The study analyzed the effects of genetics on longevity and aging traits. Residuals generated from linear and logistic regression were used to analyze the data. The study used genotyping on Affymetrix 100K SNP GeneChip and Marshfield STR genotyping to determine age at natural menopause. Statistical methods such as Cox proportional hazards, logistic regression, and linear regression were used to compute residual traits for analysis. Family-based association tests and generalized estimating equations were used to test for association between genotyped individuals and SNPs. The study found eight SNPs on chromosome 1 associated with age at death in FBAT analysis, with two sets of SNPs clustered in two regions approximately 500 kb apart. Several strategies were used to prioritize SNPs associated with longevity and aging traits, including an untar- geted approach, exploring consistency of SNP associations across related sets of traits, and investigating SNP associations in candidate genes and regions reported to be associated with longevity identified from established databases.In the study of menopause, the GEE model revealed top-ranked SNP associations including number 11, rs6910534 (p = 0.00003) near FOXO3a and number 18, rs3751591 (p = 0.00006) in CYP19A1. Table 2c presents the LOD scores of 2.0 and the corresponding 1.5-LOD support interval from genome-wide linkage for the three quantitative aging traits. None of the regions overlapped with SNPs associated with these aging traits in the FBAT and GEE analyses. Notably, for biologic age by OSS, the linkage peak on chromosome 21 confirmed a prior Framingham Study report using a genome-wide scan with 401 microsatellite markers [10]. Table 3 provides all SNP associations with a GEE or FBAT p-value < 0.01 for both traits within the two pairs of related traits. For age at death and morbidity-free survival at age 65 years, FBAT models identified seven SNPs and GEE models identified nine SNPs associated with both traits including rs2374983 near PON1 (Tables 3a and 3b). For biologic age by OSS and walking speed, thirteen SNPs in FBAT models and six SNPs in GEE models were associated with both traits (Tables 3c and 3d). The SNPs were annotated using the UCSC genome browser tables using the May 2004 assembly http://genome.ucsc.edu/[31,32]. All genes within 60 kb of the top-ranked SNPs were identified.

The longevity and aging traits available in the FHS 100K SNP resource are listed in Table 1. In this report, we consider only five of the traits listed in Table 1: multivariable-adjusted age at death, morbidity-free survival at age 65 years, age at natural menopause, walking speed, and biologic age by OSS. These traits include a pooled sample of Original Cohort and Offspring participants, with the exception of walking speed, which is reported in Offspring. We identified from the literature 79 potential candidate genes and regions associated with longevity (see Additional file 1 for listing). Of these, 12 genes had no SNPs and 67 genes had 1 to 45 SNPs within 60 kb of the gene.

Table 1: Aging and Longevity Phenotypes for Framingham Heart Study 100K Project
Exam cycle(s) Phenotype Subgroup Number of Traits N (MV**) Offspring / Original Adjustment Trait (variable name on the website*)
Survival Traits: Cox regression Survival 2 1345 (1166) Cohort & Offspring Cox regression Age at death (1. deathageX, 2. pooled Sex-specific deathageMV) 1. birth cohort 2. multivariable adjusted for birth cohort, education, smoking, obesity (BMI >30), CVD risk factors, comorbidity measured at exam 
Categorical traits: Logistic regression Survival past the ALE (1. 2 1345 (1166) Cohort & Offspring Logistic regression deathpastALEX, 2. deathpastALEMV) pooled Sex-specific 1. birth cohort 
Morbidity-free survival (free of CVD, cancer and dementia) pooled, exams closest to Sex-specific At age 65 years (1. age 65 years morbidityfree65X, 2. morbidityfree65MVX) birth cohort morbidityfree65MVX) smoking, obesity, CVD risk factors measured at exam closest to age 65 years (within a 5-year horizon) 
Quantitative Traits: Linear regression Reproductive Aging 2 438 (378) Cohort & Offspring Linear regression Age at natural menopause (1. pooled, women only 1. crude menoageX, 2. menoageMVX) 2. multivariable adjusted for smoking, BMI, parity, generation (measured at exams prior to menopause) 
Cognitive function 2 593 (462) Cohort & Offspring Linear regression MMSE at age 65 years (1. MMSE65X, pooled, exams at age 65 Sex-specific 2. MMSE65MVX) 1. birth cohort MMSE at the specified Offspring 2. multivariable adjusted for birth cohort, education, FSRP exam (1. MMSE5X, 2. MMSE5MVX, 1. measured at exam closest to age 65 years (5-year horizon) MMSE7X, 2. MMSE7MV, 1. MMSE5to7X, 2. MMSE5to7MVX) 
Physical Performance 6 764 Exam 7 Linear regression Hand grip (2. handgripThis is the first dense GWAS of longevity and aging traits in a community-based sample of adults from two generations of the same families. Over 1300 men and women have detailed information on longevity and aging. 

Table 2: Aging and Longevity Phenotypes for FHS 100K Project - Results of Association and Linkage Analyses 

2a. GEE, Top 5 p-values by Phenotype* 

Trait SNP Chromosome Physical location GEE p-value FBAT p-value Gene Region (within 60 kb) 

Age at death rs1528753 11 90,523,987 8.1 x 10^-8 0.024 
rs2371208 7 81,982,510 2.6 x 10^-6 0.031 
rs10496799 2 139,261,401 1.4 x 10^-5 0.735 NXPH2 
rs10489006 4 31,444,987 3.6 x 10^-5 0.078 
rs3757354 6 16,235,386 6.4 x 10^-5 0.316 MYLIP 

Morbidity-free survival at age 65 rs1412337 1 165,350,299 1.8 x 10^-9 0.505 DPT 
rs32566 5 5,845,507 1.9 x 10^-9 0.323 
rs10484246 6 9,559,183 8.4 x10^-8 0.928 
rs4831837*8*12,756,234*4.7*10^-7*0.182 
rs2639889*16*59,680,648*9.4*10^-7*0.903