. Author manuscript; available in PMC: 2017 Jul 29.

Published in final edited form as:Science. 2016 Aug 19;353(6301):827–830. doi:10.1126/science.aad6970

Cardiometabolic Risk Loci Share DownstreamCis- andTrans-Gene Regulation Across Tissues and Diseases

Oscar Franzén^1,^2,^*,Raili Ermel^3,^4,^*,Ariella Cohain^1,^*,Nicholas K Akers¹,Antonio Di Narzo¹,Husain A Talukdar⁵,Hassan Foroughi-Asl⁵,Claudia Giambartolomei⁶,John F Fullard⁶,Katyayani Sukhavasi³,Sulev Köks³,Li-Ming Gan⁷,Chiara Giannarelli^1,⁸,Jason C Kovacic⁸,Christer Betsholtz^9,¹⁰,Bojan Losic¹,Tom Michoel¹¹,Ke Hao¹,Panos Roussos^1,^6,¹²,Josefin Skogsberg⁵,Arno Ruusalepp^2,^3,⁴,Eric E Schadt¹,Johan LM Björkegren^1,^2,^3,⁵,The Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) Study

¹Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York 10029, NY, USA

²Clinical Gene Networks AB, Jungfrugatan 10, 114 44 Stockholm, Sweden

³Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia

⁴Department of Cardiac Surgery, Tartu University Hospital, 1a L. Puusepa St., 50406 Tartu, Estonia

⁵Division of Vascular Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles väg 2, 171 77 Stockholm, Sweden

⁶Division of Psychiatric Genomics, Department of Psychiatry and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York 10029, NY, USA

⁷Cardiovascular and Metabolic Diseases, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Pepparedsleden 1, Mölndal, 431 83, Sweden

⁸Cardiovascular Research Centre, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York 10029, NY, USA

⁹AstraZeneca-Karolinska Integrated CardioMetabolic Centre (ICMC), Karolinska Institutet, Novum, Blickagången 6, 141 57 Huddinge, Sweden

¹⁰Department of Immunology, Genetics and Pathology Dag Hammarskjölds Väg 20, 751 85 Uppsala, Sweden

¹¹Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Old College, South Bridge, Edinburgh EH8 9YL, UK

¹²Department of Psychiatry, JJ Peters VA Medical Center, Mental Illness Research Education and Clinical Center (MIRECC), JJ Peters VA Medical Center, 130 West Kingsbridge Road, Bronx, NY 10468, USA

^✉

Correspondence to:johan.bjorkegren@mssm.edu

Shared first authorship.

PMC Copyright notice

PMCID: PMC5534139 NIHMSID: NIHMS865440 PMID:27540175

Abstract

Genome-wide association studies (GWAS) have identified hundreds of cardiometabolic disease (CMD) risk loci. However, they contribute little to genetic variance, and most downstream gene-regulatory mechanisms are unknown. We genotyped and RNA-sequenced vascular and metabolic tissues from 600 coronary artery disease patients in the STARNET study. Gene expression traits associated with CMD risk SNPs identified by GWAS were more extensively found in STARNET than in tissue- and disease-unspecific gene-tissue expression studies, indicating sharing of downstreamcis-/trans-gene regulation across tissues and CMDs. In contrast, the regulatory effects of other GWAS risk SNPs were tissue-specific; abdominal fat emerged as an important gene-regulatory site for blood lipids, such as for the LDL-cholesterol and coronary artery disease risk-genePCSK9. STARNET provides insights into gene-regulatory mechanisms for CMD risk loci, facilitating their translation into opportunities for diagnosis, therapy and prevention.

In 2012, cardiovascular disease accounted for 17.5 million deaths, nearly one-third of all deaths worldwide, and >80% (14.1 million) were from coronary artery disease (CAD) and stroke. CAD is preceded by cardiometabolic diseases (CMDs) such as hypertension, impaired lipid and glucose metabolism, and systemic inflammation (1,2). Genome-wide association studies (GWAS) have identified hundreds of DNA variants associated with risk for CAD (3), hypertension (4), blood lipid levels (5), markers of plasma glucose metabolism (6–10), type 2 diabetes (6,11), body mass index (12), rheumatoid arthritis (13), systemic lupus erythematosus (14), ulcerative colitis (15) and Crohn’s disease (16). However, identifying susceptibility genes responsible for these loci has proven difficult.

