Diabetes Metab J > Volume 45(2); 2021 > Article
Yang, Yao, Ding, Chen, and Guo: Enhancer-Gene Interaction Analyses Identified the Epidermal Growth Factor Receptor as a Susceptibility Gene for Type 2 Diabetes Mellitus

Abstract

Background

Genetic interactions are known to play an important role in the missing heritability problem for type 2 diabetes mellitus (T2DM). Interactions between enhancers and their target genes play important roles in gene regulation and disease pathogenesis. In the present study, we aimed to identify genetic interactions between enhancers and their target genes associated with T2DM.

Methods

We performed genetic interaction analyses of enhancers and protein-coding genes for T2DM in 2,696 T2DM patients and 3,548 controls of European ancestry. A linear regression model was used to identify single nucleotide polymorphism (SNP) pairs that could affect the expression of the protein-coding genes. Differential expression analyses were used to identify differentially expressed susceptibility genes in diabetic and nondiabetic subjects.

Results

We identified one SNP pair, rs4947941×rs7785013, significantly associated with T2DM (combined P=4.84×10−10). The SNP rs4947941 was annotated as an enhancer, and rs7785013 was located in the epidermal growth factor receptor (EGFR) gene. This SNP pair was significantly associated with EGFR expression in the pancreas (P=0.033), and the minor allele “A” of rs7785013 decreased EGFR gene expression and the risk of T2DM with an increase in the dosage of “T” of rs4947941. EGFR expression was significantly upregulated in T2DM patients, which was consistent with the effect of rs4947941×rs7785013 on T2DM and EGFR expression. A functional validation study using the Mouse Genome Informatics (MGI) database showed that EGFR was associated with diabetes-relevant phenotypes.

Conclusion

Genetic interaction analyses of enhancers and protein-coding genes suggested that EGFR may be a novel susceptibility gene for T2DM.

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by insulin resistance and hyperglycemia. There is compelling evidence that genetic factors have a strong influence on the risk of T2DM [1]. Over the last decade, catalyzed by the ability to perform genome-wide association studies (GWASs) with ever larger samples, more than 400 robust susceptibility variants for T2DM have been identified [2,3,4,5], together with associated credible sets. The majority of these studies searched for simple additive, cumulative, and independent effects, primarily based on single-locus analyses. The joint effect of identified variants explains approximately 10% of observed T2DM heritability [5,6].
Multiple hypotheses have been put forward to explain the well-known “missing heritability” problem, which refers to a phenomenon whereby single genetic variations cannot account for much of the heritability of phenotypes. These include epigenetics, rarer variants with larger effects, and limitations of GWASs [7]. Genetic interactions can affect heritability calculations, and lack of knowledge of genetic interactions is believed to be an important cause of the missing heritability [8]. Studies have identified several epistatic mechanisms at the onset of T2DM. For instance, interactions among RAS-related genes were associated with T2DM susceptibility in a Chinese population, although the main effects of the individual loci may not be observed [9]. However, interaction analyses using single nucleotide polymorphisms (SNPs) in the whole genome usually suffer from the problem of very stringent significance [10]. To solve this problem, the majority of previous hypothesis-driven studies have restricted the search for interactions on the basis of existing biological knowledge, such as candidate genes and protein-protein interactions, or statistical features, such as marginal effects and known GWAS hits [10]. However, focusing only on interactions of SNPs with known associated loci or candidate genes may miss SNP interactions that expose no association individually but in combination contribute to disease susceptibility [11]. Therefore, it is important to solve the low power of the genome-wide epistasis analyses problem with a method that does not depend on known related loci.
A major goal in human genetics research is to understand genetic contributions to complex diseases, specifically the molecular mechanisms by which common DNA variants influence disease etiology. The functional relevance of most discovered loci, including those that have been the most reproducibly associated, remains unclear. Readily available data from the Encyclopedia of DNA Elements (ENCODE) [12] and the Roadmap Epigenomics Project [13] have made it possible to investigate regulatory elements in noncoding regions. A number of studies demonstrated that disease- and trait-associated genetic variants were enriched in regulatory elements, mostly enhancers [14]. Distant enhancers located at considerable genomic distances from gene promoters can be brought into close spatial proximity through specific chromosomal interactions, which are essential for the control of spatiotemporal gene expression [15]. Direct interactions between enhancers and promoters are central to dominant models of enhancer function [15]. In strong support of these models, the interaction between enhancers and their target genes can induce gene transcription, even in the absence of a key transcriptional activator [16]. Considering the important roles of enhancers in genetic predisposition to diseases, analyzing genomic interactions between genes and surrounding enhancers is a great knowledge-based method to solve the low power of the genome-wide epistasis analysis problem.
In this study, we performed genetic interaction analyses of protein-coding genes and surrounding enhancers to identify variations that may play a role in the risk of T2DM. By considering statistically interacting SNPs, we identified a novel susceptibility gene, the epidermal growth factor receptor (EGFR), for T2DM. Our results provide new insights into the genetic architecture of T2DM.

