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Background: The mechanisms underlying the association between diabetes and coronary artery disease (CAD) risk are unclear. We aimed to assess this association by studying genetic variants that have been shown to associate with type 2 diabetes (T2DM). If the association between diabetes and CAD is causal, we expected to observe an association of these variants with CAD as well., Methods and Results: We studied all genetic variants currently known to be associated with T2DM at a genome-wide significant level (p < 5*10(-8)) in CARDIoGRAM, a genome-wide data-set of CAD including 22,233 CAD cases and 64,762 controls. Out of the 44 published T2DM SNPs 10 were significantly associated with CAD in CARDIoGRAM (OR>1, p < 0.05), more than expected by chance (p = 5.0*10(-5)). Considering all 44 SNPs, the average CAD risk observed per individual T2DM risk allele was 1.0076 (95% confidence interval (CI), 0.9973-1.0180). Such average risk increase was significantly lower than the increase expected based on i) the published effects of the SNPs on T2DM risk and ii) the effect of T2DM on CAD risk as observed in the Framingham Heart Study, which suggested a risk of 1.067 per allele (p = 7.2*10(-10) vs. the observed effect). Studying two risk scores based on risk alleles of the diabetes SNPs, one score using individual level data in 9856 subjects, and the second score on average effects of reported beta-coefficients from the entire CARDIoGRAM data-set, we again observed a significant - yet smaller than expected - association with CAD., Conclusions: Our data indicate that an association between type 2 diabetes related SNPs and CAD exists. However, the effects on CAD risk appear to be by far lower than what would be expected based on the effects of risk alleles on T2DM and the effect of T2DM on CAD in the epidemiological setting., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

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., (Copyright 2010 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)