brandslund, Ivan, Broer, Linda, Burt, Amber A., Butterworth, Adam S., Caulfield, Mark J., Cesana, Giancarlo, Chambers, John C., Chasman, Daniel I., Chen, Yii-Der Ida, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y., Collins, Francis S., Cook, James P., Cox, Amanda J., Crosslin, David S., Danesh, John, de Bakker, Paul I. W., Denus, Simon de, Mutsert, Renée de, Dedoussis, George, Demerath, Ellen W., Dennis, Joe G., Denny, Josh C., Di Angelantonio, Emanuele, Dörr, Marcus, Drenos, Fotios, Dubé, Marie-Pierre, Dunning, Alison M., Easton, Douglas F., Elliott, Paul, Evangelou, Evangelos, Farmaki, Aliki-Eleni, Feng, Shuang, Ferrannini, Ele, Ferrieres, Jean, Florez, Jose C., Fornage, Myriam, Fox, Caroline S., Franks, Paul W., Friedrich, Nele, Gan, Wei, Gandin, Ilaria, Gasparini, Paolo, Giedraitis, Vilmantas, Girotto, Giorgia, Gorski, Mathias, Grallert, Harald, Grarup, Niels, Grove, Megan L., Gustafsson, Stefan, Haessler, Jeff, Hansen, Torben, Hattersley, Andrew T., Hayward, Caroline, Heid, Iris M., Holmen, Oddgeir L., Hovingh, G. Kees, Howson, Joanna M. M., Hu, Yao, Hung, Yi-Jen, Hveem, Kristian, Ikram, M. Arfan, Ingelsson, Erik, Jackson, Anne U., Jarvik, Gail P., Jia, Yucheng, Jørgensen, Torben, Jousilahti, Pekka, Justesen, Johanne M., Kahali, Bratati, Karaleftheri, Maria, Kardia, Sharon L. R., Karpe, Fredrik, Kee, Frank, Kitajima, Hidetoshi, Komulainen, Pirjo, Kooner, Jaspal S., Kovacs, Peter, Krämer, Bernhard K., Kuulasmaa, Kari, Kuusisto, Johanna, Laakso, Markku, Lakka, Timo A., Lamparter, David, Lange, Leslie A., Langenberg, Claudia, Larson, Eric B., Lee, Nanette R., Lee, Wen-Jane, Lehtimäki, Terho, Lewis, Cora E., Li, Huaixing, Li, Jin, Li-Gao, Ruifang, Lin, Li-An, Lin, Xu, Lind, Lars, Lindström, Jaana, Linneberg, Allan, Liu, Ching-Ti, Liu, Dajiang J., Luan, Jian’an, Lyytikäinen, Leo-Pekka, MacGregor, Stuart, Mägi, Reedik, Männistö, Satu, Marenne, Gaëlle, Marten, Jonathan, Masca, Nicholas G. D., McCarthy, Mark I., Meidtner, Karina, Mihailov, Evelin, Moilanen, Leena, Moitry, Marie, Mook-Kanamori, Dennis O., Morgan, Anna, Morris, Andrew P., Müller-Nurasyid, Martina, Munroe, Patricia B., Narisu, Narisu, Nelson, Christopher P., Neville, Matt, Ntalla, Ioanna, O’Connell, Jeffrey R., Owen, Katharine R., Pedersen, Oluf, Peloso, Gina M., Pennell, Craig E., Perola, Markus, Perry, James A., Perry, John R. B., Pers, Tune H., Ewing, Ailith, Polasek, Ozren, Raitakari, Olli T., Rasheed, Asif, Raulerson, Chelsea K., Rauramaa, Rainer, Reilly, Dermot F., Reiner, Alex P., Ridker, Paul M., Rivas, Manuel A., Robertson, Neil R., Robino, Antonietta, Rudan, Igor, Ruth, Katherine S., Saleheen, Danish, Salomaa, Veikko, Samani, Nilesh J., Schreiner, Pamela J., Schulze, Matthias B., Scott, Robert A., Segura-Lepe, Marcelo, Sim, Xueling, Slater, Andrew J., Small, Kerrin S., Smith, Blair H., Smith, Jennifer A., Southam, Lorraine, Spector, Timothy D., Speliotes, Elizabeth K., Stefansson, Kari, Steinthorsdottir, Valgerdur, Stirrups, Kathleen E., Strauch, Konstantin, Stringham, Heather M., Stumvoll, Michael, Sun, Liang, Surendran, Praveen, Swart, Karin M. A., Tardif, Jean-Claude, Taylor, Kent D., Teumer, Alexander, Thompson, Deborah J., Thorleifsson, Gudmar, Thorsteinsdottir, Unnur, Thuesen, Betina H., Tönjes, Anke, Torres, Mina, Tsafantakis, Emmanouil, Tuomilehto, Jaakko, Uitterlinden, André G., Uusitupa, Matti, van Duijn, Cornelia M., Vanhala, Mauno, Varma, Rohit, Vermeulen, Sita H., Vestergaard, Henrik, Vitart, Veronique, Vogt, Thomas F., Vuckovic, Dragana, Wagenknecht, Lynne E., Walker, Mark, Wallentin, Lars, Wang, Feijie, Wang, Carol A., Wang, Shuai, Wareham, Nicholas J., Warren, Helen R., Waterworth, Dawn M., Wessel, Jennifer, White, Harvey D., Willer, Cristen J., Wilson, James G., Wood, Andrew R., Wu, Ying, Yaghootkar, Hanieh, Yao, Jie, Yerges-Armstrong, Laura M., Young, Robin, Zeggini, Eleftheria, Zhan, Xiaowei, Zhang, Weihua, Zhao, Jing Hua, Zhao, Wei, Zheng, He, Zhou, Wei, Zillikens, M. Carola, Rivadeneira, Fernando, Borecki, Ingrid B., Pospisilik, J. Andrew, Deloukas, Panos, Frayling, Timothy M., Lettre, Guillaume, Mohlke, Karen L., Rotter, Jerome I., Kutalik, Zoltán, Hirschhorn, Joel N., Cupples, L. Adrienne, Loos, Ruth J. F., North, Kari E., and Lindgren, Cecilia M.
Published: 2019
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40. Gene-Centric Meta-Analysis of Lipid Traits in African, East Asian and Hispanic Populations

