U01HG011967

Design, Prediction, and Prioritization of Systematic Perturbations of the Human Genome - Abstract

Noncoding genetic variation that alters gene regulation is of paramount importance for health, disease, and evolution. Diseases ranging in incidence from the most common to the most rare all have substantial risk associated with regulatory variation, and most of the genetic differences between closely related species are noncoding.

Whole genome sequencing can directly identify that variation but to realize its potential to elucidate the genetic determinants of health and disease, will require accurate annotation of this noncoding variation for functionality. In coding sequence, the genetic code allows variants to be annotated to a rough hierarchy of likely functional effects and pathogenicity. In noncoding sequence, such annotation is less clear.

Perturbation assays, i.e., assays that modify genetic or epigenetic states and measure the effect of those perturbations on regulatory endpoints, offer a possible path to annotating noncoding variation. However, to fully leverage this data, novel and sophisticated statistical and machine learning approaches are required to extract useful information from those assays, to integrate that information across regulatory endpoints, and to extrapolate findings so that annotation of previously unobserved (unperturbed) variation in diverse cell types is possible.

The goal of the Duke Prediction Center is to develop the analytic approaches and tools that will allow for the routine annotation of noncoding variation for functionality and ultimately pathogenicity.

Aim 1 is to establish best practices in perturbation assay design and analysis. This will allow IGVF characterization centers to design their experiments so that, when coupled with optimized analyses, the data produced will be maximally informative for subsequent predictive modeling.

Aim 2 is to develop novel mechanistic machine learning approaches for predicting the functional effect of noncoding variation on function in diverse cell types.

Aim 3 is to identify noncoding genomic regions that are subject to functional constraint, which will be leveraged in prioritizing variants for pathogenicity.

The expected outcomes of this project will be:
(I) Robust estimates of optimal experimental design parameters and recommendations for analysis tools and best practices for the various assays used within the IGVF consortium.
(II) Predicted functional effects of observed variation to be shared through the IGVF variant/phenotype catalog, as well as a state-of-the-art machine learning method (and associated tools) that can identify previously-unknown interactions among genomic variants, both observed and novel, and predict their functional impact in diverse cell types.
(III) A list of regulatory elements subject to functional constraint shared through the IGVF variant/phenotype catalog, and a principled prioritization framework (and associated tools) for interpreting variation within patient genomes for pathogenicity.

Due to the considerable success of genetics, there are thousands of unknown regulatory causes of disease. Each of those causes is an opportunity to improve treatment, diagnostics, or prevention. This project will be a major advance towards unlocking that potential.

Duke University

NHGRI SUPPORTS THE DEVELOPMENT OF RESOURCES AND TECHNOLOGIES THAT WILL ACCELERATE GENOME RESEARCH AND ITS APPLICATION TO HUMAN HEALTH AND GENOMIC MEDICINE. A CRITICAL PART OF THE NHGRI MISSION CONTINUES TO BE THE STUDY OF THE ETHICAL, LEGAL AND SOCIAL IMPLICATIONS (ELSI) OF GENOME RESEARCH. NHGRI ALSO SUPPORTS THE TRAINING AND CAREER DEVELOPMENT OF INVESTIGATORS AND THE DISSEMINATION OF GENOME INFORMATION TO THE PUBLIC AND TO HEALTH PROFESSIONALS. THE SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM IS USED TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. THE SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM IS USED TO FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.

93.172 - Human Genome Research

National Human Genome Research Institute (NHGRI) [HHS - NIH]

Durham, North Carolina 277103011 United States

Single Zip Code

Developing Predictive Models of the Impact of Genomic Variation on Function (U01 Clinical Trial Not Allowed) (RFA-HG-20-047)

Amendment Since initial award the total obligations have increased 817% from $353,667 to $3,243,707.