GWAS on the Island of Kosrae Figure 2. Inclusion of a between-family test of association increases study power using rank as a metric. A known effect comprising 1% of phenotypic variance explained was ‘‘spiked’’ into a dataset of 770 randomly selected SNPs with MAF$0.01. Study power was evaluated for within- only (FBAT and PLINK/QFAM-Within) and within- and between-family (PLINK/QFAM-Total) tests of association. Across the 770 spiked datasets generated, study power is measured as the fraction of datasets in which the ‘‘spiked’’ SNP exceeds a particular rank. Ranking first out of 770 SNPs in each dataset approximates a rank of #440 in the context of a full genome-wide scan of ,340,000 markers. doi:10.1371/journal.pgen.1000365.g002 Comparison of the within-only vs. combined within- and between-family association test confirmed that greatest power, as measured by the rank order of the true effects, was obtained through the use of a combined within- and between-family association test (Figure 2). Within-family tests implemented in FBAT and PLINK/QFAM-Within identified the spiked SNP as the best result in 36% and 43% of all spiked datasets, respectively. A combined within- and between-family test as implemented in PLINK/QFAM-Total increased identification of the spike as the best-associated SNP to 68%. PLINK/QFAM-Total also ranked a greater proportion of spiked SNPs in the top 5 results than FBAT (78% PLINK/QFAM-Total vs. 65% FBAT), indicating that the between-family test adds substantial power to the study. In a full genome scan of ,340,000 markers, these rank thresholds approximately correspond to the top 440 or 2,200 results, respectively, for a true effect explaining 1% of the variance. We then examined p-value distributions for the PLINK/ QFAM-Within and PLINK/QFAM-Total tests (data not shown). As expected, p-values for the within-family test follow the distribution while the combined within- and between-family (QFAM-Total) test exhibited a systematic deviation from the null. Such inflation of the nominal association score is typical for genotyping artifacts as well as excess known or cryptic relatedness in a between-family test of association, and was anticipated from the known relatedness between sibships. We determined that the major source of score inflation in the combined within- and between-family association test (QFAM-Total) was relatedness in the cohort (Dataset S1, Figure S2). Conceptually, including closely related family units resembles population stratification, as the allele frequencies (from IBD) and phenotypes (from heritability and shared environment) are correlated in family members. In all subsequent analyses, we applied genomic control to adjust the association scores for excess relatedness [19]. After calibrating the distribution of test statistics to the null, we again evaluated the within-only vs. within- and between-family association tests using p-values to compare performance and to assess study power. Because the ability to estimate power accurately is poor when power is very low, we sought to improve power to discriminate between performances of the two tests by examining the spiked dataset containing an effect explaining 2% phenotypic variance. Using p-values as the measure of signifi- cance, we confirmed that the combined within- and between- family association test implemented in PLINK/QFAM-Total has 24.4% power compared to the within-family only test at 15.3% power to achieve an arbitrary threshold of p,1026 (Figure 3). Based on these preliminary analyses, we selected an analytic strategy as follows. The extended Kosrae pedigree is broken into smaller family units, namely sibships without parents. The remaining individuals are filtered on identity-by-descent estimates to produce a set of individuals related no more closely than first cousins, i.e. sibships of size one. The complete set of all sibships is filtered for each trait to include only individuals who are both successfully genotyped and phenotyped. Sibships are analyzed using a combined within- and between-family association test as implemented in PLINK/QFAM-Total, including permutation testing to correct for within-family correlation between genotype and phenotype. Finally, we compensate for relatedness between family units and any residual stratification by applying genomic control. Empirical Power Calculations We used the simulation data above to estimate the effective sample size for the BMI phenotype by direct observation for small effects (Figure 4A) and by extrapolation for larger effects (Figure 4B). Power from the 2,073 individuals analyzed in Kosraen sibships for BMI is comparable to that obtained from a study of 840 unrelated individuals. The more than two-fold size reduction from the actual cohort composed of sibships to an effective number of unrelated individuals highlights the excess of relatedness among our study participants. PLoS Genetics | www.plosgenetics.org 5 February 2009 | Volume 5 | Issue 2 | e1000365