van den Berg Marten E, Warren Helen R, Cabrera Claudia P, Verweij Niek, Mifsud Borbala, Haessler Jeffrey, Bihlmeyer Nathan A, Fu Yi-Ping, Weiss Stefan, Lin Henry J, Grarup Niels, Li-Gao Ruifang, Pistis Giorgio, Shah Nabi, Brody Jennifer A, Müller-Nurasyid Martina, Lin Honghuang, Mei Hao, Smith Albert V, Lyytikäinen Leo-Pekka, Hall Leanne M, van Setten Jessica, Trompet Stella, Prins Bram P, Isaacs Aaron, Radmanesh Farid, Marten Jonathan, Entwistle Aiman, Kors Jan A, Silva Claudia T, Alonso Alvaro, Bis Joshua C, de Boer Rudolf, de Haan Hugoline G, de Mutsert Renée, Dedoussis George, Dominiczak Anna F, Doney Alex S F, Ellinor Patrick T, Eppinga Ruben N, Felix Stephan B, Guo Xiuqing, Hagemeijer Yanick, Hansen Torben, Harris Tamara B, Heckbert Susan R, Huang Paul L, Hwang Shih-Jen, Kähönen Mika, Kanters Jørgen K, Kolcic Ivana, Launer Lenore J, Li Man, Yao Jie, Linneberg Allan, Liu Simin, Macfarlane Peter W, Mangino Massimo, Morris Andrew D, Mulas Antonella, Murray Alison D, Nelson Christopher P, Orrú Marco, Padmanabhan Sandosh, Peters Annette, Porteous David J, Poulter Neil, Psaty Bruce M, Qi Lihong, Raitakari Olli T, Rivadeneira Fernando, Roselli Carolina, Rudan Igor, Sattar Naveed, Sever Peter, Sinner Moritz F, Soliman Elsayed Z, Spector Timothy D, Stanton Alice V, Stirrups Kathleen E, Taylor Kent D, Tobin Martin D, Uitterlinden André, Vaartjes Ilonca, Hoes Arno W, van der Meer Peter, Völker Uwe, Waldenberger Melanie, Xie Zhijun, Zoledziewska Magdalena, Tinker Andrew, Polasek Ozren, Rosand Jonathan, Jamshidi Yalda, van Duijn Cornelia M, Zeggini Eleftheria, Jukema J Wouter, Asselbergs Folkert W, Samani Nilesh J, Lehtimäki Terho, Gudnason Vilmundur, Wilson James, Lubitz Steven A, Kääb Stefan, Sotoodehnia Nona, Caulfield Mark J, Palmer Colin N A, Sanna Serena, Mook-Kanamori Dennis O, Deloukas Panos, Pedersen Oluf, Rotter Jerome I, Dörr Marcus, O'Donnell Chris J, Hayward Caroline, Arking Dan E, Kooperberg Charles, van der Harst Pim, Eijgelsheim Mark, Stricker Bruno H, Munroe Patricia B
Department of Medical Informatics Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, 3000CA, Rotterdam, the Netherlands.
Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK.
Hum Mol Genet. 2017 Jun 15;26(12):2346-2363. doi: 10.1093/hmg/ddx113.
Resting heart rate is a heritable trait, and an increase in heart rate is associated with increased mortality risk. Genome-wide association study analyses have found loci associated with resting heart rate, at the time of our study these loci explained 0.9% of the variation. This study aims to discover new genetic loci associated with heart rate from Exome Chip meta-analyses.Heart rate was measured from either elecrtrocardiograms or pulse recordings. We meta-analysed heart rate association results from 104 452 European-ancestry individuals from 30 cohorts, genotyped using the Exome Chip. Twenty-four variants were selected for follow-up in an independent dataset (UK Biobank, N = 134 251). Conditional and gene-based testing was undertaken, and variants were investigated with bioinformatics methods.We discovered five novel heart rate loci, and one new independent low-frequency non-synonymous variant in an established heart rate locus (KIAA1755). Lead variants in four of the novel loci are non-synonymous variants in the genes C10orf71, DALDR3, TESK2 and SEC31B. The variant at SEC31B is significantly associated with SEC31B expression in heart and tibial nerve tissue. Further candidate genes were detected from long-range regulatory chromatin interactions in heart tissue (SCD, SLF2 and MAPK8). We observed significant enrichment in DNase I hypersensitive sites in fetal heart and lung. Moreover, enrichment was seen for the first time in human neuronal progenitor cells (derived from embryonic stem cells) and fetal muscle samples by including our novel variants.Our findings advance the knowledge of the genetic architecture of heart rate, and indicate new candidate genes for follow-up functional studies.
静息心率是一种可遗传的性状，心率增加与死亡风险升高相关。全基因组关联研究分析已发现与静息心率相关的基因座，在我们开展研究时，这些基因座解释了0.9%的变异。本研究旨在通过外显子芯片荟萃分析发现与心率相关的新基因座。心率通过心电图或脉搏记录进行测量。我们对来自30个队列的104452名欧洲血统个体的心率关联结果进行了荟萃分析，这些个体使用外显子芯片进行基因分型。选择了24个变异在一个独立数据集（英国生物银行，N = 134251）中进行后续研究。进行了条件性和基于基因的检测，并采用生物信息学方法对变异进行研究。我们发现了五个新的心率基因座，以及一个位于已确定的心率基因座（KIAA1755）中的新独立低频非同义变异。四个新基因座中的主要变异是C10orf71、DALDR3、TESK2和SEC31B基因中的非同义变异。SEC31B处的变异与心脏和胫神经组织中SEC31B的表达显著相关。从心脏组织中的长程调控染色质相互作用中检测到了更多候选基因（SCD、SLF2和MAPK8）。我们在胎儿心脏和肺的DNase I超敏位点中观察到显著富集。此外，通过纳入我们的新变异，首次在人类神经祖细胞（源自胚胎干细胞）和胎儿肌肉样本中发现了富集现象。我们的研究结果推进了对心率遗传结构的认识，并为后续功能研究指明了新的候选基因。
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