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. Author manuscript; available in PMC: 2021 Feb 15.
Published in final edited form as: Mol Genet Genomics. 2020 Mar 11;295(3):607–619. doi: 10.1007/s00438-020-01651-3

Identification of novel functional CpG‑SNPs associated with type 2 diabetes and coronary artery disease

Zun Wang 1,#, Chuan Qiu 2,#, Xu Lin 2, Lan‑Juan Zhao 2, Yong Liu 3, Xinrui Wu 4, Qian Wang 1, Wei Liu 1, Kelvin Li 2, Hong‑Wen Deng 2,5, Si‑Yuan Tang 1, Hui Shen 2
PMCID: PMC7883506  NIHMSID: NIHMS1667066  PMID: 32162118

Abstract

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) and coronary artery disease (CAD), respectively. Nevertheless, these studies were generally performed for single-trait/disease and failed to assess the pleiotropic role of the identified variants. To identify novel functional loci and the pleiotropic relationship between CAD and T2D, the targeted cFDR analysis on CpG-SNPs was performed by integrating two independent large and multi-centered GWASs with summary statistics of T2D (26,676 cases and 132,532 controls) and CAD (60,801 cases and 123,504 controls). Applying the cFDR significance threshold of 0.05, we observed a pleiotropic enrichment between T2D and CAD by incorporating pleiotropic effects into a conditional analysis framework. We identified 79 novel CpG-SNPs for T2D, 61 novel CpG-SNPs for CAD, and 18 novel pleiotropic loci for both traits. Among these novel CpG-SNPs, 33 of them were annotated as methylation quantitative trait locus (meQTL) in whole blood, and ten of them showed expression QTL (eQTL), meQTL, and metabolic QTL (metaQTL) effects simultaneously. To the best of our knowledge, we performed the first targeted cFDR analysis on CpG-SNPs, and our findings provided novel insights into the shared biological mechanisms and overlapped genetic heritability between T2D and CAD.

Keywords: Type 2 diabetes, Coronary artery disease, Genome-wide association study (GWAS), DNA methylation, CpG-SNP, cFDR

Introduction

Diabetes mellitus is a chronic disease that occurs when the pancreas can not produce enough insulin (type 1 diabetes, T1D), or when the body can not effectively use the insulin (type 2 diabetes, T2D) (Association 2014). In 2016, about 38 million deaths were directly or indirectly caused by diabetes. The most common cause of death among patients with diabetes (especially T2D) is cardiovascular disease, responsible for 70% of deaths (Szuszkiewicz-Garcia and Davidson 2014). T2D is one of the major risk factors for coronary artery diseases (CAD) (Naito and Miyauchi 2017), which is associated with two to fourfold increased mortality risk from CAD. Abundant evidence has pointed out that T2D and CAD share many common primary risk factors including hypertension, smoking, hyperlipemia, hyperglycemia, and dysbetalipoproteinemia, as well as several potential risk factors such as obesity, low physical activity, cardiovascular family history, gender, and age (Kannel 1985; Norhammar and Schenck-Gustafsson 2013).

Although plenty of genetic loci have been identified for association with T2D or CAD by genome-wide association studies (GWASs) (Nelson et al. 2017; Scott et al. 2017), most of these identified single nucleotide polymorphisms (SNPs) were located in the intron region, and only a subset of them may affect transcription process. However, these specific functional variants were generally unknown. To search for additional novel functional genetic loci for T2D and CAD, one feasible strategy is to focus on specific variants that may potentially affect regulatory factors such as DNA methylation.

DNA methylation, also known as cytosine methylation, is a major epigenetic modification of vertebrate genomes and has profound impacts on human genetic disorders (Bogdanovic and Lister 2017). CpG site is the primary substrate for methyl transfer reactions and, therefore, SNPs that can introduce or disrupt a CpG site (CpG-SNPs) may dramatically alter the methylation status at the affected loci (Shoemaker et al. 2010; Zhi et al. 2013). Targeted association analysis of CpG-SNPs has been demonstrated as an efficient strategy to identify novel functional variants associated with complex diseases and traits, such as T2D (Dayeh et al. 2013), breast cancer (Harlid et al. 2011), hypertriglyceridemia (de Toro-Martin et al. 2016), schizophrenia (Bani-Fatemi et al. 2013), and osteoporosis (Qiu et al. 2018).

