pertaining to approximately 7 million individuals, many of whom are integrated into multigenerational pedigree networks 2 to 14 generations deep [22].

the meta-analysis over the 6 studies also failed to identify variants of genome-wide significance after adjusting for multiple compar- isons. Nebel, et al. [16] conducted a GWAS for a sample of 763 Germans ranging in age from 94 to 110 years old, with a mean age 99.7 years; 1085 controls were drawn for the study, aged 45–77 years old, with a mean age of 60.2 years. After adjusting for 662,472 comparisons and post hoc quality control assessments, 16 SNPs were identified for confirmatory testing in an independent sample of 754 long-lived Germans and 860 controls. One SNP, rs4420638 near APOC1, exhibited a significant association with longevity both in the original and confirmatory tests. However, this SNP (rs4420638) is only 14 kb from the APOE locus; it is in strong linkage disequlibrium (LD) with the APOE e4 allele; and it has long been known that APOE e4 is associated with all-cause mortality [17]. Another recent GWAS study included 410 long- lived individuals and 553 younger controls from southern Italy [18]. In this study, the minor allele of one SNP (rs10491334) in the gene CAMKIV, was associated with reduced CAMKIV expression, and was underrepresented among long-lived individuals. CAMKIV appears to activate the proteins AKT, SIRT1, and FOX03A, all of which have been associated with increased longevity or longevity- associated mechanisms in humans and several model organisms [19–21].

Subjects

In two previous studies, we examined the influence of family history on longevity in the UPDB: we defined excess longevity (EL) as the difference between observed and expected lifespan for an individual; and we defined familial excess longevity (FEL) as a kinship- weighted average of EL across all the family members of a subject [11,12]. For this study, subjects were identified after evaluating several methods for selecting them, using simulated data and various combinations of EL and FEL. The goal was to maximize the positive predictive value (the probability that a subject carried a simulated longevity extending allele, given lower all-cause mortality) while maximizing potential sample size.

Simulations assumed a genetic variant with autosomal domi- nant inheritance, and an allele frequency of 0.01. Penetrance was expressed as an all-cause mortality hazard ratio, and set at 0.5. Although we considered multiple approaches to selecting subjects based on individual longevity and family longevity scores, including Markov chain Monte Carlo approaches, we found that simply combining top quartile values of both EL and FEL (EL$3.0; FEL$1.75) outperformed the other methods and was simpler to administer. Simulation tests under different assumptions about allele frequency and penetrance altered the sensitivity and positive predictive value of the selection criteria, but did not change the relative performance of the methods.

GWAS studies of longevity must overcome several important challenges. The need to control for a very large number of comparisons compromises power considerably. The studies cited above are all quite small by current GWAS standards, and therefore, have only limited power to identify alleles associated with longevity. Selection of appropriate controls is a particular problem for studies of longevity. Controls are typically selected from the current population according to study design, and are by definition born one or more generations later than the long-lived individuals to which they are compared. Under such circumstanc- es, selection, drift, and migration can affect allele frequencies in ways that might bias intergenerational GWAS results. Additional and potentially more substantial biases can result from behavioral changes across generations and time. Larger and more robust GWASs will undoubtedly contribute importantly to our under- standing 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

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; and106 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