Abstract

   Objective: Genome-wide association studies (GWAS) and the candidate
   gene based association studies have identified a panel of variants
   associated with atrial fibrillation (AF), however, most of the
   identified single nucleotide polymorphisms (SNPs) were found located
   within intergenic or intronic genomic regions, and whether the positive
   SNPs have a real biological function is unknown, and the real disease
   causing gene need to be studied.

   Results: The current results of the genetic studies including common
   variants identified by GWAS (338 index SNPs) and candidate gene based
   association studies (40 SNPs) were summarized.

   Conclusion: Our study suggests the relationship between genetic
   variants and possible targeted genes, and provides insight into
   potential genetic pathways underlying AF incidence and development. The
   results may provide an encyclopedia of AF susceptibility SNPs and shed
   light on the functional mechanisms of AF variants identified through
   genetic studies.

   Methods: We summarized AF susceptibility SNPs identified by GWAS and
   candidate gene based association studies, and give a comprehensive
   functional annotation of all these AF susceptibility loci. by genomic
   annotation, microRNA binding prediction, promoter activity analysis,
   enhancer activity analysis, transcription factors binding activity
   prediction, expression quantitative trait loci (eQTL) analysis,
   long-range transcriptional regulatory function analysis, gene ontology
   and pathway enrichment analysis.

   Keywords: atrial fibrillation, SNP, annotation, genetics, non-coding

INTRODUCTION

   Atrial fibrillation (AF), which is characterized by rapid and irregular
   beating of the atria, and known as the most common type of cardiac
   arrhythmia. According to the epidemiologic data, the prevalence of AF
   ranged from 0.7% to 1% in the general population, and up to 8% in
   elders greater than 80 years [[34]1, [35]2]. Meanwhile, AF increases
   the risk of stroke, congestive heart failure, sudden cardiac death, and
   increase the rate of substantial morbidity and mortality for about 2
   folds [[36]3].

   AF is often associated with complications such as hypertension,
   valvular heart disease, coronary artery disease, heart failure,
   hyperthyroidism, structural heart diseases, and is also clearly
   heritable [[37]4, [38]5]. The important role of genetic factors in the
   pathogenesis of AF has shown by the identification of AF-causing
   mutations or rare variants in some families with lone AF, which occurs
   in structurally normal hearts and without known risk factors
   [[39]6–[40]8]. Meanwhile, in general AF, the non-hypothesis-driven
   genome-wide association studies (GWAS) and the candidate gene based
   association studies have identified a panel of common variants confer
   risk to AF [[41]9–[42]11]. These studies have set up a key role for the
   genetic background in generating for AF.

   GWAS investigate associations between genomic variants and a disease or
   trait at the whole genome level without priori assumptions of genomic
   locations or potential functions of candidate genes. In this case, most
   of the identified single nucleotide polymorphisms (SNPs) associated
   with disease were found located within intergenic or intronic genomic
   regions, and whether the positive SNPs have a real biological function
   is unknown, and the real target gene need to be further studied
   [[43]12, [44]13]. For example, SNP rs2200733 on chromosome 4p25 is the
   first risk variant for AF identified by GWAS and is the most robustly
   replicated AF locus to date. The gene that closest proximity to
   rs2200733 and other AF susceptibility variants in 4q25 is the PITX2.
   Studies in mice showed that pitx2 haplo-insufficiency promotes an
   atrial arrhythmia [[45]14]. However, functional evidence about the
   mechanisms linking these non-coding variants with PITX2 or the
   incidence of AF is limited, until a recent study found that these
   non-coding variants in 4q25 possessing a long-range enhancer–promoter
   interactions and exert as a transcriptional regulatory directed
   function at PITX2 [[46]15]. Understanding the biological nature of
   non-coding variants associated with AF can enable us to point the real
   causal genes causing AF and provide insight into the mechanism of AF.

   Considering one of the most important challenges of AF genetic study is
   to elucidate functional mechanisms that how the susceptibility loci
   modulate AF risk, in the current study, we summarized the results of
   the studies including variants identified by GWAS and candidate gene
   based association studies, and give a comprehensive functional
   annotation of all these AF susceptibility loci. The non-synonymous SNPs
   were first identified and classified as functional SNPs, and for SNPs
   in the non-coding region, we try to predict their potential functions
   including microRNA binding, promoter activity, enhancer activity,
   transcription factors binding activity, expression quantitative trait
   loci (eQTL), and long-range transcriptional regulatory function. Our
   results may provide an encyclopedia of AF susceptibility SNPs and shed
   light on the functional mechanisms of AF variants identified through
   genetic studies.

