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首页> 外文期刊>BMC Genetics >Principal-component-based multivariate regression for genetic association studies of metabolic syndrome components
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Principal-component-based multivariate regression for genetic association studies of metabolic syndrome components

机译：基于主成分的多元回归用于代谢综合征成分的遗传关联研究

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Background Quantitative traits often underlie risk for complex diseases. For example, weight and body mass index (BMI) underlie the human abdominal obesity-metabolic syndrome. Many attempts have been made to identify quantitative trait loci (QTL) over the past decade, including association studies. However, a single QTL is often capable of affecting multiple traits, a quality known as gene pleiotropy. Gene pleiotropy may therefore cause a loss of power in association studies focused only on a single trait, whether based on single or multiple markers. Results We propose using principal-component-based multivariate regression (PCBMR) to test for gene pleiotropy with comprehensive evaluation. This method generates one or more independent canonical variables based on the principal components of original traits and conducts a multivariate regression to test for association with these new variables. Systematic simulation studies have shown that PCBMR has great power. PCBMR-based pleiotropic association studies of abdominal obesity-metabolic syndrome and its possible linkage to chromosomal band 3q27 identified 11 susceptibility genes with significant associations. Whereas some of these genes had been previously reported to be associated with metabolic traits, others had never been identified as metabolism-associated genes. Conclusions PCBMR is a computationally efficient and powerful test for gene pleiotropy. Application of PCBMR to abdominal obesity-metabolic syndrome indicated the existence of gene pleiotropy affecting this syndrome.
机译：背景技术数量性状通常是复杂疾病风险的基础。例如，体重和体重指数（BMI）是人类腹部肥胖-代谢综合征的基础。在过去的十年中，已经进行了许多尝试来确定数量性状基因座（QTL），包括关联研究。但是，单个QTL通常能够影响多种性状，即基因多效性。因此，基因多效性可能会导致仅针对单个特征的关联研究失去能力，无论是基于单个还是多个标记。结果我们建议使用基于主成分的多元回归（PCBMR）对基因多效性进行综合评估。该方法基于原始性状的主要成分生成一个或多个独立的规范变量，并进行多元回归以测试与这些新变量的关联。系统仿真研究表明，PCBMR具有强大的功能。基于PCBMR的腹部肥胖代谢综合征的多效性关联研究及其与染色体带3q27的可能联系确定了11个与显着关联的易感基因。尽管先前已经报道了其中一些基因与代谢性状相关，但其他基因从未被鉴定为与代谢相关的基因。结论PCBMR是一种计算有效且功能强大的基因多效性测试。 PCBMR在腹部肥胖代谢综合征中的应用表明存在影响该综合征的基因多效性。

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