Another limitation of GWAS is that the identified SNPs can only explain a small proportion of the heritability (10% for T2D and 13.3% for CAD) (Nikpay et al. 2015). This phenomenon of failing to explain a substantial proportion of the heritability of complex phenotypes is often described as the “missing heritability” problem (Pei et al. 2014). To excavate the “missing heritability”, one effective way is expanding the sample sizes. However, this approach can be highly expensive and time-consuming (Stahl et al. 2012). Recently, Andreassen et al. (Andreassen et al. 2013) developed a genetic-pleiotropy-informed conditional false discovery rate (cFDR) method by leveraging two independent GWASs from associated traits in a conditional analysis. This method has been widely applied to different genetically inherited diseases and phenotypes (Peng et al. 2017a; Wang et al. 2017b; Zeng et al. 2017; Zhou et al. 2017; Hu et al. 2018).

In this study, we performed a targeted cFDR analysis on CpG-SNPs for T2D and CAD to identify novel functional gene loci and the pleiotropic relationship between these traits by integrating two independent GWASs (Nikpay et al. 2015; Scott et al. 2017). Our findings provided novel insights into the shared biological mechanisms and overlapped genetic heritability between T2D and CAD.

Methods

GWAS datasets

Two independent GWASs (Nikpay et al. 2015; Scott et al. 2017) with summary statistics for T2D and CAD were downloaded from publicly available datasets. The dataset for T2D contained meta-analysis summary statistics of 26,676 T2D cases and 132,532 control subjects from 18 studies of European ancestry performed by DIAGRAM Consortium (Scott et al. 2017). The dataset for CAD contained association summary statistics of 60,801 cases and 123,504 controls from 48 studies for a GWAS meta-analysis of CAD conducted by CARDIoGRAM Consortium (Nikpay et al. 2015). Both of the datasets consisted of the summary statistics for each SNP with the p values that have undergone genomic control at the individual study level. Further details of these two studies are described in the previous publications (Nikpay et al. 2015; Scott et al. 2017).

Identification of potentially functional CpG‑SNPs

The identification of potentially functional CpG-SNPs was described in our previous study (Qiu et al. 2018). In brief, an SNP was defined as a CpG-SNP if it introduces or disrupts a CpG site, and we identified common CpG-SNPs in the human genome by interrogating common variants [minor allele frequency (MAF) > 0.05] from human reference genome (hg19) (Genomes Project et al. 2010), and our in-house whole-genome high-coverage deep re-sequencing study (Shen et al. 2013). A total of 3,811,642 common CpG-SNPs were identified throughout the human genome.

Data processing

By overlapping the 7,362,687 common SNPs in the T2D and CAD meta-analysis with the identified CpG-SNPs, a total of 2,236,577 common CpG-SNPs with association summary statistics for both traits were retrieved. Next, we performed the linkage disequilibrium (LD) based pruning (r2 ≥ 0.2) through PLINK 1.9 (Lin et al. 2018) and identified 93,408 independent CpG-SNPs, which were used for the downstream cFDR analysis.

Statistical analysis

To estimate the pleiotropic enrichment of association compared to that expected under the null hypothesis, we first constructed a conditional Q–Q plot by successively conditioning the principal trait on the SNPs with varying strengths of association in the conditional trait, then followed by a constructed fold-enrichment plots to further estimate the pleiotropic enrichment between CAD and T2D. The cFDR approach is well-established and has been widely applied to integrate independent GWASs with summary statistics and assess whether a random SNP is associated with the principal phenotype given that the observed p values for the principal and conditional traits are both smaller than two pre-defined disease-specific significance thresholds. Next, we computed the conjunction cFDR (ccFDR) to determine the pleiotropic loci. Finally, we applied conditional and conjunctional Manhattan plots to visualize the localization of the independent loci associated with T2D conditional on CAD and vice versa, as well as independent loci with a pleiotropic effect on both traits. An SNP or gene is defined as a novel one if it has not been reported in previous GWASs (Nikpay et al. 2015; Scott et al. 2017) or our previous cFDR study (Zhang et al. 2018b). Detailed information is present in the supplementary materials.

Function annotation of the pleiotropic CpG-SNPs

To explore the functional role of the identified pleiotropic CpG-SNPs in T2D and CAD, we annotated each pleiotropic CpG-SNP to various DNA features or regulatory elements using HaploReg (https://www.broadinstitute.org/mammals/haploreg/haploreg.php) and SNPnexus (https://www.snp-nexus.org/). Both tools retrieved the ENCODE (Rosenbloom et al. 2013) and RoadMap (Dayem Ullah et al. 2018) annotations for the queried CpG-SNPs as well as their LD proxy variants (r2 ≥ 0.8).

