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. 2017 Oct 31;2017:bax078. doi: 10.1093/database/bax078

Proficiency of data interpretation: identification of signaling SNPs/specific loci for coronary artery disease

Asma N Cheema 1,2,*, Samantha L Rosenthal 3, M Ilyas Kamboh 3
PMCID: PMC5737196  PMID: 29220472

Abstract

Coronary artery disease (CAD) is a complex disorder involving both genetic and non-genetic factors. Genome-wide association studies (GWAS) have identified hundreds of single nucleotides polymorphisms (SNPs) tagging over > 40 CAD risk loci. We hypothesized that some non-coding variants might directly regulate the gene expression rather than tagging a nearby locus. We used RegulomeDB to examine regulatory functions of 58 SNPs identified in two GWAS and those SNPs in linkage disequilibrium (LD) (r2 ≥ 0.80) with the GWAS SNPs. Of the tested 1200 SNPs, 858 returned scores of 1–6 by RegulomeDB. Of these 858 SNPs, 97 were predicted to have regulatory functions with RegulomeDB score of < 3. Notably, only 8 of the 97 predicted regulatory variants were genome-wide significant SNPs (LIPA/rs2246833, RegulomeDB score = 1b; ZC3HC1/rs11556924, CYP17A1-CNNM2-NT5C2/rs12413409, APOE-APOC1/rs2075650 and UBE2Z/rs46522, each with a RegulomeDB score = 1f; ZNF259-APOA5-APOA1/rs964184, SMG6/rs2281727 and COL4A1-COL4A2/rs4773144, each with a RegulomeDB score = 2b). The remainder 89 functional SNPs were in linkage disequilibrium with GWAS SNPs. This study supports the hypothesis that some of the non-coding variants are true signals via regulation of gene expression at transcription level. Our study indicates that RegulomeDB is a useful database to examine the putative functions of large number of genetic variants and it may help to identify a true association among multiple tagged SNPs in a complex disease, such as CAD.

Database URLs

http://www.regulomedb.org/; https://www.broadinstitute.org/mpg/snap/

Background

Most human DNA sequence is non-coding (98%) and hence only small portion (2%) of human genome encodes proteins (1). Although the pathogenesis of monogenic disorders is largely explained, it has been difficult to determine the underlying mechanisms of complex disorders like coronary artery disease (CAD). Before the development of genome-wide association studies (GWAS), only the APOE*4 allele showed consistent association with the risk of CAD across many populations (2–5).

The hypothesis-free GWAS approach was designed with the assumption that common DNA variants explain the bulk of the variation in common diseases (6). About 90% of GWAS-implicated variants, exert only minimal to modest effect sizes on disease phenotypes, and they are present in non-coding rather than coding regions (7). Highly sensitive molecular and computational techniques have identified different regulatory elements (DNAse hypersensitive regions, sequences affecting the binding of transcription factors and promoters or enhancers) in intergenic regions (8). Common variants located in one of these regulatory elements may affect gene expression. To predict the role of these variants in gene regulation and to differentiate between physically tagged and functional single nucleotides polymorphism (SNPs), many databases have been created (9). RegulomeDB is one of such databases that describes the role of these variants in transcriptional regulation.

Similar to many other complex diseases, GWAS have identified hundreds of risk variants associated with CAD that need to be analyzed for their functional role in gene expression (10). Recently, we have used SNAP Webportal and Regulome DB to identify potential regulatory function of variants in associated risk loci for Alzheimer’s disease (11). In this study, we have applied the same approach to identify the regulatory nature of GWAS-implicated variants with CAD and those that are in linkage disequilibrium (LD) with these variants.

Objective

The objective of our study was to assess the GWAS-implicated CAD variants and those variants in LD with GWAS variants for their potential regulatory effects on gene transcription using bioinformatics tools.

Materials and methods

SNPs selection

A total of 58 SNPs within 54 CAD loci was selected, including 52 with accepted genome-wide significant threshold (P < 5 × 10−8) and 6 with suggestive associations (P > 5 × 10−8) identified in two GWAS (12, 13). Detailed information on the selected 58 SNPs is provided in Supplementary Table S1.

Linkage disequilibrium

For the LD assessment of the selected 58 SNPs, we used SNAP web portal (https://www.broadinstitute.org/mpg/snap/, accessed 13 July 2016) (14) (Supplementary Table S2). SNAP contains data from the Northern European from Utah (CEU) population derived from the 1000 Genomes Pilot Project 1 and three different releases of the International-Hap Map Project. We used data from both the 1000 Genomes Project and HapMap 3 (release 2) to identify SNPs in strong LD (r2 ≥ 0.80) with our SNPs of interest. We did not select an array bound search, and query SNPs were included in the output. We performed the search at three thresholds—r2 ≥ 0.80, r2 ≥ 0.90 and r2 ≥ 1.0—for both SNP datasets and identified a total of 1,200 SNPs in LD with the 58 published GWAS SNPs, including the GWAS SNPs themselves. As shown in Table 1, the number of proxy SNPs decreased with the increased level of r2.

Table 1.