METHODS

Subjects

The basic characteristics of the samples used in this project are presented in Table 1. The discovery data were obtained from the Gene Environment Association Studies (GENEVA [17], http://www.genevastudy.org/). We used a subset of GENEVA data on diabetic and nondiabetic subjects from a case-control study of T2DM nested within cohorts of the Nurses' Health Study (https://www.nurseshealthstudy.org) and the Health Professionals' Follow-up Study (https://sites.sph.harvard.edu/hpfs/), two well-characterized cohort studies, which include stored blood and DNA samples, as well as detailed information on dietary and lifestyle variables of the participants. The samples were genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0 (Thermo Fisher Scientific, Waltham, MA, USA).
All the subjects in this study were reported to be of European ancestry. After selection, 2,558 diabetic and 2,983 nondiabetic subjects were available for analyses (dbGaP: phs000091.v2.p1). A replication sample was derived from the Biobank Program of the Institute of Personalized Medicine (IPM) at Mount Sinai Medical Center. The primary sample consisted of 2,867 self-identified African-Americans, European-Americans, and Hispanics. The samples were genotyped using the Affymetrix 6.0 chip. All the individuals included in the replication study were reported to be of European ancestry. After selection, 138 diabetic and 565 nondiabetic subjects were available for analyses (dbGaP: phs000388.v1.p1).

Acquisition of SNP pairs and SNP pruning

SNP pairs were selected between the protein-coding genes and enhancers around each gene. We used gene annotations from GENCODEv19 (https://www.gencodegenes.org). Only genes annotated from chromosome 1-22 were used, which resulted in a total of 19,430 coding genes. The SNPs of the protein-coding genes were limited to SNPs within 2 kb regions around the genes. Enhancers were identified by the presence of active epigenetic histone modifications, such as H3K4me1, H3K4me3, and H3K27ac. To annotate the enhancer regions, we used publicly available ChIP-seq datasets on pancreatic islets from the Roadmap Project (http://www.roadmapepigenomics.org/). Enhancer regions located within 2 kb of the transcription start sites of the protein-coding genes were removed. The SNPs intersected with at least one broad peak of H3K4me1, H3K4me3, and H3K27ac in pancreatic islets annotated as enhancers. Enhancers were further assigned to genes within 250 kb of the gene.
We filtered individuals in the discovery and replication data by an initial scan of individual relatedness. We estimated the genetic relationship matrix of all individuals from all the autosomal SNPs in the GENEVA and IPM data separately, using a tool for genome-wide complex trait analysis [18] and excluded one of each pair of individuals with an estimated genetic relationship of more than 0.025. Using this approach, 633 and 117 individuals were removed from the GENEVA and IPM datasets, respectively, to achieve unrelatedness. Quality control in the discovery data was then performed using the whole genome association analysis toolset, PLINK [19], according to the following criteria: individual missing rate <5%, SNP call rate >99%, minor allele frequency (MAF) >5%, and Hardy-Weinberg equilibrium P>0.001. A pruned subset of SNPs was further generated using 0.6 (r2) as the pairwise linkage disequilibrium (LD) threshold.