Author: Elbers, Clara C, Guo, Yiran, Tragante, Vinicius, van Iperen, Erik PA, Lanktree, Matthew B, Castillo, Berta Almoguera, Chen, Fang, Yanek, Lisa R, Wojczynski, Mary K, Li, Yun R, Ferwerda, Bart, Ballantyne, Christie M, Buxbaum, Sarah G, Chen, Yii-Der Ida, Chen, Wei-Min, Cupples, L Adrienne, Cushman, Mary, Duan, Yanan, Duggan, David, Evans, Michele K, Fernandes, Jyotika K, Fornage, Myriam, Garcia, Melissa, Garvey, W Timothy, Glazer, Nicole, Gomez, Felicia, Harris, Tamara B, Halder, Indrani, Howard, Virginia J, Keller, Margaux F, Kamboh, M Ilyas, Kooperberg, Charles, Kritchevsky, Stephen B, LaCroix, Andrea, Liu, Kiang, Liu, Yongmei, Musunuru, Kiran, Newman, Anne B, Onland-Moret, N Charlotte, Ordovas, Jose, Peter, Inga, Post, Wendy, Redline, Susan, Reis, Steven E, Saxena, Richa, Schreiner, Pamela J, Volcik, Kelly A, Wang, Xingbin, Yusuf, Salim, Zonderland, Alan B, Anand, Sonia S, Becker, Diane M, Psaty, Bruce, Rader, Daniel J, Reiner, Alex P, Rich, Stephen S, Rotter, Jerome I, Sale, Michèle M, Tsai, Michael Y, Borecki, Ingrid B, Hegele, Robert A, Kathiresan, Sekar, Nalls, Michael A, Taylor, Herman A, Hakonarson, Hakon, Sivapalaratnam, Suthesh, Asselbergs, Folkert W, Drenos, Fotios, Wilson, James G, and Keating, Brendan J
Subjects: Epidemiology, Biological Sciences, Health Sciences, Genetics, Clinical Research, Atherosclerosis, Aetiology, 2.1 Biological and endogenous factors, Alleles, Asian People, Black People, Cholesterol, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genotype, Hispanic or Latino, Humans, Lipoproteins, HDL, Lipoproteins, LDL, Polymorphism, Single Nucleotide, Triglycerides, genetic variants, loci, lipid, genotyping, CD36 deficiency, General Science & Technology
Abstract: Meta-analyses of European populations has successfully identified genetic variants in over 100 loci associated with lipid levels, but our knowledge in other ethnicities remains limited. To address this, we performed dense genotyping of ∼2,000 candidate genes in 7,657 African Americans, 1,315 Hispanics and 841 East Asians, using the IBC array, a custom ∼50,000 SNP genotyping array. Meta-analyses confirmed 16 lipid loci previously established in European populations at genome-wide significance level, and found multiple independent association signals within these lipid loci. Initial discovery and in silico follow-up in 7,000 additional African American samples, confirmed two novel loci: rs5030359 within ICAM1 is associated with total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) (p = 8.8×10(-7) and p = 1.5×10(-6) respectively) and a nonsense mutation rs3211938 within CD36 is associated with high-density lipoprotein cholesterol (HDL-C) levels (p = 13.5×10(-12)). The rs3211938-G allele, which is nearly absent in European and Asian populations, has been previously found to be associated with CD36 deficiency and shows a signature of selection in Africans and African Americans. Finally, we have evaluated the effect of SNPs established in European populations on lipid levels in multi-ethnic populations and show that most known lipid association signals span across ethnicities. However, differences between populations, especially differences in allele frequency, can be leveraged to identify novel signals, as shown by the discovery of ICAM1 and CD36 in the current report.
Published: 2012

