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Research Article
Originally Published 15 March 2018
Free Access

Genetic Control of Left Atrial Gene Expression Yields Insights into the Genetic Susceptibility for Atrial Fibrillation

Circulation: Genomic and Precision Medicine

Abstract

Background:

Genome-wide association studies have identified 23 loci for atrial fibrillation (AF), but the mechanisms responsible for these associations, as well as the causal genes and genetic variants, remain undefined.

Methods:

To identify the effect of common genetic variants on gene expression that might explain the mechanisms linking genome-wide association loci with AF risk, we performed RNA sequencing of left atrial appendages from a biracial cohort of 265 subjects.

Results:

Combining gene expression data with genome-wide single nucleotide polymorphism data, we found that approximately two-thirds of the expressed genes were regulated in cis by common genetic variants at a false discovery rate of <0.05, defined as cis-expression quantitative trait loci. Twelve of 23 reported AF genome-wide association loci displayed genome-wide significant cis-expression quantitative trait loci, at PRRX1 (chromosome 1q24), SNRNP27 (1q24), CEP68 (2p14), FKBP7 (2q31), KCNN2 (5q22), FAM13B (5q31), CAV1 (7q31), ASAH1 (8p22), MYOZ1 (10q22), C11ORF45 (11q24), TBX5 (12q24), and SYNE2 (14q23), suggesting that altered expression of these genes plays a role in AF susceptibility. Allelic expression imbalance was used as an independent method to characterize the cis-control of gene expression. One thousand two hundred forty-eight of 5153 queried genes had cis-single nucleotide polymorphisms that significantly regulated allelic expression at a false discovery rate of <0.05.

Conclusions:

We provide a genome-wide catalog of the genetic control of gene expression in human left atrial appendage. These data can be used to confirm the relevance of genome-wide association loci and to direct future functional studies to identify the genes and genetic variants responsible for complex diseases such as AF.

Introduction

See Editorial by Roberts

Clinical Perspective

Atrial fibrillation (AF) is the most common arrhythmia, and it is associated with increased risk of stroke and death. Family studies as well as genome-wide association studies have shown that genetics play a strong role in susceptibility to AF. We performed RNA sequencing along with genome-wide genotyping on left atrial appendages from a biracial cohort of 265 subjects. This allowed us to identify common genetic variants associated with the expression of nearby genes in the left atrium. We combined our data with results from the most recent AF genome-wide association study, allowing us to suggest candidate causal genes and candidate causal genetic variants, with the hypothesis that most common genetic variants resulting in increased susceptibility to AF act via regulating the expression of nearby genes. Our studies pave the way for future functional studies to confirm the causal genes and genetic variants, which can yield novel insight into AF pathogenesis and suggest novel therapeutic strategies to prevent or treat this disease.
Atrial fibrillation (AF), the most common arrhythmia in the general population, is clearly heritable.1 Genome-wide association studies (GWAS) have identified 23 loci associated with AF,26 but the mechanisms for these associations and the causal genetic variants that underlie these associations have not yet been determined for any of these loci. The current study sought to identify the genes whose expression was altered by genetic variants associated with AF susceptibility and provide a genome-wide catalog of the genetic control of gene expression in human left atrial appendage (LAA) tissue. The central hypothesis is that the mechanisms by which these GWAS variants are associated with AF is that they regulate the expression of nearby genes and that this altered expression can lead to changes in cell physiology and anatomy, which may predispose to AF.
Throughout life, thousands of genes must be expressed in a coordinated and regulated fashion for appropriate cardiac development and maintenance of physiological homeostasis. Much of this regulation is mediated by cis regulatory elements, such as enhancers, silencers, and promoters that bind proteins, alter histone methylation and acetylation, and regulate gene transcription. This regulation has been highlighted by the GTEx study,7,8 where common genetic variants have been shown to be associated with the expression of nearby genes. These regulatory variants are called cis-expression quantitative trait loci (eQTLs) and have been identified across numerous human tissues. Some of these are tissue specific, whereas others function across many tissues. To increase our understanding of the AF GWAS loci, we studied mRNA gene expression to determine the genetic control of gene expression in a primary AF target tissue, the human left atrium (LA). We previously performed transcriptome profiling via polyA+RNA sequencing (RNAseq) in 4 paired human LAA and right atrial appendage (RAA) and found 746 genes, including many long noncoding RNAs, which were differentially expressed in these 2 chambers at a false discovery rate of <0.001.9
In the current study, we performed polyA+RNAseq on LAA tissue from 235 subjects of European descent and 30 subjects of African descent. We performed eQTL analyses to define common single nucleotide polymorphisms (SNPs) associated with LAA gene expression and found that ≈2/3 of the expressed genes had genome-wide significant cis-eQTLs associated with their expression. This included 12 of the 23 reported AF GWAS loci top SNPs, with reassignment of the target candidate genes in several of these loci. We provide a public database for lookup of LAA eQTLs as a resource for future studies. We also performed allelic expression imbalance (AEI) analyses, as an independent method to identify genetic variants associated with gene expression.

