OBJECTIVE— We sought to identify type 2 diabetes susceptibility genes through a genome-wide association scan (GWAS) in the Amish.

RESEARCH DESIGN AND METHODS— DNA from 124 type 2 diabetic case subjects and 295 control subjects with normal glucose tolerance were genotyped on the Affymetrix 100K single nucleotide polymorphism (SNP) array. A total of 82,485 SNPs were tested for association with type 2 diabetes. Type 2 diabetes–associated SNPs were further prioritized by the following: 1) associations with 5 oral glucose tolerance test (OGTT) traits in 427 nondiabetic Amish subjects, and 2) in silico replication from three independent 100L SNP GWASs (Framingham Heart Study Caucasians, Pima Indians, and Mexican Americans) and a 500K GWAS in Scandinavians.

RESULTS— The strongest association (P = 1.07 × 10−5) was for rs2237457, which is located in growth factor receptor–bound protein 10 (Grb10), an adaptor protein that regulate insulin receptor signaling. rs2237457 was also strongly associated with OGTT glucose area under the curve in nondiabetic subjects (P = 0.001). Of the 1,093 SNPs associated with type 2 diabetes at P < 0.01, 67 SNPs demonstrated associations with at least one OGTT trait in nondiabetic individuals; 80 SNPs were nominally associated with type 2 diabetes in one of the three independent 100K GWASs, 3 SNPs (rs2540317 in MFSD9, rs10515353 on chromosome 5, and rs2242400 in BCAT1 were associated with type 2 diabetes in more than one population), and 11 SNPs were nominally associated with type 2 diabetes in Scandinavians. One type 2 diabetes–associated SNP (rs3845971, located in FHIT) showed replication with OGTT traits and also in another population.

CONCLUSIONS— Our GWAS of type 2 diabetes identified several gene variants associated with type 2 diabetes, some of which are worthy of further study.

Type 2 diabetes, a complex disease that is characterized by insulin resistance and impaired β-cell function, represents a serious global public health problem, with more than 100 million people affected worldwide. While the primary molecular defects in type 2 diabetes remain largely unknown, it is clear that both genetic and environmental risk factors (including diet and physical inactivity) play critical roles. More than 20 genome-wide linkage scans of type 2 diabetes have been published, with evidence for linkage reported to a number of loci, including regions on chromosomes 1, 3, 8, 10, 12, 14, and 20 (18). Of the numerous candidate genes studied for their functional role in pancreatic β-cell function, insulin action, or energy metabolism, as well as positional candidate genes identified under linkage peaks, very few have variants that are consistently associated with type 2 diabetes. Indeed, common variants in only a few genes (PPARγ, KCNJ11, CALPN10, TCF7L2, and HNF4A) have been replicated in multiple populations (9).

The Old Order Amish are a closed founder population who emigrated from Switzerland in the early 1700s. They are a well-suited population for carrying out genetic studies since they live a relatively homogeneous lifestyle and maintain extensive family history records. The Amish Family Diabetes Study (AFDS) was initiated in 1995 with the goal of identifying the genetic determinants of type 2 diabetes (10). The sibling relative risk (λs) of type 2 diabetes in the Amish is 3.28 (95% CI 1.58–6.80), similar to that observed in other Caucasian populations. Genome-wide linkage analysis of type 2 diabetes and impaired glucose tolerance conducted in AFDS pedigrees (6) revealed regions on chromosomes 1q and 14q, both of which have been implicated in linkage scans from other populations (15,7). Specific variants in several well-replicated type 2 diabetes susceptibility genes are associated with type 2 diabetes in the Amish, including TCF7L2 rs7903146 (odds ratio 1.60, P = 0.008) (11) and HNF4A rs2425640 (1.60, P = 0.03) (12). These findings suggest that the common type 2 diabetes gene variants in the Amish will likely be relevant to more outbred Caucasian populations.

Increased knowledge of common variation in the human genome learned as part of the HapMap initiative, coupled with advances in technologies, make possible the genotyping of thousands of single nucleotide polymorphisms (SNPs) in genome-wide association scans (GWAS). This is a powerful approach for identifying novel susceptibility genes for complex diseases (13,14). Recently, four GWAS studies of type 2 diabetes have identified variants at several novel loci, including SLC30A8, IGF2BP2, CKDAL1, CDKN2A/CKDN2B, and HHEX/IDE, that show strong replicated association with type 2 diabetes (1518). In this article, we report results from a GWAS of type 2 diabetes in the Amish using the Affymetrix 100K SNP genotyping platform. We further characterize our findings using diabetes-related quantitative traits measured in nondiabetic Amish individuals. Lastly, we interpret the results of this scan in the context of three recently completed 100K GWAS studies for type 2 diabetes, as part of the Type 2 Diabetes 100K GWAS Consortium, along with a publicly available 500K GWAS of type 2 diabetes recently performed in a Scandinavian population.

Study population and phenotype assessment.

Individuals with type 2 diabetes were identified from the AFDS. Details of the AFDS have been previously described (10). Phenotypic characterization of participants included medical and family history, anthropometry, and a 3-h 75-g oral glucose tolerance test (OGTT) with insulin levels. We based our primary analyses on 124 type 2 diabetic case and 295 normal glucose tolerant (NGT) control subjects. Type 2 diabetes was defined by fasting plasma glucose level (≥7 mmol/l), 2-h OGTT plasma glucose level (≥11.1 mmol/l), random plasma glucose level (≥11.1 mmol/l), the use of insulin or prescription oral glucose-lowering agents, or a diagnosis of diabetes documented by a physician. To minimize potentially misclassifying subjects with type 1 diabetes as having type 2 diabetes, case subjects with age of diagnosis <35 years were excluded. NGT control subjects were aged >38 years at the time of study and were selected based on fasting plasma glucose level (<6.1 mmol/l) and 2-h OGTT plasma glucose level (<7.8 mmol/l).

We performed secondary quantitative analyses of our mostly highly associated signals (P < 0.01) in a set of 427 nondiabetic Amish study participants, 132 of whom had impaired glucose tolerance and 295 of whom were part of the NGT control group used in our primary analysis. We estimated the mean levels of two OGTT-derived quantitative glucose traits (fasting glucose and glucose area under the curve [GAUC]) and three insulin traits (insulinogenic index [ISI], insulin area under the curve [IAUC], and homeostasis model assessment of insulin resistance [HOMA-IR]) according to the SNP genotypes in these individuals. Total GAUC and IAUC were calculated based on measurements at 0, 30, 60, 90, 120, 150, and 180 min using the trapezoidal method. The ISI was calculated as (insulin at 30 min − fasting insulin)/(glucose at 30 min − fasting glucose). HOMA-IR was calculated as fasting insulin (mU/l) × fasting glucose (mmol/l)/22.5. Table 1 describes the characteristics of this sample. The study protocol was approved by the institutional review board at the University of Maryland School of Medicine, and informed consent was obtained from each study participant.

Genotyping.

Genomic DNA from leukocytes were genotyped using the Affymetrix GeneChip Mapping 100K array set, which consists of two microarray chips (XbaI and HindIII) (Affymetrix, Santa Clara, CA). Total genomic DNA (250 ng) was digested with XbaI or HindIII restriction enzymes and processed according to the Affymetrix protocol. The GeneChip Genotyping Analysis Software (GTYPE 4.0) was used to generate dynamic modeling algorithm–derived genotypes that were reanalyzed with the BRLMM (Bayesian RLMM) genotype calling algorithm (confidence threshold of 0.33) to improve the proportion of heterozygote calls (19). As an initial quality-control measure, BRLMM-generated chip files with call rates <90% for both enzymes across all SNPs were excluded. The resulting median call rate across all of the remaining 419 case-control samples was 97.5% (97.6% for XbaI and 97.4% for HindIII). We further removed individual SNPs with genotype call rates <90%, monomorphic SNPs and SNPs with minor allele frequency < 5%, and those deviating from Hardy-Weinberg equilibrium in control subjects (P < 0.001). The number of monomorphic and low-frequency SNPs (n = 26,816) in the Amish was not appreciably different from that observed in more outbred Caucasians of the HapMap CEU sample. For this report, we focused our analysis on the 82,485 autosomal SNPs that passed our quality-control standards.

The concordance rate for 11 quality-control samples that were run twice on the Affymetrix GeneChip mapping panel was 97.5%. We also calculated a cross-platform concordance rate of 98% for 419 samples in which 61 SNPs were genotyped using the Affymetrix GeneChip Mapping 100K panel and an independent Illumina 1536-plex GoldenGate assay. Supplementary Table 3 (available in an online appendix at http://dx.doi.org/10.2337/db07-0457) summarizes the quality checks and informativeness of the data.

Association testing and SNP prioritization scheme.

Our GWAS analysis and SNP prioritization scheme is shown in Fig. 1. We selected the SNPs most highly associated with type 2 diabetes in our Amish case-control dataset based on P value rankings (P value cutoff <0.01) and then used two complementary approaches to further prioritize them. In one approach, we evaluated the most highly type 2 diabetes–associated SNPs for association with diabetes-related quantitative traits in an expanded set of 427 nondiabetic Amish subjects, 295 of whom were NGT control subjects from the primary type 2 diabetes association analysis (internal consistency). In a parallel approach, we assessed replication of the most highly associated type 2 diabetes–associated SNPs in the Amish in four independent GWASs from other populations (external replication).

Type 2 diabetes association analysis.

We performed case-control association analysis using a variance component approach as implemented in SOLAR software (20). Using a liability threshold model, we modeled the probability that the individual was a case or control subject as a function of the individual's age, sex, and genotype, conditional on the correlations in phenotype among relative pairs. Statistical testing was performed using a likelihood ratio test, in which we compared the likelihood of the data under a model in which the genotype effect was estimated with the likelihood of a nested model in which the genotype effect was constrained to be zero. Odds ratios (ORs) were computed from variance components models. We chose to report the additive model as our primary analysis. Supplementary analyses using a dominant or recessive model did not yield any SNP showing genome-wide significance. Of 82,485 SNPs, 611 had P < 0.01 under a dominant model and 569 had P < 0.01 under the recessive model. Our complete dataset with results from all models is available online (available at http://www.medschool.umaryland.edu/amishstudies/index.asp). Pairwise linkage disequilibrium (LD) correlation statistics (r2) were computed using the Helixtree software, version 5.0.2 (GoldenHelix, Bozeman, MT).