SNP×SNP interaction analyses

For each protein-coding gene, we first carried out interaction analyses of the SNPs of the gene and SNPs of the surrounding enhancers in the discovery sample. After SNP quality control and LD-based SNP pruning, 13,642 protein-coding genes with usable SNPs were used in the analysis. Finally, 1,576,465 SNP pairs of protein-coding genes and surrounding enhancers were included in the interaction analyses. The number of enhancers of protein-coding genes ranged from 1 to 99. We then performed SNP×SNP interaction analyses in PLINK for the selected SNP pairs in the discovery dataset, which fits a logistic regression model in the following equation: Y=β01×SNP12×SNP23×SNP1×SNP2+β4×Cov1+ … +βn+3×Covn+e.
The odds ratios (ORs) for the interaction were represented by the term exp(β3). Sex, age, and the first 10 principal components were used as potential covariates in the interaction analyses. The principal components were measured using genome-wide complex trait analysis software [18]. To test for associations in the replication samples, we selected SNP pairs significantly associated with T2DM (P<5×10−5).
As any imbalance between the number of diabetic and nondiabetic subjects in the replication data would affect the results, we used boosting corrections to deal with the imbalance in the replication dataset in accordance with the method of Bosco [20]. First, we divided the nondiabetic group into five overlapping subgroups, each of which was the same size as the diabetic group. We then combined each nondiabetic subgroup with the diabetic group to form a balanced pair and selected a set of phenotype-associated SNPs (P<0.01), which were considered dominant SNPs for simplicity. We assigned each subgroup confidence scores according to the frequencies of dominant loci and obtained each sample's confidence score by averaging the confidence scores of all the subgroups where it appeared. Finally, we normalized the confidence score of the sample and performed weighted logistic regression.
We used METAL software [21] to combine the results of the SNP×SNP analyses from the different populations using a sample-size weighted model. After adjusting for multiple comparisons by the Bonferroni correction, the significance threshold was set at P<3.17×10−8 (0.05/1,576,465). We also conducted genetic association analyses of the identified SNPs and T2DM in two samples using the same covariate adjustments as in the epistasis analyses to check whether the individual SNPs involved main effects.

Functional annotation

We further annotated the regions surrounding identified SNP pairs using Hi-C interaction data and topologically associating domain (TAD) data. The Hi-C interaction data in multiple available cell lines were downloaded from the 4DGenome Database [22]. TADs in the GM12878 cell line were downloaded from the Gene Expression Omnibus (GEO) data GSE63525 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63525) [23], and the DNA sequences physically interacted with each other more frequently within a TAD [24,25,26]. To check whether the identified SNPs pairs were located in the same TAD-like domain in human pancreatic islets [27], we used TAD-like domains identified using promoter capture Hi-C in pancreatic islets via a directionality index score [25].

Differential expression analyses

We examined whether the interaction effect of the identified SNP pairs affected the expression of their target genes using a linear regression model and data on the expression levels of target genes in pancreas samples from subjects in the GTEx Pilot Project [28].
To check whether the identified susceptibility genes were differentially expressed in diabetic and nondiabetic subjects, we used four GEO datasets, GSE76894, GSE25724, GSE12643, and GSE9006. GSE76894 included the gene expression profiles of 84 nondiabetic and 19 T2DM islets isolated from pancreases unsuitable for transplantation [29]. The organ donors were obtained in Pisa with the approval of the local ethics committees. We then performed a microarray analysis of the GSE25724 dataset to evaluate differences in the transcriptomes of the T2DM and nondiabetic human islet samples. Human islets were isolated from seven nondiabetic and six T2DM organ donors by collagenase digestion, followed by density gradient purification [30]. In the GSE12643 dataset, transcript levels in myotubes from 10 obese patients with T2DM and 10 healthy control subjects matched according to age and body mass index were examined [31]. In the GSE9006 dataset, to evaluate differences in the transcriptome, gene expression profiles of peripheral blood mononuclear cells from 24 healthy volunteers, 43 newly diagnosed type 1 diabetes mellitus patients, and 12 newly diagnosed T2DM patients were analyzed [32]. Only gene expression profiles from healthy and T2DM subjects were included in the study. In each GEO dataset, we performed differential expression analyses of the identified susceptibility genes using publicly available preprocessed series matrix files.

RESULTS

SNP×SNP interaction analyses

We first carried out interaction analyses of the discovery sample and selected the top 65 SNP pairs significantly associated with T2DM (P<5×10−5) to test for associations in the replication sample (Supplementary Table 1) [33]. Combining the results from the two datasets, only one SNP pair, rs4947941×rs7785013, was significantly associated with T2DM after multiple testing corrections (combined P=4.84×10−10) (Table 2). Rs4947941 and rs7785013 were in relatively low LD with each other, with an r2 of 0.011 and 0.005 in GENEVA and IPM, respectively. The directions of the effect of this SNP pair were congruent in the two datasets. The interaction OR was estimated to be 0.91 (95% confidence interval [CI], 0.88 to 0.95) in the discovery data and 0.78 (95% CI, 0.70 to 0.86) in the replication data. This indicated that the effect of the minor allele of SNP rs4947941 (T-allele, MAFGENEVA=0.443, MAFIPM=0.424) decreased 0.91-fold (interaction OR value) and 0.78-fold in T2DM patients for each copy of the minor allele of rs7785013 (A-allele, MAFGENEVA=0.158, MAFIPM=0.137). However, the single SNP association analysis of these two SNPs revealed no significant association in either sample (P>0.05) (Table 2).
Next, we checked whether the effect of the minor allele “A” of rs7785013 on T2DM differed among individuals carrying different genotypes of rs4947941 using the ORs. As shown in Fig. 1, in the GENEVA dataset, the minor allele “A” of rs7785013 was increased 1.10-fold in T2DM subjects carrying “CC” of rs4947941 and decreased 0.99-fold and 0.93-fold in T2DM subjects carrying “TC” and “TT” of rs4947941, respectively. Consistent with these findings, in the replication data derived from the IPM, the minor allele “A” of rs7785013 was increased 1.30-fold in T2DM subjects carrying “CC” of rs4947941 and decreased 0.97-fold and 0.81-fold in T2DM subjects carrying “TC” and “TT” of rs4947941, respectively. Therefore, the minor allele “A” of rs7785013 reduced the risk of T2DM with an increase in the dosage of “T” of rs4947941.

Functional annotation

The SNP rs4947941 was located in RP11-745C15.2 and overlapped with many enhancer marks, including H3k4me1, H3k4me3, and H3k27ac (Fig. 2). The SNP rs7785013 was located in the intron region of the protein-coding EGFR. The published Hi-C datasets showed that the rs4947941 frequently interacted with the EGFR. Thus, the region surrounding rs4947941 tended to be closer in space to the EGFR (Fig. 2). The TAD annotation results showed that these two SNPs were located in the same TAD region in the GM12878 cell line (Chr7: 54830000-55330000) (Fig. 2) and TAD-like domain in human pancreatic islets (Chr7: 54824893-55497476).

Differential expression analyses

Analyses of the interaction effect of rs4947941×rs7785013 on EGFR expression showed that this SNP pair was significantly associated with EGFR expression in the pancreas (P=0.033). As shown in Fig. 3, the minor allele “A” of rs7785013 was positively associated with the EGFR expression in subjects carrying “CC” and “TC” of rs4947941 but negatively associated with EGFR expression in subjects carrying “TT” of rs4947941. Therefore, the minor allele “A” of rs7785013 decreased the gene expression of the EGFR with an increasing dosage of “T” of rs4947941.
In a one-tailed t-test, we examined whether the EGFR was differentially expressed in diabetic and nondiabetic subjects in the four GEO datasets. The results showed that EGFR was significantly up-regulated in diabetic samples in GSE76894 (P=9.8×10−4) (Fig. 4A), GSE25724 (P=5.9×10−3) (Fig. 4B), GSE12643 (P=0.047) (Fig. 4C), and GSE9006 (P=0.047) (Fig. 4D). It is worth noting that these results were consistent with the effect of the SNP interaction on T2DM and EGFR expression.

Functional validation in the Mouse Genome Informatics database

To investigate the function of the EGFR, we used the Mouse Genome Informatics (MGI) database [34,35,36], which contains integrated genetic, genomic, and biological data aimed at facilitating the study of human health and disease. The results revealed that the EGFR was involved in multiple phenotypes associated with T2DM, including disorganized pancreatic islets, abnormal pancreatic beta cell morphology, abnormal pancreas morphology, and a small pancreas in murine models (http://www.informatics.jax.org/marker/phenotypes/MGI:95294) [37].