Number of SNPs in LD for all published GWAS SNPs for HapMap3 and 1000 genomes populations at tested r2 threshold

LD
r2threshold 0.80 0.90 1.0
1000 Genomes 1176 928 480
Hap Map3 210 157 74
Total (overlaps removed) 1200 934 485

Functional assessment of CAD-associated SNPs

We used RegulomeDB to identify potentially functional SNPs among the 1200 SNPs of interest. Regulome DB is a database that scores SNPs functionality based upon experimental data, such as its existence in a DNAase hypersensitive site or transcription factor binding site. These regions have been characterized biochemically, and data are drawn from published literature, Gene Expression Omnibus and ENCODE project that include a total of 962 experimental datasets, covering over 100 tissues and cell lines and representing nearly 60 million annotations. The output data can be mapped to Human genome version 19. It is a user friendly and freely accessible database (http://www.regulomedb.org/accessed 17 July 2016) (15). The functional Grades 1–6 of RegulomeDB are given in Table 2. SNPs showing the strongest evidence of being regulatory (affecting the binding of transcription factor) are given a score of 1 and SNPs demonstrating the least evidence of being functional are given a score of 6.

Table 2.

RegulomeDB category summaries (15)

Category Description
Likely to affect binding and linked to expression of a gene target
1b eQTL + TF binding + any motif + DNase footprint + DNase peak
1c eQTL + TF binding + matched TF motif + DNase peak
1d eQTL + TF binding + any motif + DNase peak
1e eQTL + TF binding + matched TF motif
1f eQTL + TF binding/DNase peak
Likely to affect binding
2a TF binding + matched TF motif + matched DNase footprint + DNase peak
2b TF binding + any motif + DNase footprint + DNase peak
2c TF binding + matched TF motif + DNase peak
Less likely to affect binding
3a TF binding + any motif + DNase peak
3b TF binding + matched TF motif
Minimal binding evidence
4 TF binding + DNase peak
5 TF binding or DNase peak
6 Motif hit

Results

Among the 1200 SNPs evaluated with RegulomeDB, 342 had no data (Supplementary Table S3). Of the 858 SNPs for which RegulomeDB provided a score, 97 had a score of <3 (likely to affect the binding) and among these only 8 SNPs were genome-wide significant, including LIPA/rs2246833 (RegulomeDB score = 1b; eQTL in monocytes), ZC3HC1/rs11556924 (RegulomeDB score = 1f; eQTL in monocytes), CYP17A1-CNNM2-NT5C2/rs12413409 (RegulomeDB score = 1f; eQTL in monocytes and lymphoblasts), APOE-APOC1/rs2075650, and UBE2Z/rs46522 (RegulomeDB score = 1f; eQTL in monocytes), ZNF259-APOA5-APOA1/rs964184, UBE2Z/rs46522, SMG6/rs2281727, COL4A1-COL4A2/rs4773144 (RegulomeDB score =2b; eQTLs in monocytes and lymphoblasts). A flow chart summarizes these results (Figure 1) . The remaining 89 SNPs with RegulomeDB scores < 3 were not identified in GWAS but they were in LD (r2 ≥ 0.80) with the 29 GWAS reported SNPs. A summary of the regulatory SNPs in LD with GWAS SNPs is provided in Table 3.

Figure 1.

Figure 1.

58 GWAS ANPs in LD with 1200 SNPs. We used SNAP webportal to determine LD SNPs. These 1200 SNPs were further evaluated by RegulomeDB to identify their functional role. RegulomeDB did not provide data for 342 SNPs. A total of 858 SNPs returned the scores of 1–6 by RegulomeDB. Of those 858 SNPs, 97 returned the scores of < 3. Among 97 functional SNPs, only 8 were GWAS SNPs. Lower the RegulomeDB score, more evidence of functionality.

Table 3.

Functional SNPs (RegluomeDB Score < 3) in LD (r2 ≥ 0.80) with published GWAS SNPs

