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Genotyping was carried out using the Affymetrix Genome-Wide Human SNP Array 6.0 (Affymetrix, Santa Clara, CA, USA), which includes 445 mtSNPs throughout the mitochondrial genome, according to the Affymetrix protocol. Briefly, approximately 250 ng of genomic DNA was digested with restriction enzymes NspI and StyI. Digested DNA was adaptor-ligated and PCR-amplified for each sample. Fragment PCR products were then labeled with biotin, denatured, and hybridized to the arrays. Arrays were then washed and stained using Phycoerythrin on Affymetrix Fluidics Station, and scanned using the GeneChip Scanner 3000 7G to quantitate fluorescence intensities. Data management and analyses were conducted using the Genotyping Command Console. SNPs were identified using Birdsuite (version 1.5.2; http://www.broad.mit.edu/mpg/birdsuite/analysis.html). In order to ensure a high quality http://www.selleck.cn/products/bmn-673.html of the genotyping data, quality control procedures were conducted as follows. First, only samples with a minimum call rate of 95% were included. After repeated experiments, all samples (n= 2286) met this criteria and the final mean call rate reached a high level of 98.93%. Second, of the initial 445 mtSNPs, we discarded mtSNPs: (1) with a call rate http://www.selleckchem.com/products/PD-0332991.html with genotyping concordance rate http://www.selleckchem.com/products/Everolimus(RAD001).html The basic characteristics of these mtSNPs are summarized in Supplementary Table S1. Before association analyses, principal component analysis implemented in EIGENSTRAT (Price et al., 2006) was used to correct for potential population stratification that may lead to spurious association results using all the nuclear SNPs in the SNP Array 6.0. The first 10 principal components emerging from the EIGENSTRAT analyses, along with sex, age, weight, height, BMI, and physical activity, were used as covariates to adjust the raw BMD values at hip and spine. The definition of the physical activity phenotype has been detailed in our previous study (De Moor et al., 2009). We classified individuals as regular versus nonexercisers. After the adjustment by the above covariates, the residuals were used for association analyses. All association analyses were performed in R v.2.11. For single-mtSNP variants, a linear regression model was used to assess the difference in subjects carrying different mtSNP alleles. A raw P value of