# Technology to understand genetic variant effects in context

> **NIH NIH RM1** · UNIVERSITY OF WASHINGTON · 2024 · $2,156,044

## Abstract

SUMMARY
A key goal of genomics is to understand how each genomic variant contributes to an individual’s traits,
including their disease risks and likely responses to drugs or other environmental perturbations. However,
genome sequencing and clinical genetic testing have far outpaced our ability to understand variant impacts.
We have developed, scaled and democratized an array of technologies to map the effects of genetic variants
on molecular and cellular phenotypes, and these have been used by us and others to measure the effect of
~11 million variants thus far. However, meeting the promise of personalized genomic medicine will require
accounting for the fact that each individual has a unique life history beginning with development and laden with
exposures to diverse environments, and each individual variant exists in the context of millions of other
variants.
In the second phase of our Center for Excellence in Genome Science (CEGS) – the Center for Multiplex
Analysis of Phenotype (CMAP) – we propose to develop technologies for mapping variant effects at scale
across a wide range of environmental conditions, genetic contexts, and in multicellular model systems; and to
develop tools that leverage multiplex functional data to improve the prediction of disease risk.
For our renewed CMAP, we propose three Specific Aims:
Aim 1, Systematically assessing variant effects in relevant environmental, cell, tissue, and developmental
contexts;
Aim 2, Systematically assessing variant impacts in relevant genetic contexts;
Aim 3, Exploiting context-aware variant effect maps to infer individualized disease risk.
We also propose an Outreach and Education plan that leverages our considerable past success to broadly
disseminate the new technologies we develop.
The foundation in context-aware variant effect mapping that we will provide offers the tantalizing prospect of
going beyond simply classifying variants as pathogenic or benign to better forecast individual disease risks.
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## Key facts

- **NIH application ID:** 10873450
- **Project number:** 2RM1HG010461-06
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Douglas M Fowler
- **Activity code:** RM1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $2,156,044
- **Award type:** 2
- **Project period:** 2019-05-08 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10873450

## Citation

> US National Institutes of Health, RePORTER application 10873450, Technology to understand genetic variant effects in context (2RM1HG010461-06). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10873450. Licensed CC0.

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