GWAS SNPs Functional proxy SNPs Regulome DB score
LIPA/rs2246833 LIPA/rs1332327 2b
LIPA/rs1332328 2b
LIPA/rs1412444 1d
LIPA/rs2246833a 1b
LIPA/rs2250644 2b
ZC3HC1/rs11556924 ZC3HC1/rs11556924a 1f
CYP17A1-CNNM2-NT5C2/rs12413409 AS3MT/rs11191454 1f
BORCS7-ASMT/rs4409766 1f
CNNM2/rs10883808 1f
MAT2A/rs1446668 2a
NT5C2/rs10883832 1f
CNNM2/rs11191479 1f
NT5C2/rs11191557 1f
CNNM2/rs11191499 1f
NT5C2/rs11191558 1f
CNNM2/rs11191514 1f
NT5C2/rs11191580 1f
CNNM2/rs11191515 1f
NT5C2/rs11191582 1f
CNNM2/rs12221064 2b
NT5C2/rs12412038 1f
CNNM2/rs12411886 1f
NT5C2/rs12413046 1f
CNNM2/rs12413409a 1f
NT5C2/rs9633712 1e
CNNM2/rs17115213 1f
NT5C2/rs11191548 1f
CNNM2/rs2297787 2a
CNNM2/rs3781285 1f
CNNM2/rs943037 1f
CNNM2/rs12219901 2b
APOE-APOC1/TOMM40/rs2075650 APOE-APOC1/rs2075650a 1f
UBE2Z/rs46522 GIP/rs2291725 1f
GIP/rs4794004 1d
SNF8/rs1994970 1f
SNF8/rs4793992 1f
UBE2Z/rs12601672 2b
UBE2Z/rs15563 1f
UBE2Z/rs3744608 2a
UBE2Z/rs3848460 1f
UBE2Z/rs46522a 1f
UBE2Z/rs11079844 1f
ZNF259-APOA5-APOA1/rs964184 ZNF259-APOA5-APOA1/rs964184 1f
SMG6/rs2281727 SMG6/rs2281727a 2b
SMG6/rs7217687 2b
SMG6/rs9908888 2b
COL4A1-COL4A2/rs4773144 COL4A1-COL4A2/rs4773144a 2b
ABO/rs579459 ABO/rs649129 2b
ADMTS7/rs7173743 LOC105370915/rs5029904 2b
PHACTR1/rs4773143 2b
CXCL12/rs501120 CXCL12/rs518594 2b
CXCL12/rs1746052 2b
FURIN-FES/rs17514846 FES/rs1894401 1b
HHIPL1/rs2895811 HHIPL1/rs28391527 2b
HHIPL1/rs4624107 2b
HHIPL1/rs7145262 2b
IL6R/rs4845625 IL6R/rs7549250 2b
IL6R/rs7549338 2b
IL6R/rs7553796 2b
KCNE2/rs9982601 KCNE2/rs28591415 2b
KIAA1462/rs2505083 KIAA1462/rs3739998 2b
LPL/rs264 LPL/rs271 1f
LPL/rs3779788 2b
MIA3/rs17465637 MIA3/rs17163301 2b
PLG/rs4252120 PLG/rs4252126 1f
PLG/rs4252135 1f
PPAP2B/rs17114036 LOC101929929/rs72664304 2a
PLPP3/rs4634932 1f
SLC22A4-SLC22A5/rs273909 SLC22A5/rs17689550 1f
SMG6/rs7217687 2b
SMG6/rs9908888 2b
SORT1/rs602633 CELSR2/rs12740374 2b
CELSR2/rs629301 1f
CELSR2/rs646776 1f
TRIB1/rs2954029 LOC105375745/rs2980853 2b
LOC105375745/rs2001844 2b
LOC105375745/rs6982636 2b
TRIB1/rs2980856 2b
VAMP5-VAMP8-GGCX/rs1561198 GGCX/rs6738645 1f
GGCX/rs10187424 1f
VAMP8/rs1009 1b
GGCX/rs6547621 1f
VAMP8/rs1348818 1f
GGCX/rs2886722 1f
VAMP8/rs3770098 1f
VAMP8/rs6757263 1f
WDR12/rs6725887 ICA1L/rs72934715 2b
NBEAL1/rs2351524 1f
WDR12/rs72936852 2b
NBEAL1/rs4675310 1f
NBEAL1/rs72934512 2b
REST-NOA1/rs17087335 REST/rs2227901 1f
REST-NOA1/rs7687767 1d
SWAP70/rs10840293 SWAP70/rs93138 1f
SWAP70/rs360136 1f
SMAD3/rs56062135 SMAD3/rs17293632 2a
SMAD3/rs1866316 2b
MTERF1/rs8032739 2b
CDKN2BAS1/rs1333049 CDKN2BAS1/rs4977574 2c
a

GWAS significant SNPs with functional evidence (RegulomeDB score < 3) are bolded.

Overall, we had 97 functional SNPs (RegulomeDB < 3). Eight of these were GWAS SNPs, and the remaining 89 were in LD (r2 ≥ 0.80) with the GWAS SNPs.

Three variants, FES/rs1894401, LIPA/rs2246833 and VAMP8/rs1009, were strongly predicted to be functional with score of 1b. FES/rs1894401 is an intronic SNP that is an eQTL for FES in thyroid and transformed lymphoblasts, is present in the binding motif of Pax5, and affects the binding of eleven transcription factors. LIPA/rs2246833 (RegulomeDB score = 1b), located in Intron 6 of LIPA, in the DNA motif of EWSRCFLI1, is a GWAS reported SNP along with 4 other functional SNPs (of 12 tested) in this region and it and it affects the binding of CTCF. It is an eQTL in the whole blood. VAMP5-VAMP8-GGCX/rs1009 is in exon 3 of VAMP8 and affects the binding of CTCF and HSF1. rs1009 of VAMP8 is an eQTL in lymhoblasts, skeletal muscles, adipose tissue and thyroid. Of 42 SNPs analyzed in this locus, we found 8 other SNPs with RegulomeDB score < 3 (Table 3).

There were 33 functional SNPs within 15 GWAS identified CAD loci: ABO (1of 10 assessed), ADAMTS7 (1 of 15 assessed), CXCL12 (2 of 36 assessed), HHIPL1 (3 of 17 assessed), KCNE2(2 of 18 assessed), KIAA1462 (1 of 9 assessed), MIA3 (1 of 27 assessed), PPAP2B (2 of 22 assessed), SORT1 (3 of 9 assessed), WDR12 (5 of 214 assessed), IL6R (3 of 14 assessed), LPL (2 of 6 assessed), PLG (2 of 41 assessed), SLC22A4-SLC22A5 (1 of 2 assessed) and TRIB1 (4 of 16 assessed).

