OBJECTIVE—The objective of this study was to identify DNA polymorphisms associated with type 2 diabetes in a Mexican-American population.
RESEARCH DESIGN AND METHODS—We genotyped 116,204 single nucleotide polymorphisms (SNPs) in 281 Mexican Americans with type 2 diabetes and 280 random Mexican Americans from Starr County, Texas, using the Affymetrix GeneChip Human Mapping 100K set. Allelic association exact tests were calculated. Our most significant SNPs were compared with results from other type 2 diabetes genome-wide association studies (GWASs). Proportions of African, European, and Asian ancestry were estimated from the HapMap samples using structure for each individual to rule out spurious association due to population substructure.
RESULTS—We observed more significant allelic associations than expected genome wide, as empirically assessed by permutation (14 below a P of 1 × 10−4 [8.7 expected]). No significant differences were observed between the proportion of ancestry estimates in the case and random control sets, suggesting that the association results were not likely confounded by substructure. A query of our top ∼1% of SNPs (P < 0.01) revealed SNPs in or near four genes that showed evidence for association (P < 0.05) in multiple other GWAS interrogated: rs979752 and rs10500641 near UBQLNL and OR52H1 on chromosome 11, rs2773080 and rs3922812 in or near RALGPS2 on chromosome 1, and rs1509957 near EGR2 on chromosome 10.
CONCLUSIONS—We identified several SNPs with suggestive evidence for replicated association with type 2 diabetes that merit further investigation.
Diabetes continues to pose a substantial and increasing burden of morbidity and mortality on society, especially among minority populations. In the U.S., ∼18 million people have diabetes, of which one-third remain undiagnosed and most (90–95%) have type 2 diabetes (1). By 2050, rates of diagnosed diabetes are projected to more than double to 39 million, with fully one-third of children born in the year 2000 expected to develop diabetes over their lifetime (1). Minority populations, such as Mexican Americans, have a disproportionate incidence of diabetes (2–5). For example, the Mexican-American population from Starr County, Texas, has the highest diabetes-specific morbidity and mortality of any county in Texas, yet it is only the 53rd largest of Texas’ 254 counties. Age-specific prevalences are three- to fivefold higher than the general U.S. population (4,6), and in the last two decades alone there has been a 74% increase in type 2 diabetes prevalence in those aged ≥25 years in this population.
Population studies, pedigree investigations, molecular studies, and animal models consistently implicate a substantial role for genes in determining risk for type 2 diabetes (see 7,8). These studies also establish that no simple genetic model adequately explains risk for diabetes. Rather, there are likely to be multiple genes with small to modest effects that interact with each other and with environmental factors to affect susceptibility (9–11). This view of the genetics of diabetes is able to explain both its population and familial aggregation and implies that we are looking for genes whose effects are neither necessary nor sufficient to cause disease.
A great deal of effort has been expended in identifying genes underlying the risk for type 2 diabetes, including genome linkage scans (see 12,13), candidate gene studies (e.g., 14), and, more recently, genome-wide association studies (GWASs) (15–19). To date, such studies have yielded several replicated type 2 diabetes–associated risk genes including CAPN10, CDKAL1, CDKN2A, HHEX, HNF4A, IGF2BP2, KCNJ11, PPARG, SLC30A8, and TCF7L2 (20–25), but none account for a large proportion of the risk of developing type 2 diabetes in the particular population under study nor are any seen universally across all populations. Again, this suggests that many more type 2 diabetes susceptibility genes remain undiscovered.
Over the past decade, we have conducted genome-wide linkage scans on Mexican-American families from Starr County, Texas, to localize genes conferring risk to type 2 diabetes and were successful in positionally cloning the CAPN10 gene as a type 2 diabetes susceptibility locus (6,20). Given the increased power in association studies over linkage studies (26) for complex genetic diseases such as type 2 diabetes, we conducted a GWAS of a >600-member case-control set to identify additional genomic regions harboring type 2 diabetes susceptibility loci in the Starr County population. We present the results of this type 2 diabetes GWAS, the first in a non-Caucasian population, along with supporting evidence for replication from available GWASs, primarily the three accompanying this one (27,28,29).
RESEARCH DESIGN AND METHODS
This study was completed in a Mexican-American population from Starr County, Texas. We selected as unrelated cases 291 individuals who represent the youngest age-at-onset individuals from the multiplex families in our previous linkage studies and for whom we have the richest phenotypic data. The comparison individuals are not true control subjects in that their diabetes status is unknown. Rather, they are a representative sample of 323 unrelated individuals drawn from a random survey of Starr County. Of this case and random control set, 281 and 280 individuals were analyzed (see “Quality control” below) and are described in Table 1. An overlapping cohort (online appendix Table 1 [available at http://dx.doi.org/10.2337/db07-0482]) of 760 individuals (including 555 of the 561 individuals analyzed) was used to verify genotypes before single nucleotide polymorphism (SNP) selection for follow-up replication.
Diabetes was classified based on earlier National Diabetes Data Group recommendations (30), namely, previously diagnosed diabetes and current or sustained use of glucose-lowering medications, fasting glucose ≥140 mg/dl on more than one occasion, or a 2-h postload glucose of ≥200 mg/dl. Individuals were considered to have type 2 diabetes unless they were diagnosed before age 30 years, had a BMI <30 kg/m2, and had used insulin continuously since diagnosis.
Genotypying.
Genomic DNA was isolated from lymphocytes and quantified by picogreen. The genotyping assay was performed according to the manufacturer protocols (Affymetrix, Santa Clara, CA) by the Functional Genomics Core Facility at the University of Chicago. In brief, 250 ng DNA was digested with the restriction enzymes XbaI and HindIII, followed by adaptor ligation. The DNA fragments were then amplified, fragmented, labeled, and hybridized overnight to the Affymetrix GeneChip Human Mapping 100K XbaI and HindIII arrays. The arrays were scanned with the Affymetrix 7G scanner and analyzed with Affymetrix GeneChip DNA Analysis Software to generate hybridization intensity files and subsequent dynamic modeling (DM) algorithm–derived genotypes.
Case and random samples were dispersed randomly throughout the plates to eliminate the possibility of spurious associations due to systematic differences in genotyping conditions between experiments. Genotypes were called using the default Affymetrix DM algorithm and two improved algorithms, (GEL) (31) and Bayesian RLMM (BRLMM) (32,33). After removal of monomorphic markers, we analyzed genotypes for 112,541 autosomal SNPs of the possible 116,204 SNPs interrogated on the array. We anticipate analyzing X chromosome polymorphisms at a later date. Genotyping in verification sets was performed using TaqMan assays on the ABI Prism 7900HT Sequence Detection System.
Statistical methods.
We examined the case-random control cohort for evidence of related individuals that went undetected during sample collection using PLINK (34). Pairs with identity-by-descent estimates >0.20 were trimmed, preferentially keeping case rather than control subjects and individuals with higher genotype call rates if the pair was a case-case or control-control.
Fisher's exact tests for allelic associations and departures from Hardy-Weinberg equilibrium (HWE) were calculated for all polymorphic SNPs. We did not remove any of the SNPs for strict quality-control reasons but, rather, cataloged quality-control indicators for each SNP and considered them during the interpretation of the data. We observed that our most significant SNPs, those with P values between 5.1 × 10−6 and 6.2 × 10−13, had highly significant departures from HWE (P < 0.001) in random control subjects or call rates <0.85, so we subsequently focused our attention on those that surpassed these thresholds. We also set a minor allele frequency (MAF) ≥0.05 criterion, as the allelic associations at SNPs below this threshold are largely driven by differences in a small number of individuals. We anticipate following-up rare polymorphisms with significant evidence for association separately at a later date. A total of 88,142 SNPs passed these criteria (Fig. 1).
False discovery rates (FDRs) were estimated by conducting the allelic association test in 1,000 permutations (permuting the case and random labels) and tabulating the P values at given thresholds. We also conducted logistic regressions between type 2 diabetes status and genotypes under an additive model, with and without a proportion of European ancestry covariate. This was not meant as a substitute for the allelic associations but simply to provide a reasonable approach to investigate how the estimated proportions of ancestry might affect the results when included as a covariate. All statistical analyses were performed using R (available at http://www.rproject.org). Measures of linkage disequilibrium (LD) were calculated using GOLD (35).