41. Variants Identified in a GWAS Meta-Analysis for Blood Lipids Are Associated with the Lipid Response to Fenofibrate

Author: Aslibekyan, Stella, Goodarzi, Mark O, Frazier-Wood, Alexis C, Yan, Xiaofei, Irvin, Marguerite R, Kim, Eric, Tiwari, Hemant K, Guo, Xiuqing, Straka, Robert J, Taylor, Kent D, Tsai, Michael Y, Hopkins, Paul N, Korenman, Stanley G, Borecki, Ingrid B, Chen, Yii-Der I, Ordovas, Jose M, Rotter, Jerome I, and Arnett, Donna K
Subjects: Genetics, Cardiovascular, Clinical Research, Atherosclerosis, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Adult, Apolipoprotein A-I, Apolipoproteins E, Cholesterol, Cholesterol, HDL, Cholesterol, LDL, Epistasis, Genetic, Female, Fenofibrate, Gene Frequency, Genome-Wide Association Study, Humans, Hypertriglyceridemia, Hypolipidemic Agents, Lipids, Male, Meta-Analysis as Topic, Microtubule-Associated Proteins, Middle Aged, Outcome Assessment, Health Care, Polymorphism, Single Nucleotide, Regression Analysis, Triglycerides, General Science & Technology
Abstract: A recent large-scale meta-analysis of genome-wide studies has identified 95 loci, 59 of them novel, as statistically significant predictors of blood lipid traits; we tested whether the same loci explain the observed heterogeneity in response to lipid-lowering therapy with fenofibrate. Using data from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, n = 861) we fit linear mixed models with the genetic markers as predictors and high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, total cholesterol, and triglyceride concentrations as outcomes. For all four traits, we analyzed both baseline levels and changes in response to treatment with fenofibrate. For the markers that were significantly associated with fenofibrate response, we fit additional models evaluating potential epistatic interactions. All models were adjusted for age, sex, and study center as fixed effects, and pedigree as a random effect. Statistically significant associations were observed between the rs964184 polymorphism near APOA1 (P-value≤0.0001) and fenofibrate response for HDL and triglycerides. The association was replicated in the Pharmacogenetics of Hypertriglyceridemia in Hispanics study (HyperTG, n = 267). Suggestive associations with fenofibrate response were observed for markers in or near PDE3A, MOSC1, FLJ36070, CETP, the APOE-APOC1-APOC4-APOC2, and CILP2. Finally, we present strong evidence for epistasis (P-value for interaction = 0.0006 in GOLDN, 0.05 in HyperTG) between rs10401969 near CILP2 and rs4420638 in the APOE-APOC1-APOC4-APOC2 cluster with total cholesterol response to fenofibrate. In conclusion, we present evidence linking several novel and biologically relevant genetic polymorphisms to lipid lowering drug response, as well as suggesting novel gene-gene interactions in fenofibrate pharmacogenetics.
Published: 2012

