An integrated genome and phenome-wide association study approach to understanding atrial fibrillation’s disease predisposition

doi:10.21203/rs.3.rs-5004510/v1

Background

We performed phenome-wide analysis (PheWAS) and two-sample Mendelian Randomization analysis to comprehensively explore the health effects of atrial fibrillation (AF) in the European population.

Methods

Initially, SNPs associated with atrial fibrillation were retrieved from the FinnGen database, subsequently compiling a comparative SNP set to serve as a control for PheWAS analysis. A set of unlinked control SNPs (from the 1000 Genomes Project) was generated using SNPsnap. A total of 43 SNPs associated with atrial fibrillation and 172 control SNPs were utilized in the PheWAS analysis, resulting in the identification of 10 associated traits. To evaluate the causal relationship between these associated traits and the risk of AF, a bidirectional two-sample Mendelian randomization analysis was conducted using the TwoSampleMR package (version 0.6.2) in R (version 4.4.0).

Results

In total, 112 phenotypes with significant associations were identified. Following the False Discovery Rate correction, 5 phenotypes with significant associations were ascertained, each of which demonstrated a causal association with atrial fibrillation as revealed by Mendelian randomization studies

Conclusion

Overall, our study confirms the association of different factors with genetic susceptibility for AF and reveals novel observations that need to be further explored.

PheWAS

Atrial fibrillation

GWAS

Mendelian randomization

Atrial fibrillation (AF), the most common form of arrhythmia, is caused by atrial structural or electrophysiological abnormalities and rapid disordered excitation of the atrium and irregular activation of the ventricles [1]. Although the burden of AF can be reduced through risk factor management, the incidence and prevalence of AF have continued to increase over the past 20 years and are expected to continue to increase in the coming decades. Genome-wide association studies (GWAS) have significantly enhanced our understanding of the etiology of atrial fibrillation. It has been demonstrated that the pathogenesis of AF involves multiple genes, including those related to immunity, lipid metabolism, tau, and amyloid-beta (Aβ) [2]. Despite the widespread application of GWAS analysis, which has been able to identify low-frequency risk alleles through meta-analyses, the gains in statistical power have become increasingly limited [3]. The Mendelian randomization (MR) method and phenome-Wide Association Studies (PheWAS) can augment traditional GWAS approaches and broaden our understanding of the underlying biology behind the pathogenesis of atrial fibrillation.

PheWAS is a method for analyzing the association of a phenotypic set with specific genetic variations of interest, recently made feasible by integrating Electronic Health Record data with large-scale genomic datasets, which fundamentally disrupts the classical GWAS approach, testing the association of numerous phenotypes with a single genetic variant [4]. GWAS and PheWAS typically employ the same multivariate regression-based framework to test SNP-trait associations, including adjustments for ancestry to control for population stratification. By expanding the list of phenotypes associated with the variant of interest, PheWAS conducted on known disease-associated SNPs can identify potential comorbid conditions and traits that may mediate the SNP's disease association [5, 6]. For instance, PheWAS of the radiation-induced lung cancer risk variant rs16969968 revealed strong associations with various smoking-related behaviors, which mediated the variant's association with radiation-induced lung cancer risk [7]. Identifying such 'intermediate traits' that connect AF risk alleles with the causal mechanisms of AF pathogenesis has clear relevance for disease prevention and population-based risk reduction. Additionally, by identifying traits with overlapping genetic architecture with the disease of interest, researchers can curate the genetic determinants of these traits to generate an empirical list of candidate SNPs and test for pleiotropic associations with the disease under study [8].

To comprehensively explore the relationship between atrial fibrillation and susceptibility risk, we conducted this study by conducting PheWAS analysis in the UK Biobank study and using the Finnish database as a bidirectional two-sample Mendelian Randomization (MR) analysis of atrial fibrillation to examine the relationship between atrial fibrillation and a wide range of diseases. We attempted to identify new risk factors for atrial fibrillation using genetic pleiotropy between atrial fibrillation and other traits, and nominated biological pathways in which atrial fibrillation risk alleles may play a pathogenic role.

Selection of AF risk alleles

Summary data of the GWAS for AF were obtained from the FinnGen study, which collected genomic and health registry data from 500,000 participants in the FinnGen biobank. The FinnGen study included 34,748 patients with atrial fibrillation, as defined by the international classification of diseases codes, and 156,457 controls, with almost no sample overlap with the GWAS results to minimize the risk of type I error. After pruning for linkage disequilibrium (r² < 0.001; kb = 10,000kb), 43 SNPs significantly associated with atrial fibrillation (P < 5 × 10^− 9) were identified and transferred to the PheWAS analysis (table S1).

