sibships of centenarians and nonagenarians [24]. A subsequent genomewide scan for healthy aging in a smaller and younger cohort provided weak support for the chromosome 4q22-25 linkage [25], whereas a targeted study of 164 sibships of nonagenarians did not find linkage to the locus [26], nor did a genomewide scan on bone characteristics as a biomarker for biological aging [27]. All these studies used microsatellite markers with 5–10 cM spacing. To assess the linkage to chromosome 4 and identify new loci, we performed the most powerful linkage scan to date on exceptional longevity. Though the evidence for linkage to chromosome 4 remains equivocal, several novel loci were discovered in our scan, including a region on chromosome 3p24-22 with an empirically genomewide significant LOD score of 4.02 and a region on chromosome 9q31-34 with a LOD score of 3.89.

Genotyping and Filtering

Genomic DNA samples were purified from blood as described previously [24], and were subjected to whole genome amplifica- tion using the GenomiPhi kit (Amersham). Amplified DNA was genotyped at 10,204 single nucleotide polymorphisms (SNPs) on the GeneChip Human Mapping 10K 2.0 Array (Affymetrix). Samples that did not achieve at least a 95% SNP Call Rate were re-genotyped or excluded. Genotype concordance checks were performed to verify sibling status and eliminate duplicate samples, monozygotic twins, and unrelated subjects. Seven sibships were found to be comprised of at least one pair of half-siblings, which were included. Among 642 successfully genotyped samples, of which 632 subjects in 279 sibships qualified for Category 1, the mean SNP Call Rate was 98.81%, ranging from 95.03% to 99.87%. There were 109 SNPs that were not assigned to a chromosome, 170 SNPs that had a Hardy-Weinberg equilibrium P value below 1023 among 282 unrelated subjects, and 267 SNPs that had a call rate of below 90% across all 642 samples. A total of 453 SNPs meeting one or more of these criteria was eliminated, resulting in a final panel of 9751 SNPs.

Methods

Ethics Statement

Subjects were recruited through Elixir Pharmaceuticals, the New England Centenarian Study (NECS) now of Boston University Medical Center, Beth Israel Deaconess Medical Center (BIDMC), and Children’s Hospital Boston (CHB), as described previously [24,28]. All participants provided written informed consent and the study was approved by the Institutional Review Boards of the above institutions. All samples were de-identified and were either available from BIDMC or CHB, or were purchased from Elixir Pharmaceuticals or NECS for a processing

Subjects

All subjects provided proof of age. There was a predominance of female subjects in our cohort, likely reflecting the original ascertainment criterion of having a proband of at least 98 years of age regardless of gender [24]. Only self-identified white or Caucasian subjects (the vast majority of our cohort) were analyzed, since population stratification can confound nonparametric linkage analysis when parental genotypes are unobserved [29]. We had available gender, age at last contact, and alive versus deceased status as of last contact. Age at last contact was not a suitable approximation for our phenotype of interest, age at death, because 70% of our cohort was living. To produce a more homogeneous phenotype that would better estimate age at death, we calculated an expected age at death, which for deceased subjects was equal to their actual age at death, and for living subjects was equal to their age at last contact plus their age-specific and gender-specific life expectancies from life tables for the 1900 birth cohort [30]. The median year of birth for our cohort was 1901.

Linkage Analysis

Linkage analysis was performed using MERLIN/MINX v1.1.2 [31]. Genotype data were converted into MERLIN-compatible input files using the Affymetrix tool GDASPort. All siblings were encoded as affected and their ungenotyped parents were encoded as phenotype unknown. Marker map positions based on the deCODE Genetics sex-averaged genetic map and marker allele frequencies for a Caucasian population were provided by Affymetrix. The Total group was also analyzed allowing MERLIN to calculate founder marker allele frequencies in each category separately, and the results were negligibly different than with the Affymetrix frequencies (data not shown).

We computed a multipoint nonparametric Kong and Cox allele-sharing LOD score with the S scoring function and the all exponential allele-sharing model [32], since the commonly used nonparametric linkage (NPL) score is overly conservative with missing data [32–34] and our parental genotypes were universally absent. Hereafter, ‘‘LOD score’’ refers to these conditions. A parametric heterogeneity LOD (hLOD) score was also computed under both a dominant and recessive model, along with a conventional parametric LOD score. The penetrance for non- susceptible genotypes (phenocopy) under each model was arbitrarily set to one-tenth the birth cohort trait prevalence for that age category, and the penetrance for the susceptible genotypes was set to 1 since we analyzed only affected subjects. The ‘‘disease’’ allele frequencies were then calculated to fit the prevalence and penetrance model (Table S1). To limit the multiple testing burden, the results were screened only on the basis of the nonparametric LOD scores; parametric hLOD and