RESULTS

AF susceptibility loci

   Through searching the public databases including GWAS catalog
   ([47]https://www.ebi.ac.uk/gwas/), GWAS central
   ([48]https://www.gwascentral.org), and literatures in Pubmed, Embase
   and Medline, we included 18 AF GWAS and exome-wide association study
   (EWAS) in our study, which published from 2007 to 2019 ([49]Table 1).
   The workflow of the current study is shown in [50]Figure 1.
   Participants of these studies were mainly European ancestry (15 of 18
   studies), and the rest were East Asian (Korean ancestry and Japanese)
   ([51]Table 1). A total of 338 SNPs (refer as index SNPs) passed the
   multiple corrections (P<5×10^-8 or corresponding multiple correction
   threshold) and showed a significant association with AF in GWAS and
   EWAS ([52]Figure 2 and [53]Supplementary Table 1). We also include 40
   common SNPs which showed significant associated with AF in case control
   or population prospective study in candidate gene based analysis, or
   replication study of GWAS loci ([54]Figure 2 and [55]Supplementary
   Table 1). Totally, we included 378 AF susceptibility SNPs in our
   further functional annotation.

Table 1. Included studies with SNPs associated with AF from 2007 to 2019.

   Number Studies Year Discovery population Replication population
   enome-wide association study
   1 Nielsen JB. et al [[56]16] 2018 60,620 European ancestry cases and
   970,216 European ancestry controls NA
   2 Roselli C. et al [[57]17] 2018 55,114 European ancestry cases and
   482,295 European ancestry controls, 8,180 Japanese ancestry cases and
   28,612 Japanese ancestry controls, 1,307 African American ancestry
   cases and 7,660 African American ancestry controls, 845 Hispanic cases
   and 4,177 Hispanic controls NA
   3 Nielsen JB. et al [[58]18] 2018 6,337 European ancestry cases and
   61,607 European ancestry controls 30,679 European ancestry cases and
   278,895 European ancestry controls
   4 Thorolfsdottir RB. et al [[59]19] 2018 14,710 cases and 373,897
   controls from Iceland, 14,792 cases and 393,863 controls from the UK
   Biobank 9,204 cases and 76,161 controls, European ancestry
   5 Lee JY. et al [[60]20] 2017 672 Korean ancestry cases and 3,700
   Korean ancestry controls 200 Korean ancestry cases and 1,812 Korean
   ancestry controls
   6 Christophersen IE. et al [[61]21] 2017 GWAS: 18,398 individuals with
   atrial fibrillation and 91,536 referents, EWAS: 22,806 AF cases and
   132,612 referents. NA
   7 Low SK. et al [[62]22] 2017 8,180 Japanese ancestry cases and 28,612
   Japanese ancestry controls 3,120 Japanese ancestry cases and 125,064
   Japanese ancestry controls, 15,993 European ancestry cases and113,719
   European ancestry controls
   8 Thorolfsdottir RB. et al [[63]23] 2017 14,255 AF cases and 374,939
   controls, Iceland 2,002 non-Icelandic cases and 12,324 controls
   9 Yamada Y. et al [[64]24] 2017 884 patients with atrial fibrillation
   and 12,282 controls, Japanese NA
   10 Lubitz SA. et al [[65]25] 2016 1,734 individuals with and 9,423
   without AF, European ancestry NA
   11 Kertai MD. et al [[66]26] 2015 620 European ancestry cases, 257
   European ancestry controls 220 cases and 84 controls
   12 Sinner MF. et al [[67]27] 2014 6,707 AF cases and 52,426 controls in
   Europeans, 843 AF and 3,350 controls in Japanese 6,691 AF cases and
   17,144 controls in Europeans, 1,618 AF cases and 17,190 controls
   13 Ellinor PT. et al [[68]28] 2012 6,707 European ancestry cases and
   52,426 European ancestry controls 5,381 European ancestry cases and
   10,030 European ancestry controls
   14 Ellinor PT. et al [[69]29] 2010 1,335 European ancestry lone AF
   cases and 12,844 European ancestry controls 1,164 European ancestry AF
   cases, 3,607 European ancestry controls
   15 Gudbjartsson DF. et al [[70]30] 2009 2,385 European ancestry cases
   and 33,752 European ancestry controls 2,427 European ancestry cases and
   3,379 European ancestry controls, 286 Han Chinese ancestry cases and
   2,763 Han Chinese ancestry controls
   16 Benjamin EJ. et al [[71]31] 2009 3,413 cases and 37,105 referents,
   European ancestry 2,145 cases and 4,073 controls, European ancestry
   17 Larson MG. et al [[72]32] 2007 151 cases and 1,190 controls from 310
   families NA
   18 Gudbjartsson DF. et al [[73]33] 2007 550 European ancestry cases and