Control SNP Set

We compiled a comparison SNP-set to serve as controls for PheWAS analyses. A set of unlinked control SNPs (1000 Genomes Project) was generated using SNPsnap. Four control SNPs were matched to the AF-associated SNPs on: minor allele frequency (± 5%), surrounding gene density (± 50%), distance to nearest gene (± 50%) and, as a proxy for haplotype block size, the number of other SNPs in LD at r² ≥ 0.50 (± 50%). For several AF risk SNPs, we could not generate more than 4 control SNPs without loosening our matching parameters, but the gain in statistical power achieved beyond a case-to-control ratio of 1:4 is typically minimal. Therefore, a total of 43 AF-associated SNPs and 172 control SNPs were carried forward for PheWAS analyses. (table S2)

PheWAS analysis

The GWAS summary dataset of 2514 phenotypes was fetched from MRC IEU UK Biobank GWAS pipeline version 2, which conducted a large GWAS analysis using a pipeline that systematically analysed every PHESANT phenotype in UK Biobank. There were previously ~ 20k traits with complete GWAS data, but a majority of these were binary traits based on very few numbers of cases. After filtering out unreliable datasets, there are 2514 traits remaining, with any binary traits removed that had fewer than 1000 cases.

We queried UK biobank for trait associations with 43 known AF risk SNPs and the control SNP-set (172 matched SNPsnap controls). Summary statistics for traits associated with each queried variant were downloaded from UK Biobank for downstream analyses. Nominally significant SNP-trait associations (P < 0.01) were carried forward to trait-enrichment analyses, as previously described. Although a more stringent p-value threshold for carrying SNP-trait associations forward was considered, this was determined to be too conservative because many of the 2514 traits in UK Biobank have high genetic correlations with each other. Additionally, these individual SNP-trait associations were carried forward for trait-enrichment comparisons between AF-associated SNPs and the control SNP-set, and as such the PheWAS significance threshold is somewhat arbitrary so long as it is uniform across all SNP-sets.

PheWAS associations of the 43 AF SNPs with 778 phenotypic traits were compared to PheWAS results for the control SNP. For traits associated with > 1 AF SNP, we used Fisher’s exact tests to compare the frequency at which individual traits were associated with AF SNPs versus control SNPs to determine if traits were enriched for association with known AF risk variants. 2514 distinct phenotypes were defined. (table S3). Fisher’s exact p-values for trait enrichment underwent FDR correction to adjust for multiple testing. After the exclusion of traits related to the disease itself (e.g., Cardiology, Atrial fibrillation and flutter), 5 phenotypes were defined. (Table 1)

Mendelian randomization analysis

To evaluate the causal relationship between associated SNPs and AF risk, we conducted a bidirectional two sample MR analysis using the TwoSampleMR package (version 0.6.2) in R (version 4.4.0). We used (1) inverse variance weighted (IVW), (2) MR Egger, and (3) weighted median methods to test the causal relationship between SNP exposure and AF risk by summarizing SNP exposure and SNP results. We investigated how the causal effect estimate (bias) and corresponding standard deviation (precision) of the MR-PRESSO outlier test were affected by horizontal pleiotropy. We then compared MR-PRESSO to other established MR methods that can correct for horizontal pleiotropy. Traits with MR-PRESSO < 0.05 will not be analyzed, as they exhibit horizontal pleiotropy, a property that confounds Mendelian randomization studies.

Figure 1 demonstrates the research design of this study. In the UK Biobank research, 43 atrial fibrillation-associated SNPs were initially identified through the FinnGen database, successfully matching 172 control SNPs at a ratio of 1:4. Subsequently, a genome-wide association study was conducted via the UK Biobank to test the association of each AF-associated SNP and control SNP with 778 traits. Following the confirmation of the enrichment of AF-associated SNPs with GWAS traits, MR analyses were conducted using data from the FinnGen and IEU to ascertain the associations observed in PheWAS.

PheWAS analysis

After PheWAS analysis, 2514 distinct phenotypes were defined. Following the FDR test, AF were associated with the risk of 10 outcomes (TableS3). After the exclusion of traits related to the disease itself, 5 phenotypes were defined, including digoxin, direct current cardioversion, warfarin, pulse rate, hyperlipidemia and personal history of long-term (current) use of anticoagulants.