Quantitative trait analysis.

For quantitative trait analyses performed in nondiabetic Amish subjects, we used the measured genotype approach, in which we estimated the likelihood of an additive genetic model given the pedigree structure (21). Before analysis, all insulin traits (IAUC, ISI, and HOMA-IR) were transformed by their natural logarithm to reduce skewness. Parameter estimates were obtained by maximum likelihood methods, and the significance of association was tested by the likelihood ratio test. Within each model, we simultaneously estimated the effects of age and sex. These analyses were performed using the SOLAR program (20).

Power calculations.

Power calculations, based on the genetic power calculator of Purcell et al. (22), indicated that our sample would provide 80% power to detect a diabetes susceptibility allele having a genotype relative risk of 1.8 (for allele frequency of 30%, 124 case and 295 control subjects, 8% population prevalence of diabetes, assuming a multiplicative model) and 80% power to detect a quantitative trait loci accounting for 4% or higher of the trait variance for a continuously distributed phenotype (427 subjects).

In silico replication samples.

We considered whether our best type 2 diabetes association signals (P value cutoff <0.01) replicated in at least one of three distinct populations (Framingham Caucasians, Mexican Americans, and Pima Indians), each with different study designs but performed using the same Affymetrix 100K genotyping platform. Descriptions of each of the Type 2 Diabetes 100K GWAS Consortium study populations are provided in accompanying articles (2325) and in supplementary Table 4. We directly checked whether any of the 1,093 SNPs with the best type 2 diabetes association signals (P < 0.01) in the Amish were also associated with type 2 diabetes based on generalized estimating equations and family-based association tests in the Framingham Heart Study, Fisher's exact allelic association test in the Mexican-American study, and case-control and sib-based association tests in the Pima Indian study. We also utilized publicly available prereleased data (March 2007) from a type 2 diabetes GWAS carried out in a Scandinavian cohort of 1,464 type 2 diabetic case and 1,467 matched control subjects and genotyped using the Affymetrix 500K platform by the Broad-Lund-Novartis Diabetes Genetics Initiative (DGI) (available at http://www.broad.mit.edu/diabetes/) (18). We specifically checked replication of 295 of 1,093 of our most highly type 2 diabetes–associated SNPs that were present on both 100K and 500K Affymetrix genotyping arrays. Since LD structure may differ across populations, and to limit multiple comparisons, we defined replication only if the same SNP was associated with type 2 diabetes at P < 0.05 with an OR in the same direction (i.e., reflective of the same allelic risk).

Following quality-control and Hardy-Weinberg equilibrium checks, 82,485 informative SNPs were included in our analyses. The median physical inter-SNP distance was 11.3 kb, and the average distance between SNPs was 29 kb. Under the additive model, a total of 1,093 SNPs, some of which were in LD, were associated (P < 0.01) with type 2 diabetes (Fig. 2). The 50 most strongly type 2 diabetes–associated SNPs (i.e., lowest P values) are shown in Table 2. The complete dataset is available online (available at http://www.medschool.umaryland.edu/amishstudies/afds.asp). No SNP was associated with type 2 diabetes at a conservative Bonferroni-corrected level. The strongest association (P = 1.07 × 10−5) was for rs2237457 on chromosome 7, which is located in intron 4 of growth factor receptor–bound protein 10 (Grb10), an adaptor protein known to regulate signaling of insulin and IGF receptors (2628). In addition to Grb10, 15 SNPs were associated with type 2 diabetes at P < 1 × 10−4 (Fig. 2 and Table 2). These SNPs are located in or near MSH6 (chromosome 2), PRKG2 (chromosome 4), COL13A1 (chromosome 10), MTHFSD (chromosome 16), and SPECC1 (chromosome 17), none of which are obvious candidate genes for type 2 diabetes. Adjustment for BMI did not have a large impact on the strength of the associations of these SNPs with type 2 diabetes (Table 2).

As a measure of internal consistency, we tested whether the 1,093 SNPS associated with type 2 diabetes (P < 0.01) were also associated with OGTT-derived quantitative traits in nondiabetic individuals. In these analyses, we considered two OGTT glucose traits (fasting glucose and GAUC) and three OGTT insulin traits (IAUC, HOMA-IR, and ISI), with P < 0.01 as our threshold for significance. Thirty-eight nonredundant (r2 < 0.80) type 2 diabetes–associated SNPs were also associated with at least one glucose trait and showed the same allelic association as that for diabetes (i.e., the diabetes risk allele was also associated with higher glucose levels), while 29 nonredundant type 2 diabetes–associated SNPs were also associated with at least one insulin-related trait (Fig. 1; Table 3). Of the top 16 SNPs associated with type 2 diabetes at P < 1 × 10−4, rs2237457 in Grb10 was the only one also associated with an OGTT trait (P = 0.001 for GAUC). Two perfectly correlated (r2 = 1) type 2 diabetes–associated SNPs in ADAMTS1 (chromosome 5) (P = 0.004–0.005) were associated with one glucose trait (P = 0.006 for GAUC) and one insulin trait (P = 0.007 for IAUC).

We next sought to determine which of our 1,093 most highly type 2 diabetes–associated SNPs were also associated with type 2 diabetes in any of three independent populations for which the same 100K Affymetrix platform was used or in the DGI Scandinavian population for which the 500K Affymetrix platform was used. We identified 80 nonredundant SNPs for which the same risk allele was also associated with type 2 diabetes in one of the three studies from the Type 2 Diabetes 100K GWAS Consortium (P < 0.05) and 11 nonredundant SNPs that showed consistent association in the DGI sample (P < 0.05) (Fig. 1; supplementary Table 3). In total, three SNPs demonstrated associations in the Amish as well as in two independent populations. The T-allele for rs2540317 in MFSD9 on chromosome 2 was associated with decreased risk of type 2 diabetes in the Amish (OR 0.72, P = 0.007) and showed nominal association in the Pima Indian dataset (case-control OR 0.67, P = 0.042; sib-based OR 0.50, P = 0.043; and summary OR 0.63, P = 0.016) and also in the Mexican-American sample (case-control OR 0.75, P = 0.047). The G-allele in rs10515353 on chromosome 5 was associated with decreased risk of type 2 diabetes in the Amish (OR 0.61, P = 0.005) and also with decreased type 2 diabetes risk in Mexican-American (0.69, P = 0.035) and DGI (0.79, P = 0.007) samples. The T-allele in rs2242400 in BCAT1 on chromosome 10 was associated with decreased risk of type 2 diabetes in the Amish (0.71, P = 0.004) and also in the Pima Indian dataset (sib-based OR 0.66, P = 0.019; summary OR 0.78, P = 0.034) and the Mexican-American dataset (OR 0.67, P = 0.009); borderline association was also seen in the DGI sample (0.86, P = 0.051). The direction of effect was the same for all studies.

Table 4 highlights our most consistent overall findings. We present 21 type 2 diabetes–associated SNPs in the Amish (P < 0.005) that also demonstrated either 1) association with a diabetes-related quantitative trait (P < 0.005) in the Amish or 2) in silico replication of type 2 diabetes association in one independent population (P < 0.005). Of interest, the T-allele in rs3845971 in FHIT was associated with increased risk of type 2 diabetes in the Amish (OR 1.42, P = 0.004) and also in Mexican Americans (1.46, P = 0.004) and with increased GAUC (P = 4.0 × 10−4) in nondiabetic Amish subjects.

In this article, we described the results of a GWAS of type 2 diabetes of 82,485 SNPs in the Old Order Amish, a genetically closed founder population with a homogeneous lifestyle. We reasoned that this population is likely to carry a subset of the same common type 2 diabetes susceptibility variants as those found in the general population and that these variants might be easier to identify.

GWAS studies are prone to false-positives due to the very large number of statistical tests that must be performed. We were restricted by our relatively modest sample size and also computationally in our attempts to define a genome-wide significance level for which follow-up was justified (i.e., variance components tests were not feasible for the many replications needed for case-control permuted family datasets in the Amish). Thus, we relied heavily on a prioritization of SNPs worthy of follow-up by testing for 1) internal consistency of type 2 diabetes–associated SNPs with OGTT-derived quantitative traits in nondiabetic Amish individuals, 2) external replication of type 2 diabetes associations in three independent non-Amish 100K SNP GWAS studies, and 3) external replication in a 500K SNP GWAS of type 2 diabetes in a large population of Scandinavians.

We found that no single SNP replicated consistently and in the same direction across all GWAS studies, nor were all SNPs associated with type 2 diabetes also associated with quantitative traits in nondiabetic individuals (supplementary Table 4). This is not particularly surprising since we expect that an appreciable number of type 2 diabetes–associated SNPs will be false-positives. Furthermore, a true susceptibility gene in one population might not be readily discernible in other populations due to inadequate sample sizes as well as differences in genetic background, LD, and environmental exposures. Similarly, a true susceptibility gene for type 2 diabetes might not show association with diabetes-related quantitative traits in nondiabetic individuals, especially since our OGTT-derived traits are only surrogates for gold-standard measures of insulin sensitivity and insulin secretion. Nevertheless, we were able to identify a number of candidate genes and loci that showed evidence for association with type 2 diabetes in more than one population and/or were also associated with OGTT-derived quantitative traits. These results are intriguing but must be interpreted with caution. None of these loci fall within previously identified linkage regions for type 2 diabetes (chromosomes 1 and 14) in the Amish.

Our strongest type 2 diabetes association signal in the Amish was observed on chromosome 7 in a functionally relevant type 2 diabetes candidate gene, Grb10. Grb10 encodes growth factor–binding protein 10 and has been shown to bind to activated insulin receptor and act as a negative regulator of insulin action and glucose uptake (2628). Overexpression of Grb10 in mice causes postnatal growth retardation and insulin resistance (29). Our 100K GWAS contained a total of 12 SNPs in Grb10, 6 of which were associated with type 2 diabetes (P < 0.05) and were in partial LD with each other (r2 = 0.16–0.78). Rs2237457, located in intron 4, provided the lowest P value for association (OR 0.61 for the G- vs. A-allele, P = 1.07 × 10−5). This SNP was also strongly associated with OGTT GAUC in nondiabetic Amish individuals (P = 0.001). Rs2237457 was not associated with type 2 diabetes in the other three populations in which this SNP was genotyped or in the 500K SNP Scandinavian type 2 diabetes GWAS; however, three SNPs (rs2190496, rs2237478, and rs7805310) in Grb10 that were genotyped in the Scandinavian cohort were associated with type 2 diabetes (P = 0.029, P = 0.01, and P = 0.004, respectively) and are in partial LD with rs2237457 (r2 = 0.12–0.49 in HapMap CEU). Lack of replication could suggest a false-positive or that variation in Grb10 is a true positive specific to the Amish due to a founder effect or context-dependent phenotypic expression of the variant due to genetic background or environmental influences. Alternatively, this variant could be in LD with a functional variant, and extended LD in the Amish enabled a type 2 diabetes association to be detected in this population and not the others.

