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American Journal of Human Genetics|July 1, 2014
Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associationsSanthi K Ganesh, Daniel I Chasman, Martin G Larson, et al.
Plos One|October 31, 2015
Drug-Gene Interactions of Antihypertensive Medications and Risk of Incident Cardiovascular Disease: A Pharmacogenomics Study from the CHARGE ConsortiumJoshua C Bis, Colleen Sitlani, Ryan Irvin, et al.
Blood|July 26, 2018
DNA methylation age is associated with an altered hemostatic profile in a multiethnic meta-analysisCavin K Ward-Caviness, Jennifer E Huffman, Karl Everett, et al.
American Journal of Human Genetics|August 4, 2016
Whole-Exome Sequencing Identifies Loci Associated with Blood Cell Traits and Reveals a Role for Alternative GFI1B Splice Variants in Human HematopoiesisLinda M Polfus, Rajiv K Khajuria, Ursula M Schick, et al.
Genetic Epidemiology|May 28, 2016
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 GroupYun Ju Sung, Thomas W Winkler, Alisa K Manning, et al.
American Journal of Human Genetics|August 1, 2017
Low-Frequency Synonymous Coding Variation in CYP2R1 Has Large Effects on Vitamin D Levels and Risk of Multiple SclerosisDespoina Manousaki, Tom Dudding, Simon Haworth, et al.
Ebiomedicine|November 17, 2018
Association of maternal prenatal smoking GFI1-locus and cardio-metabolic phenotypes in 18,212 adultsPriyanka Parmar, Estelle Lowry, Giovanni Cugliari, et al.
International Journal of Epidemiology|May 29, 2015
Adiposity as a cause of cardiovascular disease: a Mendelian randomization studySara Hägg, Tove Fall, Alexander Ploner, et al.
Nature Genetics|November 4, 2018
Author Correction: Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation mapsValentina Iotchkova, Jie Huang, John A Morris, et al.
Nature Genetics|September 27, 2016
Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation mapsValentina Iotchkova, Jie Huang, John A Morris, et al.
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