A phenomics-based strategy identifies loci on APOC1, BRAP, and PLCG1 associated with metabolic syndrome phenotype domains.
A phenomics-based strategy identifies loci on APOC1, BRAP, and PLCG1 associated with metabolic syndrome phenotype domains.
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DOI：
10.1371/journal.pgen.1002322
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发表时间：
2011-10
期刊：
影响因子：
4.5
通讯作者：
Lin DY
中科院分区：
文献类型：
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作者：
Avery CL;He Q;North KE;Ambite JL;Boerwinkle E;Fornage M;Hindorff LA;Kooperberg C;Meigs JB;Pankow JS;Pendergrass SA;Psaty BM;Ritchie MD;Rotter JI;Taylor KD;Wilkens LR;Heiss G;Lin DY
尽管有证据表明代谢综合征组分的聚集，但目前用于鉴定统一遗传机制的方法通常评估不提供足够病因学信息的临床类别。在这里，我们使用了来自19，486名欧洲裔美国人和6，287名非洲裔美国人候选基因协会资源联盟参与者的数据，以确定与代谢表型聚类相关的基因座。包括19个数量性状的6个表型域（致动脉粥样硬化性血脂异常、血管功能障碍、血管炎症、血栓前状态、向心性肥胖和血糖升高）进行了检查。主成分分析用于降低每个域的维度，使得>55%的性状方差在每个域内表示。然后，我们应用了一种统计学上有效且计算上可行的多变量方法，该方法使用加性遗传模型将来自六个领域的八个主成分与250，000个估算的SNP相关联，并包括人口统计学协变量。在欧洲裔美国人中，我们确定了代表19个位点的606个全基因组显著SNP。这些基因座中的许多仅与一个性状域相关，与非裔美国人的结果一致，并与已发表的研究结果重叠，例如中心性肥胖和FTO。然而，我们的方法，这是适用于任何一组区间尺度性状是遗传的，并表现出证据的表型聚类，确定了三个新的基因座或附近的APOC 1，BRAP，PLCG 1，这是与多个表型域。这些多效性基因座可能有助于表征代谢失调并确定干预靶点。代谢综合征代表代谢表型的聚集（例如，升高的血压、胆固醇水平和血糖以及腹部肥胖），并且与动脉粥样硬化和2型糖尿病的风险增加相关。虽然多个基因影响特定的代谢综合征的组成部分已被报道，很少有研究评估作为一个整体的综合征的遗传基础。在这里，我们描述了一种方法来评估多个聚类性状，这使我们能够测试是否共同的遗传变异影响一个或多个代谢表型的共同出现。通过检查来自五项研究的大约20，000名欧洲裔美国人和6，200名非洲裔美国人参与者，我们发现染色体12，19和20上的三个区域与多种代谢表型相关。这些遗传变异是非常有趣的候选者，可能会增加我们对代谢表型聚类的生物学基础的理解，并有助于确定早期干预的目标。
Despite evidence of the clustering of metabolic syndrome components, current approaches for identifying unifying genetic mechanisms typically evaluate clinical categories that do not provide adequate etiological information. Here, we used data from 19,486 European American and 6,287 African American Candidate Gene Association Resource Consortium participants to identify loci associated with the clustering of metabolic phenotypes. Six phenotype domains (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic state, central obesity, and elevated plasma glucose) encompassing 19 quantitative traits were examined. Principal components analysis was used to reduce the dimension of each domain such that >55% of the trait variance was represented within each domain. We then applied a statistically efficient and computational feasible multivariate approach that related eight principal components from the six domains to 250,000 imputed SNPs using an additive genetic model and including demographic covariates. In European Americans, we identified 606 genome-wide significant SNPs representing 19 loci. Many of these loci were associated with only one trait domain, were consistent with results in African Americans, and overlapped with published findings, for instance central obesity and FTO. However, our approach, which is applicable to any set of interval scale traits that is heritable and exhibits evidence of phenotypic clustering, identified three new loci in or near APOC1, BRAP, and PLCG1, which were associated with multiple phenotype domains. These pleiotropic loci may help characterize metabolic dysregulation and identify targets for intervention. The metabolic syndrome represents a clustering of metabolic phenotypes (e.g. elevated blood pressure, cholesterol levels, and plasma glucose, as well as abdominal obesity) and is associated with an increased risk of atherosclerosis and type 2 diabetes. Although multiple genes influencing the specific metabolic syndrome components have been reported, few studies have evaluated the genetic underpinnings of the syndrome as a whole. Here, we describe an approach to evaluate multiple clustered traits, which allows us to test whether common genetic variants influence the co-occurrence of one or more metabolic phenotypes. By examining approximately 20,000 European American and 6,200 African American participants from five studies, we show that three regions on chromosomes 12, 19, and 20 are associated with multiple metabolic phenotypes. These genetic variants are highly intriguing candidates that may increase our understanding of the biologic basis of the clustering of metabolic phenotypes and help identify targets for early intervention.
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