Table 5

Association results of SNPs identified in the GWAS of Sebastiani et al. in the German GWAS data set. Markers not genotyped on the Affymetrix 6.0 Chip were

Chr. Position B36 ID MAF (controls) AF (LLI) P value INFO score 1 243871287 rs10924270 0.461 0.467 0.738 0.795 1 246927264 rs4916176 0.125 0.122 0.820 – 2 5210976 rs1377638 0.132 0.123 0.380 0.979 2 171530712 rs4668356 0.054 0.065 0.185 0.991 2 173328711 rs6433379 0.123 0.131 0.527 0.914 2 234432806 rs579327 0.177 0.159 0.152 – 2 235521853 rs2042831 0.076 0.080 0.488 0.940 3 14874782 rs294636 0.183 0.189 0.566 0.963 3 97267562 rs4393926 0.341 0.330 0.543 0.662 (in LD with rs4390941,a r2=0.99) 4 80183611 rs1455311 0.177 0.172 0.767 0.991 5 55450713 rs415407 0.467 0.471 0.834 0.926 5 65819465 rs10069397 0.484 0.448 0.055 0.993 5 83892667 rs7717527 0.182 0.196 0.443 0.967 6 31237686 rs2073724 0.086 0.085 0.861 – 6 33198814 rs4713607 0.495 0.519 0.197 1.000 8 58468572 rs954295 0.212 0.207 0.764 0.992 8 58940672 rs1436013 0.214 0.219 0.714 0.885 8 79864431 rs2717536 NA NA NA – 8 110758554 rs3133926 0.215 0.202 0.444 0.973 8 135681127 rs1036819 0.112 0.106 0.727 0.969 10 67755359 rs16922827 0.093 0.109 0.139 0.942 10 99744963 rs508001 0.102 0.117 0.096 1.014 11 60282362 rs7930940 NA NA NA – 11 63782795 rs2244621 0.149 0.120 0.014 0.933 11 125565159 rs522486 0.220 0.233 0.462 1.057 (in LD with rs1695739,a r2=0.83) 15 36552135 rs11073328 0.109 0.100 0.372 – 17 9277095 rs10521157 0.223 0.227 0.758 – 17 44426482 rs1390154 0.448 0.416 0.055 – (in LD with rs9899404,a r2=0.99) 17 52793555 rs792376 0.178 0.176 0.859 – 19 50087459 rs2075650 0.143 0.109 7.5(cid:2)10(cid:3)6 0.433 20 2602925 rs1810636 0.351 0.354 0.854 – 20 3775059 rs4815617 0.097 0.102 0.630 – 22 45173805 rs9615362 0.098 0.103 0.632 –

LLI: long-lived individuals; MAF (LLI): minor allele frequency in LLI; AF (controls): frequency in controls of the minor allele in LLI; P value: P value from logistic regression dosagetest,Pvaluesareadjustedforuncertaintyinimputation;INFOscore:r2qualitymetric,qualitymeasurefortheimputedSNPs,theinfometriciscalculatedbasedonthe entire file, based on the ratio of empirical and expected variance in dosage. Values closer to 1 indicate better expected quality of imputation, http://pngu.mgh.harvard.edu/ (cid:6)purcell/plink/dosage.shtml; LD: linkage disequilibrium; NA: no information about this SNP was available. Marker was not in the imputed data set nor could a SNP in LD (r2>0.8) be found. SNPs in bold were genotyped on Affymetrix 6.0, the P value is for an allele-based case–control comparison, using Fisher’s exact test; data from all other SNPs were derived from imputation. a SNP associated in the study of Sebastiani et al. (in press).

like, for example, IGF1-insulin-signaling or repair.

additional susceptibility loci for longevity. This result is in marked contrast to other complex phenotypes, for which GWAS have successfully uncovered multiple genetic risk factors at unprece- dented speed and with high levels of reproducibility (Esparza- Gordillo et al., 2009; Franke et al., 2008; Hofmann et al., 2008). One explanation for this lack of success in our study would be that GWAS provide only limited statistical power for a polygenic phenotype with loci of small effect such as human longevity. GWAS are more suitable for the detection of genetic variants with large effects, which presumably explain only a small fraction of the total variation (Barrett et al., 2008). It is assumed that many causal gene variants, each with a weak to moderate contribution, influence our ability to become long-lived (Christensen et al., 2006). A recent study supports this notion and concludes that the alleles with small individual effects may jointly influence life span so that the resulting impact can be both substantial and significant (Yashin et al., 2010). Another case in point is the fact that the recently detected longevity gene FOXO3A (Anselmi et al., 2009; Flachsbart et al., 2009; Li et al., 2009; Pawlikowska et al., 2009; Soerensen et al., 2010; Willcox et al., 2008) and the candidate EXO1 (Nebel et al., 2009) yielded P values in our GWAS of 0.007 and CCA 0.035, respectively. These P values were far too large to qualify the corresponding SNPs for follow-up in stage 2, suggesting that most genetic factors of similarly small effect (i.e. OR   1.4) were unfortunately likely to be missed by our study. Furthermore, especially male-specific effects could not be detected in our sample due to the small number of men in the replication study. The German and French centenarian/LLI samples used here are among the largest to-date and yet, they may not have been sufficiently powerful to detect small-effect variants in a longevity GWAS. The German sample used in stage 1, for example, had a power of 3.2% to detect an association of an OR of 1.4 (minor allele frequency of 50% and applying a significance level of 7.5  10(cid:3)8 (Supplementary Fig. 5)).

Acknowledgements

We wish to acknowledge the excellent technical support by all laboratory staff at the Institute of Clinical Molecular Biology, the Popgen biobank and the Biological Resource Center of the Fondation Jean Dausset-CEPH. This study was supported by the German Federal Ministry of Education and Research (BMBF) through the German National Genome Research Network (NGFN-2 and NGFN-plus), the DFG Excellence Cluster ‘Inflammation at Interfaces’, the Helmholtz Center Munich – German Research Center for Environmental Health, the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ and the French Ministe`re de l’Enseignement Supe´rieur et de la Recherche.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.mad.2011.06.008.

the online version, at doi:

/j.mad.

References Anselmi, C.V., Malovini, A., Roncarati, R., Novelli, V., Villa, F., Condorelli, G., Southern Italian Centenarian Study. Rejuvenation Res.

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