Quality control was performed using the APT software release 1.12.0 available from Affymetrix, Inc Santa Clara, CA to conduct additional sample QC. Twenty-nine samples were dropped due to low contrast QC, and all individuals with inconsistent genders were dropped if the inconsistency could not be resolved, removing 20 samples. RELPAIR software (Epstein et al. 2000) was used to test for misspecification of pedigree relationships and obtain a list of problematic individuals to be followed up and resolved; all misspecifications were resolved, and seven samples were dropped due to sample genotyping efficiency (<95 %).

The case control analysis of SA was performed using the modified quasi-likelihood score (MQLS) method, a quasi-likelihood score test that accounts for correlations among individuals to maintain correct type I error rates and tests for allelic association (Thornton and McPeek 2007). MQLS uses a kinship variance/covariance matrix of inbreeding coefficients and kinship coefficients to account for inbreeding and relatedness between individuals using a specified pedigree structure. Population prevalence for SA of 30 % was specified, consistent with the frequency of SA in the current study. The method is computationally tractable for GWAS. Only analyses of autosomal SNPs were performed.

PLINK (Purcell et al. 2007) statistical software was used both for marker QC and to format data for analysis. Markers included in analyses had a minor allele frequency (MAF) >0.05 and a genotyping efficiency >95 %, resulting in the removal of 221,982 markers for MAF and 30,303 markers for genotyping efficiency. Markers that did not map to a chromosome, mitochondrial SNPs, and X chromosome SNPs were also excluded (23,997). A goodness of fit test was used to examine deviations from Hardy Weinberg equilibrium (HWE) in the most unrelated individuals (166 individuals determined by pairwise kinship coefficients obtained from the KinInbcoef program (Thornton and McPeek 2007)). Markers with strong deviations from HWE (p<1.0107) were removed from the analysis, removing seven markers. Genotypes on 551 individuals generated by the CAMP dementia study were also available.

The results of multipoint genome-wide linkage analysis for all chromosomes using parametric affected-only dominant, parametric affected-only recessive, and nonparametric linkage models are summarized in Fig. 1. Five regions of the genome generated multipoint LOD scores (MHLOD or MLOD*) exceeding three under at least one of the models tested. The peak MHLOD of 3.24 on chromosome 6 occurred under a dominant model at 166.19 Mb, with a 1-LOD-down support interval extending from 164.38 to 169.62 Mb. The peak MHLOD of 3.30 on chromosome 16 was obtained at 23.97 Mb under a recessive model (1-LOD-down support interval, 18.97 to 26.57 Mb). Three peaks were detected under the nonparametric model on chromosomes 10 (peak MLOD* 4.30 at 4.26 Mb, 1-LOD-down support interval 3.28 to 5.59 Mb), 17 (peak MLOD* 3.50 at 10.09 Mb, 1-LOD-down support interval 9.82 to 11.49 Mb), and 20 (peak MLOD* 3.3 at 0.93 Mb, 1-LOD-down support interval 0.58 to 4.07 Mb).

A description of the criteria used to define SA and normally aged control individuals is provided in Table 1, and a description of the study sample is provided in Table 2. SA cases were slightly younger and had a higher proportion of males than the controls. A larger proportion of SA cases were from Ohio than Indiana, although a majority of participants were from Indiana. SA cases had slightly lower butmarried). No specific gene has previously been associated with risk for SA or any other longevity or aging trait. PDE10A is involved in neurological cascades and has been linked to schizophrenia and Huntington's disease (Arnaud et al. 2008; Kehler et al. 2011; Kleiman et al. 2011). Three SNPs in the FRA6E region were located in the Parkinson juvenile disease protein 2 (PARK2) gene and were in high linkage disequilibrium (LD) (Online Resource Fig. 3) with each other, with r2 ranging from 0.86 to Table 3 shows the strongest MQLS results from binary trait analyses. The SNPs with the strongest evidence for association (p<1.00105) were not located in genes and did not show evidence for linkage. Several of these markers are on chromosome 6 within the FRA6E region, which is less than 3 Mb from the top linkage result.

Although we observed strong evidence for linkage at three additional regions on chromosomes 10, 17, and 20, markers under these linkage peaks did not show individual evidence for association. This may be due to either a lack of power to find association or possibly due to segregation of alleles in sub-pedigrees and not population-level association.

A previously published genome-wide scan associating thyroid stimulating hormone (TSH) and thyroid levels with gene variants observed a strong association with PDE8A and PDE10A in age-diverse populations (Sardinians, Tuscans, and Old Order Amish) (Arnaud-Lopez et al. 2008). The strongest association in this previously published study was in PDE8A; however, PDE10A is in the same enzyme family and is involved in the cyclic AMP (cAMP) and cyclic GMP signaling pathways. The PDE10A gene protein product has been found to be involved in the pathophysiology of human disease. TSH controls thyroid function through binding of G protein-coupled receptor, TSH receptor (TSHR), and cAMP production. TSH plays a role in muscle, bone, central nervous system, and heart physiology, and therefore may contribute to several complex disorders. In addition, the TSHR expression has previously been associated with exceptional longevity in a previously published study of Ashkenazi Jewish centenarians, which may support a role for PDE10A in SA (Atzmon et al. 2009b).