Methods

The raw RNAseq and genotyping will not be made available, but gene read counts are available in the Gene Expression Omnibus database (GSE69890). The statistical analytic methods are available on github.

Human Subjects

LAA tissues were collected from 251 patients who underwent elective cardiac surgery to treat AF, valve disease, or other cardiac disorders. AF history, type of AF, presence of structural heart disease, demographics, cardiac procedures, and other clinical data were collected in a research database. Atrial rhythm status was determined by review of electrocardiograms obtained before surgery. Fourteen additional LAA were obtained from nonfailing, unused transplant donors. All surgical patients provided informed consent for research use of discarded atrial tissue. Before 2008 verbal consent was obtained and documented in the medical records in a process approved by the Cleveland Clinic Institutional Review Board. From 2008 onward, patients provided separate Institutional Review Board approved written informed consent. Tissue was also obtained from donor hearts not used for transplant with written consent for research use provided by the family. The Institutional Review Board approved the studies included in this report.

Human LA Tissue Processing

Human LAA tissues were obtained from patients undergoing elective surgery to treat AF, valve disease, or other cardiac disorders. Specimens were snap frozen in liquid nitrogen and stored at −80°C until RNA extraction. LA tissue specimens were also obtained from nonfailing donor hearts not used for transplant. These hearts were perfused with cardioplegia before explant and processed in the same manner as hearts used for an organ transplant. As with the surgical specimens, donor tissue samples were snap frozen in liquid nitrogen and kept at −80°C until RNA extraction. Donor information was available for age, race, and sex.

Genomic DNA Isolation and SNP Microarray

DNA was extracted from 25 to 50 mg of LAA tissue. The tissue, in 1 mL of DNAzol (Invitrogen), was homogenized (PowerGen700, Fisher Scientific) with sterile Omni Tip Disposable Generator Probes (Omni International). DNA was isolated from the homogenate after the DNAzol protocol. The DNA pellet was resuspended in 20 µL of 10 mmol/L Tris buffer (pH 7.4), and the DNA concentration was optically evaluated (OD260 nm) diluted to 100 ng/µL and stored at −80°C until use. DNA was genotyped using Illumina Hap550v3 and Hap610-quad SNP microarrays. SNP data were imputed to 1000 Genomes Project phase 2 yielding ≈19 million SNPs, using IMPUTE10 after filtering out variants falling below 0.5 on IMPUTE’s information statistic. For the eQTL analysis, we excluded SNPs with <5% and <10% minor allele frequency in the European ancestry and African ancestry cohorts resulting in roughly 6.8 and 6.6 million SNPs per cohort, respectively.

RNA Isolation and Sequencing

Fifty to 100 mg of LAA tissue was used to extract RNA. The tissue, in 1 mL of TRIzol (Invitrogen), was homogenized with sterile Omni Tip Disposable Generator Probes. RNA was isolated from the homogenate after the TRIzol protocol. The RNA pellet was dried and resuspended in 80 µL of RNase-free water, with concentration determined by OD260 nm and RNA stored at −80°C. Library generation for RNAseq was done at the University of Chicago Genomics Facility using standard Illumina protocols (Part No. 15015050, Rev A). Samples were filtered based on RNA quality as ascertained on an Agilent 2100 Bioanalyzer. Unstranded 100-bp paired-end sequencing was performed on the Illumina HiSeq 2000 platform and multiplexed to 6 samples across 2 lanes. Samples were demultiplexed and aligned to hg19 using TopHat (v2.0.4)11 with the default options. Reads from exactly matched PCR duplicates were marked using Picard tools (https://broadinstitute.github.io/picard/) and excluded from further analysis. The sequence reads were mapped to the human genome to derive a digital count of the expression of genes, which were defined using the Ensembl (version 71) gene catalog.

Statistical Analyses

RNAseq and eQTL Analysis

Expression counts were obtained from aligned files using htseq12 counts against the human Ensembl gene annotation. On average, 26 million paired-end read fragments aligned to this annotation of the transcriptome across all of our samples. Reads were quantile normalized, and gene counts for eQTL analysis were variance-stabilized transformed using the R package Deseq2.13,14 Expression of each gene was adjusted by the following covariates: sex, genetic substructure based on 4 multidimensional scaling factors, and 25 expression surrogate variable analysis (SVA) covariates. The SVA method is similar to principal component analysis, which uses unsupervised mathematical models to separate out the high variance components in high dimensional data. Thus, without manual normalization, the SVA method corrects for potential large effectors of gene expression such as read-depth, batch effects, and other technical variables, as well as environmental and disease effects such as AF status, history of structural heart disease, coronary artery disease, etc. Surrogate variables were calculated from the variance-stabilizing transformation data using the sva package.15 eQTL analyses were performed using MatrixeQTL16 (2.1.0) to test associations between genotype and variance-stabilizing transformation counts. These analyses were performed separately for each racial group with β-coefficients calculated as the additive effect of 1 allelic difference on log2 gene expression. To correct for population substructure, genetic multidimensional scaling was derived from SNP array genotyping. The top 4 multidimensional scaling factors were used as covariates for the regression analyses performed for eQTL calculations. The QVALUE package was used to estimate false discovery rate from the complete list of cis-eQTL SNP/genome-wide expressed gene pairs P values.17 Linear regression and Q-Q plot comparison of the LAA eQTLs with selected tissues were performed using the version 6p analysis of GTEx project.8

