William L. Duren

A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants.

Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge. Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose-tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs. We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls. We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk. This brings the number of T2D loci now confidently identified to at least 10.

Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes

Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D). Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and ∼2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975. We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P = 5.0 × 10−14), CDC123-CAMK1D (P = 1.2 × 10−10), TSPAN8-LGR5 (P = 1.1 × 10−9), THADA (P = 1.1 × 10−9), ADAMTS9 (P = 1.2 × 10−8) and NOTCH2 (P = 4.1 × 10−8) gene regions. Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.

Newly identified loci that influence lipid concentrations and risk of coronary artery disease

To identify genetic variants influencing plasma lipid concentrations, we first used genotype imputation and meta-analysis to combine three genome-wide scans totaling 8,816 individuals and comprising 6,068 individuals specific to our study (1,874 individuals from the FUSION study of type 2 diabetes and 4,184 individuals from the SardiNIA study of aging-associated variables) and 2,758 individuals from the Diabetes Genetics Initiative, reported in a companion study in this issue. We subsequently examined promising signals in 11,569 additional individuals. Overall, we identify strongly associated variants in eleven loci previously implicated in lipid metabolism (ABCA1, the APOA5-APOA4-APOC3-APOA1 and APOE-APOC clusters, APOB, CETP, GCKR, LDLR, LPL, LIPC, LIPG and PCSK9) and also in several newly identified loci (near MVK-MMAB and GALNT2, with variants primarily associated with high-density lipoprotein (HDL) cholesterol; near SORT1, with variants primarily associated with low-density lipoprotein (LDL) cholesterol; near TRIB1, MLXIPL and ANGPTL3, with variants primarily associated with triglycerides; and a locus encompassing several genes near NCAN, with variants strongly associated with both triglycerides and LDL cholesterol). Notably, the 11 independent variants associated with increased LDL cholesterol concentrations in our study also showed increased frequency in a sample of coronary artery disease cases versus controls.

Improved inference of relationship for pairs of individuals

Linkage analyses of genetic diseases and quantitative traits generally are performed using family data. These studies assume the relationships between individuals within families are known correctly. Misclassification of relationships can lead to reduced or inappropriately increased evidence for linkage. Boehnke and Cox (1997) presented a likelihood-based method to infer the most likely relationship of a pair of putative sibs. Here, we modify this method to consider all possible pairs of individuals in the sample, to test for additional relationships, to allow explicitly for genotyping error, and to include X-linked data. Using autosomal genome scan data, our method has excellent power to differentiate monozygotic twins, full sibs, parent-offspring pairs, second-degree (2 degrees ) relatives, first cousins, and unrelated pairs but is unable to distinguish accurately among the 2 degrees relationships of half sibs, avuncular pairs, and grandparent-grandchild pairs. Inclusion of X-linked data improves our ability to distinguish certain types of 2 degrees relationships. Our method also models genotyping error successfully, to judge by the recovery of MZ twins and parent-offspring pairs that are otherwise misclassified when error exists. We have included these extensions in the latest version of our computer program RELPAIR and have applied the program to data from the Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus (FUSION) study.

The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. I. An autosomal genome scan for genes that predispose to type 2 diabetes

We performed a genome scan at an average resolution of 8 cM in 719 Finnish sib pairs with type 2 diabetes. Our strongest results are for chromosome 20, where we observe a weighted maximum LOD score (MLS) of 2.15 at map position 69.5 cM from pter and secondary weighted LOD-score peaks of 2.04 at 56.5 cM and 1.99 at 17.5 cM. Our next largest MLS is for chromosome 11 (MLS = 1.75 at 84.0 cM), followed by chromosomes 2 (MLS = 0.87 at 5.5 cM), 10 (MLS = 0.77 at 75.0 cM), and 6 (MLS = 0.61 at 112.5 cM), all under an additive model. When we condition on chromosome 2 at 8.5 cM, the MLS for chromosome 20 increases to 5.50 at 69.0 cM (P=.0014). An ordered-subsets analysis based on families with high or low diabetes-related quantitative traits yielded results that support the possible existence of disease-predisposing genes on chromosomes 6 and 10. Genomewide linkage-disequilibrium analysis using microsatellite marker data revealed strong evidence of association for D22S423 (P=.00007). Further analyses are being carried out to confirm and to refine the location of these putative diabetes-predisposing genes.

