Identification of novel genetic susceptibility loci for thoracic and abdominal aortic aneurysms via genome-wide association study using the UK Biobank Cohort

Tamara Ashvetiya; Sherry X Fan; Yi-Ju Chen; Charles H Williams; Jeffery R O’Connell; James A Perry; Charles C Hong

doi:10.1371/journal.pone.0247287

. 2021 Sep 1;16(9):e0247287. doi: 10.1371/journal.pone.0247287

Identification of novel genetic susceptibility loci for thoracic and abdominal aortic aneurysms via genome-wide association study using the UK Biobank Cohort

Tamara Ashvetiya ^1,^#, Sherry X Fan ^1,^#, Yi-Ju Chen ¹, Charles H Williams ¹, Jeffery R O’Connell ¹, James A Perry ^1,^*, Charles C Hong ^1,^*

Editor: Danillo G Augusto²

PMCID: PMC8409653 PMID: 34469433

Abstract

Background

Thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) are known to have a strong genetic component.

Methods and results

In a genome-wide association study (GWAS) using the UK Biobank, we analyzed the genomes of 1,363 individuals with AAA compared to 27,260 age, ancestry, and sex-matched controls (1:20 case:control study design). A similar analysis was repeated for 435 individuals with TAA compared to 8,700 controls. Polymorphism with minor allele frequency (MAF) >0.5% were evaluated.

We identified novel loci near LINC01021, ATOH8 and JAK2 genes that achieved genome-wide significance for AAA (p-value <5x10^-8), in addition to three known loci. For TAA, three novel loci in CTNNA3, FRMD6 and MBP achieved genome-wide significance. There was no overlap in the genes associated with AAAs and TAAs. Additionally, we identified a linkage group of high-frequency variants (MAFs ~10%) encompassing FBN1, the causal gene for Marfan syndrome, which was associated with TAA. In FinnGen PheWeb, this FBN1 haplotype was associated with aortic dissection. Finally, we found that baseline bradycardia was associated with TAA, but not AAA.

Conclusions

Our GWAS found that AAA and TAA were associated with distinct sets of genes, suggesting distinct underlying genetic architecture. We also found association between baseline bradycardia and TAA. These findings, including JAK2 association, offer plausible mechanistic and therapeutic insights. We also found a common FBN1 linkage group that is associated with TAA and aortic dissection in patients who do not have Marfan syndrome. These FBN1 variants suggest shared pathophysiology between Marfan disease and sporadic TAA.

Introduction

Aortic aneurysms (AA) carry a significant burden of morbidity and mortality. In 2018, thoracic aortic aneurysms (TAA) and abdominal aortic aneurysms (AAA) together were responsible for 9,923 deaths in the United States, typically from complications such as aortic dissection and rupture [1]. AA primarily affect elderly males in the sixth and seventh decades of life with risk factors of tobacco use, hypertension and atherosclerosis [1]. However, AAs found in patients younger than 65 years are more often attributed to genetic predisposition. For AAA, individuals with first-degree family members with the disease are at two-fold risk of developing AAA as compared to patients with no family history [2]. Genetic studies of TAA and AAA have revealed genetic heterogeneity and polygenic inheritance patterns with variable disease penetrance [2–4].

For thoracic aortic aneurysms and dissection (TAAD), rare monogenetic syndromic disorders such as Marfan, Ehlers-Danlos and Loeys-Dietz syndromes are known to dramatically increase risk in younger individuals [5]. This risk results from mutations in FBN1, COL3A1, and genes that encode TGF- β signaling proteins respectively [5]. Beyond these, up to one-fifth of patients with TAAD have a familial predisposition toward aneurysmal disease [5]. For instance, mutations in FBN1 (fibrillin-1), the causal gene for Marfan syndrome, may increase the risk of thoracic aortic aneurysms or dissections even in individuals who do not have Marfan syndrome [4,6,7]. Additionally, rare mutations in ACTA2, encoding smooth muscle protein alpha (α)-2 actin, may account for up to 14% of familial forms of TAAs [8]. Furthermore, genetic studies of TAAD show it is closely associated with bicuspid aortic valve, another condition with strong heritability [5,9].

Since AA has a strong genetic component in certain individuals, an enhanced understanding of these factors may ultimately aid the early detection of this silent disease before it progresses into life-threatening aortic dissections and ruptures. There is evidence to suggest that a strong personal or family history of aneurysms or dissection in individuals under the age of 50 should be an indication for genetic testing to diagnose inherited aortopathy [10]. In appropriately selected patients with suspected familial aneurysmal disease, the yield of genetic testing could be as high as 36% [10]. Yet, much of the underlying genetic risk factors remain unknown.

In this paper, we address the discovery of novel genetic loci that may portend increased risk for the development of thoracic and abdominal aortic aneurysms based on a genome-wide association study (GWAS) using data from the UK Biobank [11]. The UK Biobank allows for powerful association studies with tremendous potential for expanding knowledge on the genetic basis of aortic aneurysms, as well as uncovering novel genetic variants associated with clinical diseases.

Methods

Ethical approval

The present study, which involved deidentified data obtained from the UK Biobank Resource under Application Number 49852, received the proper ethical oversight, including the determination by the University of Maryland, Baltimore Institutional Review Board that the study is not human research (IRB #: HF-00088022).

Study population

The UK Biobank, which was used for the GWAS presented here, is a large, ongoing prospective cohort study that recruited 502,682 UK participants between 2006–2010, ranging in age from 40–69 years at the time of recruitment. Extensive health-related records were collected from these participants, including clinical and genetic data, with over 820,000 genotyped single nucleotide polymorphisms (SNPs) and up to 90 million imputed variants available for most individuals. We carried out a genome-wide association study using the UK Biobank to interrogate the genome for statistically significant associations between SNPs and clinical manifestations of abdominal and thoracic aortic aneurysms at the population level.

Genome-wide association study (GWAS)

Using data from the UK Biobank Resource on 487,310 subjects with imputed genotypes, we performed quality control by removing those with genetic relatedness exclusions (Data-Field 22018—UKB, https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=22018; 1532 subjects), sex chromosome aneuploidy (Data-Field 22019 –UKB, https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=22019; 651 subjects), mismatch between self-reported sex and genetically determined sex (Data-Field 31 –UKB, https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=31; Data-Field 22001 –UKB, https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=22001; 372 subjects), recommended genomic analysis exclusions (Data-Field 22010—UKB, https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=22010; 480 subjects), and outliers for heterozygosity or missing rate (Data-Field 22017 –UKB, https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=22077; 968 subjects). For “cases” we selected subjects with the following ICD10 (international classification of diseases) diagnostic codes, classified as either “main” or “secondary”: “abdominal aortic aneurysm, without mention of rupture” (1,363 patients, ICD10 code I71.4) and “abdominal aortic aneurysm, ruptured” (131 patients, ICD10 code I71.3). The selected set was purged of relatedness by removing one of each related pair in an iterative fashion until no related subjects remained. A pool of possible control subjects was generated by removing the cases and removing subjects with ICD10 codes listed as “excluded from controls” in S1 Table. Subjects with these ICD10 codes were removed from the population of controls to avoid introducing confounding factors, specifically the TAA and aortic valvular disorders, in the analysis. The pool was further reduced by removing related subjects. From the resulting reduced pool of possible controls, 20 control subjects were selected for each case subject, matched by sex, age and ancestry with sex as a required match (n = 27,260 controls). Incremental tolerances were used for age and ancestry with tolerances being expanded with each iteration until the desired number of matching controls were found for each case subject. The age tolerance ranged from 0 (exact match) up to 7 years. Ancestry matching was performed using principal components (PCs) supplied by the UK Biobank. The mathematical distance in a graph created by plotting the PC1 and PC2 eigenvalues provided by the UK Biobank was used to test ancestry similarity. The ancestry “distance” tolerated ranged from 2 PC units up to a maximum of 80 PC units (S1 Fig), where PC1 ranged from 0 to +400 and PC2 ranged from -300 to +100 units. Using these tolerances, 20 matching controls were found for every case. The analysis was repeated with patients who carried either a main or secondary diagnosis of “thoracic aortic aneurysm, without mention of rupture” (435 patients, ICD10 code I71.2) or “thoracic aortic aneurysm, ruptured” (22 patients, ICD10 code I71.1). Case to control ratio was again set at 1:20 (n = 8,700 controls), and patients with the diagnoses listed in S2 Table were excluded from the control population. Subjects with these ICD10 codes were removed from the population of controls to avoid introducing confounding factors, specifically the AAA and aortic valvular disorders, in the analysis.

The association analysis was performed with PLINK2 using logistic regression [12]. The thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) phenotypes were run against 40 million imputed variants supplied by the UK Biobank with imputation quality scores greater than 0.70. The analysis included covariates of sex, age, and principal components 1 through 5 to adjust for ancestry. Pre-calculated PC data for the first 40 principal components was supplied by the UK Biobank. Our preliminary analysis showed that only the first 5 PCs had significance with p-values less than 0.05. Thus, we used only the first 5 PCs in our GWAS.

Identification of significant SNPs for AAA and TAA Phenotypes

The SNPs identified in the analysis were filtered to include only those with minor allele frequency (MAF) of at least 0.5% and p-value <1 x 10⁻⁶, which is suggestive of genome-wide significance.

Results

Abdominal aortic aneurysm

Of the 1,363 individuals documented to have abdominal aortic aneurysms, 131 (9.61%) had rupture of the aneurysm (Table 1). Their baseline characteristics versus matched controls are shown in Table 1. The affected patients ranged in age at diagnosis from 42.42 to 79.04 years (mean = 68.08); 86.72% were male and 13.28% female. Genetic ancestry was predominantly British (93.54%); however, patients were also represented from Irish (2.64%), Indian (0.22%), Caribbean (0.51%), and African (0.15%) backgrounds.

Table 1. Basic demographics and characteristics of individuals with abdominal aortic aneurysm (AAA) and thoracic aortic aneurysm (TAA), and the respective matched (for age, sex, ancestry) controls in UK Biobank.

	AAA Cases	Matched^* Controls	TAA Cases	Matched^* Controls
Individuals (#)	1363	27260	435	8700
# with rupture	131 (9.6%)	0 (0%)	22 (5.1%)	0 (0%)
Mean Age (yr) at diagnosis or assessment ^*	68.1	68.1	65.3	65.3
Male (%) ^*	86.7	86.7	68.7	68.7
British Ancestry (%) ^*	93.5	93.5	87.6	87.6
BMI	28.7^**	27.8	28.0	27.7
Height (in)	68.2	68.1	67.9^**	67.3
Waist (in)	39.38^*8	37.9	37.6	36.9
Weight (lbs)	191^**	184	184	179
Diastolic BP	83.3	83.3	81.3^**	83
Systolic BP	145	147	143	144
Pulse Rate	69.8	69	67^**	69.2

Open in a new tab

*Characteristics that were matched.

