Breaking the Pedigree We broke the extended pedigree to create smaller units that could be feasibly analyzed by existing software packages for large numbers of markers, while maximizing the number of genotyped individuals included in the analysis (as an initial, rough proxy for power) and maintaining some degree of information about relatedness between study participants. Alternative methods for breaking the pedigree were systematically explored, as described in the Materials and Methods. We selected sibships-without-parents as the unit of analysis (Figure 1A). The 2,848 non-consanguineous genotyped Kosraens were grouped into 586 sibships consisting of two or more individuals who share a mother and father (Figure 1B). Any genotyped parents are considered only in the context of the parents’ sibship. Of the individuals not included in a sibship of size $2 (n = 612), a subset was identified in which any two members of the subset were related to the degree of first cousins or less, as determined by genome-wide IBD sharing. In the context of association analyses, this subset of individuals can be considered as sibships of size one, where relatedness between family groups is no more than first cousins. The actual number of individuals included in the association analysis varies with the availability of phenotypic data, as individuals lacking phenotype data were omitted from the analysis of each trait. The extended Kosrae pedigree was thus broken into Figure 1. Breaking the extended Kosrae pedigree. A) The extended Kosrae pedigree is broken into sibships without parents. Parent-child or cousin relationships may exist between different sibships. Tests of association are performed within sibships (gray arrows) and between sibships (black arrows). Individuals without siblings (sibships of size 1) are filtered based on genome-wide IBD sharing to produce a maximal set of individuals with pairwise relationships equivalent to first cousins or less. Panel B shows the number of sibships of each size for n = 2,848 Kosraen individuals genotyped with the Affymetrix 500 k assay. doi:10.1371/journal.pgen.1000365.g001 GWAS on the Island of Kosrae sibships for analysis of each of 15 quantitative traits. For example, in LDL-C, 560 sibships size $2 and 240 sibships of size 1 (n = 2,366 individuals total) were analyzed for association. For BMI, the analysis was limited to individuals who had reached full adult height (females age $22 and males age $24) [15], and comprised 2,073 individuals in 467 sibships of size $2 and 202 sibships of size 1. Since the Kosrae cohort spans multiple generations, members of one sibship are frequently parents or cousins of other sibships. Because traditional association tests assume independence between family groups, we anticipated that relatedness between sibships would inflate the association test statistic [16,17]. Selection of an Association Test: Within and between Families We used simulation to evaluate association tests and the distribution of association statistics, with the goal of selecting an association test that maximized power for our chosen family configuration of sibships-without-parents. We compared two different approaches for association analyses of quantitative traits: a within-family test vs. a combined within- and between-family test. We selected the FBAT software to represent within-family test statistics, with the expectation that it would be robust to population stratification and relatedness between families [18]. The QFAM module in PLINK includes options for within-only (PLINK/QFAM-Within) as well as within- and between-family tests (PLINK/QFAM-Total) [13]. Both options of PLINK/ QFAM use permutation testing to derive empirical p-values; however, we expected the between-family test to exhibit score inflation due to known relatedness between sibships. We used a modified simulation framework to evaluate and compare performance of the association approaches. An effect of known size was spiked into a Kosraen phenotype (BMI) and analyzed using observed Kosraen genotypes and family structure. We chose to modify an observed phenotype instead of simulating genotypes in order to preserve the complex familial correlations between genotype and phenotype on Kosrae. We selected BMI as a representative quantitative phenotype for its moderate heritability (h2 = 0.47 on Kosrae) and near-complete phenotyping in our cohort. Genotype data for 1,000 SNPs were randomly drawn from the larger dataset. After omitting rare SNPs (MAF,0.01), 770 SNPs remained. For each simulation, we modified the BMI phenotype to contain an association to a single SNP contributing an additional 1% to the total phenotypic variance. While this constitutes a fairly substantial single locus effect, it constitutes a small influence on the trait as a whole that does not distort the overall heritability and genome-wide relationships between genotype and phenotype. A total of 770 modified phenotypes were generated, each containing an artificial association to a different SNP in addition to the heritable and other variation in the observed BMI phenotype. Across datasets, the randomly-selected SNP associated with the spiked-in effect spanned a range of allele frequencies greater than 0.01. Each dataset was analyzed in parallel using FBAT for a within- family association test, or using PLINK/QFAM for within-only or combined within- and between-family tests. The performance of each method was evaluated by tallying across datasets the rank of the spiked SNP within its respective dataset. The method that consistently assigned a higher rank to the spiked SNP was identified as the more powerful approach for association analyses. While genomic control is used in the actual association tests to control the false positive rate, we note that rank order is not changed by genomic control, and thus we did not employ it at this stage of evaluating methods. PLoS Genetics | www.plosgenetics.org 4 February 2009 | Volume 5 | Issue 2 | e1000365