Using a population prevalence of 10%, we estimated that a case-random study was sufficiently powered (80%) to detect a genotype relative risk of ∼1.6 under dominant, recessive, and additive models in the mid-range of allele frequencies (36).
Assessing admixture proportions.
We compared the full set of genotypes for the 116,204 SNPs in the Mexican-American subjects (MA group) and in the unrelated HapMap samples (60 Europeans from Utah from the Centre d'Etude du Polymorphisme Humain [CEU group]; 60 Yoruba from Ibadan, Nigeria [YRI group]; and 89 Asians [ASN group] including Japanese subjects from Tokyo [JPT group] and Han Chinese from Beijing [CHB group]) as proxies for Native Americans (see online appendix). The Asian HapMap samples were chosen as proxies because no 100K data exist for an appropriate Native American population once thought to be ancestral to the Mexican Americans under investigation here. This leaves the Asian samples as the most appropriate proxy. After removing SNPs either not typed or monomorphic in all four populations (CEU, YRI, ASN, and MA groups), we divided the remaining 101,150 SNPs into 10 equal subsets (by taking every 10th SNP) to reduce the degree of LD between SNPs (median intermarker distance ∼250 kb in the subsets). To estimate genome-wide proportions of ancestry (POAs) for each individual, we ran structure (37) for each of the 10 subsets using the HapMap populations as learning samples (fixed population identity) and subsequently averaged the estimated POAs across the subsets. The structure runs were conducted under an admixture model with default parameter settings of 10,000 burn-in replications and 10,000 estimating replications after burn in. Altering the prior migration probability from 0.001 to 0.1 had little effect on the results, and we present the POA estimates for the 0.1 runs herein.
In silico replication.
We entered into a consortium to share results with three other groups analyzing type 2 diabetes GWAS data in three distinct populations (Amish, Pima Indians, and Framingham Heart Study [FHS]) (Tables 6 and 7), each with different study designs but using the same genotyping platform (27,28,29). Each group requested summary data for their top ∼1,000 SNPs following criteria specific to each group in the Type 2 Diabetes 100K GWAS Consortium and shared the same for the other groups’ best signals. We requested summary data for our top 1,196 most significant high-quality SNPs (those with P < 0.01 and passing the quality-control thresholds described above). We directly compared our Fisher's exact tests for allelic associations to type 2 diabetes in the Mexican Americans with the type 2 diabetes association tests under an additive model in the Amish, the type 2 diabetes association tests by generalized estimating equations and family-based association tests in the FHS, and the case-control and within-family association tests in the Pima Indians. We considered a Mexican-American type 2 diabetes–associated SNP to be in silico replicated if it was associated at P ≤ 0.05 in the same direction (i.e., the same allele was associated with type 2 diabetes) in at least one other 100K GWAS (Fig. 1).
We also queried our data against the March 2007 prereleased data from a similar study in a Scandinavian cohort (Diabetes Genetics Inititative [DGI]) (available at http://www.broad.mit.edu/diabetes/) but conducted with a denser genotyping platform. We compared our top 1,196 association signals with any SNP reaching nominal significance (P ≤ 0.05) in the other GWASs that were within 150 kb and had r2 ≥ 0.8 in either the HapMap Europeans (CEU group) or Asians (ASN group). Again, we considered a Mexican-American type 2 diabetes–associated SNP to be in silico replicated if another SNP with r2 ≥ 0.8 in the CEU or ASN groups to the Mexican-American type 2 diabetes–associated SNP was associated at P ≤ 0.05 in the same direction (i.e., the same allele was associated with type 2 diabetes) in the DGI GWAS (Fig. 1).
RESULTS
Quality control.
We selected for subsequent analysis the XbaI and HindIII chip experiment with the highest call rate for each individual and two or less discordant genotype calls for the 31 SNPs duplicated on the two chips. Of 323 random control and 291 case subjects for which genotyping was attempted, 316 and 287, respectively, met these criteria. The mean per-chip call rate using the DM algorithm was >95%, although the XbaI chip performed slightly better than the HindIII chip (95.8 and 95.2%, respectively). For both chips, >92% of experiments had call rates >90% (92.4% for XbaI and 93.3% for HindIII). Using the DM algorithm calls, we observed significant (P < 0.001) departures from HWE in a substantial number of SNPs (9.8% all samples, 5.1% random subjects only, and 5.2% case subjects only). This is largely attributable to SNPs with excess homozygosity, consistent with nonrandom missing data (heterozygotes have more “no-calls” since their intermediacy between the two homozygote classes renders them more difficult to call than the two homozygote classes).
Using GEL, both increased the call rate (97.2% mean XbaI call rate with 95.4% of experiments having >90% call rates and 96.7% mean HindIII call rate with 95.2% of experiments having >90% call rates) and reduced nonrandom missing data by increasing the proportion of heterozygote genotype calls (online appendix Table 2), which subsequently reduced the number of SNPs showing significant (P < 0.001) departures from HWE (4.0% all samples, 2.2% random subjects only, and 2.0% case subjects only). With either genotype calling algorithm (GEL or DM), there was no substantial case-random control difference throughout the majority of the distribution of per-chip genotype call rates, although there were some outliers in the tails of the distribution (online appendix Fig. 1).
For comparative purposes, we also called the genotypes with the BRLMM algorithm, which again yielded an increased proportion of heterozygotes (online appendix Table 2). In contrast to GEL and DM, which call the genotypes for each chip experiment individually, BRLMM normalizes the intensity patterns across all chip experiments, and therefore it is recommended that only chips with DM call rates >90% be used. To do this would require 83 chip-genotyping experiments (7.1%) to be removed from consideration, substantially reducing our power. We experimented with lowering the DM algorithm call rate threshold and found that BRLMM overcompensates for missing genotypes in the heterozygote class and increases the proportion of heterozygotes to unrealistic levels in chips with DM call rates <90% (online appendix Fig. 2). Given the limited number of samples under investigation, the marginal increase in genotype calls using BRLMM over GEL and the high concordance rates between GEL and BRLMM (online appendix Tables 2 and 3), we decided to report results using the GEL algorithm to retain maximal power.
Allelic associations.
The chromosomal distribution of Fisher's exact test P values for the 88,142 SNPs passing our quality-control thresholds are presented in Fig. 2. A total of 1,196 had allelic association P < 0.01 and are presented in online appendix Table 4. The 14 best (P < 10−4) SNPs (Table 2) survey 13 different regions of the genome and are in or near ANKRD50, DYRK2, EPB41L3, GRIK1, HPSE2, ICA1, IFNG, NXPH1, OR13D1, SDF2L1, SORBS1, SPRY1, SLC24A3, and TMEFF2. Two adjacent SNPs on the Affymetrix GeneChip Human Mapping 100K set (rs10518442 and rs1498024) on chromosome 4, in and near ANKRD50, respectively, are in perfect (r2 = 1) LD with each other and are associated at P < 10−5. Our most significantly associated SNP, rs1932465, has a P value of 5.6 × 10−6, approximately one order of magnitude below a conservative Bonferroni correction for multiple tests (0.05/88, 142 = 5.7 × 10−7). We note that none of the most significant signals are SNPs with low MAFs (0.05–0.10; we excluded SNPs with MAFs <0.05). While this observation is not unexpected given the reduced power for detecting susceptibility loci with allele frequencies at the tail of the MAF distribution, it remains noteworthy since nonrandom patterns of missing data and other genotyping errors not detected in quality-control analysis often lead to SNPs with low MAFs being disproportionately found among those with the most significant P values, which are subsequently poorly replicated.
Using permutations, we empirically estimated the FDR at various thresholds (online appendix Table 5) and found that we observe many more significant allelic associations than expected genome wide. For our best signals, those meeting a P ≤ 10−4 significance threshold, the FDR is estimated to be 62%. This suggests that 8–9 of the 14 SNPs will likely turn out to be false-positives. We also compared the distribution of allelic association P values against a uniform distribution (online appendix Fig. 3). Our observed distribution begins to depart from the expected uniform one at approximately P = 10−2, suggesting an appropriate threshold for investigating in silico replication in order to prioritize SNPs for follow-up.
