Figure S2). Genomic control was used to correct for score inflation introduced by relatedness between family units (sibships) [19]. We account for multiple testing by assuming a threshold of p#561028 for genome-wide significance, following the work of Pe’er et al (2008) and Dudbridge and Gusnanto (2008) [41,42]. This approach assumes approximately 106 independent tests across the genome and requires an additional p#0.05. This significance threshold is likely conservative on Kosrae, where the true number of independent tests is likely to be smaller because of the extended LD, and so alleviates the multiple testing burden. Comparison of Allele Frequencies for Known Associated Loci Association results for known, associated loci were drawn from studies in large Caucasian cohorts for multiple traits. For each Caucasian risk allele where the SNP was directly genotyped in Kosraens, we determined its frequency in HapMap CEU. We identified SNPs on the Affymetrix array that have frequencies in HapMap CEU within 2% of the frequency of the risk allele in HapMap CEU. For each SNP in that set, we calculated the difference in allele frequency between HapMap CEU and Kosrae. These values were used to generate an empirical distribution of allele frequency differences. For the risk allele, we calculate the difference in allele frequency between HapMap CEU and Kosrae, and place this difference on the empirical distribution to determine significance. Comparison of Effect Sizes for Known Associated Loci Association results for known, associated loci were drawn from studies in large Caucasian cohorts for multiple traits. Caucasian loci were limited to SNPs directly genotyped in Kosraens, or where a strong proxy was genotyped in Kosraens (r2$0.95 in HapMap CEU and ASN). SNPs with MAF,0.05 on Kosrae were omitted from comparison, as power is low to estimate effect sizes accurately for rare SNPs. We assumed the Caucasian b estimates for each of the traits to be a fixed value. A test of heterogeneity for the magnitude and direction of the effect in Caucasians and Kosraens was performed as follows [43]: bks{bkbc *T : Where bk and bc are the effect sizes for Kosrae and Caucasian populations, sb is the standard deviation on the bk, and is distributed like a T. k References 1. Arcos-Burgos M, Muenke M (2002) Genetics of population isolates. Clin Genet 61: 233–247. 2. Heutink P, Oostra BA (2002) Gene finding in genetically isolated populations. Hum Mol Genet 11: 2507–2515. 3. Shifman S, Darvasi A (2001) The value of isolated populations. Nat Genet 28: 309–310. 4. Bonnen PE, Pe’er I, Plenge RM, Salit J, Lowe JK, et al. (2006) Evaluating potential for whole-genome studies in Kosrae, an isolated population in Micronesia. Nat Genet 38: 214–217. 5. Jakobsson M, Scholz SW, Scheet P, Gibbs JR, VanLiere JM, et al. (2008) Genotype, haplotype and copy-number variation in worldwide human populations. Nature 451: 998–1003. 6. Li JZ, Absher DM, Tang H, Southwick AM, Casto AM, et al. (2008) Worldwide human relationships inferred from genome-wide patterns of variation. Science 319: 1100–1104. 7. Shmulewitz D, Auerbach SB, Lehner T, Blundell ML, Winick JD, et al. (2001) Epidemiology and factor analysis of obesity, type II diabetes, hypertension, and dyslipidemia (syndrome X) on the Island of Kosrae, Federated States of Micronesia. Hum Hered 51: 8–19. 8. Knowler WC, Bennett PH, Hamman RF, Miller M (1978) Diabetes incidence and prevalence in Pima Indians: a 19-fold greater incidence than in Rochester, Minnesota. Am J Epidemiol 108: 497–505. 9. Furubayashi JK, Look MA (2005) Type 2 diabetes in native Hawaiians and Pacific Islanders in Hawaii. Pac Health Dialog 12: 103–110. 