Identification of novel genetic variants associated with feline cardiomyopathy using targeted next-generation sequencing

Identification of novel genetic variants associated with feline cardiomyopathy using targeted next-generation sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of novel genetic variants associated with feline cardiomyopathy using targeted next-generation sequencing Jade Raffle, Jose Novo Matos, Richard J Piercy, Perry Elliott, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3943358/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 3 You are reading this latest preprint version Abstract Cardiomyopathies are the most common heritable heart diseases in cats and humans. This study aimed to identify novel genetic variants in cats with hypertrophic cardiomyopathy (HCM) and restrictive cardiomyopathy (RCM) using a targeted panel of genes associated with human cardiomyopathy. Cats were phenotyped for HCM/RCM by echocardiography or port-mortem examination. DNA was extracted from residual blood, and targeted next-generation sequencing was performed on two separate feline cohorts: an across-breed cohort (23 healthy cats and 21 HCM-affected pedigree or Domestic Shorthair cats), and a within-breed cohort of Birman pedigree cats (14-healthy, 8 HCM-affected, and 6 RCM-affected). Genome analysis toolkit for best practice was used for variant discovery. Genomic association analyses (including the covariates breed, age and sex) were conducted to identify genetic variants of interest. We identified genetic variants associated with HCM and RCM susceptibility in candidate genes based on the human literature. Novel variants of interest were identified in the sarcomeric genes ACTC1, ACTN2, MYH7, TNNT2 and the non-sarcomeric gene CSRP3. The Birman pedigree breed demonstrated shared genetic variants across the HCM and RCM phenotypes, suggesting that the same variants could be associated with both HCM and RCM phenotypes, as proposed in humans. Health sciences/Cardiology Biological sciences/Genetics Biological sciences/Genetics/Genomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cardiomyopathies are diseases of the myocardium that can lead to a range of functional and structural abnormalities of the heart. Cardiomyopathies in both humans and cats are associated with elevated risks of congestive heart failure, cardioembolic events, and sudden cardiac death 1–3 . Subcategorization of cardiomyopathies is based on the underlying disease cause, cardiac morphology and function, clinical presentation, and signs. Hypertrophic cardiomyopathy (HCM) is defined in both humans and cats as presence of a hypertrophied left ventricle (LV) in the absence of abnormal loading conditions capable of producing a similar degree of hypertrophy (such as other cardiac or systemic diseases) 3,4 . Human HCM is the most common genetic cardiovascular disease, affecting 0.2% of the world population (1 in 500 individuals) 5 . The inheritance pattern in 60% of cases is autosomal dominant 6 . Nevertheless, more recent findings propose that human HCM follows a more complex mode of inheritance 7 with reports of genotype-positive/phenotype-negative individuals and a higher estimated population prevalence of 0.5% (1 in 200 individuals) 8 . Among domesticated animals, cats are particularly predisposed to HCM, with an estimated prevalence of 15% reported in the general cat population 9 . In humans, HCM has a well-characterized genetic basis with more than 1500 variants described so far 10 . In up to 60% of human cases, HCM is caused by variants in genes encoding the cardiac sarcomeric proteins that form the cardiac contractile unit, thus HCM is generally considered a ‘disease of the sarcomere’ 11 . Variants in the genes encoding beta-myosin heavy chain ( MYH7 ) and myosin-binding protein C ( MYBPC3 ) account for the majority of cases in humans (around 70% of patients with sarcomeric variants) 11,12 . Less commonly affected genes include cardiac troponin-I ( TNNI3 ) and cardiac troponin-T ( TNNT2 ), α-tropomyosin (TPM1 ), myosin light chain (regulatory MYL2 and essential MYL3 ) and cardiac actin ( ACTC ) 11–13 . To date, four variants associated with feline HCM susceptibility have been reported within specific cat breeds. Three of these variants were exonic and located in sarcomeric genes: MYBPC3 in the Maine Coon 14 and Ragdoll cats, 15 and MYH7 in a non-pedigree domestic shorthair cat (DSH) 16 . One variant reported in Sphynx cats was in the non-sarcomeric Alstrom syndrome 1 (ALMS1) gene 17 , which is associated with cardiac development and cell regulation 18 . One further intronic (splicing) variant within the sarcomeric TNNT2 gene was associated with HCM in the Maine Coon breed 19 , however a more recent study found that this variant was present in high frequencies across multiple cat breeds, suggesting that it might not have any significant association with HCM 20 . As yet, no additional variants have been identified. In humans, HCM is most commonly associated with exonic sarcomeric gene variants. These variants cause variable degrees of left ventricular hypertrophy (LVH) and different cardiomyopathy phenotypes within the same family 21,22 . Individuals sharing the same causative variant may variously exhibit a HCM, RCM or dilated cardiomyopathy (DCM) phenotype 21,22 . This pleiotropy leading to varying disease severity/expression in cardiomyopathies suggests that factors beyond the sarcomeric variant itself may influence disease expression, such as modifier genes, environmental factors, and epigenetic modifiers 13,23 . The non-coding genome is an emerging area of study for HCM, with regulatory variants now identified in human HCM 24,25 . For most variants the exact underlying molecular mechanisms linking genotype to phenotype remain unclear. There are numerous similarities between human and feline HCM, highlighting the potential value of using cats as a model to study the human disease. In both species, the disease is spontaneous and with similar natural histories and wide phenotypic spectra. They also share genetic homology: mutations for HCM found in humans have also been identified in Maine Coon and Ragdoll cats. Studies of feline HCM could uncover further shared genetic variants, disease mechanisms and therapeutic targets. A better understanding of the genetic factors causing disease and influencing disease severity in cats could help with diagnosis and management of the disease. Our main aim was to identify novel genetic variants for HCM susceptibility within and across cat breeds. Our secondary aim was to investigate different forms of cardiomyopathies (HCM and RCM) within a cohort of related and unrelated Birman cats to identify family-specific variants and explore possible co-occurrence of cardiomyopathies within feline families. HCM has been reported to affect both pedigree and non-pedigree cats, with some breeds displaying a greater predisposition to the disease than others. We initially investigated the disease across multiple pedigree and non-pedigree cats to capture associations across a spectrum of cat breeds. In the second study we focussed on cats of the Birman breed, since Birmans are recognized as a breed with a familial tendency to developing HCM (unpublished observations). We primarily focussed on investigating HCM, since this is the most common cardiomyopathy observed in cats, but we also included Birman cats with RCM. We aimed to determine whether shared HCM/RCM phenotype causative variants exist in Birman cats as they do in humans 21,22 . Results Descriptive statistics of the studied cat populations Descriptive statistics of the clinical and echocardiographic characteristics of the Across-breeds cat cohort (n=44) and Birman cat cohort (n=28) are summarised in Table 1 and Table 2, respectively. Due to sample limitations, we included 2 DSH control cats with a left ventricular wall thickness (LVWT) = 5.5mm. Both cats were from a geriatric population (³9 years old) with no previous signs of heart disease, all other cats included in the control population had a LVWT <5.5mm. Genetic variant discovery and statistical analyses The 18 candidate genes studied are listed in Table 3. Study 1: Across-breeds Targeted next generation sequencing analysis revealed the presence of a total of 4025 variants, single nucleotide variants (SNVs) (3400) and indels (666) in the 18 candidate genes in the Across-breeds cohort. Details of the identified genetic variants are presented in Figure 1. Chi-squared comparisons The annotation of the identified variants detected 8 high impact variants, including the known Ragdoll missense variant R820W in MYBPC3 15 , which was included as a positive control. However, none of these variants had a significantly different prevalence between cases and controls when analyzed both within and across the cat breeds. Comparisons using the Chi-squared (χ²) test of the allelic and genotypic frequencies of the identified variants with a predicted moderate or modifier effect on the encoded protein identified a total of 160 variants with a significant (P£0.05) difference between HCM cases and controls. 157 of these variants were intronic: 91/157 had a higher prevalence in cases, with 8/157 being present only in cases (see Supplementary Table 1 for full list of variants with a statistically significant difference). Four of these variants remained significant after correcting for multiple testing: one intronic variant (X:101006474, T>C, P=0.0002) in Lysosomal associated membrane protein ( LAMP2) gene, one 3’UTR (F1:42194903 G>GGT, P=0.025) located in TNNT2 gene and two missense variants (B3:76167563, C>T, L88F, P=0.003; D1:76804158, T>C, I45V, P=0.004) located within the novel gene ENSFCAG00000040035 (overlapping MYH7 ) and Cysteine and Glycine Rich Protein ( CSRP3) gene respectively. The intronic SNV in LAMP2 was only present in control cats. The 3’UTR variant located in TNNT2 had a significantly higher genotypic frequency in controls. According to the Softberry FPROM human promotor predictor analysis close to this 3’UTR SNV there are two promotors. However, this SNV did not directly overlap with these promotor predictions. The two missense variants were present in both case and control cats with a higher prevalence in controls and were located within the novel gene ENSFCAG00000040035 (overlapping MYH7 ) (B3:76167563 L88F) and CSRP3 gene (D1:7680415 I45V) respectively. The CSRP3 gene missense variant has a reported ‘sorting intolerant from tolerant’ (SIFT) score of 0.34, which is considered ‘tolerated’. Genomic association analysis The genomic association analysis for the Across-breeds cohort which included age, sex and breed as covariates revealed the presence of one intronic variant within TNNT2 (present only in cases (n = 5) (F1:42199381 CA>C)) significantly associated with HCM (P=0.0006). The Softberry NSITE analysis predicted that this variant affected the motifs that were able to bind to the nearby transcription factor binding sites (TFBS), with 3 additional motifs identified in the intronic sequence compared to the control cat population sequence (see supplementary table 8). The Manhattan plot presenting the results of the genomic association analysis for HCM susceptibility in the Across-breeds cat cohort is shown in Figure 2. Study 2: Birman cats The targeted next generation sequencing analysis of the Birman cat cohort revealed the presence of 2298 genetic variants, SNVs (1921) and indels (395) across the candidate genes. Details of the identified genetic variants are presented in Figure 3. Chi-squared comparisons The annotation of the genetic variants revealed 5 high impact variants within the Birman cohort, however their frequency was not significantly different between case and control cats. When variants with a predicted moderate or modifier effect were compared using the χ² test, 177 variants were identified with significantly different frequencies (P£0.05) between cases (HCM and RCM) and controls. Among these variants was a missense SNV located in ENSFCAG00000040035 gene -Glu22Gln (B3:76167365, G>C, E22Q, P=0.04) more prevalent in cases (heterozygous in 5 HCM, 3 RCM). This missense variant is synonymous for a variant within MYH7 gene and overlaps with a microRNA (miR-208B- ENSFCAG00000018043 ) which has been previously reported to play a role in heart disease 26 . Moreover, one 3’UTR SNV (D1:76785090, C>A, P=0.03) within CSRP3 gene was found only in control cats; the Softberry analysis did not reveal any further overlapping of this 3’UTR SNV with promoter regions. Further, 14 intronic variants located in Actin alpha cardiac muscle 1 ( ACTC1), MYH7 and TNNT2 genes were more prevalent in cases. When cases were restricted to cats with HCM, the χ² comparisons revealed 153 variants that were significantly different (P£0.05) between cases and controls. Of these 153 variants, 117 had a higher prevalence in cases, with 78 intronic variants present only in cats with HCM (mainly spanning the genes ACTC1 and Actinin Alpha 2 (ACTN2) ) . Full details of the identified variants in the HCM and RCM analyses and the HCM analyses are presented in Supplementary Tables 3 and 4, respectively. Genomic association analyses I. Genomic Analysis: HCM Cases (n=8) and Controls (n=14) When cases were restricted to just HCM, the genomic association analysis which included age and sex as covariates identified 24 intronic variants with a significant association with disease susceptibility. These intronic variants were located within ACTN2 (D2:12374576 C>T ) , MYH7 (B3:76168426 G>A) and ACTC1 (B3:70080970 T>TC) genes and had a higher