Ancestry estimates in case and random control samples.
The Starr County Mexican-American population is a relatively homogeneous (97.5% Hispanic by self-report [available at factfinder.census.gov]) yet highly admixed population with contributions to the contemporary gene pool from individuals of Spanish, Native American, and African ancestry. Previous estimates using classical markers suggest ancestry proportions of 61, 31, and 8%, respectively (38). Since population substructure can yield spurious case-control associations, we investigated the patterns of ancestry in the case and random control subjects used in the GWAS. We observed no significant difference between the 10 subsets (online appendix Fig. 4), which permitted us to average the admixture proportions over them. The ancestry estimates observed using the 100K SNP sets (68% European, 27% Asian, and 6% African) were consistent with the previous estimates from classical markers. More importantly, for the purposes here, estimates of the proportion of African, Asian, and European ancestry for the case and random control subjects were indistinguishable from each other (Fig. 3). Formal comparisons by Q-Q plots show no significant differences in the case and random control distributions of ancestry proportions (online appendix Fig. 5), suggesting that spurious associations due to different ancestries of the case and random control subjects are unlikely.
We used these POA estimates as covariates in logistic regressions between type 2 diabetes status and genotype. The POA estimates indicate that 1) there is very little difference from one individual to the next in the African POA and 2) the difference in POA estimates per individual lie along an Asian versus European axis of variation. This suggests that the POA variation could be efficiently captured by using the European or Asian POA as a covariate, and we chose to use the former. Including the CEU group covariate had little impact on the association results. The P value for nearly all SNP × genotype regressions increased or decreased by less than one-half an order of magnitude (online appendix Fig. 6). We did not observe any highly significant regressions disappearing after including the CEU group POA covariate, again suggesting that spurious associations due to different ancestries of the case and random samples are highly unlikely. Instead, the difference in the regression P values distributions was skewed toward increased significance when using the European POA as a covariate.
Verification.
Before genotyping any SNP in a larger collection of individuals for replication, we wanted to first verify the association in the same set of individuals using a different genotyping platform (TaqMan). To identify the most robust SNPs for verification genotyping, we selected a subset of 10 SNPs from the 50 highly associated SNPs (Table 2) that met our quality-control criteria (HWE departure P > 0.001 in random subjects, call rates ≥0.85, and MAF ≥0.05) using both the DM and GEL algorithms. All SNPs remained significant at a P < 0.01 (8/11 P ≤ 10−3, 4/11 P ≤ 10−4, and 1/11 P ≤ 10−5), with the exception of rs861844 (near SDF2L), which dropped to P = 0.02 (online appendix Table 6). Since the overall genotyping concordance between the genotyping platforms was 99.2% (per-marker range 98.6–99.8%), the decrease in allelic association significance is not a function of differential genotyping but rather the increase in sample size.
In silico replications in other 100K type 2 diabetes GWASs.
A total of 120 SNPs (online appendix Table 4) associated in the Mexican-American subjects (P < 0.01) had the same allele associated (P < 0.05) in one of the other 100K GWASs (27,28,29). At the more stringent P < 0.001 level (Table 3), six were replicated in the Amish, three were replicated in the Pima Indians (all by case-control tests), and four were replicated in the FHS (one by generalized estimating equations alone and three by family-based association test alone). These included SNPs in or near the following genes: RALGPS2 and ANGPTL1 (chromosome 1); LCORL, NCAPG, and CSN3 (chromosome 4); HTR4 and ADRB2 (chromosome 5); UTRN (chromosome 6); LINGO2 (chromosome 9); EGR2 (chromosome 10); UBQLNL and OR52H1 (chromosome 11); and RORA (chromosome 15). Of these, one was replicated in multiple studies: rs979752*T (P = 0.0012; odds ratio [OR] 0.562) near UBQLNL and OR52H1 in the Amish (P = 0.03; 0.764) and FHS (P = 0.04; hazard rate ratio 0.709). Additionally, two nonredundant SNPs (r2 < 0.8) in or near RALGPS2 were independently replicated in the Amish (rs2773080*G; P = 0.00080 and OR 0.628 in Mexican Americans; P = 0.033 and OR 0.793 in Amish) and Pima Indians (rs3922812*G; P = 0.00088 and OR 1.523 in Mexican Americans; P = 0.028 and OR 1.311 in Pima Indian case-control subjects).
Replication in non-100K type 2 diabetes GWASs.
We also observed 31 SNPs associated (P < 0.01) with type 2 diabetes in the Mexican Americans in high LD in either the HapMap Europeans or Asians, also showing evidence for association with type 2 diabetes in a GWAS (P < 0.05) in a Scandinavian cohort (DGI; online appendix Table 7). Four of these are significant in the Mexican Americans at a more stringent P < 0.001 level and are located in or near ACTN2 on chromosome 1, GDNF and EGFLAM on chromosome 5, EGR2 on chromosome 10, and a nongenic region on chromosome 11 (Table 4).
Replication in more than one other GWAS.
We investigated the intersection of the in silico replications in the other GWAS examined and found that six SNPs associated in Mexican Americans (P < 0.01) replicated in multiple studies (P < 0.05). SNPs in or near GYPC (chromosome 2), EGR2 (chromosome 10), and a nongenic region (chromosome 18) replicated in the Pima Indians and DGI, DBC1 (chromosome 9) in the Pima Indians and FHS, and PHLDB1 (chromosome 11) in the Amish and Pima Indians (Table 5). rs10504319*T in or near MGC34646 and CHD7 was found to decrease risk in the three (Amish, Pima Indians, and DGI) of four comparative cohorts as well as the Mexican Americans. An additional region on chromosome 11 contains two redundant SNPs (r2 > 0.8) that show evidence for replication: rs979752 in or near UBQLNL and OR52H1 is replicated in the Amish and FHS and nearby rs10500641 is replicated in the DGI study. This is in addition to the multiple RALGPS2 replications discussed above.
DISCUSSION
We have carried out a GWAS of type 2 diabetes in Mexican Americans from Starr County, Texas. We observed a number of allelic associations showing replication in one of the other GWAS, and a limited number of which show multiple lines of evidence for replication. The association signals that appear to be the most robust would be the three that are significant at P < 10−3 in the Mexican- American subjects and are replicated (P < 0.05) in at least two of four other GWASs interrogated (rs979752 and rs10500641 near UBQLNL and OR52H1 on chromosome 11, rs2773080 and rs3922812 in or near RALGPS2 on chromosome 1, and rs1509957 near EGR2 on chromosome 10). These SNPs and many other significantly associated SNPs will be prioritized for further follow-up genotyping in a larger Mexican-American case-random control cohort. Our FDR estimate suggests that if we followed up the 141 associations significant at the P ≤ 10−3 threshold, a little less than half would not be false-positives. The broad replication of these three signals meeting this significance threshold suggests that they may be true rather than false-positive associations, but confirmation of such will await the results of the follow-up genotyping in the more numerous Mexican-American case-random sample cohort. Even though our most promising SNPs may turn out to be false-positives, it is tempting nonetheless to query whether any of these putative type 2 diabetes susceptibility genes identified in this GWAS have supporting biological evidence for their candidacy as type 2 diabetes genes. Of the genes implicated and discussed above, no direct links to a diabetes-related phenotype were found.