42. A Bivariate Genome-Wide Approach to Metabolic Syndrome STAMPEED Consortium

Author: Kraja, Aldi T, Vaidya, Dhananjay, Pankow, James S, Goodarzi, Mark O, Assimes, Themistocles L, Kullo, Iftikhar J, Sovio, Ulla, Mathias, Rasika A, Sun, Yan V, Franceschini, Nora, Absher, Devin, Li, Guo, Zhang, Qunyuan, Feitosa, Mary F, Glazer, Nicole L, Haritunians, Talin, Hartikainen, Anna-Liisa, Knowles, Joshua W, North, Kari E, Iribarren, Carlos, Kral, Brian, Yanek, Lisa, O’Reilly, Paul F, McCarthy, Mark I, Jaquish, Cashell, Couper, David J, Chakravarti, Aravinda, Psaty, Bruce M, Becker, Lewis C, Province, Michael A, Boerwinkle, Eric, Quertermous, Thomas, Palotie, Leena, Jarvelin, Marjo-Riitta, Becker, Diane M, Kardia, Sharon LR, Rotter, Jerome I, Chen, Yii-Der Ida, and Borecki, Ingrid B
Subjects: Biomedical and Clinical Sciences, Nutrition, Human Genome, Clinical Research, Cardiovascular, Diabetes, Obesity, Genetics, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Adult, Aged, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Meta-Analysis as Topic, Metabolic Syndrome, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract: OBJECTIVE The metabolic syndrome (MetS) is defined as concomitant disorders of lipid and glucose metabolism, central obesity, and high blood pressure, with an increased risk of type 2 diabetes and cardiovascular disease. This study tests whether common genetic variants with pleiotropic effects account for some of the correlated architecture among five metabolic phenotypes that define MetS. RESEARCH DESIGN AND METHODS Seven studies of the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel of ∼2.5 million imputed single nucleotide polymorphisms (SNPs). Phenotypes were defined by the National Cholesterol Education Program (NCEP) criteria for MetS in pairwise combinations. Individuals exceeding the NCEP thresholds for both traits of a pair were considered affected. RESULTS Twenty-nine common variants were associated with MetS or a pair of traits. Variants in the genes LPL, CETP, APOA5 (and its cluster), GCKR (and its cluster), LIPC, TRIB1, LOC100128354/MTNR1B, ABCB11, and LOC100129150 were further tested for their association with individual qualitative and quantitative traits. None of the 16 top SNPs (one per gene) associated simultaneously with more than two individual traits. Of them 11 variants showed nominal associations with MetS per se. The effects of 16 top SNPs on the quantitative traits were relatively small, together explaining from ∼9% of the variance in triglycerides, 5.8% of high-density lipoprotein cholesterol, 3.6% of fasting glucose, and 1.4% of systolic blood pressure. CONCLUSIONS Qualitative and quantitative pleiotropic tests on pairs of traits indicate that a small portion of the covariation in these traits can be explained by the reported common genetic variants.
Published: 2011

43. Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits.

Author: Speliotes, Elizabeth K, Yerges-Armstrong, Laura M, Wu, Jun, Hernaez, Ruben, Kim, Lauren J, Palmer, Cameron D, Gudnason, Vilmundur, Eiriksdottir, Gudny, Garcia, Melissa E, Launer, Lenore J, Nalls, Michael A, Clark, Jeanne M, Mitchell, Braxton D, Shuldiner, Alan R, Butler, Johannah L, Tomas, Marta, Hoffmann, Udo, Hwang, Shih-Jen, Massaro, Joseph M, O'Donnell, Christopher J, Sahani, Dushyant V, Salomaa, Veikko, Schadt, Eric E, Schwartz, Stephen M, Siscovick, David S, NASH CRN, GIANT Consortium, MAGIC Investigators, Voight, Benjamin F, Carr, J Jeffrey, Feitosa, Mary F, Harris, Tamara B, Fox, Caroline S, Smith, Albert V, Kao, WH Linda, Hirschhorn, Joel N, Borecki, Ingrid B, and GOLD Consortium
Subjects: NASH CRN, GIANT Consortium, MAGIC Investigators, GOLD Consortium, Humans, Fatty Liver, Insulin, Lipase, Blood Glucose, Adaptor Proteins, Signal Transducing, Lectins, C-Type, Membrane Proteins, Nerve Tissue Proteins, Tomography, X-Ray Computed, Case-Control Studies, Cohort Studies, Mutation, Missense, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Adult, Aged, Aged, 80 and over, Middle Aged, Male, Genome-Wide Association Study, Chondroitin Sulfate Proteoglycans, Non-alcoholic Fatty Liver Disease, Prevention, Human Genome, Digestive Diseases, Liver Disease, Hepatitis, Chronic Liver Disease and Cirrhosis, Genetics, Clinical Research, 2.1 Biological and endogenous factors, Developmental Biology
Abstract: Nonalcoholic fatty liver disease (NAFLD) clusters in families, but the only known common genetic variants influencing risk are near PNPLA3. We sought to identify additional genetic variants influencing NAFLD using genome-wide association (GWA) analysis of computed tomography (CT) measured hepatic steatosis, a non-invasive measure of NAFLD, in large population based samples. Using variance components methods, we show that CT hepatic steatosis is heritable (∼26%-27%) in family-based Amish, Family Heart, and Framingham Heart Studies (n = 880 to 3,070). By carrying out a fixed-effects meta-analysis of genome-wide association (GWA) results between CT hepatic steatosis and ∼2.4 million imputed or genotyped SNPs in 7,176 individuals from the Old Order Amish, Age, Gene/Environment Susceptibility-Reykjavik study (AGES), Family Heart, and Framingham Heart Studies, we identify variants associated at genome-wide significant levels (p
Published: 2011

44. Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research

Author: Ahalt, Stan, primary, Avillach, Paul, additional, Boyles, Rebecca, additional, Bradford, Kira, additional, Cox, Steven, additional, Davis-Dusenbery, Brandi, additional, Grossman, Robert L, additional, Krishnamurthy, Ashok, additional, Manning, Alisa, additional, Paten, Benedict, additional, Philippakis, Anthony, additional, Borecki, Ingrid, additional, Chen, Shu Hui, additional, Kaltman, Jon, additional, Ladwa, Sweta, additional, Schwartz, Chip, additional, Thomson, Alastair, additional, Davis, Sarah, additional, Leaf, Alison, additional, Lyons, Jessica, additional, Sheets, Elizabeth, additional, Bis, Joshua C, additional, Conomos, Matthew, additional, Culotti, Alessandro, additional, Desain, Thomas, additional, Digiovanna, Jack, additional, Domazet, Milan, additional, Gogarten, Stephanie, additional, Gutierrez-Sacristan, Alba, additional, Harris, Tim, additional, Heavner, Ben, additional, Jain, Deepti, additional, O'Connor, Brian, additional, Osborn, Kevin, additional, Pillion, Danielle, additional, Pleiness, Jacob, additional, Rice, Ken, additional, Rupp, Garrett, additional, Serret-Larmande, Arnaud, additional, Smith, Albert, additional, Stedman, Jason P, additional, Stilp, Adrienne, additional, Barsanti, Teresa, additional, Cheadle, John, additional, Erdmann, Christopher, additional, Farlow, Brandy, additional, Gartland-Gray, Allie, additional, Hayes, Julie, additional, Hiles, Hannah, additional, Kerr, Paul, additional, Lenhardt, Chris, additional, Madden, Tom, additional, Mieczkowska, Joanna O, additional, Miller, Amanda, additional, Patton, Patrick, additional, Rathbun, Marcie, additional, Suber, Stephanie, additional, and Asare, Joe, additional
Published: 2023
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45. Supplemental Figure 2 from Rare Variation in TET2 Is Associated with Clinically Relevant Prostate Carcinoma in African Americans