GWAS ID	Trait	Associated LVRWT SNPs	Associated control LVRWT SNPs	P-value	P-value _(FDR)
ukb-b-10272	digoxin	7	1	7.27E-05	0.01932166
ukb-b-10444	Direct current cardioversion	13	1	2.08E-09	1.05E-06
ukb-b-13248	warfarin	15	3	3.73E-09	1.56E-06
ukb-b-18103	Pulse rate	17	10	3.64E-07	0.000130626
ukb-b-7352	Personal history of long-term (current) use of anticoagulants	12	0	1.42E-09	8.95E-07

Table1 Trait more likely to be associated with atrial fibrillation (AF) risk SNPs than control SNPs in PheWAS enrichment analysis.

Mendelian randomization analyses

In this study, we aim to investigate the potential bidirectional causal relationship between AF and AF-related factors by implementing a bidirectional MR study design using AF and AF-related factors. The associations for digoxin, direct current cardioversion, warfarin, pulse rate and personal history of long-term (current) use of anticoagulants were observed in two-sample MR analysis.

The causal effect of AF on associated risk

The results of the MR analyses were shown in Table 2, the scatter plots, forest plots and funnel plots were presented in Supplementary 1. As to its components, genetic liability to AF was related to digoxin, direct current cardioversion, warfarin and personal history of long-term (current) use of anticoagulants while no associated with pulse rate. The ORs in genetically predicted AF were 1.002 (p = 6.14E-29) for digoxin, 1.004 (p = 2.16E-45) for direct current cardioversion, 1.006 (p = 1.70E-61) for warfarin, 0.985 (p = 0.0828) for pulse rate and 1.005 (p = 2.40E-49) for personal history of long-term (current) use of anticoagulants.

GWAS ID	Trait (outcome)	MR-method	No. of SNP	Beta	SE	OR	MR-PRESSO	P-value
ukb-b-10272	digoxin	IVW	85	0.002	0.0002	1.002	0.10	6.14E-29
		MR-Egger		0.004	0.0006	1.004		5.43E-10
		Weighted median		0.002	0.0003	1.002		8.45E-14
ukb-b-10444	Direct current cardioversion	IVW	132	0.004	0.0003	1.004	0.47	2.16E-45
		MR-Egger		0.009	0.0008	1.009		1.80E-20
		Weighted median		0.004	0.0004	1.004		1.89E-31
ukb-b-13248	warfarin	IVW	168	0.006	0.0004	1.006	0.09	1.70E-61
		MR-Egger		0.011	0.0007	1.011		8.50E-32
		Weighted median		0.006	0.0005	1.006		3.66E-34
ukb-b-18103	Pulse rate	IVW	204	0.016	0.0090	0.985	0.76	0.0828
		MR-Egger		0.017	0.0206	0.983		0.405
		Weighted median		0.006	0.0057	0.994		0.301
ukb-b-7352	Personal history of long-term (current) use of anticoagulants	IVW	168	0.005	0.0003	1.005	0.08	2.40E-49
		MR-Egger		0.008	0.0009	1.008		3.84E-16
		Weighted median		0.005	0.0005	1.005		1.19E-29

Table 2 Associations of genetically proxied AF with disease outcomes in two-sample Mendelian randomization analysis

The causal effect of associated risk on AF

The results of the MR analyses were shown in Table 3, the scatter plots, forest plots and funnel plots were presented in Supplementary 1. As to its components, genetic liability to AF was related to digoxin, direct current cardioversion, warfarin, pulse rate and personal history of long-term (current) use of anticoagulants. The ORs in genetically predicted AF were 3.07E+57 (p =0.01) for digoxin, 1.90E+39 (p = 1.39E-28) for direct current cardioversion, 3.84E+26 (p = 5.02E-13) for warfarin, 0.90 (p = 0.01) for pulse rate and 3.63E+29 (p = 1.00E-08) for personal history of long-term (current) use of anticoagulants.

