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A Social Network for Genes?

In the same way Facebook recommends friends and Spotify suggests music, UT researchers are using links between genes to determine which ones cause disease. Learn more.

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Social networks use known connections to help us find new connections. Facebook and LinkedIn examine your contacts for overlaps with other members’ contacts and suggest people you may want to meet. Pandora and Spotify analyze patterns in your music preferences to suggest tunes you may like.

Now there’s a network for connecting genes and diseases.

Inspired by social networking methods, researchers at UT’s Institute for Computational Engineering and Sciences (ICES) have developed techniques for discovering connections between genes and the diseases they may influence, like diabetes and Alzheimer’s Disease.

Having a new tool to help tie genes to diseases is invaluable for geneticists and for the development of genetic tests used by doctors for serious diseases, says Martin Singh-Blom, a lead author of the paper.

The techniques developed by Inderjit Dhillon, a computer scientist, and Edward Marcotte, a biochemist, outperformed all current genetic linkage tests on the market and uncovered potential new genes of interest for a variety of diseases. The research results were published in May in the online and open-access journal PLOS ONE.

“Angelina Jolie’s recent mastectomy is an example of the benefit this kind of knowledge has given us,” says Singh-Blom, referring to the preventative surgery the actress underwent after genetic tests revealed she carried a variant of the BRCA1 gene strongly associated with breast cancer.

“By improving our knowledge of how mutations in genes cause diseases we can improve our ability to assess the risks and benefits of preemptive treatments like that,” adds Singh-Blom, a former graduate student in Marcotte’s lab.

A chart illustrating a gene-phenotype network for diabetes. Phenotypes from mice, plants and humans are connected to a shared network of genes.

A gene-phenotype network for diabetes. Phenotypes from mice, plants and humans are connected to a shared network of genes. 

Dhillon and Marcotte pair met at a presentation in 2012 and found, despite their different fields, they were working toward the same goal. “We realized that both of us were basically trying to predict missing links,” says Dhillon, who studies social network analysis. Marcotte is looking for collections of genes shared across species with the goal of identifying potential disease genes in humans.

The scholars and their research teams combined forces to create the first “social network” for genes and their associated phenotypes (traits), with a focus on finding genes associated with human diseases.

The network is built on a collection of hundreds of thousands of known human gene-to-gene interactions, integrated with thousands of gene-phenotype associations that Marcotte compiled from humans and an array of other organisms, including zebra fish, fruit flies, bacteria, yeast and plants.

Identifying shared clusters of genes and their traits in different species can help locate potential disease genes in humans, even if those genes have a seemingly unrelated effect in other organisms. For example, research that Marcotte published in 2010 found that the same cluster of genes that influence cell wall repair in yeast aids in blood vessel growth in humans.

Finding genes that might influence a disease required the whole network to be scanned for gene-disease connections and the connections to be ranked. This system is similar to Facebook scanning its huge database of users for common interests to come up with a selection of “friend suggestions,” Dhillon explains.

One network map in the research is arranged around diabetes. It’s a web of known connections between genes, genes and phenotypes, and the resulting predicted connections.

In this map two new gene predictions are highlighted as being potentially important to diabetes, based on known connections: AQPI, a gene that influences mouse kidney function, and MYBL2, a gene that influences response to salt stress in plants.

“The gene AQPI might be linked to diabetes as there are multiple paths [in the gene-phenotype network] that suggest this connection,” Dhillon says.

Diabetes is just one disease that Marcotte and Dhillon investigated with this method. Known gene connections were confirmed and new potential disease genes found for leukemia, prostate cancer, schizophrenia and breast cancer, among others.

Banner illustration by Josh Gamma, University Creative Services. A longer version of this story originally appeared on the ICES website.


Inderjit Dhillon Earns ICES Distinguished Research Award