GWAS loci typically span large, noncoding, intergenic regions with numerous single-nucleotide polymorphisms (SNPs) in strong linkage disequilibrium. These regions are enriched incis-regulatory elements (17) and expression quantitative trait loci (eQTLs) (18–20), suggesting that gene regulation is the principal mechanism by which risk loci affect complex disease etiology. However, it is largely unknown whether this gene-regulatory effect includes one or several genes acting in one or multiple tissues and whether risk loci for different diseases sharecis- andtrans-gene regulation. A better understanding of gene regulation may also shed light on why known GWAS risk loci explain only ~10% of expected heritable variance in CMD risk (21). Possibly, multiple risk loci, acting through commoncis- andtrans-genes, contribute synergistically to heritability (22,23).

In the Stockholm-Tartu Atherosclerosis Reverse Networks Engineering Task study (STARNET) (fig. S1), we recruited 600 well-characterized (table S1,fig. S2) CAD patients, genotyped DNA (6,245,505 DNA variant calls with minor allele frequency >5%,fig. S3), and sequenced RNA isolated from blood, atherosclerotic-lesion-free internal mammary artery (MAM), atherosclerotic aortic root (AOR), subcutaneous fat (SF), visceral abdominal fat (VAF), skeletal muscle (SKLM), and liver (LIV) (15–30 million reads per sample,figs. S4–S11,table S2).

In total, ~8 millioncis-eQTLs were identified, and nearly half were unique SNP-gene pairs (figs. S12–S26,tables S3–S7). The STARNETcis-eQTLs were enriched in genetic associations established by GWAS for CAD, CMDs and Alzheimer’s disease (AD) (3–16,24) (figs. S27–S33) and were further enriched after epigenetic filtering (figs. S34–S39). Of 3,326 genome-wide significant risk SNPs identified by GWAS to date (25), 2,047 (61%) had a matchingcis-QTL in STARNET (Fig. 1A). Of the 54 lead risk SNPs verified in meta-analyses of CAD GWAS (3), 38cis-eQTLs with a regulatory trait concordance score (RTC) >0.9 and at least one candidate gene were identified in STARNET (table S8,fig. S27). Compared to large datasets ofcis-eQTL isolated only from blood,cis-eQTLs across all tissues in STARNET matched >10-fold more CAD and CMD-related GWAS risk SNPs (Fig. 1B). STARNETcis-eQTLs isolated from CAD-affected tissues also matched several-fold more CAD and CMD-related GWAS risk SNPs thancis-eQTLs from corresponding tissues isolated from predominantly healthy individuals in GTEx (18) (Fig. 1C). Thus, not all gene-regulatory effects of disease risk SNPs are identifiable in blood or healthy tissues. This notion was further underscored by comparing the statistical significances ofcis-eQTLs for GWAS risk SNPs in STARNET with corresponding associations in GTEx (Fig. 1D). In STARNET, gene fusions (table S9) and CAD-related loss of function mutations (table S10) were also detected.

Fig. 1 — (A) Venn diagram showing 2,047/3,326 disease-associated risk SNPs from the NHGRI GWAS catalog overlapping with at least one form of STARNET e/psi/aseQTLs. (B) Odds ratios that STARNET eQTLs coincide with CAD-associated risk SNPs (Set 1, CARDIoGRAM-C4D, n=53; Set 2, CARDIoGRAM extended, n=150) (3), blood lipids (Set 3, n=35) (5), and metabolic traits (Set 4, n=132) (6,8,10,12) versus blood eQTLs from RegulomeDB and HapMap. They-axis shows odds ratios. Error bars, 95% confidence intervals. (C) Stacked bar plots comparing tissue-specific eQTLs from STARNET and GTEx (18) coinciding with disease-associated risk SNPs in the same Sets 1–4 as in (B). (**D–I**). Q-Q plots showing associations of tissue-specific STARNET (blue) and GTEx (18) (red)*cis*-eQTLs of disease-associated risk SNPs identified by GWAS for CAD (3) (D), blood lipids (5) (E), waist-hip ratio (12) (F), fasting glucose (6) (G), AD (24) (H), and SLE (14) (I).