Allele Expression Imbalance

AEI analysis was performed pooling the European and African descent subjects, using samtools18 and pysam packages, along with custom scripts (https://www.github.com/jeffhsu3/genda), which were used to obtain RNAseq allele-specific read counts at all genotyped SNPs residing in each Ensembl gene. These SNPs in the transcript are called indicator SNPs and were used to determine the allelic ratio expression balance by counting RNAseq reads containing each allele. Indicator SNPs were then filtered on read counts (>20 heterozygotes at the indicator) for robust analysis. Outlier subjects were removed if their log2 allelic ratio was >4.32, as these appeared to be because of indicator SNP genotyping error (subjects were, in fact, homozygous for the indicator SNP) or mapping errors. A nonparametric Mann–Whitney U test was used to test the association of the absolute value of the log2 allelic ratio between homozygous and heterozygous individuals at common cis-SNPs (>5% minor allele frequency with >10 homozygotes of either allele to accurately capture the cis-SNP effect on AEI).

Results

Study Participants

The demographics, relevant medical history, and surgical indications of the study population are shown in Table 1 and included 235 subjects with self-reported European ancestry and 30 self-reported African ancestry. Using imputed SNP array data, the genetic ancestry was examined by principal component analysis, revealing that the self-identified African ancestry samples were admixed or clustered closely with African reference samples, whereas the self-reported European ancestry samples clustered closely with European reference samples (Figure I in the Data Supplement).
Table 1. Study Demographics
Patient CharacteristicsTotal n=265European Descent n=235African Descent n=30P Value*
Age, median (IQR), y62.0 (53.0–69.0)62.0 (54.5–69.5)56.5 (50.2–68.8)0.24
Sex, female, n (%)84 (31)63 (26)21 (70)0.88
BMI, median (IQR), kg/m227.2 (24.5–30.3)27.2 (24.5–30.3)26.2 (23.0–31.6)0.63
Diabetes mellitus, n (%)35 (13)28 (12)7 (24)0.07
History of CAD, n (%)160 (63)143 (64)17 (58)0.69
History of MVD, n (%)136 (54)114 (51)22 (75)0.02
Hypertension, n (%)137 (54)115 (51)22 (78)0.01
History of AF, n (%)213 (84)192 (86)21 (72)0.09
AF rhythm at surgery, n (%)130 (51)118 (53)12 (41)0.13
Surgical indication
 Donor, n (%)14 (5)13 (5)1 (3)1.00
 Valve, n (%)153 (57)129 (54)24 (80)0.01
 CABG, n (%)106 (40)95 (40)11 (36%)0.84
 Non-CABG nonvalve, n (%)52 (19)51 (21)22 (73)<0.01
AF indicates atrial fibrillation; BMI, body mass index; CABG, coronary artery bypass graft; CAD, coronary artery disease; IQR, interquartile range; and MVD, mitral valve disease.
*
Statistical significance between patients of European descent and African descent, either by t test, for quantitative traits, or χ2, for categorical traits.
Not including 1 African descent and 13 European descent donors, for which only age and sex were known.
Patients could have an indication for both valve and CABG surgeries.

Genotype Effects on Gene Expression

The overall design of our analysis of the genetics of gene expression is shown in Figure II in the Data Supplement. We performed polyA+selected RNAseq on LAA RNA to obtain expressed gene counts corresponding to the ≈65 000 protein-coding and noncoding genes contained in Ensembl gene annotation release 71. The average library size-adjusted reads for each gene for each sample gives a U-shaped frequency distribution (Figure III in the Data Supplement). Our definition of genes expressed in the LAA is those with a minimum of 1000 variance-stabilized counts summed from all 235 European descent samples which yielded 24 042 genes.
We examined the genetic effects on gene expression in LAAs in separate analyses of the European and African descent cohorts. Twenty-five SVAs were determined to be optimal for eQTL identification, and these were used as covariates in our eQTL analysis. We used a±250 Kb window (500 Kb total) around the most distal 5′ transcription start site, as annotated by Ensembl, as our definition of a cis effect. We found 15 906 genes, representing 66% of the 24 042 expressed genes, including numerous long noncoding RNAs, with at least 1 cis-eQTL that met our 0.05 Q-value threshold. Table I in the Data Supplement shows the top eQTL SNP for the 15 906 significant genes in European descent subjects and the associated eQTL Q value of the same SNP in Blacks. Only 713 significant cis-eQTLs (Q<0.05) were found in the African descent subjects because of the small sample size, and a different SNP was often the most significant in these subjects versus that of the European descent subjects (Table II in the Data Supplement). Nonetheless, there was a strong correlation (r=0.76, P=4.13E-278) of the effect size and direction between a set of 1464 cis-eQTLs in African descent subjects (meeting a more liberal Q<0.5 threshold because of the small sample size) and cis-eQTLs in European descent subjects, using the top cis-eQTL SNP for each gene (Q<0.05) defined in European descent subjects (Figure 1). All significant SNP-gene pair cis-eQTL associations in the European descent subjects can be searched using our publicly available web application (http://afeqtls.lerner.ccf.org), which also displays the eQTL association values for the African ancestry cohort.
Figure 1. cis-expression quantitative trait loci (eQTLs) replication between ethnic groups. The single nucleotide polymorphism (SNP) with the most significant cis-eQTL (Q≤0.05) in European descent subjects for a given gene was filtered in the African descent subjects with a threshold of Q<0.5, yielding a set of 1464 SNP-gene pairs in both populations. The eQTL β-coefficients’ effect on target gene expression for that SNP for both the European-American (x axis) and Black (y axis) subjects is plotted (r=0.76, P=4.13E-278).