   4,476 European ancestry controls 3,030 European ancestry cases and
   14,780 European ancestry controls, 333 Han Chinese ancestry cases and
   2,836 Han Chinese ancestry controls
   Candidate gene based association study
   19 Cao H. et al [[74]34] 2019 828 patients and 834 controls in Chinese
   population NA
   20 Xiong H. et al [[75]35] 2019 944 AF patients and 981 non-AF controls
   in Chinese population 732 cases and 1,291 controls in Chinese
   population
   21 Wang P. et al [[76]36] 2018 1,164 AF patients and 1,460 controls NA
   21 Zaw KTT. et al [[77]37] 2017 452 cases and 1,981 controls in
   Japanese NA
   22 Feng W. et al [[78]38] 2017 300 AF cases and 300 controls NA
   23 Nakano Y. et al [[79]39] 2016 577 cases and 1935 controls in
   Japanese NA
   24 Seppälä I. et al [[80]40] 2016 1,834 individuals with AF and 7,159
   unaffected individuals NA
   25 Fang Z. et al [[81]41] 2016 1,150 AF cases and 1,150 AF-free
   controls in Chinese NA
   26 Wang C. et al [[82]42] 2016 1,127 unrelated AF patients and 1,583
   non-AF subjects NA
   27 Fang Z. et al [[83]41] 2016 597 AF cases and 996 AF-free controls in
   Chinese NA
   28 Zhang R. et al [[84]43] 2016 1,132AF patients and 1,206 controls NA
   29 Roberts JD. et al [[85]44] 2016 2,601 incident of AF in a total of
   17,529 participant NA
   30 Luo Z. et al [[86]45] 2016 889 AF patients and 1015 controls,
   Chinese NA
   31 Chen S. et al [[87]46] 2015 941 cases and 562 controls, Chinese
   2,113 cases and 3,381 controls
   32 Liu Y. et al [[88]47] 2015 597 AF patients and 996 non-AF controls
   in Chinese NA
   33 Rosenberg MA. et al [[89]48] 2014 879 incident AF in a total 3,309
   participants NA
   34 Andreasen L. et al [[90]49] 2014 657 patients diagnosed with AF and
   a control group comprising 741 individuals NA
   35 Luo Z. et al [[91]50] 2014 889 AF patients and 1,015 controls in
   Chinese NA
   36 Voudris KV. et al [[92]51] 2014 509 patients of whom 203 with AF NA
   37 Liu Y. et al [[93]52] 2014 597 AF patients and 996 non-AF controls
   in Chinese NA
   37 Andreasen L. et al [[94]49] 2014 657 AF cases and 741 controls,
   European ancestry NA
   38 Lin H. et al [[95]53] 2014 948 cases and 3,330 controls, European
   ancestry NA
   39 Adamsson S. et al [[96]54] 2014 520 incident AF in a total 3,900
   subjects, European ancestry 2,247 cases, 2,208 controls
   40 Cao H. et al [[97]55] 2014 840 AF patients and 845 controls in
   Chinese NA
   41 Marott SC. et al [[98]56] 2013 358 patients with lone AF, 299
   non-lone AF, and 741 controls, European ancestry NA
   42 Jeff JM. et al [[99]57] 2014 1,288 patients with cardiac surgery,
   European ancestry NA
   43 VoudrisKv. et al [[100]51] 2014 509 patients with cardiac surgery,
   European ancestry NA
   44 Ilkhanoff L. et al [[101]58] 2014 241 cases and 3,144 controls,
   African Americans NA
   45 Marott SC. et al [[102]56] 2013 2,570 AF events in 69,611
   participants, European ancestry NA
   46 Andreasen L. et al [[103]59] 2013 358 patients with lone AF and a
   control of 751 individuals, European ancestry NA
   47 Olesen MS. et al [[104]60] 2012 209 patients with early-onset lone
   AF, and a control group consisting of 534 individuals free of AF NA
   48 Schnabel RB. et al [[105]61] 2011 European (n=18,524; 2260 AF cases
   in a total 18,524individuals cohort in European ancestry), 263 AF cases
   in a total of 3,662 African American descent. 468 AF cases and 438
   controls
   49 Wirka RC. et al [[106]62] 2011 384 early onset lone AF cases and
   3,010 population control NA
   50 Li C. et al [[107]63] 2011 650 AF patients and 1,447 non-AF controls
   NA
   51 Lubitz SA. et al [[108]64] 2010 790 case and 1,177 control subjects,
   European ancestry 5,066 case and 30,661 referent subjects, European
   ancestry
   52 Roberts JD. et al [[109]65] 2010 620 AF cases and 2,446 healthy
   controls NA
   53 Ren X. et al [[110]66] 2010 384 sporadic AF patients and 844
   controls NA
   54 Shi L. et al [[111]67] 2009 383 AF patients versus 851 non-AF
   controls NA
   55 Kääb S. et al [[112]68] 2009 3,508 AF cases and 12,173 controls,
   European ancestry NA
   56 Sinner MF. et al [[113]69] 2008 1,207 AF-cases and 2,475 controls NA
   57 Giusti B. et al [[114]70] 2007 456 AF patients and 912 matched
   controls NA
   [115]Open in a new tab

Figure 1.

