Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits.
Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits.
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代谢综合征特征的常见SNP解释了遗传力和遗传相关性。
DOI：
10.1371/journal.pgen.1002637
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发表时间：
2012
期刊：
影响因子：
4.5
通讯作者：
Chow CC
中科院分区：
文献类型：
--
作者：
Vattikuti S;Guo J;Chow CC
我们使用一个双变量（多变量）线性混合效应模型来估计狭义遗传力（h2）和遗传力解释的常见SNPs（hg2）的几个代谢综合征（MetS）性状和遗传相关性对性状的动脉粥样硬化风险在社区（ARIC）全基因组关联研究（GWAS）人口。MetS特征包括体重指数（BMI）、腰臀比（WHR）、收缩压（SBP）、空腹血糖（GLU）、空腹胰岛素（INS）、空腹甘油三酯（TG）和空腹高密度脂蛋白（HDL）。我们发现，常见SNP对h2的贡献率分别为：身高h2的58%，BMI h2的41%，WHR h2的46%，GLU h2的30%，INS h2的39%，TG h2的34%，HDL h2的25%，SBP h2的80%。我们使用ARIC人群和独立的心脏研究（FHS）人群证实了先前关于身高和BMI的报告。我们证明了多变量模型支持BMI和WHR之间以及TG和HDL之间存在较大的遗传相关性。我们还表明，MetS性状之间的遗传相关是成正比的表型相关。狭义的遗传力的性状，如身体质量指数是一个衡量的变异性的性状之间的人，这是占他们的加性遗传差异。了解这些遗传差异有助于深入了解生物学机制，从而治疗疾病。全基因组关联研究（GWAS）调查了人群中常见的大量遗传标记。他们已经确定了几个与性状和疾病相关的单一标记。然而，这些标记似乎并不能解释所有已知的狭义遗传力。在这里，我们使用最近开发的模型来量化GWAS中包含的单个性状和性状之间共享的遗传信息。我们专门研究了与2型糖尿病和心脏病相关的代谢综合征特征，我们发现，对于大多数这些特征，以前未考虑的遗传性包含在GWAS调查的常见标记中。我们还计算了性状之间的遗传相关性，这是性状共有的遗传成分的度量。我们发现这些性状之间的遗传相关可以通过它们的表型相关来预测。
We used a bivariate (multivariate) linear mixed-effects model to estimate the narrow-sense heritability (h2) and heritability explained by the common SNPs (hg2) for several metabolic syndrome (MetS) traits and the genetic correlation between pairs of traits for the Atherosclerosis Risk in Communities (ARIC) genome-wide association study (GWAS) population. MetS traits included body-mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting glucose (GLU), fasting insulin (INS), fasting trigylcerides (TG), and fasting high-density lipoprotein (HDL). We found the percentage of h2 accounted for by common SNPs to be 58% of h2 for height, 41% for BMI, 46% for WHR, 30% for GLU, 39% for INS, 34% for TG, 25% for HDL, and 80% for SBP. We confirmed prior reports for height and BMI using the ARIC population and independently in the Framingham Heart Study (FHS) population. We demonstrated that the multivariate model supported large genetic correlations between BMI and WHR and between TG and HDL. We also showed that the genetic correlations between the MetS traits are directly proportional to the phenotypic correlations. The narrow-sense heritability of a trait such as body-mass index is a measure of the variability of the trait between people that is accounted for by their additive genetic differences. Knowledge of these genetic differences provides insight into biological mechanisms and hence treatments for diseases. Genome-wide association studies (GWAS) survey a large set of genetic markers common to the population. They have identified several single markers that are associated with traits and diseases. However, these markers do not seem to account for all of the known narrow-sense heritability. Here we used a recently developed model to quantify the genetic information contained in GWAS for single traits and shared between traits. We specifically investigated metabolic syndrome traits that are associated with type 2 diabetes and heart disease, and we found that for the majority of these traits much of the previously unaccounted for heritability is contained within common markers surveyed in GWAS. We also computed the genetic correlation between traits, which is a measure of the genetic components shared by traits. We found that the genetic correlation between these traits could be predicted from their phenotypic correlation.
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van Duijn, Cornelia M.