Thecis effects of disease-associated risk loci identified by GWAS are central for understanding downstream molecular mechanisms of disease. However, thesecis-genes likely also affect downstreamtrans-genes. To identify possibletrans effects, we ran a targeted analysis to call bothcis- andtrans-genes for lead risk SNPs identified by GWAS. After assigningcis-eQTLs for 562 risk SNPs for CAD, CMDs and AD (3–16,24), we used a causal inference test (26) to conservatively call causal correlations between thecis-genes andtrans-genes by assessing the probability that an interaction was causal (SNP→cis-gene→trans-gene, false discovery rate [FDR]<1%) and not reactive (SNP→trans-gene→cis-gene,P>0.05) (26) (table S11). We found extensive sharing ofcis- andtrans-gene regulation by GWAS risk loci across tissues and CMDs. In CAD, 28 risk loci with at least one causal interaction (FDR <1%,P>0.05) had a total of 51cis-genes and 1040trans-genes. Of these, 26 risk loci, 37cis-genes (including 27 key drivers (27)), and 994trans-genes were connected in a main CAD regulatory gene network acting across all 7 tissues (Fig. 2). The trans-genes in this network were enriched with genes previously associated with CAD and atherosclerosis (Fisher’s test, 1.54-fold,P=8E-10,table S11). Sharing ofcis/trans-genes downstream of complex disease risk loci also emerged for other CMDs and AD (3–16,24) (fig. S40). In fact, we identified 33cis-genes regulated by risk SNPs across all CMDs, including CAD and AD, acting as key drivers in a pan-diseasecis/trans-gene regulatory network (Fig. 3A).

Fig. 2 — A main gene-regulatory network ofcis-andtrans-genes associated with 21/46 index SNPs for risk loci identified for CAD by meta-analysis in the CARDIoGRAM GWAS of CAD (3) inferred using a causal inference test (26).

Among CMDs,cis/trans-genes of GWAS risk SNPs for blood lipid levels (5) emerged as central (Fig. 3B) where tissue-specific down-stream effects were beside LIV (46cis- and 150trans-genes) observed in the fat tissues (SF; 45cis- and 372trans-genes: VAF; 38cis- and 465trans-genes) (fig. S41,table S11). Visceral abdominal fat examples includedABCA8/ABCA5 (rs4148008) associated with 36 downstreamtrans-genes in VAF and HDL;EVI5 (rs7515577) associated with 32 VAFtrans-genes and total cholesterol; andSTARD3 (rs11869286) associated with 7 VAFtrans-genes and HDL. In addition, thecis-geneTMEM258 (rs174546) with 22trans-genes in abdominal fat surfaced as a parallel/alternative regulatory site of plasma LDL to the proposedFADS-1,2,3 in LIV (5) (fig. S41). Other risk SNPs with VAF-specificcis-genes had few or even notrans-genes (fig. S41). For example, two risk SNPs—rs11206510 for CAD and rs12046679 for LDL cholesterol level (3,5)—regulatePCSK9 in VAF, not in LIV (Fig. 4A, B). The VAF-specificity of these eQTLsPCSK9 in were confirmed in an independent gene expression dataset from morbidly obese patients (28) (Fig. 4C,fig. S30) suggesting that PCSK9 is secreted from VAF into the portal vein to affect hepatic LDL receptor degradation, LDL plasma levels and risk for CAD (29). Interestingly and as previously suggested (30), we observed that STARNET patients in the upper, compared to the lower, 5^th–20^th percentiles of waist–hip ratio, (i.e., patients with and without “male fat”) had higher levels of circulating PCSK9 (Fig. 4D) and LDL/HDL ratio (Fig. 4E).