In a recent report by Di Paola et al. (30), the A-allele of rs4947710, a synonymous coding SNP in Grb10, was associated with decreased risk of type 2 diabetes in a relatively homogeneous population of Italian Caucasians (P = 0.0001). This SNP was not part of the 100K SNP panel nor was our most highly type 2 diabetes–associated SNP (rs2237457) genotyped in the Italian sample. We found that rs2237457 and rs4947710 are not in LD (r2 = 0) in HapMap CEU samples. However, rs10486757, another Grb10 SNP associated with type 2 diabetes in the Amish (P = 0.024), is in LD with rs4947710 (r2 = 0.64 in HapMap CEU). Further investigation of Grb10 is currently underway.

Our GWAS and replication strategy have several limitations. First, the relatively small sample size limits our ability to detect gene variants of modest effect size. Second, we recognize that the definition of external replication of our top SNPs across three independent 100K studies of type 2 diabetes might represent a skewed distribution of the overall results since replication was limited to our ∼1,000 most highly type 2 diabetes–associated SNPs. This approach was used to facilitate comparisons across populations and also to limit the number of false-positive replications due to multiple comparisons. To the extent that we attempted to pursue signals that represent the “lowest hanging fruit,” we believe that the approach we have taken is reasonable. A formal meta-analysis of the entire set of data from all four 100K studies is currently underway. Third, our replication approach was focused at the level of the SNP in order to avoid additional multiple comparisons. However, it is possible that we did not identify significantly associated SNPs in other populations that were in LD with our top SNPs. This is particularly relevant for our comparisons with the Scandinavian 500K GWAS, for which only 27% of the SNPs identified in the Amish with P < 0.01 were identified in the 500K SHP panel.

The likelihood that we missed common variants important to type 2 diabetes is high due to the relatively sparse density of the 100K SNP panel (mean intermarker distance = 29 kb) compared with other denser GWAS SNP panels. For example, SNPs in well-replicated genes (SLC30A8, IGF2BP2, CKDAL1, CDKN2A/CKDN2B, and HHEX/IDE) found in four recently published type 2 diabetes GWAS studies (1518), as well as previously known type 2 diabetes–associated variants in TCF7L2, KCNJ11, HNF4A, or CAPN10 (9), were not adequately covered on the 100K genotyping panel (i.e., r2 < 0.8 between the SNP of interest and SNPs on the 100K panel). As a positive control, we previously demonstrated that TCF7L2 SNP rs7903146 and the HNF4A promoter SNP rs2425640, neither of which is present on the 100K panel, were associated with type 2 diabetes and impaired glucose tolerance in the Amish Family Diabetes Study (OR 1.57, P = 0.008; 1.60, P = 0.04, respectively) (12,25). Interestingly, rs10509645 in HHEX on the 100K panel (r2 = 0.7 with rs7923837 found previously to be strongly associated with type 2 diabetes in other GWAS studies) was significantly associated with type 2 diabetes in the Amish (OR 1.30 for the G-allele; P = 0.02). Rs9300039 on chromosome 11, shown to be associated with type 2 diabetes in the other GWAS studies (17), was present on the 100K panel but was not significantly associated with type 2 diabetes in the Amish (OR 1.09 for the C-allele; P = 0.67).

In summary, we presented results from our initial examination of a GWAS of type 2 diabetes in the Amish. Although we did not identify any genes associated with type 2 diabetes that reached genome-wide significance, we report a number of genes and loci that are worthy of further study based on replication in other studies or on quantitative trait loci consistency. This report (and the three companion articles) provides a valuable resource for other investigators to utilize in the search for the pathogenic variants for type 2 diabetes.

FIG. 1.

Schematic diagram of analysis and SNP prioritization approach for a 100K type 2 diabetes GWAS in the Amish. FASG, fasting glucose during an OGTT; FHS, Framingham Heart Study.

FIG. 1.

Schematic diagram of analysis and SNP prioritization approach for a 100K type 2 diabetes GWAS in the Amish. FASG, fasting glucose during an OGTT; FHS, Framingham Heart Study.

FIG. 2.

SNP association P values (<0.01) across all autosomal chromosomes.

FIG. 2.

SNP association P values (<0.01) across all autosomal chromosomes.

TABLE 1

Description of sample characteristics for type 2 diabetes GWAS in the Amish

CharacteristicsType 2 diabetic case and NGT control subject dataset
Type 2 diabetic case subjectsNGT control subjects
n 124 295 
Male subjects (%) 33 52 
Age (years) 51.3 ± 10.5 64.4 ± 12.9 
BMI (kg/m229.3 ± 5.8 27.4 ± 4.7 
CharacteristicsType 2 diabetic case and NGT control subject dataset
Type 2 diabetic case subjectsNGT control subjects
n 124 295 
Male subjects (%) 33 52 
Age (years) 51.3 ± 10.5 64.4 ± 12.9 
BMI (kg/m229.3 ± 5.8 27.4 ± 4.7 
CharacteristicsOGTT-derived quantitative trait dataset
AllMenWomen
n 427 200 227 
Age (years) 51.9 ± 11.9 52.2 ± 11.9 51.7 ± 11.9 
BMI (kg/m227.7 ± 5.0 26.4 ± 4.0 28.9 ± 5.5 
Fasting glucose (mmol/l) 5.09 ± 0.45 5.12 ± 0.47 5.07 ± 0.43 
GAUC (mmol · l−1 · h−1379.2 ± 68.0 362.6 ± 65.3 393.9 ± 67.1 
IAUC (mU· l−1 · h−1137.9 ± 88.9 108.5 ± 60.6 164.5 ± 101.3 
HOMA-IR (mU per mmol/l22.6 ± 1.7 2.6 ± 2.2 2.6 ± 1.1 
ISI (units/g) 0.9 ± 1.5 0.85 ± 1.9 0.93 ± 1.0 
CharacteristicsOGTT-derived quantitative trait dataset
AllMenWomen
n 427 200 227 
Age (years) 51.9 ± 11.9 52.2 ± 11.9 51.7 ± 11.9 
BMI (kg/m227.7 ± 5.0 26.4 ± 4.0 28.9 ± 5.5 
Fasting glucose (mmol/l) 5.09 ± 0.45 5.12 ± 0.47 5.07 ± 0.43 
GAUC (mmol · l−1 · h−1379.2 ± 68.0 362.6 ± 65.3 393.9 ± 67.1 
IAUC (mU· l−1 · h−1137.9 ± 88.9 108.5 ± 60.6 164.5 ± 101.3 
HOMA-IR (mU per mmol/l22.6 ± 1.7 2.6 ± 2.2 2.6 ± 1.1 
ISI (units/g) 0.9 ± 1.5 0.85 ± 1.9 0.93 ± 1.0 

Data are means ± SD. All were nondiabetic subjects.

