A disease similarity matrix based on the uniqueness of shared genes
© The Author(s). 2017
Published: 24 May 2017
Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal previously unknown connections and form a reference base for therapy development and drug repurposing.
Here we introduce a method to measure disease similarity that incorporates the uniqueness of shared genes. For each disease pair, we calculated the uniqueness score and constructed disease similarity matrices using OMIM and Disease Ontology annotation.
Using the Disease Ontology-based matrix, we identified several interesting connections between cancer and other disease and conditions such as malaria, along with studies to support our findings. We also found several high scoring pairwise relationships for which there was little or no literature support, highlighting potentially interesting connections warranting additional study.
We developed a co-occurrence matrix based on gene uniqueness to examine the relationships between diseases from OMIM and DORIF data. Our similarity matrix can be used to identify potential disease relationships and to motivate further studies investigating the causal mechanisms in diseases.
Over the last two decades computational methods have contributed increasingly to the analysis of many diseases [1, 2]. Areas of interest include the identification and annotation of disease genes [3–5], effects of single nucleotide polymorphisms (SNPs) , studies on gene-drug interactions , semantics and ontological work [8, 9], protein interaction networks , and many others. Of particular interest is the investigation of the relationship between diseases in terms of genotypic and phenotypic similarity. Recent work with disease networks has revealed the interconnected nature of various diseases [11, 12], which begs the question; can we gain new knowledge of a disease such as cancer by studying “connected”, non-cancer diseases? Many diseases including obesity [13, 14], infection , diabetes , and possibly even psychological stress  have reported some relationship to cancer. Often the relationship type is unknown or partially known, indicating the need for further exploration of the interconnectedness of diseases. The key to understanding disease-disease similarity is to enrich the relationships with a quantifiable value and to infer new disease associations based on this enriched value.
Several strategies to measure disease similarity have been developed in previous studies. Mathur and Dinakarpandian used semantic similarity between ontological terms associated with diseases . Using formal concept analysis (FCA, closely related to bi-clustering or co-clustering), Keller and colleagues identified clusters from the previous known gene-disease associations . By investigating formal concepts they revealed hidden relationships between diseases based on common associated genes as well as genes associated with a common set of diseases. Suthram et al. integrated high-throughput mRNA expression data and protein-protein interaction networks to discover human disease relationships in a systematic and quantitative way. They revealed similarities between diseases by identifying functional modules among the protein-protein interactions and scoring their association with diseases.
An alternative way to define disease similarity is to not only to consider the number of the genes they shared, but also to take into account the uniqueness of shared genes or molecular features. In this study, we introduce a method for measuring similarity between pairs of diseases based on the number of genes they share only with each other. We assume that if a gene or set of genes is related to only one pair of diseases, the similarity between those two diseases should be higher than that of a pair of disease sharing gene associations with many other diseases.
Next, we applied symmetric approximate minimum degree permutation to reorder the disease co-occurrence matrix. This algorithm was developed by Stefan I. Larimore and Timothy A. Davis and incorporated into MATLAB . This reordering algorithm first creates a permutation vector p from a symmetric positive definite matrix A. This permutation vector, which contains a list of reordered columns from A, is then used to create a new matrix S such that S = (p, p) has a sparser Cholesky factor than the original matrix A. The end result is that the reordered matrix S is less sparse near the lower diagonal and sparser near the upper diagonal. For our disease co-occurrence matrix, this effectively clusters highly related diseases in the lower right quadrant around the diagonal.
We developed a co-occurrence matrix based on gene uniqueness to examine the relationships between diseases from the OMIM and DORIF databases. We found examples of known disease relationships as well as connections with no available evidence. This matrix serves as a preliminary reference for identifying disease-disease associations, providing a map of the connections between diseases, and directing focus toward those associations which may not otherwise be obvious. It could also be used as a first step in drug repositioning research, directing focus to new potential protein or DNA targets. It is important to note that the purpose of this study is to provide a disease similarity matrix from the uniqueness of shared genes as a reference and that it is not meant to serve as the basis for clinical decisions in patient care.
Complex diseases such as cancer are both unique and related to other diseases, and analyzing all pairwise relationships between diseases provides new perspectives. For instance, drugs used for the treatment of related non-cancer diseases may help to treat the side effects of cancer drugs. Another example lies in the complex relationship between bacteria and cancer: bacteria can be both beneficial and cancer causing. Research on disease relationships can stimulate the development of new ideas about cancer and its relationship to infection. Additionally, this research could help clarify the mechanisms and tissue-specificity of non-cancer diseases and how they may prime the cellular environment for metastasis. We expect that in the near future, due to the availability of an enormous amount of genotypic and phenotypic data related to disease, there will be a novel view point for cancer research emerging from these studies.
Publication of this article was funded by UIC and by the National Natural Science Foundation of China (No.31071167 and No.31370751).
Availability of data and materials
OMIM data is available from https://www.omim.org/. DORIF data is available from http://projects.bioinformatics.northwestern.edu/do_rif/.
MC and CL: designed the concept, developed statistical methods, collected the real data, performed the clustering analysis, and drafted the manuscript. YL and CJ developed statistical methods, implemented the coding, and approved the final manuscript. HL designed the concept, provided financial support, and approved the final manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
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About this supplement
This article has been published as part of BMC Medical Genomics Volume 10 Supplement 1, 2017: Selected articles from the 6th Translational Bioinformatics Conference (TBC 2016): medical genomics. The full contents of the supplement are available online at https://bmcmedgenomics.biomedcentral.com/articles/supplements/volume-10-supplement-1.
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