Genome-wide analysis identifies novel susceptibility loci for heart failure and nonischemic cardiomyopathy subtype

doi:10.21203/rs.3.rs-4539882/v1

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Genome-wide analysis identifies novel susceptibility loci for heart failure and nonischemic cardiomyopathy subtype

https://doi.org/10.21203/rs.3.rs-4539882/v1

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Heart failure (HF) is a serious cardiovascular condition resulting from abnormalities in multiple biological processes that affects over 64 million people worldwide. Here, we report a meta-analysis of genome-wide analysis for all-cause HF in East Asian populations (n ~ 227,000) and more precise definition of nonischemic cardiomyopathy (NICM) subtype in multi-ancestry populations (n ~ 403,000). We identified 3 previously unknown HF loci and 2 NICM loci. Follow up analyses demonstrated male-specific HF association at CDKN1A and MYBPC3 loci, and prioritized candidate causal gene at SVIL locus for NICM. Moreover, we demonstrated that SVIL deficiency aggravated cardiomyocyte hypertrophy, apoptosis and impaired cell viability in phenylephrine (PE)-treated H9C2 cells. In addition, the gene expression level of B-type natriuretic peptide (BNP) which was deemed as a hallmark for HF was further elevated by SVIL silencing in PE-stimulated H9C2 cells. RNA-sequencing analysis on H9C2 cells revealed that the function of SVIL might be mediated through pathways relevant to regulation and differentiation of heart muscle. These results further elucidate the genetic architecture of HF and provide important insight into the biological pathways underlying NICM and sex-specific relevance of CDKN1A and MYBPC3 loci.

Health sciences/Diseases/Cardiovascular diseases/Heart failure

Biological sciences/Genetics/Genetic association study/Genome-wide association studies

heart failure

nonischemic cardiomyopathy

genome-wide association study

meta-analysis

sex-specific

SVIL

Heart failure (HF) is a serious cardiovascular condition resulting from abnormalities in multiple biological processes that contribute to cardiac dysfunction. HF affects over 64 million people worldwide¹ and has a prevalence of 1.3% among adults ≥ 35 years old in China². HF is characterized by high economic burden in developed countries³. While advances have been made in diagnosis and treatment of HF, its mortality remains high with 1-year mortality of more than 30%⁴, and 5-year mortality of ~ 50%⁵. The wide spectrum of potential causes of HF and the slow disease progression make prevention and treatment of HF particularly challenging.

Both genetic factors and exposure to environmental factors contribute to HF onset, and the estimated heritability ranges from 26 to 34%⁶. Recent genome-wide association studies (GWAS) have confirmed the contribution of genetic factors to risk of HF with 57 susceptibility loci among populations of predominantly European ancestry over the last decade^7–13. However, many of the identified loci play a role via clinical HF risk factors, including coronary heart disease (CAD), atrial fibrillation (AF), and hypertension. Given the diverse etiologies and clinical heterogeneity of HF, there is a critical need to refine the HF phenotype and reveal the pathogenesis independent of clinical HF risk factors. Although recent GWAS of specific HF subtypes such as nonischemic cardiomyopathy (NICM) has been conducted in European ancestry⁸, they failed to identify any NICM-specific gene except for a previously reported gene (BAG3) for all-cause HF. To date, the genetic component of HF has not yet been extensively characterized in East Asian populations, it is possible that the previously identified genetic association from European ancestry may not be generalizable to other populations. Moreover, males and females with HF showed different characteristics, and few GWAS have examined sex differences in the genetic architecture of HF specifically. Thus, additional studies are needed to further elucidate the genetic architecture of HF independent of clinical HF risk factors in East Asian populations and determine the sex-specific relevance of HF-associated risk variants.

In the present study, we sought to expand our understanding of the genetic basis of HF and more specific NICM subtype, and evaluating the biological pathways underlying subclinical left ventricular dysfunction.

Identification of 3 Novel Loci for all-cause HF among East-Asian populations: To further elucidate the genetic architecture of heart failure, we first carried out a genome-wide association study (GWAS) in 3,972 all-cause HF cases and 11,171 controls in the China PEACE 5p-HF/ChinaHEART (Supplemental Table 1). However, this GWAS analysis failed to identify any genome-wide significantly HF-associated single-nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) > 1% probably due to limited power (Supplemental Fig. 1). We next combined our GWAS results in the China PEACE 5p-HF/ChinaHEART with summary statistics for congestive heart failure from Biobank Japan¹¹ in a fixed-effects meta-analysis that included a total of 13,385 HF cases and 214,211 controls with 5,887,000 SNPs common to both datasets (Fig. 1 and Supplemental Table 1). This analysis revealed 94 significantly associated variants distributed across 5 loci (Fig. 2A, Supplemental Fig. 2 and Supplemental Table 2). Of these loci, three were novel and associated with HF for the first time herein (Table 1, Fig. 2A and Supplemental Fig. 3). The other 2 genome-wide significant loci in our HF meta-analysis overlapped with the 57 previously known regions identified for HF^7–13 among populations of predominantly European ancestry. (Supplemental Table 3). However, despite the P-values not exceeding the genome-wide significance level of 5.0x10^− 8, our meta-analysis did yield evidence for association of 33 of the remaining 55 known HF loci at a Bonferroni-corrected threshold for testing 57 regions (P = 0.05/57 = 8.8x10^− 4), and all other 22 loci achieved significance level of 0.05 (Supplemental Table 3). Thus, our meta-analyses among East-Asian populations with the China PEACE 5p-HF/ChinaHEART and Biobank Japan replicated association signals at all known 57 regions.

