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Principal component analysis with mitochondrial SNPs passing quality control (MAF > 0.01), was used to control for hidden population substructure in the following association analyses (Biffi et al., 2010). This method has been shown to outperform haplogroup-stratified or haplogroup-adjusted association analyses with no loss in power for the detection of true associations. Principal component analysis was performed using the SNPRelate R package (Zheng et al., 2012) using only individuals of European ancestry.

Rare-Variant association testing for sequencing data was carried out to detect rare mtDNA variants involved in human longevity. For this purpose, theSKATalgorithmwasused:asupervisedandflexibleregressionmethod to test for association between rare variants in a gene or genetic region with a continuous/dichotomous trait. This algorithm was implemented in the SKAT package of R. Compared with other gene-based approach, SKAT is able to deal with variants that have different direction and magnitude of effects, allows for covariate adjustment, and also avoids arbitrary selection of threshold in burden test (Wu et al., 2011). Gene- based analysis was adjusted for the first four PCs derived from the principal component analysis previously described. To compute the SKAT P-values, we used a resampling procedure based on 10 000 bootstraps. Thefunctionusedtomeasuresthegeneticsimilaritybetweentworandom subjects i and i’ in the analyzed region via the P selected markers was the weighted linear kernel function. As suggested by Wu et al. (2011), the diagonal weight matrix was chosen using the beta distribution density function with parameters a1 = 1 and a2 = 25 evaluated at the sample minor-allele frequency or MAF (default option of SKAT package of R).

Acknowledgments

The study was supported by the GEHA Project.

Funding

This project was funded through Priority 1 (Life Sciences, Genomics and Biotechnology for Health) of European Union’s FP6, Project Number LSHM-CT-2004-503270. The research also received funding from the European Union’s Seventh Framework Programme (FP7/2007-2011) under Grant Agreement No 259679.

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