frequency in the case population aside from one intronic variant associated to controls in ACTN2 (D2:12407434 GGGGT>G). The Manhattan plot presenting these results is shown in Figure 4. II. Genomic Analysis: HCM and RCM Cases (n=14) and Controls (n=14) The genomic association analysis of the merged HCM and RCM phenotypes revealed the presence of 9 intronic variants within CSRP3 gene and 1 intronic variant within MYH7 with a statistically significant to the disease. The Manhattan plot presenting these results is shown in Figure 5. Human Comparison & Functional Impact Analysis The significant missense SNV that we identified in Birman cats (B3:76167365) corresponds to the protein change pE22Q with a SIFT score of 0.4. The protein orthologue approach confirmed the presence of an E amino acid in the protein ortholog alignment for humans (14:23415004, Human GRCh38 position). According to the Clinical Variants (ClinVar) archive, an E>Q protein change human variant at position 14:23413845 has an association with HCM and DCM that does not overlap with the variant identified in cats, but it is relatively close to the human position identified from the region comparison approach 14:23415004. Discussion We aimed to identify novel genetic variants associated with HCM and RCM in Birman cats, as well as across pedigree breeds and DSH cats and performed two studies using a targeted cardiomyopathy gene panel and with meticulously phenotyped cats. Although there are a few shared genetic variants associated with HCM resistance or susceptibility across cat breeds, the genetic architecture of the disease seems to be breed-specific. Moreover, our studies identified high impact variants within sarcomeric genes that were present at similar frequencies in both HCM cases and healthy controls. Similarly, several missense variants were present in both cases and controls, with no significant differences between the groups. These findings indicate that studies with appropriate sample sizes of cases and controls are required for the identification of truly causative HCM variants in cats. Nevertheless, we did identify a missense variant located on a novel gene ENSFCAG00000040035 , which overlaps with MYH7 and a microRNA (miR-208). This missense variant had a significantly higher prevalence in HCM cases (P = 0.048) in our Birman cat cohort. The function of ENSFCAG00000040035 gene is unknown, but MYH7 is a sarcomeric gene that encodes the protein beta myosin heavy chain; a key protein component of the sarcomere involved in contractile function 27 . Of increased interest is the presence of this variant within miR-208 (overlapping with ENSFCAG00000040035 and MYH7) , which is expressed in cardiac tissue and regulates the production of beta myosin heavy chain during cardiomyocyte development 28 . MicroRNAs (miRNA) are non-coding RNA sequences which are crucial in biochemical pathway regulation including during the heart’s development 29 . According to previous studies, genetic variants in miRNA genes can have profound effects on miRNA functionality at all levels, including miRNA transcription, maturation, and target specificity, and as such they can also contribute to disease 30–32 . Therefore miR-208 might prove to be an important biomarker in feline cardiac disease, especially since it has already been linked to myocardial infarction, hypertensive heart disease 33,34 and dilated cardiomyopathy in humans 35 . Relationships between miRNAs and cardiac conditions such as left ventricular hypertrophy and fibrosis have already been shown across studies 36,37 , including evidence for other miRNAs that overlap with the MYH7 gene serving as biomarkers for human HCM 38,39 . Nevertheless, the role of microRNAs in feline HCM remains largely unknown. There is only one previous study that compared serum miRNA of a healthy control cat cohort (n = 12) with a HCM cohort (with stage C (disease progression is causing noticeable clinical signs of heart disease)) (n = 11). In that study, 7 human HCM-associated miRNAs (causing cardiac tissue damage and disturbed blood flow) were upregulated in the HCM cat cohort, 40 suggesting that miRNAs might also play a role in feline HCM and could potentially be useful biomarkers as in humans. The miR-208 was not among the upregulated miRNAs within their cat cohort, suggesting our miR-208 variant may be specific to Birman pedigree cats. Further studies in Birman cats are needed to validate these results and confirm the potential role of miR-208 or/and ENSFCAG00000040035 in feline HCM susceptibility. Recent studies suggest intronic variants might play a more significant role in HCM susceptibility than previously thought 41,42 . Whole genome sequencing studies of human HCM have found pathogenic intronic variants that were causing disruption of splicing and TFBS 41 . In our study, we identified several intronic variants significantly associated with HCM susceptibility in cats, both across cat breeds and in the Birman cohort. Of particular interest was the intronic variant in TNNT2 (position F1:42199381) which was present only in HCM cases in the Across-breeds cat study. Softberry analysis predicted that this intronic variant affects the number of motifs in or around the TFBS which has the potential to influence the binding of transcription factors, and consequently gene regulation. Several variants in TNNT2 gene are associated with human HCM 43 . TNNT2 encodes cardiac troponin-T (cTnT) a cardiac specific protein forming part of the troponin complex associated with thin filaments. This protein plays a crucial role in cardiomyocyte contraction and relaxation through its interaction with actin and tropomyosin 44 . Further studies are needed to confirm whether this intronic variant plays a regulatory role and contributes to HCM susceptibility since Softberry tools currently provide predictions based on the human (and not the feline) genome assembly. Moreover, we found several intronic variants associate with HCM susceptibility both in the Across-breeds and Birman cat studies, but it is difficult to assess if they have a pathological impact. Further genomic and functional genomic studies with larger sample sizes are needed to shed light on the role of intronic variants in feline HCM susceptibility. We investigated RCM alongside HCM in Birman cats following previous unpublished observations of cats within the same family being diagnosed with different cardiomyopathy phenotypes, as has been observed in affected human families 45 . There are significant similarities in disease presentation between HCM and RCM, with both conditions leading to diastolic dysfunction and increased end diastolic pressure 45 , predisposing cats to heart failure. RCM in cats shares similar histological features to HCM, thus it has been proposed that RCM could be part of the spectrum of HCM 46 . An intronic variant in the CSRP3 gene was significantly associated with the combined group of HCM and RCM cases. The CSRP3 gene encodes the muscle LIM protein (MLP) of functional importance for calcium handling and signalling in cardiomyocytes 47 . Variants within this gene have been identified as causative for both human HCM and dilated cardiomyopathy 48,49 . Within our Birman cat cohort, the intronic variant was more prevalent in the control population and may have a protective role. Our results indicate the potential presence of shared protective variants against the phenotypic expression of RCM/HCM phenotype within Birman cats. To identify whether our intronic variant is playing a protective role and the exact mechanism behind this, further molecular and functional studies would need to be conducted, alongside validation in another case-control study of Birman cats. It would be useful if further studies in Birman cats were to include related cats exhibiting varying cardiomyopathies. Our Birman cohort included related cats, but these consisted of only 4 cats in the control population and 2 HCM cats. Genetic similarities identified between the human and feline cardiomyopathies point to the usefulness of cats as a translational model of human disease. Preclinical feline models of HCM have already shown promising results, with the myosin inhibitor MYK-461 (mavacamten) reducing left ventricular outflow tract (LVOT) obstruction in Maine coon cats with HCM 50 . Collaborations between human and veterinarian researchers have the potential to unravel the genetic background of cardiomyopathies, with beneficial outcomes possible for both. Conclusion In this study, we identified several high- or moderate-impact variants in sarcomeric and other candidate HCM genes that were not causative, since they were not unique to cats with HCM. As with human patients, in some cats with HCM a genetic cause cannot be identified by following a candidate gene approach and focusing only on exonic variants. In this study we identified several intronic variants statistically associated with HCM susceptibility or protection, but due to the poor annotation of regulatory elements in the cat genome it is difficult to assess their potential role in HCM. Moreover, we identified in Birman cats an exonic variant of interest located in a gene of unknown function (ENSFCAG00000040035) that overlaps with the key sarcomeric gene MYH7 and a myocardium related microRNA (miR-208). Further studies are needed to characterise the role of this variant in HCM susceptibility within this breed and assess if microRNAs play a role in HCM susceptibility in cats as in humans. Materials and Methods Ethical approval Ethical approval was received by the Clinical Research Ethical Review Board of the Royal Veterinary College (URN.2019.1942-3, URN 2016 1515, URN 2015-1378), the study followed all relevant guidelines and regulations. The study is reported in adherence to ARRIVE (https://arriveguidelines.org) guidelines. Study population Cats were recruited into the study following routine or invited echocardiographic screening for heart disease or following post-mortem examination conducted at the Royal Veterinary College (RVC). Of the cats included in the study (total n = 72), 2 HCM cats in the Birman cohort and 11 HCM cats in the Across-breeds cohort were diagnosed at necropsy. All other cats were diagnosed via echocardiography. Cats without evidence of HCM or other cardiac disease (confirmed via echocardiography) and above the age of 9 years old were used as controls. Cats of any age with a confirmed HCM phenotype were used as cases. Among the Birman cohort 2 control cats were littermates, 2 control cats shared the same dam, and 2 HCM cats shared the same dam. Our work included two studies: i) An Across-breeds cat study, totalling 44 phenotyped cats (controls = 23, HCM = 21) representing 21 non-pedigree cats (DSH) and pedigree breeds (4 Bengal, 8 British shorthair, 1 British longhair, 6 NFC, 3 Ragdoll, and 1 Maine coon). Among the HCM cases we included a Ragdoll cat confirmed homozygous for the HCM-associated MYBPC3 variant (R820W); ii) A Birman pedigree cat study, totalling 28 phenotyped Birman cats (controls = 14, cases = 17, including 8 cats with HCM, and 6 cats with RCM). Phenotyping The cardiac phenotype was defined by echocardiography and/or gross pathology and histopathology with owner consent. Echocardiography was performed by a board-certified veterinary cardiologist, or by a cardiology resident under the supervision of a board-certified veterinary cardiologist, using a Vivid E9 or Vivid I ultrasound machine (GE Systems, Hatfield, Hertfordshire, UK) with a 7.5 or 12 MHz phased-array transducer. Standard echocardiographic views were acquired, and video loops recorded 9 . All studies were measured off-line using dedicated echocardiographic software (EchoPac, GE Systems, Hatfield, Hertfordshire, UK). On echocardiography, the thickness of the left ventricular free wall (LVFW) and interventricular septum (IVS) was measured by a leading edge to leading edge technique from a 2D right parasternal long-axis (RPLA) 4- or 5-chambered view and a short-axis view at the papillary muscle level (RPSA). The thickest end-diastolic segment was averaged over 3 different cardiac cycles in each view (RPLA and RPSA). End-diastolic frames were defined as the first frame after mitral valve closure in RPLA and as the time point in the cardiac cycle of greatest left ventricular internal diameter in RPSA 9 . The greatest end-diastolic wall thickness of these measured views (RPLA septal, RPLA free wall, RPSA septal, RPSA free wall) was defined as LVWT and used for data analysis. Left atrial linear dimensions were measured as left atrial to aortic ratio (LA/Ao ratio) and left atrial diameter (LAD). The LA/Ao was measured as the ratio of the left atrium to aorta measured in 2D from a RPSA view at the heart base, in the frame after aortic valve closure 51 . The LAD was measured as the cranial-caudal LA dimension from a RPLA 4-chambered view, in the frame before mitral valve opening 52 . Left ventricular (LVFS%) fractional shortening was measured by M-Mode from a right parasternal short-axis at the papillary muscle. Systolic anterior motion of the mitral valve (SAM) was assessed on colour Doppler and 2D echo from a right parasternal long-axis 5 chamber view. HCM was defined as LVWT ³5.5 mm at end-diastole. Cases with concurrent disease that could contribute to LVH were excluded from the study. These conditions included systemic hypertension (systolic blood pressure >160mmHg) 53 , aortic stenosis or hyperthyroidism 54 . Healthy cats (control group) were defined as having a LVWT <5.5 mm and aged ³9 years old to minimise inclusion of cats with late onset HCM. Necropsy