   [116]Figure 1
   [117]Open in a new tab

   Workflow of the annotation of susceptibility SNPs associated with
   atrial fibrillation.

Figure 2.

   [118]Figure 2
   [119]Open in a new tab

   Distribution of the 378AF susceptibility SNPs and AF causal genes.

   Genomic region annotation using Variant Effect Predictor
   ([120]http://asia.ensembl.org/Homo_sapiens/Tools/VEP, GRCh38) showed
   that only a small portion of GWAS index AF SNPs was located in exon of
   known genes (21/338, 6.21%), 63.31% were found in introns (214/338),
   and 29.29% locate in intergenic regions (99/338) ([121]Figure 3). In
   candidate gene based analysis, 50% identified AF related variants
   locate in intron, and the proportion of non-synonymous variants
   associated with AF (32.50%, 13/40) was higher than in GWAS index SNPs
   (2.936%, 10/338) ([122]Figure 3).

Figure 3.

   [123]Figure 3
   [124]Open in a new tab

   Genomic region annotation distribution of the AF susceptibility SNPs.
   (A) Index SNPs associated with AF identified in GWAS, (B) AF
   susceptibility SNPs identified by candidate gene based analysis.

Functional annotation of missense and splicing related SNPs

   Through GWAS, EWAS and candidate gene based association study, a total
   of 36 SNPs were found located in exon of known genes. Missense variants
   in SPATC1L (rs113710653) [[125]24], TNFSF13 (rs11552708) [[126]24],
   SLC22A25 (rs11231397) [[127]24], RPL3L (rs140185678) [[128]16], GCOM1
   and MYZAP (rs147301839) [[129]16], UBE4B (rs187585530) [[130]17], NEBL
   (rs2296610) [[131]22], LRIG1 (rs2306272) [[132]17], PLEC (rs373243633)
   [[133]23], DNAH10OS (rs12298484) [[134]17] were associated with risk of
   AF through GWAS and EWAS approach, and candidate gene based association
   study found missense variants in AGTR1 [[135]38], AGXT2 [[136]40],
   ZFHX3 [[137]52], MTR [[138]70], KCNH2 [[139]69], KCNE1 [[140]51], NPPA
   [[141]66] confer risk to AF. Two index SNPs of GWAS, including
   rs140192228 in RPL3L [[142]16] and rs133902 in MYO18B [[143]16] were
   predicted may change the mRNA splicing.

Functional annotation of AF susceptibility SNPs in UTR

   8 AF susceptibility SNPs were found in the UTR of protein-coding genes,
   and 4 of them were predicted to alter the microRNA binding ability
   predicted by MirSNP ([144]http://bioinfo.bjmu.edu.cn/mirsnp/search) and
   miRNASNP ([145]http://bioinfo.life.hust.edu.cn/). Rs1049334 in the
   3’UTR of CAV1 was predicted change the binding with hsa-miR-125a-3p,
   hsa-miR-3620, hsa-miR-4299, hsa-miR-4726-3p, hsa-miR-4783-3p and
   hsa-miR-497-3p. Rs13385 in the 3’UTR of HBEGF was expected to alter the
   binding with hsa-miR-1207-5p and hsa-miR-4763-3p. Rs7508 in 3’UTR of
   ASAH1 may change the binding of hsa-miR-134, hsa-miR-3118, hsa-miR-5190
   and hsa-miR-628-5p. Rs951366 in NUCKS1 was found in the binding region
   of hsa-miR-3929, hsa-miR-4419b, hsa-miR-4478, hsa-miR-4649-3p and
   hsa-miR-485-5p.

Functional annotation of AF susceptibility SNPs in non-coding regions

   According to the data of the chromatin state and modification of
   histone binding, a total of 250 SNPs in non-coding regions were
   identified as located in enhancer regions or might affect the histone
   mark of promoters and enhancers ([146]Supplementary Table 1), and
   further analysis found that 65 of them may change the situation of
   interaction with transcription factors ([147]Supplementary Table 1). 40
   transcription factors were found interact with these SNPs. After
   corrected by genome-wide expected binding ability, these SNPs were
   significantly enriched for disruption of 3 TFs including STAT6
   (P=0.02), REST (P=0.05) and NFIC (P=3.86x10^-3) ([148]Figure 4).

