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A 100K Genome-Wide Association Scan for Diabetes and Related Traits in the Framingham Heart Study

Replication and Integration With Other Genome-Wide Datasets

¹ Center for Human Genetic Research and Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
² Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
³ Department of Medicine, Harvard Medical School, Boston, Massachusetts
⁴ Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
⁵ Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
⁶ National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, Massachusetts
⁷ General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts

Address correspondence and reprint requests to James B. Meigs, MD, MPH, General Medicine Division, Massachusetts General Hospital, 50 Staniford St., 9th Floor, Boston, MA 02114. E-mail: jmeigs{at}partners.org

Abbreviations: DGI, Diabetes Genetics Initiative; FPG, fasting plasma glucose; FBAT, family-based association test; FHS, Framingham Heart Study; GEE, generalized estimating equations; GWA, genome-wide association; HOMA-IR, homeostasis model assessment of insulin resistance; ISI, insulin sensitivity index; MAF, minor allele frequency; mFPG, 28-year mean fasting plasma glucose; NIH, National Institutes of Health; SNP, single nucleotide polymorphism

OBJECTIVE— To use genome-wide fixed marker arrays and improved analytical tools to detect genetic associations with type 2 diabetes in a carefully phenotyped human sample.

RESEARCH DESIGN AND METHODS— A total of 1,087 Framingham Heart Study (FHS) family members were genotyped on the Affymetrix 100K single nucleotide polymorphism (SNP) array and examined for association with incident diabetes and six diabetes-related quantitative traits. Quality control filters yielded 66,543 SNPs for association testing. We used two complementary SNP selection strategies (a "lowest P value" strategy and a "multiple related trait" strategy) to prioritize 763 SNPs for replication. We genotyped a subset of 150 SNPs in a nonoverlapping sample of 1,465 FHS unrelated subjects and examined all 763 SNPs for in silico replication in three other 100K and one 500K genome-wide association (GWA) datasets.

RESULTS— We replicated associations of 13 SNPs with one or more traits in the FHS unrelated sample (16 expected under the null); none of them showed convincing in silico replication in 100K scans. Seventy-eight SNPs were nominally associated with diabetes in one other 100K GWA scan, and two (rs2863389 and rs7935082) in more than one. Twenty-five SNPs showed promising associations with diabetes-related traits in 500K GWA data; one of them (rs952635) replicated in FHS. Five previously reported associations were confirmed in our initial dataset.

CONCLUSIONS— The FHS 100K GWA resource is useful for follow-up of genetic associations with diabetes-related quantitative traits. Discovery of new diabetes genes will require larger samples and a denser array combined with well-powered replication strategies.

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