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Diabetologia

, Volume 54, Issue 8, pp 2047–2055 | Cite as

Genome-wide association and meta-analysis in populations from Starr County, Texas, and Mexico City identify type 2 diabetes susceptibility loci and enrichment for expression quantitative trait loci in top signals

  • J. E. Below
  • E. R. Gamazon
  • J. V. Morrison
  • A. Konkashbaev
  • A. Pluzhnikov
  • P. M. McKeigue
  • E. J. Parra
  • S. C. Elbein
  • D. M. Hallman
  • D. L. Nicolae
  • G. I. Bell
  • M. Cruz
  • N. J. CoxEmail author
  • C. L. HanisEmail author
Article

Abstract

Aims/hypothesis

We conducted genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) analyses to identify and characterise risk loci for type 2 diabetes in Mexican-Americans from Starr County, TX, USA.

Method

Using 1.8 million directly interrogated and imputed genotypes in 837 unrelated type 2 diabetes cases and 436 normoglycaemic controls, we conducted Armitage trend tests. To improve power in this population with high disease rates, we also performed ordinal regression including an intermediate class with impaired fasting glucose and/or glucose tolerance. These analyses were followed by meta-analysis with a study of 967 type 2 diabetes cases and 343 normoglycaemic controls from Mexico City, Mexico.

Result

The top signals (unadjusted p value <1 × 10−5) included 49 single nucleotide polymorphisms (SNPs) in eight gene regions (PER3, PARD3B, EPHA4, TOMM7, PTPRD, HNT [also known as RREB1], LOC729993 and IL34) and six intergenic regions. Among these was a missense polymorphism (rs10462020; Gly639Val) in the clock gene PER3, a system recently implicated in diabetes. We also report a second signal (minimum p value 1.52 × 10−6) within PTPRD, independent of the previously implicated SNP, in a population of Han Chinese. Top meta-analysis signals included known regions HNF1A and KCNQ1. Annotation of top association signals in both studies revealed a marked excess of trans-acting eQTL in both adipose and muscle tissues.

Conclusions/Interpretation

In the largest study of type 2 diabetes in Mexican populations to date, we identified modest associations of novel and previously reported SNPs. In addition, in our top signals we report significant excess of SNPs that predict transcript levels in muscle and adipose tissues.

Keywords

Expression quantitative trait loci Genome-wide association study Meta-analysis Mexican-American Type 2 diabetes 

Abbreviations

CQC

Contrast quality control

CRLMM

Corrected robust linear model with maximum likelihood classification

DIAGRAM+

Diabetes Genetics Replication and Meta-analysis Consortium (expanded dataset)

eQTL

Expression quantitative trait loci

GWAS

Genome-wide association study

IBD

Identical-by-descent

IFG

Impaired fasting glucose

IGT

Impaired glucose tolerance

LCL

Lymphoblastoid cell lines

LD

Linkage disequilibrium

MAF

Minor allele frequency

NHGRI

National Human Genome Research Institute

PCA

Principal component analysis

QC

Quality control

SNP

Single nucleotide polymorphism

SQC

Skewness quality control

Introduction

Genome-wide association studies (GWASs) of type 2 diabetes in large samples of recent European descent have identified a number of susceptibility loci; it is widely accepted that, to further understand the genetic aetiology of type 2 diabetes, it will be necessary to replicate these findings and identify new loci using other populations. Mexican-Americans have disproportionately high rates of type 2 diabetes. In order to identify genetic variants contributing to type 2 diabetes susceptibility in Mexican-Americans we conducted both binary trait and ordinal GWA on 1618 individuals from Starr County with diabetes (cases), impaired glucose tolerance (IGT) or impaired fasting glucose (IFG), or normal fasting glucose and glucose tolerance (controls), using nearly 2 million directly interrogated and imputed markers. These analyses were combined in meta-analyses with the results from GWASs of admixed Mexicans from Mexico City [1]. This sample is described in detail in the companion paper. These cohorts comprise the largest type 2 diabetes analysis in Mexican and Mexican-American samples to date. In addition, we followed our association and meta-analyses by annotating SNPs with expression quantitative trait loci (eQTL) information from lymphoblastoid cell lines (LCLs) as well as adipose and muscle tissues, to characterise possible functional roles for SNPs associated with type 2 diabetes.

Methods

Study population and phenotype assessment

Using the 1979 National Diabetes Data Group criteria [2], cases (n = 990) and controls (n = 990) were selected from genetic and epidemiological resources developed in the Mexican-American population in Starr County, TX, USA (these also meet definitions set out in the 1997 ADA criteria [3]). Of the 931 successfully genotyped case samples, we selected 837 unrelated individuals for our analyses.

A representative type 2 diabetes control sample was selected from a systematic survey of the primary population centres of Starr County conducted from 2002 to 2006. Blocks were randomly selected within primary population centres in Starr County, and all individuals in each household on selected blocks were enumerated. A random individual aged 20 years and above with no history of diabetes was selected from each household for a physical evaluation including an oral glucose tolerance test with a 75 g glucose load. From 1,345 oral glucose tolerance tests, 350 individuals were classified as having diabetes based on their fasting or 2 h post-load glucose, and were excluded from consideration as controls.

From the remaining 995 individuals, 990 were selected for genotyping, and after genotype quality control and pruning related samples, the control sample included 781 unrelated Starr County residents. Of these, 345 individuals had IGT (defined as a blood glucose level of 7.8–11.0 mmol/l after a 2 h oral glucose tolerance test) or impaired fasting glucose (defined as fasting blood glucose level of 5.6–6.9 mmol/l after overnight fast) and were included in the IGT/IFG category as an intermediate phenotype in the ordinal analysis. Table 1 summarises phenotypes measured in the case, IGT/IFG and control groups.
Table 1

Characteristics of the study population

Characteristic

Cases

IFG/IGT

Controls

n

837

345

436

Sex (n, female)

504

259

299

Age (years) (age at diagnosis in cases)

46.7 ± 10.9

40.9 ± 10.5

37.6 ± 9.0

BMI (kg/m2)

31.8 ± 6.4

31.0 ± 5.8

29.5 ± 6.5

HbA1c (%)

10.9 ± 3.7

NA

NA

Data are mean ± SD, unless otherwise indicated

NA, not available

Genotyping and quality control

Genomic DNA was isolated from whole blood of 1,980 unique individuals from Starr County, and quantified by picogreen. The Affymetrix Genome-Wide SNP Array 6.0 assay was performed according to manufacturer protocols (Affymetrix, Santa Clara, CA, USA) at the Center for Inherited Disease Research. Case, IGT/IFG, and control groups were randomised to plates. Genotypes were called for 1,890 samples passing quality control (QC) using both the Affymetrix supported Birdseed v2 [4] calling algorithm as well as the corrected robust linear model with maximum likelihood classification (CRLMM) [5] and only calls in complete agreement between the two algorithms were used in analyses.

No samples uniformly failed contrast QC (CQC) and skewness QC (SQC) [6] in the Affymetrix enzyme-specific subsets as well as the full set of enzyme-specific SNPs. Plate effects were examined [6] and three plates were identified as having an excess of SNPs with low plate-association p values (132, 413, and 1,080 SNPs with p value > 1 × 10−7 on these plates). The plate showing the most severe plate effects was comprised of samples that had failed genotyping previously and had been repeated. Since no systematic bias was apparent from the battery of quality measures and no single set of individuals consistently clustered together in a genotype class in the cluster plots of the most plate-biased SNPs (rather poor clustering and miss-calling were frequently observed in these cluster plots), we chose to recall the data with CRLMM without removing any individuals. Only autosomal genotypes with perfect match calls in both the Birdseed and CRLMM datasets (‘consensus calls’) were carried forward, resulting in a modest reduction in overall call rate (from 0.997 to 0.996 across SNPs and samples). Plate effect analysis was repeated using the consensus calls; the number of SNPs with plate effect p values <1 × 10−7 for the three plates noted above are 14, 111 and 77, respectively. That a modest plate effect remains in the data may reduce our power to detect true phenotype associations, but the lack of evidence of differential bias between cases and controls suggests that the bias will not increase the false positive rate. We annotated all SNPs with their quality scores from the QC phase, and checked for flags, such as strong association with a plate, in all top signals.

Using PLINK (http://pngu.mgh.harvard.edu/∼purcell/plink/) [7], SNP and individual level call rates were calculated as well as sex incompatibilities (one mismatch identified and removed using markers on the X and Y chromosomes). All individuals passed per sample missingness of <0.1 and 7124 SNPs were removed as a result of missingness >0.1. Identical-by-descent (IBD) estimations were calculated in PLINK and an ‘unrelated’ cohort of 1,618 samples was created with a proportion of pairwise sharing IBD less than 0.28, weighting inclusion of cases and higher call rate. Individual and plate level heterozygosity was estimated in PLINK and no outliers observed. SNPs with minor allele frequency (MAF) <0.01 were removed. Departures from Hardy–Weinberg equilibrium were calculated and flagged in the full sample, cases, and controls.

Major ancestry group components for the unrelated admixed sample in a SNP set pruned of local and long-distance linkage disequilibrium (LD) were calculated in EIGENSTRAT [8] (Fig. 1a); however, neither of the first two principal components were found to be significantly correlated with type 2 diabetes (mean regression p values were 0.18, 0.39, with Pearson correlation coefficients between covariate and phenotype of 0.005 and 0.021, respectively), similar to our previous findings [9]. Although we did not see significant correlation of type 2 diabetes and European admixture in the Starr County sample, strong correlation was observed in the Mexico City study (Fig. 1b). To prevent excessive false positive rates, all analyses in the Mexico City sample controlled for the distribution along the primary axis of variation. For additional details of covariates in this sample please refer to the companion paper [10].
Fig. 1

PCA analysis of (a) study population from Starr County, TX, USA, and (b) study population from Mexico City. Cases are shown in red, IGT/IFG samples are in green, gluconormal controls are in blue. Both populations analysed together with a number of ancestral populations are shown in (c)

Imputation

We performed imputation in MACH 1.0 [11, 12] from consensus call genotypes with MAF >0.01 in 1,618 Starr County Mexican-Americans to the full panel of CEU (Centre d’Etude du Polymorphisme [Utah residents with northern and western European ancestry]), YRI (Yoruba in Ibadan, Nigeria), and JPT (Japanese in Tokyo, Japan) + CHB (Han Chinese in Beijing, China) HapMap2 phased polymorphic SNPs from release 22 [13]. In total 137,532 SNPs with an estimate of the squared correlation between imputed and true genotypes, r 2  ≤ 0.7 (as estimated by MACH) were removed. An additional 2,616 SNPs with MAF <0.01 were removed, resulting in 1,829,586 genotyped or imputed markers for analyses.

Type 2 diabetes association analyses

We used SNPtest [14] to calculate the Armitage trend test using covariates and the option score, which specifies a missing-data likelihood test. Additionally, the missing-data likelihood test was extended to a three-category (cases, IGT/IFG, and controls) ordered logistic regression using the same covariates, and implemented in R [15]. We tested the significance of the correlation of covariates in PLINK. In Starr County, 56% of people aged 70 years and older have diabetes, suggesting that substantial numbers of those with non-normal or even normal glucose tolerance will ultimately develop diabetes. Therefore, to generate a more meaningful covariate for age, we used the age at diagnosis (dxage) for cases and the age at enrolment for the controls. In the case–control analysis as well as in the data including the IGT/IFG category, age/dxage, sex and BMI were significantly correlated with phenotype (mean regression p values across directly interrogated SNPs were <1 × 10−34, <1 × 10−4 and <1 × 10−3, with Pearson correlation coefficients between covariate and ordinal phenotype of 0.36, 0.08 and 0.15 respectively).

Meta-analysis

Type 2 diabetes association results from SNPtest in Mexicans from Mexico City were combined with results from the associations in the Starr County sample using SNPtest’s companion program META [16]. We performed principal component analysis (PCA) in EIGENSTRAT on a local and long-distance LD-pruned SNP set in the Mexico City sample as well as together with parental populations, to check homogeneity between the samples (Fig. 1b, c). Population substructure of the samples within the context of HapMap CEU, YRI, CHB + JPT, and Mexican-American samples from Los Angeles, as well as samples from Bolivians, Peruvians, and Nahua, Mayan, and Spanish populations [17], was largely overlapping, suggesting the samples are derived from similar parental populations; the Starr County sample exhibited more European ancestry. The score method in SNPtest generates a relative information measure on the scale of (0,1) of the observed statistical information from the imputation for the estimate of allele frequency of the SNPs, and was used to assess confidence in the meta-analysis p values. Reported p values have a combined score statistic ≥0.5, MAF > 0.05 and p value < 0.05 in each study, and a meta-analysis p value <0.001 showing the same direction of effect.