Of 97 SNPs with RegulomeDB score < 3, 25 were in the CYP17A1-CNNM2-NT5C2 region, and one of them was a GWAS reported SNP (rs12413409). The regional LD plot of this SNP is given in Supplementary Figure S1. rs9633712 (RegulomeDB score = 1e) is located in Intron 3 of NT5C2 and is an eQTL for USMG5 in monocytes. This SNP was also found in the motifs of the following transcription factors: PU1, ELF1, Sfpil, PU.1 and c-Ets-1. It appears to affect the binding of SPI1. Twenty SNPs returned a score of 1f (likely to affect the binding), and 18 of them were in intronic regions. NT5C2/rs11191558 lies in HOXC series of DNA motifs, and CNNM2/rs3781285 lies between NF-kappaB and P50:50. NT5C2/rs2297787 returned a score of 2a, affecting the binding motifs of FOXI1, HNF3-alpha and FOXP1 and the binding of FOXA1. SNP rs12412038 is located in Intron 10 of NT5C2 and is in the binding motif of Irx. The remaining two SNPs, rs12219901 and rs12221064, lie in the CNNM2-NT5C2 intergenic region and upstream of CNNM2, respectively. They are located in DNA motifs of SRF and MAZR and affect the binding of POLRA2 and CTCF/ETS. Interestingly, rs943037 resides in exon 7 of CNNM2. Nineteen of the 25 SNPs in the region of CYP17A1-CNNM2-NT5C2 are eQTLs for USMG5 (Table 4).

Table 4.

Putative functional SNPs and corresponding motifs, eQTL and related transcription factors (Regulome DB score < 3)

