Fine Mapping Spatiotemporal Mechanisms of Genetic Variants Causing Cardiac Disease

Introduction:

Several genes and variants are involved in cardiac genome-wide association studies (GWAS, a type of study that helps in identifying the genes that are associated with a particular disease). In this study, spatiotemporal information from the cardiac samples and expression quantitative trait locus (eQTL, a locus that explains the genetic variations of the gene expression) analysis is performed. There can be colocalization (a phenomenon in which two or more traits share the genetic factors that are present in a particular locus) between GWAS and eQTL, which can help in fine mapping and identification of potential causal variants, such as pulse pressure GWAS loci is colocalized with fetal and smooth muscle eQTLs; atrial fibrillation GWAS loci with cardiac muscle eQTL; and pulse rate GWAS loci with adult and cardiac muscle eQTL.

Regulatory elements and regulatory variants regulate the expression of multiple genes in a spatiotemporal context (activation of genes occurs in specific tissues within specific times of development). Fine-mapping spatiotemporal mechanisms include GWAS signals for cardiac traits with gene expressions from different cardiac developmental tissues, stages, and cells and fine-mapping them to understand the mechanisms of cardiovascular diseases. This study uses the spatiotemporal information of around 966 RNA-seq from available cardiac samples and performs expression quantitative trait locus (eQTL) analysis on both eGenes and eIsoforms. Therefore, this showed a large number of cardiac GWAS variants impact disease and traits during different developmental stages along with tissue-specific and cell-specific fashion.

What Is a Genome-Wide Association Study (GWAS)?

Genome-wide association study (GWAS) is a type of study that helps identify the genes that are associated with a particular disease. This method involves studying the whole genome (entire DNA (deoxyribonucleic acid) which is present in an individual’s cell) of several people which helps in identifying variations called single nucleotide polymorphisms (SNPs). These frequently occurring variations (SNPs) help in identifying the genes that are involved in the disease development and help in determining the risk of getting the disease and response to treatment approaches.

What Is Fine Mapping Spatiotemporal Mechanisms of Genetic Variants Causing Cardiac Disease?

Fine mapping is a process of analyzing trait-associated regions from a GWAS study to identify specific genetic variants that are likely to cause the trait of interest. Gene expressions are regulated in specific spatial, which are tissue, organ, or cell type, and temporal, which are adult and fetal-like manner indicating the regulatory variants functions of the heart. Therefore this affects the gene expression that is associated with cardiac traits and disease during different developmental stages and cellular contexts. Hence, fine-mapping spatiotemporal mechanisms of genetic variants is a study that includes GWAS signals for cardiac traits along with gene expressions from different cardiac developmental tissues, stages, and cells and fine-mapping them to understand the mechanisms of cardiovascular diseases.

What Are The Methods And Materials Used In Fine Mapping Spatiotemporal Mechanisms Of Genetic Variants Causing Cardiac Disease Studies?

Techniques such as cell type deconvolution techniques, GTEx consortium is a project, which contains bulk RNA-seq (RNA-sequencing), that helps in the identification of gene expression in specific cell types and associated isoforms. Since many samples are not available, the help of induced pluripotent stem cell-derived cardiovascular precursor cells (iPSC-CVPCs) have developed iPSCORE, which has similar epigenomic and transcriptomic properties to fetal cardiac cells. Therefore, adult GTEx (Genotype-Tissue Expression) and deconvoluted fetal-like iPSCORE datasets are used in this study.

What Are the Results of Fine Mapping Spatiotemporal Mechanisms of Genetic Variants Causing Cardiac Disease Study?

The results include:

• Analysis of Combined eQTL: To study the cardiac gene expression and genetic variation, the data obtained from RNA-seq for fetal-like iPSC-CVPCs and GTEx Consortium are used, and to map down the regulatory effects of genetic variants on adult and fetal cardiac tissues, combined eQTL analysis is performed using linear mixed model along with kinship (family relatives) matrix to obtain relatedness among the samples. The results were:

  1. Egene contained multiple eQTL signals, and each was associated with different underlying causal variants.

  2. Gene eQTLs mainly occur in intergenic regions (part of DNA, which is located between genes).

  3. Isoform eQTLs overlap gene bodies such as splice donor sites, intros, exons, and splice acceptor sites. Therefore isoform eQTLs mainly influence transcript stability.

• Mapping Spatiotemporal Cardiovascular eQTLs: Shared, specific, or associated eQTLs are used. The results were:

  1. Cardiac muscle- eQTLs overlapped regulatory elements that are active in atrial and ventricular cardiomyocytes and less likely to overlap regulatory elements that are active for cell types like adipocytes, fibroblasts, and macrophages.

  2. Multigenic eQTL signals are active by spatiotemporal regulation. Therefore, spatiotemporally regulated eQTL shared the same genes. Fetal eQTLs are less multigenic than adult eQTLs.

  3. Cardiac genes with their antisense RNAs (noncoding RNAs, which regulate gene expression at multiple levels) share the same eQTL signals.

  4. Colocalization of eQTL signals and GWAS signals can help in identifying potential molecular mechanisms.

  5. Cardiac traits that are enriched for spatiotemporal eQTLs are; pulse pressure trait enriched for fetal-like iPSC-CVPC, endocardium, smooth muscle, aorta, arteria, and immune eQTLs; pulse rate is associated with adult arteria and atrium eQTLs; and atrial fibrillation is associated with cardiac muscle and left ventricular eQTLs.

• Fine Mapping With the Help of Colocalization: Fine mapping with the colocalization of eQLTs signals with GWAS signals showed less casual variants for hundreds of loci. Putative causal variants are associated with cardiac traits near hundreds of loci, and the majority are novel.

Conclusion:

Fine mapping, spatiotemporal mechanisms of genetic variants causing cardiac disease, showed that eGenes were associated with regulatory variants, and isoforms were associated with variants affecting post-transcriptional modifications; regulatory variants of eQTL signals were related to multiple genes which function in a spatiotemporal manner; fetal-like- eQTLs are less related to multiple eGenes than seen in the adult eQLTs; eQTL signals colocalize with cardiac GWAS traits function and are in a spatiotemporal manner; traits such as pulse pressure is enriched for fetal-like-eQTLs which indicates, genetic variants are associated with the adult trait in their function during early development of the heart. Fine mapping provides an understanding of the molecular mechanism of cardiac disease and its traits.