Table 2. Results from the haplotype-based analyses for candidate genes showing evidence for association with longevity (P 0.05/Number of haplotypes).

Group Gene SNPs Haplotypes Freq OR 95% CI P valueb PCSK1 rs3811952-rs155979 Age ≥ 90 CCA 0.302 1.00 Reference 5q15-q21 -rs3762985 (3/3)a 0.628 CCC 0.247 0.89 0.56-1.43 0.008 CGA 0.228 0.54 0.34-0.85 0.752 GCA 0.156 0.92 0.53-1.58 0.996 GCC 0.060 1.00 0.48-2.09 0.246 Rare 0.007 4.59 0.35-60.17 EGFR rs2072454-rs2293347- Female ≥ 100 CACA 0.310 1.00 Reference 7p12 rs3807362-rs884225 0.420 TGCG 0.247 0.67 0.25-1.77 (4/4) 0.003 CGCA 0.187 4.11 1.64-10.31 0.339 CGCG 0.150 1.60 0.61-4.18 0.000 TACA 0.052 0.00 − 0.329 TGCA 0.033 0.31 0.03-3.27 0.000 CATA 0.022 0.00 N.a. PAX4 rs327519-rs712700 Male ≥ 90 AA 0.683 1.00 Reference 7q32 (2/3) 0.130 AG 0.282 1.49 0.89-2.51 0.008 GG 0.035 5.14 1.54-17.14 LYN rs1450740-rs1450741- Age ≥ 90 CTCTGCA 0.504 1.00 Reference 8q13 rs1562818- rs1980042- 0.141 ATCCGCA 0.119 1.46 0.88-2.41 rs1027989-rs1546518- 0.741 CTTTGCA 0.114 1.10 0.64-1.89 rs3968371 0.325 CCCTGCA 0.077 1.37 0.73-2.58 (7/10) 0.155 ATCTGCA 0.052 1.77 0.81-3.88 0.002 CTCCGCA 0.048 3.72 1.64-8.45 0.900 CTCCGGG 0.037 0.95 0.40-2.24 0.438 ATCTCCA 0.016 0.57 0.14-2.36 0.262 ATCTCGA 0.014 0.31 0.04-2.41 0.227 ATTTGCA 0.012 2.92 0.51-16.59 0.576 Rare 0.010 1.61 0.31-8.44 Male ≥90 CTCTGCA 0.519 1.00 Reference 0.787 ATTTGCA 0.014 1.36 0.15-12.76 0.411 ATCTCGA 0.024 0.42 0.05-3.29 0.000 ATCTCCA 0.012 0.00 − 0.109 ATCTGCA 0.053 2.52 0.81-7.82 0.181 ATCCGCA 0.123 1.63 0.80-3.35 0.390 CTTTGCA 0.105 1.46 0.62-3.43 0.150 CTCCGGG 0.028 2.48 0.72-8.56 0.004 CTCCGCA 0.040 6.20 1.80-21.30 0.518 CCCTGCA 0.076 1.38 0.52-3.67 0.516 Rare 0.007 2.45 0.16-36.74

aNumber of tagSNPs among all SNPs genotyped for a candidate gene, bThe P value for each haplotype using a general linear model (GLM).

A B CcPCSK1 (5q15-q21) EGFR (7p12) PAX4 (7q32)95751875 95794708 > Pr icicailld 55242525 > a: 127037582 127043016 >SEH 3 mH +H 5         -LOG10 (P) 3& > jJ—___ 2e 2 2 2oO °°a o414e 4 1. . 1§ ‘i0 0 0Location 95792 95794 95796 55180 55200 55220 55240 427038 127040 kbt _— ' |YZ oe re ’N wo fo Nns Siempre erS 5 g a 8 x x 2 8 =I ei Bl g 8 S 3 & g &5 8 & Ss g 3 3 x & xen i ee ene eC8 is a ae 2 2 2Block 1 (5 kb)1 2 3 4 ‘3 ( py | A a9 14 12 8 8029LYN (8q13) 56954926 > 57085685 >3210Location 56960 56970 56980 56990 57000 57010 kb1 1 1 Lo 1 1 !g = 8 = g g g a 8 5Ss 5 Ss g 8 2 g 3 a 3ee a eR ee2 2 @ ® ® 2 @ @ 2 2‘ 2 0 Ss 3 00 0 it 40 > Allele genotype haplotype -Log,, (P value2 A oe oe — _Adjusted29028 1 0 1 15 15 ° e — Male>9020 14 0 : 0 : 0 uy o @ no Female>901 0 0 o @¢ — Female2100°° an oa — Exon t UTR 

PAX4 (7q32)127037582) 127043016 >3 mH +H 5 127038 127040 kb1s327519nm 1s712700© 1s327517

Figure 1. Summary results with gene and SNP information for four genes that consistently showed evidence for association with lifespan in allelic, typic, and haplotypic analyses.

values for the epistatic interaction LRT under a best fitting model among five genetic models tested for each age/gender group. Supplement Data Table S3 shows the ORs and their 95% CIs for the genotype combinations composed of each candidate SNP pair for two genes along with the numbers of cases and controls. For each age/gender group, 6, 1, 3, and 2 pairs of loci for each group

Supplement Data Table S2 presents significant P

yielded statistical significance in both the regre- ssion model and ORs for interaction. In particular, the interaction of platelet derived growth factor C (PDGFC) and glycogen synthase 2 (liver) (GYS2) for males older than 90 yr as well as the interaction of TNFSF11 and CDK10 in the group adjusted by gender showed a dose-response relationship with increasing copies of the target allele for each SNP.

sulin-processing enzyme that plays a key role in regulating insulin biosynthesis (Ohagi et al., 1996). Mutations in this gene are associated with obesity, tumorigenesis, and metastasis (Tzimas et al., 2005). The PAX4 gene, located in 7q32, is a member of the paired box family of transcription factor that mediates differentiation of insulin-pro- ducing beta-cells in the pancreas and plays critical roles during cancer growth (Li et al., 2006). EGFR is a transmembrane glycoprotein that functions as a tyrosine protein kinase (Kondo and Shimizu, 1983). Mutations in the EGFR gene, located in the 7p12.3-p12.1 region, have oncogenic potential in the development of non-small cell lung cancer (Tai et al., 2006). The LYN, located on 8q13, encodes a tyrosine kinase that is essential in immunoglo- bulin-mediated signaling, particularly in establishing B cell tolerance (Parravicini et al., 2002). Even aging mice with a Lyn up/up phenotype do not -/- display hematologic malignancies, while Lyn mice are more susceptible to tumorigenesis (Harder et al., 2001). To the best of our knowledge, this is the first reported evidence associating these genes with human longevity.

Discussion

It is well known that genetic variation exists across ethnic groups. Indeed, significant association of genes for human leukocyte antigen (HLA), ACE, APOE, MTTP, and CETP with human longevity in Caucasian populations have not been replicated in studies using Korean centenarians (Choi et al., 2003). The Korean longevity study carried out since 1998 reported significant differences in the occurrence of centenarians by gender, location, lifestyle, and diet (Lee et al., 2005). In 2005, the ratios of the Jeola province to the Kyongsang province were 1:0.97 for nonagenarians and 1:1.27 for centenarians (KNSO, 2005). Interestingly, a reverse relationship between the incidence of cancer and degree of longevity has been observed across provinces, indicating that prevention of cancers may be an important strategy to enhance lifespan (Kwon and Park, 2005). This notion could be applied to other age-related diseases such as DM and CVD.