Independent susceptibility markers for atrial fibrillation on chromosome 4q25

Background-: Genetic variants on chromosome 4q25 are associated with atrial fibrillation (AF). We sought to determine whether there is more than 1 susceptibility signal at this locus. Methods and results-: Thirty-four haplotype-tagging single-nucleotide polymorphisms (SNPs) at the 4q25 locus were genotyped in 790 case and 1177 control subjects from Massachusetts General Hospital and tested for association with AF. We replicated SNPs associated with AF after adjustment for the most significantly associated SNP in 5066 case and 30 661 referent subjects from the German Competence Network for Atrial Fibrillation, Atherosclerosis Risk In Communities Study, Cleveland Clinic Lone AF Study, Cardiovascular Health Study, and Rotterdam Study. All subjects were of European ancestry. A multimarker risk score composed of SNPs that tagged distinct AF susceptibility signals was constructed and tested for association with AF, and all results were subjected to meta-analysis. The previously reported SNP, rs2200733, was most significantly associated with AF (minor allele odds ratio 1.80, 95% confidence interval 1.50 to 2.15, P=1.2×10) in the discovery sample. Adjustment for rs2200733 genotype revealed 2 additional susceptibility signals marked by rs17570669 and rs3853445. A graded risk of AF was observed with an increasing number of AF risk alleles at SNPs that tagged these 3 susceptibility signals. Conclusions-: We identified 2 novel AF susceptibility signals on chromosome 4q25. Consideration of multiple susceptibility signals at chromosome 4q25 identifies individuals with an increased risk of AF and may localize regulatory elements at the locus with biological relevance in the pathogenesis of AF

Principal-Component Analysis for Assessment of Population Stratification in Mitochondrial Medical Genetics

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