examinations were performed by a single trained observer, and HCM was defined as a hypertrophied LV in the presence of myofiber disarray and interstitial/replacement fibrosis on histopathology 55 . In the Birman cat cohort, in addition to HCM cases we also included RCM cases, defined as the presence of left or biatrial enlargement (left and/or right atrial diameter in RPLA view>16 mm), LVWT £5.5 mm and normal left ventricular systolic function (LVFS%>30%). Blood and tissue collection and DNA extraction Myocardial samples collected at necropsy were received from Birman breeders following death with suspicion of heart disease. For the Across-breeds cats, liver samples were obtained following routine necropsy examinations at the RVC. Residual blood (derived from clinical testing) was used for this project from blood collected by either a qualified veterinarian or veterinary nurse following echocardiography (using the same equipment and expert for each diagnosis) to exclude systemic diseases that can affect the heart and to measure cardiac biomarkers. DNA was extracted from whole blood/liver/myocardial samples using two commercial kits: DNeasy Blood and Tissue Kit (Qiagen®) and GeneJet Whole Blood Genomic DNA Purification Mini Kit (Thermo Scientific®) according to the manufacturers’ instructions. DNA quality and quantity were assessed using Denovix DS-11 Series spectrophotometer and Invitrogen Qubit 4 Fluorometer, respectively. Feline HCM gene panel and Targeted Next Generation Sequencing (tNGS) We developed a gene panel for feline HCM and RCM based on candidate genes previously implicated in human cardiomyopathies (Table 3). This feline panel was equivalent to the Illumina TruSight Cardio Panel 56 which is applied in suspected cases of human cardiomyopathy. In the first study (Across-breeds cohort) we included a panel of 18 candidate genes (Table 3). The same panel was used in the second study (Birman cohort) with the exclusion of two metabolic genes. These two genes were excluded due to limited variation, with no exonic variation being identified in these genes from the first study. Targeted Next Generation Sequencing Analysis The raw sequencing data (FASTQ files) were assessed for quality control using FASTQC (v10.1) 57 and trimmed to exclude adapter sequencing using Trimmomatic (v0.36) 58 prior to mapping the reads on Felis Catus v9.0 genome assembly 59 using the BWA aligner 60 . The matching variant file for the Felis Catus v9.0 genome assembly (Ensembl release version 95) 61 was sorted against the reference dictionary to obtain known variant sites using Picard toolkit (v2.21.7) 62 . The reads were indexed, and duplicates removed using SAMTOOLS (v1.3) 63 . Base recalibration and variant calling to detect SNVs and indel variants were performed with the GATK (v.3.8) software 64 using HaplotypeCaller 65 . Joint VCF files were created for cases and controls (for each study separately). Two separate VCF files for the Birman cases were created: one including both HCM and RCM cases and another only HCM. We ran a grouped analysis for cats with HCM and RCM phenotypes, as RCM has been suggested to be part of the HCM spectrum, i.e., these two phenotypes might represent diverse expressions of the same disease 45,46,66,67 . The SNV locations were obtained from Felis Catus v9.0 genome assembly using the Ensembl genome browser release version 95 68 . SNV annotation was performed using the Ensembl variant effect predictor (VEP) tool 69 . The data from each study were analysed separately. Allelic and genotypic frequencies of genetic (SNV and Indel) variants with a predicted high, moderate, or modifier functional impact according to VEP were compared between cases and controls to assess if there are statistically significant differences between the two groups. The Chi-squared test (χ2), with a significance level set at P£0.05 was used in this respect. A correction for multiple testing (0.05 divided by number of genes tested) was also applied. To identify if any of the SNVs of interest in 3’UTR and other non-coding regions were located within a putative regulatory region we further interrogated these SNVs using Softberry software 70 . Specifically, to identify potential functional roles of our SNVs of interest we used BEDTools 71 to extract SNV sequences 1500bp either side of our SNV and ran comparisons against the corresponding 3000bp sequence extracted from our sample containing the reference allele. These 3000bp sequences were inputted into Softberry tools FPROM promotor predictor to look for predicted promotor regions in our significant 3’UTR SNVs and the NSITE tool to search for regulatory motifs in our 3’UTR and intronic regions 72 . Genomic association studies A bed genotypic file was generated from the VCF file for cases and controls using the PLINK software (v1.90) 73–75 . Each of the datasets was subjected to quality control (qc) measures using the following thresholds: call rate <90%, minor allele frequency <0.05 and Hardy-Weinberg equilibrium P<10 -6 . A genomic relationship matrix was created for all animals using the GEMMA (v0.98.1) algorithm 76 . GEMMA was used to run the genomic association analyses for HCM susceptibility using a mixed model where the genomic relationship matrix was added as a random effect to account for possible population stratification and age, sex, and breed as fixed effects in the first study (Across-breeds cat cohort), and lambda correction applied to the P-values. The same model with the exclusion of breed as a fixed effect was used in the second study (Birman cat cohort). The significance level was set at P£0.05 and a Bonferroni correction for multiple testing was applied. Python3 77 in Jupyter notebook 78 (for Mac OS) was used to create Manhattan plots to present the genomic analyses results. Feline and Human Comparisons To investigate whether the SNVs of interest identified for feline HCM susceptibility were equivalent or close to previously identified variants in humans, a relevant comparison was performed. Initially, a region comparison approach was used to compare the cat (Felis Catus v9.0) and human (GRCh38.p13) assembly by inputting the cat SNV position into Ensembl’s region comparison tool 68 . The output of this analysis provides the predicted equivalent coordinate in the human assembly per feline SNV. Moreover, a protein orthologue approach was applied by extracting the relevant amino acid sequence alignments for Felis Catus v9.0 and Human GRCh38.p13 genome assemblies using the Ensembl ortholog comparison tool 68 in ClustalW format for missense SNVs of interest. The equivalent human protein position for each missense SNV was identified through these sequence comparisons before identifying the correct international union of pure and applied chemistry (IUPAC) coding. After the equivalent human genome position was identified, ClinVar database 79 was used to search for human SNVs that have been previously reported close to the equivalent feline SNVs of interest. Abbreviations ACTC Cardiac actin ACTN2 Actinin Alpha 2 ALSM1 Alstrom syndrome 1 ARVC Arrhythmogenic right ventricular cardiomyopathy CSRP3 Cysteine and Glycine Rich Protein 3 DCM Dilated cardiomyopathy DSH Domestic shorthair HCM Hypertrophic cardiomyopathy LA/Ao Left atrium to aorta ratio LAD Left atrial diameter LV Left ventricle LVH Left ventricular hypertrophy LVWT Left ventricular wall thickness at end-diastole MYBPC3 myosin-binding protein C MYH7 beta-myosin heavy chain MYL2 Regulatory myosin light chain MYL3 Essential myosin light chain NFC Norwegian Forest Cat RCM Restrictive cardiomyopathy SNV single nucleotide polymorphisms tNGS targeted next-generation sequencing TNNI3 Cardiac troponin I TNNT2 cardiac troponin-T TPM1 α-tropomyosin Declarations Data Availability The datasets generated and analysed during the study are available in the Sequence Read Archive (SRA) repository, BioProject ID PRJNA1083230. Acknowledgements The authors acknowledge the financial support provided by the Biotechnology and Biological Sciences Research Council (BBSRC) studentship. Funding was provided via the UKRI funder award reference 1903448. The authors acknowledge the Petplan Charitable Trust for making the research possible through funding provided to projects S18-6930731 and S190735-774. The authors also acknowledge the Winn Feline Foundation and the Birman Cat Club for their continued support of the research. The authors express gratitude to the team at the Queen Mother hospital for Animals at the Royal Veterinary College. The authors thank Petros Syrris (University College London) for their guidance on human hypertrophic cardiomyopathy and Lois Wilkie (Royal Veterinary College) for performing the post-mortem examinations on cats included in the study and providing in-depth pedigree information of the Birman cat cohort. The authors acknowledge Oliver Foreman (Animal Health Trust) for their help with devising the original candidate gene panel and co-ordinates. Author Contributions Jade Raffle (JR): Collected clinical data on study participants, performed the bioinformatic and statistical analysis and interpretation of the sequencing datasets, and prepared the manuscript. Jose Novo Matos (JNM): Provided expertise on feline cardiology, recruited the study participants, and performed the sample DNA extractions for submission to outsourced sequencing, contributed to data collection and study design, and assisted in revising the manuscript. Androniki Psifidi (AP): Provided expertise on clinical genetics, guidance on the research design and data interpretation and assisted in revising the manuscript. Virginia Luis Fuentes (VLF): Provided expertise on feline cardiology, conducted echocardiographic assessments for the study, research design and data interpretation and assisted in revising the manuscript. David J Connolly (DJC): Provided expertise on feline cardiology, conducted echocardiographic assessments for the study, research design and data interpretation and assisted in revising the manuscript. Perry Elliott (PE): Provided guidance on the human cardiology genomic background and the development of the feline targeted HCM panel. Richard Piercy (RP): Contributed to securing funding and experimental design alongside reviewing the manuscript and providing feedback. AP, VLF, DJC, RP: conceived and designed the genetic study of HCM resistance and secured funding. References Wexler, R., Elton, T., Pleister, A. & Feldman, D. Cardiomyopathy: an overview. Am Fam Physician (2009). Payne, J. R. et al. Prognostic indicators in cats with hypertrophic cardiomyopathy. J Vet Intern Med 27, 1427–1436 (2013). Luis Fuentes, V. et al. ACVIM consensus statement guidelines for the classification, diagnosis, and management of cardiomyopathies in cats. J Vet Intern Med 34, 1062–1077 (2020). Ommen, S. 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Descriptive statistics of clinical and echocardiographic characteristics of the Across-breeds cohort aiming to identify genetic variants for HCM susceptibility using a targeted cardiomyopathy gene panel. Controls (n = 23) HCM (n = 21) Age (years) 12 [9 – 17] 6.9 [1.8 - 20] Weight (Kg) 4.2 [2.41 – 5.65] 4.8 [3 – 8.55] Sex: males (%) 8 (35%) 14 (67%) Echocardiography LVWT (mm) 4.8 [4 – 5.5] 7.6 [5.7 – 11.6] LAD (mm) 14.1 [11.5 - 17] 19.15 [11.2 - 31] LA/Ao 1.25 [1 – 1.5] 1.8 [1.12 – 2.7] Results are presented as median [range] for each variable for the Hypertrophic cardiomyopathy (HCM) and control cats. Abbreviations: LAD, left atrial diameter; LA/Ao, left atrium to aorta ratio; LVWT, left ventricular wall thickness at end-diastole. Table 2. Descriptive statistics of clinical and echocardiographic characteristics of the Birman cat study aiming to identify genetic variants for HCM susceptibility using a cardiomyopathy targeted gene panel. Controls (n = 14) HCM (n = 8) RCM (n = 6) Age (years) 11.3 [9.2 - 17] 8.35 [1.4 – 16.4] 8.25 [4 – 17.9] Weight (Kg) 3.53 [2.63 – 5.2] 4.15 [3.5 – 5.2] 4.4 [3.54 – 5.2] Sex: males (%) 3 (21%) 6 (75%) 3 (50%) Echocardiography LVWT (mm) 4.4 [2.8 – 4.8] 6 [5.5 – 8.4] 5.1 [4.8 – 5.2] LAD (mm) 13.5 [11.4 – 15.4] 14 [12 – 19.4] 21.9 [16 – 27.3] LA/Ao 1.4 [1.3 - 1.6] 1.3 [1 – 1.8] 2 [1.8 – 2.9] Results are presented as median [range] for each variable for the cardiomyopathies (HCM, RCM) and control cats. Abbreviations: LAD, left atrial diameter; LA/Ao, left atrium to aorta ratio; LVWT, left ventricular wall thickness at end-diastole. Table 3. Feline cardiomyopathy target gene panel. Gene names and gene positions based on FelCat9 assembly. *genes exclusive to the 18-gene panel for Cohort (i) Across-breeds Cat tNGS Study; ˆgenes are exclusive to the 16-gene panel Cohort (ii) Birman Pedigree Cat Study. Gene Acronym Gene Name Chromosome Start Position End Position MYL3 Myosin light chain 3 A2 16290210 16296259 TNNC1 Troponin C1 A2 21044756 21047572 PRKAG2* Protein Kinase AMP-Activated Non-Catalytic Subunit Gamma 2 A2 165581663 165842537 CAV3ˆ Caveolin-3 A2 50098852 50115062 PDLIM3* PDZ and LIM domain protein 3 B1 16445054 16474763 PLN Phospholamban B2 109748495 109748653 MYH7 Myosin heavy chain gene B3 76134518 76188380 TPM1 Tropomyosin 1 B3 43987226 44036037 ACTC1 Actin alpha cardiac muscle 1 B3 70080059 70085659 MYH6 Myosin Heavy Chain 6 B3 76134518 76188380 MYBPC3 Myosin Binding Protein C D1 101324989 101341953 CSRP3 Cysteine and Glycine Rich Protein 3 D1 76776148 76804617 ACTN2 Actinin Alpha 2 D2 12369479 12442749 MYL2 Myosin light chain 2 D3 8973170 8980956 TCAP Telethonin E1 40752572 40753322 TNNI3 Troponin I3, Cardiac Type) E2 3439821 3450055 TNNT2 Troponin T2 F1 42194772 42209527 GLA