Given the large amount of data generated in a GWAS, one might naively think that this study represents a comprehensive interrogation of the human genome for type 2 diabetes susceptibility genes, but this is simply not true (39). Although the mean intermarker distance for the 116,204 SNPs genotyped on the Affymetrix GeneChip Human Mapping 100K set is only 8.5 kb, the 100K platform does not completely cover the genome given the patterns of LD and uneven SNP density (40). Nowhere is this more evident than searching for associations at previously identified and replicated type 2 diabetes genes. For example, the Mexican-American population under investigation here is the same in which CAPN10 was identified through positional cloning studies subsequent to genome linkage scans. However, the nearest SNPs to CAPN10 on the 100K platform are 187 and 250 kb in either direction, well beyond the LD block in which CAPN10 resides. The results for other “known” type 2 diabetes genes in our study are presented in online appendix Table 8. Like CAPN10, there are no SNPs on the Affymetrix GeneChip Human Mapping 100K set near HNF4A or KCNJ11 and HHEX. The previously identified type 2 diabetes–associated variant (rs1801282) in PPARG is included on the 100K set but is not associated with type 2 diabetes in Mexican Americans (P = 1.0). For TCF7L2, the SNP (rs7100927) in highest LD (r2 = 0.5) with the previously identified type 2 diabetes–associated variant (rs7903146) also shows no significant associations to type 2 diabetes in the Mexican-American subjects (P = 0.952). The SNPs in or near two genes (IGF2BP2 and SLC30A8), previously identified in other GWASs as containing type 2 diabetes risk alleles, show no evidence of association and have modest LD between the previously associated variant and the SNPs on the 100K platform. However, we did observe significant associations with SNPs in CDKAL1 and CDKN2A (P < 0.01) but only at SNPs not in LD with the originally associated SNP, so these could not be considered direct replication of the original signal but may point to other variation contributing to risk of type 2 diabetes in Mexican Americans.
The lack of difference in the POA estimates between the case and random samples speaks not just to a reduced likelihood of spurious associations due to substructure but also to a larger issue. Given the high prevalence of type 2 diabetes among Native Americans, it has been previously hypothesized that the high prevalence of type 2 diabetes in Mexican Americans may be due to their Native American ancestry (41,42). In support of this hypothesis is our estimate that ∼30% of the contemporary Mexican-American gene pool is Native American derived. Given the prevalence of diabetes among Native Americans, the predicted prevalence in Mexican Americans parallels that expected based on this degree of admixture (43). However, if type 2 diabetes in Mexican Americans was largely Native American derived, a higher proportion of Asian (proxy for Native American) ancestry would have been observed in the case subjects than in the random control subjects; we did not observe this.
The POAs are genome-wide estimates. We assume these may be highly variable from one genomic region to the next, so it remains possible that for any given gene associated with type 2 diabetes in Mexican Americans, it is the Native American–derived variant that is the risk allele. We also noted that the difference in the distributions of the regression P values was skewed toward increased significance when using the European POA as a covariate. This suggests that we may be able to exploit this when admixture mapping methods are used in the future.
In conclusion, we observed many SNPs associated with type 2 diabetes, some of which were replicated in at least one of four other GWASs we queried. This study represents our initial examination of the Mexican-American 100K GWAS data; more sophisticated approaches will follow, including a meta-analysis of four 100K GWASs. It may also be that subsequent investigations of this GWAS with haplotypes or genes as the unit of investigation, rather than SNPs, will prove to be more informative. Nonetheless, we have highlighted several interesting putative type 2 diabetes genes for follow-up in the hopes that it may further elucidate the etiology of type 2 diabetes and identify new avenues for both the treatment and prevention of this complex disease.
Fisher's exact test −log10(P values) for tests of association between the 88702 high-quality autosomal SNPs (SNPs with HWE departure P > 0.001 in random subjects, call rates ≥0.85, and MAF ≥0.05) and type 2 diabetes affection status.
Fisher's exact test −log10(P values) for tests of association between the 88702 high-quality autosomal SNPs (SNPs with HWE departure P > 0.001 in random subjects, call rates ≥0.85, and MAF ≥0.05) and type 2 diabetes affection status.
Estimates of ancestry proportions in case and random control subjects. The genome-wide average ancestral proportions of ancestry for each individual is plotted in a triangular matrix in which each point of the triangle represents 100% ancestry for the indicated ancestral population. The average proportion of each ancestral population in the Starr County Mexican Americans is listed after each ancestral population. Red = case subjects; blue = random control subjects.
Estimates of ancestry proportions in case and random control subjects. The genome-wide average ancestral proportions of ancestry for each individual is plotted in a triangular matrix in which each point of the triangle represents 100% ancestry for the indicated ancestral population. The average proportion of each ancestral population in the Starr County Mexican Americans is listed after each ancestral population. Red = case subjects; blue = random control subjects.
Descriptive statistics for the individuals with type 2 diabetes in the primary GWAS set from Starr County, Texas
| n | 281 |
| Sex (n female) | 174 |
| Age (years) | 57.9 ± 10.7 |
| Age at diagnosis (years) | 45.9 ± 10.1 |
| Fasting glucose (mg/dl) | 190.3 ± 75.0 |
| A1C (%) | 11.6 ± 3.5 |
| BMI (kg/m2) | 31.4 ± 6.2 |
| n | 281 |
| Sex (n female) | 174 |
| Age (years) | 57.9 ± 10.7 |
| Age at diagnosis (years) | 45.9 ± 10.1 |
| Fasting glucose (mg/dl) | 190.3 ± 75.0 |
| A1C (%) | 11.6 ± 3.5 |
| BMI (kg/m2) | 31.4 ± 6.2 |
Data are means ± SD, unless otherwise indicated.
SNPs most significantly associated with type 2 diabetes
| dbSNP rs | Chr. | Position* | Gene* | Allele 1/2 | Control frequency (a2) | Case frequency (a2) | P† | OR† |
|---|---|---|---|---|---|---|---|---|
| rs1932465 | 1 | 104418527 | C/G | 0.881 | 0.958 | 5.61 × 10−6 | 3.102 | |
| rs10497723 | 2 | 192817829 | TMEFF2 | A/G | 0.195 | 0.095 | 8.45 × 10−6 | 0.431 |
| rs1498024 | 4 | 125912629 | SPRY1/ANKRD50 | C/T | 0.844 | 0.735 | 1.68 × 10−5 | 0.513 |
| rs6136651 | 20 | 19144096 | SLC24A3 | A/G | 0.834 | 0.725 | 1.81 × 10−5 | 0.525 |
| rs757705 | 7 | 8313535 | ICA1/NXPH1 | A/G | 0.498 | 0.63 | 2.79 × 10−5 | 1.713 |
| rs861844 | 22 | 20330773 | SDF2L1 | G/T | 0.086 | 0.172 | 3.21 × 10−5 | 2.214 |
| rs10518442 | 4 | 125951873 | ANKRD50 | A/C | 0.861 | 0.761 | 3.22 × 10−5 | 0.516 |
| rs10492202 | 12 | 66628005 | DYRK2/IFNG | C/T | 0.11 | 0.203 | 3.48 × 10−5 | 2.054 |
| rs1159006 | 10 | 100396273 | HPSE2 | C/T | 0.266 | 0.39 | 4.01 × 10−5 | 1.765 |
| rs10512332 | 9 | 104554018 | OR13D1 | C/T | 0.789 | 0.678 | 4.60 × 10−5 | 0.564 |
| rs1536558 | 10 | 97222258 | SORBS1 | G/T | 0.261 | 0.155 | 4.77 × 10−5 | 0.519 |
| rs1941011 | 18 | 5649096 | EPB41L3 | A/T | 0.675 | 0.551 | 4.96 × 10−5 | 0.590 |
| rs458685 | 21 | 30099382 | GRIK1 | A/G | 0.105 | 0.194 | 6.54 × 10−5 | 2.048 |
| rs2831605 | 21 | 28467064 | A/G | 0.945 | 0.874 | 6.82 × 10−5 | 0.405 |
| dbSNP rs | Chr. | Position* | Gene* | Allele 1/2 | Control frequency (a2) | Case frequency (a2) | P† | OR† |
|---|---|---|---|---|---|---|---|---|
| rs1932465 | 1 | 104418527 | C/G | 0.881 | 0.958 | 5.61 × 10−6 | 3.102 | |
| rs10497723 | 2 | 192817829 | TMEFF2 | A/G | 0.195 | 0.095 | 8.45 × 10−6 | 0.431 |
| rs1498024 | 4 | 125912629 | SPRY1/ANKRD50 | C/T | 0.844 | 0.735 | 1.68 × 10−5 | 0.513 |
| rs6136651 | 20 | 19144096 | SLC24A3 | A/G | 0.834 | 0.725 | 1.81 × 10−5 | 0.525 |
| rs757705 | 7 | 8313535 | ICA1/NXPH1 | A/G | 0.498 | 0.63 | 2.79 × 10−5 | 1.713 |
| rs861844 | 22 | 20330773 | SDF2L1 | G/T | 0.086 | 0.172 | 3.21 × 10−5 | 2.214 |
| rs10518442 | 4 | 125951873 | ANKRD50 | A/C | 0.861 | 0.761 | 3.22 × 10−5 | 0.516 |
| rs10492202 | 12 | 66628005 | DYRK2/IFNG | C/T | 0.11 | 0.203 | 3.48 × 10−5 | 2.054 |
| rs1159006 | 10 | 100396273 | HPSE2 | C/T | 0.266 | 0.39 | 4.01 × 10−5 | 1.765 |
| rs10512332 | 9 | 104554018 | OR13D1 | C/T | 0.789 | 0.678 | 4.60 × 10−5 | 0.564 |
| rs1536558 | 10 | 97222258 | SORBS1 | G/T | 0.261 | 0.155 | 4.77 × 10−5 | 0.519 |
| rs1941011 | 18 | 5649096 | EPB41L3 | A/T | 0.675 | 0.551 | 4.96 × 10−5 | 0.590 |
| rs458685 | 21 | 30099382 | GRIK1 | A/G | 0.105 | 0.194 | 6.54 × 10−5 | 2.048 |
| rs2831605 | 21 | 28467064 | A/G | 0.945 | 0.874 | 6.82 × 10−5 | 0.405 |
Affymetrix NetAffx annotation;
allele 2 vs. 1. Chr., chomosome.