Coordinate 0-based SNP ID RegulomeDB score Gene/Locus Position eQTL Motif Protein-binding
chr15:91429041 rs1894401 1b FES Intron 2 FES Pax5 SPI1
USF1
POLR2A
GABPA
BHLHE40
CEBPB
CTCF
MAX
RFX5
RUNX3
STAT5A
chr10:91005853 rs2246833 1b LIPA Intron 6 LIPA EWSR-FLI1 CTCF
znf143
chr2:85808736 rs1009 1b VAMP8 Exon 3 VAMP8 CTCF
LOC388969 HSF1
chr17:47038470 rs4794004 1d GIP Intron 4 ATP5G1 Gata5 NR3C1
UBE2Z IN3AK20
CREB1
TAF1
TCF12
CTCF
POLR2A
USF1
FOXA1
FOXA2
RBBP5
chr10:91002926 rs1412444 1d LIPA Intron3 LIPA SAP1a ATF2
ELK1 FOXM1
ELK3 SP1
ELK4 SPI1
MECP2 MTA3
ERF RUNX3
ERG
ETS1
ETV1
ETV2
ETV3
Gabpa
chr10:104873760 rs9633712 1e NT5C2 Intron 3 UMG5 PU1 SP11
ELF-1
Sfpil
PU.1
c-Ets-1
chr11:116648916 rs964184 1f ZPR1 Downstream ZRP1 TAGLN FOXJ2
chr1:109818529 rs646776 1f CELSR2 Upstream CELSR2 PSMA5 CTCF
HEY1
REST
POLR2A
ZBTB7A
TAF7
chr10:104616662 rs4409766 1f BORCS7-ASMT Intron 1 C10orf77 Tcf3 BACH1
USMG5 MAFF
MAFK
chr17:47008206 rs4793992 1f SNF8 Intron 7 ATP5G1 POLR2A
UBE2Z TEAD4
chr6:161152293 rs4252126 1f PLG Intron 11 PLG CTCF
RUNX3
TEAD4
RAD21
chr6:161154231 rs4252135 1f PLG Intron 12 PLG CTCF
FOXA1
NFKB1
RAD21
ZNF263
SMC3
ZNF143
FOXA2
chr10:104846177 rs11191548 1f NT5C2 gene region Downstream NT5C2 USMG5 TEAD1
TEAD3
chr10:104864613 rs11191557 1f NT5C2 Intron 5 USMG5
chr10:104864677 rs11191558 1f NT5C2 Intron 5 USMG5 HOXC13
Hoxa13
Hoxc13
Hoxd12
HOXA13
HOXD9
HOXC11
chr10:104871203 rs12413046 1f NT5C2 Intron 3 USMG5 NR3C1
TRIM28
CTCF
ATF2
IKZF1
TCF7L2
ZNF263
chr10:104871278 rs10883832 1f NT5C2 Intron 3 USMG5 TRIM28
TCF7L2
chr10:104913652 rs11191582 1f NT5C2 Intron 2 USMG5 EP300
NFIC
TCF12
TEAD4
STAT1
ARID3A
EP300
JUN
RCOR1
chr10:104906210 rs11191580 1f NT5C2 Intron 2 USMG5 TRIM28
SETDB1
GATA1
GTF2F1
CEBPB
FOS
JUND
ZNF263
chr10:104856161 rs12412038 1f NT5C2 Intron 10 USMG5 Irx-3
Irx-2
Irx-4
Irx-6
chr2:85807081 rs1348818 1f VAMP8 Intron 2 GGCX HMGIY EBF1,
Mtf1
Srf
Zfp105
HMGIY
chr2:85805366 rs3770098 1f VAMP8 Intron1 VAMP8 POLR2A
LOC388969 BHLHE40
E2F6
KDM5B
MAX
MXI1
MYC
NFIC
WRNIP1
chr2:85803541 rs6757263 1f VAMP8 Upstream VAMP8 GGCX SP1
VAMP8 EP300
LOC388969 NFIC
chr17:46988596 rs46522 1f UBE2Z Intron 2 ATP5G1 NFKB
UBE2Z NFYB
RUNX3
chr19:45395618 rs2075650 1f TOMM40 Intron 2 TOMM40 RREB1
chr1:56996190 rs4634932 1f PLPP3 Intron 2 PPAP2B POLR2A
chr2:203880833 rs4675310 1f NBEAL1 Intron 1 ALS2CR13
chr10:104681142 rs17115213 1f CNNM2 Intron 1 USMG5
chr10:104721125 rs10883808 1f CNNM2 Intron 1 USMG5
chr10:104723619 rs11191479 1f CNNM2 Intron 1 USMG5 GATA1
TAL1
CEBPB
chr10:104773363 rs11191514 1f CNNM2 Intron 1 USMG5 PAX5
chr10:104776526 rs11191515 1f CNNM2 Intron 1 USMG5
chr10:104825664 rs3781285 1f CNNM2 Intron 4 USMG5 NF-kappaB IKZF1
P50:50
chr10:104835918 rs943037 1f CNNM2 Exon 7 USMG5 TBX20
Foxj1
chr8:19813701 rs271 1f LPL Intron 6 LPL
chr17:47039131 rs2291725 1f GIP Exon 4 GIP GATA2
TCF4
FOSL2
EGR1
ELF1
FOS
NR3C1
EP300
RXRA
CHD2
JUND
POLR2A
RAD21
FOSL1
REST
chr2:203880991 rs2351524 1f NBEAL1 5' UTR ALS2CR13
chr1:109818305 rs629301 1f CELSR2 3' UTR PSRC1 CTCF
POLR2A
chr2:85774675 rs6547621 1f 3' UTR GGCX ELK4
POLR2A
chr10:104660003 rs11191454 1f AS3MT Intron10 USMG5
chr10:104685298 rs12411886 1f CNNM2 Intron1 USMG5 Zec
chr10:104719095 rs12413409 1f CNNM2 Intron1 USMG5 POLR3A
chr10:104764270 rs11191499 1f CNNM2 Intron1 USMG5
chr17:47014126 rs1994970 1f SNF8 Intron4 ATP5G1 TFII-I
UBE2Z
chr2:85742296 rs2886722 1f Pseudogene LOC388969 TCF7L2
chr2:85783127 rs6738645 1f GGCX Intron5 Evi-1 POLR2A
chr2:85794296 rs10187424 1f Pseudogene GGCX
LOC388969
chr5:131723064 rs17689550 1f RAPGEF6
chr7:129663495 rs11556924 1f ZC3HC1 Exon8 KIAA0265
chr17:4702833 rs11079844 1f Pseudogene ATP5G1
chr17: 47005192 rs15563 1f UBE2Z Exon7 ATP5G1 PRDM1
chr17:47047113 rs3848460 1f UBE2Z ATP5G1 CEBPB
chr10:104680136 rs2297787 2a CNNM2 Intron 1 Freac-7 FOXA1
HFH3(FOXI1) SIN3A
HNF3alpha ZNF263
FOXP1 HNF4G
Elf3 FOXA1
Foxl1
Srf
Tcf3
Tcfap2e
Zfp105
HFH(FOXl1)
chr1:56948289 rs72664304 2a C8B Intron 6 FOXA1 FOXA1
Foxa2 FOXA2
