Next, we considered whether individuals met standard “ ” cutoffs for high function on the self-reported meas- ures of physical function: total scores of 0 or 1 on the activities of daily living (ADL) and instrumental (IADL) scales, indicating no assistance or partial as- sistance on only a single item needed; Nagi score of 0 or 1 (Nagi 1976), indicating no difficulty or difficulty – on only one item; Rosow Breslau score of 3 or 4, indicating limitation on zero items or one item. Lower extremity function was considered by limiting SA to individuals scoring in the top 1/3 of the sample on the Established Populations for Epidemiologic Studies of the Elderly (EPESE) short physical performance bat- tery summary score (>8 on a 12 point scale). Finally, individuals considered SA self-reported to be satisfied or very satisfied with life.

There were a total of 263 Amish individuals included in this study after performing sample qual- ity control (QC). Among these 263 individuals, 74 were classified as SA and 189 were classified as controls. This study has been reviewed and ap- proved by the Institutional Review Boards at the University of Miami and Vanderbilt University Medical Center.

In addition to these self-reported measures of func- tioning, a short physical performance test developed for use in the EPESE was used to assess lower ex- tremity function (Seeman et al. 1994). The tasks in- clude three measures of balance (side by side, semi- tandem, and tandem) for 10 s, the time required to sit and stand from a chair five times without using the hands and the time required to walk 10 ft.

Collection of clinical and demographic data The baseline examination included collection of demo- graphic characteristics and a screening of cognitive and physical function. Demographic information and family relationships (parents and grandparents) were recorded in standard format to facilitate pedigree construction.

Genotyping

The Affymetrix Human SNP Array 6.0 (Affymetrix®, Inc Santa Clara, CA) was used for genome-wide SNP genotyping. DNA was obtained from the Hussman In- stitute of Human Genomics at the University of Miami and the Center for Human Genetics Research at Van- derbilt University DNA Banks. Genomic DNA was quantitated via the ND-8000 spectrophotometer, and DNA quality was evaluated via gel electrophoresis. μ The genomic DNA (250 ng/5 l) samples were pro- cessed according to standard Affymetrix procedures for processing of the assay. The arrays were then scanned using the GeneChip Scanner 3000 7 G operated by the Affymetrix® GeneChip® Command Console® soft- ware. The data were processed for genotype calling using the Affymetrix® Power Tools (APT) software using the birdseed calling algorithm version 2.0 Affy- metrix®, Inc Santa Clara, CA (Korn et al. 2008).

Participants underwent cognitive screening using a revision of the 3MS examination (Teng and Chui 1987) developed for use in the Cache County Memory Study. We established the 3MS cutoff point of <87 (after education adjustment), indicating potential cog- nitive impairment after examining the receiver operat- ing curves in predicting subsequent dementia and ’ prodromal Alzheimer s disease in the Cache County Memory Study. This cut point was selected as having maximal utility in detection of dementia (Khachaturian et al. 2000), optimizing the sensitivity of case detection (98.4 %). Depression was screened using the 15-item GDS (Yesavage 1988), and life satisfaction was “ probed using a single question: Overall how sat- isfying is your life: very satisfying, satisfying, not ” so satisfying?

Samples with call rates less than 95 % were reexamined by re-clustering analysis. The follow- ing sample QC procedures were applied: (1) one CEPH sample was placed on each plate to ensure reproducibility of results across the plates; (2) all samples with call rates <95 % were rechecked individually to ensure quality of genotypes; the DNA sample was examined using gel electropho- resis to determine the quality of the actual DNA sample before making a call to exclude it from analysis; (3) A sample call rate of 95 % was applied to all samples, if the call rate fell below this average after several chips were run, genotyp- ing was stopped, and molecular procedures and equipment were tested for potential problems.

for these same markers and were used for SNP allele frequency estimation, determining deviations from HWE, and setting linkage phase in the pedigrees.

Linkage analysis We used the software program Ped- Cut (Liu et al. 2008) to subdivide the 13 generation pedigree into computationally tractable sub-pedigrees. The 74 SA individuals and their 7 full siblings were grouped into 16 sub-pedigrees informative for linkage (e.g., at least two SA individuals in each pedigree) that ≤ were small enough (bit size 24) to generate multi- – point LOD scores using the Lander Green algorithm implemented in Merlin (Abecasis et al. 2002). PLINK was used to select a set of 4,941 markers in low linkage disequilibrium (pairwise r2<0.16 for all markers) for multipoint linkage analysis. Inter-marker map distances were obtained from the Rutgers Com- – bined Physical Linkage Map (Matise et al. 2007). Multipoint LOD scores allowing for heterogeneity (multipoint heterogeneity LOD score (MHLOD)) were calculated using affected-only dominant (trait 0 allele frequency 0.001), affected-only recessive (trait 0 allele frequency 0.20), and nonparametric allele- sharing (multipoint nonparametric allele-sharing LOD score (MLOD*)) models.

Statistical analysis