Next, we tested whether the identified CpG-SNPs or their LD proxy variants (r2 ≥ 0.8) have expression quantitative trait loci (eQTL), methylation QTL (meQTL) or metabolic QTL (metaQTL) effects. The eQTL hits were obtained from HaploReg based on GTEx and other eQTL results; independent cis- and trans- meQTLs in whole blood were obtained from Bonder’s study (Bonder et al. 2016) (https://genenetwork.nl/biosqtlbrowser/); and metaQTLs were obtained from SNiPA (https://snipa.helmholtz-muenchen.de/snipa3/) which summarized recently published metaQTL studies.

Gene ontology (GO) enrichment and protein–protein interaction

The GOEAST program (https://omicslab.genetics.ac.cn/GOEAST/) was applied to identify significantly enriched gene ontology terms among the list of genes associated with pleiotropic CpG-SNPs. The p values were calculated by hypergeometric tests and adjusted for multiple comparisons by stringent Yekutieli method (FDR under dependency) (Yekutieli and Benjamini 1999). Next, to partially explore and characterize the functional relationship of the identified genes, the corresponding protein association networks were constructed using the STRING 10.5 database (https://string-db.org/).

Mendelian randomization analysis

To further explore the causal effect of T2D on CAD, we applied two-sample Mendelian randomization (MR) analysis using T2D as exposure and CAD as outcome through “Two-SampleMR” package (https://github.com/MRCIEU/TwoSampleMR) (Hemani et al. 2018). Similar to Chen’s study (Chen et al. 2018), instead of testing at the whole genome level, we selected out a subset of genes to operate “gene-specific MR”. In detail, we first mapped T2D-related CpG-SNPs to their corresponding genes. Then, we extracted all the SNPs mapped to these genes in the original T2D GWAS dataset as a candidate for exposure instruments while using CAD as the outcome. Similar to our previous study (Zhang et al. 2018a), the candidate instrumental SNPs should meet the selection criteria where: (1) SNPs have significant association with the exposure (T2D, p ≤ 5 × 10−8); (2) among the SNPs that in high LD (r2 ≥ 0.01), only the SNP with the highest effect size (β value) will be retained. Finally, 17 SNPs were included as instrumental variables (Table S5), and MR was performed using MR Egger, inverse-variance weighted (IVW), weighted median, and weighted mode.

Results

Assessment of pleiotropic enrichment

We observed a clear separation between the different Q-Q curves, and the proportion of actual effects in T2D varied considerably across different levels of association for CAD (Fig. 1a, c), which indicated a strong enrichment of T2D-associated SNPs. A similar enrichment pattern was also identified in CAD conditioned on T2D (Fig. 1b, d). We observed an evident upward shift from the expected baseline. It clearly demonstrated the pleiotropy between T2D and CAD (T2D|CAD, CAD|T2D), where T2D conditioned on CAD (T2D|CAD) achieved the most significant pleiotropic enrichment, as an enrichment fold over 20 was observed in Y-axis while comparing the most stringent subset to all SNPs.

Fig. 1.

Fig. 1

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Stratified Q–Q plots and fold-enrichment plots. Stratified Q–Q plots of nominal vs. empirical − log 10(p) values in principal trait below the standard GWAS threshold of p ≤ 5 × 10−8 as a function of the significance of the association with conditional trait at the level of p ≤ 1, p ≤ 0.1, p ≤ 0.01, p ≤ 0.001, and p ≤ 0.0001, respectively. a T2D as a function of the significance of the association with CAD, and b CAD as a function of the significance of the association with T2D. Fold-enrichment plots of enrichment vs. nominal − log 10(p) values (corrected for inflation) corresponding to levels of p ≤ 1, p ≤ 0.1, p ≤ 0.01, p ≤ 0.001, respectively in c T2D below the standard GWAS threshold of p ≤ 5 × 10−8 as a function of significance of the association with CAD; and in d CAD below the standard GWAS threshold of p ≤ 5 × 10−8 as a function of significance with T2D. Dashed lines indicate the null-hypothesis

T2D loci identified by cFDR

We identified 98 significant (cFDR ≤ 0.05) CpG-SNPs associated with T2D conditioned on CAD (Fig. 2a), including 79 novel SNPs that have not been reported in association with T2D in previous studies (Scott et al. 2017; Zhang et al. 2018b) (Table S1). Using a more conservative threshold of cFDR ≤ 0.01, 55 CpG-SNPs (37 novel CpG-SNPs) remained as significant variants.

Fig. 2.

