Features | Partner Sites | Information | LinkXpress
Sign In
GLOBETECH PUBLISHING LLC
GLOBETECH PUBLISHING LLC
GLOBETECH MEDIA

New Tool Developed to Identify Genetic Risk Factors

By BiotechDaily International staff writers
Posted on 13 Feb 2014
Image: Pathway-based Human Phenotype Network classification shows relationships between diseases and traits based on shared etiology for certain phenotypes. The diseases and traits in these clusters have more connections to each other than to others in the network: the bolder the line, the stronger the connection (Photo courtesy of Dartmouth College).
Image: Pathway-based Human Phenotype Network classification shows relationships between diseases and traits based on shared etiology for certain phenotypes. The diseases and traits in these clusters have more connections to each other than to others in the network: the bolder the line, the stronger the connection (Photo courtesy of Dartmouth College).
A new biological pathway-based computational model has been developed to identify underlying genetic connections between different diseases.

The model called the Pathway-based Human Phenotype Network (PHPN) mines the data present in large publicly available disease datasets to find shared single nucleotide polymorphisms (SNPs), genes, or pathways and expresses them in a visual form.

Geneticists at the Geisel School of Medicine at Dartmouth (Hanover, NH, USA) built a pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits, based on about 2,300 genes and 1,200 biological pathways. Using genome-wide association study (GWAS) phenotype-to-genes associations, and pathway data from a free, open-source, curated and peer reviewed pathway database Reactome, they connected 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.

PHPN supports the integration of genomic and phenotypic data to uncover significant links between traits, attributes, and disease. This offers tremendous potential in identifying risk factors for certain diseases. At the same time, it can reveal important targets for therapeutic intervention. The automatic classification of phenotypes into “phenotype classes,” using the network’s topological modularity and a standard community detection algorithm, showed very promising results. Two traits that were connected in the PHPN but did not share any common associated genes or any clear-cut biological relationship were von Willebrand factor and FVIII levels (vWF) and hippocampal atrophy (HA).

Christian Darabos, PhD, the lead author of the study, said, “The intuitive network representation of the knowledge mined from several large-scale datasets makes the information accessible to anyone. It lies at the crossroads of computational genetics, systems biology, information theory, and network science. As a proof of concept, the PHPN has proven capable of identifying well-documented interactions, and many novel links that remain to be explored in depth. The PHPN is a hypothesis-generating tool, potentially capable of identifying yet uncharacterized common drug targets.” As a next step, the scientists will refine statistical methods, isolate networks for optimal results, and compare previous work on phenotype networks. The study was published on January 25, 2014, in the journal BioDataMining.

Related Links:

Geisel School of Medicine at Dartmouth
Reactome



Channels

Drug Discovery

view channel
Image: Star-like glial cells in red surround alpha-beta plaques in the cortex of a mouse with a model of Alzheimer\'s disease (Photo courtesy of Strittmatter laboratory/Yale University).

Experimental Cancer Drug Reverses Symptoms in Mouse Model of Alzheimer's Disease

An experimental, but clinically disappointing drug for treatment of cancer has been found to be extremely effective in reversing the symptoms of Alzheimer's disease (AD) in a mouse model.... Read more

Biochemistry

view channel
Image:  Model depiction of a novel cellular mechanism by which regulation of cryptochromes Cry1 and Cry2 enables coordination of a protective transcriptional response to DNA damage caused by genotoxic stress (Photo courtesy of the journal eLife, March 2015, Papp SJ, Huber AL, et al.).

Two Proteins Critical for Circadian Cycles Protect Cells from Mutations

Scientists have discovered that two proteins critical for maintaining healthy day-night cycles also have an unexpected role in DNA repair and protecting cells against genetic mutations that could lead... Read more

Business

view channel

NanoString and MD Anderson Collaborate on Development of Novel Multi-Omic Expression Profiling Assays for Cancer

The University of Texas MD Anderson Cancer Center (Houston, TX, USA) and NanoString Technologies, Inc. (Seattle, WA, USA) will partner on development of a revolutionary new type of assay—simultaneously profiling gene and protein expression, initially aiming to discover and validate biomarker signatures for immuno-oncology... Read more
 
Copyright © 2000-2015 Globetech Media. All rights reserved.