Fig. 4 — (A)*PCSK9* was expressed in STARNET LIV and VAF but only associated with the CAD risk SNP rs11206510 in VAF (FDR<0.001). Box plot of allelicPCSK9 expression of the CAD risk SNP rs11206510 showing dosage effect of the T allele (P=3.91e-15; FDR=4e-04). (B) Regional plot of thePCSK9 locus. rs2479394, linked to plasma LDL levels by GWAS (5), acts independently of rs11206510 as the lead eQTL ofPCSK9 expression in VAF. rs2479394 was not an eQTL ofPCSK9 in STARNET LIV. (C) Box plots of allelicPCSK9 expression in VAF of rs11206510 and rs2479394 in a gene-tissue expression study of morbidly obese patients (fig. S29) (28). Box plots of PCSK9 levels (D) and ratios of LDL/HDL (E) in plasma isolated from the STARNET patients within the upper and lower 5^th–20^th percentile of waist-hip ratio (WHR) (PCSK9; 5^th,P=8.0e-11; 10^th,P=1.9e-11; 15^th,P=5.9e-05; 20^th,P=0.004: LDL/HDL ratio; 5^th,P=0,007; 10^th,P=0.001; 15^thP=0.0005; 20^th,P=0.0009.

STARNET provides new insights into tissue-specific gene-regulatory effects of disease-associated risk SNPs identified by GWAS, as exemplified by abdominal fat for blood lipids, and will be a complementary resource for exploring GWAS findings moving forward. Furthermore, STARNET also revealed unexpected sharing ofcis- andtrans-genes downstream of risk loci for CMDs across both tissues and diseases. We anticipate that the identifiedcis/trans-gene regulatory networks will help elucidate the complex downstream effects of risk loci for common complex diseases, including possible epistatic effects that could shed light on the missing heritability of CMD risk. Given the detailed phenotypic data on STARNET patients, we can begin to identify how genetic variability interacts with environmental perturbations across tissues to cause pathophysiological alterations and complex diseases.

Supplementary Material

Figure S1

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Figure S38

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Figure S40

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Figure S41

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Supplementary text and figures

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Table S1

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Table S10

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Table S11

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Table S3

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Table S4

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Table S5

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Table S6

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Table S7

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Table S8

NIHMS865440-supplement-Table_S8.xlsx^{(1.5MB, xlsx)}

Acknowledgments

The STARNET study was supported by the University of Tartu (SP1GVARENG (JLMB)), the Estonian Research Council (ETF grant #8853 (AR and JLMB)), the Astra-Zeneca Translational Science Centre-Karolinska Institutet (a joint research program in translational science, (JLMB)), Clinical Gene Networks AB (CGN) as an SME of the FP6/FP7 EU-funded integrated projectCVgenes@target (HEALTH-F2-2013-601456), the Leducq transatlantic networks; CAD Genomics (CG, EES and JLMB) and Sphingonet (CB), the Torsten and Ragnar Söderberg Foundation (CB), the Knut and Alice Wallenberg Foundation (CB), the American Heart Association (A14SFRN20840000, JK, EES and JLMB), the National Institutes of Health (NIH NHLBI, R01HL125863, JLMB; NIH NHLBI R01HL71207, EES; R01AG050986, Roussos; NIH NHLBI K23HL111339, CG; NIH NHLBI K08HL111330, JK) and the Veterans Affairs (Merit grant BX002395, PR). The DNA genotyping and RNA sequencing were in part performed by the SNP&SEQ technology platform at Science for Life, the National Genomics Infrastructure (NGI) in Uppsala and Stockholm supported by Swedish Research Council (VR-RF1), Knut and Alice Wallenberg Foundation and UPPMAX. CGN has financially contributed to the STARNET study. JLMB is the founder and chairman of CGN. JLMB, EES and AR are on the board of directors for CGN. JLMB, TM and AR own equity in CGN and receive financial compensation from CGN. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. The STARNET data is accessible through dbGAP.

Footnotes

Supplementary online materials

Material and MethodsFigures S1–S41 Tables S1–S11

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