Fig. 2

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Conditional Manhattan plot. SNPs with − log 10(cFDR) ≥ 1.3 (cFDR ≤ 0.05) for a T2D given CAD (T2D|CAD) and b CAD given T2D (CAD|T2D), or c − log 10(ccFDR) ≥ 1.3 (ccFDR ≤ 0.05) are shown above the red line

We performed a series of bioinformatics analyses to explore the potential regulatory function for these CpG-SNPs. Assuming these CpG-SNPs were partially effective through DNA methylation, we analyzed the meQTL effects of identified CpG-SNPs, where 13 of them showed significant meQTL effects in whole blood (Table S3). In addition, we investigated whether these CpG-SNPs were also associated with T2D through metabolomics regulation. Interestingly, 11 CpG-SNPs showed metaQTL effects, including 4 SNPs (i.e., rs7723, rs1596972, rs17202418, rs2237892) whose associated metabolites (e.g., 1-oleoylglycerophosphocholine, 2-methylbutyroylcarnitine, gamma-glutamylvaline, etc.) have direct connections with the susceptibility of T2D (Table S4) (Lontchi-Yimagou et al. 2013; Leitner et al. 2017; Zhao and Li 2019). Notably, 7 novel CpG-SNPs (i.e., rs10786044, rs2066612, rs6770420, rs7004862, rs7094128, rs7723, and rs7732628) showed meQTL, eQTL, and metaQTL effects simultaneously and several of them were enriched in enhancer/promoter elements in various tissues (Table 2).

Table 2.

Functional annotation for ten CpG-SNPs showing significant effects in meQTL, eQTL, and metaQTL

rsID GENCODE genes Traits meQTL (p) eQTL hits metaQTL (metabolites) Promoter histone marks Enhancer histone marks DNAse Proteins bound Motifs changed
rs579459 ABO Pleiotropic 1.45E–09 Five hits ADp BLD Gl Four tissues NFYA, POL2 Hmx, Nkx2
rsl 1172113 LRP1 CAD 1.43E–07 Four hits SM C18:1 Eight tissues 15 tissues 17 tissues FOX Al AP-2, Hiel, PU.l
rs3105748 SLC22A3 CAD 7.67E–13 Four hits Succinylcarnitine 6 tissues Four altered motifs
rsl 0786044 AL161652.1 T2D 9.93E–05 Whole blood 2-Hydroxypalmi-
tate		 Five tissues RAPI, ZNF263 HNF6, ZEB1
rs2066612 DLEU1 T2D 4.06E–07 Whole blood X-11847 BHLHE40, Foxa, HEY1
rs6770420 EIF5A2 T2D 1.04E–08 Whole blood Ursodeoxycholate Hsf, SIX5
rs7004862 1NTS8 T2D ≤ 1.00E–50 23 hits SM C24:0 BLD HDAC2,Mef2
rs7094128 LRRC20 T2D 5.05E–08 Fiboblast, bood 1-Methylxanthine BLD IPSC, ADRL AP-4, HEN1, Lmo2-complex
rs7723 TMED4 T2D 2.14E–47 9 hits 1-Oleoylglycero Four tissues Four tissues Four altered motifs
rs7732628 CTC-564N23.3 T2D 6.41E–05 Heart artery X-12206 FAT, PANC 15 tissues CFOS AP-1, ERalpha-a
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meQTL methylation quantitative trait locus (including associated SNPs with an LD r2≥0.8, Table S3), eQTL expression quantitative trait locus, metaQTL metabòlic quantitative trait locus, DNAse deoxyribonuclease, Adp ADpSGEGDFXAEGGGVR, 1 -oleoylglycero 1-oleoylglycerophosphocholine

CAD loci identified by cFDR

Likewise, we identified 80 significant (cFDR ≤ 0.05) CpG-SNPs associated with CAD conditioned on T2D (Fig. 2b, Table S2), including 61 novel variants for CAD. Using a more conservative threshold of cFDR ≤ 0.01, 49 significant loci remained (32 novel SNPs).

By exploring the potential regulatory functions of these significant CpG-SNPs, we found 15 CpG-SNPs showing significant meQTL effects in whole blood (Table S3), and 9 CpG-SNPs associated with various metabolites (Table S4), including some candidates (e.g., butyrylcarnitine, ethylmalonate, cholesterol, etc.) that have been implicated in the pathophysiology of CAD. In particular, two novel CpG-SNPs, rs11172113 and rs3105748, showed meQTL, eQTL, and metaQTL effects simultaneously (Table 2).

