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Identifying genetic markers associated with susceptibility to cardiovascular diseases.

ABSTRACT: The development of cardiovascular diseases (CVDs) is due to a complex interaction between the genome and the environment. Understanding how genetic differences in individuals contribute to their susceptibility to CVDs can help guide practitioners to give the best advice to achieve a favorable outcome for the patient. As genome technologies evolve, genotyping of individuals could be available to all patients using a simple saliva test. Large-scale genome-wide association studies and meta analyses have provided powerful insights into polymorphisms that may be predictive of disease and an individual's response to certain nutrients, but moving forward it is imperative that these insights can be applied in the medical setting to reduce the incidence and mortality of CVDs.

SUBMITTER: Shukla H

PROVIDER: S-EPMC6331704 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Identifying genetic markers associated with susceptibility to cardiovascular diseases.

Shukla Hitesh H Mason Jessica Louise JL Sabyah Abdullah A

Future science OA 20181026 1

The development of cardiovascular diseases (CVDs) is due to a complex interaction between the genome and the environment. Understanding how genetic differences in individuals contribute to their susceptibility to CVDs can help guide practitioners to give the best advice to achieve a favorable outcome for the patient. As genome technologies evolve, genotyping of individuals could be available to all patients using a simple saliva test. Large-scale genome-wide association studies and meta analyses ...[more]

PMID: 30652019

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Project description:Although largely preventable, cardiovascular diseases (CVDs) are the biggest cause of death worldwide. Common complex cardiovascular disorders (e.g., coronary heart disease, hypertonia, or thrombophilia) result from a combination of genetic alterations and environmental factors. Recent advances in the genomics of CVDs have fostered huge expectations about future use of susceptibility variants for prevention, diagnosis, and treatment. Our aim was to summarize the latest developments in the field from a public health perspective focusing on the applicability of data on single-nucleotide polymorphisms (SNPs), through a systematic review of studies from the last decade on genetic risk estimating for common CVDs.Several keywords were used for searching the PubMed, Embase, CINAHL, and Web of Science databases. Recent advances were summarized and structured according to the main public health domains (prevention, early detection, and treatment) using a framework suggested recently for translational research. This framework includes four recommended phases: "T1. From gene discovery to candidate health applications; T2. From health application to evidence-based practice guidelines; T3. From evidence-based practice guidelines to health practice; and T4. From practice to population health impacts."The majority of translation research belongs to the T1 phase "translation of basic genetic/genomic research into health application"; there are only a few population-based impacts estimated. The studies suggest that an SNP is a poor estimator of individual risk, whereas an individual's genetic profile combined with non-genetic risk factors may better predict CVD risk among certain patient subgroups. Further research is needed to validate whether these genomic profiles can prospectively identify individuals at risk to develop CVDs. Several research gaps were identified: little information is available on studies suggesting "Health application to evidence-based practice guidelines"; no study is available on "Guidelines to health practice." It was not possible to identify studies that incorporate environmental or lifestyle factors in the risk estimation.Currently, identifying populations having a larger risk of developing common CVDs may result in personalized prevention programs by reducing people's risk of onset or disease progression. However, limited evidence is available on the application of genomic results in health and public health practice.

Project description:ObjectiveDuring cardiovascular disease progression, molecular systems of myocardium (e.g., a proteome) undergo diverse and distinct changes. Dynamic, temporally-regulated alterations of individual molecules underlie the collective response of the heart to pathological drivers and the ultimate development of pathogenesis. Advances in high-throughput omics technologies have enabled cost-effective, temporal profiling of targeted systems in animal models of human diseases. However, computational analysis of temporal patterns from omics data remains challenging. In particular, bioinformatic pipelines involving unsupervised statistical approaches to support cardiovascular investigations are lacking, which hinders one's ability to extract biomedical insights from these complex datasets.Approach and resultsWe developed a non-parametric data analysis platform to resolve computational challenges unique to temporal omics datasets. Our platform consists of three modules. Module I preprocesses the temporal data using either cubic splines or principal component analysis (PCA), and it simultaneously accomplishes the tasks on missing data imputation and denoising. Module II performs an unsupervised classification by K-means or hierarchical clustering. Module III evaluates and identifies biological entities (e.g., molecular events) that exhibit strong associations to specific temporal patterns. The jackstraw method for cluster membership has been applied to estimate p-values and posterior inclusion probabilities (PIPs), both of which guided feature selection. To demonstrate the utility of the analysis platform, we employed a temporal proteomics dataset that captured the proteome-wide dynamics of oxidative stress induced post-translational modifications (O-PTMs) in mouse hearts undergoing isoproterenol (ISO)-induced hypertrophy.ConclusionWe have created a platform, CV.Signature.TCP, to identify distinct temporal clusters in omics datasets. We presented a cardiovascular use case to demonstrate its utility in unveiling biological insights underlying O-PTM regulations in cardiac remodeling. This platform is implemented in an open source R package (https://github.com/UCLA-BD2K/CV.Signature.TCP).

Dataset Information

Identifying genetic markers associated with susceptibility to cardiovascular diseases.

Publications

Identifying genetic markers associated with susceptibility to cardiovascular diseases.

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