Cardioinformatics: the nexus of bioinformatics and precision cardiology

Abstract

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.

Keywords: bioinformatics; cardiology; cardiovascular disease; computational biology.

Figure 1

The status of CVD research. (A) Number of deaths by non-communicable diseases in the United States and (B) funding by the NIH for research on cancer and CVDs. (C) PubMed queries reveal a large body of CVD research, out of which only a small percentage involve bioinformatics. (D) Relative to the total pool of bioinformatics papers (in any field), there are far more cancer papers that utilize bioinformatics methods than CVD papers that utilize such methods. Since all the queries are based on the manual MeSH catalog, more recent tallies will lag behind the true volume of publication.

Figure 2

The number of genes associated with CVD. CVD is defined to include all phenotypes under the term ‘Abnormality of the cardiovascular system’ (HP:0001626) in the Human Phenotype Ontology [248]. Annotations of each phenotype was pooled from OMIM [249], Orphanet [250] and DECIPHER [251].

Figure 3

Distribution of SNPs that have been associated with a phenotypic trait. The associations are downloaded from NHGRI-EBI GWAS Catalog in which only those with P-value were retained.

Figure 4

Molecular assays on GEO. (A) The cumulative number of molecular assays (i.e. unique combinations of biosample, study and platform) deposited in GEO by CV research. (B) Breakdown of high-throughput sequencing assays by the type of study. Expression profiling by high-throughput sequencing, i.e. mRNA-seq assays, are often coupled with another profiling technique, for example, to provide functional read-out of transcription factor binding profiled by ChIP-seq. Note that to avoid excessive over-counting of irrelevant samples such as those from plants or unrelated model organisms, we only counted samples deposited with a PubMed ID pointing to a CV study. Surveys were done on the GEOmetadb database [176] updated on 17 November 2018.

Figure 5

Multi-omics data. (A) The multiple layers of omics data that are now accessible to researchers. Genome/exome, transcriptome, proteome, metabolome, as well as the microbiome and chemical compounds in the exposome can be profiled by assays on a single class of molecules (DNA, RNA, proteins or small molecules), while the other layers depend on the ability to capture DNA–protein or RNA–protein interactions. The phenome is less well defined as phenotypic measures vary greatly from physical measurements to laboratory tests, from descriptive to quantitative traits. Sources of comprehensive phenotypic data comparable to the other omics can be obtained, for example, from EHRs. Beyond the genome, omics datasets become highly complicated, due to the variation across tissues and cell types. (B) Large omics datasets that are (or will be) available for CVD research. For each dataset, the number of samples being assayed across multiple omics are indicated on the right. This number is often smaller than the total number of samples/participants in a given project, because not every sample is run on multiple assays. Sources are provided in Supplementary Data.