DISCUSSION

As noted earlier, multiple hypotheses have been put forward to explain the well-known “missing heritability” problem. Epigenetics and rarer variants with larger effects are among the reasons advanced to explain the limitations of GWASs [7]. In the previous studies, we reported a method that integrating epigenetic features for predicting SNPs associated with T2DM and other complex disorders [38,39]. In this study, considering the important role of the interaction between an enhancer and its target gene in gene regulation and disease pathogenesis, we performed genetic interaction analyses of variants between enhancers and protein-coding genes to identify susceptibility loci associated with T2DM.
We identified one significant interaction pair, rs4947941×rs7785013, which was associated with T2DM after multiple testing corrections. Previous GWASs have not reported the relationships between these two SNPs and T2DM or other disorders. In the present study, rs4947941 overlapped with many enhancer marks, and rs7785013 was located in the intron region of the EGFR. The annotation results showed that these two SNPs were located in the same TAD region. Furthermore, the region surrounding rs4947941 tended to be closer in space to the EGFR. Thus, the EGFR may serve as a susceptibility gene for T2DM.
The EGFR encodes a type of transmembrane glycoprotein, which is a member of the protein kinase superfamily. Prior to the GWAS catalog [40], no previous GWASs reported the relationship between EGFR polymorphisms and T2DM. In the present study, we detected associations between all EGFR polymorphisms and T2DM in two GWAS datasets, and no significant association results were obtained after multiple testing corrections (Supplementary Table 2) [33]. A previous study revealed that inhibition of EGFR tyrosine kinase activity ameliorated insulin resistance [41]. In the present study, differential expression analyses using four GEO datasets confirmed that EGFR may be a novel gene for the risk of T2DM. Furthermore, the EGFR was significantly up-regulated in diabetic samples.
In parallel with our study, recent research integrated epigenomics and TAD data and discovered T2DM-associated enhancer-promoter SNP pairs from imputed data where neither SNP achieved independent genome-wide significance [42]. Manduchi et al. [42] reported that one enhancer-promoter SNP pair, rs7991210-rs3742250, was significantly associated with T2DM in pancreatic islets after main effect filtering (combined P=2.16×10−9). In our data, we attempted to validate this epistasis effect of the SNP pair, rs7991210-rs3742250. As the promoter SNP, rs3742250, was not included in the discovery data, we used two other SNPs that were in LD with rs3742250 (D′>0.75) as surrogates using Phase 3 data from the 1000 Genomes project as a reference panel [43]. Combining the results of the two datasets, we found that these two SNP pairs were significantly associated with T2DM (P<0.05) (Supplementary Table 3) [33]. In addition, our approach did not restrict the analysis to the promoter region of the gene. Lee et al. [44] detected dynamic enhancer contacts throughout the gene bodies that tracked with elongating RNA polymerase II and the leading edge of RNA synthesis. Thus, we considered all enhancer-gene interactions, an approach that could yield more interesting results.
Some limitations of the current study should be addressed. The genetic susceptibility and etiology of T2DM may differ among populations of distinct ancestral origin. In the present study, most of the genotype data in the dbGaP database were based on Europeans, and only subjects who reported European ancestry were included in the epistasis analyses. Further studies are recommended to investigate the association between the rs4947941-rs7785013 interaction and T2DM in different ethnic groups. The sample size may be an additional limitation. We identified only one significant SNP pair for T2DM after multiple corrections, which was far fewer than we excepted. It is possible that the sample size was not large enough to obtain an accurate estimate. The Bonferroni correction employed to reduce type I errors may have increased the probability of false negatives. Consequently, a further study with a larger sample size is needed to validate our results.
In summary, we performed genetic interaction analyses of enhancers and protein-coding genes for T2DM. We identified one SNP pair, rs4947941×rs7785013, that was significantly associated with T2DM. Further annotation, differential expression, and functional validation studies suggested that the EGFR may be a susceptibility gene for T2DM.

SUPPLEMENTARY MATERIALS

Supplementary materials related to this article can be found online at https://doi.org/10.4093/dmj.2019.0204.

Supplementary Table 1

Top 65 associated SNP pairs
dmj-2019-0204-s001.pdf

Supplementary Table 2

The association between all EGFR polymorphisms and T2DM
dmj-2019-0204-s002.pdf

Supplementary Table 3

The interaction effects of the previously identified SNP pairs
dmj-2019-0204-s003.pdf

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conception or design: Y.Y., S.Y., J.M.D., W.C., Y.G.

Acquisition, analysis, or interpretation of data: S.Y., J.M.D.

Drafting the work or revising: S.Y., Y.G.

Final approval of the manuscript: Y.Y., S.Y., J.M.D., W.C., Y.G.

FUNDING

This study was supported by the National Natural Science Foundation of China (31871264, 81872490), Innovative Talent Promotion Plan of Shaanxi Province for Young Sci-Tech New Star (2018KJXX-010), Natural Science Foundation of Zhejiang Province (LGF18C060002, LWY20H060001), Major Science and Technology Projects in Xiaoshan District (2018224), Natural Science Basic Research Program Shaanxi Province (2018JQ3058) and the Fundamental Research Fund for Central Universities. This study was also supported by the High Performance Computing Platform of Xi’an Jiaotong University and the China Scholarship Council.

ACKNOWLEDGMENTS

We acknowledge use of data from the GENEVA consortium and IPM Biobank Project. In performing the current study, we did not collaborate with the investigators of these projects. Therefore, our study does not necessarily reflect their opinions. The datasets were obtained through dbGaP authorized access (accession numbers: phs000091.v2.p1 and phs000388.v1.p1).

