Our limited ability to relate genotype to phenotype is a major obstacle for biomedical research and personalized medicine. Currently only ~2% of germline missense variants have clinical interpretations, and the remainder, variants of uncertain significance (VUS), offer no information to inform diagnosis or guide treatment. As the clinical use of whole exome and genome sequencing increases, the number of VUS will skyrocket. Large-scale functional assays in model organisms are the only methods for variant interpretation currently poised to match the pace of variant discovery, and here we propose to extend their use to interpret genetic complexity. Our approach leverages the advent of low-cost, large-scale gene synthesis and the development of high throughput in vivo assays of protein function in model organisms, such as yeast. We propose a generalizable approach for determining the functional consequences of polymorphisms in human disease genes, including individual alleles, combinations of alleles in the same gene, and combinations of alleles in multiple genes in a pathway, on a massively parallel scale. The quantitative nature of our assay and the structure of our experimental design will allow us to compare the impact of allele combinations with their individual effects, and thus detect genetic epistasis (nonlinear genetic interactions) arising from naturally occurring human genetic variation outside of the limits of outbred human populations. Through this novel approach, we will not only explore the extent to which nonlinear interactions between human genes are pervasive or rare, but by placing them in the context of protein and metabolic pathway structure, we will gain insight into their molecular underpinnings. Our study will also provide an unprecedented amount of information about the contribution of individual amino acids to the function of the three disease-relevant enzymes in our study, and we will analyze our results in the context of their published crystal structures. Finally, we will develop new methods and assays that will expand the throughput, combinatorics, and number of assays available for functional analysis of human variation. We will pilot our approach using three human genes (OTC, ASS1, and ASL) associated with a class of metabolic disorders known as urea cycle disorders (UCD). Neonatal UCD is associated with severe enzyme deficiency. These infants rapidly develop high levels of ammonia, cerebral edema, and symptoms that can include seizures, coma, and death. Less severe forms may remain undiagnosed into childhood or adulthood. Late onset UCDs generally involve an environmental trigger (e.g. surgery, pregnancy, or chemical exposure) in individuals with reduced enzyme function. Diagnosis of the adult onset form is hampered by the fact that it often presents with symptoms such as episodic psychosis, bipolar disorder and major depression, and without treatment, prognosis is poor. Thus, knowledge of the functional implications of genetic variation in these genes has the potential to reduce the morbidity and mortality associated with delayed treatment or underdiagnosis.

Public Health Relevance

Although we can sequence a person's genome quickly and cheaply, we often don't know whether the sequence changes that we find are harmful or benign. This project will develop methods for predicting the functional consequences of human genetic variation at unprecedented scales, making it possible to predict the effects of multiple changes in the same gene or in different genes. Because we are testing this approach on three human genes whose loss of function cause a class of severe, but treatable diseases (urea cycle disorders), our results may shorten the time to diagnosis and initiation of potentially life-saving treatments.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM134274-01
Application #
9800540
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Krasnewich, Donna M
Project Start
2019-09-19
Project End
2023-07-31
Budget Start
2019-09-19
Budget End
2020-07-31
Support Year
1
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Pacific Northwest Research Institute
Department
Type
DUNS #
041332172
City
Seattle
State
WA
Country
United States
Zip Code
98122