**P-value <0.05 in comparison to matched controls.

Based on GWAS of these 1,363 individuals, we identified four independent loci near the LINC01021, ADAMTS8, ATOH8 and JAK2 genes (7 SNPs in total) that achieved a genome-wide significance level of p-value <5 x 10⁻⁸ for variants with MAF ≥0.5% (Fig 1A). Of these, LINC01021, ADAMTS8 and JAK2 represent novel loci associated with AAA, and each of the four loci harbors SNPs that possess features suggestive of functional importance, with biological plausibility as disease susceptibility loci (Table 2). In addition, we found 7 additional variants in CDKN2B-AS1 and CELSR2, while not reaching genome-wide significance, are within the suggestive threshold for significance (p-value <1 x 10⁻⁶), replicate findings from earlier studies and possess a strong basis for biologic plausibility (Table 2). Full GWAS results for AAA are included in S5 Table for variants with MAF ≥0.5% and p-value <1 x 10⁻⁶. Quantile-quantile plots (QQ Plots) are provided in S2 Fig to illustrate that the GWAS quality was well controlled.

Fig 1 — **(A)** Manhattan plot of GWAS results (MAF >0.5%) for AAA. Significance is displayed on the y-axis as -log₁₀ of the p-value, with results ordered along the x-axis by chromosome (each bar represents a different chromosome). **(B-G)** Prevalence of abdominal aortic aneurism (AAA) per 100,000 participants in the UK Biobank by genotype. Bars labeled with ratio of cases: Controls. **(B)** Prevalence of AAA decreases with *ADAMTS8* variant rs7936928 status (P-value = 7.51x10^-9, OR per T allele = 0.786). Decrease in AAA prevalence is noted in the homozygotes for the minor allele (T/T) in comparison to the heterozygotes (C/T) and the noncarriers (C/C) in a stepwise, “dosage-dependent” manner. **(C)** Prevalence of AAA increases with *JAK2* variant rs193181528 status (P-value = 3.26x10^-8, OR per C allele = 2.776). **(D)** Prevalence of AAA increases with *ATOH8* variant rs113626898 status (P-value = 9.06x10^-9, OR per A allele = 2.714). **(E)** Prevalence of AAA increases with *LINC01021* variant rs116390453 status (P-value = 4.26 x10^-9, OR per T allele = 2.505). **(F)** Prevalence of AAA decreases with *CDKN2B-AS1* variant rs1537373 status (P-value = 6.68x10^-7, OR per T allele = 0.8211). **(G)** Prevalence of AAA decreases with *CELSR2* variant rs12740374 status (P-value = 2.04x10^-7, OR per T allele = 0.7668).

Table 2. Top SNPs associated with abdominal aortic aneurysm.

SNP	Chr: BP (GRCh37)	Allele	Nearest Gene	Type	MAF (%)	OR (95% CI)	P-value
rs116390453	5:27,997,008	C/T	*LINC01021*	Intergenic	0.77	2.505 (1.84–3.4)	4.26x10^-9
rs7936928	11:130,279,168	C/T	ADAMTS8	Intronic	39.3	0.786 (0.724–0.853)	7.51x10^-9
rs4936099	11:130,280,725	A/C	ADAMTS8	Intronic	40.7	0.789 (0.727–0.856)	1.05x10^-8
rs11222084	11:130,273,230	A/T	ADAMTS8	Intergenic	36.7	0.785 (0.723–0.853)	1.12x10^-8
rs3740888	11:130,278,210	T/C	ADAMTS8	Intronic	39.4	0.790 (0.728–0.858)	1.59x10^-8
rs113626898	2:86,015,431	G/A	*ATOH8*	UTR3	0.59	2.714 (1.93–3.82)	9.06x10^-9
rs193181528	9:5,059,543	T/C	*JAK2*	Intronic	0.58	2.776 (1.93–3.99)	3.26x10^-8
rs1537373	9:22,103,341	G/T	CDKN2B-AS1	ncRNA	49.4	0.821 (0.76–0.887)	6.68x10^-7
rs12740374	1:109,817,590	G/T	CELSR2	3’-UTR	21.8	0.767 (0.694–0.848	2.04x10^-7
rs629301	1:109,818,306	T/G	CELSR2	3’-UTR	22	0.770 (0.697–0.851)	2.78x10^-7
rs646776	1:109,818,530	T/C	CELSR2	downstream	22	0.770 (0.697–0.851)	2.83x10^-7
rs3832016	1:109,818,158	C/CT	CELSR2	3’-UTR	21.3	0.768 (0.694–0.85)	3.35x10^-7
rs660240	1:109,817,838	C/T	CELSR2	3’-UTR	21.3	0.771 (0.696–0.853)	4.36x10^-7
rs7528419	1:109,817,192	A/G	CELSR2	3’-UTR	22	0.777 (0.703–0.858)	6.81x10^-7

Open in a new tab

7 variants in 4 genes, LINC01021, ADAMTS8, ATOH8 and JAK2, reached genome-wide significance P-value of < 5 x10^-8, while 7 additional variants in CDKN2B-AS1 and CELSR2, while not statistically significant, replicated findings from earlier studies. Of these, LINC01021, ATOH8 and JAK2 are novel AAA-associated loci identified in the present study (bold faced). Chr:BP denotes the chromosome location and NCBI Build 37 SNP physical position. Variants that are in linkage disequilibrium (LD) are identically colored. MAF, minor allele frequency. OR, odds ratio.

Among the SNPs with genome-wide significance, we identified a linkage group of 4 variants in close proximity to the ADAMTS8 gene, which encodes the ADAM metallopeptidase with thrombospondin type 1 motif 8, an inflammation-regulated enzyme expressed in macrophage-rich areas of atherosclerotic plaques [13] (Table 2 and Fig 1B; p-values 7.51 x 10⁻⁹–1.59 x 10⁻⁸, MAF 36.7%-40.7%, and odds ratios 0.785–0.790). Prior studies have described upregulation of ADAMTS8 in the macrophages of patients with abdominal aortic aneurysms [14]. Of note, the ADAMTS8 locus was recently identified among 14 novel AAA-risk loci identified from a study of the Million Veteran Program [15].

Other notable variants identified in this genome-wide analysis include the intronic variant rs193181528 (Table 2 and Fig 1C; p-value 3.26 x 10⁻⁸, MAF 0.58%, OR 2.776), located within the gene encoding JAK2 tyrosine kinase. Mutations in JAK2 are suspected to play a potential role in the progression of AAAs [16,17]. Among human aortic tissues collected from patients undergoing AAA surgery, JAK2 expression levels were higher in patients with AAA as compared to controls [16]. Treatment with JAK2/STAT3 pathway inhibitors attenuated experimental AAA progression by reducing the expression of pro-inflammatory cytokines and matrix metalloproteinases as well as inflammatory cell infiltration [16,17].

The analysis also identified variant rs113626898 (Table 2 and Fig 1D; p-value 9.06 x10^-9, MAF 0.59%, OR 2.714) in the gene encoding atonal bHLH transcription factor 8 (ATOH8), which plays a role in myogenesis and contributes to endothelial cell differentiation, proliferation and migration [18]. Dysregulation of this gene could plausibly contribute to aneurysmal formation given its important role in the cell cycles of myocytes and endothelial cells.

Variant rs116390453 (Table 2 and Fig 1E; p-value 4.26 x 10⁻⁹, MAF 0.77%, OR 2.505) is within a long intergenic non-coding RNA (LINC01021), also known as p53 upregulated regulator of p53 levels (PURPL). To date, PURPL has not been linked to aneurysmal formation.

In addition, we identified several distinct variants that do not reach the standard threshold for genome-wide significance for association with AAA, but are nevertheless within the suggestive threshold for genome-wide significance (p-value <1 x 10⁻⁶) (Table 2). Variant rs1537373 (Fig 1F; p-value 6.68 x 10⁻⁷, MAF 49.9%, OR 0.821) in CDKN2B-AS1, encoding the long non-coding RNA known as cyclin dependent kinase inhibitor CDKN2B antisense RNA1, is located within the CDKN2B-CDKN2A gene cluster at chromosome 9p21, a major genetic susceptibility locus for coronary artery disease, atherosclerosis and myocardial infarction [19]. This locus has also been previously associated with intracranial aneurysm and AAA formation [15,20–23]. Thus, CDKN2B-AS1 variant rs1537373 may increase the risk of AAA formation indirectly through the development of atherosclerosis, a major clinical risk factor for AAA.

Finally, our analysis identified a linkage group of six SNPs within the CELSR2 gene, encoding the cadherin EGF LAG seven-pass G-type receptor 2 (Table 2 and Fig 1G; p-values 2.04 x10-7–6.81 x10^-7, MAF 21.3–22%, OR 0.767–0.777). While CELSR2 SNPs do not meet traditional P-value cutoff for genome-wide significance, our findings corroborate prior GWAS associations of CELSR2 with AAA [15,24,25], and identify new common SNPs that are associated with AAA (specifically rs3832016 and rs660240) (Table 2).

Thoracic aortic aneurysm

Of the 435 individuals with thoracic aortic aneurysms, 22 (5.06%) had rupture of the aneurysm (Table 1). The affected patients ranged in age at diagnosis from 36.47 to 78.65 years (mean = 65.28); 68.74% were male and 31.26% female, with genetic ancestry of British (87.59%), Irish (2.07%), Indian (1.61%), Caribbean (1.38%), and African (0.23%) origins. Based on GWAS, we identified three SNPs that achieved a genome-wide significance level (p-value <5 x 10⁻⁸) together with a MAF ≥0.5% (Fig 2A and Table 3). Full GWAS results for TAA are included in S6 Table for variants with MAF ≥ 0.5%. Quantile-quantile plots (QQ Plots) are provided in S2 Fig to illustrate that the GWAS quality was well controlled.

Fig 2 — **(A)** Manhattan plot of GWAS results (MAF >0.5%) for TAA. **(B-D)** Prevalence of thoracic aortic aneurism (AAA) per 100,000 participants in the UK Biobank by genotype. Bars labeled with ratio of cases: Controls. **(B)** Prevalence of TAA increases with *CTNNA3* variant rs149014140 status (P-value = 1.82x10^-8, OR per G allele = 4.268). **(C)** Prevalence of TAA increases with *FRMD6* variant rs148927240 status (P-value = 2.19x10^-8, OR per A allele = 4.23). **(D)** Prevalence of TAA increases with *MPB* variant rs78851735 status (P-value = 3.79x10^-8, OR per T allele = 3.446).

Table 3. SNPs in 3 novel loci associated with thoracic aortic aneurysm.

SNP	Chr:BP	Allele	Nearest Gene	Type	MAF (%)	OR (95% CI)	P-value
rs149014140	10:68,863,297	A/G	*CTNNA3*	Intronic	0.78	4.268 (2.57–7.07)	1.82x10^-8
rs148927240	14:52,239,510	G/A	*FRMD6*	Intergenic	0.71	4.23 (2.55–7.01)	2.19x10^-8
rs78851735	18:74,774,680	C/T	*MBP*	Intronic	0.98	3.446 (2.22–5.35)	3.79x10^-8

Open in a new tab

CTNNA3, FRMD6 and MBP are novel TAA-associated loci identified in the present study (bold faced). Chr:BP denotes the chromosome location and NCBI Build 37 SNP physical position. MAF, minor allele frequency; OR, odds ratio.

Among the SNPs with significant p-values, variant rs149014140 in CTNNA3 gene is of particular interest (Fig 2B; p-value 1.82 x 10⁻⁸, MAF 0.78%, OR 4.268) since CTNNA3 encodes a vinculin/alpha-catenin family protein known to play a role in cell-to-cell adhesion of muscle cells [26]. Another significant variant is rs148927240 (Fig 2C; p-value 2.19 x 10⁻⁸, MAF 0.71%, OR 4.23), an intergenic variant located between the long non-coding RNA FERM domain containing 6 (FRMD6), involved in cell contact inhibition and cell cycle regulation [27], and the gene encoding one of the gamma subunits of a guanine nucleotide-binding protein (GNG2). Finally, variant rs78851735 (Fig 2D; p-value 3.79 x 10⁻⁸, MAF 0.98%, OR 3.446) is an intronic variant within the gene encoding myelin basic protein (MBP), which is the major protein in myelin sheaths of the nervous system [28]. The biological relevance of FRMD6, GNG2 and MBP with respect to the development of aortic aneurysms is unclear at this time.

In addition, we identified a linkage group of high-frequency variants (MAFs 9.56–9.97%, odds ratios 1.615–1.644) that do not reach the standard threshold for genome-wide significance for association with TAA, but fall in within FBN1, which encodes the fibrillin-1 protein FBN1 encodes the fibrillin-1 protein and is implicated in the pathogenesis of Marfan syndrome [29] (Table 4). Fibrillin-1 is important in maintaining the integrity of connective tissues throughout the body, as it serves as a structural component of calcium-binding myofibrils [29].

Table 4. Linkage group of FBN1 variants associated with thoracic aortic aneurysm.

SNP	Chr: BP	Allele	Type	MAF (%)	OR (95% CI)	P-value
rs1561207	15: 48,858,971	G/T	Intronic	9.89	1.615 (1.33–1.96)	1.57x10^-6
rs689304	15: 48,922,360	C/T	Intronic	9.93	1.642 (1.35–1.99)	5.38x10^-7
rs625034	15: 48,926,202	T/C	Intronic	9.9	1.64 (1.35–1.99)	6.15x10^-7
rs1036476	15: 48,914,775	T/C	Intronic	9.89	1.636 (1.35–1.99)	6.93x10^-7
rs2028109	15: 48,919,103	A/C	Intronic	9.9	1.634 (1.35–1.99)	7.54x10^-7
rs2455925	15: 48,893,649	T/C	Intronic	9.97	1.625 (1.34–1.97)	9.37x10^-7
rs4775769	15: 48,939,888	G/T	Intergenic	9.56	1.644 (1.35–2.01)	9.70x10^-7

Open in a new tab

Chr:BP denotes the chromosome location and NCBI Build 37 SNP physical position. MAF, minor allele frequency; OR, odds ratio.

Interestingly, this haplotype, as illustrated by the FBN1 intronic variant rs1561207, demonstrated a pronounced dose-dependence: homozygotes had significantly higher prevalence of thoracic aortic aneurysm than heterozygotes (Fig 3A). Comorbid conditions of TAA patients with these FBN1 variants (S3 Table), as well as gender and age at diagnosis (S4 Table) were similar to the overall population of TAA patients. In FinnGen PheWeb, comprised of genetic and clinical information on 178,899 Finnish cohort, this haplotype was associated with aortic dissection (p-value 2.3 x 10⁻⁵; S3 Fig). Interestingly, GWAS of thoracic aorta images in the UK Biobank revealed similar association of FBN1 with dilated aorta [30]. Thus, our study strengthens the emerging functional association between FBN1 and nonsyndromic aortopathy [31].

Fig 3 — **(A)** Increase in TAA prevalence per 100,000 participants in the UK Biobank is noted in the homozygotes for the minor allele (T/T) in comparison to the heterozygotes (C/T) and the noncarriers (C/C) in a pronounced stepwise, “dosage-dependent” manner (P-value = 1.57x10^-6, OR per T allele 1.615). Bars labeled with ratio of cases: Controls. **(B)** Prevalence of TAA rises with bradycardia (purple, heart rate ≤ 54 beats per minute, bpm) in a stepwise manner from tachycardia (orange, defined as heart rate ≥ 91 bpm, OR = 1.89) to normal rate (green, heart rate 55 to 90 bpm, OR = 1.62) to bradycardia (purple, heart rate ≤ 54 bpm, OR = 2.09). P-value = 0.01622 by Pearson’s Chi-squared test. (C) This relationship is seen in both *FBN1* variant carriers (GT) and noncarriers (GG), but the impact of bradycardia is more dramatic in homozygous variant carriers (TT).

Relationship between pulse rate and prevalence of aortic aneurysms

UK Biobank contains a wealth of baseline clinical information of participants, including height, weight, body mass index, blood pressure and pulse rate [11]. Our UKB OASIS (Omics Analysis, Search & Information System) permits high-throughput analysis of associations between clinical and genetic information (unpublished), When we analyzed AA prevalence by baseline characteristics of BMI, height, waist circumference, blood pressure and heart rate (Table 1), an unexpected correlation emerged between baseline heart rate and prevalence of thoracic aortic aneurysms (TAA). For TAA, but not AAA, there was a general trend toward increased prevalence in individuals with bradycardia (defined as heart rate ≤54 beats per minute), regardless of genotype (Fig 3B). Interestingly, this trend seems more marked for homozygous carriers of FBN1 variants (Fig 3C). In contrast, a general trend toward slightly increased AAA prevalence is seen with tachycardia, although the effect is smaller overall (S4 Fig).

Discussion

Genome wide association analysis of abdominal and thoracic aortic aneurysmal disease in the UK Biobank revealed novel loci near LINC01021, ATOH8 and JAK2 genes associated with AAA, and novel loci near CTNNA3, FRMD6 and MBP genes associated with TAA. Out of the 24 loci previously established for AAA, three were replicated by our analysis, ADAMTS8, CELSR2 and CDKN2B-AS1 [15,32]. Based on the data compiled here with the thresholds for p-value and minor allele frequency as set forth in the Methods section, there was no significant overlap in the SNPs associated with AAAs and those associated with TAAs. While this is consistent with growing evidence for a distinct underlying genetic architecture and a distinct pathophysiology of these two aortopathies [33], further studies are necessary to specifically address this question.

A potentially clinically relevant finding is the identification of a linkage group of SNPs encompassing the FBN1 gene, which is associated with TAA in the UK Biobank and with aortic dissection in FinnGen cohorts. This haplotype demonstrated a pronounced dose-dependence, with homozygous carriers associated with ~2.2-fold higher prevalence of thoracic aortic aneurysm than heterozygotes. Given the relatively high minor allele frequencies for these SNPs (9.56–9.97%), as well as the well-defined role of FBN1 in the pathogenesis of connective tissues disorders including Marfan syndrome, we hypothesize that mutations within this linkage group may account for a non-trivial portion of nonsyndromic thoracic aortic aneurysms and dissections, particularly those within the context of positive family history. Therefore, these variants could merit inclusion in genetic screening panels for familial thoracic aneurysmal disease. Taken together with the findings of Pirruccello, et al., our finding suggests some degree of shared pathophysiology between aortic disease in Marfan syndrome and sporadic thoracic aortic aneurysm [30].

An unexpected finding of this study is the apparent association of TAA prevalence and baseline bradycardia. It is unknown whether bradycardia is a consequence of beta-adrenergic blocker usage in those diagnosed with TAA. Indeed, higher percentage of AAA cases are on beta-blockers than controls (24.3% versus 10.3%; S7 Table). Nonetheless, the fact that this association is not seen with AAA even though AAA cases are also prescribed beta-blockers at a higher rate than their respective controls (24.1% versus 12.8%; S7 Table), suggests some biological basis and warrants further investigation, particularly for those with FBN variants.

The approach used in this paper has several limitations. As with any GWAS study, the discovery of novel loci associated with aortopathies does not prove functional causality, and the findings described herein need to be validated by analysis of other databases, ideally in a patient population of more diverse genetic origins than the UK Biobank. There are certain limitations inherent to a population study based on ICD10 codes in comparison to a study dedicated specifically to aortopathies. For example, an ICD10-based studies are limited by the fact that, as in many real-world situations, many diseases and medical conditions are underdiagnosed. In a UKB-based study, this is especially important with respect to the matched controls since they are selected randomly from a pool of individuals who simply do not carry the ICD10 codes, rather than those specifically ruled out for the disease by a focused survey. For instance, valvular disorders are a common co-morbidity of aortic aneurysm, and aortic dissection is a complication of aortic aneurysm (S1 Table), but these subjects may not be coded as having aortic aneurysm per se and inappropriately included among the controls. To account for this, we excluded from controls not just the subjects with ICD10 codes corresponding to aortic aneurysms, but also those with common comorbid conditions and complications of aortic aneurysms, to increase the probability that the controls are truly free of the diagnosis we are studying. We acknowledge this may have introduced small bias for detecting genetic associations indirectly related to aortopathies.

