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二维基因组扫描确定原发性高血压的新上位基因座。

Two-dimensional genome-scan identifies novel epistatic loci for essential hypertension.

作者信息

Bell Jordana Tzenova, Wallace Chris, Dobson Richard, Wiltshire Steven, Mein Charles, Pembroke Janine, Brown Morris, Clayton David, Samani Nilesh, Dominiczak Anna, Webster John, Lathrop G Mark, Connell John, Munroe Patricia, Caulfield Mark, Farrall Martin

机构信息

Department of Cardiovascular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.

出版信息

Hum Mol Genet. 2006 Apr 15;15(8):1365-74. doi: 10.1093/hmg/ddl058. Epub 2006 Mar 16.

Abstract

It is well established that gene interactions influence common human diseases, but to date linkage studies have been constrained to searching for single genes across the genome. We applied a novel approach to uncover significant gene-gene interactions in a systematic two-dimensional (2D) genome-scan of essential hypertension. The study cohort comprised 2076 affected sib-pairs and 66 affected half-sib-pairs of the British Genetics of HyperTension study. Extensive simulations were used to establish significance thresholds in the context of 2D genome-scans. Our analyses found significant and suggestive evidence for loci on chromosomes 5, 9, 11, 15, 16 and 19, which influence hypertension when gene-gene interactions are taken into account (5q13.1 and 11q22.1, two-locus lod score=5.72; 5q13.1 and 19q12, two-locus lod score=5.35; 9q22.3 and 15q12, two-locus lod score=4.80; 16p12.3 and 16q23.1, two-locus lod score=4.50). For each significant and suggestive pairwise interaction, the two-locus genetic model that best fitted the data was determined. Regions that were not detected using single-locus linkage analysis were identified in the 2D scan as contributing significant epistatic effects. This approach has discovered novel loci for hypertension and offers a unique potential to use existing data to uncover novel regions involved in complex human diseases.

摘要

基因相互作用影响常见人类疾病,这一点已得到充分证实,但迄今为止,连锁研究一直局限于在全基因组中寻找单个基因。我们应用了一种新方法,在原发性高血压的系统性二维(2D)基因组扫描中发现显著的基因-基因相互作用。研究队列包括英国高血压遗传学研究中的2076对患病同胞对和66对患病半同胞对。在2D基因组扫描的背景下,使用广泛的模拟来确定显著性阈值。我们的分析发现,在考虑基因-基因相互作用时,5号、9号、11号、15号、16号和19号染色体上的位点有显著和提示性证据,这些位点会影响高血压(5q13.1和11q22.1,两位点对数似然比分数=5.72;5q13.1和19q12,两位点对数似然比分数=5.35;9q22.3和15q12,两位点对数似然比分数=4.80;16p12.3和16q23.1,两位点对数似然比分数=4.50)。对于每一个显著和提示性的成对相互作用,确定了最适合数据的两位点遗传模型。在2D扫描中发现,单基因座连锁分析未检测到的区域对显著上位效应有贡献。这种方法发现了高血压的新位点,并提供了利用现有数据揭示复杂人类疾病相关新区域的独特潜力。

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