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

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.

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.

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.

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.

Author notes

*

Citation details: Cheema,A.N., Rosenthal,S.L., and Kamboh,M.I. Proficiency of data interpretation: Identification of signaling SNPs/specific loci for coronary artery disease. Database (2017) Vol. 2017: article ID bax078; doi:10.1093/database/bax078