Validation of any novel association results in our study is hampered by the lack of genome-wide scans in cohorts with an ethnic origin and population history similar to Kosrae. The majority of studies to date have been performed in Caucasians. Further work is required to assess in Kosraens the extent of genetic drift and selection under strikingly different environmental pressures. Rep- lication of true ‘‘island’’ variants would likely be difficult or impossible in existing Caucasian cohorts and underscores the need for the inclusion of diverse ethnic groups in genetic studies. It is also worth noting that although extended LD on Kosrae facilitates locus identification through greater genome coverage, it hampers fine-mapping efforts. In the event that novel association results can be validated or replicated in other populations, a comparison of LD patterns between populations will likely be important to identify the causal variant. While future methods will no doubt improve on our analytical approach, we describe approaches which may be useful to others undertaking genetic studies in population isolates. Tools for validating pedigrees with genetic data greatly facilitated the review of millions of pairwise IBD estimates, highlighted inconsistencies in the reported pedigree, and assisted in the identification of previously unknown first-degree relationships. We show that applying a combined within- and between-family test of association to the subunits of a large extended pedigree increases study power. In addition, the simulation framework we describe for empirical power calculations will be useful for evaluating and comparing the performance of other methods for association analyses as they become available. The current analysis assesses the role of common variants influencing phenotype on the island of Kosrae, but does not evaluate the role of rare variants. In fact, the analytic challenges posed by extensive relatedness in this cohort and the previously demonstrated extended LD in the population suggest that Kosraens may be particularly informative for other analytic methods such as homozygosity mapping. Recent, severe popula- tion bottlenecks and subsequent rapid expansion have greatly enriched Kosraens for long stretches of homozygosity. These homozygous segments act as proxies for rare recessive variants segregating in the population and are predicted to greatly increase our power to detect such variants. We are currently developing methods for homozygosity mapping in this unique population. We anticipate that homozygosity approaches for the detection of rare, recessive alleles, coupled with direct sequencing studies to characterize variation on Kosrae not captured by existing genotyping platforms, will complement the association studies for common variants presented here. Together, these three approach- es will provide a more complete picture of genetic variation in population isolates, and the underlying role of drift and natural selection on the architecture of metabolic traits on Kosrae. Materials and Methods Ethics Statement The study was approved by the Institutional Review Boards at all participating institutions, including Rockefeller University (protocol #JFN-0282-0707), Massachusetts Institute of Technol- ogy (COUHES protocol #0602110607) and Massachusetts General Hospital (protocol #2006-P-000211/6; MGH). All patients provided written informed consent (in English or Kosraen) for the collection of samples and subsequent analysis. Sample Collection During screenings performed in 1994, 2001 and 2003, patients were recruited by public announcements and came to the clinic GWAS on the Island of Kosrae following an overnight fast. The 1994 screen was described previously [7,12]. Briefly, informed consent was obtained from each patient (forms available in Kosraen), along with self-reported information on identity of family members, medical history, current medications, lifestyle, diet, exercise, and ethnicity. Blood was collected from Kosraens in the fasted state and centrifuged. Plasma and buffy coats were frozen and shipped to Rockefeller University for serological assays and DNA extraction, respectively. IRB approval was obtained from all participating institutions. Clinical Data Quantitative trait measurements were log- or square root- transformed to approach normality, adjusted for age and gender where applicable, and converted to Z-scores. An average Z-score was used for patients screened in multiple collection years or monozygotic twins. Individual trait descriptions are available in the Supplemental materials (Dataset S1). Genotypes were analyzed for association with fifteen quantita- tive traits: body mass index (BMI), height, weight, waist circumference, plasma leptin, percent body fat, diastolic and systolic blood pressure, fasting plasma glucose, thyroid stimulating hormone, HDL-C, LDL-C, total plasma cholesterol, triglycerides and high-sensitivity plasma C-reactive protein. Genotyping Data from the Affymetrix 500 k assay were generated at Affymetrix, South San Francisco, CA. Genotypes were called with the BRLMM algorithm. A minimum call rate of 95% was required for each chip (Table S1). The two chips in the 500 k assay (enzyme fractions Sty and Nsp) were matched by genotype concordance and gender concordance between each chip and the clinical data for that sample. Of the ,3,100 subjects ascertained, 2,906 individuals were successfully genotyped according to these criteria. Per-SNP quality filters included: mapping to a unique genomic location, minimum per-SNP call rate of 95%, fewer than 10 Mendelian errors, and minor allele frequency (MAF) .0. 408,775 SNPs met these criteria. For the purposes of SNP quality control, allele frequencies were estimated assuming all 2,906 genotyped individuals were unrelated. Hardy-Weinberg equilibri- um was not used as a quality filter, as it cannot be assessed by standard formulae in our highly related cohort. Autosomal SNPs with MAF$0.01 were analyzed for association with each trait, where MAF was calculated using the individuals phenotyped for that trait. Sibling relationships were accounted for according to default procedures in PLINK. The number of SNPs analyzed ranged from 332,890 (TSH) to 345,026 (Height). Pedigree Study participants provided names and birthdates of their relatives during the patient interview. Information from multiple patient records was cross-referenced and used to reconstruct extended pedigrees. Relationships reported by subjects in the 1994 screen were validated genetically using Mendelian inheritance checks and identity-by-state analyses with microsatellite markers and the pedigree was modified to reflect the genetically accurate relationships [12,39]. Subjects screened in 2001 and 2003 were originally incorporated into the pedigree on the basis of genealogical information. SNP genotyping data were subjected to identity-by-descent estimation using PLINK [13]. Thresholds of IBD sharing for parent-child, full-sibling, and half-sibling relationships were empirically determined from the distribution of genome-wide IBD scores for known relationships. We used empirical ratios of total sharing and the proportion of genome shared in 0, 1, or 2 PLoS Genetics | www.plosgenetics.org 14 February 2009 | Volume 5 | Issue 2 | e1000365