GWAS ID	Trait (exposure)	MR-method	No. of SNP	Beta	SE	OR	MR-PRESSO	P-value
ukb-b-10272	digoxin	IVW	9	132.37	37.09	3.07E+57	<0.01	0.01
		MR-Egger		314.36	97.32	3.36E+136		0.01
		Weighted median		18.16	10.68	77385168.62		0.09
ukb-b-10444	Direct current cardioversion	IVW	21	90.44	8.15	1.90E+39	0.74	1.39E-28
		MR-Egger		139.54	13.01	4.00E+60		1.68E-09
		Weighted median		66.66	9.09	8.91E+28		2.26E-13
ukb-b-13248	warfarin	IVW	21	61.21	8.47	3.84E+26	0.10	5.02E-13
		MR-Egger		107.19	17.31	3.56E+46		5.98E-06
		Weighted median		22.18	5.16	4282635216		1.74E-05
ukb-b-18103	Pulse rate	IVW	856	0.10	0.04	0.90	0.75	0.01
		MR-Egger		0.41	0.09	0.67		1.20E-05
		Weighted median		0.14	0.05	0.87		<0.01
ukb-b-7352	Personal history of long-term (current) use of anticoagulants	IVW	15	68.07	11.88	3.63E+29	0.48	1.00E-08
		MR-Egger		115.15	24.98	1.03E+50		<0.01
		Weighted median		15.39	5.28	4812157.42		<0.01

Table 3 Associations of genetically proxied AF with disease exposure in two-sample Mendelian randomization analysis

To systematically explore the causes and consequences of atrial fibrillation, we conducted a phenome-wide, bidirectional MR analysis of atrial fibrillation, spanning thousands of traits. In this study, PheWAS and two-sample MR analyses found robust evidence in support of associations of genetically predicted AF with risk of digoxin, direct current cardioversion, warfarin, pulse rate and personal history of long-term (current) use of anticoagulants in of which direct current cardioversion, warfarin and personal history of long-term (current) use of anticoagulants have a bidirectional causal relationship.

In this work, multiple risk factors showed potential evidence of a role in the aetiology of AF and most of them were related to different phenotypes of the heart, which is consistent with the findings reported in previous study. Liu et al. successfully pinpointed numerous genes and DNAm sites that potentially exert causal influences on atrial fibrillation (AF) [9]. Concurrently, extensive genome-wide association studies (GWAS) have revealed over a hundred loci linked to AF, offering crucial insights for devising future research endeavors aimed at elucidating the functional mechanisms underlying the pathogenesis of AF triggered by DNA mutations. Furthermore, our research substantiates the existence of a bidirectional causality between atrial fibrillation and direct current cardioversion, a relationship that has seldom been reported in the previous MR study. Direct current cardioversion is a common rhythm control strategy in patients with symptomatic atrial fibrillation, which is capable of delivering high-energy electrical impulses to the myocardial tissue within an extremely brief timeframe, resulting in the simultaneous depolarization of the majority of myocardial fibers (approximately over 75%) [10]. A multicenter randomized controlled trial has demonstrated that patients treated with baseline cardioversion appeared to have a significantly lower mortality risk during follow-up after adjustment for known confounders (event rates per 100 patient years 2.52 v 3.87, weighted hazard ratio 0.75, 95% confidence interval 0.67 to 0.85, P < 0.001) [11]. However, the results of another study indicate that a higher incidence of intracardiac thrombus is observed following direct current cardioversion treatment [12]. Despite the high success rate in restoring sinus rhythm, the incidence of rapid and slow arrhythmia complications in patients with atrial fibrillation is significantly elevated compared to the control group. Notably, the participants in this study were in high cardiovascular risk (e.g. cardiac amyloidosis), which is different from the UK Biobank individuals with a generally good health status.

Warfarin, a derivative of dicumarol, exerts its anticoagulant effect by inhibiting the interconversion of vitamin K and its 2,3-epoxide (vitamin K epoxide) [13]. According to previous MR study, it has been hypothesized that warfarin administration may induce pathological changes in the skin cells of patients with AF, subsequently precipitating chronic myelogenous leukemia [14]. However, to date, no definitive literature exists to report a direct causal relationship between AF and warfarin administration. Based on PheWAS, a bidirectional causal relationship has been ascertained between AF and the risk associated with warfarin, which partly supported the association between AF and warfarin as reported by previous MR studies [15]. Furthermore, Current guidelines in the United States and Europe recommend the use of warfarin in AF, but between 2010 and 2014, the use of warfarin decreased from 32.8% in 2010 to 22.9% in 2014, which may be related to significant differences in age, gender, income level, insurance status, geographical region, stroke risk, use of anticoagulants, and choice of anticoagulants [16, 17]. In another registry study based on the multi-center nature of the United States, even after controlling for clinical and socio-demographic characteristics, black patients were significantly less likely to receive direct-acting oral warfarin than white patients, with no difference between white and Hispanic patients [18]. It is noteworthy that the studies were conducted exclusively within the United States, exhibiting certain distinctions from the data of the UK Biobank.

