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American Journal of Hypertension|December 15, 2010
Five blood pressure loci identified by an updated genome-wide linkage scan: meta-analysis of the Family Blood Pressure ProgramJeannette Simino, Gang Shi, Rezart Kume, et al.
European Journal of Human Genetics : EJHG|March 22, 2022
Gene-lifestyle interactions in the genomics of human complex traitsVincent Laville, Timothy Majarian, Yun J Sung, et al.
American Journal of Hypertension|March 7, 2009
The association of cell cycle checkpoint 2 variants and kidney function: findings of the Family Blood Pressure Program and the Atherosclerosis Risk In Communities studyNora Franceschini, Kari E North, Donna Arnett, et al.
Frontiers in Genetics|December 22, 2022
Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumptionTimothy D Majarian, Amy R Bentley, Vincent Laville, et al.
European Journal of Human Genetics : EJHG|September 29, 2018
Combined linkage and association analysis identifies rare and low frequency variants for blood pressure at 1q31Heming Wang, Priyanka Nandakumar, Fasil Tekola-Ayele, et al.
Molecular Psychiatry|November 22, 2019
Three genetic-environmental networks for human personalityIgor Zwir, Coral Del-Val, Javier Arnedo, et al.
Circulation. Cardiovascular Genetics|November 20, 2015
Genetic Susceptibility to Lipid Levels and Lipid Change Over Time and Risk of Incident Hyperlipidemia in Chinese PopulationsXiangfeng Lu, Jianfeng Huang, Zengnan Mo, et al.
Plos Genetics|January 24, 2013
A systematic mapping approach of 16q12.2/FTO and BMI in more than 20,000 African Americans narrows in on the underlying functional variation: results from the Population Architecture using Genomics and Epidemiology (PAGE) studyUlrike Peters, Kari E North, Praveen Sethupathy, et al.
Circulation. Cardiovascular Genetics|October 30, 2013
Genome-wide association study identifies 8 novel loci associated with blood pressure responses to interventions in Han ChineseJiang He, Tanika N Kelly, Qi Zhao, et al.
Scientific Reports|May 30, 2024
Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scoresYana Hrytsenko, Benjamin Shea, Michael Elgart, et al.
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