eQTL analysis

The top type 2 diabetes association signals from SNPtest and from the three-category ordered logistic regression were evaluated for their effect on transcript levels in LCLs, muscle, and adipose tissue. The set of eQTLs in lymphocytes among the type 2 diabetes associated SNPs were retrieved from SCAN [18], which holds the results of eQTL mapping of HapMap SNPs from 90 CEU and 90 YRI samples, to transcriptional expression evaluated in LCLs from these individuals by the Affymetrix GeneChip Human Exon 1.0 ST Array (Affymetrix). For the analyses of muscle and adipose eQTLs, samples were chosen from among 184 study participants for availability of both tissues, and are described in Sharma et al. [19]. Sixty-two study participants at the tails of the distribution of insulin sensitivity (p value <1 × 10−5) adjusted for age, sex, BMI and per cent fat were genotyped on the Illumina 1M platform (Illumina, San Diego, CA, USA) using standard Illumina protocols. Transcript levels were assayed using Human Whole Genome 4 × 44 arrays (Agilent Technologies, Santa Clara, CA, USA). eQTL mapping in each tissue was done by testing each marker for the additive effect of allele dosage on normalised probe-level expression intensity. In our annotations, the best proxy SNPs were determined for those type 2 diabetes associated SNPs not directly typed on the Illumina platform used in the eQTL analyses of muscle and adipose tissues.

Using SNP eQTL annotations in LCLs, and adipose and muscle tissues, we conducted simulations to test for an enrichment of eQTLs (p value <1 × 10−4) among the top signals (p value <1 × 10−3) associated with type 2 diabetes. We generated 1,000 random SNP sets, each of the same size (n = 722) as the original list of LD-pruned top signals (r 2 > 0.30), containing variants matched on MAF distribution, sampled without replacement from the set of typed SNPs on the Illumina 1M platform; MAF matching was done by drawing from the platform SNPs, which had been grouped into discrete MAF bins, each spanning 5% of the allele frequency spectrum. These simulations (n = 1,000) yield an empirical p value, calculated as the proportion of simulations in which the number of eQTLs exceeds the observed number, Q, in the list of top type 2 diabetes signals.

Results

Association and meta-analyses

All analyses were performed using the combined age/dxage measurement and sex as covariates and association tests were conducted with and without correcting for BMI. We estimated the inflation factors (the ratio of the trimmed means of the observed and expected values), λ age/dxage+sex = 1.034 and λ age/dxage+sex+BMI = 1.026 in the case–control analysis and 1.038 and 1.023 (respectively) in the three-category ordered logistic regression. The Manhattan and quantile–quantile (QQ) plots summarising results from the ordinal logistic regression without BMI as a covariate are presented in Fig. 2a, b (others are in ESM Figs 13). Forty-nine high quality SNPs, with an uncorrected p value <1 × 10−5 in at least one analysis, fell within 14 genomic regions, and are listed, with p values from the analysis, in Table 2 and depicted in Fig. 2a (p values for these markers in each analysis can be found in ESM Table 1). These top signals fall within eight genic regions: PER3, PARD3B, EPHA4, TOMM7, PTPRD, HNT (also known as RREB1), LOC729993 (also known as SHISA9), IL34, and six intergenic regions. In general, the SNPs within these genic and intergenic regions represent single blocks of LD (r 2 > 0.7). Aside from the SNPs in PARD3B and EPHA4, which have MAF of about 0.05, these signals are driven by common variation (MAF > 0.1) (Table 2). We calculated heterogeneity in META (www.stats.ox.ac.uk/∼jsliu/meta.html); we observed no excess of heterogeneity genome-wide and all Starr County top signals have heterogeneity p values >1 × 10−4 (Table 2) [20]. Results for SNPs in LD with previously reported associated type 2 diabetes SNPs from the National Human Genome Research Institute (NHGRI) catalogue [21], Diabetes Genetics Replication and Meta-analysis Consortium (expanded dataset) (DIAGRAM+) [22], as well as top signals identified in the previous GWAS in Starr County [9], were examined in detail (ESM Table 2). We also examined our results for variation within CAPN10, previously reported as a type 2 diabetes gene in Mexican-Americans, but key SNPs in the associated CAPN10 haplotypes were neither directly interrogated nor imputed.
Fig. 2

Manhattan plot of −log10 (p values) at 1.8 million autosomal SNPs (a) and QQ plot of observed vs expected p values (b) of results of ordinal regression analysis on type 2 diabetes adjusted for age/dxage and sex

Table 2

Summary of top regions from GWAS of type 2 diabetes in Starr County Mexican-Americans