Additionally, it has been observed that personal history of long-term (current) use of anticoagulants exhibits a bidirectional causative relationship with atrial fibrillation. Previous network meta-analyses have revealed that the strategy of administering standard-dose anticoagulants for the long-term treatment of patients with AF significantly reduces the risk of stroke/systemic embolism, intracranial hemorrhage, and all-cause mortality, however, no statistically significant differences were observed in the incidence of major bleeding or other complications [19]. Although current research has not directly demonstrated a causal relationship between personal history of long-term (current) use of anticoagulants and the onset of AF, it has been observed that the history of long-term oral anticoagulation therapy is strongly associated with the prognosis of atrial fibrillation, an association that was independent of gender and age [20]. Concurrently, current therapeutic guidelines stipulate that patients with atrial fibrillation were required to undergo long-term anticoagulant therapy, which was also in accordance with the conclusions that we have arrived at.

Digoxin is a cardiac glycoside used as drug in case of heart problems, including congestive heart failure, atrial fibrillation or flutter, and certain cardiac arrhythmias [21]. Our study reveals a unidirectional causal relationship between AF and digoxin, a finding not previously demonstrated in previous MR studies. However, due to the minor positive effect (odds ratio = 1.002), it is postulated that this may be associated with limitations related to ethnicity and geographical regions. The number of digoxin users in the United States declined by 47% from 766 users per 100,00 adults in 2010–2011 to 402 users per 100,000 adults in 2016–2017, however, digoxin remains prevalent among Asian and Hispanic populations in the United States [22]. Furthermore, more than 20 years following the publication of trials related to digoxin, there exist many reports concerning the safety of digoxin treatment that are characterized by contradictory data [23]. Despite the aforementioned controversies and discrepant data, digoxin remains a universally utilized medication. It continues to be an excellent choice for the pharmacological control of heart rate in patients with atrial fibrillation.

Current observational studies and MR research have demonstrated a causative relationship between pulse rate and AF, which is consistent with the results of our research. Existing research findings support an inverse causality between genetically determined resting heart rates and AF, for those below 65 beats per minute [24]. As the same time, additional research has demonstrated that pulse rate can predicts AF events in a large, population-based cohort [25]. In summary, the resting pulse rate may constitute a useful component in elucidating the intricate mechanisms underlying the initiation of AF.

In previous Mendelian randomization studies, several other health outcomes have been causally associated with atrial fibrillation. These associations encompass renal function [26], stroke [27], and the causal effects of obstructive sleep apnea [28], as well as the frequency of its episodes, among others. However, these findings were not captured in our PheWAS analysis, potentially due to the lack of power in our study to detect such associations, such as diseases with low prevalence or small case numbers among the UK Biobank participants, including multiple sclerosis, Alzheimer's disease, and Parkinson's disease. Future research with larger sample sizes and independent study populations is required to replicate and validate these reported MR findings.

The strength of this study lies in the employment of a hypothesis-free approach to assess the bidirectional causal effects of the full phenotypic traits of atrial fibrillation. To address the issue of multiple testing, we utilized the Bonferroni correction and compared the consistency of results across three distinct MR methods. We acknowledge that sample overlap between exposure and outcome GWAS may induce overfitting in cases of weak instrumentation. However, considering the adequate F-statistics for risk factors in univariate MR models, the potential for bias should be minimal.

Competing interests

The authors declare no competing interests.

Funding Declaration

National Natural Science Foundation of China (82102506), Sichuan leading carder health care research project (2023-1301) and Sichuan Science and Technology Program (2023NSFSC1477).

Author Contribution

H contributed substantially to the conception, design, analysis, and interpretation of data for the work; and drafted and revised the work. M contributed substantially to the acquisition, analysis, interpretation of data, and revising the intellectual content. K made substantial contributions to the interpretation of data and revising the intellectual content. Y made substantial contributions to the interpretation of data and revising the intellectual content. X and F made substantial contributions to the interpretation of data and revising the intellectual content. K and M contributed substantially to the conception, design, acquisition, analysis, and interpretation of data for the work; and drafted and revised the work. All authors read and approved the final manuscript.

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No competing interests reported.

An integrated genome and phenome-wide association study approach to understanding atrial fibrillation’s disease predisposition

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

INTRODUCTION

MATERIALS AND METHODS

Selection of AF risk alleles

Control SNP Set

PheWAS analysis

Mendelian randomization analysis

RESULTS

DISCUSSION

Declarations

Competing interests

Author Contribution

References

Additional Declarations

Supplementary Files

Status:

Version 1