REFERENCES

1. Willemsen G, Ward KJ, Bell CG, Christensen K, Bowden J, Dalgard C, et al. The concordance and heritability of type 2 diabetes in 34,166 twin pairs from international twin registers: the discordant twin (DISCOTWIN) consortium. Twin Res Hum Genet 2015;18:762-771.
crossref pmid
2. Scott RA, Scott LJ, Magi R, Marullo L, Gaulton KJ, Kaakinen M, et al. DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 2017;66:2888-2902.
crossref pmid pmc
3. Xue A, Wu Y, Zhu Z, Zhang F, Kemper KE, Zheng Z, et al. eQTLGen Consortium. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun 2018;9:2941.
crossref pmid pmc pdf
4. Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet 2018;50:1505-1513.
crossref pmid pmc pdf
5. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature 2016;536:41-47.
crossref pmid pmc pdf
6. DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium. Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium. South Asian Type 2 Diabetes (SAT2D) Consortium. Mexican American Type 2 Diabetes (MAT2D) Consortium. Type 2 Diabetes Genetic Exploration by Nex-generation sequencing in muylti-Ethnic Samples (T2D-GENES) Consortium. Mahajan A, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 2014;46:234-244.
crossref pmid pmc pdf
7. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature 2009;461:747-753.
crossref pmid pmc pdf
8. Zuk O, Hechter E, Sunyaev SR, Lander ES. The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci U S A 2012;109:1193-1198.
crossref pmid pmc
9. Yang JK, Zhou JB, Xin Z, Zhao L, Yu M, Feng JP, et al. Interactions among related genes of renin-angiotensin system associated with type 2 diabetes. Diabetes Care 2010;33:2271-2273.
crossref
10. Wei WH, Hemani G, Haley CS. Detecting epistasis in human complex traits. Nat Rev Genet 2014;15:722-733.
crossref pmid pdf
11. Shen J, Li Z, Song Z, Chen J, Shi Y. Genome-wide two-locus interaction analysis identifies multiple epistatic SNP pairs that confer risk of prostate cancer: a cross-population study. Int J Cancer 2017;140:2075-2084.
crossref pmid
12. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 2012;489:57-74.
crossref pmid pmc
13. Roadmap Epigenomics Consortium. Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature 2015;518:317-330.
crossref pmid pmc pdf
14. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 2012;337:1190-1195.
crossref pmid pmc
15. Bulger M, Groudine M. Functional and mechanistic diversity of distal transcription enhancers. Cell 2011;144:327-339.
crossref pmid pmc
16. Deng W, Lee J, Wang H, Miller J, Reik A, Gregory PD, et al. Controlling long-range genomic interactions at a native locus by targeted tethering of a looping factor. Cell 2012;149:1233-1244.
crossref pmid pmc
17. Cornelis MC, Agrawal A, Cole JW, Hansel NN, Barnes KC, Beaty TH, et al. GENEVA Consortium. The Gene, Environment Association Studies consortium (GENEVA): maximizing the knowledge obtained from GWAS by collaboration across studies of multiple conditions. Genet Epidemiol 2010;34:364-372.
crossref pmid pmc
18. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 2011;88:76-82.
crossref pmid pmc
19. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-575.
crossref pmid pmc
20. Bao F, Deng Y, Zhao Y, Suo J, Dai Q. Bosco: boosting corrections for genome-wide association studies with imbalanced samples. IEEE Trans Nanobioscience 2017;16:69-77.
crossref pmid
21. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010;26:2190-2191.
crossref pmid pmc pdf
22. Teng L, He B, Wang J, Tan K. 4DGenome: a comprehensive database of chromatin interactions. Bioinformatics 2015;31:2560-2564.
crossref pmid pmc pdf
23. Rao SS, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 2014;159:1665-1680.
crossref pmid pmc
24. Pombo A, Dillon N. Three-dimensional genome architecture: players and mechanisms. Nat Rev Mol Cell Biol 2015;16:245-257.
crossref pmid pdf
25. Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 2012;485:376-380.
crossref pmid pmc pdf
26. Nora EP, Lajoie BR, Schulz EG, Giorgetti L, Okamoto I, Servant N, et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 2012;485:381-385.
crossref pmid pmc pdf
27. Miguel-Escalada I, Bonas-Guarch S, Cebola I, Ponsa-Cobas J, Mendieta-Esteban J, Atla G, et al. Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes. Nat Genet 2019;51:1137-1148.
crossref pmid pmc pdf
28. GTEx Consortium. Human genomics. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 2015;348:648-660.
crossref pmid pmc
29. Solimena M, Schulte AM, Marselli L, Ehehalt F, Richter D, Kleeberg M, et al. Systems biology of the IMIDIA biobank from organ donors and pancreatectomised patients defines a novel transcriptomic signature of islets from individuals with type 2 diabetes. Diabetologia 2018;61:641-657.
crossref pmid pdf
30. Dominguez V, Raimondi C, Somanath S, Bugliani M, Loder MK, Edling CE, et al. Class II phosphoinositide 3-kinase regulates exocytosis of insulin granules in pancreatic beta cells. J Biol Chem 2011;286:4216-4225.
crossref pmid
31. Frederiksen CM, Hojlund K, Hansen L, Oakeley EJ, Hemmings B, Abdallah BM, et al. Transcriptional profiling of myotubes from patients with type 2 diabetes: no evidence for a primary defect in oxidative phosphorylation genes. Diabetologia 2008;51:2068-2077.
crossref pmid pdf
32. Kaizer EC, Glaser CL, Chaussabel D, Banchereau J, Pascual V, White PC. Gene expression in peripheral blood mononuclear cells from children with diabetes. J Clin Endocrinol Metab 2007;92:3705-3711.
crossref pmid pdf
33. Shi Y. Enhancer-gene interaction analyses identified EGFR as a susceptibility gene for type 2 diabetes supplementary files, 2019 cited 2020 May 19. Available from: https://figshare.com/articles/Enhancer-Gene_Interaction_Analyses_Identified_EGFR_as_a_Susceptibility_Gene_for_Type_2_Diabetes_Supplementary_Files/9757391/1.
34. Smith CL, Blake JA, Kadin JA, Richardson JE, Bult CJ. Mouse Genome Database Group. Mouse Genome Database (MGD)-2018: knowledgebase for the laboratory mouse. Nucleic Acids Res 2018;46:D836-D842.
crossref pmid pdf
35. Finger JH, Smith CM, Hayamizu TF, McCright IJ, Xu J, Law M, et al. The mouse Gene Expression Database (GXD): 2017 update. Nucleic Acids Res 2017;45:D730-D736.
crossref pmid pdf
36. Bult CJ, Krupke DM, Begley DA, Richardson JE, Neuhauser SB, Sundberg JP, et al. Mouse Tumor Biology (MTB): a database of mouse models for human cancer. Nucleic Acids Res 2015;43:D818-D824.
crossref pmid pdf
37. Miettinen PJ, Huotari M, Koivisto T, Ustinov J, Palgi J, Rasilainen S, et al. Impaired migration and delayed differentiation of pancreatic islet cells in mice lacking EGF-receptors. Development 2000;127:2617-2627.
pmid
38. Yao S, Guo Y, Dong SS, Hao RH, Chen XF, Chen YX, et al. Regulatory element-based prediction identifies new susceptibility regulatory variants for osteoporosis. Hum Genet 2017;136:963-974.
crossref pmid pmc pdf
39. Dong SS, Guo Y, Yao S, Chen YX, He MN, Zhang YJ, et al. Integrating regulatory features data for prediction of functional disease-associated SNPs. Brief Bioinform 2019;20:26-32.
crossref pmid pdf
40. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al. The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 2014;42:D1001-D1006.
crossref pmid pdf
41. Li Z, Li Y, Overstreet JM, Chung S, Niu A, Fan X, et al. Inhibition of epidermal growth factor receptor activation is associated with improved diabetic nephropathy and insulin resistance in type 2 diabetes. Diabetes 2018;67:1847-1857.
crossref pmid pmc
42. Manduchi E, Williams SM, Chesi A, Johnson ME, Wells AD, Grant SFA, et al. Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS. Hum Genet 2018;137:413-425.
crossref pmid pmc pdf
43. 1000 Genomes, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature 2015;526:68-74.
crossref pmid pmc pdf
44. Lee K, Hsiung CC, Huang P, Raj A, Blobel GA. Dynamic enhancer-gene body contacts during transcription elongation. Genes Dev 2015;29:1992-1997.
crossref pmid pmc
Fig. 1

Association of the minor allele “A” of rs7785013 with type 2 diabetes mellitus in subjects carrying different genotypes of rs4947941 in the Gene Environment Association Studies (GENEVA) and Institute of Personalized Medicine (IPM) datasets. The odds ratios (ORs) of the association analyses results are shown in the y-axis. TT, subjects carrying “TT” of rs4947941; TC, subjects carrying “TC” of rs4947941; CC, subjects carrying “CC” of rs4947941.

dmj-2019-0204f1.jpg
Fig. 2

Epigenetic annotation for the region surrounding rs4947941 and rs7785013. The topologically associating domain (TAD) data in the GM12878 cell line were downloaded from the Gene Expression Omnibus (GEO) data, GSE63525. TAD-like domains were identified using promoter capture Hi-C in human pancreatic islets. Chromatin interaction data in multiple cell lines were downloaded from the 4DGenome Database. Active histone modifications, including H3k4me1, H3k4me3, and H3k27ac, in pancreatic islets were obtained from the Roadmap Project using the WashU EpiGenome Browser. EGFR, epidermal growth factor receptor.

dmj-2019-0204f2.jpg
Fig. 3

Association of the minor allele “A” of rs7785013 with the expression of the epidermal growth factor receptor (EGFR) in the pancreas of subjects carrying different genotypes of rs4947941 in the GTEx Pilot Project. The beta values of the association analyses results are shown in the y-axis. CC, subjects carrying “CC” of rs4947941; TC, subjects carrying “TC” of rs4947941; TT, subjects carrying “TT” of rs4947941.

dmj-2019-0204f3.jpg
Fig. 4

The results of the differential expression analyses of the epidermal growth factor receptor (EGFR) in diabetic and nondiabetic subjects in the (A) GSE76894, (B) GSE25724, (C) GSE12643, and (D) GSE9006 datasets.

dmj-2019-0204f4.jpg
Table 1.
Basic characteristics of subjects
Characteristic Discovery GENEVA
Replication IPM
Case Control Total Case Control Total
Sample size 2,558 2,983 5,541 138 565 703
Male/Female 1,109/1,449 1,275/1,708 2,384/3,157 101/37 370/195 471/232
Age, yr 57.36±7.71 57.12±7.66 57.23±7.69 58.71±15.32 64.83±10.31 59.91±14.67

Values are presented as mean±standard deviation.

GENEVA, Gene Environment Association Studies initiative in Type 2 Diabetes; IPM, Biobank Program of the Institute of Personalized Medicine.

Table 2.
The interaction SNP pairs identified for type 2 diabetes mellitus
SNP pair Chr Positiona Geneb Allelec GENEVA
IPM
Combined P valued
MAF CR OR (95% CI) P value MAF CR OR (95% CI) P value
rs4947941 7 54871175 RP11-745C15.2 T/C 0.443 1 1.00 (0.98-1.02) 0.824 0.424 1 0.97 (0.92-1.02) 0.831 0.911
rs7785013 7 55152667 EGFR A/G 0.158 0.999 1.00 (0.98-1.03) 0.830 0.137 0.999 0.98 (0.91-1.05) 0.977 0.998
rs494794×rs7785013 - - - - - - 0.91 (0.88-0.95) 9.41×10−7 - - 0.78 (0.70-0.86) 1.73×10−6 4.84×10−10

SNP, single nucleotide polymorphism; Chr, chromosome; GENEVA, Gene Environment Association Studies initiative in Type 2 Diabetes; IPM, Biobank Program of the Institute of Personalized Medicine; MAF, minor allele frequency; CR, call rate; OR, odds ratio; CI, confidence interval; EGFR, epidermal growth factor receptor.

a Position was relative to the hg19 version of the human genome,

b The physical location of identified SNP,

c The former allele represents the minor allele,

d Meta-analyses results by using two genome-wide association study samples.

TOOLS
METRICS Graph View
  • 0 Crossref
  • 0 Scopus
  • 897 View
  • 26 Download
ORCID iDs

Yang Yang
https://orcid.org/0000-0001-5909-4660

Shi Yao
https://orcid.org/0000-0002-4574-8106

Wei Chen
https://orcid.org/0000-0002-7499-9836

Yan Guo
https://orcid.org/0000-0002-7364-2392

Funding Information

National Natural Science Foundation of China
https://doi.org/10.13039/501100001809
31871264
81872490

Innovative Talent Promotion Plan of Shaanxi Province for Young Sci-Tech New Star

2018KJXX-010

Natural Science Foundation of Zhejiang Province
https://doi.org/10.13039/501100004731
LGF18C060002
LWY20H060001

Major Science and Technology Projects in Xiaoshan District

2018224

Natural Science Basic Research Program Shaanxi Province

2018JQ3058

Fundamental Research Fund for Central Universities

Related articles

Effects of Anti-Vascular Endothelial Growth Factor (VEGF) on Pancreatic Islets in Mouse Model of Type 2 Diabetes Mellitus.2009 June;33(3)

Serum Soluble Epidermal Growth Factor Receptor Level Increase in Patients Newly Diagnosed with Type 2 Diabetes Mellitus2018 August;42(4)



ABOUT
BROWSE ARTICLES
FOR CONTRIBUTORS
Editorial Office
101-2104, Lotte Castle President, 109 Mapo-daero, Mapo-gu, Seoul 04146, Korea​
Tel: +82-2-714-9064    Fax: +82-2-714-9084    E-mail: diabetes@kams.or.kr                

Copyright © 2021 by Korean Diabetes Association. All rights reserved.

Developed in M2PI

Close layer