SNPs associated with type 2 diabetes in Mexican Americans (P < 0.001) with replication in at least one other 100K GWAS
| dbSNP rs | Chr. | Position* | Gene* | Allele 1/2 | Mexican Americans (P value)† | Mexican Americans (OR)† | Amish (P value)‡ | Amish (OR)‡ | Pima Indians (CC P)§ | Pima Indians (CC OR)§ | Pima Indian sibs (P)‖ | Pima Indian sibs (OR)‖ | FHS GEE (P)¶ | FHS FBAT (P)# | FHS HRR** |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rs979752 | 11 | 5495715 | UBQLNL/OR52H1 | C/T | 0.00012 | 0.562 | 0.030 | 0.764 | 0.040 | 0.658 | 0.709 | ||||
| rs7164773 | 15 | 58855240 | RORA | C/T | 0.00016 | 1.639 | 0.021 | 1.300 | |||||||
| rs981864 | 9 | 28293495 | LINGO2 | G/T | 0.00024 | 2.138 | 0.020 | 1.035 | |||||||
| rs10498761 | 6 | 45850484 | A/C | 0.00036 | 1.675 | 0.041 | 1.290 | ||||||||
| rs1517645 | 2 | 223998255 | C/G | 0.00072 | 0.475 | 0.005 | 0.766 | ||||||||
| rs3775745 | 4 | 71293834 | CSN3 | A/C | 0.00078 | 0.636 | 0.002 | 0.698 | |||||||
| rs1833714 | 5 | 148035093 | HTR4/ADRB2 | C/T | 0.00078 | 0.643 | 0.026 | 0.713 | |||||||
| rs2773080 | 1 | 175428407 | RALGPS2 | A/G | 0.00080 | 0.628 | 0.033 | 0.793 | |||||||
| rs1509957 | 10 | 64280724 | EGR2 | A/G | 0.00084 | 0.652 | 0.032 | 0.721 | |||||||
| rs3922812 | 1 | 175391590 | RALGPS2/ANGPTL1 | A/G | 0.00088 | 1.523 | 0.028 | 1.311 | |||||||
| rs10516322 | 4 | 18099836 | LCORL/NCAPG | C/G | 0.00089 | 0.656 | 0.050 | 0.806 | |||||||
| rs6929370 | 6 | 145395271 | UTRN | C/T | 0.00095 | 1.533 | 0.033 | 3.988 |
| dbSNP rs | Chr. | Position* | Gene* | Allele 1/2 | Mexican Americans (P value)† | Mexican Americans (OR)† | Amish (P value)‡ | Amish (OR)‡ | Pima Indians (CC P)§ | Pima Indians (CC OR)§ | Pima Indian sibs (P)‖ | Pima Indian sibs (OR)‖ | FHS GEE (P)¶ | FHS FBAT (P)# | FHS HRR** |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rs979752 | 11 | 5495715 | UBQLNL/OR52H1 | C/T | 0.00012 | 0.562 | 0.030 | 0.764 | 0.040 | 0.658 | 0.709 | ||||
| rs7164773 | 15 | 58855240 | RORA | C/T | 0.00016 | 1.639 | 0.021 | 1.300 | |||||||
| rs981864 | 9 | 28293495 | LINGO2 | G/T | 0.00024 | 2.138 | 0.020 | 1.035 | |||||||
| rs10498761 | 6 | 45850484 | A/C | 0.00036 | 1.675 | 0.041 | 1.290 | ||||||||
| rs1517645 | 2 | 223998255 | C/G | 0.00072 | 0.475 | 0.005 | 0.766 | ||||||||
| rs3775745 | 4 | 71293834 | CSN3 | A/C | 0.00078 | 0.636 | 0.002 | 0.698 | |||||||
| rs1833714 | 5 | 148035093 | HTR4/ADRB2 | C/T | 0.00078 | 0.643 | 0.026 | 0.713 | |||||||
| rs2773080 | 1 | 175428407 | RALGPS2 | A/G | 0.00080 | 0.628 | 0.033 | 0.793 | |||||||
| rs1509957 | 10 | 64280724 | EGR2 | A/G | 0.00084 | 0.652 | 0.032 | 0.721 | |||||||
| rs3922812 | 1 | 175391590 | RALGPS2/ANGPTL1 | A/G | 0.00088 | 1.523 | 0.028 | 1.311 | |||||||
| rs10516322 | 4 | 18099836 | LCORL/NCAPG | C/G | 0.00089 | 0.656 | 0.050 | 0.806 | |||||||
| rs6929370 | 6 | 145395271 | UTRN | C/T | 0.00095 | 1.533 | 0.033 | 3.988 |
Affymetrix NetAffx annotation;
allele 2 vs. 1;
genotype 22 vs. 12;
case-control tests, allele 2 vs. 1;
within-family tests, allele 2 vs. 1;
generalized estimating equations (GEEs), allele 2 vs. 1;
family-based association test (FBAT), allele 2 vs. 1;
hazard rate ratio (HRR), allele 2 vs. 1. Chr., chromosome.