Table 1

5 novel loci identified for heart failure (HF) and non-ischemic cardiomyopathy (NICM) in the meta-analysis.
SNP	CHR	BP (hg19)	Nearest Gene(s)	EA/NEA	EAF*	OR (95%CI)	P	P-het
				Meta-analysis for HF (13,385/214,211)**
rs17076	2q31.2	179,595,117	TTN	C/G	0.32	1.09 (1.06–1.12)	1.2E-09	0.12
rs12542527	8q24.13	125,876,444	MTSS1	A/G	0.61	1.08 (1.05–1.11)	2.6E-08	0.33
rs139616510	11p11.2	47,266,755	MYBPC3	T/C	0.005	1.74 (1.44–2.11)	1.2E-08	0.18
				Meta-analysis for NICM (3,603/399,497)***
rs2182400	10p11.23	29,707,551	SVIL-AS1	G/A	0.53	1.16 (1.10–1.22)	4.6E-08	0.53
rs75959316	13q34	113,065,400	MCF2L	A/C	0.009	1.48 (1.29–1.69)	1.3E-08	7.6E-39
HF, heart failure; NICM, non-ischemic cardiomyopathy; CHR, chromosome; BP, base pair position (hg19); EA, effect allele; NEA, non-effect allele; EAF, effect allele frequency (*only available from the China PEACE 5p-HF/ChinaHEART); OR, odds ratio; 95%CI, 95% confidence interval; P-het, p-value for heterogeneity between the China PEACE 5p-HF/ChinaHEART and Biobank Japan for meta-analysis of HF, and between the China PEACE 5p-HF/ChinaHEART and the UK Biobank for meta-analysis of NICM.
Number in parentheses indicate number of cases/controls.
**3 novel loci identified for HF in meta-analysis of the China PEACE 5p-HF/ChinaHEART and Biobank Japan.
***2 novel loci identified for NICM in meta-analysis of the China PEACE 5p-HF/ChinaHEART and the UK Biobank.

Sex-stratified meta-analysis for HF in the China PEACE 5p-HF/ChinaHEART and Biobank Japan: We next carried out sex-stratified meta-analysis for all-cause HF with GWAS results in the China PEACE 5p-HF/ChinaHEART and summary level GWAS data in Biobank Japan in males and females separately. The meta-analysis in males revealed 19 significantly associated variants distributed across 2 loci (Fig. 3A, Supplemental Fig. 4 and Supplemental Table 4). Of these, 1 locus (MYBPC3) overlapped with the 3 novel loci for all-cause HF identified in the combined analysis (Table 1) whereas the other one (CDKN1A) was a previously known locus for HF (Supplemental Table 3). Nonetheless, this meta-analysis failed to identify any genome-wide significantly associated SNPs in females (Fig. 3A and Supplemental Fig. 5). We further used sex-stratified meta-analysis results to explore how association signals at the 5 HF susceptibility loci differed in males and females. Interestingly, one of the novel loci (MYBPC3) at 11p11.2 yielded nominal P-value for interaction with sex (P-het) that was < 0.05, and the effect sizes were stronger in males (OR = 2.04, 95% CI 1.59–2.62, P = 1.8x10^− 8) than in females (OR = 1.34, 95% CI 0.98–1.82, P = 0.063) (Fig. 3B and Supplemental Table 5).

Identification of 2 Novel Loci for NICM: To facilitate the discovery of genetic signals that act independent of traditional HF risk factors, we performed a GWAS in 1,787 NICM cases and 11,171 controls in the China PEACE 5p-HF/ChinaHEART (Fig. 1 and Supplemental Table 1), followed by a meta-analysis with the UK Biobank⁸, since the NICM GWAS result is not available in Biobank Japan. This meta-analysis for NICM in 3,603 cases and 399,497 controls resulted in 51 significantly associated variants distributed across 3 loci (Supplemental Table 6, Fig. 2B and Supplemental Fig. 6). Of these loci, two (SVIL-AS1 and MCF2L) were novel and associated with NICM for the first time herein (Table 1 and Supplemental Fig. 7). The other locus (PITX2) overlapped with the 57 previously known regions identified for HF^7–13(Supplemental Table 3).

Association of Novel Loci with HF Risk Factors and Echocardiographic Traits: We next explored whether the novel genome-wide significant loci were associated with HF risk factors and echocardiographic traits. Based on publicly available data, the risk allele of lead variants at the TTN and MTSS1 loci identified in the meta-analysis for all-cause HF were associated with increased risk of atrial fibrillation (AF)¹⁴ at the Bonferroni-corrected P of 7.7x10^− 4 (0.05/65) for testing 5 loci and 13 traits (Supplemental Table 7). Moreover, the TTN, MTSS1, and SVIL loci were associated with reduced left ventricular ejection fraction (LVEF) and increased left ventricular end-systolic volume (LVESV) in a recent large-scale GWAS of echocardiographic traits¹⁵ (Supplemental Table 8). Since the lead SNP rs139616510 at the MYBPC3 locus has a low MAF (~ 0.1%) in European populations and a relatively higher MAF (0.5%) in East Asians, we alternatively evaluated its association with echocardiographic traits in a subset of the China PEACE 5p-HF Study who went through echocardiography (N = ~ 3000). Consistent with a role in left ventricular remodeling, the MYBPC3 locus was associated with increased left ventricular end-diastolic volume (LVEDV) (Supplemental Table 8).

Enrichment of HF-associated Variants in Biological Pathways or Tissues: To gain biological insight into the newly identified HF loci, we used bioinformatics tool to carry out enrichment analyses for regulatory elements and pathways. A Data-driven Expression-Prioritized Integration for Complex Trait (DEPICT) analysis revealed that the most significantly enriched biological pathways and tissues were abnormal myocardial trabeculae morphology, thin ventricular wall, actin monomer binding, abnormal heart development, heart atria, and atrial appendage (Supplemental Tables 9 and 10). Taken together, these results further highlight the myocardial structure and heart development as an important component in the pathophysiology of HF.

Prioritization of Positional Candidate Genes: To identify candidate causal genes at the newly identified loci, we first used multi-tissue gene expression (eQTL) and splice (sQTL) data from the GTEx Project (version 8)¹⁶ and other datasets, such as the eQTLgen Consortium, or previously published studies available through the PhenoScanner database¹⁷. For 2q31.2, 8q24.13 and 10q11.23, at least one candidate gene could be prioritized based on the lead SNP yielding a cis eQTL in one or more tissues relevant to HF, such as aorta, heart, and blood (Supplemental Table 11). Positional candidate genes with highly significant cis eQTLs were TTN, TTN-AS1 and FKBP7 on 2q31.2, MTSS1 on 8q24.13, and SVIL, SVIL-AS1, and PTCHD3P1 on 10q11.23 (Supplemental Table 11). We further prioritized loci by focusing on positional candidates that yielded eQTLs in heart tissues (atrial appendage, left ventricle) and skeleton muscle, since the heart is composed mostly of muscle and stromal tissue. SVIL and SVIL-AS1 on chromosomes 10q11.23 yielded the most significant eQTLs in these HF-related tissues (Supplemental Table 11). In support of the gene regulatory effect, the lead SNP on chromosomes 10q11.23 also displayed significant cis sQTL with SVIL and SVIL-AS1 in skeleton muscle in the GTEx Project (Supplemental Table 12). Supervillin, encoded by the SVIL gene, belongs to the villin/gelsolin superfamily of actin-binding proteins¹⁸, which plays a vital role in the structural integrity of muscle fibres in humans¹⁹. According to the Human Protein Atlas

(HPA) and GTEx Project, SVIL shows the highest expression in skeletal muscle, followed by heart muscle (Supplemental Fig. 8A). Furthermore, SVIL also shows the highest expression in skeletal myocytes and cardiomyocytes at a cellular level (Supplemental Fig. 8B), supporting a role for this protein in heart. In addition, the lead SNP on chromosome 10q11.23 yielded cis eQTLs for SVIL-AS1 in left ventricle and SVIL in whole blood, where the NICM risk allele (G) was associated with increased expression of SVIL-AS1 and decreased expression of SVIL (Fig. 4A and Supplemental Table 11). As a regulatory element, SVIL-AS1 may suppress the expression level of SVIL at multiple levels including transcription, post-transcription and epigenetic modification. Taken together, we prioritized SVIL as a strong causal positional candidate gene for functional validation (Fig. 1).

Reduction of SVIL aggravates the cardiomyocyte injury in the cell model of heart failure: In order to assess the role of SVIL, H9C2 cells were transfected with siRNA targeting SVIL, and the efficiency was measured by quantitative real-time PCR (RT-qPCR) (Supplemental Fig. 8C). In the cell model of heart failure, H9C2 cells were exposed to phenylephrine (PE) for 48 hours based on previous studies^20–22. Visible hypertrophic response was observed in PE-treated cells shown as cytoskeletal rearrangement in F-actin stress fibers (stained by FITC-labelled Phalloidin), and this effect was more profound when SVIL has been silenced (Fig. 4B, left panel). The cell surface area of siSVIL + PE group was increased to 1.2-fold (P < 0.001) compared to that in PE group (Fig. 4B, right panel). Overall, the data of cell morphology indicated that SVIL deficiency was prone to cause cardiomyocyte hypertrophy. Then, RT-qPCR was carried out to detect the gene expression of B-type natriuretic peptide (BNP) and atrial natriuretic peptide (ANP), whose elevation were used to diagnose heart failure. As shown in Fig. 4C, both expressions of BNP and ANP were significantly increased in siSVIL + PE group in contrast to those in control (siCtrl + Vehicle), although SVIL silencing alone had no obvious effect on those gene expressions. Moreover, we found that SVIL deficiency can further raise the expression level of BNP even though it had already been stimulated by PE. Next, cell apoptosis, a main pathogenesis of heart failure was assessed. The proportions of H9C2 cells that undergone early and late apoptosis induced by PE were significantly raised by SVIL siRNA interference (Fig. 4D), and SVIL deficiency alone could increase the proportion of cells in the state of early apoptosis (Fig. 4D, left panel). Furthermore, the level of cell viability was notably decreased owing to SVIL knockdown with PE exposure (Fig. 4E). All these observations revealed that the loss of SVIL could play a role in the pathogenic process of heart failure. Subsequently, the effect of SVIL on other genes associated with cardiomyopathy was assessed by western blot analysis (Fig. 4F). As expected, SVIL knockdown markedly decreased the levels of caveolin-3 and α-sarcoglycan which were previously identified in familial cardiomyopathy^22,23 .

Differentially expressed genes (DEG) and Gene Ontology (GO) Pathway analysis: To reveal the molecular mechanism associated with SVIL gene, a DEG analysis was performed to identify gene expression changes between SVIL knockdown and controls in H9C2 cells. A total of 435 DEGs (FDR ≤ 0.05 and fold-change ≥ 2.0) were detected, of which 187 genes were upregulated (higher expression in SVIL knockdown) and 248 genes were downregulated (Supplemental Table 13). According to the functional enrichment results, 17 GO pathways, including 13 biological process (BP) term, 3 cellular component (CC) term and 1 molecular function (MF) term were statistically significantly enriched in DEGs with rich factor > 5. The most enriched GO pathway is response to muscle activity, with 3 upregulated genes (PERM1, POSTN, PRKAA2) and 2 downregulated genes (AKAP5 and MYOG) involved (Supplemental Table 14). Among the 17 GO significant pathways, 5 involved the biological process and cellular component of heart muscle, including response to muscle activity, negative regulation of striated muscle cell differentiation, neuromuscular junction, myofibril assembly and regulation of myotube differentiation (Fig. 4G and Supplemental Table 14).