SNP

Chr

Pos

Risk allele

Non-risk allele

Ordinal analysis, age- and sex-adjusted

Meta-analysis p value

Gene

Location

OR (95% CI)

p value

Freq (all)

Freq (cases)

Freq (IFG/IGT)

Freq (controls)

Imputation r2

rs10462020

1

7803270

T

G

1.43 (1.27–1.59)

8.39 × 10−6

0.83

0.85

0.8

0.8

1

8.57 × 10−3

PER3

Missense

rs6728803

2

66001970

G

A

1.38 (1.26–1.51)

6.80 × 10−7

0.52

0.56

0.5

0.48

0.83

9.16 × 10−3

KRT18P33-LOC729348

Intergenic

rs849230

2

205969274

A

G

1.83 (1.57–2.1)

6.86 × 10−6

0.93

0.95

0.92

0.91

0.88

PARD3B

Intron

rs1879976

3

149826207

A

G

1.34 (1.21–1.47)

8.91 × 10−6

0.59

0.61

0.59

0.54

0.9

LOC344741-RPL38P1

Intergenic

rs2240727

7

22819037

T

C

1.37 (1.24–1.5)

2.79 × 10−6

0.34

0.38

0.29

0.31

0.9

7.72 × 10−3

TOMM7

3′ UTR

rs4766119

12

3419017

A

G

1.65 (1.44–1.85)

2.65 × 10−6

0.87

0.89

0.85

0.84

0.72

1.25 × 10−3

LOC100128253-LOC100129223

Intergenic

rs149228

16

13211975

C

A

1.43 (1.28–1.57)

1.49 × 10−6

0.24

0.27

0.21

0.2

0.89

1.61 × 10−3

LOC729993

Intron

rs4522658

2

134203633

G

A

1.28 (1.17–1.4)

3.40 × 10−5

0.46

0.49

0.46

0.42

0.95

1.27 × 10−4

NAP5-MGAT5

Intergenic

rs16862811

2

222109330

G

T

1.8 (1.53–2.08)

2.65 × 10−5

0.94

0.95

0.95

0.91

0.94

4.12 × 10−4

EPHA4

Intron

rs10192201

2

237468147

G

C

1.26 (1.13–1.4)

4.44 × 10−4

0.64

0.65

0.67

0.58

0.93

1.54 × 10−3

LOC100128709-LOC93463

Intergenic

rs649891

9

10420602

C

T

1.29 (1.16–1.43)

1.69 × 10−4

0.41

0.43

0.44

0.35

0.78

5.80 × 10−6

PTPRD

Intron

rs3099797

11

131626596

C

T

1.3 (1.18–1.43)

4.60 × 10−5

0.64

0.67

0.63

0.58

1

3.38 × 10−5

HNT

Intron

rs8033124

15

89712478

C

A

1.3 (1.16–1.44)

1.83 × 10−4

0.42

0.44

0.44

0.37

0.74

LOC100128403-LOC441732

Intergenic

rs3813905

16

69238351

C

G

1.41 (1.25–1.57)

3.35 × 10−5

0.75

0.77

0.73

0.73

0.73

9.73 × 10−4

IL34

5′ UTR

Representative SNPs from each genomic region with p values below 1 × 10−5 in at least one analysis (regression on the ordinal or binary phenotype, with/without covariate BMI). Reported statistics are derived from the ordinal regression on type 2 diabetes adjusted for age/dxage and sex. Reported meta-analysis p values are derived from the binary case–control analysis combined with the analogous analysis in the Mexico City dataset. Any evidence for association is driven solely by the Starr County study, as all p values in the Mexico City sample for these SNPs were >0.15 (SNPs in the table without high quality Mexico City genotypes are labelled with a dash); p values for heterogeneity are greater than 1 × 10−4 for all meta-analysed SNPs.

Chr, chromosome; Freq, frequency; Pos, position; UTR, untranslated region

The top meta-analysis signals fell within and near the genes CSMD1 and HNF1A; additional signals implicated LSAMP, FGF12, RP11-354K1.1, KCNQ1, ANK2, NIPAL2, RP11-74C3.1, CIT, C22orf30 (also known as PRR14L), EPHB2, MCPH1 and DEPDC5. Follow-up meta-analysis of top signals from the combined Mexican/Mexican-American studies with the recent DIAGRAM+ dataset identified genome-wide significant signals (p value <5 × 10−8) in or near susceptibility genes HNF1A and CDKN2A/CDKN2B, and suggestive evidence for IGF2BP2, KCNQ1 and the previously unreported C14orf70. For additional details of the meta-analyses of these samples and follow-up of top signals in DIAGRAM+, please refer to the companion paper.

