Circulation: Genomic and Precision Medicine最新文献

Background: Brugada syndrome is an inheritable arrhythmia condition that is associated with rare, loss-of-function variants in SCN5A. Interpreting the pathogenicity of SCN5A missense variants is challenging, and ≈79% of SCN5A missense variants in ClinVar are currently classified as variants of uncertain significance. Automated patch clamp technology enables high-throughput functional studies of ion channel variants and can provide evidence for variant reclassification.

Methods: An in vitro SCN5A-Brugada syndrome automated patch clamp assay was independently performed at Vanderbilt University Medical Center and Victor Chang Cardiac Research Institute. The assay was calibrated according to ClinGen Sequence Variant Interpretation recommendations using high-confidence variant controls (n=49). Normal and abnormal ranges of function were established based on the distribution of benign variant assay results. Odds of pathogenicity values were derived from the experimental results according to ClinGen Sequence Variant Interpretation recommendations. The calibrated assay was then used to study SCN5A variants of uncertain significance observed in 4 families with Brugada syndrome and other arrhythmia phenotypes associated with SCN5A loss-of-function.

Results: Variant channel parameters generated independently at the 2 research sites showed strong correlations, including peak I_Na density (R²=0.86). The assay accurately distinguished benign controls (24/25 concordant variants) from pathogenic controls (23/24 concordant variants). Odds of pathogenicity values were 0.042 for normal function and 24.0 for abnormal function, corresponding to strong evidence for both American College of Medical Genetics and Genomics/Association for Molecular Pathology benign and pathogenic functional criteria (BS3 and PS3, respectively). Application of the assay to 4 clinical SCN5A variants of uncertain significance revealed loss-of-function for 3/4 variants, enabling reclassification to likely pathogenic.

Conclusions: This validated high-throughput assay provides clinical-grade functional evidence to aid the classification of current and future SCN5A-Brugada syndrome variants of uncertain significance.

Background: Metabolomics may reveal novel biomarkers for coronary heart disease (CHD). We aimed to identify circulating metabolites and construct a metabolite risk score (MRS) associated with incident CHD among racially and geographically diverse populations.

Methods: Untargeted metabolomics was conducted using baseline plasma samples from 900 incident CHD cases and 900 age-/sex-/race-matched controls (300 pairs of Black Americans, White Americans, and Chinese adults, respectively), which detected 927 metabolites with known identities among ≥80% of samples. After quality control, 896 case-control pairs remained and were randomly divided into discovery (70%) and validation (30%) sets within each race. In the discovery set, conditional logistic regression and least absolute shrinkage and selection operator over 100 subsamples were applied to identify metabolites robustly associated with CHD risk and construct the MRS. The MRS-CHD association was evaluated using conditional logistic regression and the C-index. Mediation analysis was performed to examine if MRS mediated associations between conventional risk factors and incident CHD. The results from the validation set were presented as the main findings.

Results: Twenty-four metabolites selected in ≥90% of subsamples comprised the MRS, which was significantly associated with incident CHD (odds ratio per 1 SD, 2.21 [95% CI, 1.62-3.00] after adjusting for sociodemographics, lifestyles, family history, and metabolic health status). MRS could distinguish incident CHD cases from matched controls (C-index, 0.69 [95% CI, 0.63-0.74]) and improve CHD risk prediction when adding to conventional risk factors (C-index, 0.71 [95% CI, 0.65-0.76] versus 0.67 [95% CI, 0.61-0.73]; P<0.001). The odds ratios and C-index were similar across subgroups defined by race, sex, socioeconomic status, lifestyles, metabolic health, family history, and follow-up duration. The MRS mediated large portions (46.0%-74.2%) of the associations for body mass index, smoking, diabetes, hypertension, and dyslipidemia with incident CHD.

Conclusions: In a diverse study sample, we identified 24 circulating metabolites that, when combined into an MRS, were robustly associated with incident CHD and modestly improved CHD risk prediction beyond conventional risk factors.

Genetic hypertrophic cardiomyopathy (HCM) is classically caused by pathogenic/likely pathogenic variants in sarcomere genes (G+). Currently, HCM is diagnosed if there is unexplained left ventricular (LV) hypertrophy with LV wall thickness ≥15 mm in probands or ≥13 mm in at-risk relatives. Although LV hypertrophy is a key feature, this binary metric does not encompass the full constellation of phenotypic features, particularly in the subclinical stage of the disease. Subtle phenotypic manifestations can be identified in sarcomere variant carriers with normal LV wall thickness, before diagnosis with HCM (G+/LV hypertrophy-; subclinical HCM). We conducted a systematic review to summarize current knowledge about the phenotypic spectrum of subclinical HCM and factors influencing penetrance and expressivity. Although the mechanisms driving the development of LV hypertrophy are yet to be elucidated, activation of profibrotic pathways, impaired relaxation, abnormal Ca²⁺ signaling, altered myocardial energetics, and microvascular dysfunction have all been identified in subclinical HCM. Progression from subclinical to clinically overt HCM may be more likely if early phenotypic manifestations are present, including ECG abnormalities, longer mitral valve leaflets, lower global E' velocities on Doppler echocardiography, and higher serum N-terminal propeptide of B-type natriuretic peptide. Longitudinal studies of variant carriers are critically needed to improve our understanding of penetrance, characterize the transition to disease, identify risk predictors of phenotypic evolution, and guide the development of novel treatment strategies aimed at influencing disease trajectory.