In the present study, we identified 3 novel regions associated with all-cause HF and 2 for NICM and replicated all 57 previously identified regions. Our results add to the growing body of genetic and bioinformatics evidence that left ventricular remodeling and sarcomere structure as important drivers of HF pathophysiology.

Our results highlighted the functional roles of several potential causal genes related to sarcomere structure based on the bioinformatic analyses and literature review. The 2q31.2 locus tagged by the lead SNP rs17076 harbored TTN, a gene that encodes for the largest-known human protein, at which rare truncating mutations were detected in 25% of familial DCM and 18% of idiopathic DCM²⁴. TTN has a vital role in the regulation of sarcomere assembly and the preservation of diastolic and systolic function²⁵. Since the lead SNP in our GWAS is intronic, we assessed whether it could affect expression or splicing of the TTN gene using the GTEx Project. Interestingly, the HF risk allele C was associated with increased intron-excision ratio of TTN gene and this splicing effect was stronger than its expression consequence in sarcomere relevant tissues including skeleton muscle and heart. While the role of truncation mutations of TTN in cardiomyopathy is accepted, it is likely the mutant allele would produce alterations in titin isoform composition or expression level with abnormal properties that contributes to HF.

When gene expression data were used to prioritize candidate causal genes, the MTSS1 (metastasis suppressor protein 1) gene emerged as a strong candidate causal gene for all-cause HF. MTSS1 interacts with actin cytoskeleton and cell membrane to regulate cell structure and intercellular junctions²⁶. When investigating the cis-associations with gene expression in the eQTLgen Consortium and GTEx database, the lead variant rs12542527 could be linked to HF via a gene regulatory effect on MTSS1 expression.In this regard, previous studies have shown the reduction in MTSS1 may promote favorable LV remodeling in both mice²⁷ and humans^28,29.

In our analysis, the lead SNP (rs139616510; C > T) yielded the strongest effect size for HF (OR = 1.74) out of all risk variants. Rs139616510 is in high LD (r² = 1) with the missense variant rs573916965, which leads to a rare (~ 0.5% frequency in East Asian populations, and ~ 0.5% in European populations) but computationally predicted deleterious Glu334Lys substitution in exon 12 of MYBPC3 encoding the cardiac myosin-binding protein C. Identified first in Japanese hypertrophic cardiomyopathy (HCM) patients³⁰, Glu334Lys mutation destabilizes its protein through the ubiquitin-proteasome system (UPS)³⁰, and contributes to cardiac dysfunction by altering the protein levels of Ca²⁺ handling proteins and action potential durations³¹. This is the first time Glu334Lys mutation being identified in large-scale genetic analysis for HF in the East Asian populations, and interestingly, this substitution was not specific to HCM as the Glu334Lys mutation was carried by 21 patients with a diagnosis of dilated cardiomyopathy (DCM) in the China PEACE 5p-HF/ChinaHEART. This hypothesis was further corroborated by the significant association of Glu334Lys mutation with increased LVEDV in the China PEACE 5p-HF/ChinaHEART, which are the structural and functional echocardiographic features of DCM.

Another important aspect of our study is the in vivo functional validation of SVIL as potential candidate causal gene at the chromosome 10q11.23 locus, which may point to a role of the pathways involving myocardial structure and heart development in the development of HF. In the China PEACE 5p-HF/ChinaHEART, the G allele of the lead SNP rs2182400 at the SVIL locus increase the risk of NICM with stronger magnitude (OR = 1.14, P = 6.5x10^− 4) than with all-cause HF (OR = 1.09, P = 3.5x10^− 3), indicating the association signal is not primarily mediated via ischemic conditions induced by CAD and MI. This hypothesis is further validated in the phenome-wide association approach (PheWAS) as there is no associations between rs2182400 and common heart failure risk factors including CAD, MI, AF, BP, BMI, lipid and T2D. Consistent with our observation, rs2182400 was only nominally associated with increased risk of all-cause HF (OR = 1.02, P = 2.2x10^− 3) from previous large-scale GWAS in European population¹². Unlike the loss of function variant, our observation suggest that a regulatory mediator is most likely involved, as the G allele of rs2182400 would potentially increase splicing and decrease SVIL gene expression level.

In cardiac and skeletal muscles, the most abundantly expressed isoform is archvillin-the largest (∼250 kDa), differentially spliced isoform 2 of supervillin^32,33. It is worth noting that loss of supervillain has been recently reported in four myopathy patients presented with myofibrillar structural abnormalities¹⁹. As a binding partner to major sarcolemma-associated proteins, supervillain is known to function as part of a system keeping the structural integrity of muscle fibres³⁴. Loss of supervillin leads to impaired myofibrillar cytoskeleton and formation of autophagic vacuoles¹⁹, which is responsible for cardiomyopathy with altered proteostasis and myofibrillar disorganization. Furthermore, the important role of supervillin in maintaining normal cardiac function was further validated with animal models. Previous study confirmed a significant decrease in atrial conduction velocity following morpholino knockdown of SVIL in a zebrafish model³⁵.

Multiple data from our functional and bioinformatics analyses support the mechanistic roles of SVIL in the pathogenesis of NICM. First, SVIL deficiency aggravated cardiomyocyte hypertrophy, apoptosis and impaired cell viability in phenylephrine (PE)-treated H9C2 cells. In addition, increased levels of HF markers were observed in SVIL knockdown H9C2 cell. Second, the sarcomere related protein (α-sarcoglycan and caveolin-3) were reduced when the expression of SVIL was inhibited. Specifically, previous studies have shown that the loss of caveolin-3, a component of caveolae, causes cardiac hypertrophy via hyperactivation of p42/44 MAPK cascade³⁶ and the dysregulation of muscle calcium homeostasis³⁷. Third, GO pathway analysis on DEGs by SVIL knockdown revealed that the function of SVIL might be mediated through pathways relevant to regulation and differentiation of heart muscle. In support of this notion, SVIL showed strong evidence of association with LVEF, LVESV and right ventricular ejection fraction in previous study^15,38. Nevertheless, we cannot rule out the possibility that SVIL could also influence HF via other pathways since SVIL has been identified as one of the strongest GWAS signals for the descending aorta in 39,688 UK BioBank individuals³⁹. Most strongly expressed in vascular smooth muscle cells within the aorta, increased SVIL expression corresponded to a larger descending aortic diameter. Since the exact physiological role of supervillin in the heart remains to be established, a better understanding of SVIL’s biological functions will be needed to determine its role in the development of HF, particularly with respect to NICM.

Males and females with HF showed different characteristics across the EF spectrum, and male sex is a key driver for HF with reduced ejection fraction (HFrEF)⁴⁰. However, few GWAS have examined sex differences in the genetic architecture of HF specifically. MYBPC3, one of the previously unknown loci for all-cause HF, was identified in the male-specific GWAS and showed stronger effect in males than in females. One explanation might be the higher expression of proteins associated with apoptosis (i.e., Fas and TNFR1) in males compared to females, whose level results in lower level of LV recovery for patients with recent-onset cardiomyopathy⁴¹. However, the missense lead SNP at the MYBPC3 locus was associated with decreased LVEF, increased LVEDV and LVESD to a greater extent in females than males in the China PEACE 5p-HF Study (data not shown). Previous studies have also shown that sex affects the penetrance and pathology of genetic cardiomyopathies with male being more severely affected⁴². Here, due to the unavailability of sex-stratified GWAS results in the UK Biobank, we did not perform sex-stratified meta-analyses for NICM. Whether sex-specific genetic associations exist will be an important area of future study.

While our results point to novel and distinct genetic determinants of HF and NICM, certain limitations should be taken into considerations. First, the China PEACE 5p-HF/ChinaHEART is primarily Chinese population, whereas the majority of subjects in the UK Biobank were of European ancestry and possibly a confounding factor for the NICM meta-analysis. However, the NICM GWAS result is not available in Biobank Japan, and we have controlled this potential bias by performing the heterogeneity test. Second, there could also have been misclassification in our analyses since, for example, HF and NICM may not have been defined exactly the same way in the China PEACE 5p-HF/ChinaHEART, the UK Biobank, and Biobank Japan. We note that if such misclassifications had occurred, they would have most likely been non-differential and biased the results towards the null. Finally, even though SNPs with MAF as low as 0.1% were included in our analyses, our study was primarily focused on discovery of main effects with common susceptibility alleles. However, rare variants or gene-environment interactions still likely play important roles in modulating risk of HF, which will require additional investigation.

In summary, large-scale genetics analyses identified 3 previously unknown risk loci for all-cause HF in East Asian populations and 2 loci for NICM in multi-ancestry populations. Follow up analyses demonstrated male-specific HF association at CDKN1A and MYBPC3 loci, and functionally validated SVIL as one candidate causal gene that are associated with subclinical left ventricular dysfunction. These results provide opportunities for further exploration of the biological mechanisms underlying the pathogenesis of HF and left ventricular remodeling, which may inform new therapeutic strategies.

Study Populations

The China Patient-centered Evaluative Assessment of Cardiac Events Prospective Heart Failure Study (China PEACE 5p-HF Study) enrolled patients hospitalized primarily for HF from 52 Chinese hospitals located in 20 provinces between August 2016 and May 2018. The protocol of the China PEACE 5p-HF Study has been published⁴³. In brief, 4,866 patients aged 18 years or older, and hospitalized with a primary diagnosis of new-onset HF or decompensation of chronic HF assessed by local physicians, were enrolled in the study, and 4,363were genotyped using Illumina genotyping arrays. In the current analysis, we used data from 3,972 all-cause HF cases and 1,787 NICM cases that passed genotyping QC procedures. The central ethics committee at Fuwai Hospital and local ethics committees at participating hospitals approved the China PEACE 5p-HF Study. The study was registered at www.clinicaltrials.gov (NCT02878811). The investigation conformed with the principles outlined in the Declaration of Helsinki.

The China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) project is a government funded public health programme designed for screening of cardiovascular disease risk and for intervention in community based populations throughout China. The design of ChinaHEART project has been described previously⁴⁴. Briefly, from 20 November 2014 through 31 December 2021, 351 county level regions in all 31 provinces in the mainland of China were selected as study sites to provide diversity in geographical distribution, population structure, and exposure to risk factors and disease patterns. Study site selection also considered population size, population stability, and local capacity to support the project. A total of 4.5 million residents were recruited, and 14,853 were genotyped using Illumina genotyping arrays. In this study, a subset of 11,171 controls who were free from cardiovascular disease (CVD) and HF at baseline and during follow up between 2014 and 2017 were available as control subjects for the China PEACE 5p-HF Study. The central ethics committee at the China National Center for Cardiovascular Diseases approved this study (approval no. 2014-574). All enrolled participants provided written informed consent.