In addition to physical proximity, we considered the roles that SNPs in the top signals play in transcript prediction. Annotation of LD-pruned top signals (p value <1 × 10−3, r 2 < 0.3) from the ordinal regression analysis in Starr County, with sex and age/dxage as covariates, identified 258 SNPs predicting transcript levels in LCLs, 289 in muscle, and 359 in adipose tissue. The mean number of frequency matched SNPs that predict transcript levels in LCLs in 1,000 simulations was 252 (SD 12.17), suggesting no enrichment. However, the maximum number of muscle and adipose eQTLs observed in 1,000 simulations was 173 and 193, respectively. The distributions of eQTL counts established by simulations, as well as the actual observed counts in muscle and adipose tissues, are depicted in Fig. 3a, b (see ESM Fig. 4 for LCLs). Notably, nearly all of the enrichment in muscle and adipose tissue is driven by trans-acting eQTLs; only three of the eQTLs observed in muscle, and eight in adipose tissue, were cis-acting. In addition, we replicated the observed enrichment in muscle and adipose tissues in the top signals (defined as described above) from the analysis in the Mexico City sample (Fig. 4a, b). A number of SNPs showing association for type 2 diabetes in our studies predicted gene expression across multiple tissues; 14 SNPs predict expression in all three tissues tested (ESM Fig. 5).
Fig. 3

Simulated distribution of eQTL counts for SNPs with allele frequencies matched to signals with p value <1 × 10−3 from the ordinal regression on type 2 diabetes adjusted for age/dxage and sex in the Starr County study population. The black dot represents the actual eQTL count observed in SNPs with p value <1 × 10−3 in this analysis. Results are shown for transcription prediction in adipose (a) and muscle (b) tissue

Fig. 4

Simulated distribution of eQTL counts for SNPs with allele frequencies matched to signals with p value <1 × 10−3 from the ordinal regression on type 2 diabetes adjusted for age/dxage and sex in the Mexico City study population. The black dot represents the actual eQTL count observed in SNPs with p value <1 × 10−3 in this analysis. Results are shown for transcription prediction in adipose (a) and muscle (b) tissue

Discussion

As studies of genetic association in type 2 diabetes are pursued in non-European populations, it is important to recognise that, in populations with markedly increased disease rates, there may be room to improve on the case–control study design. In this case, we favour an approach that uses more information and recognises the staging of the disease, enabling us to recover a substantial number of individuals. In general, the results of the ordinal and binary analyses are very similar, but including the intermediate class allows us to include more of the individuals in the sample, and should, therefore, provide a more complete picture of the association of genetic variation with the spectrum of phenotypes related to type 2 diabetes. Among the top signals in the ordinal analysis is a functional missense polymorphism in the clock gene PER3. Substantial research has implicated circadian clock function in metabolic diseases, coronary function and obesity [23, 24, 25], but this signal is not replicated in the Mexico City sample (p value ∼0.78).

Another top signal from the Starr County sample, as well as in the meta-analysis with Mexico City, implicates a previously reported type 2 diabetes risk gene PTPRD. PTPRD, protein tyrosine phosphatase receptor type delta gene [26], was recently identified by GWAS in Han Chinese populations [26]. Although the SNPs are both intronic, our result is 1.6 Mb downstream and represents an independent signal from within the gene (the SNPs are in linkage equilibrium in HapMap CHB + JPT as well as CEU samples). The role of PTPRD in type 2 diabetes is not yet understood; however, when ablated in mice, genes in this subfamily induce defects in glucose homeostasis and insulin sensitivity [27, 28].

There is little literature suggesting roles for IL34, TOMM7, HNT or PARD3B, nor the intergenic regions, in diabetes; however, in HapMap Europeans, LD extends from several of these top regions to include candidate genes for type 2 diabetes. For example, two intergenic SNPs on chromosome 12 are in high LD with PRMT8 (r 2 = 0.764), a gene known to influence lipid levels [29]. Yet, LD with a type 2 diabetes-related gene alone is not sufficient evidence of a SNP’s functional role. To assess functional consequences of associated variants, we explored our findings in the context of gene regulation.