GTEx Multiple Tissue Comparison

We examined the cross-tissue replication of our 15 906 LAA eQTLs (SNP-gene pairs) by comparison against eQTL data from 6 tissues in the GTEx project7,8 including 2 heart tissues, the left ventricle, and the RAA. There was a remarkable conservation of the LAA eQTLs with the 6 other tissues, as the effect and direction of the cis-SNPs on gene expression (β-coefficients) were highly correlated with r2 values ranging from 0.39 (whole blood) to 0.67 (RAA; Figure IV in the Data Supplement). We found 586 LAA cis-eQTLs that were found not identified in left ventricle and RAA tissues (Table III in the Data Supplement). The 23 AF GWAS SNPs identified in Table 2 were used to determine whether the LAA eQTLs were stronger than those found in other tissues. Q-Q plots were generated against a uniform distribution. When compared with 3 GTEx tissues (blood, left ventricle, and RAA), the LAA eQTLs showed greater significance, indicating that the LAA AF GWAS eQTL set is the most relevant for AF (Figure V in the Data Supplement).
Table 2. eQTLs and AEI in AF GWAS Loci
AF GWAS LocusNominal Gene AttributionAF GWAS SNP*Top eQTL GeneTop eQTL SymboleQTL βeQTL P ValueeQTL Q ValueGWAS SNP Evidence AEI (P Value)AEI Indicator SNP
1q21.3KCNN3, PMVKrs6666258ENSG00000173207CKS1B−0.054.53E-035.87E-02NA§NA
1q24.2METTL11Brs12044963ENSG00000231437RP11-88H9.20.171.51E-027.36E-01NANA
1q24.2PRRX1rs3903239ENSG00000116132PRRX1−0.162.86E-059.33E-04NANA
2p13.3ANXA4, GMCL1rs3771537ENSG00000124380SNRNP27−0.056.22E-063.11E-04NANA
2p14CEP68rs2540953ENSG00000011523CEP68−0.104.98E-137.13E-11NANA
2q31.2TTN, TTN-AS1rs2288327ENSG00000079150FKBP70.102.40E-061.33E-04NANA
3p25.1CAND2rs4642101ENSG00000144712CAND2−0.048.10E-038.85E-02NANA
4q25PITX2rs6817105ENSG00000250103PANCR0.027.44E-017.76E-01NANA
5q22.3KCNN2rs337711ENSG00000080709KCNN2−0.081.55E-033.35E-02NANA
5q31.2WNT8Ars2040862ENSG00000031003FAM13B−0.246.91E-304.83E-272.10E-05rs3777118
6q22.31SLC35F1, PLNrs4946333ENSG00000111860CEP85L−0.047.70E-024.19E-01NANA
6q22.31GJA1rs12664873ENSG00000152661GJA1−0.071.62E-02NDNANA
7q31.2CAV1rs3807989ENSG00000105974CAV1−0.101.55E-078.96E-061.98E-9rs1049337
8p22ASAH1, PCM1rs7508ENSG00000104763ASAH10.081.69E-071.22E-055.60E-08rs3810
9q22.32C9ORF3rs10821415ENSG00000158169FANCC0.041.84E-021.54E-01NANA
10q22.2SYNPO2Lrs10824026ENSG00000177791MYOZ1−1.872.90E-383.65E-35NANA
10q24.33NEURL1rs6584555ENSG00000173915USMG50.034.13E-022.52E-01NANA
10q24.33SH3PXD2Ars2047036ENSG00000107957SH3PXD2A−0.013.80E-016.53E-01NANA
11q24KCNJ5rs75190942ENSG00000174370C11orf450.291.59E-069.20E-05NANA
12q24.21TBX5rs10507248ENSG00000089225TBX50.074.87E-041.03E-02NANA
14q23.2SYNE2rs1152591ENSG00000054654SYNE2−0.172.31E-196.73E-173.02E-09rs35648226
15q24.1HCN4rs7164883ENSG00000183324REC114−0.156.71E-023.27E-01NANA
16q22.3ZFHX3rs2106261ENSG00000259768Antisense-ZFHX3−0.091.57E-014.80E-01NANA
AEI indicates allelic expression imbalance; AF, atrial fibrillation; eQTL, expression quantitative trait loci; GWAS, genome-wide association studies; and SNP, single nucleotide polymorphism.
*
AF GWAS SNPs.3,6
A positive β indicates higher expression with more dosages of the AF risk allele.
Meets our P value and Q value significance threshold of <0.05
§
Not enough heterozygous individuals at any indicator SNP in the transcript to perform AEI analysis.
GWAS SNP is 706 Kb from GJA1.