Participating cohorts in the BioBank Japan have been previously described in detail⁴⁵. Briefly, Biobank Japan is a hospital-based Japanese national biobank project including data from approximately 200,000 patients enrolled between 2003-2007. Participants were recruited at 12 medical institutes throughout Japan.

The UK Biobank recruited participants between 40-69 years of age who were registered with a general practitioner of the UK National Health Service (NHS). Between 2006-2010, a total 503,325 individuals were included. All study participants provided informed consent and the study was approved by the North West Multi-centre Research Ethics Committee. Detailed methods used by the UK Biobank have been described elsewhere⁴⁶.

Phenotyping

All-cause HF cases included participants with a clinical diagnosis of HF of any aetiology from the China PEACE 5p-HF Study. To avoid any misclassification of cases, we also excluded samples with a diagnosis of hypertrophic cardiomyopathy (HCM) due to the substantial Mendelian inheritance pattern of hypertrophic cardiomyopathy. Among all-cause HF patients, nonischemic cardiomyopathy (NICM) was defined on the basis of left ventricular dysfunction and absence of coronary heart disease (CHD), acute myocardial infarction (AMI) and ischemic cardiomyopathy. Non-HF controls were those who were free from cardiovascular disease (CVD) and HF at baseline and during follow up between 2014 and 2017 from the ChinaHEART project to eliminate the possibility of CVD individuals progressing to HF in controls. This definition strategy resulted in 3,972 all-cause HF cases, 1,787 NICM cases and 11,171 controls.

Genotyping and imputation

Genome-wide genotyping of single-nucleotide polymorphisms (SNPs) in the China PEACE 5p-HF Study and the ChinaHEART project was performed on the Illumina Infinium Global Screening Array chip. Based on sample quality control procedures described in Supplemental Table 15, we excluded duplicate samples (positive controls) and samples with a low call rate (<90%), samples with first-degree relatives (prioritizing the cases and/or samples with higher call-rates from each pair) and samples with discordant ethnicity from Han population. Quality control procedures for genotyped SNPs excluded SNPs with significant departure from Hardy-Weinberg Equilibrium (HWE) in controls (P<1x10⁻⁶) and SNPs with a low call rate <97% (Supplemental Table 16). This resulted in 640,842 autosomal SNPs that were available for imputation in 15,143 participants.

Imputation procedures were performed using IMPUTE2⁴⁷ and genotype calls were based on phase3 1000 G Mixed population. Analyses with the imputed data set excluded SNPs with INFO scores <0.8 and with minor allele frequencies (MAF) <0.1% (Supplemental Table 16). This resulted in 6,976,826 autosomal SNPs that were available for a GWAS analysis in 15,143 Han Chinese subjects.

GWAS for HF and NICM in the China PEACE 5p-HF/ChinaHEART

Autosomal SNPs were tested for association with HF and NICM using logistic regression, assuming additive genetic effects. GWAS analyses for HF were also conducted in a sex-specific fashion. GWAS analyses were performed with PLINK v2.0 with adjustment for age, sex (except for sex-stratified GWAS), and the first 3 principal components⁴⁸. The genome-wide significance threshold was set at P=5.0x10^-8. Manhattan and quantile-quantile plots were constructed using ‘qqman’ package (v0.1.8) in R.

Meta-analyses for HF in the China PEACE 5p-HF/ChinaHEART and BioBank Japan

Publicly available summary statistics for HF with 8,678,732 SNPs from the Biobank Japan with 9,413 cases and 203,040 controls were downloaded¹¹. We carried out a fixed-effects meta-analysis in 13,385 cases and 214,211 controls with 5,887,000 SNPs common to the China PEACE 5p-HF/ChinaHEART and the Biobank Japan datasets assuming an additive model, as implemented in METAL⁴⁹. The genome-wide threshold for significant association was set at P=5.0x10^-8. A locus was defined as novel if our lead SNP was >1Mb away or in weak or no (r²≤0.1) linkage disequilibrium (LD) with the lead variants at the 57 previously reported loci for HF^7-13. Replication of the 57 known HF loci was considered significant at a Bonferroni-corrected threshold of P=8.8x10^-4 (0.05/57).

Sex-stratified Meta-analyses for HF in the China PEACE 5p-HF/ChinaHEART and BioBank Japan

Sex-stratified meta-analyses for all-cause HFassociation was carried out with GWAS result in the China PEACE 5p-HF/ChinaHEART and summary level data in BioBank Japan¹¹ in males (8,419 HF cases and 107,613 controls) and females (4,966 HF cases and 106,598 controls) separately (Supplemental Table 1). Cochran’s Q statistics for heterogeneity P-values between males and females (P-het) were considered significant at the Bonferroni-corrected threshold for testing 5 novel loci (0.05/5=0.01).

Meta-analyses for NICM in the China PEACE 5p-HF/ChinaHEART and the UK BioBank

Publicly available summary statistics for NICM with 7,736,183 SNPs from the UK Biobank with 1,816 cases and 388,326 controls were downloaded⁸. We carried out a fixed-effects meta-analysis in 3,603 cases and 399,497 controls with 4,566,441 SNPs common to the China PEACE 5p-HF/ChinaHEART and the UK Biobank datasets assuming an additive model, as implemented in METAL⁴⁹. The genome-wide threshold for significant association was set at P=5.0x10^-8. A locus was defined as novel if our lead SNP was >1Mb away or in weak or no (r²≤0.1) linkage disequilibrium (LD) with the lead variants at the 57 previously reported loci for HF^7-13.

Association of novel loci with HF risk factors

The lead variants at the newly identified HF loci were evaluated for association with various HF risk factors from previously published studies, including body mass index (BMI)⁵⁰, blood pressure (systolic blood pressure, diastolic blood pressure, and pulse pressure)⁵¹, plasma lipids (total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides)⁵², estimated glomerular filtration rate (eGFR)⁵³, atrial fibrillation (AF)¹⁴, coronary artery disease (CAD)⁵⁴, myocardial infarction (MI)⁵⁵, and type 2 diabetes (T2D)⁵⁶. A Bonferroni-corrected P=7.7x10^-4 (0.05/65) for testing 5 loci and 13 traits was considered as the threshold for significant associations with HF risk factors.

Association of novel loci with cardiac magnetic resonance imaging (MRI)-derived left ventricular measure

Evaluation of the novel HF loci for association with cardiac magnetic resonance imaging (MRI)-derived left ventricular measure, including left ventricular ejection fraction (LVEF), left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), and stroke volume (SV) were carried out using a previously published study in the UK Biobank¹⁵. Associations with P≤ Bonferroni-corrected threshold for testing 5 SNPs and 4 traits (0.05/20=2.5x10^-3) were considered significant.

Data-driven Expression-Prioritized Integration for Complex Traits (DEPICT)

Additional gene prioritization analysis and tissue enrichment analysis were carried out by DEPICT (version 1.1)⁵⁷ using 207 SNPs with false discovery rates (FDRs) < 0.05 (P<1.75x10^-6) associated with HF that were identified in the meta-analysis with the China PEACE 5p-HF/ChinaHEARTand BioBank Japan. Both nominal P-values and FDRs were calculated for gene set enrichment and tissue enrichment.

Expression Quantitative Trait Locus (eQTL) and Splice Quantitative Trait Locus (sQTL) analyses

Functional evaluation of SNPs at the 5 novel HF loci and prioritization of candidate causal genes was determined using multi-tissue expression (eQTL) and splice (sQTL) data from the GTEx Project (version 8)¹⁶ and other datasets, such as the eQTLgen Consortium⁵⁸, the Human Protein Atlas (HPA)⁵⁹ or previously published studies available through the PhenoScanner database¹⁷. Consideration was only given to cis eQTLs and cis sQTLs with P<5.0x10⁻⁵that were derived from our lead GWAS variants.

Cell culture and treatment

H9C2 cells obtained from ATCC (Manassas, VA, USA) were cultured in Dulbecco’s modified Eagle’s medium (iCell Bioscience) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin/streptomycin (Gibco) in a humidified incubator with 5% CO₂at 37℃. When the cell coverage rate reached 70%, trypsin (Gibco) was used to dissociate H9C2. In the cell model of heart failure, phenylephrine (PE) was utilized as agonists according to previous studies^60-62. Firstly, H9C2 cells were seeded in 12-well culture plates or confocal dishes at the density of 2×10⁵ cells per well, and then incubated in complete medium for 24 h. Next, serum-free medium was adopted for another 24 h, siRNA transfection could be performed in the meanwhile. Subsequently, PE dissolved in PBS was added to the medium at a final concentration of 100 μM for 48 h. Equal volumes of PBS (vehicle) were administered in control group. After incubation, subsequent experiments were performed, including total RNA extraction for RT-qPCR and cell surface area measured by staining of FITC-labelled Phalloidin. All the cell experiments were repeated three times independently.

SiRNA transfection

The expression levels of SVIL in H9C2 cells were suppressed by siRNA-mediated knockdown targeting SVIL gene. Lipofectamine RNAimax (Invitrogen) was utilized for siSVIL (RiboBio) or non-targeting siRNA (siCtrl) transfection following the manufacturer’s instruments.

In vitro hypertrophy assays

H9C2 cells prepared for immunofluorescence microscopy were seeded in confocal dishes and exposed to hypertrophic agonist PE for 48 h. After activation, cells were fixed in 4% paraformaldehyde and stained with FITC-conjugated phalloidin (Biosharp) for 30 min. DAPI was used for cell nuclei staining. A Leica TCS SP8 STED microscope and Image J software was used to acquire images and measure cell surface area. Fold changes in the average surface area of treated cells were calculated relative to that of untreated ones.

Quantitative real-time PCR

Total RNA was extracted from H9C2 cells using the TRIzol reagent (Invitrogen) as described in the manufacturer’s manual. The obtained RNA was then used for cDNA synthesis with HiScript III RT SuperMix (Vazyme) and subsequently performed qPCR with AceQ qPCR SYBR Green Master Mix (Vazyme). The mRNA expression levels of BNP and ANP were normalized to the housekeeping gene GAPDH. Relative gene expression levels were calculated using ΔΔCt-method. The following primers were used for qRT-PCR: BNP, forward, 5’-GAACAATCCACGATGCAGAAGC-3’ and reverse, 5’-GGGCCTTGGTCCTTTGAGAG-3’; ANP, forward, 5’-GAGGAGAAGATGCCGGTAG-3’ and reverse, 5’- CTAGAGAGGGAGCTAAGTG-3’.

Western blot

For immunoblotting, cells were homogenized in cell lysis buffer (150 mM NaCl, 50 mM HEPES pH 7.4, 1% Triton X-100). Bicinchoninic Acid (BCA) Protein Assay Kit (Thermo Fisher) was used to determine the protein concentration of the samples. Samples were loaded on 10% SDS-PAGE gels and then transferred to polyvinylidene fluoride (PVDF) membranes. The PVDF membrane was incubated in blocking buffer (Beyotime) for 1 h at room temperature. Subsequently, primary antibodies diluted as 1:1000 were applied to the blot overnight at 4℃ with gentle rocking. Antibodies were purchased as follows: supervillin (Santa; sc-53556), caveolin-3 (Proteintech; 28358-1-AP), α-sarcoglycan (Santa; ab189254), GAPDH (ZSGB-bio; TA-08). HRP‑labeled antibody diluted as 1:5000 was used as the secondary antibody and incubated with the membrane for 1 h. Enhanced chemiluminescence (ECL) solution was added on the membrane for its development and photograph.