We also note that our study identified fewer associations than recent GWAS studies on the Million Veterans Program (MVP) cohort and on aortic images in the UK Biobank [15,30]. The fewer associations we identified compared to the MVP study may due to the fact that we examined 1,363 patients with AAA whereas the MVP study examined 7,642 patients [15]. Additionally, the greater number of associations found by Pirruccello et al. could reflect the fact that they used imaging to identify individuals with subclinical aortopathy not captured by ICD10 codes [30]. Finally, we also note that, in addition to the variants and loci discussed here, there are many more that didn’t make the genome-wide significance cutoff of p < 5 x 10⁻⁸ or MAF cutoff ≥ 0.5%. Thus, much of the genetic underpinnings of abdominal and thoracic aortic aneurysm formation remain to be discovered.

Supporting information

S1 Fig. Principle components (PCs) by Ethnicity for UK Biobank participants.

When selecting controls for comparison with cases, control subjects were picked from subjects within 80 units on the PC1 vs. PC2 graph. The size of 80 units is illustrated with the red boxes around subjects who are primarily European, Chinese or African Ethnicity based on the PC1 and PC2 eigenvalues provided by the UK Biobank.

(TIF)

Click here for additional data file.^{(642.2KB, tif)}

S2 Fig. Quantile-quantile plots (QQ Plots) for the AAA and TAA phenotypes showing that the quality of the association analysis is well controlled with minimal confounding present.

The genomic control (GC Lambda) values of 1.04 (AAA) and 1.05 (TAA) are within the generally accepted range for GWAS.

(TIF)

Click here for additional data file.^{(538.7KB, tif)}

S3 Fig. In FinnGen cohort, FBN1 variant rs625034 is associated with increased prevalence of aortic dissection (P = 2.3 x 10–5).

Manhattan plot of phenome wide association study (PheWAS) is shown.

(TIF)

Click here for additional data file.^{(1.6MB, tif)}

S4 Fig. Association between pulse rate and prevalence of abdominal aortic aneurysm formation.

A general trend toward slightly increased AAA prevalence is seen with tachycardia.

(TIF)

Click here for additional data file.^{(634.7KB, tif)}

S1 Table. ICD10 diagnostic codes excluded from controls for GWAS of Abdominal Aortic Aneurysm (AAA).

(XLSX)

Click here for additional data file.^{(9.8KB, xlsx)}

S2 Table. ICD10 diagnostic codes excluded from controls for GWAS of Thoracic Aortic Aneurysm (TAA).

(XLSX)

Click here for additional data file.^{(9.8KB, xlsx)}

S3 Table. Comorbidities of patients with one copy of the SNPs described in the linkage group encompassing FBN1 are similar to comorbidities of all patients with TAA.

This table includes comorbidities with frequency ≥ 25%.

(XLSX)

Click here for additional data file.^{(10.1KB, xlsx)}

S4 Table. Age at diagnosis for all TAA patients, and for TAA patients with at least one copy of the SNPs described in the linkage group encompassing FBN1.

(XLSX)

Click here for additional data file.^{(10KB, xlsx)}

S5 Table. Complete results for SNPs associated with AAA.

(XLSX)

Click here for additional data file.^{(19.3KB, xlsx)}

S6 Table. Complete results for SNPs associated with TAA.

(XLSX)

Click here for additional data file.^{(14.5KB, xlsx)}

S7 Table. Beta-blocker usage in AAA and TAA cases, and in respective controls.

(XLSX)

Click here for additional data file.^{(9.7KB, xlsx)}

Acknowledgments

This research was conducted using the UK Biobank Resource under Application Number 49852. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Abbreviations

AA: aortic aneurysm
AAA: abdominal aortic aneurysm
GWAS: genome-wide association study
ICD: international classification of diseases
LD: linkage disequilibrium
MAF: minor allele frequency
MVP: Million Veteran Program
PC: principal component
SNP: single nucleotide polymorphism
TAA: thoracic aortic aneurysm
TAAD: thoracic aortic aneurysms and dissection
UK: United Kingdom

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

This work was supported by National Institute of General Medical Sciences, R01GM118557 and National Heart, Lung, and Blood Institute, R01HL1351291 to CCH, and National Heart, Lung, and Blood Institute, 1U01HL137181 to JP. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.CDC. Aortic Aneurysm | cdc.gov. Centers for Disease Control and Prevention 2020. https://www.cdc.gov/heartdisease/aortic_aneurysm.htm. Accessed October 3, 2020.
2.Shibamura H, Olson JM, van Vlijmen-Van Keulen C, et al. Genome scan for familial abdominal aortic aneurysm using sex and family history as covariates suggests genetic heterogeneity and identifies linkage to chromosome 19q13. Circulation 2004;109:2103–2108. doi: 10.1161/01.CIR.0000127857.77161.A1 [DOI] [PubMed] [Google Scholar]
3.Guo D, Hasham S, Kuang SQ, et al. Familial thoracic aortic aneurysms and dissections: genetic heterogeneity with a major locus mapping to 5q13-14. Circulation 2001;103:2461–2468. doi: 10.1161/01.cir.103.20.2461 [DOI] [PubMed] [Google Scholar]
4.Vaughan CJ, Casey M, He J, et al. Identification of a chromosome 11q23.2-q24 locus for familial aortic aneurysm disease, a genetically heterogeneous disorder. Circulation 2001;103:2469–2475. doi: 10.1161/01.cir.103.20.2469 [DOI] [PubMed] [Google Scholar]
5.Ostberg NP, Zafar MA, Ziganshin BA, Elefteriades JA. The Genetics of Thoracic Aortic Aneurysms and Dissection: A Clinical Perspective. Biomolecules 2020;10. doi: 10.3390/biom10020182 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Milewicz DM, Michael K, Fisher N, Coselli JS, Markello T, Biddinger A. Fibrillin-1 (FBN1) mutations in patients with thoracic aortic aneurysms. Circulation 1996;94:2708–2711. doi: 10.1161/01.cir.94.11.2708 [DOI] [PubMed] [Google Scholar]
7.LeMaire SA, McDonald M-LN, Guo D-C, et al. Genome-wide association study identifies a susceptibility locus for thoracic aortic aneurysms and aortic dissections spanning FBN1 at 15q21.1. Nat Genet 2011;43:996–1000. doi: 10.1038/ng.934 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Guo D-C, Pannu H, Tran-Fadulu V, et al. Mutations in smooth muscle alpha-actin (ACTA2) lead to thoracic aortic aneurysms and dissections. Nat Genet 2007;39:1488–1493. doi: 10.1038/ng.2007.6 [DOI] [PubMed] [Google Scholar]
9.Gould RA, Aziz H, Woods CE, et al. ROBO4 variants predispose individuals to bicuspid aortic valve and thoracic aortic aneurysm. Nat Genet 2019;51:42–50. doi: 10.1038/s41588-018-0265-y [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Hicks KL, Byers PH, Quiroga E, Pepin MG, Shalhub S. Testing patterns for genetically triggered aortic and arterial aneurysms and dissections at an academic center. J Vasc Surg 2018;68:701–711. doi: 10.1016/j.jvs.2017.12.023 [DOI] [PubMed] [Google Scholar]
11.Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 2018;562:203–209. doi: 10.1038/s41586-018-0579-z [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 2015;4:7. doi: 10.1186/s13742-015-0047-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Wågsäter D, Björk H, Zhu C, et al. ADAMTS-4 and -8 are inflammatory regulated enzymes expressed in macrophage-rich areas of human atherosclerotic plaques. Atherosclerosis 2008;196:514–522. doi: 10.1016/j.atherosclerosis.2007.05.018 [DOI] [PubMed] [Google Scholar]
14.Lamblin N, Ratajczak P, Hot D, et al. Profile of macrophages in human abdominal aortic aneurysms: a transcriptomic, proteomic, and antibody protein array study. J Proteome Res 2010;9:3720–3729. doi: 10.1021/pr100250s [DOI] [PubMed] [Google Scholar]
15.Klarin D, Verma SS, Judy R, et al. Genetic Architecture of Abdominal Aortic Aneurysm in the Million Veteran Program. Circulation 2020;142:1633–1646. doi: 10.1161/CIRCULATIONAHA.120.047544 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Xiao J, Wei Z, Chen X, et al. Experimental abdominal aortic aneurysm growth is inhibited by blocking the JAK2/STAT3 pathway. Int J Cardiol 2020;312:100–106. doi: 10.1016/j.ijcard.2020.03.072 [DOI] [PubMed] [Google Scholar]
17.Wu Q-Y, Cheng Z, Zhou Y-Z, et al. A novel STAT3 inhibitor attenuates angiotensin II-induced abdominal aortic aneurysm progression in mice through modulating vascular inflammation and autophagy. Cell Death Dis 2020;11:131. doi: 10.1038/s41419-020-2326-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Fang F, Wasserman SM, Torres-Vazquez J, et al. The role of Hath6, a newly identified shear-stress-responsive transcription factor, in endothelial cell differentiation and function. J Cell Sci 2014;127:1428–1440. doi: 10.1242/jcs.136358 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Roberts R, Stewart AFR. 9p21 and the genetic revolution for coronary artery disease. Clin Chem 2012;58:104–112. doi: 10.1373/clinchem.2011.172759 [DOI] [PubMed] [Google Scholar]
20.Samuel N, Radovanovic I. Genetic basis of intracranial aneurysm formation and rupture: clinical implications in the postgenomic era. Neurosurg Focus 2019;47:E10. doi: 10.3171/2019.4.FOCUS19204 [DOI] [PubMed] [Google Scholar]
21.Hitchcock E, Gibson WT. A Review of the Genetics of Intracranial Berry Aneurysms and Implications for Genetic Counseling. J Genet Couns 2017;26:21–31. doi: 10.1007/s10897-016-0029-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Helgadottir A, Thorleifsson G, Manolescu A, et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 2007;316:1491–1493. doi: 10.1126/science.1142842 [DOI] [PubMed] [Google Scholar]
23.Helgadottir A, Thorleifsson G, Magnusson KP, et al. The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm. Nat Genet 2008;40:217–224. doi: 10.1038/ng.72 [DOI] [PubMed] [Google Scholar]
24.Jones GT, Bown MJ, Gretarsdottir S, et al. A sequence variant associated with sortilin-1 (SORT1) on 1p13.3 is independently associated with abdominal aortic aneurysm. Hum Mol Genet 2013;22:2941–2947. doi: 10.1093/hmg/ddt141 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Carino D, Sarac TP, Ziganshin BA, Elefteriades JA. Abdominal Aortic Aneurysm: Evolving Controversies and Uncertainties. Int J Angiol 2018;27:58–80. doi: 10.1055/s-0038-1657771 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.van Hengel J, Calore M, Bauce B, et al. Mutations in the area composita protein αT-catenin are associated with arrhythmogenic right ventricular cardiomyopathy. Eur Heart J 2013;34:201–210. doi: 10.1093/eurheartj/ehs373 [DOI] [PubMed] [Google Scholar]
27.Xu Y, Wang K, Yu Q. FRMD6 inhibits human glioblastoma growth and progression by negatively regulating activity of receptor tyrosine kinases. Oncotarget 2016;7:70080–70091. doi: 10.18632/oncotarget.12148 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Wąsik N, Sokół B, Hołysz M, et al. Serum myelin basic protein as a marker of brain injury in aneurysmal subarachnoid haemorrhage. Acta Neurochir (Wien) 2020;162:545–552. doi: 10.1007/s00701-019-04185-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Sakai LY, Keene DR, Renard M, De Backer J. FBN1: The disease-causing gene for Marfan syndrome and other genetic disorders. Gene 2016;591:279–291. doi: 10.1016/j.gene.2016.07.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Pirruccello JP, Chaffin MD, Fleming SJ, et al. Deep learning enables genetic analysis of the human thoracic aorta. bioRxiv 2020:2020.05.12.091934. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Anon. FinnGen Documentation of R4 release. https://finngen.gitbook.io/documentation/. Accessed December 16, 2020.
32.Jones GT, Tromp G, Kuivaniemi H, et al. Meta-Analysis of Genome-Wide Association Studies for Abdominal Aortic Aneurysm Identifies Four New Disease-Specific Risk Loci. Circ Res 2017;120:341–353. doi: 10.1161/CIRCRESAHA.116.308765 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Pinard A, Jones GT, Milewicz DM. Genetics of Thoracic and Abdominal Aortic Diseases. Circulation Research 2019;124:588–606. doi: 10.1161/CIRCRESAHA.118.312436 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0247287.r001