Galactosidase X 83631654 83639229 LAMP2* Lysosomal associated membrane protein X 100973530 101012558 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3943358","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":278936339,"identity":"4f234512-45db-45a9-8ee9-310848b5ce88","order_by":0,"name":"Jade Raffle","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACxgYGhg8VBQxycBEJIrQwzjhjwGBMvBaQLpCWxAaitTDPPvyw4YCBTfr2GckPGH7UMCTObCCghbEvzRCoJS13zo00A8aeYwyJswm6qofB/PEHg8O5M3jOMDDwNjAkziOshf0j0Jb/6RJALYx/idPCA3LYgQQJ9h4GZpAtRDiMpxCoJdlwBnubwWGZYxLGBL1v2MO+seFAhZ28BDPzw4dvamxkZxwgpAXZzANERaQ8YSWjYBSMglEw4gEAqjc++eFSM2wAAAAASUVORK5CYII=","orcid":"","institution":"Royal Veterinary College","correspondingAuthor":true,"prefix":"","firstName":"Jade","middleName":"","lastName":"Raffle","suffix":""},{"id":278936341,"identity":"dee5e242-5409-47c2-a58e-c8e61f628d0f","order_by":1,"name":"Jose Novo Matos","email":"","orcid":"","institution":"Royal Veterinary College","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"Novo","lastName":"Matos","suffix":""},{"id":278936343,"identity":"dd4c22e5-ed63-4fe4-9803-d5c54bf14b47","order_by":2,"name":"Richard J Piercy","email":"","orcid":"","institution":"Royal Veterinary College","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"J","lastName":"Piercy","suffix":""},{"id":278936345,"identity":"3c557fd2-b661-46e5-8329-16ad0bd9cbdf","order_by":3,"name":"Perry Elliott","email":"","orcid":"","institution":"University College London, University of London","correspondingAuthor":false,"prefix":"","firstName":"Perry","middleName":"","lastName":"Elliott","suffix":""},{"id":278936348,"identity":"3645602b-aa07-4b06-92de-195d85b5e619","order_by":4,"name":"David J Connolly","email":"","orcid":"","institution":"Royal Veterinary College","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"J","lastName":"Connolly","suffix":""},{"id":278936349,"identity":"c10ac8b7-83e9-4889-8cda-9f353b1a942a","order_by":5,"name":"Virginia Luis Fuentes","email":"","orcid":"","institution":"Royal Veterinary College","correspondingAuthor":false,"prefix":"","firstName":"Virginia","middleName":"Luis","lastName":"Fuentes","suffix":""},{"id":278936351,"identity":"da265e08-b3fc-4cd7-8b17-f01fead44a93","order_by":6,"name":"Androniki Psifidi","email":"","orcid":"","institution":"Royal Veterinary College","correspondingAuthor":false,"prefix":"","firstName":"Androniki","middleName":"","lastName":"Psifidi","suffix":""}],"badges":[],"createdAt":"2024-02-09 15:06:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3943358/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3943358/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-87852-5","type":"published","date":"2025-01-31T15:57:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52651489,"identity":"f4d448ea-e5de-4b2c-9f70-bc658d1051aa","added_by":"auto","created_at":"2024-03-14 05:43:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97809,"visible":true,"origin":"","legend":"\u003cp\u003eStatistics of the identified, annotated using the Variant Effect Predictor tool of Ensembl, variants in the Across-breeds cohort using targeted generation sequencing of 18 cardiomyopathy related genes based on the human literature.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3943358/v1/57da74a0528e7434bf43cfd5.png"},{"id":52651488,"identity":"cd9fdaa8-bc29-4bf2-9bbd-d9be6724871d","added_by":"auto","created_at":"2024-03-14 05:43:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65596,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot presenting the results of the genomic association analysis for HCM susceptibility in the Across-breeds cat cohort. Genomic location (horizontal axis) is plotted against -log10(P-value); significantly (P≤0.05) significant threshold, after correcting for multiple testing, is shown as the red line.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3943358/v1/6fd3b7ea77e69bd5892ed8da.png"},{"id":52651491,"identity":"c26e7740-9cea-4ac6-a91e-fc7505c47962","added_by":"auto","created_at":"2024-03-14 05:43:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166400,"visible":true,"origin":"","legend":"\u003cp\u003eStatistics of the identified (annotated using the Variant Effect Predictor tool of Ensembl) variants in the Birman cat cohort using targeted cardiomyopathy gene panel.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3943358/v1/9521225ac204a558e93c0df5.png"},{"id":52651490,"identity":"3202aed8-1678-4f67-b316-5e7268779724","added_by":"auto","created_at":"2024-03-14 05:43:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114092,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot presenting the results of the genomic association analysis for hypertrophic cardiomyopathy in Birman cats. Genomic location (horizontal axis) is plotted against -log10(P-value); significantly (P≤0.05) significant threshold, after correcting for multiple testing, is shown as the red line.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3943358/v1/c695864061fa37586d977a39.png"},{"id":52651492,"identity":"5d462e87-d2aa-4de8-b0ba-4a1d1a98790f","added_by":"auto","created_at":"2024-03-14 05:43:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55379,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot presenting the results of the genomic association analysis for hypertrophic cardiomyopathy and restrictive cardiomyopathy in Birman cats. Genomic location (horizontal axis) is plotted against -log10(P-value); significantly (P≤0.05) significant threshold, after correcting for multiple testing, is shown as the red line.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3943358/v1/034180f11ab995e35dd67322.png"},{"id":75351169,"identity":"f7ff9b59-833a-4fc2-bf99-7887dc13395e","added_by":"auto","created_at":"2025-02-03 16:07:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1441757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3943358/v1/a2667aa2-7a98-4c6c-9a31-ae3ea4579a02.pdf"},{"id":52651493,"identity":"929401d6-a0d0-44d6-ad79-71ff5f5ef228","added_by":"auto","created_at":"2024-03-14 05:43:22","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5681682,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataFiletNGSManuscript231108.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3943358/v1/b1b2068e7329034f5d7916c7.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of novel genetic variants associated with feline cardiomyopathy using targeted next-generation sequencing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiomyopathies are diseases of the myocardium that can lead to a range of functional and structural abnormalities of the heart. Cardiomyopathies in both humans and cats are associated with elevated risks of congestive heart failure, cardioembolic events, and sudden cardiac death\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. Subcategorization of cardiomyopathies is based on the underlying disease cause, cardiac morphology and function, clinical presentation, and signs.\u003c/p\u003e \u003cp\u003eHypertrophic cardiomyopathy (HCM) is defined in both humans and cats as presence of a hypertrophied left ventricle (LV) in the absence of abnormal loading conditions capable of producing a similar degree of hypertrophy (such as other cardiac or systemic diseases)\u003csup\u003e3,4\u003c/sup\u003e. Human HCM is the most common genetic cardiovascular disease, affecting 0.2% of the world population (1 in 500 individuals)\u003csup\u003e5\u003c/sup\u003e. The inheritance pattern in 60% of cases is autosomal dominant\u003csup\u003e6\u003c/sup\u003e. Nevertheless, more recent findings propose that human HCM follows a more complex mode of inheritance\u003csup\u003e7\u003c/sup\u003e with reports of genotype-positive/phenotype-negative individuals and a higher estimated population prevalence of 0.5% (1 in 200 individuals)\u003csup\u003e8\u003c/sup\u003e. Among domesticated animals, cats are particularly predisposed to HCM, with an estimated prevalence of 15% reported in the general cat population\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn humans, HCM has a well-characterized genetic basis with more than 1500 variants described so far\u003csup\u003e10\u003c/sup\u003e. In up to 60% of human cases, HCM is caused by variants in genes encoding the cardiac sarcomeric proteins that form the cardiac contractile unit, thus HCM is generally considered a \u0026lsquo;disease of the sarcomere\u0026rsquo;\u003csup\u003e11\u003c/sup\u003e. Variants in the genes encoding beta-myosin heavy chain (\u003cem\u003eMYH7\u003c/em\u003e) and myosin-binding protein C (\u003cem\u003eMYBPC3\u003c/em\u003e) account for the majority of cases in humans (around 70% of patients with sarcomeric variants)\u003csup\u003e11,12\u003c/sup\u003e. Less commonly affected genes include cardiac troponin-I (\u003cem\u003eTNNI3\u003c/em\u003e) and cardiac troponin-T (\u003cem\u003eTNNT2\u003c/em\u003e), α-tropomyosin \u003cem\u003e(TPM1\u003c/em\u003e), myosin light chain (regulatory \u003cem\u003eMYL2\u003c/em\u003e and essential \u003cem\u003eMYL3\u003c/em\u003e) and cardiac actin (\u003cem\u003eACTC\u003c/em\u003e)\u003csup\u003e11\u0026ndash;13\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo date, four variants associated with feline HCM susceptibility have been reported within specific cat breeds. Three of these variants were exonic and located in sarcomeric genes: \u003cem\u003eMYBPC3\u003c/em\u003e in the Maine Coon\u003csup\u003e14\u003c/sup\u003e and Ragdoll cats,\u003csup\u003e15\u003c/sup\u003e and \u003cem\u003eMYH7\u003c/em\u003e in a non-pedigree domestic shorthair cat (DSH)\u003csup\u003e16\u003c/sup\u003e. One variant reported in Sphynx cats was in the non-sarcomeric Alstrom syndrome 1 \u003cem\u003e(ALMS1)\u003c/em\u003e gene\u003csup\u003e17\u003c/sup\u003e, which is associated with cardiac development and cell regulation\u003csup\u003e18\u003c/sup\u003e. One further intronic (splicing) variant within the sarcomeric \u003cem\u003eTNNT2\u003c/em\u003e gene was associated with HCM in the Maine Coon breed\u003csup\u003e19\u003c/sup\u003e, however a more recent study found that this variant was present in high frequencies across multiple cat breeds, suggesting that it might not have any significant association with HCM\u003csup\u003e20\u003c/sup\u003e. As yet, no additional variants have been identified.\u003c/p\u003e \u003cp\u003eIn humans, HCM is most commonly associated with exonic sarcomeric gene variants. These variants cause variable degrees of left ventricular hypertrophy (LVH) and different cardiomyopathy phenotypes within the same family\u003csup\u003e21,22\u003c/sup\u003e. Individuals sharing the same causative variant may variously exhibit a HCM, RCM or dilated cardiomyopathy (DCM) phenotype\u003csup\u003e21,22\u003c/sup\u003e. This pleiotropy leading to varying disease severity/expression in cardiomyopathies suggests that factors beyond the sarcomeric variant itself may influence disease expression, such as modifier genes, environmental factors, and epigenetic modifiers\u003csup\u003e13,23\u003c/sup\u003e. The non-coding genome is an emerging area of study for HCM, with regulatory variants now identified in human HCM\u003csup\u003e24,25\u003c/sup\u003e. For most variants the exact underlying molecular mechanisms linking genotype to phenotype remain unclear.\u003c/p\u003e \u003cp\u003eThere are numerous similarities between human and feline HCM, highlighting the potential value of using cats as a model to study the human disease. In both species, the disease is spontaneous and with similar natural histories and wide phenotypic spectra. They also share genetic homology: mutations for HCM found in humans have also been identified in Maine Coon and Ragdoll cats. Studies of feline HCM could uncover further shared genetic variants, disease mechanisms and therapeutic targets. A better understanding of the genetic factors causing disease and influencing disease severity in cats could help with diagnosis and management of the disease.\u003c/p\u003e \u003cp\u003eOur main aim was to identify novel genetic variants for HCM susceptibility within and across cat breeds. Our secondary aim was to investigate different forms of cardiomyopathies (HCM and RCM) within a cohort of related and unrelated Birman cats to identify family-specific variants and explore possible co-occurrence of cardiomyopathies within feline families.\u003c/p\u003e \u003cp\u003eHCM has been reported to affect both pedigree and non-pedigree cats, with some breeds displaying a greater predisposition to the disease than others. We initially investigated the disease across multiple pedigree and non-pedigree cats to capture associations across a spectrum of cat breeds. In the second study we focussed on cats of the Birman breed, since Birmans are recognized as a breed with a familial tendency to developing HCM (unpublished observations). We primarily focussed on investigating HCM, since this is the most common cardiomyopathy observed in cats, but we also included Birman cats with RCM. We aimed to determine whether shared HCM/RCM phenotype causative variants exist in Birman cats as they do in humans\u003csup\u003e21,22\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e\u003cstrong\u003eDescriptive statistics of the studied cat populations\u003c/strong\u003e\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics of the clinical and echocardiographic characteristics of the Across-breeds cat cohort (n=44) and Birman cat cohort (n=28) are summarised in Table 1 and Table 2, respectively. Due to sample limitations, we included 2 DSH control cats with a left ventricular wall thickness (LVWT) = 5.5mm. Both cats were from a geriatric population (\u0026sup3;9 years old) with no previous signs of heart disease, all other cats included in the control population had a LVWT \u0026lt;5.5mm.