Here we report the results of the Fertility, Longevity and Aging (FLAG) study, a genome-wide genetic linkage study of familial exceptional longevity. Subjects were selected from the Utah Population Database [22,23] under a design that differs from most others in several important characteristics: 1) subjects were selected on the basis of both individual longevity, and a strong family history of longevity; 2) relatives of varying degrees of relationship were included; and 3) linkage analysis was based on a microsatellite marker set (deCODE Genetics 1100 marker set) more appropriate than a conventional 400 marker linkage panel to the extended family structures of subjects drawn from the UPDB, and hence, relatively short regions of identity by descent.

Methods

Utah Population Database

Table 1 gives the demographic characteristics of study subjects. The unexpected relative excess of males is explained by two factors: first, the computation of excess longevity adjusts for sex, effectively counting males as females approximately two years older. Second, and more importantly, it proved easier to locate extremely aged men, by current residence, than women in Utah. Women in the UPDB are more likely to have changed their names as a result of marriage, are less likely to have driver’s licenses

The Utah Population Database (UPDB) is a repository of longitudinal information on Utahans and their families. Originally constructed from genealogical data [23], the database has developed by successive record linking to integrate cancer registry data, Utah death certificates, U.S. Census data, Utah birth certificates, Utah driver license records, and more recently, medical records data. Currently the database includes information

Table 1. Demographic Characteristics of Study Subjects.

Typed Affected Relative Pairs

Relative Pairs: ‘‘Total’’ is a count of all pairs of a given type among genotyped affected individuals. ‘‘Closest’’ is a tabulation of the closest genotyped affected among all genotyped affected individuals. doi:10.1371/journal.pone.0034746.t001

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 [31], 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.

where d is the Kronecker delta function (1 if the two arguments are equal, 0 otherwise).

The APM statistic is the sum of the random variable S over all i ij and j. As a test for significance, a null distribution of marker alleles was generated using a ‘‘gene-dropping’’ algorithm: first alleles are randomly assigned to founders in proportion to their expected frequency; then they are transmitted by descent throughout the pedigree. Five hundred iterations of this algorithm were performed for each marker, and Z-scores computed as the difference between the mean score under the null and the observed score, divided by the null standard deviation. P-values were estimated by reference to a standard normal distribution, and empirical p-values computed as the number of times a randomly-generated test statistic exceeded each observed value.

Analytical methods

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 [24], Allegro [25], or Merlin [26] required more computer memory than was available; Markov chain Monte Carlo (MCMC) methods, as employed by Loki [27], Genibd [28], SOLAR [29], and SIMWALK [30] 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 singlepoint 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 18q22-23 appears more significant by multipoint (@109 cM; adjusted p = 0.25) than by the singlepoint (adjusted p = 0.59 for D18S1161 @111.05 cM) method.