Decision Letter 0

Danillo G Augusto

7 Apr 2021

PONE-D-21-03668

Identification of Novel Genetic Susceptibility Loci for Thoracic and Abdominal Aortic Aneurysms Via Genome-Wide Association Study Using the UK Biobank Cohort

PLOS ONE

Dear Dr. Hong,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The reviewers raised significant concerns about methods, analyses, clarity, and language. Please address each comment carefully, changing the manuscript accordingly. Please notice that all data must be provided as part of the manuscript or its supporting information, or deposited to a public repository. For more information, check PLOS Data policy.

Please submit your revised manuscript by May 22 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Danillo G Augusto

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please amend either the title on the online submission form (via Edit Submission) or the title in the manuscript so that they are identical.

3. Thank you for stating the following in the Acknowledgments Section of your manuscript:

[This research was conducted using the UK Biobank Resource under Application Number 49852. This work was supported by NIGMS R01GM118557 and NHLBI R01HL1351291 to CCH, and NHLBI 1U01HL137181to JP. The funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; or decision to submit the manuscript for publication.]

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

[The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.]

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

4. Please include a copy of Table 5 which you refer to in your text on page 14.

5. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors performed a genome-wide association study (GWAS) on abdominal aortic aneurysms (AAA) and thoracic aortic aneurysms (TAA) using data from the UK Biobank. They identified three new risk loci for AAA and replicated three existing loci. For TAA, they also identified three new risk loci. For both traits, no GWAS has been performed in the UK Biobank, as existing efforts focused on aortic aneurysms as a whole. For AAA, two large GWAS non-overlapping efforts have been published before (Klarin, et al. Circulation. 2020, and Jones, et al. Circ Res. 2017), studying 7,642 and 10,204 cases, respectively. For TAA, a GWAS was performed in 2011 on 765 cases (LeMaire, et al. Nat Genet. 2011). None of these studies included the UK Biobank. Although the current study does not outperform earlier efforts by sheer sample size, the release of GWAS summary statistics from the UK Biobank using a thorough analysis pipeline could have added value for the scientific community.

General remarks:

I have some general concerns about the study

1) The GWAS quality control and methods in general are lacking a lot of essential details making assessment of the quality of the study methods difficult. I am also missing quantification of confounding (lambdaGC, LD-score regression intercept). I provide suggestions in the point-by-point comments.

2) The AAA scientific community would benefit most from a meta-analysis of existing GWAS datasets. I think making summary statistics available is a good first step, but I would like to urge the authors to pursue a joint effort.

3) The authors claim that the lack of overlapping risk loci between AAA and TAA identified in the present study is evidence for distinct disease mechanisms. I think the analyses performed in the study are not sufficient to conclude this. I urge the authors to properly test this hypothesis. In the point-by-point comments below I address where I think the conclusions are incomplete.

Point-by-point comments:

Page 6: “As with any GWAS study, the discovery of novel loci associated with aortopathies does not prove functional causality, and the findings described herein needs to be validated by analysis of other databases, ideally in a patient population of more diverse genetic origins than the UK Biobank.”

The part about replication ideally in a diverse population is missing context. How would this improve the study, or how would this specifically support the abovementioned limitations?

Page 9: The GWAS methods are missing important parameters making thorough assessment of the quality of the methods difficult. In each quality control step, please specify the thresholds used for filtering. Also specify any tools used.

Page 9: Please specify what “recommended genomic analysis exclusions” are.

Page 9: “Subjects with these ICD10 codes were removed from the population of controls to avoid introducing confounding factors, specifically the TAA and aortic valvular disorders, in the analysis.“

I don’t agree with this statement (or I don’t fully understand). If only the controls are filtered to exclude persons with these disorders, you are depleting the controls of these conditions. Thereby introducing bias because the prevalence of these conditions will be higher in cases. If cases with these conditions were also excluded then indeed are you reducing potential confounding. Please clarify and if needed adjust the inclusion criteria.

Page 9: Regarding tolerances. Please provide a table with baseline characteristics of cases and controls separately.

Page 10: The PC-based control selection in not completely clear. I would not be able to reproduce this method. What is a PC unit? This does not seem to be value in an eigenvector as +400 is rather large. Please also provide a supplementary figure showing the first few PCs plotted (perhaps against hapmap samples) highlighting the cases and controls in order to see the overlap and distance.

Page 10: “Our preliminary analysis showed that only the first 5 PCs had significance.” What does this mean and how was this tested?

Page 10: “The potential functional significance of associated variants was assessed by Eigen PC scores, presence of promoter or enhancer elements, and presence of DNase hypersensitivity sites in the affected regions of the genome.”

Does this mean that the variants of interest are present in promoter or enhancer elements, or that these are nearby (and within what window)? Provide a reference for Eigen-PC describe the method for obtaining these.

Page 11: The authors do not provide any estimate for presence of confounding. Please provide a lambdaGC value for both GWAS and preferable also an LD-score regression intercept or equivalent metric.

Page 11: “Genetic ancestry was predominantly British (93.54%); however, patients were also represented from Irish (2.64%), Indian (0.22%), Caribbean (0.51%), and African (0.15%) backgrounds“.

Mixed ancestry could bias the association analyses if not accounted for properly. It is the major source for confounding in GWAS in general. Indeed, the authors perform a PC-matched analysis and includes PCs as covariates, but this does not guarantee to fully avoid confounding. It is essential to quantify confounding (as mentioned in an earlier comment). I would suggest to perform a sensitivity analysis excluding non-European ancestry persons and use the metrics for confounding, as well as the Manhattan plots, as comparison.

Page 11 and Figure 1: some loci (especially AAA chromosome 2) contain lonely associated SNPs without accompanying LD SNP. This is not necessarily bad, but it would be good to see some additional metrics: 1) Hardy-Weinberg equilibrium P-values added to supplementary Tables 5 and 6, 2) sensitivity analysis excluding non-European ancestry samples, and 3) are there nearby SNPs that just fall below the MAF threshold and not plotted for that reason?

Page 11. Typo: “(p-value <1e-6; Table 2)”

Page 11: “In addition, we found several distinct variants that do not reach the definitive threshold for genome-wide significance but nevertheless possess a strong basis for biologic plausibility and are within the suggestive threshold for genome-wide significance (p-value <1e-6; Table 2).”

In my opinion it is too much to use the Eigen-PC score to compensate for lack of statistical association. In any genomic region there will be some SNPs that are functionally relevant, but this does not imply disease specificity. The score should rather be used to prioritize SNP within a risk locus. If suggestive SNPs were also found at a suggestive level of significance in other GWAS of AAA/TAA, or are bona fide loci in related traits, that would be a good motivation to prioritize these, rather than by Eigen-PC score alone.

Page 12: “The significant linkage group of ADAMTS8 variants that we identified includes rs7936928 (intronic), rs4936099 (intronic), rs11222084 (intergenic), and rs3740888 (intronic); these variants have p-values 7.51 x 10-9-1.59 x 10-8, MAF 36.7%-40.7%, and odds ratios 0.785-0.790”.

These four SNPs are described as a linkage group, meaning (if my interpretation is correct) that these are in strong LD. This means their P-values (and MAFs) are closely related. Mentioning all four of them could lead to an unwanted feeling of replication or importance to the reader. Reporting only the lead SNP or some prioritized SNP would be sufficient.

Page 12: Top SNPs are linked to genes. There is no clear description of the motivation of prioritizing genes. It looks like the nearest genes are selected, or some nearby gene with disease relevance. I would like to see a data-driven approach to gene prioritization, for which there are many different options.

Page 12: “In addition, we identified several distinct variants that do not reach the standard threshold for genome-wide significance for association with AAA, but are nevertheless within the suggestive threshold for genome-wide significance (p-value <1 x 10-6) and possess a strong basis for biologic plausibility (Table 2).”

Indeed, finding these variants which are bona fide players in other diseases is important. However, the term biological plausibility in this context is ambiguous. The finding supports a pleiotropic role for these loci and indeed support that “something is happening” at that locus. The term biological plausibility/importance implies some functional evidence.

Page 13: “Of these variants, rs12740374, rs660240 and rs7528419 have a particularly high Eigen PC score of 4.4, 3.8 and 3.2, respectively, suggesting a functional role (Table 2).”

Please provide context of the interpretation of specific value of the Eigen score.

Page 13: The same remark about ancestry as for AAA applies here.

Page 14. Typo: “In addition, we identified we identified a linkage group of high-frequency variants”

Page 14: “but nevertheless have a strong basis for biologic plausibility since they fall in a large region of linkage disequilibrium encompassing FBN1 (Table 5).”

Here, the term biologic plausibility is again confusing. Indeed, the position of the hit near FBN1 is interesting, but is no real genetic or biological evidence for these variants. Are there specific SNPs or LD-SNPs identified in earlier GWAS of TAA or related traits?