TABLE 2

Fifty SNPs most highly associated with type 2 diabetes from Amish GWAS

SNPChromo-somePosition*GeneAlleles 1/2StrandCase subjects (n)Control subjects (n)Allele 2 case subjectsAllele 2 control subjectsType 2 diabetes POR§Type 2 diabetes P (BMI)
rs2237457 50693638 Grb10 A/G − 124 293 0.53 0.68 1.07 × 10−5 0.61 1.46 × 10−5 
rs980720 82272087 PRKG2 A/G − 122 287 0.80 0.90 1.25 × 10−5 0.52 4.87 × 10−5 
rs10509199 10 65295820  G/T − 124 295 0.31 0.46 1.68 × 10−5 0.62 4.46 × 10−5 
rs1373147 17 20147237 SPECC1 A/T − 124 293 0.47 0.60 2.79 × 10−5 0.63 5.26 × 10−6 
rs4082516 10 71374662 COL13A1 C/G − 122 293 0.77 0.88 3.19 × 10−5 0.53 1.27 × 10−5 
rs10509195 10 65193372  A/C − 118 281 0.42 0.28 3.30 × 10−5 1.62 7.1 × 10−5 
rs1395931 123535443  A/G 124 293 0.68 0.58 6.36 × 10−5 1.57 2.90 × 10−5 
rs3136279 47871272 MSH6 G/T − 124 294 0.20 0.11 6.81 × 10−5 1.80 9.2 × 10−5 
rs1446732 134374210  G/T − 121 295 0.51 0.37 7.38 × 10−5 1.55 0.0001 
rs10485249 70161908  C/G 124 295 0.82 0.92 7.96 × 10−5 0.54 0.0004 
rs2703813 17 20055907 SPECC1 A/G − 123 285 0.51 0.64 8.12 × 10−5 0.64 2.36 × 10−5 
rs1916412 10 65340801  C/T − 124 293 0.62 0.49 8.35 × 10−5 1.54 0.0002 
rs1916411 10 65340839  C/G − 123 292 0.38 0.51 8.48 × 10−5 0.65 0.0002 
rs3751797 16 85124993 MTHFSD A/T − 123 285 0.76 0.64 8.74 × 10−5 1.60 0.0005 
rs930621 134418548  C/T 122 290 0.54 0.41 8.74 × 10−5 1.55 0.0001 
rs10509201 10 65342248  A/G − 123 293 0.62 0.49 8.80 × 10−5 1.53 0.0002 
rs2158473 17 20079682 SPECC1 C/T 119 292 0.51 0.39 0.0001 1.54 3.00 × 10−5 
rs2502497 75399083  G/T 116 279 0.05 0.10 0.0001 0.43 0.0002 
rs430123 106109632  A/G − 114 272 0.16 0.27 0.0002 0.61 0.0002 
rs10504553 75038957 TCEB1 A/G − 115 275 0.77 0.89 0.0002 0.56 0.0003 
rs9287428 133912538 FLJ34870 C/T 124 287 0.83 0.71 0.0002 1.62 0.0002 
rs10507601 13 55047379  A/G 123 294 0.04 0.12 0.0002 0.44 0.0006 
rs994952 78319674  A/G 120 287 0.52 0.42 0.0002 1.52 0.0002 
rs7604549 133923233 FLJ34870 A/G − 123 294 0.26 0.40 0.0002 0.65 0.0002 
rs2737245 116727757 TRPS1 A/C − 113 276 0.58 0.71 0.0002 0.67 0.0003 
rs1513287 114754898 SIDT1 A/G 120 289 0.58 0.44 0.0002 1.49 0.0003 
rs7817780 119486147 SAMD12 C/T − 120 294 0.91 0.80 0.0002 1.85 0.0002 
rs297765 20 4435111  A/G 124 295 0.75 0.63 0.0002 1.55 0.0004 
rs4410442 112434500  A/G − 123 294 0.48 0.34 0.0002 1.52 0.0008 
rs1351916 139479918  A/T − 122 288 0.37 0.56 0.0002 0.67 0.0006 
rs1507666 123532455  A/T − 119 285 0.44 0.56 0.0002 0.66 0.0002 
rs10498934 83516566  C/T − 124 293 0.57 0.69 0.0002 0.66 0.0004 
rs721729 174671967  A/G − 119 285 0.43 0.56 0.0002 0.65 0.0002 
rs1492908 159869201 GFM1 A/G 122 291 0.20 0.29 0.0002 0.62 0.0004 
rs10484725 78325880  A/G 124 294 0.19 0.11 0.0002 1.75 0.0002 
rs10487440 125763796  G/T − 121 284 0.17 0.23 0.0002 0.59 0.0002 
rs10498881 72999327 RIMS1 C/G − 121 289 0.88 0.95 0.0002 0.48 0.0019 
rs9317821 13 69008405  C/T − 123 290 0.52 0.64 0.0002 0.67 0.0008 
rs10487442 125850492  A/G 124 295 0.83 0.76 0.0003 1.68 0.0003 
rs723397 107375942 FBXL17 C/T 123 295 0.24 0.36 0.0003 0.65 0.0014 
rs6897150 165833376  A/G − 123 294 0.26 0.14 0.0003 1.60 0.0003 
rs1458405 78321198  G/T − 112 270 0.51 0.40 0.0003 1.54 0.0002 
rs10521205 17 12809867 KIAA0672 A/G − 122 292 0.16 0.26 0.0003 0.60 0.0007 
rs1023738 174685924  C/T − 123 294 0.44 0.55 0.0003 0.67 0.0002 
rs1367313 159848232 GFM1 C/T − 124 295 0.79 0.71 0.0003 1.57 0.0005 
rs10521204 17 12809385 KIAA0672 A/C 124 293 0.83 0.74 0.0003 1.65 0.0007 
rs2034531 137364060  A/G − 123 289 0.80 0.88 0.0003 0.59 0.0006 
rs1282090 112779804 CD96 A/G 124 295 0.82 0.68 0.0004 1.58 0.0011 
rs9328099 2382544  A/G − 122 294 0.74 0.84 0.0004 0.63 0.0004 
rs10497567 181111709 KIAA1604 A/G 124 295 0.93 0.85 0.0004 1.98 0.0005 
SNPChromo-somePosition*GeneAlleles 1/2StrandCase subjects (n)Control subjects (n)Allele 2 case subjectsAllele 2 control subjectsType 2 diabetes POR§Type 2 diabetes P (BMI)
rs2237457 50693638 Grb10 A/G − 124 293 0.53 0.68 1.07 × 10−5 0.61 1.46 × 10−5 
rs980720 82272087 PRKG2 A/G − 122 287 0.80 0.90 1.25 × 10−5 0.52 4.87 × 10−5 
rs10509199 10 65295820  G/T − 124 295 0.31 0.46 1.68 × 10−5 0.62 4.46 × 10−5 
rs1373147 17 20147237 SPECC1 A/T − 124 293 0.47 0.60 2.79 × 10−5 0.63 5.26 × 10−6 
rs4082516 10 71374662 COL13A1 C/G − 122 293 0.77 0.88 3.19 × 10−5 0.53 1.27 × 10−5 
rs10509195 10 65193372  A/C − 118 281 0.42 0.28 3.30 × 10−5 1.62 7.1 × 10−5 
rs1395931 123535443  A/G 124 293 0.68 0.58 6.36 × 10−5 1.57 2.90 × 10−5 
rs3136279 47871272 MSH6 G/T − 124 294 0.20 0.11 6.81 × 10−5 1.80 9.2 × 10−5 
rs1446732 134374210  G/T − 121 295 0.51 0.37 7.38 × 10−5 1.55 0.0001 
rs10485249 70161908  C/G 124 295 0.82 0.92 7.96 × 10−5 0.54 0.0004 
rs2703813 17 20055907 SPECC1 A/G − 123 285 0.51 0.64 8.12 × 10−5 0.64 2.36 × 10−5 
rs1916412 10 65340801  C/T − 124 293 0.62 0.49 8.35 × 10−5 1.54 0.0002 
rs1916411 10 65340839  C/G − 123 292 0.38 0.51 8.48 × 10−5 0.65 0.0002 
rs3751797 16 85124993 MTHFSD A/T − 123 285 0.76 0.64 8.74 × 10−5 1.60 0.0005 
rs930621 134418548  C/T 122 290 0.54 0.41 8.74 × 10−5 1.55 0.0001 
rs10509201 10 65342248  A/G − 123 293 0.62 0.49 8.80 × 10−5 1.53 0.0002 
rs2158473 17 20079682 SPECC1 C/T 119 292 0.51 0.39 0.0001 1.54 3.00 × 10−5 
rs2502497 75399083  G/T 116 279 0.05 0.10 0.0001 0.43 0.0002 
rs430123 106109632  A/G − 114 272 0.16 0.27 0.0002 0.61 0.0002 
rs10504553 75038957 TCEB1 A/G − 115 275 0.77 0.89 0.0002 0.56 0.0003 
rs9287428 133912538 FLJ34870 C/T 124 287 0.83 0.71 0.0002 1.62 0.0002 
rs10507601 13 55047379  A/G 123 294 0.04 0.12 0.0002 0.44 0.0006 
rs994952 78319674  A/G 120 287 0.52 0.42 0.0002 1.52 0.0002 
rs7604549 133923233 FLJ34870 A/G − 123 294 0.26 0.40 0.0002 0.65 0.0002 
rs2737245 116727757 TRPS1 A/C − 113 276 0.58 0.71 0.0002 0.67 0.0003 
rs1513287 114754898 SIDT1 A/G 120 289 0.58 0.44 0.0002 1.49 0.0003 
rs7817780 119486147 SAMD12 C/T − 120 294 0.91 0.80 0.0002 1.85 0.0002 
rs297765 20 4435111  A/G 124 295 0.75 0.63 0.0002 1.55 0.0004 
rs4410442 112434500  A/G − 123 294 0.48 0.34 0.0002 1.52 0.0008 
rs1351916 139479918  A/T − 122 288 0.37 0.56 0.0002 0.67 0.0006 
rs1507666 123532455  A/T − 119 285 0.44 0.56 0.0002 0.66 0.0002 
rs10498934 83516566  C/T − 124 293 0.57 0.69 0.0002 0.66 0.0004 
rs721729 174671967  A/G − 119 285 0.43 0.56 0.0002 0.65 0.0002 
rs1492908 159869201 GFM1 A/G 122 291 0.20 0.29 0.0002 0.62 0.0004 
rs10484725 78325880  A/G 124 294 0.19 0.11 0.0002 1.75 0.0002 
rs10487440 125763796  G/T − 121 284 0.17 0.23 0.0002 0.59 0.0002 
rs10498881 72999327 RIMS1 C/G − 121 289 0.88 0.95 0.0002 0.48 0.0019 
rs9317821 13 69008405  C/T − 123 290 0.52 0.64 0.0002 0.67 0.0008 
rs10487442 125850492  A/G 124 295 0.83 0.76 0.0003 1.68 0.0003 
rs723397 107375942 FBXL17 C/T 123 295 0.24 0.36 0.0003 0.65 0.0014 
rs6897150 165833376  A/G − 123 294 0.26 0.14 0.0003 1.60 0.0003 
rs1458405 78321198  G/T − 112 270 0.51 0.40 0.0003 1.54 0.0002 
rs10521205 17 12809867 KIAA0672 A/G − 122 292 0.16 0.26 0.0003 0.60 0.0007 
rs1023738 174685924  C/T − 123 294 0.44 0.55 0.0003 0.67 0.0002 
rs1367313 159848232 GFM1 C/T − 124 295 0.79 0.71 0.0003 1.57 0.0005 
rs10521204 17 12809385 KIAA0672 A/C 124 293 0.83 0.74 0.0003 1.65 0.0007 
rs2034531 137364060  A/G − 123 289 0.80 0.88 0.0003 0.59 0.0006 
rs1282090 112779804 CD96 A/G 124 295 0.82 0.68 0.0004 1.58 0.0011 
rs9328099 2382544  A/G − 122 294 0.74 0.84 0.0004 0.63 0.0004 
rs10497567 181111709 KIAA1604 A/G 124 295 0.93 0.85 0.0004 1.98 0.0005 
*

Genome build 36.1.

Genic region that contains associated SNPs.

P values derived using variance components analysis under an additive genetic model, adjusted for age, sex, and family structure.

§

OR calculated from a liability threshold model in SOLAR and estimated as allele 2 versus allele 1.

P values derived using variance components analysis under an additive genetic model, adjusted for age, sex, BMI, and family structure. Our complete dataset with results from all models are available online (available at http://www.medschool.umaryland.edu/amishstudies/index.asp).