10. Sundborn G, Metcalf P, Scragg R, Schaaf D, Dyall L, et al. (2007) Ethnic differences in the prevalence of new and known diabetes mellitus, impaired GWAS on the Island of Kosrae Supporting Information Figure S1 Marker quality control for SNPs in the Affymetrix 500 k assay. Found at: doi:10.1371/journal.pgen.1000365.s001 (0.01 MB PDF) Figure S2 Excess relatedness is the major source of association score inflation. Association score inflation was evaluated in three subsets of the Kosrae cohort using the BMI phenotype. Score inflation is greatly reduced in ‘‘unrelated’’ (less than first cousins) individuals and in a set of sibships filtered to remove all parent- child relationships between sibships, as compared to all available sibships in the cohort. Found at: doi:10.1371/journal.pgen.1000365.s002 (0.06 MB PDF) Figure S3 Quantile-quantile plots showing genome-wide asso- ciation results for 15 quantitative traits. For each trait, the number of individuals used in the analysis, heritability, and genomic control correction factor (lambda) are given. Found at: doi:10.1371/journal.pgen.1000365.s003 (0.17 MB PDF) Table S1 Genotyping statistics for the Affymetrix 500 k assay. Found at: doi:10.1371/journal.pgen.1000365.s004 (0.04 MB DOC) Dataset S1 Supplemental Trait Descriptions. Found at: doi:10.1371/journal.pgen.1000365.s005 (0.81 DOC) MB Acknowledgments We are deeply grateful to the people of Kosrae, the Kosrae Department of Health Services, and Dr. Hiroshi Ismael for making this study possible. We thank Connie Zhao, Caroline Jones, Nicolette Oleng’, Ryan Tewhey and Noe¨l Burtt for technical assistance; Steve Heymsfield for assistance with the percent body fat phenotype; and Shaun Purcell for helpful discussions about the association analyses. Author Contributions Conceived and designed the experiments: JKL JBM IP BMN VS JLB MS MJD DMA JMF. Performed the experiments: JKL JBM IP JS JLS MLB VS LG MLS HEL. Analyzed the data: JKL JBM BMN JS EEK JGS CNC MJD DMA. Contributed reagents/materials/analysis tools: JKL JBM IP BMN JS EEK JLS RB JGS WJ MN JNF MLB LG AL HF SBA RPL CNC JLB MS MJD JMF. Wrote the paper: JKL JGS CNC DMA. glucose tolerance, and impaired fasting glucose. Diabetes Heart and Health Survey (DHAH) 2002–2003, Auckland New Zealand. N Z Med J 120: U2607. 11. Bourgain C, Genin E (2005) Complex trait mapping in isolated populations: Are specific statistical methods required? Eur J Hum Genet 13: 698–706. 12. Shmulewitz D, Heath SC, Blundell ML, Han Z, Sharma R, et al. (2006) Linkage analysis of quantitative traits for obesity, diabetes, hypertension, and dyslipide- mia on the island of Kosrae, Federated States of Micronesia. Proc Natl Acad Sci U S A 103: 3502–3509. 13. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575. 14. Coop G, Wen X, Ober C, Pritchard JK, Przeworski M (2008) High-resolution mapping of crossovers reveals extensive variation in fine-scale recombination patterns among humans. Science 319: 1395–1398. 15. Roche AF, Davila GH (1972) Late adolescent growth in stature. Pediatrics 50: 874–880. 16. Macgregor S (2008) Optimal two-stage testing for family-based genome-wide association studies. Am J Hum Genet 82: 797–799; author reply 799– 800. 17. Skol AD, Scott LJ, Abecasis GR, Boehnke M (2007) Optimal designs for two- stage genome-wide association studies. Genet Epidemiol 31: 776–788. 18. Laird NM, Horvath S, Xu X (2000) Implementing a unified approach to family- based tests of association. Genet Epidemiol 19 Suppl 1: S36–42. 19. Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55: 997–1004. PLoS Genetics | www.plosgenetics.org 16 February 2009 | Volume 5 | Issue 2 | e1000365