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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.

Type 1 diabetes is an autoimmune disease with a Th1 phenotype in which insulin-producing β-cells in the pancreas are destroyed. The T-cell–specific transcription factor TCF7 activates genes involved in immune regulation and is a candidate locus for genetic susceptibility to type 1 diabetes. A nonsynonymous single nucleotide polymorphism (SNP) (Pro to Thr) in the TCF7 gene, C883A, was examined in samples from 282 Caucasian multiplex type 1 diabetic families. HLA-DRB1 and -DQB1 genotypes were previously determined for these samples, allowing data stratification based on HLA-associated risk. The transmission disequilibrium test showed significant overtransmission of the A allele from fathers (64.1%, P < 0.007) and nonsignificant overtransmission (57.4%, P < 0.06) of the A allele to patients who do not carry the highest-risk HLA-DR3/DR4 genotype. Elliptical sib pair analysis showed significant associations of the A allele with type 1 diabetes in paternal transmissions (P < 0.03), transmissions to male children (P < 0.04), and in the non-DR3/DR4 group (P < 0.04). These data also suggest that TCF7 C883A may affect age of disease onset. Analysis of genotype data from surrounding SNPs suggests that this TCF7 polymorphism may itself represent a risk factor for type 1 diabetes.

Maternal metabolism during pregnancy impacts the developing fetus, affecting offspring birth weight and adiposity. This has important implications for metabolic health later in life (e.g., offspring of mothers with pre-existing or gestational diabetes mellitus have an increased risk of metabolic disorders in childhood). To identify genetic loci associated with measures of maternal metabolism obtained during an oral glucose tolerance test at ∼28 weeks’ gestation, we performed a genome-wide association study of 4,437 pregnant mothers of European (n = 1,367), Thai (n = 1,178), Afro-Caribbean (n = 1,075), and Hispanic (n = 817) ancestry, along with replication of top signals in three additional European ancestry cohorts. In addition to identifying associations with genes previously implicated with measures of glucose metabolism in nonpregnant populations, we identified two novel genome-wide significant associations: 2-h plasma glucose and HKDC1, and fasting C-peptide and BACE2. These results suggest that the genetic architecture underlying glucose metabolism may differ, in part, in pregnancy.

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.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.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.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.

We have investigated, in 282 multiplex Caucasian families (the Human Biological Data Interchange Repository), the association of type 1 diabetes with polymorphisms in the IL4R gene. IL4R encodes a subunit of the interleukin-4 receptor, a molecule critical to T-helper cell development. By genotyping eight different IL4R single-nucleotide polymorphisms (SNPs) and identifying haplotypes (complex alleles) in the multiplex type 1 diabetic families who were stratified for HLA genotype, we have observed significant evidence of linkage and association of the IL4R gene to type 1 diabetes. In particular, we have identified a specific haplotype that appears to be protective and observed that this protective effect is strongest among individuals not carrying the HLA DR3/DR4 genotype (which confers the strongest genetic risk for type 1 diabetes). These findings suggest an important role for the IL4R gene in immune-related disease susceptibility and illustrate the value of using multi-SNP haplotype information in association studies.