Genomics Researchers Discover Road Map to Disease Origin
Researchers from the UA Health Sciences, the University of Pennsylvania and Vanderbilt University have advanced the understanding of the genetic and biological basis of diseases like cancer, diabetes, Alzheimer’s and rheumatoid arthritis. The team's newly published paper was picked as one of the best 30 of the year in computational biology and bioinformatics.

UAHS Office of Public Affairs
May 3, 2016

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Understanding the genetic and biologic basis of diseases like rheumatoid arthritis, cancer, diabetes and Alzheimer’s will advance the development of new therapies. From left:  Yves Lussier, Ikbel  Achour and Haiquan Li.
Understanding the genetic and biologic basis of diseases like rheumatoid arthritis, cancer, diabetes and Alzheimer’s will advance the development of new therapies. From left: Yves Lussier, Ikbel Achour and Haiquan Li.


Researchers are one step closer to understanding the genetic and biological basis of diseases like cancer, diabetes, Alzheimer's and rheumatoid arthritis – and identifying new drug targets and therapies – thanks to work by three computational biology research teams from the University of Arizona Health Sciences, the University of Pennsylvania and Vanderbilt University.

The three computational biology research teams are striving to better understand the common genetic and biological backgrounds that make certain people susceptible to the same disease.

To understand this, the researchers have developed a method to demonstrate how individual, disease-associated DNA variants share similar biological properties that provide a road map for disease origin.

"The discovery of these shared properties offer the opportunity to broaden our understanding of the biological basis of disease and identify new therapeutic targets," said Dr. Yves A. Lussier, lead and senior corresponding author of the study and UAHS associate vice president for health sciences and director of the UAHS Center for Biomedical Informatics and Biostatistics.

The researchers' findings – a method demonstrating that independent DNA variants linked to a disease share similar biological properties – were published online in the April 27 edition of Npj Genomic Medicine.

Over the last 10 years, genetics researchers have conducted large data set studies, called genome wide association studies, that analyze DNA variants across thousands of human genomes to identify those that are more frequent in people with a disease.

However, exactly how these disease-associated variants affect the function and regulation of genes remains elusive, making clinical interpretation difficult.

A method to explore the biological impact of these variants and how they are linked to disease was developed through the collaboration of bioinformatics and the following systems biology researchers and their teams, including Lussier and: Haiquan Li, research associate professor and director for translational bioinformatics in the Department of Medicine at the UA College of Medicine – Tucson; Ikbel Achour, director for precision health at the UA Center for Biomedical Information and Biostatistics; Jason H. Moore, director of the Institute for Biomedical Informatics at the University of Pennsylvania's Perelman School of Medicine; and Dr. Joshua C. Denny, an associate professor of biomedical informatics and medicine at Vanderbilt University.

In their new paper, the researchers used computational modeling of 2 million pairs of disease-associated single-nucleotide polymorphisms to demonstrate that DNA risk variants can affect biological activities such as gene expression and cellular machinery, which together provide a more comprehensive picture of disease biology.

When DNA risk variants for a given disease were analyzed in combination, similar biological activities were discovered, suggesting that distinct risk variants can affect the same or shared biological functions and thus cause the same disease.

More detailed analyses of variants linked to bladder cancer, Alzheimer's disease and rheumatoid arthritis showed that two variants can contribute to disease independently, but also interact genetically. Therefore, the precise combination of DNA variants of a patient may work to increase or decrease the relative risk of disease.

The team of researchers also is pursuing the development of methods to unveil the biological incidence of long-overlooked DNA variants with the aim to more precisely inform clinical decisions with treatments tailored to a patient's genetic and biological background. Since two of these research teams (Lussier's and Denny's) recently committed to the White House Precision Medicine Initiative, this innovative study constitutes an early contribution and demonstrates how strategic collaboration is key to making precision medicine a reality, Lussier said.

The paper, "Integrative genomics analyses unveil downstream biological effectors of disease-specific polymorphisms buried in intergenic regions," has been identified as one of the best 30 of the year in computational biology and bioinformatics, and will be presented as a "highlight of the year" at the 2016 Intelligent Systems for Molecular Biology conference, the largest international conference of computational biology/bioinformatics, in July in Orlando, Florida.

In addition to Lussier, Li, Achour, Denny and Moore, study contributors included: Joanne Berghout, Vincent Gardeux, Jianrong Li and Kenneth S. Ramos of the UA Health Sciences; Lisa Bastarache from Vanderbilt; Younghee Lee from the University of Utah; and Lorenzo Pesce, Xinan Yang and Ian Foster from the University of Chicago. Achour, Berghout, Gardeux, Haiquan Li, Jianrong Li and Lussier also are members of the UA BIO5 Institute, and Lussier is a member of the University of Arizona Cancer Center. The work also was conducted in part at the University of Illinois.

Extra info

The study was supported in part by grants from the Computation Institute BEAGLE Cray Supercomputer of the University of Chicago and Argonne National Laboratory (NIH 1S10RR029030-01), the NIH National Library of Medicine (R01-LM010685, K22-LM008308, LM009012, LM010098, LM010685), the University of Arizona Cancer Center (NCI P30CA023074), the University of Arizona Health Sciences (UL1RR024975), the University of Illinois CTSA (UL1TR000050) and the Vanderbilt University CTSA (UL1TR000445).

Abstract

Functionally altered biological mechanisms arising from disease-associated polymorphisms, remain difficult to characterise when those variants are intergenic, or, fall between genes. We sought to identify shared downstream mechanisms by which inter- and intragenic single-nucleotide polymorphisms (SNPs) contribute to a specific physiopathology. Using computational modelling of 2 million pairs of disease-associated SNPs drawn from genome-wide association studies (GWAS), integrated with expression Quantitative Trait Loci (eQTL) and Gene Ontology functional annotations, we predicted 3,870 inter-intra and inter-intra SNP pairs with convergent biological mechanisms (FDR<0.05). These prioritised SNP pairs with overlapping messenger RNA targets or similar functional annotations were more likely to be associated with the same disease than unrelated pathologies (OR>12). We additionally confirmed synergistic and antagonistic genetic interactions for a subset of prioritised SNP pairs in independent studies of Alzheimer's disease (entropy P=0.046), bladder cancer (entropyP=0.039), and rheumatoid arthritis (PheWAS case–control P<10−4). Using ENCODE data sets, we further statistically validated that the biological mechanisms shared within prioritised SNP pairs are frequently governed by matching transcription factor binding sites and long-range chromatin interactions. These results provide a "road map" of disease mechanisms emerging from GWAS and further identify candidate therapeutic targets among downstream effectors of intergenic SNPs.

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