UW–Madison receives NIH grant to study how genetic variation alters human genome function
A new National Institutes of Health (NIH)-funded project is highlighting UW–Madison’s unique strengths in data science, computational network inference, genomics, and high-throughput screening, to tackle an incredibly challenging and important series of questions related to how genomic variation influences biological function in humans.
This project is part of the new flagship program within the National Human Genome Research Institute (NHGRI) to develop new approaches to understanding the human genome.
Mark Craven, professor of biostatistics and medical informatics, and affiliate faculty member in the Department of Computer Sciences is the principal investigator for the nearly $3M project titled, “Linking variants to multi-scale phenotypes via a synthesis of subnetwork inference and deep learning.”
Co-investigators on the project include Audrey Gasch, director of the Center for Genomic Science Innovation and professor of genetics, Qiongshi Lu, assistant professor of biostatistics and medical informatics, and Robert Steiner, clinical professor of pediatrics.
The project is one of 25 NIH National Human Genome Research Institute grants to support an Impact of Genomic Variation on Function (IGVF) consortium. The NIH is providing approximately $185 million over five years to IGVF consortium investigators.
The UW grant is an off-shoot of Gasch and Craven’s recently funded Research Forward project, “An integrative computational and experimental system for interpreting genomic variation,” which involves 15 UW faculty.
The Office of the Vice Chancellor for Research and Graduate Education’s Research Forward initiative seeks to stimulate and support highly innovative and groundbreaking research at the UW–Madison. The initiative is supported by the Wisconsin Alumni Research Foundation. Projects such as Gasch’s and Craven’s were chosen because they have the potential to fundamentally transform a field of study, but require significant development prior to the submission of applications for external funding such as NIH.
Researchers have identified millions of human genomic variants that differ across the world, including thousands of disease-associated ones.
However, a significant limitation is that in most cases the functional impact of those genetic differences is difficult to predict.
A major goal of the IGVF is to develop new computational modeling approaches to predict the impact of human genetic variants on genome function. Methods will be guided by large-scale genomic datasets generated and shared by other experimental IGVF centers.
“By complementing experimental approaches with advanced computational methods, such as machine learning, the IGVF consortium will be able to predict which variants in the genome impact health and disease, even if they haven’t yet been experimentally characterized,” Craven says.
“Being able to characterize the impact of genetic variants on human traits is critical for interpreting the roles these variants play in human health and disease,” Gasch says. “This research will significantly advance our ability to predict the impact of genetic variants. This in turn will advance many fields, including boosting the effectiveness of genomic analyses to study common diseases and identify the genetic causes of rare diseases”
“We will also develop and apply active learning algorithms to identify the most informative experiments for subsequent analysis by IGVF consortium,” Craven says.
Active learning is an approach whereby computer algorithms analyze existing data and identify which additional experiments will be most informative. Approaches developed by Craven’s group will provide critical guidance for data collection by other IGVF centers.
“Throughout the project, we will work closely with other IGVF centers to guide experimental data collection, benchmark methods from across centers, and contribute to the variant-element-phenotype catalog which will have broad applications by the community,” Craven adds.
The IGVF consortium will develop a variant catalog summarizing the results as well as approaches used in their studies. The catalog of variant impacts will provide an important resource for clinicians and experimentalists, by providing new information relevant for diseases and other human traits. All information generated by the consortium will be made freely available to the research community via a web portal to assist with future research projects.
— By Natasha Kassulke, email@example.com, (608) 219-8042