Changwei Li1, Jiang He2, Jing Chen2, Jinying Zhao2, Dongfeng Gu2, James E Hixson2, Dabeeru C Rao2, Cashell E Jaquish2, Treva K Rice2, Yun Ju Sung2, Tanika N Kelly2. 1. From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.). changwei.li@uga.edu. 2. From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.).
Abstract
BACKGROUND: Gene-environmental interaction analysis can identify novel genetic factors for blood pressure (BP). We performed genome-wide analyses to identify genomic loci that interact with potassium to influence BP using single-marker (1 and 2 df joint tests) and gene-based tests among Chinese participants of the GenSalt study (Genetic Epidemiology Network of Salt Sensitivity). METHODS AND RESULTS: Among 1876 GenSalt participants, the average of 3 urine samples was used to estimate potassium excretion. Nine BP measurements were taken using a random-zero sphygmomanometer. A total of 2.2 million single nucleotide polymorphisms were imputed using Affymetrix 6.0 genotype data and the Chinese Han of Beijing and Japanese of Tokyo HapMap reference panel. Promising findings (P<1.00×10-4) from GenSalt were evaluated for replication among 775 Chinese participants of the MESA (Multi-ethnic Study of Atherosclerosis). Single nucleotide polymorphism and gene-based results were meta-analyzed across the GenSalt and MESA studies to determine genome-wide significance. The 1 df tests identified interactions for ARL15 rs16882447 on systolic BP (P=2.83×10-9) and RANBP3L rs958929 on pulse pressure (P=1.58×10-8). The 2 df tests confirmed the ARL15 rs16882447 signal for systolic BP (P=1.15×10-9). Genome-wide gene-based analysis identified CC2D2A (P=2.59×10-7) at 4p15.32 and BNC2 (P=4.49×10-10) at 9p22.2 for systolic BP, GGNBP1 (P=1.18×10-8), and LINC00336 (P=1.36×10-8) at 6p21 for diastolic BP, DAB1 (P=1.05×10-13) at 1p32.2, and MIR4466 (P=5.34×10-8) at 6q25.3 for pulse pressure. The BNC2 (P=3.57×10-8) gene was also significant for mean arterial pressure. CONCLUSIONS: We identified 2 novel BP loci and 6 genes through the examination of single nucleotide polymorphism- and gene-based interactions with potassium.
BACKGROUND: Gene-environmental interaction analysis can identify novel genetic factors for blood pressure (BP). We performed genome-wide analyses to identify genomic loci that interact with potassium to influence BP using single-marker (1 and 2 df joint tests) and gene-based tests among Chinese participants of the GenSalt study (Genetic Epidemiology Network of Salt Sensitivity). METHODS AND RESULTS: Among 1876 GenSaltparticipants, the average of 3 urine samples was used to estimate potassium excretion. Nine BP measurements were taken using a random-zero sphygmomanometer. A total of 2.2 million single nucleotide polymorphisms were imputed using Affymetrix 6.0 genotype data and the Chinese Han of Beijing and Japanese of Tokyo HapMap reference panel. Promising findings (P<1.00×10-4) from GenSalt were evaluated for replication among 775 Chinese participants of the MESA (Multi-ethnic Study of Atherosclerosis). Single nucleotide polymorphism and gene-based results were meta-analyzed across the GenSalt and MESA studies to determine genome-wide significance. The 1 df tests identified interactions for ARL15rs16882447 on systolic BP (P=2.83×10-9) and RANBP3Lrs958929 on pulse pressure (P=1.58×10-8). The 2 df tests confirmed the ARL15rs16882447 signal for systolic BP (P=1.15×10-9). Genome-wide gene-based analysis identified CC2D2A (P=2.59×10-7) at 4p15.32 and BNC2 (P=4.49×10-10) at 9p22.2 for systolic BP, GGNBP1 (P=1.18×10-8), and LINC00336 (P=1.36×10-8) at 6p21 for diastolic BP, DAB1 (P=1.05×10-13) at 1p32.2, and MIR4466 (P=5.34×10-8) at 6q25.3 for pulse pressure. The BNC2 (P=3.57×10-8) gene was also significant for mean arterial pressure. CONCLUSIONS: We identified 2 novel BP loci and 6 genes through the examination of single nucleotide polymorphism- and gene-based interactions with potassium.
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