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eGenetic variant discovery and statistical analyses\u003c/h3\u003e\n\u003ch3\u003eThe 18 candidate genes studied are listed in\u0026nbsp;Table 3.\u003c/h3\u003e\n\u003ch3\u003eStudy 1: Across-breeds\u003c/h3\u003e\n\u003cp\u003eTargeted next generation sequencing analysis revealed the presence of a total of 4025 variants, single nucleotide variants (SNVs) (3400) and indels (666) in the 18 candidate genes in the Across-breeds cohort. Details of the identified genetic variants are presented in\u0026nbsp;Figure 1.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eChi-squared comparisons\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe annotation of the identified variants detected 8 high impact variants, including the known Ragdoll missense variant R820W in \u003cem\u003eMYBPC3\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003e, which was included as a positive control. However, none of these variants had a significantly different prevalence between cases and controls\u0026nbsp;when analyzed both within and across the cat breeds.\u003c/p\u003e\n\u003cp\u003eComparisons using the Chi-squared (\u0026chi;\u0026sup2;) test of the allelic and genotypic frequencies of the identified variants with a predicted moderate or modifier effect on the encoded protein identified a total of 160 variants with a significant (P\u0026pound;0.05) difference between HCM cases and controls. 157 of these variants were intronic: 91/157 had a higher prevalence in cases, with 8/157 being present only in cases (see Supplementary Table 1 for full list of variants with a statistically significant difference). Four of these variants remained significant after correcting for multiple testing: one intronic variant (X:101006474, T\u0026gt;C, P=0.0002) in Lysosomal associated membrane protein (\u003cem\u003eLAMP2)\u003c/em\u003e gene, one 3\u0026rsquo;UTR (F1:42194903 G\u0026gt;GGT, P=0.025) located in \u003cem\u003eTNNT2\u003c/em\u003e gene and two missense variants (B3:76167563, C\u0026gt;T, L88F, P=0.003; D1:76804158, T\u0026gt;C, I45V, P=0.004) located within the novel gene \u003cem\u003eENSFCAG00000040035\u0026nbsp;\u003c/em\u003e(overlapping \u003cem\u003eMYH7\u003c/em\u003e) and Cysteine and Glycine Rich Protein \u003cem\u003e(\u003c/em\u003e\u003cem\u003eCSRP3)\u0026nbsp;\u003c/em\u003egene respectively. The intronic SNV in \u003cem\u003eLAMP2\u003c/em\u003e was only present in control cats. The 3\u0026rsquo;UTR variant located in \u003cem\u003eTNNT2\u003c/em\u003e had a significantly higher genotypic frequency in controls. According to the Softberry FPROM human promotor predictor analysis close to this 3\u0026rsquo;UTR SNV there are two promotors. However, this SNV did not directly overlap with these promotor predictions.\u003c/p\u003e\n\u003cp\u003eThe two missense variants were present in both case and control cats with a higher prevalence in controls and were located within the novel gene \u003cem\u003eENSFCAG00000040035\u0026nbsp;\u003c/em\u003e(overlapping \u003cem\u003eMYH7\u003c/em\u003e) (B3:76167563 L88F) and \u003cem\u003eCSRP3\u003c/em\u003e gene (D1:7680415 I45V) respectively. The \u003cem\u003eCSRP3\u003c/em\u003e gene missense variant has a reported \u0026lsquo;sorting intolerant from tolerant\u0026rsquo; (SIFT) score of 0.34, which is considered \u0026lsquo;tolerated\u0026rsquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGenomic association analysis\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe genomic association analysis for the Across-breeds cohort which included age, sex and breed as covariates revealed the presence of one intronic variant within \u003cem\u003eTNNT2\u003c/em\u003e (present only in cases (n = 5) (F1:42199381 CA\u0026gt;C)) significantly associated with HCM (P=0.0006). The Softberry NSITE analysis predicted that this variant affected the motifs that were able to bind to the nearby transcription factor binding sites (TFBS), with 3 additional motifs identified in the intronic sequence compared to the control cat population sequence (see supplementary table 8). The Manhattan plot presenting the results of the genomic association analysis for HCM susceptibility in the Across-breeds cat cohort is shown in Figure 2.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eStudy 2: Birman cats\u003c/h3\u003e\n\u003cp\u003eThe targeted next generation sequencing analysis of the Birman cat cohort revealed the presence of 2298 genetic variants, SNVs (1921) and indels (395) across the candidate genes.\u0026nbsp;Details of the identified genetic variants are presented in\u0026nbsp;Figure 3.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eChi-squared comparisons\u003c/strong\u003e\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe annotation of the genetic variants revealed 5 high impact variants within the Birman cohort, however their frequency was not significantly different between case and control cats. When variants with a predicted moderate or modifier effect were compared using the \u0026chi;\u0026sup2; test, 177 variants were identified with significantly different frequencies (P\u0026pound;0.05) between cases (HCM and RCM) and controls. Among these variants was a missense SNV located in \u003cem\u003eENSFCAG00000040035 gene\u003c/em\u003e-Glu22Gln (B3:76167365, G\u0026gt;C, E22Q, P=0.04) more prevalent in cases (heterozygous in 5 HCM, 3 RCM). This missense variant is synonymous for a variant within \u003cem\u003eMYH7\u003c/em\u003e gene and overlaps with a microRNA (miR-208B- \u003cem\u003eENSFCAG00000018043\u003c/em\u003e) which has been previously reported to play a role in heart disease\u003csup\u003e26\u003c/sup\u003e. Moreover, one 3\u0026rsquo;UTR SNV (D1:76785090, C\u0026gt;A, P=0.03) within \u003cem\u003eCSRP3\u0026nbsp;\u003c/em\u003egene was found only in control cats;\u0026nbsp;the Softberry analysis did not reveal any further overlapping of this 3\u0026rsquo;UTR SNV with\u0026nbsp;promoter regions.\u0026nbsp;Further, 14 intronic variants located in\u0026nbsp;Actin alpha cardiac muscle 1\u003cem\u003e\u0026nbsp;(\u003c/em\u003e\u003cem\u003eACTC1), MYH7\u003c/em\u003e and \u003cem\u003eTNNT2\u003c/em\u003e genes were more prevalent in cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen cases were restricted to cats with HCM,\u0026nbsp;the \u0026chi;\u0026sup2; comparisons revealed 153 variants that were significantly different (P\u0026pound;0.05) between cases and controls. Of these 153 variants, 117 had a higher prevalence in cases, with 78 intronic variants present only in cats with HCM (mainly\u0026nbsp;spanning\u0026nbsp;the genes \u003cem\u003eACTC1\u003c/em\u003e and\u0026nbsp;Actinin Alpha 2 \u003cem\u003e(ACTN2)\u003c/em\u003e)\u003cem\u003e.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFull details of the identified variants in the HCM and RCM analyses and the HCM analyses are presented in Supplementary Tables 3 and 4, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGenomic association analyses\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eI. Genomic Analysis: HCM Cases (n=8) and Controls (n=14)\u003c/p\u003e\n\u003cp\u003eWhen cases were restricted to just HCM, the genomic association analysis which included age and sex as covariates identified 24 intronic variants with a significant association with disease susceptibility. These intronic variants were located within \u003cem\u003eACTN2\u003c/em\u003e (D2:12374576 C\u0026gt;T\u003cem\u003e)\u003c/em\u003e, \u003cem\u003eMYH7\u0026nbsp;\u003c/em\u003e(B3:76168426 G\u0026gt;A) and \u003cem\u003eACTC1\u003c/em\u003e (B3:70080970 T\u0026gt;TC) genes and had a higher frequency in the case population aside from one intronic variant associated to controls in \u003cem\u003eACTN2\u003c/em\u003e (D2:12407434 GGGGT\u0026gt;G). The Manhattan plot presenting these results is shown in Figure 4.\u003c/p\u003e\n\u003cp\u003eII. Genomic Analysis: HCM and RCM Cases (n=14) and Controls (n=14)\u003c/p\u003e\n\u003cp\u003eThe genomic association analysis of the merged HCM and RCM phenotypes revealed the presence of 9 intronic variants within \u003cem\u003eCSRP3\u0026nbsp;\u003c/em\u003egene and 1 intronic variant within \u003cem\u003eMYH7\u0026nbsp;\u003c/em\u003ewith a statistically significant to the disease. The Manhattan plot presenting these results is shown in Figure 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Comparison \u0026amp; Functional Impact Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe significant missense SNV that we identified in Birman cats (B3:76167365) corresponds to the protein change pE22Q with a SIFT score of 0.4. The protein orthologue approach confirmed the presence of an E amino acid in the protein ortholog alignment for humans (14:23415004, Human GRCh38 position). According to the Clinical Variants (ClinVar) archive, an E\u0026gt;Q protein change human variant at position 14:23413845 has an association with HCM and DCM that does not overlap with the variant identified in cats, but it is relatively close to the human position identified from the region comparison approach 14:23415004.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe aimed to identify novel genetic variants associated with HCM and RCM in Birman cats, as well as across pedigree breeds and DSH cats and performed two studies using a targeted cardiomyopathy gene panel and with meticulously phenotyped cats. Although there are a few shared genetic variants associated with HCM resistance or susceptibility across cat breeds, the genetic architecture of the disease seems to be breed-specific.\u003c/p\u003e \u003cp\u003eMoreover, our studies identified high impact variants within sarcomeric genes that were present at similar frequencies in both HCM cases and healthy controls. Similarly, several missense variants were present in both cases and controls, with no significant differences between the groups. These findings indicate that studies with appropriate sample sizes of cases and controls are required for the identification of truly causative HCM variants in cats. Nevertheless, we did identify a missense variant located on a novel gene \u003cem\u003eENSFCAG00000040035\u003c/em\u003e, which overlaps with \u003cem\u003eMYH7\u003c/em\u003e and a microRNA (miR-208). This missense variant had a significantly higher prevalence in HCM cases (P\u0026thinsp;=\u0026thinsp;0.048) in our Birman cat cohort. The function of \u003cem\u003eENSFCAG00000040035\u003c/em\u003e gene is unknown, but \u003cem\u003eMYH7\u003c/em\u003e is a sarcomeric gene that encodes the protein beta myosin heavy chain; a key protein component of the sarcomere involved in contractile function\u003csup\u003e27\u003c/sup\u003e. Of increased interest is the presence of this variant within miR-208 (overlapping with \u003cem\u003eENSFCAG00000040035\u003c/em\u003e and \u003cem\u003eMYH7)\u003c/em\u003e, which is expressed in cardiac tissue and regulates the production of beta myosin heavy chain during cardiomyocyte development\u003csup\u003e28\u003c/sup\u003e. MicroRNAs (miRNA) are non-coding RNA sequences which are crucial in biochemical pathway regulation including during the heart\u0026rsquo;s development\u003csup\u003e29\u003c/sup\u003e. According to previous studies, genetic variants in miRNA genes can have profound effects on miRNA functionality at all levels, including miRNA transcription, maturation, and target specificity, and as such they can also contribute to disease\u003csup\u003e30\u0026ndash;32\u003c/sup\u003e. Therefore miR-208 might prove to be an important biomarker in feline cardiac disease, especially since it has already been linked to myocardial infarction, hypertensive heart disease\u003csup\u003e33,34\u003c/sup\u003e and dilated cardiomyopathy in humans\u003csup\u003e35\u003c/sup\u003e. Relationships between miRNAs and cardiac conditions such as left ventricular hypertrophy and fibrosis have already been shown across studies\u003csup\u003e36,37\u003c/sup\u003e, including evidence for other miRNAs that overlap with the \u003cem\u003eMYH7\u003c/em\u003e gene serving as biomarkers for human HCM\u003csup\u003e38,39\u003c/sup\u003e. Nevertheless, the role of microRNAs in feline HCM remains largely unknown. There is only one previous study that compared serum miRNA of a healthy control cat cohort (n\u0026thinsp;=\u0026thinsp;12) with a HCM cohort (with stage C (disease progression is causing noticeable clinical signs of heart disease)) (n\u0026thinsp;=\u0026thinsp;11). In that study, 7 human HCM-associated miRNAs (causing cardiac tissue damage and disturbed blood flow) were upregulated in the HCM cat cohort,\u003csup\u003e40\u003c/sup\u003e suggesting that miRNAs might also play a role in feline HCM and could potentially be useful biomarkers as in humans. The miR-208 was not among the upregulated miRNAs within their cat cohort, suggesting our miR-208 variant may be specific to Birman pedigree cats. Further studies in Birman cats are needed to validate these results and confirm the potential role of miR-208 or/and \u003cem\u003eENSFCAG00000040035\u003c/em\u003e in feline HCM susceptibility.