@article {1159, title = {Genome-wide association study of PR interval.}, journal = {Nat Genet}, volume = {42}, year = {2010}, month = {2010 Feb}, pages = {153-9}, abstract = {

The electrocardiographic PR interval (or PQ interval) reflects atrial and atrioventricular nodal conduction, disturbances of which increase risk of atrial fibrillation. We report a meta-analysis of genome-wide association studies for PR interval from seven population-based European studies in the CHARGE Consortium: AGES, ARIC, CHS, FHS, KORA, Rotterdam Study, and SardiNIA (N = 28,517). We identified nine loci associated with PR interval at P < 5 x 10(-8). At the 3p22.2 locus, we observed two independent associations in voltage-gated sodium channel genes, SCN10A and SCN5A. Six of the loci were near cardiac developmental genes, including CAV1-CAV2, NKX2-5 (CSX1), SOX5, WNT11, MEIS1, and TBX5-TBX3, providing pathophysiologically interesting candidate genes. Five of the loci, SCN5A, SCN10A, NKX2-5, CAV1-CAV2, and SOX5, were also associated with atrial fibrillation (N = 5,741 cases, P < 0.0056). This suggests a role for common variation in ion channel and developmental genes in atrial and atrioventricular conduction as well as in susceptibility to atrial fibrillation.

}, keywords = {Aged, Atrial Fibrillation, Cohort Studies, Electrocardiography, Female, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Heart Conduction System, Humans, Male, Meta-Analysis as Topic}, issn = {1546-1718}, doi = {10.1038/ng.517}, author = {Pfeufer, Arne and van Noord, Charlotte and Marciante, Kristin D and Arking, Dan E and Larson, Martin G and Smith, Albert Vernon and Tarasov, Kirill V and M{\"u}ller, Martina and Sotoodehnia, Nona and Sinner, Moritz F and Verwoert, Germaine C and Li, Man and Kao, W H Linda and K{\"o}ttgen, Anna and Coresh, Josef and Bis, Joshua C and Psaty, Bruce M and Rice, Kenneth and Rotter, Jerome I and Rivadeneira, Fernando and Hofman, Albert and Kors, Jan A and Stricker, Bruno H C and Uitterlinden, Andr{\'e} G and van Duijn, Cornelia M and Beckmann, Britt M and Sauter, Wiebke and Gieger, Christian and Lubitz, Steven A and Newton-Cheh, Christopher and Wang, Thomas J and Magnani, Jared W and Schnabel, Renate B and Chung, Mina K and Barnard, John and Smith, Jonathan D and Van Wagoner, David R and Vasan, Ramachandran S and Aspelund, Thor and Eiriksdottir, Gudny and Harris, Tamara B and Launer, Lenore J and Najjar, Samer S and Lakatta, Edward and Schlessinger, David and Uda, Manuela and Abecasis, Goncalo R and M{\"u}ller-Myhsok, Bertram and Ehret, Georg B and Boerwinkle, Eric and Chakravarti, Aravinda and Soliman, Elsayed Z and Lunetta, Kathryn L and Perz, Siegfried and Wichmann, H-Erich and Meitinger, Thomas and Levy, Daniel and Gudnason, Vilmundur and Ellinor, Patrick T and Sanna, Serena and K{\"a}{\"a}b, Stefan and Witteman, Jacqueline C M and Alonso, Alvaro and Benjamin, Emelia J and Heckbert, Susan R} } @article {1150, title = {Meta-analyses of genome-wide association studies identify multiple loci associated with pulmonary function.}, journal = {Nat Genet}, volume = {42}, year = {2010}, month = {2010 Jan}, pages = {45-52}, abstract = {

Spirometric measures of lung function are heritable traits that reflect respiratory health and predict morbidity and mortality. We meta-analyzed genome-wide association studies for two clinically important lung-function measures: forced expiratory volume in the first second (FEV(1)) and its ratio to forced vital capacity (FEV(1)/FVC), an indicator of airflow obstruction. This meta-analysis included 20,890 participants of European ancestry from four CHARGE Consortium studies: Atherosclerosis Risk in Communities, Cardiovascular Health Study, Framingham Heart Study and Rotterdam Study. We identified eight loci associated with FEV(1)/FVC (HHIP, GPR126, ADAM19, AGER-PPT2, FAM13A, PTCH1, PID1 and HTR4) and one locus associated with FEV(1) (INTS12-GSTCD-NPNT) at or near genome-wide significance (P < 5 x 10(-8)) in the CHARGE Consortium dataset. Our findings may offer insights into pulmonary function and pathogenesis of chronic lung disease.

}, keywords = {Databases, Genetic, Female, Forced Expiratory Volume, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Humans, Lung, Lung Diseases, Male, Meta-Analysis as Topic, Polymorphism, Single Nucleotide, Spirometry, Vital Capacity}, issn = {1546-1718}, doi = {10.1038/ng.500}, author = {Hancock, Dana B and Eijgelsheim, Mark and Wilk, Jemma B and Gharib, Sina A and Loehr, Laura R and Marciante, Kristin D and Franceschini, Nora and van Durme, Yannick M T A and Chen, Ting-Hsu and Barr, R Graham and Schabath, Matthew B and Couper, David J and Brusselle, Guy G and Psaty, Bruce M and van Duijn, Cornelia M and Rotter, Jerome I and Uitterlinden, Andr{\'e} G and Hofman, Albert and Punjabi, Naresh M and Rivadeneira, Fernando and Morrison, Alanna C and Enright, Paul L and North, Kari E and Heckbert, Susan R and Lumley, Thomas and Stricker, Bruno H C and O{\textquoteright}Connor, George T and London, Stephanie J} } @article {6088, title = {Genome-wide joint meta-analysis of SNP and SNP-by-smoking interaction identifies novel loci for pulmonary function.}, journal = {PLoS Genet}, volume = {8}, year = {2012}, month = {2012}, pages = {e1003098}, abstract = {

Genome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second (FEV(1)), and its ratio to forced vital capacity (FEV(1)/FVC). Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV(1) and FEV(1)/FVC across 19 studies (total N = 50,047). We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER (smallest P(JMA = )5.00{\texttimes}10(-11)), HLA-DQB1 and HLA-DQA2 (smallest P(JMA = )4.35{\texttimes}10(-9)), and KCNJ2 and SOX9 (smallest P(JMA = )1.28{\texttimes}10(-8)) were associated with FEV(1)/FVC or FEV(1) in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects.