TABLE 3

SNPs associated with type 2 diabetes (P < 0.01) and at least one OGTT-derived trait (P < 0.01) in nondiabetic Amish subjects

SNPChromo-somePosition*Gene†StrandAlleles 1/2Frequency allele 2Type 2 diabetes
OGTT trait analysis
POR§TraitMean 11Mean 12Mean 22P
rs667222 55090696 DHCR24 − A/G 0.75 0.005 0.71 GAUC 212.21 199.85 192.58 0.008 
rs6588186 66319597 PDE4B − A/G 0.78 0.008 1.45 GAUC 192.09 185.94 201.07 0.002 
rs570021 71217978 PTGER3 A/T 0.27 0.007 0.70 IAUC 550.64 613.59 690.13 0.007 
rs6424414 71228058 PTGER3 C/T 0.27 0.007 0.70 IAUC 548.85 614.86 683.35 0.007 
rs211706 75802445 SLC44A5 − A/G 0.08 0.002 0.52 Fasting glucose 2.83 2.74 2.68 0.006 
rs10493580 76277500 LOC729766 C/G 0.15 0.003 0.60 GAUC 199.25 186.85 189.20 0.005 
rs1030414 81241030  − C/T 0.07 0.005 0.51 GAUC 197.22 182.88 154.78 0.003 
rs2257963 105672501  − C/T 0.61 0.009 0.74 GAUC 208.52 195.36 190.49 0.009 
rs1516150 105758114  A/G 0.39 0.010 1.36 GAUC 190.76 195.70 208.75 0.009 
rs2576216 215202099 ESRRG A/G 0.16 0.006 0.64 GAUC 200.81 187.83 190.21 0.002 
rs2818781 215203396 ESRRG C/T 0.84 0.003 1.61 GAUC 168.17 189.21 201.24 2.2 × 10−4 
rs2576212 215204637 ESRRG − A/G 0.84 0.005 1.58 GAUC 178.70 188.25 200.71 0.001 
rs1343747 240794106  A/C 0.23 0.002 1.43 HOMA-IR 2.22 2.53 2.72 0.002 
         IAUC 555.29 646.59 694.29 0.002 
rs10495824 34604049  − C/T 0.76 0.008 1.41 IAUC 677.25 635.10 545.83 0.002 
rs10490049 40426854 SLC8A1 − A/C 0.17 0.002 1.54 Fasting glucose 2.79 2.88 2.89 0.001 
         GAUC 192.09 204.49 216.33 0.001 
rs897097 55539162  A/G 0.61 0.001 1.46 HOMA-IR 2.09 2.37 2.49 0.010 
rs2540317 102716239 MFSD9 − C/T 0.78 0.007 0.72 GAUC 216.62 199.07 189.80 3.4 × 10−4 
rs4324336 105593460  − A/T 0.78 0.005 1.44 Fasting glucose 2.78 2.76 2.85 0.002 
rs2321201 134136406  − C/T 0.37 0.006 1.35 IAUC 539.03 627.77 628.08 0.006 
rs10510530 22459609  A/T 0.86 0.003 0.65 Fasting glucose 2.90 2.86 2.79 0.007 
rs3845971 59975712 FHIT C/T 0.72 0.004 1.42 GAUC 185.46 188.48 203.71 1.0 × 10−4 
rs1373340 66984977  − A/C 0.86 0.007 0.67 HOMA-IR 1.89 2.14 2.43 0.006 
         IAUC 529.61 519.62 610.68 0.009 
rs2587015 147740690 PLSCR1 − C/T 0.76 0.007 1.40 IAUC 751.72 609.81 547.07 0.001 
rs2587014 147741153 PLSCR1 A/G 0.24 0.009 0.72 IAUC 548.12 610.88 743.82 0.001 
rs2587012 147741360 PLSCR1 − G/T 0.24 0.009 0.72 IAUC 549.20 618.96 758.40 4.0 × 10−4 
rs837678 191168575 LEPREL1 − C/T 0.23 0.007 1.39 Fasting glucose 2.79 2.82 3.02 0.008 
rs10517351 35352269  − A/G 0.83 0.010 0.71 ISI 0.41 0.47 0.59 0.010 
rs4128879 162098442  − A/G 0.85 0.007 0.67 ISI 1.46 0.62 0.52 0.009 
rs10521005 33638471 ADAMTS1 − C/T 0.46 0.004 0.74 IAUC 659.01 568.57 550.30 0.007 
rs9292501 33639167 ADAMTS1 A/G 0.45 0.005 0.75 GAUC 201.46 197.18 186.88 0.006 
         IAUC 655.02 570.18 550.12 0.008 
rs9292502 33639516 ADAMTS1 A/G 0.46 0.003 0.74 GAUC 201.78 197.24 186.94 0.006 
         IAUC 661.14 571.39 551.03 0.006 
rs709668 96199942  − C/T 0.28 0.009 0.73 GAUC 201.47 191.03 183.01 0.002 
rs950664 103381835  C/T 0.62 0.009 1.35 GAUC 188.26 190.43 205.91 1.4 × 10−4 
rs990133 108073268  C/T 0.86 0.004 1.58 GAUC 169.38 189.40 197.90 0.006 
rs7720835 117269389  C/T 0.20 0.006 1.41 HOMA-IR 2.26 2.48 3.25 0.002 
rs4365869 117282887  − A/T 0.79 0.009 0.72 HOMA-IR 3.24 2.46 2.27 0.003 
rs1423003 147427736 SPINK5 − A/G 0.45 0.004 1.38 HOMA-IR 2.51 2.32 2.12 0.008 
rs1862446 147460749 SPINK5 A/C 0.45 0.006 1.37 HOMA-IR 2.50 2.32 2.12 0.009 
rs1422930 167330171 ODZ2 C/T 0.09 0.003 0.52 HOMA-IR 2.27 2.72 1.45 0.002 
rs7770797 6566961 LY86 − C/T 0.43 0.005 0.73 IAUC 536.16 579.90 649.95 0.010 
rs10484908 108410287  C/G 0.11 0.009 1.53 GAUC 192.64 207.68 212.03 0.001 
rs10237701 8860569  C/T 0.45 0.009 1.33 ISI 0.67 0.49 0.51 0.008 
rs10487563 48905713  − C/T 0.23 0.002 0.68 ISI 0.49 0.60 0.80 1.4 × 10−4 
rs2237457 50693638 Grb10 − A/G 0.63 1.1 × 10−5 0.61 GAUC 214.90 197.81 191.93 0.001 
rs10499761 56161137 LOC650200 − A/G 0.75 0.007 1.42 GAUC 178.50 192.32 201.72 0.001 
rs3753107 91467087 AKAP9 − C/T 0.77 0.006 0.72 HOMA-IR 2.88 2.50 2.27 0.007 
rs10488510 91498161 AKAP9 G/T 0.23 0.006 1.39 HOMA-IR 2.29 2.54 2.76 0.010 
rs647055 114159262   A/G 0.25 0.007 1.41 IAUC 551.83 620.21 780.97 3.8 × 10−4 
         ISI 0.49 0.65 0.68 0.001 
rs10488284 119742580 KCND2 − A/G 0.15 0.001 0.60 HOMA-IR 2.49 2.14 2.53 0.003 
rs192392 53094411  A/C 0.54 0.004 0.74 IAUC 650.81 600.14 540.14 0.007 
rs2450148 53112873  − C/T 0.47 0.010 1.31 IAUC 537.54 580.77 669.94 0.002 
rs10504133 53152586  C/T 0.51 0.002 0.72 IAUC 659.06 595.05 525.29 0.001 
rs7001645 105192526 RIMS2 C/G 0.44 0.007 1.34 ISI 0.63 0.53 0.46 0.008 
rs9297357 106211509  − A/G 0.23 0.006 0.68 GAUC 201.81 185.25 199.13 0.001 
rs10505229 115908561  C/T 0.18 0.002 1.50 GAUC 193.84 200.43 230.74 0.004 
rs10511574 11941204  A/C 0.09 0.002 1.82 GAUC 193.05 209.84 170.35 0.007 
rs10511777 26773463  − A/G 0.94 0.006 0.53 Fasting glucose 3.19 2.92 2.80 0.002 
rs10491665 29401568  A/C 0.09 0.006 0.58 GAUC 198.29 185.73 171.34 0.007 
rs2804498 10 33660713 NRP1 − A/G 0.40 0.001 1.42 Fasting glucose 2.78 2.82 2.89 0.003 
rs768676 10 44022687  A/T 0.89 0.002 0.61 Fasting glucose 3.00 2.89 2.80 3.9 × 10−4 
rs1111803 10 72426094  C/T 0.55 0.008 0.75 GAUC 208.11 193.50 191.12 0.004 
rs2437871 10 90268360 C10orf5 A/C 0.54 0.003 0.72 ISI 0.47 0.51 0.68 0.001 
rs10509589 10 91804145  − A/C 0.11 0.004 1.61 HOMA-IR 2.34 2.74 2.51 0.010 
rs1887979 10 110133240  A/T 0.41 0.009 0.74 GAUC 201.35 193.00 187.75 0.010 
rs10500651 11 5530156  G/T 0.73 0.006 1.42 HOMA-IR 2.88 2.46 2.27 0.005 
rs7119814 11 107941856 EXPH5 C/T 0.14 0.007 1.50 ISI 0.58 0.46 0.29 0.008 
rs10506173 12 39431860 CNTN1 A/G 0.95 0.007 0.55 Fasting glucose 2.97 2.91 2.81 0.007 
rs312272 12 39534200 CNTN1 C/T 0.69 0.007 1.37 Fasting glucose 2.77 2.78 2.86 0.002 
rs192852 12 39537359 CNTN1 − C/T 0.32 0.009 0.74 Fasting glucose 2.86 2.77 2.77 0.001 
rs2289522 12 39616798 CNTN1 C/T 0.55 0.001 1.46 Fasting glucose 2.76 2.81 2.86 0.004 
rs3794247 12 39646028 CNTN1 A/C 0.57 0.002 1.42 Fasting glucose 2.76 2.81 2.86 0.003 
rs10521210 17 12966928  C/T 0.65 0.009 1.34 GAUC 184.59 193.63 199.65 0.009 
rs9915220 17 13630703  A/G 0.95 0.001 2.72 Fasting glucose 2.69 2.73 2.83 0.009 
rs530205 18 42639148  C/T 0.61 0.009 0.76 IAUC 642.24 605.90 538.52 0.008 
rs2953271 18 47696031  − A/T 0.38 0.002 0.69 GAUC 200.87 195.67 183.77 0.004 
rs10502971 18 49141959 DCC A/G 0.59 0.009 1.34 GAUC 186.36 193.47 203.52 0.001 
rs615696 18 49946910 MBD2 C/T 0.79 0.006 0.69 Fasting glucose 2.89 2.84 2.78 0.010 
rs739453 19 40771776  A/G 0.60 0.005 0.73 IAUC 685.23 586.92 536.51 0.002 
rs3745718 19 53406965 CARD8 − A/G 0.40 0.010 0.75 GAUC 202.60 193.05 186.48 0.003 
rs297765 20 4435111  A/G 0.67 2.1 × 10−4 1.55 Fasting glucose 2.74 2.80 2.85 0.004 
         GAUC 186.37 191.30 203.12 0.001 