Cell apoptosis detection

Annexin V-FITC Apoptosis Detection Kit (abcam, ab14085) was used to evaluate the effect of SVIL deficiency and PE treatment on H9C2 cell apoptosis. In brief, H9C2 cells were collected and washed twice with PBS precooled at 4°C. After centrifugation, the supernatant was discarded and the cell pellet was resuspended with 500 μL of 1 × binding buffer. Then, 5 μL annexin V-FITC and 5 μL propidium iodide (PI) were added, mixed gently, and incubated at room temperature in darkness for 15 min. A cell group without staining was set as negative control, and a single dye staining group (stained with only one dye) were set for the voltage of flow cytometry and its compensation, and the apoptosis rate of the cells was detected. Finally, the data were analyzed and plotted with FlowJo V10.

Cell viability assay

The Cell Counting Kit 8 (CCK8) (YEASEN) was applied to measure cell viability. H9C2 cells were seeded into 96-well plates at a density of 2×10³ cells/well. After 48 h-treatment, 10 μL CCK8 solution was added into the wells and the cells were incubated for 4 h at 37℃ in the dark. Finally, the absorbance was measured at 450 nm by a microplate reader.

Library construction for RNA-seq and sequencing procedures

Total RNA was isolated using MJzol animal RNA Extraction Kit (MagBeads). Paired-end libraries were synthesized using the TruSeq® RNA Sample Preparation Kit (Illumina, USA) following TruSeq® RNA Sample Preparation Guide. Briefly, the poly-A containing mRNA molecules were purified using poly-T oligo-attached magnetic beads. Following purification, the mRNA was fragmented into small pieces using divalent cations under 94℃ for 8 min. The cleaved RNA fragments were copied into first strand cDNA using reverse transcriptase and random primers, followed by second strand cDNA synthesis using DNA Polymerase I and RNase H. These cDNA fragments then went through an end repair process, the addition of a single ‘A’ base, and then ligation of the adapters. The products were purified and enriched with PCR to create the final cDNA library. Purified libraries were quantified by Qubit® 2.0 Fluorometer (Life Technologies, USA) and validated by Agilent 2100 bioanalyzer (Agilent Technologies, USA) to confirm the insert size and calculate the mole concentration. Cluster was generated by cBot with the library diluted to 10 pM and then sequenced on the Illumina Nova 6000 (Illumina, USA).

Data analysis and identification of differentially expressed genes (DEGs)

Sequencing raw reads were preprocessed by filtering out rRNA reads, sequencing adapters, short-fragment reads and other low-quality reads. We used Hisat2 (version:2.0.4)⁶³ to map the cleaned reads to the rat Rnor_6.0 reference genome with two mismatches. After genome mapping, Stringtie (version:1.3.0)^64,65 was used with a reference annotation to generate fragments per kilobase per million reads (FPKM) values for known gene models. Differentially expressed genes (DEGs) were identified using edgeR⁶⁶. The p-value significance threshold in multiple tests was set by the FDR. The fold-changes were also estimated according to the FPKM in each sample. The DEGs were selected using the following filter criteria: FDR ≤0.05 and fold-change ≥2.

Gene Ontology (GO) enrichment analysis

For functional enrichment analysis, all DEGs were mapped to terms in the GO databases, and then significantly enriched GO terms were defined with P<0.05 and rich factor ≥5 as the threshold. GO term analysis was classified into three subgroups, namely biological process (BP), cellular component (CC) and molecular function (MF).

URLs. Biobank Japan, https://biobankjp.org/english/index.html; The UK Biobank, https://www.ukbiobank.ac.uk/ ; METAL, http://csg.sph.umich.edu/abecasis/metal/index.html;

Genotype-Tissue Expression Project, http://gtexportal.org/; Phenoscanner, http://www.phenoscanner.medschl.cam.ac.uk/phenoscanner; The Human Protein Atlas, https://www.proteinatlas.org; The eQTLgen Consortium, https://www.eqtlgen.org; R statistical software, http://www.R-project.org/.

Data Availability Statement

Full summary statistics relating to the meta-analysis with the China PEACE 5p-HF/ChinaHEART, the Biobank Japan and the UK Biobank will be deposited with The NHGRI-EBI Catalog of published genome-wide association studies (https://www.ebi.ac.uk/gwas/docs/about). All other relevant data are available upon request from the authors.

Acknowledgments

This project was supported by the Medical and Health Technology Innovation Project of the Chinese Academy of Medical Sciences (2021-I2M-1-009),the National Key Technology R&D Program (2015BAI12B02) from the Ministry of Science and Technology of China, the CAMS Innovation Fund for Medical Science (2021-I2M-1-011),and Beijing Nova Program (Z211100002121035).

Author Contributions

Concept and design: Yi Han, Yun Hong, Xi Li, and Jing Li. Acquisition, analysis, and interpretation of data: Yi Han, Yun Hong, Yan Gao, Jiapeng Lu, Bowang Chen, Lihuang Zhang, Xiaofang Yan, Ying Sun, Liping Zhang, Jiangling Liu, Xi Li, and Jing Li. Drafting of the manuscript: Yi Han, Yun Hong, Xi Li, and Jing Li. Critical revision of the manuscript for important intellectual content: All authors.

Disclosures

None.

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Genome-wide analysis identifies novel susceptibility loci for heart failure and nonischemic cardiomyopathy subtype

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