Author: Koboldt, Daniel C., primary, Kanchi, Krishna L., primary, Gui, Bin, primary, Larson, David E., primary, Fulton, Robert S., primary, Isaacs, William B., primary, Kraja, Aldi, primary, Borecki, Ingrid B., primary, Jia, Li, primary, Wilson, Richard K., primary, Mardis, Elaine R., primary, and Kibel, Adam S., primary
Published: 2023
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46. Supplemental Table 2 from Rare Variation in TET2 Is Associated with Clinically Relevant Prostate Carcinoma in African Americans

Author: Koboldt, Daniel C., primary, Kanchi, Krishna L., primary, Gui, Bin, primary, Larson, David E., primary, Fulton, Robert S., primary, Isaacs, William B., primary, Kraja, Aldi, primary, Borecki, Ingrid B., primary, Jia, Li, primary, Wilson, Richard K., primary, Mardis, Elaine R., primary, and Kibel, Adam S., primary
Published: 2023
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47. Supplemental Table 1 from Rare Variation in TET2 Is Associated with Clinically Relevant Prostate Carcinoma in African Americans

Author: Koboldt, Daniel C., primary, Kanchi, Krishna L., primary, Gui, Bin, primary, Larson, David E., primary, Fulton, Robert S., primary, Isaacs, William B., primary, Kraja, Aldi, primary, Borecki, Ingrid B., primary, Jia, Li, primary, Wilson, Richard K., primary, Mardis, Elaine R., primary, and Kibel, Adam S., primary
Published: 2023
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48. Supplemental Figure 1 from Rare Variation in TET2 Is Associated with Clinically Relevant Prostate Carcinoma in African Americans

Author: Koboldt, Daniel C., primary, Kanchi, Krishna L., primary, Gui, Bin, primary, Larson, David E., primary, Fulton, Robert S., primary, Isaacs, William B., primary, Kraja, Aldi, primary, Borecki, Ingrid B., primary, Jia, Li, primary, Wilson, Richard K., primary, Mardis, Elaine R., primary, and Kibel, Adam S., primary
Published: 2023
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49. Legend for Supplemental Tables and Figures from Rare Variation in TET2 Is Associated with Clinically Relevant Prostate Carcinoma in African Americans

Author: Koboldt, Daniel C., primary, Kanchi, Krishna L., primary, Gui, Bin, primary, Larson, David E., primary, Fulton, Robert S., primary, Isaacs, William B., primary, Kraja, Aldi, primary, Borecki, Ingrid B., primary, Jia, Li, primary, Wilson, Richard K., primary, Mardis, Elaine R., primary, and Kibel, Adam S., primary
Published: 2023
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50. Genetic identification of familial hypercholesterolemia within a single U.S. health care system

Author: Abul-Husn, Noura S., Manickam, Kandamurugu, Jones, Laney K., Wright, Eric A., Hartzel, Dustin N., Gonzaga-Jauregui, Claudia, O'Dushlaine, Colm, Leader, Joseph B., Kirchner, H. Lester, Lindbuchler, D'Andra M., Barr, Marci L., Giovanni, Monica A., Ritchie, Marylyn D., Overton, John D., Reid, Jeffrey G., Metpally, Raghu P. R., Wardeh, Amr H., Borecki, Ingrid B., Yancopoulos, George D., Baras, Aris, Shuldiner, Alan R., Gottesman, Omri, Ledbetter, David H., Carey, David J., Dewey, Frederick E., and Murray, Michael F.
Published: 2016

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