rs2255140 21 31975858 SFRS15 C/T 0.87 0.009 1.57 ISI 0.37 0.48 0.60 0.004 
SNPChromo-somePosition*Gene†StrandAlleles 1/2Frequency allele 2Type 2 diabetes
OGTT trait analysis
POR§TraitMean 11Mean 12Mean 22P
rs667222 55090696 DHCR24 − A/G 0.75 0.005 0.71 GAUC 212.21 199.85 192.58 0.008 
rs6588186 66319597 PDE4B − A/G 0.78 0.008 1.45 GAUC 192.09 185.94 201.07 0.002 
rs570021 71217978 PTGER3 A/T 0.27 0.007 0.70 IAUC 550.64 613.59 690.13 0.007 
rs6424414 71228058 PTGER3 C/T 0.27 0.007 0.70 IAUC 548.85 614.86 683.35 0.007 
rs211706 75802445 SLC44A5 − A/G 0.08 0.002 0.52 Fasting glucose 2.83 2.74 2.68 0.006 
rs10493580 76277500 LOC729766 C/G 0.15 0.003 0.60 GAUC 199.25 186.85 189.20 0.005 
rs1030414 81241030  − C/T 0.07 0.005 0.51 GAUC 197.22 182.88 154.78 0.003 
rs2257963 105672501  − C/T 0.61 0.009 0.74 GAUC 208.52 195.36 190.49 0.009 
rs1516150 105758114  A/G 0.39 0.010 1.36 GAUC 190.76 195.70 208.75 0.009 
rs2576216 215202099 ESRRG A/G 0.16 0.006 0.64 GAUC 200.81 187.83 190.21 0.002 
rs2818781 215203396 ESRRG C/T 0.84 0.003 1.61 GAUC 168.17 189.21 201.24 2.2 × 10−4 
rs2576212 215204637 ESRRG − A/G 0.84 0.005 1.58 GAUC 178.70 188.25 200.71 0.001 
rs1343747 240794106  A/C 0.23 0.002 1.43 HOMA-IR 2.22 2.53 2.72 0.002 
         IAUC 555.29 646.59 694.29 0.002 
rs10495824 34604049  − C/T 0.76 0.008 1.41 IAUC 677.25 635.10 545.83 0.002 
rs10490049 40426854 SLC8A1 − A/C 0.17 0.002 1.54 Fasting glucose 2.79 2.88 2.89 0.001 
         GAUC 192.09 204.49 216.33 0.001 
rs897097 55539162  A/G 0.61 0.001 1.46 HOMA-IR 2.09 2.37 2.49 0.010 
rs2540317 102716239 MFSD9 − C/T 0.78 0.007 0.72 GAUC 216.62 199.07 189.80 3.4 × 10−4 
rs4324336 105593460  − A/T 0.78 0.005 1.44 Fasting glucose 2.78 2.76 2.85 0.002 
rs2321201 134136406  − C/T 0.37 0.006 1.35 IAUC 539.03 627.77 628.08 0.006 
rs10510530 22459609  A/T 0.86 0.003 0.65 Fasting glucose 2.90 2.86 2.79 0.007 
rs3845971 59975712 FHIT C/T 0.72 0.004 1.42 GAUC 185.46 188.48 203.71 1.0 × 10−4 
rs1373340 66984977  − A/C 0.86 0.007 0.67 HOMA-IR 1.89 2.14 2.43 0.006 
         IAUC 529.61 519.62 610.68 0.009 
rs2587015 147740690 PLSCR1 − C/T 0.76 0.007 1.40 IAUC 751.72 609.81 547.07 0.001 
rs2587014 147741153 PLSCR1 A/G 0.24 0.009 0.72 IAUC 548.12 610.88 743.82 0.001 
rs2587012 147741360 PLSCR1 − G/T 0.24 0.009 0.72 IAUC 549.20 618.96 758.40 4.0 × 10−4 
rs837678 191168575 LEPREL1 − C/T 0.23 0.007 1.39 Fasting glucose 2.79 2.82 3.02 0.008 
rs10517351 35352269  − A/G 0.83 0.010 0.71 ISI 0.41 0.47 0.59 0.010 
rs4128879 162098442  − A/G 0.85 0.007 0.67 ISI 1.46 0.62 0.52 0.009 
rs10521005 33638471 ADAMTS1 − C/T 0.46 0.004 0.74 IAUC 659.01 568.57 550.30 0.007 
rs9292501 33639167 ADAMTS1 A/G 0.45 0.005 0.75 GAUC 201.46 197.18 186.88 0.006 
         IAUC 655.02 570.18 550.12 0.008 
rs9292502 33639516 ADAMTS1 A/G 0.46 0.003 0.74 GAUC 201.78 197.24 186.94 0.006 
         IAUC 661.14 571.39 551.03 0.006 
rs709668 96199942  − C/T 0.28 0.009 0.73 GAUC 201.47 191.03 183.01 0.002 
rs950664 103381835  C/T 0.62 0.009 1.35 GAUC 188.26 190.43 205.91 1.4 × 10−4 
rs990133 108073268  C/T 0.86 0.004 1.58 GAUC 169.38 189.40 197.90 0.006 
rs7720835 117269389  C/T 0.20 0.006 1.41 HOMA-IR 2.26 2.48 3.25 0.002 
rs4365869 117282887  − A/T 0.79 0.009 0.72 HOMA-IR 3.24 2.46 2.27 0.003 
rs1423003 147427736 SPINK5 − A/G 0.45 0.004 1.38 HOMA-IR 2.51 2.32 2.12 0.008 
rs1862446 147460749 SPINK5 A/C 0.45 0.006 1.37 HOMA-IR 2.50 2.32 2.12 0.009 
rs1422930 167330171 ODZ2 C/T 0.09 0.003 0.52 HOMA-IR 2.27 2.72 1.45 0.002 
rs7770797 6566961 LY86 − C/T 0.43 0.005 0.73 IAUC 536.16 579.90 649.95 0.010 
rs10484908 108410287  C/G 0.11 0.009 1.53 GAUC 192.64 207.68 212.03 0.001 
rs10237701 8860569  C/T 0.45 0.009 1.33 ISI 0.67 0.49 0.51 0.008 
rs10487563 48905713  − C/T 0.23 0.002 0.68 ISI 0.49 0.60 0.80 1.4 × 10−4 
rs2237457 50693638 Grb10 − A/G 0.63 1.1 × 10−5 0.61 GAUC 214.90 197.81 191.93 0.001 
rs10499761 56161137 LOC650200 − A/G 0.75 0.007 1.42 GAUC 178.50 192.32 201.72 0.001 
rs3753107 91467087 AKAP9 − C/T 0.77 0.006 0.72 HOMA-IR 2.88 2.50 2.27 0.007 
rs10488510 91498161 AKAP9 G/T 0.23 0.006 1.39 HOMA-IR 2.29 2.54 2.76 0.010 
rs647055 114159262   A/G 0.25 0.007 1.41 IAUC 551.83 620.21 780.97 3.8 × 10−4 
         ISI 0.49 0.65 0.68 0.001 
rs10488284 119742580 KCND2 − A/G 0.15 0.001 0.60 HOMA-IR 2.49 2.14 2.53 0.003 
rs192392 53094411  A/C 0.54 0.004 0.74 IAUC 650.81 600.14 540.14 0.007 
rs2450148 53112873  − C/T 0.47 0.010 1.31 IAUC 537.54 580.77 669.94 0.002 
rs10504133 53152586  C/T 0.51 0.002 0.72 IAUC 659.06 595.05 525.29 0.001 
rs7001645 105192526 RIMS2 C/G 0.44 0.007 1.34 ISI 0.63 0.53 0.46 0.008 
rs9297357 106211509  − A/G 0.23 0.006 0.68 GAUC 201.81 185.25 199.13 0.001 
rs10505229 115908561  C/T 0.18 0.002 1.50 GAUC 193.84 200.43 230.74 0.004 
rs10511574 11941204  A/C 0.09 0.002 1.82 GAUC 193.05 209.84 170.35 0.007 
rs10511777 26773463  − A/G 0.94 0.006 0.53 Fasting glucose 3.19 2.92 2.80 0.002 
rs10491665 29401568  A/C 0.09 0.006 0.58 GAUC 198.29 185.73 171.34 0.007 
rs2804498 10 33660713 NRP1 − A/G 0.40 0.001 1.42 Fasting glucose 2.78 2.82 2.89 0.003 
rs768676 10 44022687  A/T 0.89 0.002 0.61 Fasting glucose 3.00 2.89 2.80 3.9 × 10−4 
rs1111803 10 72426094  C/T 0.55 0.008 0.75 GAUC 208.11 193.50 191.12 0.004 
rs2437871 10 90268360 C10orf5 A/C 0.54 0.003 0.72 ISI 0.47 0.51 0.68 0.001 
rs10509589 10 91804145  − A/C 0.11 0.004 1.61 HOMA-IR 2.34 2.74 2.51 0.010 
rs1887979 10 110133240  A/T 0.41 0.009 0.74 GAUC 201.35 193.00 187.75 0.010 
rs10500651 11 5530156  G/T 0.73 0.006 1.42 HOMA-IR 2.88 2.46 2.27 0.005 
rs7119814 11 107941856 EXPH5 C/T 0.14 0.007 1.50 ISI 0.58 0.46 0.29 0.008 
rs10506173 12 39431860 CNTN1 A/G 0.95 0.007 0.55 Fasting glucose 2.97 2.91 2.81 0.007 
rs312272 12 39534200 CNTN1 C/T 0.69 0.007 1.37 Fasting glucose 2.77 2.78 2.86 0.002 
rs192852 12 39537359 CNTN1 − C/T 0.32 0.009 0.74 Fasting glucose 2.86 2.77 2.77 0.001 
rs2289522 12 39616798 CNTN1 C/T 0.55 0.001 1.46 Fasting glucose 2.76 2.81 2.86 0.004 
rs3794247 12 39646028 CNTN1 A/C 0.57 0.002 1.42 Fasting glucose 2.76 2.81 2.86 0.003 
rs10521210 17 12966928  C/T 0.65 0.009 1.34 GAUC 184.59 193.63 199.65 0.009 
rs9915220 17 13630703  A/G 0.95 0.001 2.72 Fasting glucose 2.69 2.73 2.83 0.009 
rs530205 18 42639148  C/T 0.61 0.009 0.76 IAUC 642.24 605.90 538.52 0.008 
rs2953271 18 47696031  − A/T 0.38 0.002 0.69 GAUC 200.87 195.67 183.77 0.004 
rs10502971 18 49141959 DCC A/G 0.59 0.009 1.34 GAUC 186.36 193.47 203.52 0.001 
rs615696 18 49946910 MBD2 C/T 0.79 0.006 0.69 Fasting glucose 2.89 2.84 2.78 0.010 
rs739453 19 40771776  A/G 0.60 0.005 0.73 IAUC 685.23 586.92 536.51 0.002 
rs3745718 19 53406965 CARD8 − A/G 0.40 0.010 0.75 GAUC 202.60 193.05 186.48 0.003 
rs297765 20 4435111  A/G 0.67 2.1 × 10−4 1.55 Fasting glucose 2.74 2.80 2.85 0.004 
         GAUC 186.37 191.30 203.12 0.001 
rs2255140 21 31975858 SFRS15 C/T 0.87 0.009 1.57 ISI 0.37 0.48 0.60 0.004 

