Principal-Component Analysis for Assessment of Population Stratification in Mitochondrial Medical Genetics
Principal-Component Analysis for Assessment of Population Stratification in Mitochondrial Medical Genetics
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DOI：
10.1016/j.ajhg.2010.05.005
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发表时间：
2010-06-11
影响因子：
9.8
通讯作者：
Rosand, Jonathan
中科院分区：
文献类型：
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作者：
Biffi, Alessandro;Anderson, Christopher D.;Rosand, Jonathan
虽然遗传性线粒体遗传变异可导致人类疾病，但由于线粒体群体分层（PS），目前还没有有效的方法来控制混杂。我们试图确定一个可靠的方法，在线粒体医学遗传学的PS评估。我们分析了来自1513名欧洲裔美国人的线粒体SNP数据，同时使用先前验证的144种线粒体标记物以及Affyphase 6.0（n = 432），Illumina 610-Quad（n = 458）或Illumina 660（n = 623）平台进行基因分型。在人类基因组多样性小组（HGDP）（Illumina 650）的938名参与者中进行了额外的分析。我们比较了以下方法来控制PS：单倍型组分层分析，线粒体主成分分析（PCA），并结合常染色体线粒体PCA。我们计算了线粒体的基因组膨胀因子（mtGIF），并对模拟的病例对照和连续表型（各10，000次模拟）进行了统计学检验，这些表型与线粒体的祖先具有不同程度的相关性。然后比较不同调整方法的结果。我们还计算了权力的发现，真正的协会在每种方法下，使用模拟的方法。线粒体！PCA概括了单倍群信息，但单倍群分层分析在控制PS方面不如线粒体PCA。细胞核和线粒体主成分之间的相关性非常有限。核PC的调整对模拟表型的线粒体分析没有影响。线粒体！使用来自商业上可获得的全基因组阵列的数据进行的PCA与使用详尽的线粒体标记物组进行的PCA强烈相关。最后，我们通过模拟证明，使用线粒体PCA检测真实关联的功率没有损失。
Although inherited mitochondrial genetic variation can cause human disease, no validated methods exist for control of confounding due to mitochondrial population stratification (PS). We sought to identify a reliable method for PS assessment in mitochondrial medical genetics. We analyzed mitochondria' SNP data from 1513 European American individuals concomitantly genotyped with the use of a previously validated panel of 144 mitochondrial markers as well as the Affymetrix 6.0 (n = 432), Illumina 610-Quad (n = 458), or Illumina 660 (n = 623) platforms. Additional analyses were performed in 938 participants in the Human Genome Diversity Panel (HGDP) (Illumina 650). We compared the following methods for controlling for PS: haplogroup-stratified analyses, mitochondrial principal-component analysis (PCA), and combined autosomal-mitochondrial PCA. We computed mitochondria' genomic inflation factors (mtGIFs) and test statistics for simulated case-control and continuous phenotypes (10,000 simulations each) with varying degrees of correlation with mitochondria' ancestry. Results were then compared across adjustment methods. We also calculated power for discovery of true associations under each method, using a simulation approach. Mitochondria! PCA recapitulated haplogroup information, but haplogroup-stratified analyses were inferior to mitochondria' PCA in controlling for PS. Correlation between nuclear and mitochondria' principal components (PCs) was very limited. Adjustment for nuclear PCs had no effect on mitochondria' analysis of simulated phenotypes. Mitochondria! PCA performed with the use of data from commercially available genome-wide arrays correlated strongly with PCA performed with the use of an exhaustive mitochondria' marker panel. Finally, we demonstrate, through simulation, no loss in power for detection of true associations with the use of mitochondria' PCA.