Figure 4.

   [149]Figure 4
   [150]Open in a new tab

   Transcription factor enrichment analysis results.

eQTL analyses

   SNPs in the non-coding region may associate with the expression levels
   and act as eQTL. We assessed the data from GTEx database
   ([151]https://gtexportal.org/home/) and evaluate whether AF
   susceptibility SNPs affect the target gene expression levels. The
   results showed that 151 SNPs can affect the expression levels of a
   total of 328 target genes, and 81 of them associated with the
   expression levels of the closest gene ([152]Supplementary Table 1).
   Combined with the TF binding data, 39 eQTL effect SNPs were found may
   alter the binding with transcription factors ([153]Supplementary Table
   1).

Long-range transcriptional regulatory function predictions

   We used 3dSNP database ([154]http://cbportal.org/3dsnp/) to analyze
   whether AF susceptibility SNPs affect distal target genes through
   topological interactions and function as long-range transcriptional
   regulatory elements. Results indicated that a total of 211 SNPs
   interact with distal target genes, and 104 of them exert as an eQTL
   effect ([155]Supplementary Table 1).

Gene ontology and pathway enrichment analyses of eQTL targeted genes

   eQTL targeted genes of AF were mapped onto Gene ontology (GO) database
   using three primary categories including molecular function, protein
   class and biological process via PANTHER
   ([156]http://www.pantherdb.org). The results showed that AF related
   genes were mainly enriched in binding, cellular process, metabolic
   process, protein modifying enzyme, gene-specific transcriptional
   regulator and membrane traffic protein ([157]Figure 5).

Figure 5.

   [158]Figure 5
   [159]Open in a new tab

   Gene ontology analyses of AF eQTL targeted genes. (A) Molecular
   function. (B) Biological process. (C) Protein class.

   All eQTL targeted genes of AF were subjected to pathway enrichment
   analysis in the Search Tool for the Retrieval of Interacting Genes
   (STRING, v11.0, [160]http://string-db.org). Statistical enrichment
   tests were executed on gene lists within the STRING by Gene Ontology
   and pathway annotations. The results uncovered some signaling pathway
   may play roles in AF including “organelle organization”, “striated
   muscle cell development”, “nuclear migration”, “endomembrane system
   organization” and “striated muscle cell differentiation” ([161]Table
   2).

Table 2. Significantly enriched pathways of AF eQTL targeted genes.

   #term ID term description observed gene count false discovery rate
   matching proteins in your network (labels)
   GO:0006996 organelle organization 56 2.0x10^-4
   BAZ2A,BRD8,CASQ2,CAV1,CAV2,CDC23,CEP68,CFL2,CHRAC1,CISD2,CTC1,DEK,DNM1L
   ,GATAD1,GORAB,HDAC5,HEATR2,HIP1R,HPS6,IMMT,KANSL1,KDM1B,KIF3C,MAPT,MTHF
   R,MTSS1,MYH10,MYOZ1,NDUFB10,NEK6,NEURL1,NKX2-5,NR3C1,NSF,NUCKS1,PCID2,P
   CM1,PEX26,PFDN1,PTK2,RAB29,RAB3IP,REEP1,REEP2, REEP4, RPL3L, SCMH1,
   SEC24C, SYNE2, TMEM70, UBE2D3, USP36, VPS37B, WIPF1, ZNF462, ZPBP2
   GO:0055002 striated muscle cell development 8 0.03 CASQ2, CAV2, CFL2,
   CHRNB1, FLNC, MYH10, MYOZ1, NKX2-5
   GO:0007097 nuclear migration 4 0.04 MYH10, PCM1, PTK2, SYNE2
   GO:0010256 endomembrane system organization 12 0.04 CAV1, CAV2, MTSS1,
   MYH10, NEK6, RAB29, REEP1, REEP2, REEP4, SYNE2, VPS37B, ZPBP2
   GO:0051146 striated muscle cell differentiation 9 0.04 BMP4, CASQ2,
   CAV2, CFL2, CHRNB1, FLNC, MYH10, MYOZ1, NKX2-5
   [162]Open in a new tab

DISCUSSION

   Population-based genetic analysis including GWAS and candidate gene
   based analysis has identified several SNPs associated with the risk of
   atrial fibrillation, here, we summarized the current results of the
   common variants conferred risk to AF and totally including 378 SNPs.
   Considering most of the AF susceptibility SNPs were located in the
   non-coding genomic regions, we give a comprehensive functional
   annotation of all these AF susceptibility SNPs through microRNA binding
   prediction, promoter and enhancer activity prediction, transcription
   factors binding activity prediction, eQTL analysis, and long-range
   transcriptional regulatory function predictions.

   Our functional annotation found that 151 AF susceptibility SNPs showed
   an eQTL effect, and 238 SNPs in non-coding regions were identified as
   located in enhancer regions or might affect the histone mark of
   promoters and enhancers, Previous studies also showed that 50-60% of
   the traits associated non-coding variants identified by GWAS were found
   located in DNase I hypersensitivity regions [[163]71, [164]72], and
   these results also suggest that most of the SNPs identified by the GWAS
   as predisposing to atrial fibrillation may have biological functions
   and exert regulatory effects. Our results also showed that only 81 of
   the 151 eQTL SNPs associated with the expression levels of the closest
   genes, and a total of 328 target genes were identified affected by AF
   susceptibility SNPs. Our results identify novel genes that may be
   associated with the occurrence or development of AF. For example,
   rs35006907 located in 139bp upstream of a non-coding RNA gene
   LINC00964, was found associated with the expression level of MTSS1 gene
   (P=2.02×10^-18) in the left ventricle, which 119 kb downstream of
   rs35006907. Rs35006907 was predicted within an enhancer in several
   types of tissues including the right ventricle and right atrium, and
   long-range transcriptional regulatory function predictions also showed
   that rs35006907 and its located enhancer can interact with MTSS1
   through long-range 3D chromatin loops. MTSS1 can promote actin assembly
   at intercellular junctions and a recent functional study indicated that
   rs35006907 showed a cardioprotective effect [[165]73].

   Another interesting finding is about AF susceptibility loci in 10q22,
   which was reported as the first genetic locus for familial atrial
   fibrillation by Brugada R et al in 1997, and SNPs including rs10824026
   [[166]28, [167]44], rs7394190 [[168]21], rs6480708 [[169]17] and
   rs60212594 [[170]17] in 10q22 and upstream of SYNPO2L gene were found
   robustly associated with AF in several GWAS project. What is more, a
   missense variant in SYNPO2L, rs3812629 (p.Pro707Leu) was found to
   confer risk to AF in the Framingham population by Whole Exome
   Sequencing in Atrial Fibrillation [[171]25] ([172]Figure 6A). However,
   our eQTL analysis using GTEx data showed that all these GWAS positive
   AF SNPs including rs10824026, rs7394190, rs6480708, and rs60212594 were
   strong associated with the expression level of MYOZ1 in human atrial
   appendage tissue with a P value from 1.3x10^-28 to 1.4x10^-45
   ([173]Figure 6B). Furthermore, the missense variant in SYNPO2L,
   rs3812629 (p.Pro707Leu), which confer risk to AF, was also found
   associated with MYOZ1 expression level in human atrial appendage
   tissue, and the median normalized expression level of MYOZ1 in
   homozygous risk allele GG carriers was -0.28 and extremely lower than
   in heterozygous GA carriers (0.94) ([174]Figure 6B). MYOZ1 encode
   myozenin 1, which is an intracellular binding protein involved in
   linking Z-disk proteins, and was known as a calcineurin-interacting
   protein, and help tether calcineurin to the sarcomere of skeletal and
   cardiac muscle [[175]74–[176]76]. Mutations in MYOZ1 were found in the
   patient with dilated cardiomyopathy [[177]77–[178]78]. These results
   suggested that MYOZ1, but not SYNPO2L is the causal gene of AF.

Figure 6.

   [179]Figure 6
   [180]Open in a new tab

   Association of SNPs in 10q22 with AF and eQTL analysis. (A) Regional
   plots for significant association with AF in 10q22. The P value was
   obtained from GWAS catalog database. SNPs plotted by their positions
   (UCSC hg19) on the corresponding chromosome against –log10 (P).
   Estimated recombination rates from 1000 genomes EUR populations were
   plotted in blue to reflect the local linkage disequilibrium (LD)
   structure on a secondary y axis. The most significant lead SNP
   (diamond) is denoted with the SNP identification number. Flanking SNPs
   (circles) are color-coded to represent the pairwise r^2 measure of LD
   with the lead SNP: red, r^2 ≥ 0.8; orange, 0.6 ≤ r^2< 0.8; green, 0.4 ≤
   r^2<0.6; light blue, 0.2 ≤ r^2<0.4; blue, r2< 0.2. These plots were
   generated by Locuszoom ([181]https://statgen.sph.umich.edu/locuszoom/).
   (B) eQTL analysis showed that the association between AF susceptibility
   SNPs in 10q22 with the expression level of MYOZ1 in human atrial
   appendage tissues (n=372). eQTL analysis were performed using GTEx
   data.

