The polygenic architecture of left ventricular mass mirrors the clinical epidemiology
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Marc S. Williams | W. Chung | H. Hakonarson | Thomas J. Wang | D. Roden | J. Denny | I. Kullo | E. Larson | G. Jarvik | C. Shaffer | T. Edwards | A. Giri | J. Mosley | Q. Wells | C. Stein | D. Crosslin | R. Levinson | E. Farber-Eger | J. Hellwege | A. Hung | M. Shuey | Mingjian Shi | E. Brittain | A. Arruda-Olson | K. Borthwick
[1] D. Levy,et al. Left Ventricular Mass and Incidence of Coronary Heart Disease in an Elderly Cohort , 2020 .
[2] P. Munroe,et al. Genome-Wide Analysis of Left Ventricular Image-Derived Phenotypes Identifies Fourteen Loci Associated With Cardiac Morphogenesis and Heart Failure Development , 2019, Circulation.
[3] Matthew S. Lebo,et al. The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype , 2018, Genetic epidemiology.
[4] Laura J. Scott,et al. Trans-ethnic association study of blood pressure determinants in over 750,000 individuals , 2018, Nature Genetics.
[5] B. Psaty,et al. Coronary Heart Disease Genetic Risk Score Predicts Cardiovascular Disease Risk in Men, Not Women: The Multi-Ethnic Study of Atherosclerosis , 2018, Circulation. Genomic and precision medicine.
[6] Shu Ye,et al. Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults , 2018, Journal of the American College of Cardiology.
[7] William K. Thompson,et al. A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers , 2018, Nature Communications.
[8] P. Visscher,et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry , 2018, bioRxiv.
[9] M. Kanai,et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases , 2018, Nature Genetics.
[10] S. Yusuf,et al. Penetrance of Polygenic Obesity Susceptibility Loci across the Body Mass Index Distribution. , 2017, American journal of human genetics.
[11] N. Cox,et al. Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record , 2017, PloS one.
[12] Giovanni Malerba,et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes , 2017, Nature Genetics.
[13] Jie Huang,et al. Large-scale genome-wide analysis identifies genetic variants associated with cardiac structure and function , 2017, The Journal of clinical investigation.
[14] William K. Thompson,et al. Investigating the Genetic Architecture of the PR Interval Using Clinical Phenotypes , 2017, Circulation. Cardiovascular genetics.
[15] William K. Thompson,et al. Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data , 2016, Circulation. Cardiovascular genetics.
[16] Alan M. Kwong,et al. Next-generation genotype imputation service and methods , 2016, Nature Genetics.
[17] J. Witte,et al. Determining Which Phenotypes Underlie a Pleiotropic Signal , 2016, Genetic epidemiology.
[18] Hae Kyung Im,et al. Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues , 2016, bioRxiv.
[19] Po-Ru Loh,et al. A Robust Example of Collider Bias in a Genetic Association Study. , 2016, American journal of human genetics.
[20] J. Danesh,et al. A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease , 2016 .
[21] Tamara S. Roman,et al. New genetic loci link adipose and insulin biology to body fat distribution , 2014, Nature.
[22] Steven F. Lehrer,et al. Cohort of birth modifies the association between FTO genotype and BMI , 2014, Proceedings of the National Academy of Sciences.
[23] Joshua C. Denny,et al. R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment , 2014, Bioinform..
[24] Melissa A. Basford,et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data , 2013, Nature Biotechnology.
[25] Melissa A. Basford,et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future , 2013, Genetics in Medicine.
[26] Xiang Zhou,et al. Polygenic Modeling with Bayesian Sparse Linear Mixed Models , 2012, PLoS genetics.
[27] O. Delaneau,et al. Supplementary Information for ‘ Improved whole chromosome phasing for disease and population genetic studies ’ , 2012 .
[28] David Levine,et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data , 2012, Bioinform..
[29] J. Marchini,et al. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing , 2012, Nature Genetics.
[30] M. Stephens,et al. Genome-wide Efficient Mixed Model Analysis for Association Studies , 2012, Nature Genetics.
[31] Dana C Crawford,et al. Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality , 2011, Genetic epidemiology.
[32] P. Visscher,et al. Estimating missing heritability for disease from genome-wide association studies. , 2011, American journal of human genetics.
[33] Yun Li,et al. METAL: fast and efficient meta-analysis of genomewide association scans , 2010, Bioinform..
[34] P. Visscher,et al. Common SNPs explain a large proportion of heritability for human height , 2011 .
[35] Marylyn D. Ritchie,et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations , 2010, Bioinform..
[36] P. Visscher,et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder , 2009, Nature.
[37] D. Roden,et al. Development of a Large‐Scale De‐Identified DNA Biobank to Enable Personalized Medicine , 2008, Clinical pharmacology and therapeutics.
[38] Manuel A. R. Ferreira,et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.
[39] Richard B Devereux,et al. Recommendations for chamber quantification: a report from the American Society of Echocardiography's Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardio , 2005, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.
[40] W. Kannel. Left ventricular hypertrophy as a risk factor: the Framingham experience , 1991, Journal of hypertension. Supplement : official journal of the International Society of Hypertension.
[41] D. Levy,et al. Prognostic implications of echocardiographically determined left ventricular mass in the Framingham Heart Study. , 1990, The New England journal of medicine.
[42] D. Levy,et al. Association of echocardiographic left ventricular mass with body size, blood pressure and physical activity (the Framingham Study). , 1990, The American journal of cardiology.
[43] D. Levy,et al. Left ventricular mass and incidence of coronary heart disease in an elderly cohort. The Framingham Heart Study. , 1989, Annals of internal medicine.
[44] J. C. Christiansen,et al. Echocardiographically detected left ventricular hypertrophy: prevalence and risk factors. The Framingham Heart Study. , 2020, Annals of internal medicine.