Pleiotropic loci in T2D and CAD identified with ccFDR

To further investigate whether any of the SNPs were associated with both T2D and CAD, we computed ccFDR and constructed a ccFDR Manhattan plot (Fig. 2c). Finally, 18 novel pleiotropic CpG-SNPs reached the significance level (ccFDR ≤ 0.05, 8 CpG-SNPs with ccFDR ≤ 0.01) and were annotated to 17 different genes (Table 1). None of these CpG-SNPs have been identified as pleiotropic loci for T2D and CAD in our previous study (Zhang et al. 2018b), yet 6 SNPs have been associated with CAD or T2D separately (Nikpay et al. 2015; Zhang et al. 2018b). Then we analyzed the eQTL, meQTL and metaQTL effects of identified pleiotropic CpG-SNPs and found 8 SNPs have at least one effect. Importantly, one pleiotropic CpG-SNP, rs579459, showed meQTL, eQTL, and metaQTL effects simultaneously, as well as other predicted functions such as alteration of transcription factor binding motif (Table 2).

Table 1.

Conjunction cFDR for 18 pleiotropic CpG-SNPs in T2D and CAD (ccFDR ≤ 0.05)

rsID Chr Pos Allele Gene Location eQTL/meQTL/metaQTL SNP type Gene type cFDR_CAD cFDR_T2D ccFDR
rs7604944 2 65319288 C/T SPRED2 Intronic Novel Novel 4.70E–02 2.57E–02 4.70E–02
rs2252641 2 145043894 T/C TEX41 Non-coding intronic CAD Novel 3.24E–02 2.46E–03 3.24E–02
rsl515114 2 226233671 A/G AC062015.1 48.3 kb upstream eQTL (adipose) Novel Novel 4.64E–39 7.20E–04 7.20E–04
rs1428387 5 123369550 C/T CEP120 Intronic Novel Novel 3.55E–03 3.20E–04 3.55E–03
rs1262557 6 126733443 C/T RPS4XP9 49.6 kb upstream Novel Novel 9.10E–03 2.60E–13 9.10E–03
rs17405606 7 107630918 C/T WBP1LP2 1.4 kb upstream Novel Novel 9.40E–30 7.40E–04 7.40E–04
rs7049105 9 22028802 A/G AL359922.1, CDKN2B-AS1 Intronic, non-coding intronic eQTL (blood), meQTL (LD tors 10120688) CAD Novel, confirmed 4.58E–02 1.20E–02 4.58E–02
rs1333050 9 22125914 C/T CDKN2B-AS1 4.8 kb upstream CAD Confirmed 2.96E–09 7.65E–06 7.65E–06
rs7045889 9 22133252 G/A CDKN2B-AS1 12.2 kb upstream Novel Confirmed 9.39E–03 1.19E–02 1.19E–02
rs7018475 9 22137686 T/G CDKN2B-AS1 16.6 kb upstream T2D Confirmed 2.16E–02 8.90E–03 2.16E–02
rs579459 9 133278724 T/C ABO 3.5 kb upstream eQTL (5 hits), meQTL, metaQTL CAD Novel 1.51E–02 8.23E–03 1.51E–02
rs7904519 10 113014168 A/G TCF7L2 Intronic eQTL (artery), meQTL (LD to rs7077247) T2D Novel 3.78E–02 2.97E–02 3.78E–02
rs1169302 12 120994499 T/G HNF1A Intronic eQTL (3 hits), meQTL (LD to rs2259816) Novel Novel 2.55E–02 2.53E–19 2.55E–02
rs9940128 16 53766842 G/A FTO Intronic Novel Novel 3.58E–02 4.50E–02 4.50E–02
rs2291725 17 48961770 T/C GIP Coding nonsyn eQTL (29 hits), meQTL (LD to rs3895874) Novel Novel 6.73E–05 2.66E–03 2.66E–03
rs521663 18 60155007 T/C AC090771.2 Non-coding intronic Novel Novel 7.05E–03 7.20E–03 7.20E–03
rs10408179 19 45653746 T/C RN7SL836P 1.9 kb upstream eQTL (blood), meQTL (LD tors 10406431) Novel Novel 1.14E–02 4.05E–35 1.14E–02
rs4823044 22 29528836 T/C THOC5,AC005529.1 Intronic, non-coding intronic eQTL (33 hits), meQTL (LD to rs9614006) Novel Novel 2.76E–02 4.44E–03 2.76E–02
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The allele was exhibited as reference allele/alter allele; SNP type and gene type means whether identified CpG-SNPs and genes have been reponed in previous GWAS or in our previous related cFDR studies

chr chromosome, Pos chromosomal position (GRCh38/hg38), eQTL expression quantitative trait locus, meQTL methylation quantitative trait locus (including associated SNPs with an LD r2≥0.8, Table S3), metaQTL metabolic quantitative trait locus, T2D type 2 diabetes, CAD coronary artery disease, cFDR conditional false discovery rate, ccFDR conjunctional conditional false discovery rate