SNPs with P < 0.01 for type 2 diabetes associations were tested for consistency in a sample of nondiabetic individuals (295 of whom overlapped with the type 2 diabetes association dataset). Direction of association for glucose traits was required to be higher for diabetes risk allele. Neighboring SNPs in bold are in high LD (r2 > 0.80).

*

Genic region that contains associated SNPs.

P values derived using the additive genetic model, adjusted for age, sex, and family structure. The complete dataset including results for dominant and recessive models are available online (available at http://www.medschool.umaryland.edu/amishstudies/index.asp).

§

OR calculated from a liability threshold model for allele 2 versus allele 1.

Mean values for traits are presented by genotype, with alleles shown in alphabetical order as “1/2.” All insulin traits (IAUC, HOMA-IR, and ISI) were natural log transformed prior to analysis.

TABLE 4

SNPs associated with type 2 diabetes in the Amish (P < 0.005) and providing evidence for either internal consistency (association with an OGTT-derived quantitative trait in the Amish) or external replication (associated with type 2 diabetes in an independent population)

SNPChromo-somePosition*GeneStrandAssociated with type 2 diabetes in Amish (P < 0.005)
Internal consistency with OGTT-derived quantitative traits in Amish (P < 0.005)
External replication with type 2 diabetes in independent populations (P < 0.005)
Allele 1/2Allele 2 case subjectsAllele 2 control subjectsPORTraitPPopulationP§OR
rs689157 183074135 C1orf24 A/T 0.69 0.80 0.001 0.67   Pima Indians 0.002 0.65 
rs2818781 215203396 ESRRG C/T 0.89 0.81 0.003 1.61 GAUC 0.002    
rs1343747 240794106 — A/C 0.30 0.21 0.002 1.43 HOMA-IR 0.002    
          IAUC 0.002    
rs10490049 40426854 SLC8A1 − A/C 0.23 0.14 0.002 1.54 Fasting glucose 0.001    
          GAUC 0.001    
rs3845971 59975712 FHIT C/T 0.79 0.68 0.004 1.42 GAUC 4.0 × 10−4 Mexican Americans 0.004 1.46 
rs9312113 63643792 — C/G 0.75 0.64 0.004 1.41   FHS 0.013, 0.003 1.66 
rs3775745 71147663 CSN3 − A/C 0.26 0.39 0.002 0.70   Mexican Americans 0.003 0.68 
rs1422930 167330171 ODZ2 C/T 0.04 0.11 0.003 0.52 HOMA-IR 0.002    
rs9321743 140005650 — − A/C 0.42 0.32 0.003 1.42   FHS 0.002, 0.024 1.60 
rs10487563 48905713 — − C/T 0.15 0.27 0.002 0.68 ISI 0.001    
rs2237457 50693638 Grb10 − A/G 0.53 0.68 1.1 × 10−5 0.61 GAUC 0.001    
rs10488284 119742580 KCND2 − A/G 0.12 0.17 0.001 0.60 HOMA-IR 0.003    
rs10504133 53152586 — C/T 0.43 0.54 0.002 0.72 IAUC 0.001    
rs10505229 115908561 — C/T 0.27 0.15 0.002 1.50 GAUC 0.004    
rs2804498 10 33660713 NRP1 − A/G 0.49 0.36 0.001 1.42 Fasting glucose 0.003    
rs768676 10 44022687 — A/T 0.85 0.91 0.002 0.61 Fasting glucose 0.004    
rs2437871 10 90268360 C10orf59 A/C 0.47 0.57 0.003 0.72 ISI 0.001    
rs2289522 12 39616798 CNTN1 C/T 0.64 0.52 0.001 1.46 Fasting glucose 0.004    
rs7986010 13 91517260 GPC5 A/G 0.44 0.34 0.002 1.41   FHS 0.004, 0.042 1.59 
rs2953271 18 47696031 — − A/T 0.27 0.43 0.002 0.69 GAUC 0.004    
rs297765 20 4435111 — A/G 0.75 0.63 2.1 × 10−4 1.55 Fasting glucose 0.004    
SNPChromo-somePosition*GeneStrandAssociated with type 2 diabetes in Amish (P < 0.005)
Internal consistency with OGTT-derived quantitative traits in Amish (P < 0.005)
External replication with type 2 diabetes in independent populations (P < 0.005)
Allele 1/2Allele 2 case subjectsAllele 2 control subjectsPORTraitPPopulationP§OR
rs689157 183074135 C1orf24 A/T 0.69 0.80 0.001 0.67   Pima Indians 0.002 0.65 
rs2818781 215203396 ESRRG C/T 0.89 0.81 0.003 1.61 GAUC 0.002    
rs1343747 240794106 — A/C 0.30 0.21 0.002 1.43 HOMA-IR 0.002    
          IAUC 0.002    
rs10490049 40426854 SLC8A1 − A/C 0.23 0.14 0.002 1.54 Fasting glucose 0.001    
          GAUC 0.001    
rs3845971 59975712 FHIT C/T 0.79 0.68 0.004 1.42 GAUC 4.0 × 10−4 Mexican Americans 0.004 1.46 
rs9312113 63643792 — C/G 0.75 0.64 0.004 1.41   FHS 0.013, 0.003 1.66 
rs3775745 71147663 CSN3 − A/C 0.26 0.39 0.002 0.70   Mexican Americans 0.003 0.68 
rs1422930 167330171 ODZ2 C/T 0.04 0.11 0.003 0.52 HOMA-IR 0.002    
rs9321743 140005650 — − A/C 0.42 0.32 0.003 1.42   FHS 0.002, 0.024 1.60 
rs10487563 48905713 — − C/T 0.15 0.27 0.002 0.68 ISI 0.001    
rs2237457 50693638 Grb10 − A/G 0.53 0.68 1.1 × 10−5 0.61 GAUC 0.001    
rs10488284 119742580 KCND2 − A/G 0.12 0.17 0.001 0.60 HOMA-IR 0.003    
rs10504133 53152586 — C/T 0.43 0.54 0.002 0.72 IAUC 0.001    
rs10505229 115908561 — C/T 0.27 0.15 0.002 1.50 GAUC 0.004    
rs2804498 10 33660713 NRP1 − A/G 0.49 0.36 0.001 1.42 Fasting glucose 0.003    
rs768676 10 44022687 — A/T 0.85 0.91 0.002 0.61 Fasting glucose 0.004    
rs2437871 10 90268360 C10orf59 A/C 0.47 0.57 0.003 0.72 ISI 0.001    
rs2289522 12 39616798 CNTN1 C/T 0.64 0.52 0.001 1.46 Fasting glucose 0.004    
rs7986010 13 91517260 GPC5 A/G 0.44 0.34 0.002 1.41   FHS 0.004, 0.042 1.59 
rs2953271 18 47696031 — − A/T 0.27 0.43 0.002 0.69 GAUC 0.004    
rs297765 20 4435111 — A/G 0.75 0.63 2.1 × 10−4 1.55 Fasting glucose 0.004    
*

Genome build 36.1.

Genic region that contains associated SNPs.

OR calculated from a liability threshold model in SOLAR and estimated as allele 2 versus allele 1.

§

Case-control general estimating equation and family-based association test P values given for Framingham Heart Study (FHS) data; summary P value given for Pima Indians. All insulin traits (HOMA-IR, IAUC, and ISI) were natural log transformed prior to analysis.

Published ahead of print at http://diabetes.diabetesjournals.org on 10 September 2007. DOI: 10.2337/db07-0457.

Additional information for this article can be found in an online appendix at http://dx.doi.org/10.2337/db07-0457.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

This work was supported by the following National Institutes of Health Research Grants: Training Grant in Cardiac and Vascular Cell Biology (T32 HL072751), R01 DK54261, the University of Maryland General Clinical Research Center (M01 RR16500), Hopkins Bayview General Clinical Research Center (M01 RR02719), the Maryland Clinical Nutrition Research Unit (P30 DK072488), and the Baltimore Veterans Administration Geriatric Research and Education Clinical Center.

We extend our thanks to our collaborators in the Type 2 Diabetes 100K GWAS Consortium for sharing prepublication results from the Starr County Health Studies, Framingham Heart Study, and the Pima Indians as well as to the Broad-Lund-Novartis Diabetes Genomic Initiative for access to results from their GWAS.

We also thank Soren Snitker for helpful comments and Adam Naj for help with formatting figures for this manuscript. Lastly, we gratefully acknowledge our Amish liaisons and research staff and the extraordinary cooperation and support of the Amish community, without whom these studies would not be possible.