Page 14: “(Figure 3A; p-value 1.57 x10-6, MAF 9.89%, OR 1.615) is of special interest given its high Eigen PC score”.

Please provide Eigen-PC score in the text.

Page 14: “Interestingly, each of these FBN1 variants demonstrated a pronounced dose- dependence: homozygotes had significantly higher prevalence of thoracic aortic aneurysm than heterozygotes (Figure 3A)”. Note the use of “significantly”.

This implies the difference between heterozygotes and homozygous carriers was tested statistically. Also note that “each of these variants” does not add information over “the variants / the lead variant”, but could lead to an unwanted sense of replication, while these variants are highly correlated.

Page 15: It is unclear why the authors studied the effect of pulse rate on TAA and AAA. Please clarify what the hypothesis was, or whether this was a coincidental finding.

Page 15 and Figure 3B: When we analyzed AA prevalence by baseline pulse rate for each of the statistically significant SNPs identified in this study”.

This effect seems present at some extent when looking at the plots. However, the authors do not provide a statistical test for this effect. To claim this effect to be true, it is essential to properly test this. There are probably several ways, but one would be to add an interaction term in the PLINK model.

Page 16: “Out of the 24 loci previously established for AAA, three were replicated by our analysis, ADAMTS8, CELSR2 and CDKN2B-AS1(14, 31).”

Please describe what the authors define as being replicated (for example: they were genome-wide significant in the present study). I did not see a specific analysis looking at overlap of loci.

Page 16: “Based on the data compiled here with the thresholds for p- value and minor allele frequency as set forth in the methods section, there was no significant overlap in the SNPs associated with AAAs and those associated with TAAs. This suggests a distinct underlying genetic architecture, and a distinct pathophysiology, of these two aortopathies.”

I don’t think the (lack of) overlap was thoroughly tested. I agree that the specific loci identified given the power of the studies do not overlap, but this is not in any way sufficient to conclude that the diseases have distinct genetic architecture or pathophysiology. At least, I would like to see a genome-wide comparison of the traits, for example by genetic correlation analysis using LD-score regression. If power is too low for genetic correlation, a genome-wide replication could be done. Please also include the other GWASs of AAA and TAA in these analyses to make sure lack of overlap is not due to lack of power alone.

Page 16: “It is unknown whether bradycardia is a consequence of beta-adrenergic blocker usage in those diagnosed with TAA.”

The UK Biobank has data on medication use. I don’t know how detailed this is for beta-blockers, but a sensitivity analysis excluding persons using this medication could see if this effect holds and be a potential biologically relevant effect.

Page 17: “ideally in a patient population of more diverse genetic origins than the UK Biobank.”

I would like to see some context on how this would improve our understanding, prevention or treatment. Without context if feels a bit like an empty gesture.

The methods section is missing important details. This makes reviewing the methods difficult.

-I think the English language of the manuscript could be improved. Perhaps a check by a native speaker could be beneficial. The order of word is not always optimal, and some terms are used incorrectly or abundantly

-There are two other GWAS studies of AAA published in the last four years, without overlapping samples and by different groups. I think the scientific community would benefit most from a joint analysis of these datasets. It seems that both other efforts have some sort of restriction on the use of their data. I don't know if this has any historical reasons, but in my opinion it would be good to encourage the authors to pursue collaboration.

-I was unable to find a data availability statement for the GWAS summary statistics (including all SNP effect, and not just the significant ones as provided in Supplementary Tables 5 and 6).

Reviewer #2: Very nicely conducted study. Important. Thank you.

I agree that the bradycardia finding is likely a "red herring", probably reflecting medication difference between the groups.

Where do you suggest that the work in this area go from here? How should your findings be reflected in further scientific and clinical work?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Mark K. Bakker

Reviewer #2: Yes: John A. Elefteriades, MD

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2021 Sep 1;16(9):e0247287. doi: 10.1371/journal.pone.0247287.r002

Author response to Decision Letter 0

8 Jun 2021

Responses to Reviewer Comments. Our responses are Italicized.

1) Reviewer #1: The authors performed a genome-wide association study (GWAS) on abdominal aortic aneurysms (AAA) and thoracic aortic aneurysms (TAA) using data from the UK Biobank. They identified three new risk loci for AAA and replicated three existing loci. For TAA, they also identified three new risk loci. For both traits, no GWAS has been performed in the UK Biobank, as existing efforts focused on aortic aneurysms as a whole. For AAA, two large GWAS non-overlapping efforts have been published before (Klarin, et al. Circulation. 2020, and Jones, et al. Circ Res. 2017), studying 7,642 and 10,204 cases, respectively. For TAA, a GWAS was performed in 2011 on 765 cases (LeMaire, et al. Nat Genet. 2011). None of these studies included the UK Biobank. Although the current study does not outperform earlier efforts by sheer sample size, the release of GWAS summary statistics from the UK Biobank using a thorough analysis pipeline could have added value for the scientific community.

We agree with the reviewer in principle; however, If we were to provide all summary statistics for the full GWAS, it would be this data for 40 million variants for each trait, for a total of 80 million sets of summary statistics. Presently, UK Biobank has stated that it are not ready to receive GWAS summary statistics, but it will be providing a mechanism for returning GWAS results for sharing with others. When that mechanism is in place, we will be sending our GWAS summary statistics. In meanwhile, we have provided summary statistics for variants with MAF > 0.5% and p-value < 1E-6 in Supplementary Table 5 & 6.

I have some general concerns about the study.

2) The GWAS quality control and methods in general are lacking a lot of essential details making assessment of the quality of the study methods difficult. I am also missing quantification of confounding (lambdaGC, LD-score regression intercept). I provide suggestions in the point-by-point comments.

These are provided in Supplementary Figures 2 (see also #11, #12).

3) The AAA scientific community would benefit most from a meta-analysis of existing GWAS datasets. I think making summary statistics available is a good first step, but I would like to urge the authors to pursue a joint effort.

The nature of MTA precludes us from directly sharing data outside our home institution. While efforts are in place at UK Biobank to eventually allow sharing of data with the greater research community, such joint effort is beyond the scope of this work.

The authors claim that the lack of overlapping risk loci between AAA and TAA identified in the present study is evidence for distinct disease mechanisms. I think the analyses performed in the study are not sufficient to conclude this. I urge the authors to properly test this hypothesis. In the point-by-point comments below I address where I think the conclusions are incomplete.

Point-by-point comments:

4) Page 6 and 15: “As with any GWAS study, the discovery of novel loci associated with aortopathies does not prove functional causality, and the findings described herein needs to be validated by analysis of other databases, ideally in a patient population of more diverse genetic origins than the UK Biobank.”

The part about replication ideally in a diverse population is missing context. How would this improve the study, or how would this specifically support the above-mentioned limitations?

This is general statement of the limitations. As the reviewer states, meta-analysis of existing GWAS datasets would support this statement; however, this is beyond the scope of the present story.

5) Page 9: The GWAS methods are missing important parameters making thorough assessment of the quality of the methods difficult. In each quality control step, please specify the thresholds used for filtering. Also specify any tools used.

We have added information for UKB Data-Field’s for each item used in the quality control step. If it would be better to use references/links, they are:

https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=22018

https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=22019

https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=31 and

https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=22001

https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=22010

https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=22077

6) Page 9: Please specify what “recommended genomic analysis exclusions” are.

We have added UKB Data Field info, as above.

7) Page 9: “Subjects with these ICD10 codes were removed from the population of controls to avoid introducing confounding factors, specifically the TAA and aortic valvular disorders, in the analysis.“

We believe our approach attempts to overcome certain inherent limitations of a population study based on ICD10 codes in comparison to a study dedicated specifically to aortopathies. An ICD10 based studies are limited by the fact that, as in many real-world situations, many diseases and medical conditions are underdiagnosed. This is especially important the controls in a UKB-based study since they are selected randomly from a pool of those who simply do not carry the ICD10 codes, and not specifically ruled out for the disease by a focused survey. For instance, valvular disorders are common co-morbidity of aortic aneurysm, and aortic dissection is a complication of aortic aneurysm, but those subjects may not be coded as aortic aneurysm per se and inappropriately included among the controls. Thus, for controls, we purposely excluded subjects with ICD10 codes but also common comorbid conditions and complications of aortic aneurysms, to increase the probability that the controls are truly free of the diagnosis we are studying. The UK Biobank is a very large cohort so there is ample supply of controls. We have included this explanation in the discussion.

8) Page 9: Regarding tolerances. Please provide a table with baseline characteristics of cases and controls separately.

These have been included in Table 1.

9) Page 10: The PC-based control selection in not completely clear. I would not be able to reproduce this method. What is a PC unit? This does not seem to be value in an eigenvector as +400 is rather large. Please also provide a supplementary figure showing the first few PCs plotted (perhaps against hapmap samples) highlighting the cases and controls in order to see the overlap and distance.

We have updated the description in the method such that others could easily reproduce what was done. Additionally, we have added Supplementary Figure 2 to show the range that was used for ancestry matching. This addresses the issue and graphically illustrates what was done. A plot as described by the reviewer #2, with all cases and controls would not give a meaningful picture because the 20 cases for each control would obliterate the view and the overlaps would not be discernable.

9) Page 10: “Our preliminary analysis showed that only the first 5 PCs had significance.” What does this mean and how was this tested?

We have updated the manuscript.

10) Page 10: “The potential functional significance of associated variants was assessed by Eigen PC scores, presence of promoter or enhancer elements, and presence of DNase hypersensitivity sites in the affected regions of the genome.”

Given the degree of objections to the sue of EigenPC score, we removed their mention form the manuscript. In any case, the reference for EigenPC Score is as follows: Lonita-Laza, Iuliana, et al. "A spectral approach integrating functional genomic annotations for coding and noncoding variants." Nature Genetics 48.2 (2016): 214.

11) Page 11: The authors do not provide any estimate for presence of confounding. Please provide a lambdaGC value for both GWAS and preferable also an LD-score regression intercept or equivalent metric.

These are provided in Supplementary Figure 2 (see also #2, #12).

12) Page 11: “Genetic ancestry was predominantly British (93.54%); however, patients were also represented from Irish (2.64%), Indian (0.22%), Caribbean (0.51%), and African (0.15%) backgrounds“.

We are providing the QQ Plots (Supp Figure 2), which have the GC Lambda value that show confounding is under control.

13) Hardy-Weinberg equilibrium P-values added to supplementary Tables 5 and 6;

Hardy-Weinberg equilibrium are not meaningful here because we have mixed ancestry which, a priori, violates the assumptions that HWE requires.