   Previously genetic studies in familial or sporadic AF have identified
   numerous mutations or rare variants that putatively cause AF [[182]5,
   [183]79–[184]83], and to recently, 44 genes that putatively cause AF
   were mapped to pathway of ion channels/ion channels related (ABCC9,
   HCN4, KCNA5, KCNE1, KCND3, JPH2, KCNE2, KCNE3, KCNE4, KCNE5, KCNH2,
   KCNJ2, KCNJ5, KCNJ8, KCNK3,KCNN3, KCNQ1, RYR2, SCN1B, SCN2B, SCN3B,
   SCN4B, SCN5A, SCN10A), transcription factors (GATA4, GATA5, GATA6,
   NKX2-5, NKX2-6, PITX2, SHOX2, SOX5, TBX5, ZFHX3), myocardial structural
   components (GJA1, GJA5, LMNA, MYH6, MYL4, SYNE2), signaling, protein
   turnover and others (GREM2, NPPA,SH3PXD2A, PLN). Compared to the list
   AF susceptibility genes including the 328 eQTL target genes what we
   have identified and combined with the closest gene of GWAS index SNPs,
   only 10 genes including HCN4, KCND3, KCNJ5, KCNN3, PITX2, TBX5, ZFHX3,
   GJA1, SYNE2, SH3PXD2A, and PLN were found have bath rare variants and
   common variants related with AF. These may result from most mutation
   screening were carried out in familial AF, early-onset AF or lone AF,
   and AF patients in GWAS were more complex.

   In conclusion, we summarized the current results of the genetic studies
   including common variants identified by GWAS (338 index SNPs) and
   candidate gene based association studies (40 SNPs), and performed a
   comprehensive annotation of all these AF susceptibility loci found by
   GWAS and candidate gene based association. We identified 4 AF
   susceptibility SNPs in UTRs may change the microRNA binding ability,
   and 250 AF susceptibility SNPs in non-coding regions were identified as
   located in enhancer regions or might affect the histone mark of
   promoters and enhancers, 65 SNPs may change the situation of
   interaction with transcription factors and totally 40 transcription
   factors were found interact with these SNPs. Our results also showed
   that 151 SNPs can affect the expression levels of a total of 328 target
   genes and 81 of them associated with the expression levels of the
   closest gene. Long-range transcriptional regulatory function
   predictions showed that 211 SNPs interact with distal target genes, and
   104 of them exert as an eQTL effect. We also performed a GO and pathway
   enrichment of the AF eQTL genes. Taken together, our study suggested
   the relationship between genetic variants and possible targeted genes,
   and provides insight into potential genetic pathways underlying AF
   incidence and development.

MATERIALS AND METHODS

Acquisition of AF susceptibility variants and search strategy

   The workflow of the current study is presented in [185]Figure 1. First,
   results of the current GWAS of AF were extracted from three public
   databases, including GWAS catalog ([186]https://www.ebi.ac.uk/gwas/),
   GWAS central ([187]https://www.gwascentral.org) and phenotype–genotype
   integrator ([188]https://www.ncbi.nlm.nih.gov/gap/phegeni). We also
   searched the literature in Pumbed
   ([189]https://pubmed.ncbi.nlm.nih.gov) to include all studies of AF
   GWAS. The keywords include atrial fibrillation, genome wide association
   or GWAS.

   Besides the GWAS, several candidate gene based association studies have
   also identified a panel of genetic variants confer risk to AF. Results
   of these associated genetic variants were obtained by searching from
   the PubMed, EMBASE ([190]https://www.embase.com) and Medline
   ([191]https://www.nlm.nih.gov/bsd/medline.html) ([192]Figure 1), and
   the searching keywords of medical subject headings (MeSH) including
   “atrial fibrillation” combined with “polymorphism, polymorphisms,
   variant, variants, single nucleotide polymorphism, single nucleotide
   polymorphisms, SNP, SNPs”. The results of literature searching were
   eligibility screened by two reviewers based on titles and abstracts.
   Studies published between 1 January 2007 and 1 November 2019 were
   included. Only case control association studies or cohort-based
   prospective studies were included. Functional researches, animal model
   studies or studies not performed in a population were excluded.

   Information of AF GWAS index SNPs was extracted from the database of
   GWAS catalog, and the threshold of significant level for the
   association was set as P value lower than 5×10^-8. For the SNPs from
   candidate gene based association studies, publications were reviewed by
   two reviewers independently and extracted the information about the
   variant(s) and the details of the population. Discrepancies in data
   extraction were resolved by discussion or submitted to a third reviewer
   if required. We divided the variants analyzed in candidate gene based
   studies into three groups, (i) replication study of the GWAS identified
   susceptibility loci of AF, (ii) novel variants with minor allele
   frequency (MAF) ≥0.1% (according to 1000 genome phase III global data),
   (iii) rare variants with a low frequency (MAF<0.1%) associated with AF
   by candidate gene association study or mutation screening. In our
   study, we excluded rare variants and mutations in (iii) from our
   further annotations, for the causal genes harbored mutations or rare
   variants of AF which were found in families or cohort were well
   summarized in previously reviews [[193]8, [194]84] The significant
   level for SNPs in candidate gene based association studies was set as
   satisfying the Bonferroni correction. To reduce the probability of
   false positives, we exclude case controls studies if the statistical
   power <70%. The power was extracted from publications or calculated by
   PS: Power and Simple Size Calculation software [[195]85].