Replication of Mexican-American SNPs (P < 0.001) with non-100K Type 2 Diabetes GWAS results
| Mexican American (dbSNP rs) | Mexican American (chromosomes) | Mexican American (position)* | Mexican American (gene)† | Mexican American (1/2) | Mexican American (P)‡ | Mexican American (OR)‡ | DGI (dbSNP rs) | DGI (allele) | DGI (chromosomes) | DGI (position)* | DGI (P) | DGI (OR) | HapMap (r2) | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CEU | ASN | ||||||||||||||
| rs819639 | 1 | 233219640 | ACTN2 | C/T | 0.00022 | 1.611 | rs819639 | T | 1 | 233219640 | 0.01731 | 1.194 | N/A | N/A | |
| rs1509957 | 10 | 64280724 | EGR2 | A/G | 0.00084 | 0.652 | rs1509957 | G | 10 | 64280724 | 0.00820 | 0.831 | N/A | N/A | |
| rs270568 | 5 | 38058636 | GDNF/EGFLAM | C/T | 0.00085 | 0.448 | rs270565 | C | 5 | 38061529 | 0.01440 | 0.766 | 1.000 | 1.000 | |
| rs951432 | 11 | 111854649 | C/T | 0.00090 | 0.639 | rs7116022 | T | 11 | 111856182 | 0.02057 | 0.866 | 0.964 | 0.910 | ||
| Mexican American (dbSNP rs) | Mexican American (chromosomes) | Mexican American (position)* | Mexican American (gene)† | Mexican American (1/2) | Mexican American (P)‡ | Mexican American (OR)‡ | DGI (dbSNP rs) | DGI (allele) | DGI (chromosomes) | DGI (position)* | DGI (P) | DGI (OR) | HapMap (r2) | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CEU | ASN | ||||||||||||||
| rs819639 | 1 | 233219640 | ACTN2 | C/T | 0.00022 | 1.611 | rs819639 | T | 1 | 233219640 | 0.01731 | 1.194 | N/A | N/A | |
| rs1509957 | 10 | 64280724 | EGR2 | A/G | 0.00084 | 0.652 | rs1509957 | G | 10 | 64280724 | 0.00820 | 0.831 | N/A | N/A | |
| rs270568 | 5 | 38058636 | GDNF/EGFLAM | C/T | 0.00085 | 0.448 | rs270565 | C | 5 | 38061529 | 0.01440 | 0.766 | 1.000 | 1.000 | |
| rs951432 | 11 | 111854649 | C/T | 0.00090 | 0.639 | rs7116022 | T | 11 | 111856182 | 0.02057 | 0.866 | 0.964 | 0.910 | ||
Position in March 2006 University of California Santa Cruz build;
Affymetrix annotations;
allele 2 vs. 1. N/A, not applicable (i.e., identical SNP).
SNPs associated with type 2 diabetes in Mexican Americans (P < 0.01) with replication in more than one other GWAS
| dbSNP rs | Chr. | Position* | Gene* | Allele (1/2) | Mexican American (P)† | Mexican American (OR)† | Amish (P)‡ | Amish (OR)‡ | Pima Indian case-control (P)§ | Pima Indian case-control (OR)§ | Pima Indian sibs (P)‖ | Pima Indian sibs (OR)‖ | FHS GEE (P)¶ | FHS FBAT (P)# | FHS HRR** | DGI (rs) | DGI (P)† | DGI (OR)† |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rs979752 | 11 | 5495715 | UBQLNL/OR52H1 | C/T | 0.00012 | 0.562 | 0.030 | 0.764 | 0.040 | 0.658 | 0.709 | |||||||
| rs2773080 | 1 | 175428407 | RALGPS2 | A/G | 0.00080 | 0.628 | 0.033 | 0.793 | ||||||||||
| rs1509957 | 10 | 64280724 | EGR2 | A/G | 0.00084 | 0.652 | 0.032 | 0.721 | 64280724 | 0.00820 | 0.831 | |||||||
| rs3922812 | 1 | 175391590 | RALGPS2/ANGPTL1 | A/G | 0.00088 | 1.523 | 0.028 | 1.311 | ||||||||||
| rs2191678 | 9 | 119433899 | DBC1 | A/G | 0.00316 | 0.678 | 0.035 | 0.543 | 0.009 | 0.797 | ||||||||
| rs2077173 | 11 | 117997801 | PHLDB1 | A/G | 0.00503 | 0.703 | 0.030 | 0.796 | 0.035 | 0.651 | ||||||||
| rs360248 | 2 | 126731609 | GYPC | C/G | 0.00602 | 0.676 | 0.014 | 0.618 | rs360256 | 0.017 | 0.881 | |||||||
| rs10504319 | 8 | 62089326 | MGC34646/CHD7 | C/T | 0.00656 | 0.571 | 0.010 | 0.549 | 0.043 | 0.726 | rs1509957 | 0.008 | 0.831 | |||||
| rs10500641 | 11 | 5426152 | C/T | 0.00656 | 1.522 | rs1391619 | 0.01489 | 0.746 | ||||||||||
| rs1845386 | 18 | 29572254 | C/G | 0.00901 | 1.398 | 0.045 | 1.487 | rs7237379 | 0.005 | 1.203 |
| dbSNP rs | Chr. | Position* | Gene* | Allele (1/2) | Mexican American (P)† | Mexican American (OR)† | Amish (P)‡ | Amish (OR)‡ | Pima Indian case-control (P)§ | Pima Indian case-control (OR)§ | Pima Indian sibs (P)‖ | Pima Indian sibs (OR)‖ | FHS GEE (P)¶ | FHS FBAT (P)# | FHS HRR** | DGI (rs) | DGI (P)† | DGI (OR)† |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rs979752 | 11 | 5495715 | UBQLNL/OR52H1 | C/T | 0.00012 | 0.562 | 0.030 | 0.764 | 0.040 | 0.658 | 0.709 | |||||||
| rs2773080 | 1 | 175428407 | RALGPS2 | A/G | 0.00080 | 0.628 | 0.033 | 0.793 | ||||||||||
| rs1509957 | 10 | 64280724 | EGR2 | A/G | 0.00084 | 0.652 | 0.032 | 0.721 | 64280724 | 0.00820 | 0.831 | |||||||
| rs3922812 | 1 | 175391590 | RALGPS2/ANGPTL1 | A/G | 0.00088 | 1.523 | 0.028 | 1.311 | ||||||||||
| rs2191678 | 9 | 119433899 | DBC1 | A/G | 0.00316 | 0.678 | 0.035 | 0.543 | 0.009 | 0.797 | ||||||||
| rs2077173 | 11 | 117997801 | PHLDB1 | A/G | 0.00503 | 0.703 | 0.030 | 0.796 | 0.035 | 0.651 | ||||||||
| rs360248 | 2 | 126731609 | GYPC | C/G | 0.00602 | 0.676 | 0.014 | 0.618 | rs360256 | 0.017 | 0.881 | |||||||
| rs10504319 | 8 | 62089326 | MGC34646/CHD7 | C/T | 0.00656 | 0.571 | 0.010 | 0.549 | 0.043 | 0.726 | rs1509957 | 0.008 | 0.831 | |||||
| rs10500641 | 11 | 5426152 | C/T | 0.00656 | 1.522 | rs1391619 | 0.01489 | 0.746 | ||||||||||
| rs1845386 | 18 | 29572254 | C/G | 0.00901 | 1.398 | 0.045 | 1.487 | rs7237379 | 0.005 | 1.203 |
Affymetrix NetAffx annotation;
allele 2 vs. 1;
genotype 22 vs. 12;
case-control tests, allele 2 vs. 1;
within-family tests, allele 2 vs. 1;
generalized estimating equations (GEEs), allele 2 vs. 1;
family-based association test (FBAT), allele 2 vs. 1;
hazard rate ratio (HRR), allele 2 vs. 1. Chr., chromosome.
