Linking gene knockouts in human cells to traits using omics and computer models
Statistical Methods for Inferring Gene-Phenotype Associations Using Omic Data from Gene Knockout and Human Phenotype Studies
This project builds computer tools that connect what happens when specific human genes are turned off in lab-grown cells to traits and disease signals, to help researchers find genes linked to health conditions.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Fred Hutchinson Cancer Center NIH-funded |
| Lab location | 1 site (Seattle, United States) |
| Project ID | NIH-11142645 on NIH RePORTER |
What this research studies
Researchers will create a catalog of molecular and cellular changes caused by turning off about 1,000 human genes in lab-grown cell systems that model early human development. They will combine multiple types of omics data (like gene expression and epigenetics) and lab measurements across cell types and stages. The team will build a dynamic gene-regulatory network called moDAG and use biologically informed deep learning to model tissue signaling and predict the effects of gene knockouts. These models aim to prioritize genes and guide follow-up experiments that could clarify how genetic changes influence health.
Who could benefit from this research
Good fit: People with known genetic variants in the genes being studied or those willing to donate cells or biosamples for research would be the most directly relevant participants.
Not a fit: People looking for immediate clinical treatments or whose conditions are not linked to the genes under study are unlikely to receive direct benefit from this project.
Why it matters
Potential benefit: If successful, this work could make it easier to interpret genetic variants and point to genes that might be targets for future diagnostics or treatments.
How similar studies have performed: Previous large-scale knockout catalogs and multi-omic studies have yielded useful gene-function insights, but combining multi-omic directed networks with deep learning at this scale is a newer, less-tested approach.
Where this research is happening
Seattle, United States
- Fred Hutchinson Cancer Center — Seattle, United States (Active)
Researchers
- Principal investigator: Sun, Wei — Fred Hutchinson Cancer Center
- Study coordinator: Sun, Wei
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.