14) sensitivity analysis excluding non-European ancestry samples, and

This analysis was done with the UK Biobank data which is 90% European ancestry. Thus, excluding those subjects is not a practical approach because it would reduce the sample size 10-fold.

15) are there nearby SNPs that just fall below the MAF threshold and not plotted for that reason?

We are reporting variants with MAF > 1%. The Manhattan Plot shows variants with MAF > 0.5%, so we already are showing variants “below the filter” (Figure; Supplementary Tables 5 and 6).

16) Page 11. Typo: “(p-value <1e-6; Table 2)”

Corrected!

17) Page 11: “In addition, we found several distinct variants that do not reach the definitive threshold for genome-wide significance but nevertheless possess a strong basis for biologic plausibility and are within the suggestive threshold for genome-wide significance (p-value <1e-6; Table 2).”

What the reviewer #2 describes is exactly what we did. The Eigen-PC score was used to prioritize variants within a locus and that were already suggestive; however, given the objections, we have removed EigenPC score from the analysis and results.

18) Page 12: “The significant linkage group of ADAMTS8 variants that we identified includes rs7936928 (intronic), rs4936099 (intronic), rs11222084 (intergenic), and rs3740888 (intronic); these variants have p-values 7.51 x 10-9-1.59 x 10-8, MAF 36.7%-40.7%, and odds ratios 0.785-0.790”.

We have modified this section accordingly.

19) Page 12: Top SNPs are linked to genes. There is no clear description of the motivation of prioritizing genes. It looks like the nearest genes are selected, or some nearby gene with disease relevance. I would like to see a data-driven approach to gene prioritization, for which there are many different options.

The tables are labeled as “Nearest Gene” (see column headers for the tables listing genes).

20) Page 12: “In addition, we identified several distinct variants that do not reach the standard threshold for genome-wide significance for association with AAA, but are nevertheless within the suggestive threshold for genome-wide significance (p-value <1 x 10-6) and possess a strong basis for biologic plausibility (Table 2).”

We have removed “and possess a strong basis for biologic plausibility”

21) Page 13: “Of these variants, rs12740374, rs660240 and rs7528419 have a particularly high Eigen PC score of 4.4, 3.8 and 3.2, respectively, suggesting a functional role (Table 2).”

Please provide context of the interpretation of specific value of the Eigen score.

This section has been removed.

22) Page 13: The same remark about ancestry as for AAA applies here.

Please see #12, 13, 14 above.

23) Page 14. Typo: “In addition, we identified we identified a linkage group of high-frequency variants”

Corrected!

24) Page 14: “but nevertheless have a strong basis for biologic plausibility since they fall in a large region of linkage disequilibrium encompassing FBN1 (Table 5).”

Mention of biological plausibility has been removed.

25) Page 14: “(Figure 3A; p-value 1.57 x10-6, MAF 9.89%, OR 1.615) is of special interest given its high Eigen PC score”.

Please provide Eigen-PC score in the text.

Again, we have removed this section. No more Eigen-PC score.

26) Page 14: “Interestingly, each of these FBN1 variants demonstrated a pronounced dose- dependence: homozygotes had significantly higher prevalence of thoracic aortic aneurysm than heterozygotes (Figure 3A)”. Note the use of “significantly”.

“Each of these variants” have been replaced with “this haplotype.”

27) Page 15: It is unclear why the authors studied the effect of pulse rate on TAA and AAA. Please clarify what the hypothesis was, or whether this was a coincidental finding.

We have added the following. “UK Biobank contains a wealth of baseline clinical information of participants, including height, weight, body mass index, bone mineral density, basic laboratory values, blood pressure and pulse rate. Our UKB OASIS (Omics Analysis, Search & Information System) permits high-throughput analysis of associations between clinical and genetic information (unpublished), When we analyzed AA prevalence by baseline characteristics (Supplementary Table 7) for each of the statistically significant SNPs identified in this study, an unexpected correlation emerged between baseline heart rate and prevalence of aortic aneurysms.

28) Page 15 and Figure 3B: When we analyzed AA prevalence by baseline pulse rate for each of the statistically significant SNPs identified in this study”.

P-value for nongenetic analysis of pulse rate-TAA association is provided (Figure 3)

29) Page 16: “Out of the 24 loci previously established for AAA, three were replicated by our analysis, ADAMTS8, CELSR2 and CDKN2B-AS1(14, 31).”

Please describe what the authors define as being replicated (for example: they were genome-wide significant in the present study). I did not see a specific analysis looking at overlap of loci.

“Replicated” means that we got the same results as other studies identified by the reference numbers 14 and 31 in our statement. We did a literature search to determine this. No specific analysis is necessary to confirm that the genes listed in other articles matches the gene names found in our work.

30) Page 16: “Based on the data compiled here with the thresholds for p- value and minor allele frequency as set forth in the methods section, there was no significant overlap in the SNPs associated with AAAs and those associated with TAAs. This suggests a distinct underlying genetic architecture, and a distinct pathophysiology, of these two aortopathies.”

We have modified the text to reflect the fact that, while these finding are suggestive, further studies are needed.

31) Page 16: “It is unknown whether bradycardia is a consequence of beta-adrenergic blocker usage in those diagnosed with TAA.”

We now include evidence that despite the fact that higher percentages of both TAA and AAA cases were on beta-blockers compared to respective controls, the association with bradycardia is noted only for the TAA, suggesting potential biological basis.

32) Page 17: “ideally in a patient population of more diverse genetic origins than the UK Biobank.”

I would like to see some context on how this would improve our understanding, prevention or treatment. Without context if feels a bit like an empty gesture.

The methods section is missing important details. This makes reviewing the methods difficult.

All the requests for more details in the methods have now been addressed.

33) I think the English language of the manuscript could be improved. Perhaps a check by a native speaker could be beneficial. The order of word is not always optimal, and some terms are used incorrectly or abundantly

Just for information the first and the corresponding authors were born in the USA. English is our native language. In over 150 publications by the corresponding authors, this is the first time this issue has been pointed out to us.

34) -There are two other GWAS studies of AAA published in the last four years, without overlapping samples and by different groups. I think the scientific community would benefit most from a joint analysis of these datasets. It seems that both other efforts have some sort of restriction on the use of their data. I don't know if this has any historical reasons, but in my opinion it would be good to encourage the authors to pursue collaboration.

While this is a wonderful idea, but this is currently not possible since, under the existing MTA/Data Use Agreement UKB data cannot be shared outside our institution. That said, other investigators need to obtain data directly from UKB.

35) -I was unable to find a data availability statement for the GWAS summary statistics (including all SNP effect, and not just the significant ones as provided in Supplementary Tables 5 and 6).

As stated in response to #1, #3 and #34, the UK Biobank will be providing a mechanism for returning GWAS results for sharing with others. When that mechanism is in place, we will be sending our GWAS summary statistics.

Reviewer #2: Very nicely conducted study. Important. Thank you.

Thank you very much.

I agree that the bradycardia finding is likely a "red herring", probably reflecting medication difference between the groups.

Where do you suggest that the work in this area go from here? How should your findings be reflected in further scientific and clinical work?

Our work suggesting a degree of shared pathophysiology between with aortic disease in Marfan syndrome and sporadic thoracic aneurysm, should be followed up. If confirmed, one possible application of this work may be inclusion of the FBN variants reported here in genetic screening panel for familial thoracic aneurysmal disease. Our finding of association of bradycardia with thoracic aortic aneurysm warrant further investigation, particularly for those with FBN variants. These points are raised in the discussions.

Attachment

Submitted filename: Responses to reviewer comments 2021-6-2.docx

Click here for additional data file.^{(29.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0247287.r003

Decision Letter 1

Danillo G Augusto

12 Aug 2021

Identification of Novel Genetic Susceptibility Loci for Thoracic and Abdominal Aortic Aneurysms Via Genome-Wide Association Study Using the UK Biobank Cohort

PONE-D-21-03668R1

Dear Dr. Hong,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Danillo G Augusto

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors provided clear responses to all points addressed. The revised manuscript is clear, technically sounds and presents important findings. I think it is a valuable contribution to science and I support its publication in the journal.

I want to apologise to the authors for my remark regarding the English language. This was a blunt comment and I was wrong to make assumptions about native language. It was a humbling lesson for me and thank you for pointing this out.

Reviewer #2: Thank you for revisions. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: M. Bakker

Reviewer #2: Yes: John A. Elefteriades, MD

PLoS One. doi: 10.1371/journal.pone.0247287.r004

Acceptance letter

Danillo G Augusto

23 Aug 2021

PONE-D-21-03668R1

Identification of Novel Genetic Susceptibility Loci for Thoracic and Abdominal Aortic Aneurysms Via Genome-Wide Association Study Using the UK Biobank Cohort

Dear Dr. Hong:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Danillo G Augusto

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Principle components (PCs) by Ethnicity for UK Biobank participants.

(TIF)

Click here for additional data file.^{(642.2KB, tif)}

S2 Fig. Quantile-quantile plots (QQ Plots) for the AAA and TAA phenotypes showing that the quality of the association analysis is well controlled with minimal confounding present.

The genomic control (GC Lambda) values of 1.04 (AAA) and 1.05 (TAA) are within the generally accepted range for GWAS.

(TIF)

Click here for additional data file.^{(538.7KB, tif)}

S3 Fig. In FinnGen cohort, FBN1 variant rs625034 is associated with increased prevalence of aortic dissection (P = 2.3 x 10–5).

Manhattan plot of phenome wide association study (PheWAS) is shown.

(TIF)

Click here for additional data file.^{(1.6MB, tif)}

S4 Fig. Association between pulse rate and prevalence of abdominal aortic aneurysm formation.

A general trend toward slightly increased AAA prevalence is seen with tachycardia.

(TIF)

Click here for additional data file.^{(634.7KB, tif)}

S1 Table. ICD10 diagnostic codes excluded from controls for GWAS of Abdominal Aortic Aneurysm (AAA).

(XLSX)

Click here for additional data file.^{(9.8KB, xlsx)}

S2 Table. ICD10 diagnostic codes excluded from controls for GWAS of Thoracic Aortic Aneurysm (TAA).

(XLSX)

Click here for additional data file.^{(9.8KB, xlsx)}

S3 Table. Comorbidities of patients with one copy of the SNPs described in the linkage group encompassing FBN1 are similar to comorbidities of all patients with TAA.

This table includes comorbidities with frequency ≥ 25%.

(XLSX)

Click here for additional data file.^{(10.1KB, xlsx)}

S4 Table. Age at diagnosis for all TAA patients, and for TAA patients with at least one copy of the SNPs described in the linkage group encompassing FBN1.

