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首页> 外文期刊>BMC Veterinary Research >A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes
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A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes

机译：基于多表型的全基因组关联研究以挖掘多效性基因的方法

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BackgroundCurrent Genome-Wide Association Studies (GWAS) are performed in a single trait framework without considering genetic correlations between important disease traits. Hence, the GWAS have limitations in discovering genetic risk factors affecting pleiotropic effects.ResultsThis work reports a novel data mining approach to discover patterns of multiple phenotypic associations over 52 anthropometric and biochemical traits in KARE and a new analytical scheme for GWAS of multivariate phenotypes defined by the discovered patterns. This methodology applied to the GWAS for multivariate phenotype highLDLhighTG derived from the predicted patterns of the phenotypic associations. The patterns of the phenotypic associations were informative to draw relations between plasma lipid levels with bone mineral density and a cluster of common traits (Obesity, hypertension, insulin resistance) related to Metabolic Syndrome (MS). A total of 15 SNPs in six genes (PAK7, C20orf103, NRIP1, BCL2, TRPM3, and NAV1) were identified for significant associations with highLDLhighTG. Noteworthy findings were that the significant associations included a mis-sense mutation (PAK7:R335P), a frame shift mutation (C20orf103) and SNPs in splicing sites (TRPM3).ConclusionsThe six genes corresponded to rat and mouse quantitative trait loci (QTLs) that had shown associations with the common traits such as the well characterized MS and even tumor susceptibility. Our findings suggest that the six genes may play important roles in the pleiotropic effects on lipid metabolism and the MS, which increase the risk of Type 2 Diabetes and cardiovascular disease. The use of the multivariate phenotypes can be advantageous in identifying genetic risk factors, accounting for the pleiotropic effects when the multivariate phenotypes have a common etiological pathway.
机译：背景技术当前的全基因组关联研究（GWAS）是在单一性状框架内进行的，没有考虑重要疾病性状之间的遗传相关性。因此，GWAS在发现影响多效性效应的遗传危险因素方面存在局限性。结果这项工作报告了一种新颖的数据挖掘方法，用于发现KARE中52种人类学和生化特征的多个表型关联的模式，以及一种新的GWAS定义的多表型分析方案。发现的模式。该方法适用于GWAS，用于从表型关联的预测模式中得出的多变量表型highLDLhighTG。表型关联的模式有助于得出血浆脂质水平与骨矿物质密度和一组与代谢综合征（MS）相关的常见特征（肥胖，高血压，胰岛素抵抗）之间的关系。鉴定出六个基因（PAK7，C20orf103，NRIP1，BCL2，TRPM3和NAV1）中的15个SNP与高LDLhighTG显着相关。值得注意的发现是重要的关联包括错义突变（PAK7：R335P），移码突变（C20orf103）和剪接位点的SNP（TRPM3）。结论这六个基因分别与大鼠和小鼠的数量性状基因座（QTL）相对应。已显示出与常见特征相关，例如特征明确的MS甚至肿瘤易感性。我们的发现表明，这六个基因可能在脂质代谢和MS的多效性作用中起重要作用，从而增加2型糖尿病和心血管疾病的风险。多元表型的使用在鉴定遗传风险因素方面可能是有利的，当多元表型具有共同的病因途径时，考虑了多效性效应。

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