It has long been thought that related individuals share a familial predisposition to longevity, and for more than a century numerous studies have investigated the degree to which human longevity might be an inherited characteristic [21-2]. Most studies of this type have reported small (<10%) to moderate (<30%) heritability of human longevity amid differences in definitions of longevityResults. Additional and potentially more substantial biases can result from behavioral changes across generations and time. Larger and more robust genome-wide association studies (GWASs) will undoubtedly contribute importantly to our understanding of the genetics of longevity; but for now, other approaches, including family studies, remain competitive. The University of Utah Health Sciences Institutional Review Board and the University of Louisville Biomedical Institutional Review Board approved the study protocol; all recruited subjects provided their written consent to be included in this study. A total of 732 study subjects were genotyped by deCODE Genetics, Reykjavik, Iceland. Of these, 433 were identified as expressing an excess longevity phenotype (ELP) and were considered affected. We recruited and genotyped 139 offspring of affected individuals and considered these putative carriers of the ELP. Last, we recruited and genotyped 160 controls: 54 offspring of deceased siblings of affected individuals were considered putative non-carriers of the long-lived phenotype; and 106 randomly drawn individuals from the UPDB population complete the control group. Ages of the 433 ELP-affected individuals ranged from 86 to 109 years old. Only 325 of these were related to another genotyped affected individual. Subjects were enrolled between August 2003 and January 2009. By 2009, 117 ELP-affected subjects had died. The total number of affected pairs over all classes of relatives was 607 because many affected individuals were related to multiple others through multiple lines of descent. The complex and overlapping nature of the genealogical data makes delineation of discrete families difficult: by the broadest definition, all study subjects could be connected to one another by one or more known genealogical links (including marriage). On the other hand, the vast majority of affected pairs had kinship values of zero, i.e., they were not biologically related by any known common ancestry. In spite of the genealogical complexity, no converged to obviously incorrect solutions after consuming literally weeks of CPU time. (especially at advanced ages), and are less likely to have health care billing records in their own names than are men. All these characteristics make it more difficult to find a correct current residence using the available records. Therefore, we used a simplified version of the venerable affected member (APM) test, originally devised by Weeks and Lange, as a primary test for linkage of EL to each region. Let G and G represent the two alleles of i1 i2 given marker carried by person i; G and G are the j1 j2 alleles carried by a family member j of person i, q(g) is the allele frequency for marker allele g. Then the statistic S is: 

Genotypes. Genotyping was performed at deCODE Genetics, Reykjavik, Iceland, using the deCODE 1100 microsatellite marker set. Here we report only the autosomal marker results. Any marker successfully typed in at least 50% of subjects was analyzed. The maximum number of markers successfully typed in any sample was 1074, and 1051 markers (97.8%) met the analysis inclusion criterion (returned types for >50% of subjects). The number of alleles observed per marker ranged from 2 (D9S1152, DG19S135) to 38 (DG19S265). Average marker spacing over the 22 autosomes was 3.4 cM. Allele frequencies were estimated from control subjects by simple counting and checked against the HapMap European Reference (CEU) allele frequencies.

The relatively large and complex sample, in particular, the large number of ungenotyped individuals intervening in the paths of relationship between genotyped affected family members presented an analytical challenge. It proved impossible with existing software to obtain reliable estimates of identity by descent (IBD) probabilities. Exact estimation with software such as Genehunter, Allegro, or Merlin required more computer memory than was available; Markov chain Monte Carlo (MCMC) methods, as employed by Loki, Genibd, SOLAR, and SIMWALK failed to converge or chromosome 3p24.1 @56.45 cM (or 30.1 Mb). D3S1266 and D3S3547 are immediately adjacent to one another on 3p24.1, as are D18S469 and D18S1161 on 18q22.3. Tables 2 and 3 also show p-values adjusted for multiple comparisons, as quantiles of the minimum simulated p-value observed at each locus. Alone, none of these results achieves genome-wide significance after adjusting for the number of hypotheses tested. The most significant genome-wide result is the single-point estimate for D3S3547 (adjusted p = 0.09). The same approximate location (@55 cM) yields a multipoint adjusted p of 0.20. The linked region on 18interest is TGFBR2, which has been associated with age-related diseases such as cancer and osteoporosis [41,42]. The identification of these genes in the linked region provides a starting point for further investigation into the molecular mechanisms underlying exceptional longevity.