Background: Inflammatory heart disease can be triggered by a variety of causes, both infectious and noninfectious in nature. We hypothesized that inflammatory cardiomyopathy is potentially related to microbial infection.

Methods: In this retrospective study, we used deep RNA sequencing on formalin-fixed paraffin-embedded heart tissue specimens to detect pathogenic agents. We first investigated 4 single-sample cases to test the feasibility of this diagnostic protocol and further 3 control-sample paired cases to improve the protocol with differential metatranscriptomics next-generation sequencing (mtNGS) analysis.

Results: We demonstrate that differential mtNGS allows identification of various microbials as potentially pathogenic, for example, Cutibacterium acnes, Corynebacterium aurimucosum, and Pseudomonas denitrificans, which are usually commensal in healthy individuals. Differential mtNGS also allows characterization of human host response in each individual by profiling alterations of gene expression, networked pathways, and inferred immune cell compositions, information of which is beneficial for us to understand different etiologies and immunity roles in each case. Additionally, differential mtNGS allows the identification of genetic variants in patients that may contribute to their susceptibility to particular microbial infections.

Conclusions: The demonstrated power of differential mtNGS in simultaneous capture of both the infectious microbial(s) and the status of human host immune response could help us better understand the pathogenesis of complex inflammatory cardiomyopathy, if conducted on a larger scale of the population. The developed differential mtNGS method could also shed light on its translation and adoption of such a laboratory test in clinic practice, allowing for a more effective diagnosis to guide therapeutic treatment of the disease.

Background: Chronic kidney disease (CKD) is highly prevalent in Central America, and genetic factors may contribute to CKD risk. To understand the influences of genetic admixture on CKD susceptibility, we conducted an admixture mapping screening of CKD traits and risk factors in US Hispanic and Latino individuals from Central America country of origin.

Methods: We analyzed 1023 participants of HCHS/SOL (Hispanic Community Health Study/Study of Latinos) who reported 4 grandparents originating from the same Central America country. Ancestry admixture findings were validated on 8191 African Americans from WHI (Women's Health Initiative), 3141 American Indians from SHS (Strong Heart Study), and over 1.1 million European individuals from a multistudy meta-analysis.

Results: We identified 3 novel genomic regions for albuminuria (chromosome 14q24.2), CKD (chromosome 6q25.3), and type 2 diabetes (chromosome 3q22.2). The 14q24.2 locus driven by a Native American ancestry had a protective effect on albuminuria and consisted of 2 nearby regions spanning the RGS6 gene. Variants at this locus were validated in American Indians. The 6q25.3 African ancestry-derived locus, encompassing the ARID1B gene, was associated with increased risk for CKD and replicated in African Americans through admixture mapping. The European ancestry type 2 diabetes locus at 3q22.2, encompassing the EPHB1 and KY genes, was validated in European individuals through variant association.

Conclusions: US Hispanic/Latino populations are culturally and genetically diverse. This study focusing on Central America grandparent country of origin provides new loci discovery and insights into the ancestry-of-origin influences on CKD and risk factors in US Hispanic and Latino individuals.

Omics refers to the measurement and analysis of the totality of molecules or biological processes involved within an organism. Examples of omics data include genomics, transcriptomics, epigenomics, proteomics, metabolomics, and more. In this review, we present the available literature reporting omics data on heart failure that can inform the development of novel treatments or innovative treatment strategies for this disease. This includes polygenic risk scores to improve prediction of genomic data and the potential of multiomics to more efficiently identify potential treatment targets for further study. We also discuss the limitations of omic analyses and the barriers that must be overcome to maximize the utility of these types of studies. Finally, we address the current state of the field and future opportunities for using multiomics to better personalize heart failure treatment strategies.

Background: Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRS_CHD) for 5 genetic ancestry groups.

Methods: We derived ancestry-specific and multi-ancestry PRS_CHD based on pruning and thresholding (PRS_PT) and ancestry-based continuous shrinkage priors (PRS_CSx) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRS_CHD in 176,988 individuals across 9 diverse cohorts.

Results: Multi-ancestry PRS_PT and PRS_CSx outperformed ancestry-specific PRS_PT and PRS_CSx across a range of tuning values. Two best-performing multi-ancestry PRS_CHD (ie, PRS_PTmult and PRS_CSxmult) and 1 ancestry-specific (PRS_CSxEUR) were taken forward for validation. PRS_PTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian ancestry (1.56 [1.50-1.61]), Hispanic/Latino ancestry (1.38 [1.24-1.54]), and African ancestry (1.16 [1.11-1.21]). PRS_CSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38-3.00]) and European ancestry (1.65 [1.59-1.71]), lower in East Asian ancestry (1.59 [1.54-1.64]), Hispanic/Latino ancestry (1.51 [1.35-1.69]), and the lowest in African ancestry (1.20 [1.15-1.26]).

Conclusions: The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRS_CHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRS_CHD.