Increased power of mixed models facilitates association mapping of 10 loci for metabolic traits in an isolated population.

The potential benefits of using population isolates in genetic mapping, such as reduced genetic, phenotypic and environmental heterogeneity, are offset by the challenges posed by the large amounts of direct and cryptic relatedness in these populations confounding basic assumptions of independence. We have evaluated four representative specialized methods for association testing in the presence of relatedness; (i) within-family (ii) within- and between-family and (iii) mixed-models methods, using simulated traits for 2906 subjects with known genome-wide genotype data from an extremely isolated population, the Island of Kosrae, Federated States of Micronesia. We report that mixed models optimally extract association information from such samples, demonstrating 88% power to rank the true variant as among the top 10 genome-wide with 56% achieving genome-wide significance, a >80% improvement over the other methods, and demonstrate that population isolates have similar power to non-isolate populations for observing variants of known effects. We then used the mixed-model method to reanalyze data for 17 published phenotypes relating to metabolic traits and electrocardiographic measures, along with another 8 previously unreported. We replicate nine genome-wide significant associations with known loci of plasma cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, thyroid stimulating hormone, homocysteine, C-reactive protein and uric acid, with only one detected in the previous analysis of the same traits. Further, we leveraged shared identity-by-descent genetic segments in the region of the uric acid locus to fine-map the signal, refining the known locus by a factor of 4. Finally, we report a novel associations for height (rs17629022, P< 2.1 × 10⁻8).

Genome-wide association study of electrocardiographic conduction measures in an isolated founder population: Kosrae.

BACKGROUND: Cardiac conduction, as assessed by electrocardiographic PR interval and QRS duration, is an important electrophysiological trait and a determinant of arrhythmia risk. OBJECTIVE: We sought to identify common genetic determinants of these measures. METHODS: We examined 1604 individuals from the island of Kosrae, Federated States of Micronesia, an isolated founder population. We adjusted for covariates and estimated the heritability of quantitative electrocardiographic QRS duration and PR interval and, secondarily, its subcomponents, P-wave duration and PR segment. Finally, we performed a genome-wide association study (GWAS) in a subset of 1262 individuals genotyped using the Affymetrix GeneChip Human Mapping 500K microarray. RESULTS: The heritability of PR interval was 34% (standard error [SE] 5%, P = 4 x 10(-18)); of PR segment, 31% (SE 6%, P = 3.2 x 10(-13)); and of P-wave duration, 17% (SE 5%, P = 5.8 x 10(-6)), but the heritablility of QRS duration was only 3% (SE 4%, P = .20). Hence, GWAS was performed only for the PR interval and its subcomponents. A total of 338,049 single nucleotide polymorphisms (SNPs) passed quality filters. For the PR interval, the most significantly associated SNPs were located in and downstream of the alpha-subunit of the cardiac voltage-gated sodium channel gene SCN5A, with a 4.8 ms (SE 1.0) or 0.23 standard deviation increase in adjusted PR interval for each minor allele copy of rs7638909 (P = 1.6 x 10(-6), minor allele frequency 0.40). These SNPs were also associated with P-wave duration (P = 1.5 x 10(-4)) and PR segment (P = .01) but not with QRS duration (P > or =.22). CONCLUSIONS: The PR interval and its subcomponents showed substantial heritability in a South Pacific islander population and were associated with common genetic variation in SCN5A.