TCF4
SP1
HNF4G
HNF4A
HDAC2
JUND
EP300
chr17:46993232 rs3744608 2a UBE2Z Intron 3 Zfp740 SPI1
MZF1 POLR2A
MAZR IKZF1
SP1 MAX
SP1:SP3 TFAP2A
WT1ZNF21 TFAP2C
Zfp281 SP1
ZFp740 CEBPB
WT1 NR3C1
ZNF219 BATF
ZNF740 BCL11A
SP4 MEF2A
NFKB1
JUND
EP300
STAT3
IRF4
EBF1
FOSL2
BATF
NR3C1
RUNX3
MYC
STAT3
chr2:85764959 rs1446668 2a MAT2A nc transcript CTCF CTCF
Upstream MAT2A POLR2A
TAF1
RFX5
RAD21
HEY1
CDX2
HNF4A
ZNF263
NR3C1
CTCF
MYC
AR
MYBL2
TEAD4
MAZ
CHD2
SMC3
TBP
ZNF143
CDX2
E2F6
MAX
NR3C1
SIN3A
YY1
REST
HMGN3
chr17:47006492 rs12601672 2b UBE2Z Downstream UBE2Z Zfx POLR2A
EGR1
SPI1
ELF1
chr10:30316071 rs3739998 2b KIAA1462 Exon 2 RELA CTCF
MYC
PAX5
ZNF143
chr8:126476378 rs2980856 2b TRIB1 gene Intergenic region pax-8 JUND
region Downstream TRIB1 Sox17 POLR2A
TFAP2C
MXI1
CEBPB
chr9:136154303 rs649129 2b ABO gene region Intergenic region IRF NFYA
Upstream ABO POLR2A
FOS
IRF1
NFYB
PML
chr10:104840966 rs12219901 2b CNNM2 gene Intergenic region SRF POLR2A
region Downstream CXCL12
chr10:44778545 rs1746052 2b CXCL12 gene Intergenic region GATA1 TAL1
region Downstream CXCL12
chr21:35593826 rs28451064 2b LINC00310 gene Intergenic region PPAR SP1
region Downstream FOXA2
LINC00310
chr17:2098271 rs7217687 2b SMG6 Intron 13 - NF-1 SIN3A
TCF12
MAX
YY1
ZNF263
EP300
TEAD4
chr13:110960711 rs4773144 2b COL4A2 Intron 3 STAT3:STAT3 POLR2A
EZH2
chr14:100116251 rs28391527 2b HHIPL1 Intron 3 MyoD BHLHE40
SCRT1 USF1
FIGLA FOXA1
MAX
chr1:154404335 rs7549250 2b IL6R Intron 3 TBX15 MXI1
FOS
JUNB
MAX
JUND
JUN
STAT3
FOSL1
MAFK
RCOR1
MYC
USF2
TEAD4
RCOR1
YY1
chr1:154404379 rs7549338 2b IL6R Intron 3 GR FOS
AR JUNB
JUND
JUN
STAT3
chr1:154404405 rs7553796 2b IL6R Intron 3 NF-kappaB, FOS
JUND
JUN
STAT3
chr14:100127439 rs4624107 2b HHIPL1 Intron 7 Pax5 JUND
chr10:91011457 rs1332328 2b LIPA Intron 9 UF1H3BETA CREBBP
ZNF263
CDX2
ELF1
ZEB1
TBP
TFAPC2
TBP
POLR2A
ETS1
GABPA
HEY1
chr17:2117944 rs2281727 2b SMG6 Intron 13 SRY CREBBP
Srf EP300
Zfp105 STAT3
TRIM28
MYC
RBBP5
chr8:126479314 rs6982636 2b LOC105375745 Intron1 MAF SMARCC1
RFX3
POLR2A
GATA2
CHD2
GTF2F1
chr14:100125720 rs7145262 2b HHIPL1 Intron4 ESR2 SMARC4
ZBTB7A
SMARB1
POLR2A
EZH2
RAD21
BACH1
chr10:104677125 rs12221064 2b CNNM2 Upstream CNNM2 MAZR, CTCF, ETS1
ETS1
chr8:126478349 rs2980853 2b LOC105375745 Upstream Pit-1, RFX3
LOC105375745
chr15:79152421 rs5029904 2b LOC105370915 Upstream NeuroD USF1
LOC105370915 POLR2A
YY1
FOXA1
E2F4
MAX
TAF7
TAF1
MXI1
chr8:126478744 rs2001844 2b LOC105375745 Upstream HSF1 RFX3
LOC105375745
chr10:91011680 rs1332327 2b LIPA 5' UTR AP-4, CREBBP
CDX2
ELF1
TBP
SPI1
NRF1
ELF1
ETS1
GABPA
SPI1
PAX5
SREBF1
chr17:2102452 rs9908888 2b SMG6 Intron10 GR CEBPB
AR
chr10:91008878 rs2250644 2b LIPA Intron1 Oct-1 RUNX3
XBP-1
MAfb
Mafk
MAFB
MAFK
NRL
chr1:109817589 rs12740374 2b CELSR2 Exon34 HNF1 EBF1
HNF1A
DUXA
chr1:222794090 rs17163301 2b MIA3 Intron1 HNF1 EBF1
HNF1A
HNF1B
DUXA
chr21:35644028 rs28591415 2b pseudogene PPAR EP300
FOXA1
HDAC2
NFIC
SP1
chr2:203713279 rs72934715 2b ICA1L Intron2 HMGIY ATF2
NFIC
EBF1
EP300
NFKB1
PAX5
chr2:203775474 rs72936852 2b WDR12 Intron1 AR MAFF
chr2:203926270 rs72934512 2b NBEAL1 Intron6 TEAD1 TEAD4
TEAD3
chr8:19803092 rs3779788 2b LPL Intron1 TGIF CEBPB
chr10:44757106 rs518594 2b CXCL12 Downstream intergenic E2A FOXM1
NRSE NFIC
NRSF MAX
EBF1
TBL1XR1
chr4:57824931 rs7687767 1d CECR6 Sox8 FOXA1
chr11:5759712 rs93138 1f SWAP70 Intron8
rs360136 1f SWAP70 Exon13
chr:457798188 rs2227901 1f REST-NOA1 Exon6 SPI1
chr:1567442595 rs17293632 2a SMAD3 Intron4 Bach1 SIN3A
AP-1 TCF7L2
JundM2 TFAP2A
Pou1f1 TFAP2C
Pou3f1 ZNF217
Sox5 POLR2A
JDP2 STAT1
MXI1
TAF1
E2F1
POLR2A
HDAC1
TCF7L2
MYC
POLR2A
ESR1
EP300
CDX2
HNF4A
CEBPB
EP300
FOS
FOSL1
GATA3
JUNB
JUN
MYC
NR2F2
NR3C1
RAD21
RCOR1
TCF12
JUND
MAFK
EGR1
MAX
chr15:67441996 rs18866316 2b SMAD3 Exon4 AIRE ESR1
Elf3 NR3C1
Srf POLR2A
Tcf3
Tcfap2e
chr15:67448898 rs8032739 2b MTERF Intron4 SRF CEBPB
FOS
MYC
STAT3
chr9:22098573 rs4977574 2c CDKN2BAS1 Intron16 AR AR
NR3C1
NR3C2
Ar
Elk-1
c-Ets-1(p54)

