from http://biomedgerontology.oxfordjournals.org/ at Univ of Iowa-Law Library on August 8, 2015

Methods

Demographics

GD approach

Peripheral blood was obtained from 1,072 (569 women and 503 men) unrelated individuals, 18- to 106-years old, recruited from the same geographical area of Central Italy (Marche region), on the eastern side of the Apennines. The whole population studied is composed of Caucasian individuals. The same donors provided information con- cerning their health condition so as to ascertain that no pathological condition existed (eg, cancer, diabetes, heart diseases, hypertension, obesity, and chronic inflammatory diseases). Participant’s age is self-reported and is corrobo- rated with official documentation. Almost all participants were recruited in blood donor centers and through family physicians. The population under study was divided into three sex-specific age classes: for men (women), the first class consists of individuals younger than 66 years (<73- years old), the second class of individuals aged 66 to 88 years (73- to 91-years old), and the third class of individuals older than 88 years (>91-years old). These gender-specific age classes were defined according to demographic informa- tion and accounted for the different survival rates of men and women in the Italian population (12). Briefly, the age classes ranging from 18 to 106 years were obtained on the basis of a synthetic survival curve constructed using historic mortality data taken from the Italian population from 1890 onward (computed using death counts per calendar year and per year of birth as reported for the Italian population in the Human Mortality Database, available online at www. mortality.org [12]). The study protocol was approved by the Joint Ethical Committee (JEC) University of Camerino– Azienda ASUR Marche ZT-10 Camerino, in accordance with the Declaration of Helsinki in its revised edition and with international and local regulatory requirements.

The survival function of participants was determined by applying a GD approach. The method allows the estima- tion of a survival function for candidate alleles and geno- types in cross-sectional samples of unrelated individuals (14). A detailed description of the analytical procedure is reported elsewhere (15). Briefly, in order to take into account the changes in mortality experienced by all the cohorts considered in the study, a synthetic survival func- tion of the general population was constructed. Then, the marginal survival functions Sa(x) and Sb(x) of carri- ers and noncarriers, respectively, of the TP53*P72 allele were determined according to the following relationship: Ss(x) = PSa(x) + (1 − P)Sb(x), where Ss(x) is the survival function of the whole Italian population (synthetic curve) and P(1 − P) is the initial frequency of carriers (noncarriers) in the population. Because this frequency should be that before survival selection begins to operate consistently, the sample of 18- to 35-year-old participants, in reproductive age, was used for estimating the parameter P. Statistical significance of the differences between the survival curves of carriers/noncarriers/general population was obtained by a random permutation procedure, by applying 1,000 rep- licates. Demographic data on the Italian population were downloaded by the website www.mortality.org. The Matlab 6.1 package was used for GD analyses.

Statistical analysis

Allele and genotype frequency were calculated by geno- type counting method. Hardy–Weinberg Equilibrium was assessed by comparing the genotype frequencies with the expected values using a contingency table χ2 statistics. Genotype association was tested under additive, dominant, and recessive models. Cochran–Armitage test of trend was employed for the additive model. Pearson’s χ2 or Fisher’s Exact test (when expected is <5) were used to evaluate the dominant model, the recessive model, and the allelic association. The strength of association was expressed as odds ratios (oR) with 95% confidence intervals (ci).

Genomic DNA extraction was carried out from periph- eral blood through standardized salting-out method and DNA was stored at −

°C until gene analysis.

Genomic DNA extraction was carried out from periph- eral blood through standardized salting-out method and DNA was stored at −20°C until gene analysis.

For rs1042522 genotyping, PCR was performed using PCR Master Mix (2×; Fermentas), with a primer set,

difference between the frequencies is statistically signifi- cant (oR 0.586; 95% ci 0.420–0.817; p = .002).

The TP53*P72 was considered the “risk allele” because of the previous associations found between *P72 allele and enhanced survival in Dutch and Swedish long-lived people

In men, the difference between the survival functions of carriers (SA(x)), noncarriers (SB(x)), and the male popula- tion (S(x)) is not even significant (p = .930), but in this case, no crossing of the survival curves was observed, suggesting no association of this polymorphism with survival in the male sample.

Statistical analyses were performed using SPSS 16.0 (SPSS Inc., Chicago, IL). Two-tailed probability values of less than .05 were regarded as statistically significant. Bonferroni’s correction for multiple testing was not performed, since we are assessing specific questions on a gene previously tested for association with human longevity, and we are not searching for associations without a priori hypotheses (16).

Discussion