Genomic region annotations

   All AF susceptibility SNPs including index SNPs identified by GWAS and
   SNPs identified by candidate gene based association studies were first
   annotated using Variant Effect Predictor
   ([196]http://asia.ensembl.org/Homo_sapiens/Tools/VEP, GRCh38) in
   Ensembl to obtain their genomic region information.

Functional annotation of AF susceptibility SNPs in exon

   According to the genomic region information obtained from Variant
   Effect Predictor, non-synonymous SNPs were directly recognized as
   functional variants. SNPs in untranslated region (UTR) were analyzed
   the microRNA binding ability using MirSNP tool
   ([197]http://bioinfo.bjmu.edu.cn/mirsnp/search) [[198]86] and miRNASNP
   v2.0 ([199]http://bioinfo.life.hust.edu.cn/miRNASNP2/) [[200]87].

Enhancer prediction and transcription factor (TF) binding sites prediction of
AF susceptibility SNPs in non-coding regions

   Splicing variants identified by Variant Effect Predictor were
   classified as functional SNPs directly. Next, for intronic or
   intergenic SNPs, the chromatin states data from the Roadmap and ENCODE
   to analyze whether they are overlapping any enhancers in possible AF
   related tissues and cell types.

   For the AF susceptibility SNPs in the non-coding genomic regions,
   including in UTR, promoter, intron, and intergenic regions, SNP2TFBS
   database ([201]http://ccg.vital-it.ch/snp2tfbs/) was used to predict
   potential binding ability between SNPs and transcription factors
   [[202]88].

Histone modification analysis

   SNPs in promoter, intron, and intergenic regions may modify the histone
   binding ability, and here, using HaploReg (version 4.1)
   ([203]https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php)
   [[204]89], we analyzed whether the identified non-coding AF associated
   SNPs overlap the major histone modifications (H3K9ac and H3K4me3 for
   promoter regions, H3K27ac and H3K4me1for enhancer regions) in AF
   related tissues and cell types.

Long-range transcriptional regulatory function predictions

   SNPs in the noncoding region may reside within or near regulatory
   elements controlling the expression of distal target genes through
   topological interactions, and using 3DSNP [[205]90], we annotated the
   possible regulatory effect of identified AF associated SNPs by
   examining their 3D interactions with distal genes mediated by chromatin
   loops.

Expression quantitative trait loci analyses

   Genotype-Tissue Expression (GTEx) data were used in determining whether
   identified AF associated SNPs affect gene expression levels. eQTL
   analysis were performed bases on raw RNA-Seq data (RPKM) by genes from
   the GTEx V6 analysis freeze (dbGaP Accession phs000424.v6.p1) and
   included 25 tissues.

Gene ontology (GO) and pathway enrichment analysis of eQTL targeted genes

   Gene Ontology including biological process, molecular function, and
   protein class were annotated using PANTHER
   ([206]http://www.pantherdb.org). KEGG pathway enrichment analysis were
   used in the Search Tool for the Retrieval of Interacting Genes (STRING,
   v11.0, [207]http://string-db.org).

Supplementary Material

   Supplementary Table 1
   [208]aging-12-103615-s001..xlsx^ (53.5KB, xlsx)

Footnotes

   AUTHOR CONTRIBUTIONS: Study concept and design: Chengqi Xu, data
   collection: Chengqi Xu, Pengyun Wang and Rongfeng Zhang, Critical
   revision of the manuscript for important intellectual content: Yunlong
   Xia. Statistical analysis: Chengqi Xu and Rongfeng Zhang. Obtained
   funding: Wang PY and Wei Yang. Study supervision: Wei Yang.

   CONFLICTS OF INTEREST: The authors declare that the research was
   conducted in the absence of any commercial or financial relationships
   that could be construed as a potential conflicts of interest.

   FUNDING: This study was supported by the National Natural Science
   Foundation of China Program (Grant No. 81600263, 31671302 and
   81971276), National Key R&D Program of China (Grant #2018YFC1311600),
   Young and Middle-aged Talents of Wuhan Municipal Health Commission (to
   Wang PY), Hubei health and family planning scientific research project
   (Grant No.WJ2019Q037).

REFERENCES