Trans-eQTL Analysis

We identified 58 trans-eQTLs in the subjects of European descent using the conservative definition of the trans-SNP and its regulated transcript being on different chromosomes. We used a stringent Bonferroni adjusted P-value threshold of <1E-13 (Table IV in the Data Supplement) as trans-eQTLs show modest to low replication between studies.19,20 We examined the effect size of these 58 trans-eQTLs in the Black subjects, and there was significant correlation (Table IV in the Data Supplement; Figure VI in the Data Supplement; r=0.35, P=1.91E-3), indicating replication for most of these trans-eQTLs, with several outliers.

cis-eQTLs at AF GWAS Loci to Identify Candidate Causal Variants

Among the 23 top independent AF GWAS SNPs (Table 2), we found 12 SNPs that had ≥1 genome-wide significant cis-eQTLs (Q<0.05, using all eQTL SNP-gene pairs in 500 Kb intervals that overlap with the 23 GWAS SNPs). Another 6 AF GWAS SNPs had cis-eQTLs that met the more liberal threshold of P<0.05. RNAseq confirmed our prior finding that the AF-associated SNPs at chr 4q25 are not associated with LAA expression of PITX2c.21 The top eQTL gene in each GWAS locus was not always the nominally attributed closest candidate gene. For example, at the chr. 5q31 locus the most significant eQTL was for FAM13B, whereas this locus had previously been putatively associated with WNT8A. Similarly, the chr. 10q22 locus was most strongly associated with MYOZ1, whereas SYNPO2L was the closest gene. Table V in the Data Supplement provides a complete list of all genes in each GWAS locus with their eQTLs values for the corresponding GWAS SNP. Table V in the Data Supplement additionally provides the top cis-eQTL SNP for each gene in these GWAS loci. This analysis demonstrates that the GWAS SNP can be a significant cis-eQTL for ≥2 genes. For example, the chr. 10q22 AF GWAS SNP has genome-wide significant cis-eQTLs for both SYNPO2L and MYOZ1. We found that the GWAS SNP was often not the strongest cis-eQTL SNP. For example, in the chr. 11q24 locus a significant eQTL for C11ORF45 was found, but the strongest eQTL SNP (rs4937390) was not the GWAS SNP (rs75190942) and was not in strong linkage disequilibrium (LD) with the GWAS SNP (r2=0.21).
The most significant AF GWAS SNP cis-eQTL was for rs10824026 on chr. 10q22 for MYOZ1 (Q=3.65E-35; Table 2; Figure 2), as previously reported in a microarray study of 53 LA samples.22 In addition, the SYNPO2L gene has the second strongest eQTL at this locus (Q=1.63E-10; Figure 2B; Table V in the Data Supplement). As shown in Figure 2, there are additional SNPs in LD with rs10824026 that have stronger cis-eQTLs than the GWAS SNP rs10824026, indicating that the AF GWAS SNP may not be the causal SNP at this locus. The AF risk allele of rs10824026 was associated with decreased expression of the MYOZ1 gene but increased expression of the SYNPO2L gene (Figure 2), indicating that altered expression of either or both of these genes might contribute to AF susceptibility.
Figure 2. MYOZ1 and SYNPO2L cis-expression quantitative trait loci (eQTLs) at the chr 10q22 atrial fibrillation (AF) risk locus. A, Partial regression plot showing the effect of the AF genome-wide association studies (GWAS) single nucleotide polymorphism (SNP) rs10824026 on the left atrial (LA) expression of the MYOZ1 gene in European descent subjects after adjusting for genetic multidimensional scaling (MDS), transcription surrogate variable analyses (SVAs), and gender. Blue dots are subjects homozygous for the minor allele, green dots are heterozygotes, and red dots are homozygous for the major (risk) allele (r2=0.68, Q value=3.65E-35). B, eQTL P values and linkage disequilibrium (LD) relationship (color scale) with rs10824026 in the 0.5 Mb region around the MYOZ1 transcription start site (TSS) in European subjects, with the grey bar indicating the gene boundary. C, Effect of rs10824026 on the LA expression of the SYNPO2L gene in European subjects (r2=0.76, Q value=8.31E-11). D, eQTL P values and LD relationship with rs10824026 in the 0.5 Mb region around the SYNPO2L TSS European subjects.
The second strongest AF GWAS SNP cis-eQTL (Table 2) was found at chr. 5q31 for the FAM13B gene (Q=4.83E-27; Figure 3A and 3B). Although this GWAS SNP was nominally attributed by proximity to the WNT8A gene, WNT8A is not expressed in our LA samples and our eQTL analysis suggests that the gene at this locus responsible for AF susceptibility is FAM13B, encoding a poorly characterized member of the RhoGAP (Rho GTPase activation protein) domain gene family. The AF risk allele is associated with lower LAA FAM13B expression. The cis-eQTL plot for LA FAM13B expression in European descent subjects shows many equally significant eQTL SNPs in the same LD block stretching over ≈150 Kb, including the AF GWAS SNP rs2040862 (Figure 3B).
Figure 3. cis-expression quantitative trait loci (eQTLs) for genome-wide association studies (GWAS) genes. A, C, E, G, I, K, Partial regression plot showing the effect of the atrial fibrillation (AF) GWAS single nucleotide polymorphisms (SNPs) rs2040862, rs1152591, rs2540953, rs3807989, rs3903239, and rs10507248 on the left atrial (LA) expression of the FAM13B, SYNE2, CEP68, CAV1, PRRX1, and TBX5 genes in European descent subjects after adjusting for genetic multidimensional scaling (MDS), transcription surrogate variable analyses (SVAs), and gender. Blue dots are subjects homozygous for the reference allele (GRCh37 build), green dots are heterozygotes, and red dots are homozygous for the variant allele. B, D, F, H, J, L, eQTL P values and linkage disequilibrium (LD) relationship (color scale) with the GWAS SNP (arrow) in the 0.5 Mb region around the transcription start site (TSS) of the FAM13B, SYNE2, CEP68, CAV1, PRRX1, and TBX5 genes in European subjects.
The third strongest GWAS SNP eQTL was for the SYNE2 gene (Q=6.72E-17; Figure 3C and 3D). SYNE2 encodes the nuclear envelope protein nesprin 2, which links the nuclear envelope to the cytoskeleton and controls subcellular nuclear localization.23,24 The GWAS SNP (rs1152591) risk allele is also associated with lower LAA expression of SYNE2.
GWAS SNPs at 9 other loci were also significantly associated in cis with the expression of PRRX1, SNRNP27, CEP68, FKBP7, KCNN2, CAV1, AHAH1, C11ORF45, and TBX5 (Figure 3E through 3L; Figure VII in the Data Supplement). There were 6 additional GWAS SNPs with cis-eQTLs that met the more liberal P<0.05 threshold; these SNPs were associated with the expression of CKS1B, RP11-88H9.2, CAND2, GJA1, FANCC, and USMG5 (Table 2; Table V in the Data Supplement).