\u003c/p\u003e \u003cp\u003eRecent studies suggest intronic variants might play a more significant role in HCM susceptibility than previously thought\u003csup\u003e41,42\u003c/sup\u003e. Whole genome sequencing studies of human HCM have found pathogenic intronic variants that were causing disruption of splicing and TFBS\u003csup\u003e41\u003c/sup\u003e. In our study, we identified several intronic variants significantly associated with HCM susceptibility in cats, both across cat breeds and in the Birman cohort. Of particular interest was the intronic variant in \u003cem\u003eTNNT2\u003c/em\u003e (position F1:42199381) which was present only in HCM cases in the Across-breeds cat study. Softberry analysis predicted that this intronic variant affects the number of motifs in or around the TFBS which has the potential to influence the binding of transcription factors, and consequently gene regulation. Several variants in \u003cem\u003eTNNT2\u003c/em\u003e gene are associated with human HCM\u003csup\u003e43\u003c/sup\u003e. \u003cem\u003eTNNT2\u003c/em\u003e encodes cardiac troponin-T (cTnT) a cardiac specific protein forming part of the troponin complex associated with thin filaments. This protein plays a crucial role in cardiomyocyte contraction and relaxation through its interaction with actin and tropomyosin\u003csup\u003e44\u003c/sup\u003e. Further studies are needed to confirm whether this intronic variant plays a regulatory role and contributes to HCM susceptibility since Softberry tools currently provide predictions based on the human (and not the feline) genome assembly. Moreover, we found several intronic variants associate with HCM susceptibility both in the Across-breeds and Birman cat studies, but it is difficult to assess if they have a pathological impact. Further genomic and functional genomic studies with larger sample sizes are needed to shed light on the role of intronic variants in feline HCM susceptibility.\u003c/p\u003e \u003cp\u003eWe investigated RCM alongside HCM in Birman cats following previous unpublished observations of cats within the same family being diagnosed with different cardiomyopathy phenotypes, as has been observed in affected human families\u003csup\u003e45\u003c/sup\u003e. There are significant similarities in disease presentation between HCM and RCM, with both conditions leading to diastolic dysfunction and increased end diastolic pressure\u003csup\u003e45\u003c/sup\u003e, predisposing cats to heart failure. RCM in cats shares similar histological features to HCM, thus it has been proposed that RCM could be part of the spectrum of HCM\u003csup\u003e46\u003c/sup\u003e. An intronic variant in the \u003cem\u003eCSRP3\u003c/em\u003e gene was significantly associated with the combined group of HCM and RCM cases. The \u003cem\u003eCSRP3\u003c/em\u003e gene encodes the muscle LIM protein (MLP) of functional importance for calcium handling and signalling in cardiomyocytes\u003csup\u003e47\u003c/sup\u003e. Variants within this gene have been identified as causative for both human HCM and dilated cardiomyopathy\u003csup\u003e48,49\u003c/sup\u003e. Within our Birman cat cohort, the intronic variant was more prevalent in the control population and may have a protective role. Our results indicate the potential presence of shared protective variants against the phenotypic expression of RCM/HCM phenotype within Birman cats. To identify whether our intronic variant is playing a protective role and the exact mechanism behind this, further molecular and functional studies would need to be conducted, alongside validation in another case-control study of Birman cats. It would be useful if further studies in Birman cats were to include related cats exhibiting varying cardiomyopathies. Our Birman cohort included related cats, but these consisted of only 4 cats in the control population and 2 HCM cats.\u003c/p\u003e \u003cp\u003eGenetic similarities identified between the human and feline cardiomyopathies point to the usefulness of cats as a translational model of human disease. Preclinical feline models of HCM have already shown promising results, with the myosin inhibitor MYK-461 (mavacamten) reducing left ventricular outflow tract (LVOT) obstruction in Maine coon cats with HCM\u003csup\u003e50\u003c/sup\u003e. Collaborations between human and veterinarian researchers have the potential to unravel the genetic background of cardiomyopathies, with beneficial outcomes possible for both.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we identified several high- or moderate-impact variants in sarcomeric and other candidate HCM genes that were not causative, since they were not unique to cats with HCM. As with human patients, in some cats with HCM a genetic cause cannot be identified by following a candidate gene approach and focusing only on exonic variants. In this study we identified several intronic variants statistically associated with HCM susceptibility or protection, but due to the poor annotation of regulatory elements in the cat genome it is difficult to assess their potential role in HCM. Moreover, we identified in Birman cats an exonic variant of interest located in a gene of unknown function \u003cem\u003e(ENSFCAG00000040035)\u003c/em\u003e that overlaps with the key sarcomeric gene \u003cem\u003eMYH7\u003c/em\u003e and a myocardium related microRNA (miR-208). Further studies are needed to characterise the role of this variant in HCM susceptibility within this breed and assess if microRNAs play a role in HCM susceptibility in cats as in humans.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch3\u003eEthical approval\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eEthical approval was received by the Clinical Research Ethical Review Board of the Royal Veterinary College (URN.2019.1942-3, URN 2016 1515, URN 2015-1378), the study followed all relevant guidelines and regulations. The study is reported in adherence to ARRIVE (https://arriveguidelines.org) guidelines.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eStudy population\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eCats were recruited into the study following routine or invited echocardiographic screening for heart disease or following post-mortem examination conducted at the Royal Veterinary College (RVC). Of the cats included in the study (total n = 72), 2 HCM cats in the Birman cohort and 11 HCM cats in the Across-breeds cohort were diagnosed at necropsy. All other cats were diagnosed via echocardiography. Cats without evidence of HCM or other cardiac disease (confirmed via echocardiography) and above the age of 9 years old were used as controls. Cats of any age with a confirmed HCM phenotype were used as cases.\u0026nbsp;Among the Birman cohort 2 control cats were littermates, 2 control cats shared the same dam, and 2 HCM cats shared the same dam.\u003c/p\u003e\n\u003cp\u003eOur work included two studies: i) An Across-breeds cat study, totalling 44 phenotyped cats (controls = 23, HCM = 21) representing 21 non-pedigree cats (DSH) and pedigree breeds (4 Bengal, 8 British shorthair, 1 British longhair, 6 NFC, 3 Ragdoll, and 1 Maine coon). Among the HCM cases we included a Ragdoll cat confirmed homozygous for the HCM-associated \u003cem\u003eMYBPC3\u003c/em\u003e variant (R820W); ii) A Birman pedigree cat study, totalling 28 phenotyped Birman cats (controls = 14, cases = 17, including 8 cats with HCM, and 6 cats with RCM).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003ePhenotyping\u003c/h3\u003e\n\u003cp\u003eThe cardiac phenotype was defined by echocardiography and/or gross pathology and histopathology with owner consent. Echocardiography was performed by a\u0026nbsp;board-certified veterinary cardiologist, or by a cardiology resident under the supervision of a board-certified veterinary cardiologist,\u0026nbsp;using a Vivid E9 or Vivid I ultrasound machine (GE Systems, Hatfield, Hertfordshire, UK) with a 7.5 or 12 MHz phased-array transducer. Standard echocardiographic views were acquired, and video loops recorded\u003csup\u003e9\u003c/sup\u003e. All studies were measured off-line using dedicated echocardiographic software (EchoPac,\u0026nbsp;GE Systems, Hatfield, Hertfordshire, UK).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn echocardiography, the thickness of the left ventricular free wall (LVFW) and interventricular septum (IVS) was measured by a leading edge to leading edge technique from a 2D right parasternal long-axis (RPLA) 4- or 5-chambered view and a short-axis view at the papillary muscle level (RPSA). The thickest end-diastolic segment was averaged over 3 different cardiac cycles in each view (RPLA and RPSA). End-diastolic frames were defined as the first frame after mitral valve closure in RPLA and as the time point in the cardiac cycle of greatest left ventricular internal diameter in RPSA\u003csup\u003e9\u003c/sup\u003e. The greatest end-diastolic wall thickness of these measured views (RPLA septal, RPLA free wall, RPSA septal, RPSA free wall) was defined as LVWT and used for data analysis. Left atrial linear dimensions were measured as left atrial to aortic ratio (LA/Ao ratio) and left atrial diameter (LAD). The LA/Ao was measured as the ratio of the left atrium to aorta measured in 2D from a RPSA view at the heart base, in the frame after aortic valve closure\u003csup\u003e51\u003c/sup\u003e. The LAD was measured as the cranial-caudal LA dimension from a RPLA 4-chambered view,\u0026nbsp;in the frame before mitral valve opening\u003csup\u003e52\u003c/sup\u003e. Left ventricular (LVFS%) fractional shortening was measured by M-Mode from a right parasternal short-axis at the papillary muscle.\u0026nbsp;Systolic anterior motion of the mitral valve (SAM) was assessed on colour Doppler and 2D echo from a right parasternal long-axis 5 chamber view.\u003c/p\u003e\n\u003cp\u003eHCM was defined as LVWT\u0026nbsp;\u0026sup3;5.5 mm at end-diastole. Cases with concurrent disease that could contribute to LVH were excluded from the study. These conditions included systemic hypertension (systolic blood pressure \u0026gt;160mmHg)\u003csup\u003e53\u003c/sup\u003e, aortic stenosis or hyperthyroidism\u003csup\u003e54\u003c/sup\u003e. Healthy cats (control group) were defined as having a LVWT \u0026lt;5.5 mm and aged\u0026nbsp;\u0026sup3;9 years old to minimise inclusion of cats with late onset HCM. Necropsy examinations were performed by a single trained observer, and HCM was defined as a hypertrophied LV in the presence of myofiber disarray and interstitial/replacement fibrosis on histopathology\u003csup\u003e55\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn the Birman cat cohort, in addition to HCM cases we also included RCM cases, defined as the presence of left or biatrial enlargement (left and/or right atrial diameter in RPLA view\u0026gt;16 mm), LVWT\u0026nbsp;\u0026pound;5.5 mm and normal left ventricular systolic function (LVFS%\u0026gt;30%).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eBlood and tissue collection and DNA extraction\u003c/h3\u003e\n\u003cp\u003eMyocardial samples collected at necropsy were received from Birman breeders following death with suspicion of heart disease. For the Across-breeds cats, liver samples were obtained following routine necropsy examinations at the RVC. Residual blood (derived from clinical testing) was used for this project from blood collected by either a qualified veterinarian or veterinary nurse following echocardiography (using the same equipment and expert for each diagnosis) to exclude systemic diseases that can affect the heart and to measure cardiac biomarkers. DNA was extracted from whole blood/liver/myocardial samples using two commercial kits: DNeasy Blood and Tissue Kit (Qiagen\u0026reg;) and GeneJet Whole Blood Genomic DNA Purification Mini Kit (Thermo Scientific\u0026reg;) according to the manufacturers\u0026rsquo; instructions. DNA quality and quantity were assessed using Denovix DS-11 Series spectrophotometer and Invitrogen Qubit 4 Fluorometer, respectively.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eFeline HCM gene panel and Targeted Next Generation Sequencing (tNGS)\u003c/h3\u003e\n\u003cp\u003eWe developed a gene panel for feline HCM and RCM based on candidate genes previously implicated in human cardiomyopathies (Table 3). This feline panel was equivalent to the Illumina TruSight Cardio Panel\u003csup\u003e56\u003c/sup\u003e which is applied in suspected cases of human cardiomyopathy. In the first study (Across-breeds cohort) we included a panel of 18 candidate genes (Table 3). The same panel was used in the second study (Birman cohort) with the exclusion of two metabolic genes. These two genes were excluded due to limited variation, with no exonic variation being identified in these genes from the first study.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTargeted Next Generation Sequencing Analysis\u003c/h3\u003e\n\u003cp\u003eThe raw sequencing data (FASTQ files) were assessed for quality control using FASTQC (v10.1)\u003csup\u003e57\u003c/sup\u003e and trimmed to exclude adapter sequencing using Trimmomatic (v0.36)\u003csup\u003e58\u003c/sup\u003e prior to mapping the reads on Felis Catus v9.0 genome assembly\u003csup\u003e59\u003c/sup\u003e using the BWA aligner\u003csup\u003e60\u003c/sup\u003e. The matching variant file for the Felis Catus v9.0 genome assembly (Ensembl release version 95)\u003csup\u003e61\u003c/sup\u003e was sorted against the reference dictionary to obtain known variant sites using Picard toolkit (v2.21.7)\u003csup\u003e62\u003c/sup\u003e. The reads were indexed, and duplicates removed using SAMTOOLS (v1.3)\u003csup\u003e63\u003c/sup\u003e. Base recalibration and variant calling to detect SNVs and indel variants were performed with the GATK (v.3.8) software\u003csup\u003e64\u003c/sup\u003e using HaplotypeCaller\u003csup\u003e65\u003c/sup\u003e.