GO enrichment analysis and protein–protein interaction analysis

By performing GO enrichment analysis for genes that were nearest to the identified significant CpG-SNPs, we revealed that genes associated with T2D were significantly enriched in biological processes of “pancreas development” (p = 9.73 × 10−6) and “regulation of cellular ketone metabolic process” (p = 3.4 × 10−5), and genes associated with CAD were enriched in GO terms like “apolipoprotein binding” (p = 0.014) (Table 3).

Table 3.

Gene ontology (GO) lerms enriched for SNP-annotated genes with FDR ≤ 0.05

Traits GO terms Term description Gene counts FDR
T2D GO.0031016 Pancreas development 9 9.73E–06
GO.0010565 Regulation of cellular ketone metabolic process 11 3.40E–05
GO.0031018 Endocrine pancreas development 7 3.40E–05
GO.0048522 Positive regulation of cellular process 49 3.40E–05
GO.0048518 Positive regulation of biological process 53 3.62E–05
CAD GO.0034185 Apolipoprotein binding 3 1.40E–02
GO.0005515 Protein binding 24 1.50E–02
Pleiotropic GO:0043102 Amino acid salvage 2 3.51E–02
GO:0071265 l-Methionine biosynthetic process 2 3.57E–02
GO:0046883 Regulation of hormone secrction 4 4.14E–02
GO:0071267 l-Methionine salvage 2 4.38E–02
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To further explore and visualize the functional partnership among identified T2D- and CAD-targeted genes, we conducted protein–protein interaction analysis with STRING 10.5 database (Fig. 3). In the protein network plot of T2D (Fig. 3a), proteins including PCSK9, LDLR, PLG, MMP13, and IGF2R, have very close contact and have been proven to have a strong relationship with diabetes (Graham et al. 2013; Chanprasertyothin et al. 2015; Eroglu et al. 2016; Ference et al. 2016; Ribeiro et al. 2016). In the protein network of CAD (Fig. 3b), proteins including MTNR1B, GLP2R, GIP, ETS1, and CAMK2G, have close contact and have been associated with CAD (Jiang et al. 2015; Winsvold et al. 2015; Amare et al. 2017; Iacobellis et al. 2017; Gong et al. 2018).

Fig. 3.

Fig. 3

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Functional protein association network analysis. Connections are based on co-expression and experimental evidence with a STRING 10.5 summary score above 0.4. Each filled node denotes a gene; edges between nodes indicate protein–protein interactions between protein products of the corresponding genes in a T2D and b CAD. Different edge colors represent the types of evidence for the association

The causal effect of T2D on CAD

Though the causal relationship between T2D and CAD has been studied on the whole genome level (Dale et al. 2017), the key genes that contribute to such a causality remain unknown. To investigate the effect of specific genes on the causality between T2D and CAD, we applied a gene-specific two-sample MR analysis (see “Methods”) to characterize the causal effect of T2D on CAD. After gene selection, LD assessment, and data harmonization, a total of 17 T2D-related SNPs annotated to 16 different genes (including THADA, CDKN2B-AS1, MTNR1B, etc.) were selected as instrumental variables (the detailed information was summarized in Table S5). The selected instrumental SNPs showed no direct evidence of significant association (Table S5) with CAD, suggesting that these instrument variables are less likely to violate the “no horizontal pleiotropy” assumption (i.e., instrumental variables affect the outcome through a pathway other than the exposure) (Hemani et al. 2017). This was further supported by the results from the MR-Egger regression test where the estimated value for the intercept term was null for T2D and CAD (β = − 0.006, p = 0.374). These results indicated that horizontal pleiotropy does not heavily influence our results. We detected a significant causal relationship of these selected genes between T2D and CAD using MR Egger and IVW methods as main models while using simple mode, weighted median, and weighted mode as additional validations (Figs. 4, S1–S3, Table 4). The result suggests that these genes may be the key factors of the causality between T2D and CAD.

Fig. 4.

Fig. 4

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Forest plot of gene-specific MR estimates T2D on CAD. The causal effect of T2D on CAD estimated by MR analysis on 16 T2D-associated genes was expressed by IVW [β = 0.076, 95% CI (0.05, 0.09), p = 0.003] and MR Egger [β = 0.124, 95% CI (0.066, 0.154), p = 0.050]

Table4.

