and Y chromosome phylogeny. Unfortunately, although some initial steps have been taken in this direction [5], such detailed data does not yet exist. For the moment, our analysis must be limited to mtDNA sequences.

mellitus patients (mean age: 5865 years; range 43–65 years), HN or healthy non-obese young males (mean age: 2063 years; range 18–25 years), ON or obese young males (mean age: 2162 years; range 18–25 years), KA or patients with Alzheimer’s disease (mean age: 77610 years; range 47–93 years), PD or patients with Parkinson’s disease (mean age: 6269 years; range 39–81 years) and GC/TC or Gifu/Tokyo centenarians (mean age: 10061, range 99–105 years) (see [7] and http://mtsnp.tmig.or.jp/mtsnp/ index_e.shtml for more information). In addition, we include new data from 112 complete mtDNA sequences from semi-super- centenarians (labeled SSC in this paper), ranging in age from 105– 115 years (mean age 107.361.2 years). These semi-supercente- narians included 96 females and 16 males and were recruited from all over Japan. The study protocol complied with the Declaration of Helsinki and was approved by the Committees on the Ethics of Human Research of Keio University School of Medicine, Gifu International Institute of Biotechnology, and Tokyo Metropolitan Institute of Gerontology. Written informed consent was obtained from each participant or a member of his/her immediate family. A detailed description of the methods used in the collection, extraction and sequencing of the mtDNA samples used in the present study is given in our website (http://mtsnp.tmig.or.jp/ mtsnp/annex_e.html). In brief, sequences were analyzed with a 3130xl Genetic Analyzer from Applied Biosystems. Complete sequences were aligned, assembled, and compared with the program Sequencher 4.1 (Gene Codes, Ann Arbor, MI). For verification, visual inspection of each candidate mtSNP was carried out. At least 2 overlapping DNA templates amplified with different primer pairs were used for identification of each mtSNP. Newly obtained mtDNA sequences of semi-supercentenarians were deposited in GenBank as the following accession numbers

In this paper we develop techniques to identify mutations for longevity by looking for correlations between the longevity phenotype, mtSNPs and mtDNA haplogroups. The correlations could be due to functional polymorphisms or to population structure. We analyze the correlations using two different methods. In the first, we use several statistical scores to directly test correlations between haplogroups and the longevity phenotype. In the second, we apply Principal Component Spectral Analysis to separate correlations due to functional polymorphisms from those due to population structure. The results are similar. We find that there is a strong selection for Longevity in the D super-haplogroup and strong progressive correlations in the DRD4RD4a lineage. However, using Gaussian generated SNPs distributions we find that this association is most likely due to population structure (ie. it depends on the details of the phylogeny of the M Clade within which D is a sub-clade and does not identify functional SNPs). We identify the mtSNP 14979C (Cytb: Ile78Thr) in D4a to be correlated with extreme longevity. To study whether this mtSNPs might be hitchhiking on nuclear SNPs, we looked for association signatures of enrichment in the Japanese D clade samples in HapMaP [5]. However, the SNP density in HapMaP is too low to allow any definite conclusions.

Materials and Methods

Figure 1 shows the age distribution of the samples used in the present study. The data comes from two sources. The first is Tanaka et al [6] and consists of 672 complete mitochondrial DNA sequences divided into sets of 96 samples in each of seven phenotypes: JD or diabetic patients with severe angiopathy (mean

  

Figure 1. Age distribution across the samples (JD, ND, HN, KA, PD, TC/GC, doi:10.1371/journal.pone.0002421.g001

sequences are attached in FASTA format in Data S1. The other sequences used in this paper can be downloaded

centenarian (ie non-target or NT) groups, corresponding to healthy non-obese males, obese young males, diabetics with and without angiopathy, Alzheimer’s and Parkinson’s disease patients. The statistics L is motivated by an analogy with the t-test. S