Comparison of four GWA studies
| FHS | Mexican American | Pima C/C | Pima family | Amish | |
|---|---|---|---|---|---|
| n | 1,087 | 281 case/280 control | 300 case/334 control | 172 sibships | 124 case/295 control |
| Type | Population, family-based | Case/control | Case/control | Family | Case/control |
| Ethnicity | Non-Hispanic white | Mexican American | American Indian | American Indian | Non-Hispanic white |
| Male/female | 527/560 | ||||
| Case | 108/173 | 114/186 | 48/92 | 41/83 | |
| Control | 69/211 | 160/174 | 57/64 | 153/142 | |
| Mean age (years) | 51.5 ± 9.8 | ||||
| Case | 57.7 ± 10.8 | 19.2 ± 4.5 | 41.0 ± 8.3 | 51.3 ± 10.5 | |
| Control | — | 55.5 ± 9.8 | 27.8 ± 7.9 | 64.4 ± 12.9 | |
| Mean BMI (kg/m2) | 27.5 ± 5.2 | ||||
| Case | 31.5 ± 6.2 | 38.9 ± 8.4 | 38.9 ± 9.3 | 29.3 ± 5.8 | |
| Control | — | 35.4 ± 8.0 | 36.9 ± 8.9 | 27.4 ± 4.7 | |
| Quantitative glycemic traits analyzed | FPG, tFPG, A1C, FI, HOMA-IR, Gutt ISI 0_120 | FASTG, glucose AUC, insulin AUC, HOMA-IR, insulin secretion index | |||
| Notes | 91 cases of incident diabetes | Controlled for admixture | Age of onset <25 years | Sibships overlap with C/C | Cases have (+) FH; active lifestyle |
| SNPs analyzed | 66,543 | 88,702 | 80,044 | 80,044 | 82,485 |
| SNPs failed | 39,205 MAF <10%; 4,064 HWE P < 0.001; 10,438 call rate <90% | 19,032 MAF <5%; 1,562 HWE P < 0.001; 4,818 call rate <85% | 28,215 MAF <1%; 2,429 HWE P < 0.001; 5,122 call rate <85% and/or error rate >3% in duplicate samples | 28,215 MAF <1%; 2,429 HWE P < 0.001; 5,122 call rate <85% and/or error rate >3% in duplicate samples | 26,816 MAF <5%; 2,573 HWE P < 0.001; 1,866 call rate <90% |
| P value thresholds | Strategy 1: P < 0.01 in all three glucose traits (FPG, tFPG, and A1C) or in all 3 insulin traits (FI, HOMA-IR, and Gutt) or two glucose and two insulin traits or incident type 2 diabetes; Strategy 2: P < 0.001 | Primary analysis: P < 0.01 for type 2 diabetes; In silico replication: P < 0.05 | In silico replication:P < 0.007 in combined within-family and case-control analysis (weighted to give priority to within-family test); Additional genotyping: P < 0.001 in combined within-family and case-control analysis as above | In silico replication: P < 0.007 in combined within-family and case-control analysis (weighted to give priority to within-family test); Additional genotyping: P < 0.001 in combined within-family and case- control analysis as above | Primary analysis: P < 0.01 for type 2 diabetes; Internal replication: type 2 diabetes P < 0.01 and one glucose trait (FASTG or GAUC) or one insulin trait (ISI, IAUC, or HOMA-IR) P < 0.01 |
| Replication cohort | 1,465 unrelated FHS participants (non-overlapping) | 760 overlapping individuals for SNP verification | Non-overlapping Pima Indians: 1,207 case/1,627 control | Non-overlapping Pima Indians: 1,207 case/1,627 control | 427 nondiabetic Amish participants (295 control from primary type 2 analysis) |
| In silico replication | T2D 100K Consortium + DGI 500K | T2D 100K Consortium + DGI 500K | T2D 100K Consortium + DGI 500K | T2D 100K Consortium + DGI 500K | T2D 100K Consortium + DGI 500K |
| FHS | Mexican American | Pima C/C | Pima family | Amish | |
|---|---|---|---|---|---|
| n | 1,087 | 281 case/280 control | 300 case/334 control | 172 sibships | 124 case/295 control |
| Type | Population, family-based | Case/control | Case/control | Family | Case/control |
| Ethnicity | Non-Hispanic white | Mexican American | American Indian | American Indian | Non-Hispanic white |
| Male/female | 527/560 | ||||
| Case | 108/173 | 114/186 | 48/92 | 41/83 | |
| Control | 69/211 | 160/174 | 57/64 | 153/142 | |
| Mean age (years) | 51.5 ± 9.8 | ||||
| Case | 57.7 ± 10.8 | 19.2 ± 4.5 | 41.0 ± 8.3 | 51.3 ± 10.5 | |
| Control | — | 55.5 ± 9.8 | 27.8 ± 7.9 | 64.4 ± 12.9 | |
| Mean BMI (kg/m2) | 27.5 ± 5.2 | ||||
| Case | 31.5 ± 6.2 | 38.9 ± 8.4 | 38.9 ± 9.3 | 29.3 ± 5.8 | |
| Control | — | 35.4 ± 8.0 | 36.9 ± 8.9 | 27.4 ± 4.7 | |
| Quantitative glycemic traits analyzed | FPG, tFPG, A1C, FI, HOMA-IR, Gutt ISI 0_120 | FASTG, glucose AUC, insulin AUC, HOMA-IR, insulin secretion index | |||
| Notes | 91 cases of incident diabetes | Controlled for admixture | Age of onset <25 years | Sibships overlap with C/C | Cases have (+) FH; active lifestyle |
| SNPs analyzed | 66,543 | 88,702 | 80,044 | 80,044 | 82,485 |
| SNPs failed | 39,205 MAF <10%; 4,064 HWE P < 0.001; 10,438 call rate <90% | 19,032 MAF <5%; 1,562 HWE P < 0.001; 4,818 call rate <85% | 28,215 MAF <1%; 2,429 HWE P < 0.001; 5,122 call rate <85% and/or error rate >3% in duplicate samples | 28,215 MAF <1%; 2,429 HWE P < 0.001; 5,122 call rate <85% and/or error rate >3% in duplicate samples | 26,816 MAF <5%; 2,573 HWE P < 0.001; 1,866 call rate <90% |
| P value thresholds | Strategy 1: P < 0.01 in all three glucose traits (FPG, tFPG, and A1C) or in all 3 insulin traits (FI, HOMA-IR, and Gutt) or two glucose and two insulin traits or incident type 2 diabetes; Strategy 2: P < 0.001 | Primary analysis: P < 0.01 for type 2 diabetes; In silico replication: P < 0.05 | In silico replication:P < 0.007 in combined within-family and case-control analysis (weighted to give priority to within-family test); Additional genotyping: P < 0.001 in combined within-family and case-control analysis as above | In silico replication: P < 0.007 in combined within-family and case-control analysis (weighted to give priority to within-family test); Additional genotyping: P < 0.001 in combined within-family and case- control analysis as above | Primary analysis: P < 0.01 for type 2 diabetes; Internal replication: type 2 diabetes P < 0.01 and one glucose trait (FASTG or GAUC) or one insulin trait (ISI, IAUC, or HOMA-IR) P < 0.01 |
| Replication cohort | 1,465 unrelated FHS participants (non-overlapping) | 760 overlapping individuals for SNP verification | Non-overlapping Pima Indians: 1,207 case/1,627 control | Non-overlapping Pima Indians: 1,207 case/1,627 control | 427 nondiabetic Amish participants (295 control from primary type 2 analysis) |
| In silico replication | T2D 100K Consortium + DGI 500K | T2D 100K Consortium + DGI 500K | T2D 100K Consortium + DGI 500K | T2D 100K Consortium + DGI 500K | T2D 100K Consortium + DGI 500K |
T2D, type 2 Diabetes.