1.
Elbein SC, Hoffman MD, Teng K, Leppert MF, Hasstedt SJ: A genome-wide search for type 2 diabetes susceptibility genes in Utah Caucasians.
Diabetes
48
:
1175
–1182,
1999
2.
Wiltshire S, Hattersley AT, Hitman GA, Walker M, Levy JC, Sampson M, O'Rahilly S, Frayling TM, Bell JI, Lathrop GM, Bennett A, Dhillon R, Fletcher C, Groves CJ, Jones E, Prestwich P, Simecek N, Rao PV, Wishart M, Foxon R, Howell S, Smedley D, Cardon LR, Menzel S, McCarthy MI: A genomewide scan for loci predisposing to type 2 diabetes in a U.K. population (the Diabetes UK Warren 2 Repository): analysis of 573 pedigrees provides independent replication of a susceptibility locus on chromosome 1q.
Am J Hum Genet
69
:
553
–569,
2001
3.
Du W, Sun H, Wang H, Qiang B, Shen Y, Yao Z, Gu J, Xiong M, Huang W, Chen Z, Zuo J, Hua X, Gao W, Sun Q, Fang F: Confirmation of susceptibility gene loci on chromosome 1 in northern China Han families with type 2 diabetes.
Chin Med J (Engl)
114
:
876
–878,
2001
4.
Meigs JB, Panhuysen CI, Myers RH, Wilson PW, Cupples LA: A genome-wide scan for loci linked to plasma levels of glucose and HbA1c in a community-based sample of Caucasian pedigrees: the Framingham Offspring Study.
Diabetes
51
:
833
–840,
2002
5.
Hanson RL, Ehm MG, Pettitt DJ, Prochazka M, Thompson DB, Timberlake D, Foroud T, Kobes S, Baier L, Burns DK, Almasy L, Blangero J, Garvey WT, Bennett PH, Knowler WC: An autosomal genomic scan for loci linked to type II diabetes mellitus and body-mass index in Pima Indians.
Am J Hum Genet
63
:
1130
–1138,
1998
6.
Hsueh WC, St. Jean PL, Mitchell BD, Pollin TI, Knowler WC, Ehm MG, Bell CJ, Sakul H, Wagner MJ, Burns DK, Shuldiner AR: Genome-wide and fine-mapping linkage studies of type 2 diabetes and glucose traits in the Old Order Amish: evidence for a new diabetes locus on chromosome 14q11 and confirmation of a locus on chromosome 1q21–q24.
Diabetes
52
:
550
–557,
2003
7.
Ng MC, So WY, Cox NJ, Lam VK, Cockram CS, Critchley JA, Bell GI, Chan JC: Genome-wide scan for type 2 diabetes loci in Hong Kong Chinese and confirmation of a susceptibility locus on chromosome 1q21–q25.
Diabetes
53
:
1609
–1613,
2004
8.
McCarthy MI: Growing evidence for diabetes susceptibility genes from genome scan data.
Curr Diab Rep
3
:
159
–167,
2003
9.
Owen KR, McCarthy MI: Genetics of type 2 diabetes.
Curr Opin Genet Dev
17
:
239
–244,
2007
10.
Hsueh W-C, Mitchell BD, Aburomia R, Pollin T, Sakul H, Ehm MG, Michelsen BK, Wagner MJ, St. Jean PL, Knowler WC, Burns DK, Bell CJ, Shuldiner AR: Diabetes in the old order Amish: characterization and heritability analysis of the Amish Family Diabetes Study.
Diabetes Care
23
:
595
–601,
2000
11.
Damcott CM, Pollin TI, Reinhart LJ, Ott SH, Shen H, Silver KD, Mitchell BD, Shuldiner AR: Polymorphisms in the transcription factor 7-like 2 (TCF7L2) gene are associated with type 2 diabetes in the Amish: replication and evidence for a role in both insulin secretion and insulin resistance.
Diabetes
55
:
2654
–2659,
2006
12.
Damcott CM, Hoppman N, Ott SH, Reinhart LJ, Wang J, Pollin TI, O'Connell JR, Mitchell BD, Shuldiner AR: Polymorphisms in both promoters of hepatocyte nuclear factor 4-α are associated with type 2 diabetes in the Amish.
Diabetes
53
:
3337
–3341,
2004
13.
Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, San Giovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C, Hoh J: Complement factor H polymorphism in age-related macular degeneration.
Science
308
:
385
–389,
2005
14.
Gibbs JR, Singleton A: Application of genome-wide single nucleotide polymorphism typing: simple association and beyond.
PLoS Genet
2
:
e150
,
2006
15.
Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, Timpson NJ, Perry JR, Rayner NW, Freathy RM, Barrett JC, Shields B, Morris AP, Ellard S, Groves CJ, Harries LW, Marchini JL, Owen KR, Knight B, Cardon LR, Walker M, Hitman GA, Morris AD, Doney AS, the Wellcome Trust Case Control Consortium (WTCCC), McCarthy MI, Hattersley AT: Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes.
Science
316
:
1336
–1341,
2007
16.
Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, Walters GB, Styrkarsdottir U, Gretarsdottir S, Emilsson V, Ghosh S, Baker A, Snorradottir S, Bjarnason H, Ng MC, Hansen T, Bagger Y, Wilensky RL, Reilly MP, Adeyemo A, Chen Y, Zhou J, Gudnason V, Chen G, Huang H, Lashley K, Doumatey A, So WY, Ma RC, Andersen G, Borch-Johnsen K, Jorgensen T, van Vliet-Ostaptchouk JV, Hofker MH, Wijmenga C, Christiansen C, Rader DJ, Rotimi C, Gurney M, Chan JC, Pedersen O, Sigurdsson G, Gulcher JR, Thorsteinsdottir U, Kong A, Stefansson K: A variant in CDKAL1 influences insulin response and risk of type 2 diabetes.
Nat Genet
39
:
770
–775,
2007
17.
Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely KN, Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, Buchanan TA, Watanabe RM, Valle TT, Kinnunen L, Abecasis GR, Pugh EW, Doheny KF, Bergman RN, Tuomilehto J, Collins FS, Boehnke M: A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants.
Science
316
:
1341
–1345,
2007
18.
Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Roix JJ, Kathiresan S, Hirschhorn JN, Daly MJ, Hughes TE, Groop L, Altshuler D, Almgren P, Florez JC, Meyer J, Ardlie K, Bengtsson BK, Isomaa B, Lettre G, Lindblad U, Lyon HN, Melander O, Newton-Cheh C, Nilsson P, Orho-Melander M, Rastam L, Speliotes EK, Taskinen MR, Tuomi T, Guiducci C, Berglund A, Carlson J, Gianniny L, Hackett R, Hall L, Holmkvist J, Laurila E, Sjogren M, Sterner M, Surti A, Svensson M, Svensson M, Tewhey R, Blumenstiel B, Parkin M, Defelice M, Barry R, Brodeur W, Camarata J, Chia N, Fava M, Gibbons J, Handsaker B, Healy C, Nguyen K, Gates C, Sougnez C, Gage D, Nizzari M, Gabriel SB, Chirn GW, Ma Q, Parikh H, Richardson D, Ricke D, Purcell S: Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.
Science
316
:
1331
–1336,
2007
19.
BRLMM: an improved genotype calling method for the GeneChip Human Mapping 500K Array Set [article online],
2006
. Available from http://www.affymetrix.com/support/technical/whitepapers/brlmm_whitepaper.pdf. Accessed 5 September 2007
20.
Almasy L, Blangero J: Multipoint quantitative-trait linkage analysis in general pedigrees.
Am J Hum Genet
62
:
1198
–1211,
1998
21.
Boerwinkle E, Chakraborty R, Sing CF: The use of measured genotype information in the analysis of quantitative phenotypes in man. I. Models and analytical methods.
Ann Intern Med
50
:
181
–194,
1986
22.
Purcell S, Cherny SS, Sham PC: Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits.
Bioinformatics
19
:
149
–150,
2003
23.
Florez JC, Manning AK, Dupuis J, McAteer J, Irenze K, Gianniny L, Mirel DB, Fox CS, Cupples LA, Meigs JB: A 100K genome-wide association scan for diabetes and related traits in the Framingham Heart Study: replication and integration with other genome-wide datasets.
Diabetes
56
:
3063
–3074,
2007
24.
Hanson RL, Bogardus C, Duggan D, Kobes S, Knowlton M, Infante AM, Marovich L, Benitez D, Baier LJ, Knowler WC: A search for variants associated with young-onset type 2 diabetes in American Indians in a 100K genotyping array.
Diabetes
56
:
3045
–3052,
2007
25.
Hayes MG, Pluzhnikov A, Miyake K, Sun Y, Ng MCY, Roe CA, Below JE, Nicolae RI, Konkashbaev A, Bell GI, Cox NJ, Hanis CL: Identification of type 2 diabetes genes in Mexican Americans through genome-wide association studies.
Diabetes
56
:
3033
–3042,
2007
26.
Deng Y, Bhattacharya S, Swamy OR, Tandon R, Wang Y, Janda R, Riedel H: Growth factor receptor-binding protein 10 (Grb10) as a partner of phosphatidylinositol 3-kinase in metabolic insulin action.
J Biol Chem
278
:
39311
–39322,
2003
27.
Langlais P, Dong LQ, Ramos FJ, Hu D, Li Y, Quon MJ, Liu F: Negative regulation of insulin-stimulated mitogen-activated protein kinase signaling by Grb10.
Mol Endocrinol
18
:
350
–358,
2004
28.
Mounier C, Lavoie L, Dumas V, Mohammad-Ali K, Wu J, Nantel A, Bergeron JJ, Thomas DY, Posner BI: Specific inhibition by hGrb10zeta of insulin-induced glycogen synthase activation: evidence for a novel signaling pathway.
Mol Cell Endocrinol
173
:
15
–27,
2001
29.
Shiura H, Miyoshi N, Konishi A, Wakisaka-Saito N, Suzuki R, Muguruma K, Kohda T, Wakana S, Yokoyama M, Ishino F, Kaneko-Ishino T: Meg1/Grb10 overexpression causes postnatal growth retardation and insulin resistance via negative modulation of the IGF1R and IR cascades.
Biochem Biophys Res Commun
329
:
909
–916,
2005
30.
Di Paola R, Ciociola E, Boonyasrisawat W, Nolan D, Duffy J, Miscio G, Cisternino C, Fini G, Tassi V, Doria A, Trischitta V: Association of hGrb10 genetic variations with type 2 diabetes in Caucasian subjects.
Diabetes Care
29
:
1181
–1183,
2006