Table 2

Genes inthe pro–anti-oxidant pathway showing a nominally significant association (p b 0.05) with at least one traitunder study(ADL, HG, WS, MMSE, CCS) plus survival.

⁎ Gene Effect numSNPS Chr Test stat P-value Best SNP Test stat P-value Best SNP Test stat P-value Best SNP ADL HG WS ACOX1 PRO 10 17 2.503 0.131 rs7226127 1.515 0.516 rs17583163 0.958 0.839 rs12430 GCLC ANTI 14 6 2.183 0.177 rs7742367 2.121 0.188 rs16883912 3.919 0.017 rs670548 GLRX ANTI 8 5 1.140 0.714 rs3756704 2.714 0.109 rs1047420 1.914 0.355 rs2007 GSTP1 ANTI 4 11 0.668 0.924 rs1695 1.826 0.357 rs1138272 0.954 0.776 rs7941395 LOX PRO 1 5 0.053 0.957 rs17352686 1.213 0.477 rs17352686 0.165 0.905 rs17352686 MT1A ANTI 3 16 1.928 0.363 rs4784701 0.419 0.940 rs4784701 1.051 0.687 rs8049883 NDUFS1 OXPHOS 5 2 0.898 0.790 rs11695633 4.196 0.022 rs6435324 4.431 0.025 rs6435326 NDUFV1 OXPHOS 1 11 0.570 0.701 rs12793832 2.863 0.179 rs12793832 0.092 0.949 rs12793832 PRDX3 ANTI 6 10 2.307 0.228 rs11198811 3.515 0.076 rs3377 0.425 0.955 rs4752257 SOD2 ANTI 5 6 2.503 0.164 rs911847 1.573 0.464 rs5746136 3.230 0.085 rs2842980 TXNRD1 ANTI 15 12 4.157 0.010 rs7310505 1.100 0.784 rs7301631 3.252 0.053 rs7310505 UCP2 ANTI 4 11 1.225 0.588 rs2632725 1.807 0.350 rs7109266 4.706 0.032 rs659366 UCP3 ANTI 4 11 1.154 0.624 rs3781907 4.868 0.018 rs11235972 4.715 0.027 rs1685354 UQCRFS1 OXPHOS 4 19 2.272 0.240 rs10420904 0.619 0.888 rs759628 1.936 0.357 rs759628 MMSE CCS Survival ACOX1 PRO 10 17 1.276 0.663 rs7219716 3.105 0.048 rs8065144 1.201 0.738 rs3643 GCLC ANTI 14 6 0.887 0.887 rs534957 1.197 0.699 rs534957 1.620 0.438 rs2397147 GLRX ANTI 8 5 3.382 0.041 rs871775 1.689 0.411 rs4561 0.820 0.909 rs1047420 GSTP1 ANTI 4 11 5.446 0.009 rs1695 2.778 0.126 rs1138272 2.558 0.171 rs7927381 LOX PRO 1 5 1.052 0.532 rs17352686 3.034 0.167 rs17352686 7.597 0.013 rs17352686 MT1A ANTI 3 16 6.231 0.006 rs4784701 1.306 0.546 rs8049883 1.356 0.543 rs4784701 NDUFS1 OXPHOS 5 2 1.260 0.588 rs11695633 0.204 0.998 rs6435326 2.236 0.242 rs6435324 NDUFV1 OXPHOS 1 11 9.613 0.005 rs12793832 4.422 0.079 rs12793832 0.178 0.893 rs12793832 PRDX3 ANTI 6 10 4.068 0.049 rs1553850 3.718 0.065 rs3740562 0.742 0.811 rs3377 SOD2 ANTI 5 6 1.264 0.615 rs5746136 0.812 0.840 rs5746151 4.553 0.012 rs2758331 TXNRD1 ANTI 15 12 1.779 0.371 rs4964778 1.896 0.308 rs4964735 2.257 0.184 rs7310505 UCP2 ANTI 4 11 1.019 0.685 rs2632725 1.093 0.666 rs659366 1.823 0.359 rs659366 UCP3 ANTI 4 11 2.048 0.285 rs11235972 0.990 0.699 rs647126 2.566 0.182 rs11235972 UQCRFS1 OXPHOS 4 19 4.115 0.038 rs759628 2.527 0.178 rs759628 4.094 0.038 rs759628

Association showing a significance below p b 0.05 are indicated in bold. ⁎ Note:Theprevalenteffectofthedifferentgenesinsidethepro–anti-oxidantmetabolicpathwaywasreported.PRO:pro-oxidant;ANTI:anti-oxidant;OXPHOS:involvedinmitochondrial oxidative phosphorylation.

level, samples were excluded if they had a call fraction lower than 90%. Onthe genotype level, SNPs were excludedif they had (1) a missingfre- quency (MiF) higher than 10% and (2) a frequency of rare allele (MAF)

As it regards the HG performance, two nominally significant associ- ationswere detected. Theformer, in linewithpreviously published data (Dato et al., 2012b), involved the UCP3 gene (p = 0.019) with the best SNP rs11235972 confirming the association with less HG strength pre- viously reported in the 1905 cohort; the latter involved NDUFS1 gene (p = 0.022). In the case of WS performance, the strongest association was detected for GCLC gene (p = 0.017). An effect on WS was found also for UCP2 (p = 0.032), UCP3 (p = 0.027) and NDUFS1 (p = 0.025) too, the last two confirming the association with physical status sug- gested by the correlation with HG scores; a borderline association with WS performance was detected for TXNRD1 gene too (p = 0.053), that was also associated with ADL levels in this cohort.

2.4.2. Single-SNP analysis

The association between the analyzed genetic variants and the quantitative/dichotomous traits was assessed by using linear/logistic regression models. ANOVAand thelikelihood ratio tests,asappropriate, were adopted to assess the significance of the resulting associations. Since for each SNP three different coding schemes were adopted (dom- inant, recessive and additive), the significance of the association was summarized with the MAX statistic approach used in Li et al. (2008). A complete description of this procedure is reported in the Supplemen- tary material (see Statistical Appendix).

As for MMSE, three strong associations were found, related to the NDUFV1 gene (p = 0.005), the MT1A gene (p = 0.006) and the GSTP1 gene (p = 0.009), all having a favorable effect on cognitive functioning at single SNP level. Less significant but still under the nominal level were the associations with MMSE levels of UQCRFS1 gene (p = 0.038) andGLRX(p= 0.041).Borderlinesignificantwastheassociationbetween PRDX3geneandcognitivefunctioning(p= 0.049).Nooneofthesegenes was found associated with the performance at CCS, which showed a bor- derline association with ACOX1 genetic variability only (p = 0.048).

The association between genetic variants and survival time was car- ried out with the same approach employed for the quantitative and di- chotomous traits. Cox regression models were used in place of linear and logistic models, in order to better capture the idiosyncrasies of the survival outcome (see Statistical Appendix).