One SNP rs2075650 lies in Intron 2 of ApoEApoC1/TOMM40 with a RegulomeDB score of 1f. It is located in RREB1 DNA motif and is an eQTL for TOMM40 (Table 4).

In total 3 of 107 SMG6 associated SNPs, rs2281727, rs7217687 and rs9908888 had a score of 2b and they affect the binding of EP300. rs2281727 is a genome-wide significant SNP located in Intron 9 of SMG6. It is in binding motifs of SRY, Srf and Zfp105 and affects the binding of CREBBP, EP300, STAT3, TRIM28, MYC and RBBP5 (Table 4).

The UBE2Z region had 10 functional SNPs, including a GWAS reported SNP, UBE2Z/rs46522 (RegulomeDB score of 1f). The SNP with the most evidence of regulatory function in this locus is rs4794004 with a score of 1d. It is in DNA motif of Gata5 that alters the expression of UBE2Z and ATP5G1and affects the binding of NR3C1, IN3AK20, CREB1, TAF12, CTCF, POLR2A, USF1, FOXA1, FOXA2 and RBBP5. The other 5 SNPs in this region have a score of 1f. The remaining two regulatory SNPs, rs3744608 and rs12601672, have scores of 2a and 2b, respectively. rs3744608 is located in Intron 3 of UBE2Z and it affects the binding of large number of transcription factors (Table 4).

COL4A1-COL4A2/rs473144 is a GWAS reported SNP, achieving a RegulomeDB score of 2b. This SNP lies in Intron 3 of COL4A2 between STST3:STAT3 DNA motif and affects the binding of POLR2A and EZH2 (Table 4). ZNF259-APOA5-APOA1/rs964184 is a GWAS significant SNP with a score of 1f and is an eQTL for TAGLN. This SNP is located downstream of this gene region and is present in FOXJ2 DNA motif. Another GWAS significant SNP, ZC3HC1/rs11556924 is an exonic variant and the only functional SNP (score = 1f) in this locus; it is also an eQTL for ZC3HC1 (Table 4).

REST-NOA1/rs17087335 is in LD with two functional SNPs (rs2227901 and rs7687767 with RegulomeDB scores of 1f and 1d, respectively). rs768776 lies in DNA motif of Sox8 and affects the binding of FOXA1. SWAP70 has two functional SNPs, rs93138 and rs360136, each with a RegulomeDB score of 1f. SWAP70/rs93138 is an eQTL as evidenced in monocytes.

SMAD3 has three functional SNPs, SMAD3/rs17293632 and SMAD3/rs1866316 and MTERF1/rs8032739 with RegulomeDB scores of 2a, 2a and 2b, respectively. Both are in LD with a lead GWAS SNP (SMAD3/rs56062135). CDKN2BAS1/rs1333049 has one functional SNP(rs4977574) only with RegulomeDB score of 3c. It is a part of a gene cluster on chromosome 9p21 and it maps to Intron 16 of cyclin dependent kinase, an important regulator of cell cycle.

Discussion

Following the sequencing of human genome, a large number of SNPs have been identified that affect disease phenotypes, but their exact roles remain unclear (16). One possible explanation is that some variation affects disease expression at the transcriptional level other than at the protein level. For example, a base pair change in a transcription factor binding site may affect the binding affinity of transcription factors that consequently may alter the transcription of the related genes. These effects are indirect and may seem subtle, but their interactions with other genetic or environmental factors may result in the pathogenesis of common diseases.

Like other complex disorders, a large number of CAD associated risk variants have been discovered by multiple GWAS (12, 13, 17). ENCODE provides information regarding the functionality of human genome (18). This data requires careful interpretation and helps to define the biological function of previously termed ‘junk DNA’. Using bioinformatics tools, we may generate new hypotheses about the gene regulation of complex disorders. In this study, we have used two bioinformatics tools, SNAP and RegulomeDB, in order to identify the putative roles of CAD-associated SNPs.

We examined a total 1,200 SNPs in 54 loci implicated by GWAS, including 58 genome-wide significant SNPs. Ninety-seven SNPs were predicted to have regulatory functions with a RegulomeDB score of <3, but only 8 of them were genome-wide significant. Interestingly, all 8 genome-wide significant SNPs with suggested regulatory function are located either in intronic or intergenic regions, suggesting that these are true associations that regulate gene expression at the transcriptional level.

Among these eight GWAS reported functional SNPs, the SNP with the top RegulomeDB score was LIPA/rs2246833 (Regulome DB score = 1b). This variant is located in Intron 6 of lipase A (LIPA) and is an eQTL for the same gene which catalyzes intracellular triglyceride and hydrolyses cholesterol ester (19).

ZC3HC1/rs11556924 is a GWAS significant CAD associated SNP that returned a score of 1f. rs11556924 is a coding SNP located in the ZC3HC1 gene region encoding NIPA (Nuclear Interaction Partner of ALK) protein. This polymorphism is responsible for arginine-histidine amino acid alteration at position 363 (R363H). The SNP has been associated with essential hypertension in Finnish population (20, 21).