Previous studies have identified enrichment of eQTLs in trait associated SNPs [30]. A recent study into the functional role of type 2 diabetes associated SNPs found an excess of eQTLs among signals from the Wellcome Trust Case Control Consortium and the Diabetes Genetics Initiative in muscle and adipose [31, 32]. The enrichment in these cases was driven almost exclusively by trans-acting eQTLs. This enrichment was robust to thresholds of eQTL p value (1 × 10−6 was also considered and a similar level of enrichment is still observed) and threshold of significance of type 2 diabetes association (top 1,000 and top 10,000 signals were analysed and enrichment was seen in both). We find similar enrichment in both the Starr County and Mexico City datasets. The lack of enrichment of eQTLs in LCLs, despite significant enrichment of eQTLs from muscle and adipose tissue, is likely to be attributable to the fact that LCLs as a cell type are not as relevant as muscle and adipose tissue to type 2 diabetes risk, and only a proportion of eQTLs are shared across tissues [31, 32]. The number of SNPs predicting transcript level in both muscle and adipose tissue is markedly higher (178) than those overlapping LCLs (33), suggesting concordance of regulatory action in muscle and adipose tissues in a large proportion of top type 2 diabetes associated SNPs in our study. Previous studies of eQTLs in LCLs in top type 1 diabetes associated loci have found significant enrichment [30], suggesting that lymphoblasts are a relevant tissue for type 1 diabetes; this is reassuring given the role of autoimmunity in the disease.

Of the 53 previously reported SNPs considered (drawn from the NHGRI catalogue, DIAGRAM+, and Hayes et al. [9], and interrogated or represented by proxy SNP in the Starr County analyses) we identified seven markers with p value <0.05 and odds ratios in the same direction. These markers are in or near IGF2BP2, WFS1, JAZF1, OR13D1-OR13D3P, KCNQ1, VPS33B, and EPB41L3-LOC645355. An additional 20 SNPs do not reach marginal significance in our dataset, but do show odds ratios in the same direction. Seventeen SNPs have previously reported confidence intervals for the odds ratios which overlap those observed in the Starr County dataset (all data shown in ESM Table 2).

As in all GWASs of type 2 diabetes published to date, we lack power to detect the kinds of small effect we now know are likely to contribute to disease risk (see ESM Fig. 6). However, the absence of support for a number of established type 2 diabetes genes is notable in this dataset. Of particular interest is the lack of association of SNPs within TCF7L2, the gene with the strongest associations with type 2 diabetes observed in Europeans. While the allele frequency of the variant most strongly associated in Europeans is lower in Asian populations, estimates of the odds ratios were quite similar. In contrast, the frequency of the risk allele is similar in Mexican-Americans and Europeans, but the odds ratios are lower in both the Mexican-American and Mexico City datasets; in a fixed effects meta-analysis of SNP rs7903146 in our data and that of Voight et al. [22], the heterogeneity p value is 0.05. Our confidence intervals for rs7903146 also overlap, but just barely, according to DIAGRAM estimates.

The winner’s curse, the tendency of a biased overestimate of effect in the first study to report an association and be successfully published, may explain the somewhat higher overestimates of effect in the top signals in the Starr County and Mexico City studies [33]. In addition, ascertainment can contribute to these effects. For example, in Starr County, patients from families with two or more affected members were specifically targeted for the GWAS, which improves power, but leads to estimates of effect size that are larger than would be estimated in an unselected case series. In general, however, lack of replication is likely to be due to the fact that all studies of type 2 diabetes to date are under-powered to detect small effects, and therefore only detect association signals from some (probably different) subset of the contributing variants.

To identify, confirm, and characterise additional genetic risk factors for type 2 diabetes it is likely that we will need to continue studies in non-European populations, to explore new study designs and methods of analysis in high-risk groups, and to enhance GWAS with eQTL and other functional studies.