Allelic Expression Imbalance

We examined the effects of genetic variation on gene expression using AEI, an analysis method that eliminates the most common sources of variation among our human LA samples. In this method, we count expression of the 2 alleles in the RNAseq data within each individual that is heterozygous for an exonic indicator SNP. These indicator SNPs were identified by DNA SNP array genotyping. Combining our subjects of European and African descent, we examined all expressed genes with at least 20 subjects heterozygous for ≥1 indicator SNPs. To find the top cis-SNP associated with AEI, which we call the cis-AEI SNP, we used a nonparametric 2-tailed t test, as previously described,25 to calculate the P value for association of each cis-SNP within ±250 Kb of the transcription start site with AEI for each indicator SNP. For example, the SYNE2 transcript contains the indicator SNP rs35648226 that was heterozygous in 116 subjects, and we ranked the log2 expression of the minor/major allele counts for each of these subjects (Figure 4A). Each bar represents 1 subject that was color-coded as homozygous (green) or heterozygous (orange) for the cis-AEI SNP rs2738413, which is in strong LD with the SYNE2 locus AF GWAS SNP rs1152591. The top cis-AEI SNP, rs2738413 (P=3.02E-9), and all other SNPs in this locus are shown in an AEI Manhattan plot (Figure 4B). It is evident that the cis-AEI SNP was not in perfect LD with the indicator SNP (LD between indicator and cis-AEI SNP r2=0.39 in our data set), as heterozygotes for the cis-AEI SNP were found at both ends of the allelic ratio plot (Figure 4A). We compared the Manhattan plots for the cis-AEI and cis-eQTLs in this locus (Figure 4B and 4C) and found excellent concordance, although a different variant (chr14:64673560:D) in strong LD with rs1152591 had the strongest eQTL.
Figure 4. Allelic expression imbalance and cis-expression quantitative trait loci (eQTLs) for the SYNE2 gene. A, The log2 allelic expression ratio is plotted in ascending order for the 116 subjects who were heterozygous for the exonic indicator single nucleotide polymorphism (SNP) rs35648226. The green and orange bars represent subjects homozygous and heterozygous for the top cis-AEI SNP rs2738413, respectively. B, cis-allelic expression imbalance (AEI) SNP Manhattan plot of the –log10 P values for the association of each cis-SNP in this locus with AEI, revealing that the genome-wide association studies (GWAS) SNP rs1152591 was not the top cis-AEI SNP. Linkage disequilibrium (LD) with the top cis-AEI SNP rs2738413 is indicated by the color scale and the gray shading shows the SYNE2 gene boundaries. C, cis-eQTL Manhattan plot of the –log10 P values for the association of each cis-SNP in this locus with SYNE2 expression, showing that there is a different top eQTL SNP, but this SNP is in high LD with both the cis-AEI and GWAS SNPs.
Global AEI analysis was performed for 5153 genes that met our genotype and expression criteria. We found an AEI association for 1248 genes (24% of queried genes) using a Q-value threshold of 0.05 (Table VI in the Data Supplement). The major reason that only a fraction of the analyzed genes had significant AEI was that for many of these genes the number of subjects heterozygous for the indicator SNP was underpowered. In many cases, the top AEI SNP differed from the top eQTL SNP. AEI analysis may provide insight into the functional SNP, as the AEI method controls for expression within each subject and eliminates many variables in the eQTL analysis. AEI within the AF-GWAS loci is shown in Table V in the Data Supplement.