Genetic association between T2D and CAD by Mendelian randomization analysis on 16 T2D-associated genes

Method nSNP β (95% CI) p
MR Egger 17 0.124 (0.066, 0.154) 0.050
Weighted median 17 0.096 (0.067, 0.111) 0.001
Inverse variance weighted 17 0.076 (0.05, 0.09) 0.003
Simple mode 17 0.074 (0.019, 0.103) 0.200
Weighted mode 17 0.099 (0.071,0.114) 0.003
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Detailed SNPs information is exhibited in Table S5

nSNP number of SNPs applied in the test, β effect size, 95% CI 95% confidence interval, p p value

Discussion

Our study represents the first targeted cFDR analysis for CpG-SNPs that are associated with both CAD and T2D. In this study, by leveraging the power of two independent GWAS datasets from T2D and CAD, and by identifying functional CpG-SNPs, we discovered 98 CpG-SNPs for T2D and 80 CpG-SNPs for CAD. Most of these genes have not been reported to be significantly associated with T2D or CAD before. By applying the ccFDR methods, we further identified 18 novel CpG-SNPs associated with both T2D and CAD.

CpG-SNPs have been suggested as an important mechanism where genetic variants can affect gene expression and function via epigenetic mechanisms (Tsuboi et al. 2017; Qiu et al. 2018). Therefore, it is necessary to analyze the genetic effects of CpG-SNPs on different levels. Under this consideration, as well as to investigate other epigenomic functions of CpG-SNPs, we analyzed eQTL, meQTL, and metaQTL effects of identified CpG-SNPs. Interestingly, we identified 10 T2D-/CAD- associated CpG-SNPs that showed eQTL, meQTL, and metaQTL effects (Table 2), suggesting that these genetic loci may play essential roles in T2D and/or CAD pathogenesis. These CpG-SNPs were annotated to ten different genes, including two novel genes DLEU1 and EIF5A2, which have not been significantly associated with either T2D or CAD in previous studies. The DLEU1 gene participates in the biological processes of circulating OCSFA (odd-numbered chain saturated fatty acids) metabolism (de Oliveira Otto et al. 2018) and can alter CDK1 expression (Wang et al. 2017a), which is related to diabetes (Zhao and Li 2019). EIF5A2 is a direct and functional target of miR-203 (Deng et al. 2016), and c-Jun/miR-203/SOCS3 signaling pathway is associated with insulin resistance (Zhou et al. 2015). In addition, we found one pleiotropic CpG-SNP, rs579459, showing meQTL, eQTL, and metaQTL effects simultaneously. rs579459 was annotated to the ABO gene, which has recently been suggested to have a critical role in metabolic disease (Suhre et al. 2011) and CAD (Chen et al. 2016). Furthermore, the metabolite (ADpSGEGDFXAEGGGVR) associated with rs579459 belongs to phosphorylated fibrinogen peptides A (FPA), which has been implicated in the pathogenesis of T2D (Yang et al. 2018). These facts suggested that rs579459 (representing ABO) may be a crucial locus in connecting pathogenesis of both T2D and CAD. We also identified several genes (SLC22A2, KCNQ1, CDKN2B-AS1, and CDKAL1) contained multiple significant CpG-SNPs, implying that multiple neigh-boring CpG-SNPs may synergistically mediate the DNA methylation and gene expression of the target genes. Interestingly, SLC22A2 only showed a suggestive association (p = 7.159 × 10−5) with CAD in previous GWAS (Higgins et al. 1996), but it reached genome-wide significance (cFDR = 1.40 × 10−11) in our study. SLC22A2 plays a significant role in lipid metabolism (Ober et al. 2009), which is an essential factor for CAD (Ma et al. 2019).

By functional annotation using GO enrichment, we identified several terms involved in the pathogenesis of T2D such as “pancreas development” and “regulation of cellular ketone metabolic process”, suggesting that the CpG-SNPs might be associated with T2D via regulation of beta-cell development in pancreas and may also regulate ketone metabolism in T2D patients. We also identified terms associated with CAD, such as “apolipoprotein binding”, indicating the regulation of apolipoprotein activity and lipid metabolism might be one of the mechanisms relating CpG-SNPs to the CAD (Pechlaner et al. 2017). Interestingly, the enriched biological functions in the pleiotropic genes were mostly related to l-methionine metabolism, which was previously found in relation to both diseases (Yin et al. 2018; Zaghloul et al. 2019). By protein–protein interaction network, we constructed functional networks for T2D and CAD. Specially, we found several key proteins that contribute to the network of both diseases, including FTO, IGF, GLP, and SLC. By the gene-specific MR analysis, we also partially conclude the causality between T2D-related genes, including FTO, IGF2BP2, and SLC30A8, implying that these genes could be the key factors in the co-regulation of T2D and CAD. Those findings shed light on the biological understanding of how CpG-SNPs co-regulate the pathogenesis of T2D and CAD.

