Exploring pleiotropy using principal components.
Exploring pleiotropy using principal components.
复制标题
使用主要成分探索多效性。
DOI：
10.1186/1471-2156-4-s1-s53
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发表时间：
2003-12-31
期刊：
影响因子：
2.9
通讯作者：
Xu, JF
中科院分区：
文献类型：
--
作者：
Bensen, JT;Lange, LA;Langefeld, CD;Chang, BL;Bleecker, ER;Meyers, DA;Xu, JF
标准的多变量主成分(PCS)方法被用来识别可能由一个或多个共同基因(多效性)控制的变量簇。对6个个体性状(总胆固醇(CHOL)、高密度脂蛋白和低密度脂蛋白、甘油三酯(TG)、体重指数(BMI)和收缩压(SBP))和每台PC进行遗传力估计和连锁分析，以比较我们识别主要基因效应的能力。使用来自基因分析研讨会13的模拟数据(第11年的队列1和2数据)，首先根据年龄、性别和吸烟(每天吸烟)对数量性状进行调整。对调整后的变量进行标准化，计算PCs，然后进行正交变换(varimax旋转)。然后对旋转的PC进行遗传度和定量多点连锁分析。前三个PC可解释总表型变异的73%。所有三台个人电脑的遗传度估计都在0.60以上。我们对PCs和个体性状进行了连锁分析。大多数多效性和性状特异性基因都没有被鉴定出来。标准的PCS分析方法不利于识别影响模拟数据集中检查的六个性状的多效性基因。此外，使用传统的数量性状连锁分析无法在这个模拟数据集中识别遗传力估计超过0.60的性状变异的20%。在某些情况下，缺乏对多效性和特性特异性基因的识别可能反映了它们对所检查的特性/PC的低贡献，或者更重要的是，反映了所分析的样本组的特性，而不仅仅是PC方法本身的失败。
A standard multivariate principal components (PCs) method was utilized to identify clusters of variables that may be controlled by a common gene or genes (pleiotropy). Heritability estimates were obtained and linkage analyses performed on six individual traits (total cholesterol (Chol), high and low density lipoproteins, triglycerides (TG), body mass index (BMI), and systolic blood pressure (SBP)) and on each PC to compare our ability to identify major gene effects. Using the simulated data from Genetic Analysis Workshop 13 (Cohort 1 and 2 data for year 11), the quantitative traits were first adjusted for age, sex, and smoking (cigarettes per day). Adjusted variables were standardized and PCs calculated followed by orthogonal transformation (varimax rotation). Rotated PCs were then subjected to heritability and quantitative multipoint linkage analysis. The first three PCs explained 73% of the total phenotypic variance. Heritability estimates were above 0.60 for all three PCs. We performed linkage analyses on the PCs as well as the individual traits. The majority of pleiotropic and trait-specific genes were not identified. Standard PCs analysis methods did not facilitate the identification of pleiotropic genes affecting the six traits examined in the simulated data set. In addition, genes contributing 20% of the variance in traits with over 0.60 heritability estimates could not be identified in this simulated data set using traditional quantitative trait linkage analyses. Lack of identification of pleiotropic and trait-specific genes in some cases may reflect their low contribution to the traits/PCs examined or more importantly, characteristics of the sample group analyzed, and not simply a failure of the PC approach itself.