Discussion

We found that most of the LAA-expressed genes (66% in the European descent subjects) were controlled by nearby common genetic variants (cis-eQTLs). This is concordant with other large human transcriptomic studies, as ≈69% of the genes expressed in whole blood had cis-eQTLs in 5626 subjects.26 Thus, common variants have large cis-effects on global gene expression and robust eQTL identification is detectable for sample sizes in the low hundreds.
GWAS studies have been invaluable in identifying common genetic variants associated with complex diseases such as AF. However, it is still an arduous task to identify causal SNPs and the mechanisms by which they alter disease susceptibility, particularly when the disease-associated genetic variants are intergenic. Here, we use an intermediate phenotype, gene expression, to identify potential causal genetic variants, based on the hypothesis that these SNPs work by regulating gene expression. To gain deeper insight into the functional role of the AF GWAS SNPs, we evaluated their roles as regulatory SNPs by ascertaining their effects on the expression of nearby genes via RNAseq of a large biracial set of human LAAs.
Overall, genome-wide significant cis-eQTLs were discovered for 12 of the 23 AF GWAS SNPs, and another 6 GWAS SNPs displayed uncorrected cis-eQTL P values<0.05. One reason for the incomplete representation of genome-wide cis-eQTLs for the GWAS SNPs is that the remaining SNPs may control gene expression at a different time (during heart development or maturation) or place (eg, LA pulmonary veins). This may explain the absence of an identifiable eQTL at the chr. 4q25 locus for PITX2c in adult LA tissue, despite this locus harboring the strongest AF-GWAS SNP.21
The AF-GWAS SNP with the strongest eQTL was rs10824026, associated with expression of both the MYOZ1 and SYNPO2L gene, although in opposite directions. Although a previously reported study identified rs3740293, an SNP in LD rs10824026, as a cis-eQTL for MYOZ1 in LA and RA tissue, it did not identify the SNP as an eQTL for SYNPO2L.22 MYOZ1 encodes the myozenin-1 protein, which interacts with calcineurin and colocalizes with the Z-disc protein α-actinin. SYNPO2L encodes synaptopodin 2-like protein, initially described in cardiomyocytes derived from pluripotent stem cells; this protein is also highly expressed in the Z-disc and, like myozenin-1, it interacts with α-actinin. Also called CHAP (cytoskeletal heart-enriched actin-associated protein), its knockdown in zebrafish leads to aberrant heart and skeletal muscle development, disorganized sarcomeres, and diminished cardiac contractility.27 The inverse regulation of the expression of these 2 adjacent genes suggests that the ratio of the encoded proteins that share a similar subcellular localization may play a role in AF susceptibility.
The second strongest GWAS SNP eQTL was for FAM13B, at a locus previously attributed to WNT8A. At this locus, the minor allele of rs2040862 is the AF risk allele, and this allele is associated with decreased expression of FAM13B. The FAM13B gene encodes an uncharacterized protein containing a RhoGAP. A different SNP at chr. 5q31 (rs1004989) that was previously associated with the electrocardiographic QT-interval was found to have a significant eQTL for FAM13B expression in left ventricle tissue samples28; however, rs1004989 is not in LD with the AF GWAS SNP (rs2040862) or the LA FAM13B top eQTL SNP (rs17171731), perhaps illuminating tissue-specific regulatory variants controlling FAM13B gene expression.
Recently, an AF candidate gene and GWAS region SNP eQTL study was reported using 122 RAA samples.29 cis-eQTLs were identified for PITX2a in RAA, but the LAA-expressed PITX2c transcript isoform was not expressed in RAA. We found no comparable cis-eQTL for PITX2c in LAA tissue. Additional RAA cis-eQTLs were identified for CAV1, MOYZ1, C9ORF3, and FANCC,29 genes for which we also identified LAA cis-eQTLs.
Limitations of our study include statistical power; thus, the absence of an eQTL does not guarantee that there is no genetic control of gene expression at any given locus.30 We also cannot detect the effects of rare coding mutations. Exome sequencing studies have recently found short truncating mutations at the 10q22 AF locus in SYNPOL2 that may potentially play a role in AF.31 In addition, gene expression is most likely controlled by multiple variants, both local and distant. Our combination of eQTL and AEI analysis may, in some cases, help to identify causal candidate SNPs that are stronger than the top AF GWAS SNPs, which can then be tested in functional genomic studies. The effect of heart explanting on gene expression is unknown, but this covariate and all other phenotypic and environmental effects on gene expression are potentially corrected for by our SVA method. Another limitation is that our study was performed on LAA, often surgically removed to reduce stroke risk; although, other regions such as those in and nearer the pulmonary vein may be more relevant to AF pathogenesis. Finally, our study provides a valuable resource to the community with an LA-specific eQTL browser, which may be particularly useful for genetic studies of AF, and more broadly for the identification of functional SNPs for other cardiovascular traits and diseases.
Data access: for variance normalized gene expression levels for each subject are available in the GEO database, accession number GSE69890.