The CYP17A1-CNNM2-NT5C2 gene region has the highest number of regulatory SNPs, including one GWAS significant SNP, rs12413409. This locus affects diastolic blood pressure, systolic blood pressure and body mass index. All three measures are important risk factors for CAD (22). There are 25 putative regulatory SNPs in LD with rs12413409 that are located across four genes on chromosome 10 (CNNM2, NT5C2, AS3MT and BORCS7/ASMT) but affect the expression of same protein USMG5. These findings suggest that USMG5 should be investigated as an important player for CAD pathogenesis. USMG5 (upregulated during skeletal muscle growth protein 5) is also known as diabetes-associated protein in insulin-sensitive tissues that plays a crucial role in the maintenance of ATP synthase structure in mitochondria (23). Chen et al. (24) have purified this protein from bovine heart mitochondria and suggested its role in cell energy metabolism.

APOE-APOC1/TOMM40/rs2075650 is present in the TOMM40 gene region near the APOE-C1 cluster. TOMM40 encodes TOMM40 protein, which is an important subunit 40 of outer mitochondrial membrane protein complex. rs2075650 risk allele has shown an association with low levels of CRP in CAD patients (25).

rs46522, an intronic SNP in Ubiquitin-conjugating enzyme E2Z (UBE2Z) gene region returns a RegulomeDB score of 1f. This SNP is associated with CAD in Iranian and Han Chinese populations (26, 27). The exact mechanism by which genetic alteration in UBE2Z can attribute to the CAD risk is not yet clear; however, rs46522 is in strong LD with the causal SNPs in gastric inhibitory peptide (GIP) gene that encodes GIP protein, a protein that modifies the glucose and lipid metabolism potentially mediating known CAD risk factors.

ZNF259-APOA5APOA1/rs964184 is also an important regulatory SNP. ZNF259 protein polymorphism has been associated with metabolic syndrome in Chinese population. Aung et al. (28) have also shown its association with lipid levels. ZNF259 is located close to APOA5. Overexpression of APOA5 in mice reduces plasma triglyceride levels and mice lacking APOA5 have hypertriglyceridemia (29).

COL4A2/rs4773144 has been identified as functional lead SNP by RegulomeDB (score = 2b). This gene controls collagen proliferation, indicating a potential functional role in atherosclerotic plaque strengthening (30).

SMG6/rs2281727 is an intronic SNP. The potential function of SMG6 in CAD is not yet established. This gene promotes the endonuclease activity and is responsible for protection of telomere ends of chromosomes (16).

Although regulatory elements are most often found in non-coding regions of the genome, we found 5 loci with exonic regulatory SNPs (VAMP8/rs1009, CNNM2/rs943037, GIP/rs2291725, KIAA1462/rs3739998 and UBE2Z/rs15563), indicating the presence of regulatory signals inside the coding sequences as well.

USF1 is an upstream transcription factor whose binding is affected by three SNPs (GIP/rs4794004, FES/rs1894401, HHIPL1/rs28391527), suggests a potential functional link between FES, GIP and HHIPL1 (31).

RegulomeDB identified three important functional SNPs affecting CAD phenotype. Among these, REST-NOA1/rs17087335 is the lead GWAS SNP that encodes a transcription factor which suppresses the voltage gated sodium and potassium channels and it has shown to maintain vascular smooth muscle cells in non-proliferative phase (32). SWAP70/rs10840293 encodes a signaling molecule that is implicated in cell adhesion and migration and it appears to be a potential regulator of leukocyte migration and their adhesion to endothelial cells (33). SMAD3 is a major regulator of TGF-ß. A study on mice has shown that mutations in this gene lead to decreased connective tissue deposition in response to vascular injury (34).

It should be noted that 342 SNPs had returned ‘No Data’ when queried by RegulomeDB. This suggests that current evidence does not support a functional role for those variants. Our results also showed that some loci harbor markedly more regulatory SNPs as compared with other regions. We caution against interpreting this finding to meant that one region is more functionally relevant, as regions with ‘fewer’ functional SNPs may have yet to be interrogated as thoroughly and thus have fewer annotations.

Since these loci are mostly in Europeans, and only 5 of them are replicated in South Asians (35), the findings may not be as relevant to other populations as they are to Europeans as genetic effects can differ across populations. The cause of this varying association with disease phenotype may be the ethnic admixture resulting in population stratification. It is also noteworthy that robust associations of variants with different diseases have been reported in Europeans while other populations (Africans, Asians and Hispanics) failed to demonstrate those associations (36, 37).

Though the cellular mechanisms underlying CAD pathogenesis are established, the molecular basis is not yet agreed upon. Comprehending the molecular basis of disease is crucial before pathogenesis is completely described. The study has identified 97 regulatory SNPs associated with CAD. In summary, our results highlight the importance of considering both disease-associated SNPs and those SNPs in LD, as well as the regulatory function of these SNPs to help identify the causal genetic mechanisms of CAD. The methods which we have implemented here can inform planning of more complete and better directed functional genomic studies.

Supplementary data

Supplementary data are available at Database Online.

Funding

This study was partially supported by Higher Education Commission of Pakistan and the US National Institutes of Health grants (AG030653 and AG041718).

Conflict of interest. None declared.

Supplementary Material

Supplementary Table 1
Supplementary Table 2
Supplementary Table 3
Supplementary Figure 1

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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 Table 1
Supplementary Table 2
Supplementary Table 3
Supplementary Figure 1

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