\u0026nbsp;Joint VCF files were created for cases and controls (for each study separately). Two\u0026nbsp;separate\u0026nbsp;VCF files for the Birman cases\u0026nbsp;were created: one including both HCM and RCM cases and another only HCM. We ran a grouped analysis for cats with HCM and RCM phenotypes, as RCM has been suggested to be part of the HCM spectrum, i.e., these two phenotypes might represent diverse expressions of the same disease\u003csup\u003e45,46,66,67\u003c/sup\u003e. The SNV locations were obtained from Felis Catus v9.0 genome assembly using the Ensembl genome browser release version 95\u003csup\u003e68\u003c/sup\u003e. SNV annotation was performed using the Ensembl variant effect predictor (VEP) tool\u003csup\u003e69\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data from each study were analysed separately. Allelic and genotypic frequencies of genetic (SNV and Indel) variants with a predicted high, moderate, or modifier functional impact according to VEP were compared between cases and controls to assess if there are statistically significant differences between the two groups. The Chi-squared test (\u0026chi;2), with a significance level set at P\u0026pound;0.05 was used in this respect. A correction for multiple testing (0.05 divided by number of genes tested) was also applied.\u003c/p\u003e\n\u003cp\u003eTo identify if any of the SNVs of interest in 3\u0026rsquo;UTR and other non-coding regions were located within a putative regulatory region we further interrogated these SNVs using Softberry software\u003csup\u003e70\u003c/sup\u003e. Specifically, to identify potential functional roles of our SNVs of interest we used BEDTools\u003csup\u003e71\u003c/sup\u003e to extract SNV sequences 1500bp either side of our SNV and ran comparisons against the corresponding 3000bp sequence extracted from our sample containing the reference allele. These 3000bp sequences were inputted into Softberry tools FPROM promotor predictor to look for predicted promotor regions in our significant 3\u0026rsquo;UTR SNVs and the NSITE tool to search for regulatory motifs in our 3\u0026rsquo;UTR and intronic regions\u003csup\u003e72\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eGenomic association studies\u003c/h3\u003e\n\u003cp\u003eA bed genotypic file was generated from the VCF file\u0026nbsp;for cases and controls\u0026nbsp;using the PLINK software (v1.90)\u003csup\u003e73\u0026ndash;75\u003c/sup\u003e. Each of the datasets was subjected to quality control (qc) measures using the following thresholds: call rate \u0026lt;90%, minor allele frequency \u0026lt;0.05 and Hardy-Weinberg equilibrium P\u0026lt;10\u003csup\u003e-6\u003c/sup\u003e. A genomic relationship matrix was created for all animals using the GEMMA (v0.98.1) algorithm\u003csup\u003e76\u003c/sup\u003e. GEMMA was used to run the genomic association analyses for HCM susceptibility using a mixed model where the genomic relationship matrix was added as a random effect to account for possible population stratification and age, sex, and breed as fixed effects in the first study (Across-breeds cat cohort), and lambda correction applied to the P-values. The same model with the exclusion\u0026nbsp;of breed\u0026nbsp;as\u0026nbsp;a\u0026nbsp;fixed effect was used in the second study (Birman cat cohort). The significance level was set at P\u0026pound;0.05 and a Bonferroni correction for multiple testing was applied.\u0026nbsp;Python3\u003csup\u003e77\u003c/sup\u003e in Jupyter notebook\u003csup\u003e78\u003c/sup\u003e (for Mac OS) was used to create Manhattan plots to present the genomic analyses results.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eFeline and Human Comparisons\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eTo investigate\u0026nbsp;whether\u0026nbsp;the SNVs of interest identified for feline HCM susceptibility were equivalent or close to previously identified variants in humans, a relevant comparison was performed. Initially, a region comparison approach was used to compare the cat (Felis Catus v9.0) and human (GRCh38.p13) assembly by inputting the cat SNV position into Ensembl\u0026rsquo;s region comparison tool\u003csup\u003e68\u003c/sup\u003e. The output of this analysis provides the predicted equivalent coordinate in the human assembly per feline SNV.\u003c/p\u003e\n\u003cp\u003eMoreover, a protein orthologue approach was applied\u0026nbsp;by extracting the relevant amino acid sequence alignments for Felis Catus v9.0 and Human GRCh38.p13 genome assemblies using the Ensembl\u0026nbsp;ortholog comparison tool\u003csup\u003e68\u003c/sup\u003e in ClustalW format for missense SNVs of interest. The equivalent human protein position for each missense SNV was identified through these sequence comparisons before identifying the correct\u0026nbsp;international union of pure and applied chemistry\u0026nbsp;(IUPAC)\u0026nbsp;coding. After the equivalent human genome position was identified, ClinVar\u0026nbsp;database\u003csup\u003e79\u003c/sup\u003e was used to search for human SNVs that have been previously reported close to the equivalent feline SNVs of interest.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eACTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eCardiac actin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eACTN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eActinin Alpha 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eALSM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eAlstrom syndrome 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eARVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eArrhythmogenic right ventricular cardiomyopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eCSRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eCysteine and Glycine Rich Protein 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eDCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eDilated cardiomyopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eDSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eDomestic shorthair\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eHCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eHypertrophic cardiomyopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eLA/Ao\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eLeft atrium to aorta ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eLAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eLeft atrial diameter\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eLV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eLeft ventricle\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eLVH\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eLeft ventricular hypertrophy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eLVWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eLeft ventricular wall thickness at end-diastole\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eMYBPC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003emyosin-binding protein C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eMYH7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003ebeta-myosin heavy chain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eMYL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eRegulatory myosin light chain\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eMYL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eEssential myosin light chain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eNFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eNorwegian Forest Cat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eRCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eRestrictive cardiomyopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003esingle nucleotide polymorphisms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003etNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003etargeted next-generation sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eTNNI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eCardiac troponin I\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eTNNT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003ecardiac troponin-T\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eTPM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.53846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026alpha;-tropomyosin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analysed during the study are available in the Sequence Read Archive (SRA) repository, BioProject ID PRJNA1083230.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors acknowledge the financial support provided by the Biotechnology and Biological Sciences Research Council (BBSRC) studentship. Funding was provided via the UKRI funder award reference 1903448. The authors acknowledge the Petplan Charitable Trust for making the research possible through funding provided to projects S18-6930731 and S190735-774. The authors also acknowledge the Winn Feline Foundation and the Birman Cat Club for their continued support of the research.\u003c/p\u003e\n\u003cp\u003eThe authors express gratitude to the team at the Queen Mother hospital for Animals at the Royal Veterinary College. The authors thank Petros Syrris (University College London) for their guidance on human hypertrophic cardiomyopathy and Lois Wilkie (Royal Veterinary College) for performing the post-mortem examinations on cats included in the study and providing in-depth pedigree information of the Birman cat cohort. The authors acknowledge Oliver Foreman (Animal Health Trust) for their help with devising the original candidate gene panel and co-ordinates.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eJade Raffle (JR): Collected clinical data on study participants, performed the bioinformatic and statistical analysis and interpretation of the sequencing datasets, and prepared the manuscript.\u003c/p\u003e\n\u003cp\u003eJose Novo Matos (JNM): Provided expertise on feline cardiology, recruited the study participants, and performed the sample DNA extractions for submission to outsourced sequencing, contributed to data collection and study design, and assisted in revising the manuscript.\u003c/p\u003e\n\u003cp\u003eAndroniki Psifidi (AP): Provided expertise on clinical genetics, guidance on the research design and data interpretation and assisted in revising the manuscript.\u003c/p\u003e\n\u003cp\u003eVirginia Luis Fuentes (VLF): Provided expertise on feline cardiology, conducted echocardiographic assessments for the study, research design and data interpretation and assisted in revising the manuscript.\u003c/p\u003e\n\u003cp\u003eDavid J Connolly (DJC): Provided expertise on feline cardiology, conducted echocardiographic assessments for the study, research design and data interpretation and assisted in revising the manuscript.\u003c/p\u003e\n\u003cp\u003ePerry Elliott (PE): Provided guidance on the human cardiology genomic background and the development of the feline targeted HCM panel.\u003c/p\u003e\n\u003cp\u003eRichard Piercy (RP): Contributed to securing funding and experimental design alongside reviewing the manuscript and providing feedback.\u003c/p\u003e\n\u003cp\u003eAP, VLF, DJC, RP: conceived and designed the genetic study of HCM resistance and secured funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWexler, R., Elton, T., Pleister, A. \u0026amp; Feldman, D. 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J. \u003cem\u003eet al.\u003c/em\u003e ClinVar: improving access to variant interpretations and supporting evidence. \u003cem\u003eNucleic Acids Res\u003c/em\u003e 46, D1062 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable\u0026nbsp;1. Descriptive statistics of clinical and echocardiographic characteristics of the Across-breeds cohort aiming to identify genetic variants for HCM susceptibility using a targeted cardiomyopathy gene panel.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"580\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.586919104991395%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.292598967297764%\" valign=\"top\"\u003e\n \u003cp\u003eControls (n = 23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.120481927710845%\" valign=\"top\"\u003e\n \u003cp\u003eHCM (n = 21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.586919104991395%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.292598967297764%\" valign=\"top\"\u003e\n \u003cp\u003e12 [9 \u0026ndash; 17]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.120481927710845%\" valign=\"top\"\u003e\n \u003cp\u003e6.9 [1.8 - 20]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.586919104991395%\" valign=\"top\"\u003e\n \u003cp\u003eWeight (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.292598967297764%\" valign=\"top\"\u003e\n \u003cp\u003e4.2 [2.41 \u0026ndash; 5.65]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.120481927710845%\" valign=\"top\"\u003e\n \u003cp\u003e4.8 [3 \u0026ndash; 8.55]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.586919104991395%\" valign=\"top\"\u003e\n \u003cp\u003eSex: males (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.292598967297764%\" valign=\"top\"\u003e\n \u003cp\u003e8 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.120481927710845%\" valign=\"top\"\u003e\n \u003cp\u003e14 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.586919104991395%\" valign=\"top\"\u003e\n \u003cp\u003eEchocardiography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.292598967297764%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.120481927710845%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.586919104991395%\" valign=\"top\"\u003e\n \u003cp\u003eLVWT (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.292598967297764%\" valign=\"top\"\u003e\n \u003cp\u003e4.8 [4 \u0026ndash; 5.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.120481927710845%\" valign=\"top\"\u003e\n \u003cp\u003e7.6 [5.7 \u0026ndash; 11.6]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.586919104991395%\" valign=\"top\"\u003e\n \u003cp\u003eLAD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.292598967297764%\" valign=\"top\"\u003e\n \u003cp\u003e14.1 [11.5 - 17]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.120481927710845%\" valign=\"top\"\u003e\n \u003cp\u003e19.15 [11.2 - 31]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.586919104991395%\" valign=\"top\"\u003e\n \u003cp\u003eLA/Ao\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.292598967297764%\" valign=\"top\"\u003e\n \u003cp\u003e1.25 [1 \u0026ndash; 1.5]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.120481927710845%\" valign=\"top\"\u003e\n \u003cp\u003e1.8 [1.12 \u0026ndash; 2.7]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults are presented as median [range] for each variable for the Hypertrophic cardiomyopathy (HCM) and control cats. Abbreviations: LAD, left atrial diameter; LA/Ao, left atrium to aorta ratio; LVWT, left ventricular wall thickness at end-diastole.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2. Descriptive statistics of clinical and echocardiographic characteristics of the Birman cat study aiming to identify genetic variants for HCM susceptibility using a cardiomyopathy targeted gene panel.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"890\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.78226711560045%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003eControls (n = 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003eHCM (n = 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.763187429854096%\"\u003e\n \u003cp\u003eRCM (n = 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.78226711560045%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e11.3 [9.2 - 17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e8.35\u0026nbsp;[1.4 \u0026ndash; 16.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.763187429854096%\"\u003e\n \u003cp\u003e8.25\u0026nbsp;[4 \u0026ndash; 17.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.78226711560045%\"\u003e\n \u003cp\u003eWeight (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e3.53 [2.63 \u0026ndash; 5.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e4.15\u0026nbsp;[3.5 \u0026ndash; 5.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.763187429854096%\"\u003e\n \u003cp\u003e4.4 [3.54 \u0026ndash; 5.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.78226711560045%\"\u003e\n \u003cp\u003eSex: males (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e3 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e6 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.763187429854096%\"\u003e\n \u003cp\u003e3 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.78226711560045%\"\u003e\n \u003cp\u003eEchocardiography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.763187429854096%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.78226711560045%\"\u003e\n \u003cp\u003eLVWT (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e4.4 [2.8 \u0026ndash; 4.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e6 [5.5 \u0026ndash; 8.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.763187429854096%\"\u003e\n \u003cp\u003e5.1\u0026nbsp;[4.8 \u0026ndash; 5.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.78226711560045%\"\u003e\n \u003cp\u003eLAD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e13.5 [11.4 \u0026ndash; 15.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e14\u0026nbsp;[12 \u0026ndash; 19.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.763187429854096%\"\u003e\n \u003cp\u003e21.9\u0026nbsp;[16 \u0026ndash; 27.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.78226711560045%\"\u003e\n \u003cp\u003eLA/Ao\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e1.4\u0026nbsp;[1.3 - 1.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e1.3 [1 \u0026ndash; 1.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.763187429854096%\"\u003e\n \u003cp\u003e2\u0026nbsp;[1.8 \u0026ndash; 2.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults are presented as median [range] for each variable for the cardiomyopathies (HCM, RCM) and control cats. Abbreviations: LAD, left atrial diameter; LA/Ao, left atrium to aorta ratio; LVWT, left ventricular wall thickness at end-diastole.\u003cem\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3. Feline cardiomyopathy target gene panel. Gene names and gene positions based on FelCat9 assembly. *genes exclusive to the 18-gene panel for Cohort (i) Across-breeds Cat tNGS Study; \u0026circ;genes are exclusive to the 16-gene panel Cohort (ii) Birman Pedigree Cat Study.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eGene Acronym\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003eGene Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eChromosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003eStart Position\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003eEnd Position\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMYL3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMyosin light chain 3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e16290210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e16296259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTNNC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTroponin C1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e21044756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e21047572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePRKAG2*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eProtein Kinase AMP-Activated\u0026nbsp;\u003cbr\u003e\u0026nbsp;Non-Catalytic Subunit Gamma 2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e165581663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e165842537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCAV3\u0026circ;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCaveolin-3\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e50098852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e50115062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePDLIM3*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePDZ and LIM\u0026nbsp;\u003cbr\u003e\u0026nbsp;domain protein 3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e16445054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e16474763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePLN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePhospholamban\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e109748495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e109748653\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMYH7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMyosin heavy chain gene\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e76134518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e76188380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTPM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTropomyosin 1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e43987226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e44036037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eACTC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eActin alpha cardiac muscle 1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e70080059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e70085659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMYH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMyosin Heavy Chain 6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e76134518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e76188380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMYBPC3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMyosin Binding Protein C\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e101324989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e101341953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCSRP3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCysteine and Glycine Rich\u0026nbsp;\u003cbr\u003e\u0026nbsp;Protein 3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e76776148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e76804617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eACTN2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eActinin Alpha 2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e12369479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e12442749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMYL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMyosin light chain 2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e8973170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e8980956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTCAP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTelethonin\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e40752572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e40753322\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTNNI3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTroponin I3, Cardiac Type)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e3439821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e3450055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTNNT2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTroponin T2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e42194772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e42209527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGLA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGalactosidase\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e83631654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e83639229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eLAMP2*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.437603993344425%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eLysosomal associated\u0026nbsp;\u003cbr\u003e\u0026nbsp;membrane protein\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.96838602329451%\" valign=\"top\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.475873544093178%\" valign=\"top\"\u003e\n \u003cp\u003e100973530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.808652246256239%\" valign=\"top\"\u003e\n \u003cp\u003e101012558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3943358/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3943358/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCardiomyopathies are the most common heritable heart diseases in cats and humans. This study aimed to identify novel genetic variants in cats with hypertrophic cardiomyopathy (HCM) and restrictive cardiomyopathy (RCM) using a targeted panel of genes associated with human cardiomyopathy. Cats were phenotyped for HCM/RCM by echocardiography or port-mortem examination. DNA was extracted from residual blood, and targeted next-generation sequencing was performed on two separate feline cohorts: an across-breed cohort (23 healthy cats and 21 HCM-affected pedigree or Domestic Shorthair cats), and a within-breed cohort of Birman pedigree cats (14-healthy, 8 HCM-affected, and 6 RCM-affected). Genome analysis toolkit for best practice was used for variant discovery. Genomic association analyses (including the covariates breed, age and sex) were conducted to identify genetic variants of interest. We identified genetic variants associated with HCM and RCM susceptibility in candidate genes based on the human literature. Novel variants of interest were identified in the sarcomeric genes \u003cem\u003eACTC1, ACTN2, MYH7, TNNT2\u003c/em\u003e and the non-sarcomeric gene \u003cem\u003eCSRP3.\u003c/em\u003e The Birman pedigree breed demonstrated shared genetic variants across the HCM and RCM phenotypes, suggesting that the same variants could be associated with both HCM and RCM phenotypes, as proposed in humans.\u003c/p\u003e","manuscriptTitle":"Identification of novel genetic variants associated with feline cardiomyopathy using targeted next-generation sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-14 05:43:17","doi":"10.21203/rs.3.rs-3943358/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvited","content":"","date":"2024-03-12T17:02:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-12T16:54:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-02-09T15:03:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e849c18b-6a57-48e5-aed5-90d154770ae7","owner":[],"postedDate":"March 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29377307,"name":"Health sciences/Cardiology"},{"id":29377308,"name":"Biological sciences/Genetics"},{"id":29377309,"name":"Biological sciences/Genetics/Genomics"}],"tags":[],"updatedAt":"2025-02-03T15:59:38+00:00","versionOfRecord":{"articleIdentity":"rs-3943358","link":"https://doi.org/10.1038/s41598-025-87852-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-31 15:57:03","publishedOnDateReadable":"January 31st, 2025"},"versionCreatedAt":"2024-03-14 05:43:17","video":"","vorDoi":"10.1038/s41598-025-87852-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-87852-5","workflowStages":[]},"version":"v1","identity":"rs-3943358","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3943358","identity":"rs-3943358","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}