Our study presents several advantages. First, compared with our previous cFDR studies (Lv et al. 2017; Peng et al. 2017b; Zhang et al. 2018b), by focusing on potential functional CpG-SNPs, false-negative rates of missing functional SNPs may be reduced. Second, by applying the cFDR method, the statistical power was increased because the integration of two large GWAS datasets can provide an increased effective sample size. Although the meta-analysis of GWAS data can achieve a similar purpose, it only allows for more powerful detection of loci with the same direction of allelic effects in the phenotypes. The cFDR method, on the other hand, allows for detecting loci regardless of their effect directions and has the ability to identify pleiotropic loci for both traits. Third, by integrating evidence from eQTL, meQTL, and metaQTL studies, we further prioritized a list of strong candidates of functional variants contributing to the pathogenesis of T2D and/or CAD. Inevitably, our study also has some limitations. First, we could not provide information about the effect estimates of pleiotropic loci on the phenotypes since we only had access to summary statistics but not genotypes of individual GWASs. If we could obtain the raw genotype data in the future, we will perform a linear regression for the novel SNPs and ones identified earlier, so that we could estimate how much our findings would contribute to the total heritability. Second, though the meQTL effects of CpG-SNPs were investigated, we only used the summary data from one study (Bonder et al. 2016), which will underestimate the portion of meQTL loci. And third, although we conducted the gene-specific MR method to investigate the roles of specific genes in the causal relationship between T2D and CAD, the effect of individual gene remains unknown. Therefore, we cannot determine the rank of the importance among these genes. Also, the causal effects of methylation and metabolomics on these traits are still unknown. In our future study, we will consider applying multivariable MR method into our study, which is capable of measuring multiple risk factors (Burgess and Thompson 2015).

In conclusion, we performed a targeted cFDR analysis for potential functional CpG-SNPs and detected several novel pleiotropic CpG-SNPs of potential functions for T2D and CAD. Our findings provided novel insights into mutual genetic mechanisms underlying the pathogenesis of T2D and CAD, which may lay a foundation for further biological experiments and clinical studies.

Supplementary Material

Supplementary file1

Acknowledgments

Funding This research was partially supported or benefited by grants from the National Institutes of Health (R01AR059781, P20GM109036, R01MH107354, R01MH104680, R01GM109068, R01AR069055, U19AG055373, R01AG061917), the Franklin D. Dickson/Missouri Endowment, and the Edward G. Schlieder Endowment and the Drs. W. C. Tsai and P. T. Kung Professorship in Biostatistics from Tulane University, the National Natural Science Foundation of China (Nos. 81370974, 81500056), the Natural Science Foundation of Hunan Province, China (No. 2016JJ3182), and Central South Univesity (2018zzts886).

Footnotes

Conflict of interest ZW and CQ wrote the main manuscript text; XL, YL, and XW conducted data analysis; WL, QW prepared all figures; LJZ and KL prepared supplementary information; the study was designed and supervised by HS, HWD, and SYT. All authors reviewed the manuscript. All authors have no conflicts of interest to declare.

Compliance with ethical standards

Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors.

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00438-020-01651-3) contains supplementary material, which is available to authorized users.

Data availability

The sequencing data generated from the “whole-genome high-coverage deep re-sequencing study (Shen et al. 2013)” and the list of identified CpG-SNP are available from the corresponding author on request. The T2D GWAS dataset used in this study was obtained from DIAGRAM Consortium (https://diagram-consortium.org/downloads.html). The CAD GWAS dataset used in this study was obtained from CARDIoGRAM Consortium (https://www.cardiogramplusc4d.org/data-downloads/).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1
NIHMS1667066-supplement-Supplementary_file1.docx (236.8KB, docx)

Data Availability Statement

The sequencing data generated from the “whole-genome high-coverage deep re-sequencing study (Shen et al. 2013)” and the list of identified CpG-SNP are available from the corresponding author on request. The T2D GWAS dataset used in this study was obtained from DIAGRAM Consortium (https://diagram-consortium.org/downloads.html). The CAD GWAS dataset used in this study was obtained from CARDIoGRAM Consortium (https://www.cardiogramplusc4d.org/data-downloads/).

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