Scientists at Dartmouth report the development of a novel biological pathway-based computational model to identify underlying genetic connections between different diseases. The pathway-based human phenotype network (PHPN) mines the data present in large publicly available disease datasets to find shared SNPs, genes, or pathways and expresses them in a visual form, according to the researchers.
The Dartmouth team’s study (“The Multiscale Backbone of the Human Phenotype Network based on Biological Pathways”) was published in BioDataMining this week.
“The PHPN offers a bird’s eye view of the diseases and phenotype’s relationships at the systems level,” said Christian Darabos, Ph.D., a postdoctoral fellow at the Institute for Quantitative Biomedical Sciences at Dartmouth.
The PHPN uses information in human disease networks in conjunction with network science tools to show clusters of related disorders sharing common genetic backgrounds. It does so without the typical clinical classification of disease, in which all heart disease or all cancers are grouped together, based on clinical presentation, explained Dr. Darabos.
“In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built a pathway-based human phenotype network of over 800 physical attributes, diseases, and behavioral traits, based on about 2,300 genes and 1,200 biological pathways,” wrote the investigators. “Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects and expanding previous studies from a gene-centric approach to that of shared cell-processes.”
In other words, PHPN explores the connections between the layers of the networks to find patterns and relationships. It supports the integration of genomic and phenotypic data to uncover significant links between traits, attributes, and disease, according to Dr. Darabos.
This offer tremendous potential in identifying risk factors for certain diseases, he noted, adding that at the same time, it can reveal important targets for therapeutic intervention.