Dataset Information

Unfolding the genotype-to-phenotype black box of cardiovascular diseases through cross-scale modeling.

ABSTRACT: Complex traits such as cardiovascular diseases (CVD) are the results of complicated processes jointly affected by genetic and environmental factors. Genome-wide association studies (GWAS) identified genetic variants associated with diseases but usually did not reveal the underlying mechanisms. There could be many intermediate steps at epigenetic, transcriptomic, and cellular scales inside the black box of genotype-phenotype associations. In this article, we present a machine-learning-based cross-scale framework GRPath to decipher putative causal paths (pcPaths) from genetic variants to disease phenotypes by integrating multiple omics data. Applying GRPath on CVD, we identified 646 and 549 pcPaths linking putative causal regions, variants, and gene expressions in specific cell types for two types of heart failure, respectively. The findings suggest new understandings of coronary heart disease. Our work promoted the modeling of tissue- and cell type-specific cross-scale regulation to uncover mechanisms behind disease-associated variants, and provided new findings on the molecular mechanisms of CVD.

SUBMITTER: Xi X

PROVIDER: S-EPMC9386115 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Unfolding the genotype-to-phenotype black box of cardiovascular diseases through cross-scale modeling.

Xi Xi X Li Haochen H Chen Shengquan S Lv Tingting T Ma Tianxing T Jiang Rui R Zhang Ping P Wong Wing Hung WH Zhang Xuegong X

iScience 20220720 8

Complex traits such as cardiovascular diseases (CVD) are the results of complicated processes jointly affected by genetic and environmental factors. Genome-wide association studies (GWAS) identified genetic variants associated with diseases but usually did not reveal the underlying mechanisms. There could be many intermediate steps at epigenetic, transcriptomic, and cellular scales inside the black box of genotype-phenotype associations. In this article, we present a machine-learning-based cross ...[more]

PMID: 35992073

Similar Datasets

Project description:BackgroundThe ability to discover genetic variants in a patient runs far ahead of the ability to interpret them. Databases with accurate descriptions of the causal relationship between the variants and the phenotype are valuable since these are critical tools in clinical genetic diagnostics. Here, we introduce a comprehensive and global genotype-phenotype database focusing on rare diseases.MethodsThis database (CentoMD ®) is a browser-based tool that enables access to a comprehensive, independently curated system utilizing stringent high-quality criteria and a quickly growing repository of genetic and human phenotype ontology (HPO)-based clinical information. Its main goals are to aid the evaluation of genetic variants, to enhance the validity of the genetic analytical workflow, to increase the quality of genetic diagnoses, and to improve evaluation of treatment options for patients with hereditary diseases. The database software correlates clinical information from consented patients and probands of different geographical backgrounds with a large dataset of genetic variants and, when available, biomarker information. An automated follow-up tool is incorporated that informs all users whenever a variant classification has changed. These unique features fully embedded in a CLIA/CAP-accredited quality management system allow appropriate data quality and enhanced patient safety.ResultsMore than 100,000 genetically screened individuals are documented in the database, resulting in more than 470 million variant detections. Approximately, 57% of the clinically relevant and uncertain variants in the database are novel. Notably, 3% of the genetic variants identified and previously reported in the literature as being associated with a particular rare disease were reclassified, based on internal evidence, as clinically irrelevant.ConclusionsThe database offers a comprehensive summary of the clinical validity and causality of detected gene variants with their associated phenotypes, and is a valuable tool for identifying new disease genes through the correlation of novel genetic variants with specific, well-defined phenotypes.

Dataset Information

Unfolding the genotype-to-phenotype black box of cardiovascular diseases through cross-scale modeling.

Publications

Unfolding the genotype-to-phenotype black box of cardiovascular diseases through cross-scale modeling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets