SurvivalanalysisofUCP3haplotypeswascarriedoutbyusingAFTmodelsaswell. In particular, first the probabilities of the individual haplotype combinations (diplotypes) were estimated by using an Expectation Maximization (EM) algorithm implemented in the haplo.stats package of R. Second, N dummy variables, corresponding to the number of different observed haplotypes, were obtained coding the reconstructed diplotype in an additive fashion (the count of a particular haplotype in the diplotype). Finally, AFT models were used for estimating the statisticalsignificanceofeachhaplotype,withtheEM–estimatedprobabilitiesused asweights.Sincethisapproachunderestimatesthestandarderrorsofrarehaplotypes, they were not included in the model when their frequency was lower than 5%. As in the previous case, sex and BMI were used as adjunctive covariates in the model.

2.3. SNP selection and genotyping

For the analysis of UCP3 genetic variability, SNPs were chosen in the genomic region 73,387,985–73,402,778 of chromosome 11, which corresponds to the coding region of UCP3 gene plus 5000 base pairs (bp) upstream and 1000 bps down stream (NCBI B36 assembly), in order to take into consideration possible regulatory elements of the gene. Tagging SNPs covering as much as possible of the known common genetic variation in UCP3 were chosen by analyzing HapMap genotype SNP data in HaploView software (http://www.broadinstitu- te.org/haploview/haploview, Barrett et al., 2005). In this analysis, the following criteria were adopted: Minimum Allele Frequency (MAF) of at least 5%, ‘pair wise tagging only’, r2   0.8, LOD = 3 and a minimum distance between SNPs = 60 bp. The same program has been used for obtaining the graphical overview of Linkage Disequilibrium (LD) (definition of blocks based on confidence interval algorithm, as reported in Gabriel et al., 2002). Pairwise measures of LD between the analyzed loci were quantified by the correlation coefficient r2.

All statistical analyses were carried out using R statistical environment (R Development Core Team, 2010. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria 2011, URL http://www.R-project.org). In particular, survival package was used for survival analyses; haplo.stats and Design packages for haplotypes association analyses (Sinnwell and Schaid, 2009; Harrell and Frank, 2009). A significance level of 0.05 was set for all the tests.

3. Results

DNA was isolated from blood spot samples using the QIAamp DNA Mini and Micro Kits (Qiagen). Genotyping of the 4 UCP3 SNPs (rs11235972, rs1685354, rs3781907 and rs647126, general characteristics reported in Table 1SM, Supplementary material) was performed as described in Soerensen et al. (2012), by using the Illumina GoldenGate platform (Illumina Inc) and data cleaning was carried out according to Illumina Inc’s recommendations.

Table 1 shows the distribution of the observed genotypes and allele frequencies of UCP3 SNPs in the MADT and 1905 Cohorts. For each SNP no significant departure from Hardy–Weinberg equilibrium was observed in both samples.

Table 1

Absolute (Abs) and relative (Rel) genotypic and allelic frequencies(cid:3)standard errors (SE) of UCP3 gene polymorphisms in the MADT and 1905 Cohorts.

dbSNP ID Genotype/allele MADT Cohort 1905 Cohort Abs (Rel(cid:3)SE) p-value (HWE) Abs (Rel(cid:3)SE) p-value (HWE) rs647126 G/G 267 (0.29(cid:3)0.015) 0.141 217 (0.31(cid:3)0.017) 0.447 G/A 471 (0.52(cid:3)0.017) 359 (0.51(cid:3)0.019) A/A 170 (0.19(cid:3)0.013) 132 (0.19(cid:3)0.015) G 1005 (0.55(cid:3)0.012) 793 (0.56(cid:3)0.013) A 811 (0.45) 623 (0.44) rs1685354 A/A 494 (0.54(cid:3)0.017) 0.270 381 (0.54(cid:3)0.019) 0.244 A/G 343 (0.38(cid:3)0.016) 285 (0.40(cid:3)0.018) G/G 71 (0.08(cid:3)0.009) 42 (0.06(cid:3)0.009) A 1331 (0.73(cid:3)0.010v 1047 (0.74(cid:3)0.012) G 485 (0.27) 369 (0.26) rs3781907 A/A 424 (0.47(cid:3)0.017) 0.538 330 (0.47(cid:3)0.019) 0.602 A/G 399 (0.44(cid:3)0.016) 311 (0.44(cid:3)0.019) G/G 85 (0.09(cid:3)0.010) 67 (0.09(cid:3)0.011) A 1247 (0.69(cid:3)0.011) 971 (0.69(cid:3)0.012) G 569 (0.31) 445 (0.31) rs11235972 G/G 475 (0.52(cid:3)0.017) 0.614 376 (0.53(cid:3)0.019) 0.698 G/A 368 (0.41(cid:3)0.016) 283 (0.40(cid:3)0.018) A/A 65 (0.07(cid:3)0.009) 49 (0.07(cid:3)0.010) G 1318 (0.73(cid:3)0.010) 1035 (0.73(cid:3)0.012) A 498 (0.27) 381 (0.27)

The table clearly shows that the variability of the rs1685354 is significantly associated to the HG performance (p = 0.029), with subjects carrying GG genotype (recessive model) showing lower HG scores than the allele A carriers. A marginal effect for rs647126 was also detected, for which carrying the A allele (dominant model) significantly increased the HG scores (p = 0.075).

In order to model the effect of the UCP3 haplotypes on the HG performance, firstly we evaluated the degree of LD between pairs of loci. We found a modest degree of LD between the analyzed polymorphisms. In particular, in the 1905 Cohort, we found LD between rs647126 and rs1685354 (r2 = 0.271), which in turn was also linked to the rs3781907 (r2 = 0.102), which in turn was linked to the rs11235972 (r2 = 0.587). The same LD pattern was observed in the MADT Cohort. In fact, in this sample we found LD between rs647126 and rs1685354 (r2 = 0.263), which in turn was also linked to the rs3781907 (r2 = 0.121), which in turn was linked to the rs11235972 (r2 = 0.543). The LD structures of UCP3 SNPs in the two analyzed samples are shown in Fig. 1(a and b).

3.2. Haplotypic analysis in the MADT Cohort

We evaluated the effect of the UCP3 haplotypes on the HG performance by applying a GLM based algorithm. Table 3 reports the estimation of the haplotype frequencies of the combined UCP3 SNPs and their association with the HG performance in the MADT Cohort.

3.1. Single-locus analysis in the MADT Cohort