(XLSX)

Click here for additional data file.^{(10KB, xlsx)}

S5 Table. Complete results for SNPs associated with AAA.

(XLSX)

Click here for additional data file.^{(19.3KB, xlsx)}

S6 Table. Complete results for SNPs associated with TAA.

(XLSX)

Click here for additional data file.^{(14.5KB, xlsx)}

S7 Table. Beta-blocker usage in AAA and TAA cases, and in respective controls.

(XLSX)

Click here for additional data file.^{(9.7KB, xlsx)}

Attachment

Submitted filename: Responses to reviewer comments 2021-6-2.docx

Click here for additional data file.^{(29.8KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting information files.

[pone.0247287.ref001] 1.CDC. Aortic Aneurysm | cdc.gov. Centers for Disease Control and Prevention 2020. https://www.cdc.gov/heartdisease/aortic_aneurysm.htm. Accessed October 3, 2020.

[pone.0247287.ref002] 2.Shibamura H, Olson JM, van Vlijmen-Van Keulen C, et al. Genome scan for familial abdominal aortic aneurysm using sex and family history as covariates suggests genetic heterogeneity and identifies linkage to chromosome 19q13. Circulation 2004;109:2103–2108. doi: 10.1161/01.CIR.0000127857.77161.A1 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref003] 3.Guo D, Hasham S, Kuang SQ, et al. Familial thoracic aortic aneurysms and dissections: genetic heterogeneity with a major locus mapping to 5q13-14. Circulation 2001;103:2461–2468. doi: 10.1161/01.cir.103.20.2461 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref004] 4.Vaughan CJ, Casey M, He J, et al. Identification of a chromosome 11q23.2-q24 locus for familial aortic aneurysm disease, a genetically heterogeneous disorder. Circulation 2001;103:2469–2475. doi: 10.1161/01.cir.103.20.2469 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref005] 5.Ostberg NP, Zafar MA, Ziganshin BA, Elefteriades JA. The Genetics of Thoracic Aortic Aneurysms and Dissection: A Clinical Perspective. Biomolecules 2020;10. doi: 10.3390/biom10020182 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref006] 6.Milewicz DM, Michael K, Fisher N, Coselli JS, Markello T, Biddinger A. Fibrillin-1 (FBN1) mutations in patients with thoracic aortic aneurysms. Circulation 1996;94:2708–2711. doi: 10.1161/01.cir.94.11.2708 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref007] 7.LeMaire SA, McDonald M-LN, Guo D-C, et al. Genome-wide association study identifies a susceptibility locus for thoracic aortic aneurysms and aortic dissections spanning FBN1 at 15q21.1. Nat Genet 2011;43:996–1000. doi: 10.1038/ng.934 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref008] 8.Guo D-C, Pannu H, Tran-Fadulu V, et al. Mutations in smooth muscle alpha-actin (ACTA2) lead to thoracic aortic aneurysms and dissections. Nat Genet 2007;39:1488–1493. doi: 10.1038/ng.2007.6 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref009] 9.Gould RA, Aziz H, Woods CE, et al. ROBO4 variants predispose individuals to bicuspid aortic valve and thoracic aortic aneurysm. Nat Genet 2019;51:42–50. doi: 10.1038/s41588-018-0265-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref010] 10.Hicks KL, Byers PH, Quiroga E, Pepin MG, Shalhub S. Testing patterns for genetically triggered aortic and arterial aneurysms and dissections at an academic center. J Vasc Surg 2018;68:701–711. doi: 10.1016/j.jvs.2017.12.023 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref011] 11.Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 2018;562:203–209. doi: 10.1038/s41586-018-0579-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref012] 12.Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 2015;4:7. doi: 10.1186/s13742-015-0047-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref013] 13.Wågsäter D, Björk H, Zhu C, et al. ADAMTS-4 and -8 are inflammatory regulated enzymes expressed in macrophage-rich areas of human atherosclerotic plaques. Atherosclerosis 2008;196:514–522. doi: 10.1016/j.atherosclerosis.2007.05.018 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref014] 14.Lamblin N, Ratajczak P, Hot D, et al. Profile of macrophages in human abdominal aortic aneurysms: a transcriptomic, proteomic, and antibody protein array study. J Proteome Res 2010;9:3720–3729. doi: 10.1021/pr100250s [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref015] 15.Klarin D, Verma SS, Judy R, et al. Genetic Architecture of Abdominal Aortic Aneurysm in the Million Veteran Program. Circulation 2020;142:1633–1646. doi: 10.1161/CIRCULATIONAHA.120.047544 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref016] 16.Xiao J, Wei Z, Chen X, et al. Experimental abdominal aortic aneurysm growth is inhibited by blocking the JAK2/STAT3 pathway. Int J Cardiol 2020;312:100–106. doi: 10.1016/j.ijcard.2020.03.072 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref017] 17.Wu Q-Y, Cheng Z, Zhou Y-Z, et al. A novel STAT3 inhibitor attenuates angiotensin II-induced abdominal aortic aneurysm progression in mice through modulating vascular inflammation and autophagy. Cell Death Dis 2020;11:131. doi: 10.1038/s41419-020-2326-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref018] 18.Fang F, Wasserman SM, Torres-Vazquez J, et al. The role of Hath6, a newly identified shear-stress-responsive transcription factor, in endothelial cell differentiation and function. J Cell Sci 2014;127:1428–1440. doi: 10.1242/jcs.136358 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref019] 19.Roberts R, Stewart AFR. 9p21 and the genetic revolution for coronary artery disease. Clin Chem 2012;58:104–112. doi: 10.1373/clinchem.2011.172759 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref020] 20.Samuel N, Radovanovic I. Genetic basis of intracranial aneurysm formation and rupture: clinical implications in the postgenomic era. Neurosurg Focus 2019;47:E10. doi: 10.3171/2019.4.FOCUS19204 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref021] 21.Hitchcock E, Gibson WT. A Review of the Genetics of Intracranial Berry Aneurysms and Implications for Genetic Counseling. J Genet Couns 2017;26:21–31. doi: 10.1007/s10897-016-0029-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref022] 22.Helgadottir A, Thorleifsson G, Manolescu A, et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 2007;316:1491–1493. doi: 10.1126/science.1142842 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref023] 23.Helgadottir A, Thorleifsson G, Magnusson KP, et al. The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm. Nat Genet 2008;40:217–224. doi: 10.1038/ng.72 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref024] 24.Jones GT, Bown MJ, Gretarsdottir S, et al. A sequence variant associated with sortilin-1 (SORT1) on 1p13.3 is independently associated with abdominal aortic aneurysm. Hum Mol Genet 2013;22:2941–2947. doi: 10.1093/hmg/ddt141 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref025] 25.Carino D, Sarac TP, Ziganshin BA, Elefteriades JA. Abdominal Aortic Aneurysm: Evolving Controversies and Uncertainties. Int J Angiol 2018;27:58–80. doi: 10.1055/s-0038-1657771 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref026] 26.van Hengel J, Calore M, Bauce B, et al. Mutations in the area composita protein αT-catenin are associated with arrhythmogenic right ventricular cardiomyopathy. Eur Heart J 2013;34:201–210. doi: 10.1093/eurheartj/ehs373 [DOI] [PubMed] [Google Scholar]

[pone.0247287.ref027] 27.Xu Y, Wang K, Yu Q. FRMD6 inhibits human glioblastoma growth and progression by negatively regulating activity of receptor tyrosine kinases. Oncotarget 2016;7:70080–70091. doi: 10.18632/oncotarget.12148 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref028] 28.Wąsik N, Sokół B, Hołysz M, et al. Serum myelin basic protein as a marker of brain injury in aneurysmal subarachnoid haemorrhage. Acta Neurochir (Wien) 2020;162:545–552. doi: 10.1007/s00701-019-04185-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref029] 29.Sakai LY, Keene DR, Renard M, De Backer J. FBN1: The disease-causing gene for Marfan syndrome and other genetic disorders. Gene 2016;591:279–291. doi: 10.1016/j.gene.2016.07.033 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref030] 30.Pirruccello JP, Chaffin MD, Fleming SJ, et al. Deep learning enables genetic analysis of the human thoracic aorta. bioRxiv 2020:2020.05.12.091934. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref031] 31.Anon. FinnGen Documentation of R4 release. https://finngen.gitbook.io/documentation/. Accessed December 16, 2020.

[pone.0247287.ref032] 32.Jones GT, Tromp G, Kuivaniemi H, et al. Meta-Analysis of Genome-Wide Association Studies for Abdominal Aortic Aneurysm Identifies Four New Disease-Specific Risk Loci. Circ Res 2017;120:341–353. doi: 10.1161/CIRCRESAHA.116.308765 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0247287.ref033] 33.Pinard A, Jones GT, Milewicz DM. Genetics of Thoracic and Abdominal Aortic Diseases. Circulation Research 2019;124:588–606. doi: 10.1161/CIRCRESAHA.118.312436 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Identification of novel genetic susceptibility loci for thoracic and abdominal aortic aneurysms via genome-wide association study using the UK Biobank Cohort

Tamara Ashvetiya

Sherry X Fan

Yi-Ju Chen

Charles H Williams

Jeffery R O’Connell

James A Perry

Charles C Hong

Roles

Abstract

Background

Methods and results

Conclusions

Introduction

Methods

Ethical approval

Study population

Genome-wide association study (GWAS)

Identification of significant SNPs for AAA and TAA Phenotypes

Results

Abdominal aortic aneurysm

Table 1. Basic demographics and characteristics of individuals with abdominal aortic aneurysm (AAA) and thoracic aortic aneurysm (TAA), and the respective matched (for age, sex, ancestry) controls in UK Biobank.

Fig 1. Top SNPs associated with AAA.

Table 2. Top SNPs associated with abdominal aortic aneurysm.

Thoracic aortic aneurysm

Fig 2. Top SNPs associated with TAA.

Table 3. SNPs in 3 novel loci associated with thoracic aortic aneurysm.

Table 4. Linkage group of FBN1 variants associated with thoracic aortic aneurysm.

Fig 3. Prevalence of TAA increases with FBN1 variant rs1561207 status and bradycardia.

Relationship between pulse rate and prevalence of aortic aneurysms

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Danillo G Augusto

Roles

Author response to Decision Letter 0

Decision Letter 1

Danillo G Augusto

Roles

Acceptance letter

Danillo G Augusto

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases