Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA.
Am J Hum Genet. 2010 Jun 11;86(6):904-17. doi: 10.1016/j.ajhg.2010.05.005. Epub 2010 May 27.
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 mitochondrial 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 mitochondrial genomic inflation factors (mtGIFs) and test statistics for simulated case-control and continuous phenotypes (10,000 simulations each) with varying degrees of correlation with mitochondrial ancestry. Results were then compared across adjustment methods. We also calculated power for discovery of true associations under each method, using a simulation approach. Mitochondrial PCA recapitulated haplogroup information, but haplogroup-stratified analyses were inferior to mitochondrial PCA in controlling for PS. Correlation between nuclear and mitochondrial principal components (PCs) was very limited. Adjustment for nuclear PCs had no effect on mitochondrial analysis of simulated phenotypes. Mitochondrial PCA performed with the use of data from commercially available genome-wide arrays correlated strongly with PCA performed with the use of an exhaustive mitochondrial marker panel. Finally, we demonstrate, through simulation, no loss in power for detection of true associations with the use of mitochondrial PCA.
尽管遗传的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），目前尚无有效的方法来控制混杂因素。我们试图找到一种可靠的方法来评估线粒体医学遗传学中的 PS。我们分析了来自 1513 名欧洲裔美国人的线粒体 SNP 数据，这些人同时使用先前经过验证的 144 个线粒体标记物面板以及 Affymetrix 6.0（n = 432）、Illumina 610-Quad（n = 458）或 Illumina 660（n = 623）平台进行了基因分型。在人类基因组多样性面板（HGDP）中的 938 名参与者中进行了额外的分析（Illumina 650）。我们比较了以下用于控制 PS 的方法：单倍群分层分析、线粒体主成分分析（PCA）和联合常染色体-线粒体 PCA。我们计算了具有不同程度与线粒体祖源相关的模拟病例对照和连续表型的线粒体基因组膨胀因子（mtGIFs）和检验统计量（每种情况各进行 10,000 次模拟）。然后比较了调整方法的结果。我们还使用模拟方法计算了在每种方法下发现真实关联的功效。线粒体 PCA 再现了单倍群信息，但在控制 PS 方面，单倍群分层分析不如线粒体 PCA。核和线粒体主成分（PCs）之间的相关性非常有限。调整核 PCs 对模拟表型的线粒体分析没有影响。使用商业全基因组阵列获得的数据进行线粒体 PCA 与使用详尽的线粒体标记物面板进行的 PCA 相关性很强。最后，我们通过模拟证明，使用线粒体 PCA 检测真实关联的功效没有损失。