}, keywords = {Forced Expiratory Volume, Gene Expression, Genome, Human, Genome-Wide Association Study, HLA-DQ Antigens, HLA-DQ beta-Chains, Humans, Lung, Nerve Tissue Proteins, Polymorphism, Single Nucleotide, Potassium Channels, Inwardly Rectifying, Pulmonary Disease, Chronic Obstructive, Receptors, Cell Surface, Smoking, SOX9 Transcription Factor, Vital Capacity}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1003098}, author = {Hancock, Dana B and Soler Artigas, Maria and Gharib, Sina A and Henry, Amanda and Manichaikul, Ani and Ramasamy, Adaikalavan and Loth, Daan W and Imboden, Medea and Koch, Beate and McArdle, Wendy L and Smith, Albert V and Smolonska, Joanna and Sood, Akshay and Tang, Wenbo and Wilk, Jemma B and Zhai, Guangju and Zhao, Jing Hua and Aschard, Hugues and Burkart, Kristin M and Curjuric, Ivan and Eijgelsheim, Mark and Elliott, Paul and Gu, Xiangjun and Harris, Tamara B and Janson, Christer and Homuth, Georg and Hysi, Pirro G and Liu, Jason Z and Loehr, Laura R and Lohman, Kurt and Loos, Ruth J F and Manning, Alisa K and Marciante, Kristin D and Obeidat, Ma{\textquoteright}en and Postma, Dirkje S and Aldrich, Melinda C and Brusselle, Guy G and Chen, Ting-Hsu and Eiriksdottir, Gudny and Franceschini, Nora and Heinrich, Joachim and Rotter, Jerome I and Wijmenga, Cisca and Williams, O Dale and Bentley, Amy R and Hofman, Albert and Laurie, Cathy C and Lumley, Thomas and Morrison, Alanna C and Joubert, Bonnie R and Rivadeneira, Fernando and Couper, David J and Kritchevsky, Stephen B and Liu, Yongmei and Wjst, Matthias and Wain, Louise V and Vonk, Judith M and Uitterlinden, Andr{\'e} G and Rochat, Thierry and Rich, Stephen S and Psaty, Bruce M and O{\textquoteright}Connor, George T and North, Kari E and Mirel, Daniel B and Meibohm, Bernd and Launer, Lenore J and Khaw, Kay-Tee and Hartikainen, Anna-Liisa and Hammond, Christopher J and Gl{\"a}ser, Sven and Marchini, Jonathan and Kraft, Peter and Wareham, Nicholas J and V{\"o}lzke, Henry and Stricker, Bruno H C and Spector, Timothy D and Probst-Hensch, Nicole M and Jarvis, Deborah and Jarvelin, Marjo-Riitta and Heckbert, Susan R and Gudnason, Vilmundur and Boezen, H Marike and Barr, R Graham and Cassano, Patricia A and Strachan, David P and Fornage, Myriam and Hall, Ian P and Dupuis, Jos{\'e}e and Tobin, Martin D and London, Stephanie J} } @article {6602, title = {Generalized estimating equations for genome-wide association studies using longitudinal phenotype data.}, journal = {Stat Med}, volume = {34}, year = {2015}, month = {2015 Jan 15}, pages = {118-30}, abstract = {

Many longitudinal cohort studies have both genome-wide measures of genetic variation and repeated measures of phenotypes and environmental exposures. Genome-wide association study analyses have typically used only cross-sectional data to evaluate quantitative phenotypes and binary traits. Incorporation of repeated measures may increase power to detect associations, but also requires specialized analysis methods. Here, we discuss one such method-generalized estimating equations (GEE)-in the contexts of analysis of main effects of rare genetic variants and analysis of gene-environment interactions. We illustrate the potential for increased power using GEE analyses instead of cross-sectional analyses. We also address challenges that arise, such as the need for small-sample corrections when the minor allele frequency of a genetic variant and/or the prevalence of an environmental exposure is low. To illustrate methods for detection of gene-drug interactions on a genome-wide scale, using repeated measures data, we conduct single-study analyses and meta-analyses across studies in three large cohort studies participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium-the Atherosclerosis Risk in Communities study, the Cardiovascular Health Study, and the Rotterdam Study.

}, keywords = {Aged, Aging, Cardiovascular Diseases, Cohort Studies, Computer Simulation, Cross-Sectional Studies, Epidemiologic Research Design, Gene-Environment Interaction, Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, Longitudinal Studies, Meta-Analysis as Topic, Models, Genetic, Pharmacogenetics, Risk Assessment, United States}, issn = {1097-0258}, doi = {10.1002/sim.6323}, author = {Sitlani, Colleen M and Rice, Kenneth M and Lumley, Thomas and McKnight, Barbara and Cupples, L Adrienne and Avery, Christy L and Noordam, Raymond and Stricker, Bruno H C and Whitsel, Eric A and Psaty, Bruce M} }