A 100K GENOME-WIDE ASSOCIATION SCAN FOR DIABETES AND RELATED TRAITS IN THE FRAMINGHAM HEART STUDY: REPLICATION AND INTEGRATION WITH OTHER GENOME-WIDE DATASETS

Abstract

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: 1,087 Framingham Heart Study (FHS) family members were genotyped on the Affymetrix 100K single nucleotide polymorphism (SNP) array, and examined for association with risk of incident diabetes and with 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 non-overlapping 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 the association 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.