Association of transcription factor 7-like 2 (TCF7L2) variants with type 2 diabetes in a Finnish sample.

Transcription factor 7-like 2 (TCF7L2) is part of the Wnt signaling pathway. Genetic variants within TCF7L2 on chromosome 10q were recently reported to be associated with type 2 diabetes in Icelandic, Danish, and American (U.S.) samples. We previously observed a modest logarithm of odds score of 0.61 on chromosome 10q, ∼1 Mb from TCF7L2, in the Finland-United States Investigation of NIDDM Genetics study. We tested the five associated TCF7L2 single nucleotide polymorphism (SNP) variants in a Finnish sample of 1,151 type 2 diabetic patients and 953 control subjects. We confirmed the association with the same risk allele (P value <0.05) for all five SNPs. Our strongest results were for rs12255372 (odds ratio [OR] 1.36 [95% CI 1.15–1.61], P = 0.00026) and rs7903146 (1.33 [1.14–1.56], P = 0.00042). Based on the CEU HapMap data, we selected and tested 12 additional SNPs to tag SNPs in linkage disequilibrium with rs12255372. None of these SNPs showed stronger evidence of association than rs12255372 or rs7903146 (OR ≤1.26, P ≥ 0.0054). Our results strengthen the evidence that one or more variants in TCF7L2 are associated with increased risk of type 2 diabetes.

Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels

Identifying the genetic variants that regulate fasting glucose concentrations may further our understanding of the pathogenesis of diabetes. We therefore investigated the association of fasting glucose levels with SNPs in 2 genome-wide scans including a total of 5,088 nondiabetic individuals from Finland and Sardinia. We found a significant association between the SNP rs563694 and fasting glucose concentrations (P = 3.5 x 10(-7)). This association was further investigated in an additional 18,436 nondiabetic individuals of mixed European descent from 7 different studies. The combined P value for association in these follow-up samples was 6.9 x 10(-26), and combining results from all studies resulted in an overall P value for association of 6.4 x 10(-33). Across these studies, fasting glucose concentrations increased 0.01-0.16 mM with each copy of the major allele, accounting for approximately 1% of the total variation in fasting glucose. The rs563694 SNP is located between the genes glucose-6-phosphatase catalytic subunit 2 (G6PC2) and ATP-binding cassette, subfamily B (MDR/TAP), member 11 (ABCB11). Our results in combination with data reported in the literature suggest that G6PC2, a glucose-6-phosphatase almost exclusively expressed in pancreatic islet cells, may underlie variation in fasting glucose, though it is possible that ABCB11, which is expressed primarily in liver, may also contribute to such variation.

The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. II. An autosomal genome scan for diabetes-related quantitative-trait loci