Supplemental Material

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Go to Circulation: Genomic and Precision Medicine
Circulation: Genomic and Precision Medicine
PubMed: 29545482

History

Received: 7 March 2017
Accepted: 23 January 2018
Published in print: March 2018
Published online: 15 March 2018

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Keywords

  1. alleles
  2. atrial appendage
  3. atrial fibrillation
  4. genomics
  5. quantitative trait loci

Subjects

Authors

Affiliations

Jeffrey Hsu, PhD
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
Shamone Gore-Panter, PhD
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
Gregory Tchou, PhD
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
Laurie Castel, BS
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
Beth Lovano, BS
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
Christine S. Moravec, PhD
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
Gosta B. Pettersson, MD, PhD
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
Eric E. Roselli, MD
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
A. Marc Gillinov, MD
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
Kenneth R. McCurry, MD
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
Nicholas G. Smedira, MD
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
John Barnard, PhD*
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
David R. Van Wagoner, PhD*
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
Mina K. Chung, MD*
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.
Jonathan D. Smith, PhD*
From the Departments of Cellular and Molecular Medicine (J.H., G.T., J.D.S.), Quantitative Health Sciences (J.B.), Molecular Cardiology (S.G.-P., L.C., B.L., C.S.M., D.R.V.W., M.K.C.), Cardiovascular Medicine (C.S.M., D.R.V.W., M.K.C., J.D.S.), and Cardiothoracic Surgery (G.B.P., E.E.R., A.M.G., K.R.M., N.G.S.), Cleveland Clinic, Cleveland, OH.

Notes

*
Drs Barnard, Van Wagoner, Chung, and Smith contributed equally to this work.
Circ Genom Precis Med is available at http://circgenetics.ahajournals.org.
Correspondence to: Jonathan D. Smith, PhD, Department of Cellular and Molecular Medicine, Cleveland Clinic, Box NC10, 9500 Euclid Ave, Cleveland, OH 44195. E-mail [email protected]

Disclosures

None.

Sources of Funding

This study was supported by the National Institutes of Health (NIH) grants R01 HL 090620 and R01 HL 111314 to Drs Chung, Wagoner, Barnard, and Smith, the NIH National Center for Research Resources for Case Western Reserve University and Cleveland Clinic Clinical and Translational Science Award UL1-RR024989, the Cleveland Clinic Department of Cardiovascular Medicine philanthropy research funds, and the Tomsich Atrial Fibrillation Research Fund. Dr Hsu was supported by the NIH training grant T32 GM 088088. Dr Smith was supported by the Geoffrey Gund Endowed Chair for Cardiovascular Research.

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  1. Genetic basis and causal relationship between atrial fibrillation and sinus node dysfunction: Evidence from comprehensive genetic analysis, International Journal of Cardiology, 418, (132609), (2025).https://doi.org/10.1016/j.ijcard.2024.132609
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  3. Comprehensive mendelian randomization reveals atrial fibrillation-breast cancer relationship and explores common druggable targets, Frontiers in Pharmacology, 15, (2024).https://doi.org/10.3389/fphar.2024.1435545
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  8. Pathogenetics of Cardiomyopathy, Генетика, 59, 6, (615-632), (2023).https://doi.org/10.31857/S0016675823050107
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  10. Connecting the Dots From GWAS to Function in Atrial Fibrillation for ZFHX3, Circulation Research, 133, 4, (330-332), (2023)./doi/10.1161/CIRCRESAHA.123.323281
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Genetic Control of Left Atrial Gene Expression Yields Insights into the Genetic Susceptibility for Atrial Fibrillation
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