Notes

Acknowledgements

Supported in part by DK073451, HL084715, and DK20595, the FOFOI IMSS-2004/014, 2005/2/I/363, 2006/1B/1/002, Fundacion IMSS A. C., and Fundacion Gonzalo Rio Arronte I. A. P. Mexico. Also supported in part by National Institutes of Health grant T32GM007197 and grant CTSA T32 UL1 RR024999. M. Cruz is a recipient of a Fundación IMSS Scholarship, Mexico. We also gratefully acknowledge the willing participation of the residents of Starr County and the efforts of the field staff in Starr County responsible for the collection of these data. Genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

125_2011_2188_MOESM1_ESM.xlsx (15 kb)
ESM Table 1 Summary of top regions from GWAS of type 2 diabetes in Starr County Mexican-Americans (XLSX 13.4 kb)
125_2011_2188_MOESM2_ESM.xlsx (21 kb)
ESM Table 2 Results of Starr County GWAS for known type 2 diabetes-associated SNPs (XLSX 18.8 kb)
125_2011_2188_MOESM3_ESM.pdf (168 kb)
ESM Fig. 1 Manhattan plot of −log10 (p values) at 1.8 million autosomal SNPs (a) and QQ plot of observed versus expected p values (b) of results of ordinal regression analysis on type 2 diabetes adjusted for age/dxage, sex, BMI (PDF 167 kb)
125_2011_2188_MOESM4_ESM.pdf (161 kb)
ESM Fig 2 Manhattan plot of −log10 (p values) at 1.8 million autosomal SNPs (a) and QQ plot of observed versus expected p values (b) of results of regression analysis on a binary type 2 diabetes phenoytpe adjusted for age/dxage and sex (PDF 160 kb)
125_2011_2188_MOESM5_ESM.pdf (165 kb)
ESM Fig 3 Manhattan plot of −log10 (p values) at 1.8 million autosomal SNPs (a) and QQ plot of observed versus expected p values (b) of results of regression analysis on the binary type 2 diabetes phenoytpe adjusted for age/dxage, sex, and BMI (PDF 164 kb)
125_2011_2188_MOESM6_ESM.pdf (131 kb)
ESM Fig 4 Simulated distribution of LCL eQTL counts for SNPs with allele frequencies matched to signals with p value <1 × 10−3 from the ordinal regression on type 2 diabetes adjusted for age/dxage and sex in the Starr Co. Study population. The black dot represents the actual LCL eQTL count observed in SNPs with p value <1 × 10−3 in this analysis (PDF 131 kb)
125_2011_2188_MOESM7_ESM.pdf (112 kb)
ESM Fig 5 Venn diagram of type 2 diabetes associated (p-value <1 × 10−3 from the ordinal regression on type 2 diabetes adjusted for age/dxage and sex in the Starr Co. study population) SNPs organised by the tissues in which they predict gene expression (eQTL p value <1 × 10−4) (PDF 111 kb)
125_2011_2188_MOESM8_ESM.pdf (145 kb)
ESM Fig 6 Power calculations for Starr County and Mexico City combined sample. Genome-wide significance threshold is 5 × 10−8, individuals with impaired fasting glucose and/or impaired glucose tolerance are divided into cases and controls (PDF 144 kb)

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • J. E. Below
    • 1
  • E. R. Gamazon
    • 2
  • J. V. Morrison
    • 2
  • A. Konkashbaev
    • 2
  • A. Pluzhnikov
    • 2
  • P. M. McKeigue
    • 3
  • E. J. Parra
    • 4
  • S. C. Elbein
    • 5
  • D. M. Hallman
    • 6
  • D. L. Nicolae
    • 1
    • 2
    • 7
    • 8
  • G. I. Bell
    • 1
    • 8
  • M. Cruz
    • 9
  • N. J. Cox
    • 1
    • 2
    • 8
    • 10
    Email author
  • C. L. Hanis
    • 6
    Email author
  1. 1.Department of Human GeneticsUniversity of ChicagoChicagoUSA
  2. 2.Section of Genetic Medicine, KCBD 3220University of ChicagoChicagoUSA
  3. 3.Public Health Sciences Section, Division of Community Health SciencesUniversity of Edinburgh Medical SchoolEdinburghUK
  4. 4.Department of AnthropologyUniversity of TorontoMississaugaCanada
  5. 5.Section on Endocrinology and MetabolismWake Forest University School of MedicineWinston-SalemUSA
  6. 6.Human Genetics CenterUniversity of Texas Health Science Center at HoustonHoustonUSA
  7. 7.Department of StatisticsUniversity of ChicagoChicagoUSA
  8. 8.Department of MedicineUniversity of ChicagoChicagoUSA
  9. 9.Unidad de Investigacion Medica en BioquimicaHospital de Especialidades, Centro Medico ‘Siglo XXI’, IMSSMexico CityMexico
  10. 10.Section of Genetic MedicineUniversity of ChicagoChicagoUSA

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