Replication evidence across four GWA studies
| Study | SNP | Chr. | Position (hg17) | Gene | Gene name | OMIM | Initial finding | QT | Internal replication | External replication 100K T2D Consortium | External replication 500K DGI |
|---|---|---|---|---|---|---|---|---|---|---|---|
| FHS | rs952635 | 1p31.2 | 66403906 | PDE4B | cAMP-specific phosphodiesterase | 600127 | G-allele protective for type 2 diabetes (HR 0.56 [0.40–0.79], Cox P = 0.0007) | Same protective allele associated with higher Gutt ISI and lower FPG, mFPG, A1C, and HOMA-IR (GEE P ∼ 0.003) | FPG and HOMA-IR (P < 0.05) | No | rs6664618 in moderate LD (r2 = 0.6); consistent trends in FPG (P = 0.04) and HOMA-IR (P = 0.057) |
| FHS | rs2863389 | 3q26.1 | 167631594 | 200 kb from nearest gene | N/A | Minor T-allele at SNP rs2863389 protective against diabetes (HR 0.41 [0.25–0.69], Cox P = 0.0006) | Same protective allele associated with lower FPG and mFPG in the FHS sample (GEE P = 0.005 and 0.0005, respectively) | No | T-allele protects from type 2 diabetes in Mexican Americans (OR 0.43, nominal P = 0.03) and in the Amish (OR 0.71, nominal P = 0.04) with similar trends in the Pimas | rs9829442 in perfect LD (r2 = 1.0); minor T-allele shows opposite nominal trend in increasing risk of type 2 diabetes (OR 1.15, P = 0.059) | |
| FHS | rs7935082 | 11q12.2 | 59911576 | MS4A7 | Membrane-spanning 4-domains subfamily A member 7 | 606502 | T-allele associated with lower FPG in FHS (FBAT P = 0.0006) | Same | No | T-allele was nominally protective from diabetes in the Mexican Americans (OR 0.53, P = 0.049) and in the Pimas (OR 0.58, P = 0.009) | rs950803 in perfect LD (r2 = 1.0); minor T-allele shows consistent nominal trend in protection from type 2 diabetes (OR 0.89, P = 0.097) |
| Amish | rs2237457 | 7p12.2 | 50693638 | GRB10 | Growth factor receptor bound protein 10 | 601523 | G-allele protective for type 2 diabetes (OR 0.61, P = 0.00001) | Same protective allele associated with lower GAUC (P = 0.001) | No | No | Not with rs2237457; six additional GRB10 SNPs in LD (r2 0.16–0.78) are associated with type 2 diabetes (P < 0.05) |
| Amish | rs3845971 | 3p14.2 | 59975712 | FHIT | Fragile histidine triad gene | 601153 | T-allele increased type 2 diabetes risk (OR 1.42, P = 0.004) | Same risk allele associated with increased GAUC (P = 0.0004) | No | Mexican-American type 2 diabetes OR 1.46 (P = 0.004) | No |
| Pima | rs10500938 | 11p14.3 | 22601179 | FANCF | Fanconia anemia, complementation group F | 603467 | A-allele increased type 2 diabetes risk (OR 2.14, P = 0.0004) | A-allele OR=1.65 (p=0.0010) | No | (A-allele absent in CEU) | |
| Pima | rs686989 | 11q23.1 | 113544435 | ZTBT16 | Zinc finger and BTB domain containing-16 | 176797 | A-allele increased type 2 diabetes risk (OR 3.26, P = 0.0004) | A-allele OR 1.27 (P = 0.0333) | No | No | |
| MA | rs979752 | 11p15 | 4326380 | UBQLNL | Ubiquilin3 | 605473 | T-allele protective for type 2 diabetes (OR 0.562, P = 0.00012) | N/A | N/A | Amish type 2 diabetes OR 0.764 (P = 0.030) and FHS HRR 0.709 (P = 0.040) | No |
| MA | rs2773080 | 1q25 | 176963373 | RALGPS2 | Ral GEF with PH domain and SH3 binding motif 2 | 611154 | G-allele protective for type 2 diabetes (OR 0.628, P = 0.0008) | N/A | N/A | Amish type 2 diabetes OR 0.793 (P = 0.033) | No |
| MA | rs1509957 | 10q21.1 | 64280724 | EGR2 | Early growth response 2 | 129010 | G-allele protective for type 2 diabetes (OR 0.652, P = 0.00084) | N/A | N/A | Pima type 2 diabetes OR 0.721 (P=0.032) | Same SNP OR 0.831 (P = 0.0082) |
| Study | SNP | Chr. | Position (hg17) | Gene | Gene name | OMIM | Initial finding | QT | Internal replication | External replication 100K T2D Consortium | External replication 500K DGI |
|---|---|---|---|---|---|---|---|---|---|---|---|
| FHS | rs952635 | 1p31.2 | 66403906 | PDE4B | cAMP-specific phosphodiesterase | 600127 | G-allele protective for type 2 diabetes (HR 0.56 [0.40–0.79], Cox P = 0.0007) | Same protective allele associated with higher Gutt ISI and lower FPG, mFPG, A1C, and HOMA-IR (GEE P ∼ 0.003) | FPG and HOMA-IR (P < 0.05) | No | rs6664618 in moderate LD (r2 = 0.6); consistent trends in FPG (P = 0.04) and HOMA-IR (P = 0.057) |
| FHS | rs2863389 | 3q26.1 | 167631594 | 200 kb from nearest gene | N/A | Minor T-allele at SNP rs2863389 protective against diabetes (HR 0.41 [0.25–0.69], Cox P = 0.0006) | Same protective allele associated with lower FPG and mFPG in the FHS sample (GEE P = 0.005 and 0.0005, respectively) | No | T-allele protects from type 2 diabetes in Mexican Americans (OR 0.43, nominal P = 0.03) and in the Amish (OR 0.71, nominal P = 0.04) with similar trends in the Pimas | rs9829442 in perfect LD (r2 = 1.0); minor T-allele shows opposite nominal trend in increasing risk of type 2 diabetes (OR 1.15, P = 0.059) | |
| FHS | rs7935082 | 11q12.2 | 59911576 | MS4A7 | Membrane-spanning 4-domains subfamily A member 7 | 606502 | T-allele associated with lower FPG in FHS (FBAT P = 0.0006) | Same | No | T-allele was nominally protective from diabetes in the Mexican Americans (OR 0.53, P = 0.049) and in the Pimas (OR 0.58, P = 0.009) | rs950803 in perfect LD (r2 = 1.0); minor T-allele shows consistent nominal trend in protection from type 2 diabetes (OR 0.89, P = 0.097) |
| Amish | rs2237457 | 7p12.2 | 50693638 | GRB10 | Growth factor receptor bound protein 10 | 601523 | G-allele protective for type 2 diabetes (OR 0.61, P = 0.00001) | Same protective allele associated with lower GAUC (P = 0.001) | No | No | Not with rs2237457; six additional GRB10 SNPs in LD (r2 0.16–0.78) are associated with type 2 diabetes (P < 0.05) |
| Amish | rs3845971 | 3p14.2 | 59975712 | FHIT | Fragile histidine triad gene | 601153 | T-allele increased type 2 diabetes risk (OR 1.42, P = 0.004) | Same risk allele associated with increased GAUC (P = 0.0004) | No | Mexican-American type 2 diabetes OR 1.46 (P = 0.004) | No |
| Pima | rs10500938 | 11p14.3 | 22601179 | FANCF | Fanconia anemia, complementation group F | 603467 | A-allele increased type 2 diabetes risk (OR 2.14, P = 0.0004) | A-allele OR=1.65 (p=0.0010) | No | (A-allele absent in CEU) | |
| Pima | rs686989 | 11q23.1 | 113544435 | ZTBT16 | Zinc finger and BTB domain containing-16 | 176797 | A-allele increased type 2 diabetes risk (OR 3.26, P = 0.0004) | A-allele OR 1.27 (P = 0.0333) | No | No | |
| MA | rs979752 | 11p15 | 4326380 | UBQLNL | Ubiquilin3 | 605473 | T-allele protective for type 2 diabetes (OR 0.562, P = 0.00012) | N/A | N/A | Amish type 2 diabetes OR 0.764 (P = 0.030) and FHS HRR 0.709 (P = 0.040) | No |
| MA | rs2773080 | 1q25 | 176963373 | RALGPS2 | Ral GEF with PH domain and SH3 binding motif 2 | 611154 | G-allele protective for type 2 diabetes (OR 0.628, P = 0.0008) | N/A | N/A | Amish type 2 diabetes OR 0.793 (P = 0.033) | No |
| MA | rs1509957 | 10q21.1 | 64280724 | EGR2 | Early growth response 2 | 129010 | G-allele protective for type 2 diabetes (OR 0.652, P = 0.00084) | N/A | N/A | Pima type 2 diabetes OR 0.721 (P=0.032) | Same SNP OR 0.831 (P = 0.0082) |
Chr., chromosome.
Published ahead of print at http://diabetes.diabetesjournals.org on 10 September 2007. DOI: 10.2337/db07-0482.
Additional information for this article can be found in an online appendix at http://dx.doi.org/10.2337/db07-0482.
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.
Article Information
This study was supported in part by U.S. Public Health Service Grants DK-20595, DK-47486, DK-47487, DK-55889, and HL-84715 and a gift from the Kovler Family Foundation. M.G.H. was supported by a mentor-based fellowship from the American Diabetes Association.
We thank Laura Martinolich, Xinmin Li, Edwin Cook, and Carole Ober for providing technical assistance with the Affymetrix genotyping assays. We also thank the 100K Type 2 Diabetes Consortium members and authors of the three other 100K GWAS studies (27,28,29) for valuable discussion and comments regarding the data, analysis, and manuscript preparation.