Type 2 diabetes mellitus is a complex disorder encompassing multiple metabolic defects. We report results from an autosomal genome scan for type 2 diabetes-related quantitative traits in 580 Finnish families ascertained for an affected sibling pair and analyzed by the variance components-based quantitative-trait locus (QTL) linkage approach. We analyzed diabetic and nondiabetic subjects separately, because of the possible impact of disease on the traits of interest. In diabetic individuals, our strongest results were observed on chromosomes 3 (fasting C-peptide/glucose: maximum LOD score [MLS] = 3.13 at 53.0 cM) and 13 (body-mass index: MLS = 3.28 at 5.0 cM). In nondiabetic individuals, the strongest results were observed on chromosomes 10 (acute insulin response: MLS = 3.11 at 21.0 cM), 13 (2-h insulin: MLS = 2.86 at 65.5 cM), and 17 (fasting insulin/glucose ratio: MLS = 3.20 at 9.0 cM). In several cases, there was evidence for overlapping signals between diabetic and nondiabetic individuals; therefore we performed joint analyses. In these joint analyses, we observed strong signals for chromosomes 3 (body-mass index: MLS = 3.43 at 59.5 cM), 17 (empirical insulin-resistance index: MLS = 3.61 at 0.0 cM), and 19 (empirical insulin-resistance index: MLS = 2.80 at 74.5 cM). Integrating genome-scan results from the companion article by Ghosh et al., we identify several regions that may harbor susceptibility genes for type 2 diabetes in the Finnish population.

Screening of 134 Single Nucleotide Polymorphisms (SNPs) Previously Associated With Type 2 Diabetes Replicates Association With 12 SNPs in Nine Genes

More than 120 published reports have described associations between single nucleotide polymorphisms (SNPs) and type 2 diabetes. However, multiple studies of the same variant have often been discordant. From a literature search, we identified previously reported type 2 diabetes–associated SNPs. We initially genotyped 134 SNPs on 786 index case subjects from type 2 diabetes families and 617 control subjects with normal glucose tolerance from Finland and excluded from analysis 20 SNPs in strong linkage disequilibrium (r2 > 0.8) with another typed SNP. Of the 114 SNPs examined, we followed up the 20 most significant SNPs (P < 0.10) on an additional 384 case subjects and 366 control subjects from a population-based study in Finland. In the combined data, we replicated association (P < 0.05) for 12 SNPs: PPARG Pro12Ala and His447, KCNJ11 Glu23Lys and rs5210, TNF −857, SLC2A2 Ile110Thr, HNF1A/TCF1 rs2701175 and GE117881_360, PCK1 −232, NEUROD1 Thr45Ala, IL6 −598, and ENPP1 Lys121Gln. The replication of 12 SNPs of 114 tested was significantly greater than expected by chance under the null hypothesis of no association (P = 0.012). We observed that SNPs from genes that had three or more previous reports of association were significantly more likely to be replicated in our sample (P = 0.03), although we also replicated 4 of 58 SNPs from genes that had only one previous report of association.

Comprehensive Association Study of Type 2 Diabetes and Related Quantitative Traits with 222 Candidate Genes

OBJECTIVE—Type 2 diabetes is a common complex disorder with environmental and genetic components. We used a candidate gene–based approach to identify single nucleotide polymorphism (SNP) variants in 222 candidate genes that influence susceptibility to type 2 diabetes. RESEARCH DESIGN AND METHODS—In a case-control study of 1,161 type 2 diabetic subjects and 1,174 control Finns who are normal glucose tolerant, we genotyped 3,531 tagSNPs and annotation-based SNPs and imputed an additional 7,498 SNPs, providing 99.9% coverage of common HapMap variants in the 222 candidate genes. Selected SNPs were genotyped in an additional 1,211 type 2 diabetic case subjects and 1,259 control subjects who are normal glucose tolerant, also from Finland. RESULTS—Using SNP- and gene-based analysis methods, we replicated previously reported SNP-type 2 diabetes associations in PPARG, KCNJ11, and SLC2A2; identified significant SNPs in genes with previously reported associations (ENPP1 [rs2021966, P = 0.00026] and NRF1 [rs1882095, P = 0.00096]); and implicated novel genes, including RAPGEF1 (rs4740283, P = 0.00013) and TP53 (rs1042522, Arg72Pro, P = 0.00086), in type 2 diabetes susceptibility. CONCLUSIONS—Our study provides an effective gene-based approach to association study design and analysis. One or more of the newly implicated genes may contribute to type 2 diabetes pathogenesis. Analysis of additional samples will be necessary to determine their effect on susceptibility.