# Capturing the phenotypic landscape of single-nucleotide variation via systematic genome editing

> **NIH NIH R01** · STANFORD UNIVERSITY · 2021 · $627,527

## Abstract

PROJECT SUMMARY
A major challenge common to understanding phenotypic diversity, modeling selection in evolution, and
developing precision medicine is enhancing our currently limited ability to predict disease and phenotypic
outcomes based on genome sequence and environmental exposures. A comprehensive understanding of
genetic variation and its role in conditioning phenotypes requires systematic, perturbation-based testing of
genetic variants across the genome in multiple environments and in an isogenic background. Previous
systematic genome perturbation efforts have focused primarily on engineering loss-of-function, but naturally
occurring variants have the most relevance to understanding medically relevant phenotypes like human traits
and disease. Such variants have been studied via genome-wide association studies (GWAS) and quantitative
trait locus (QTL) analysis, but these approaches are limited to the haplotypes that appear in the study
population, and only in few cases have the actual causative variants been identified. Advances in genome
editing technologies have made engineering specific genetic variants feasible at a large scale. This proposal
aims to systematically engineer and functionally profile a genome-wide `variation collection' in three
genetically distinct strains that cover all natural single-nucleotide variants (SNVs) in the
Saccharomyces cerevisiae species as well as SNVs associated with human diseases. The collection will
be constructed by a high-throughput CRISPR approach, leveraging an in-house sequence parsing technology
(Recombinase Directed Indexing, or REDI) that will allow rapid, inexpensive isolation of sequence-verified
variant strains among the millions that will be generated. Because some variants only exert their effects in
certain environments, this strain collection will be profiled in hundreds of conditions, including exposure to
various stresses and drugs. DNA barcodes integrated into the genome of each strain will enable pooled,
competitive growth, and allow the comprehensive identification of variants in a genome that modulate fitness
in a given condition in a single experiment. Finally, to dissect the genetic architecture of pathways underlying
diseases and identify key interactions, strains carrying combinations of SNVs will be analyzed. The strain
collection will be made available to the community for further phenotypic investigations. In addition to the
gene x environment (GxE) dataset that will likely be the largest produced to date, the technological, analytical,
and visualization pipelines will be publicly shared and integrated into community resources. This work will
constitute an unprecedented investigation of the consequences of genetic variation and their dependence
upon environment, while providing valuable resources for the scientific community. It will lay technological
and conceptual groundwork for systematic perturbation-based studies of genetic variation in human cells that
will inform the p...

## Key facts

- **NIH application ID:** 10218202
- **Project number:** 5R01GM121932-05
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Lars M Steinmetz
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $627,527
- **Award type:** 5
- **Project period:** 2017-09-18 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10218202

## Citation

> US National Institutes of Health, RePORTER application 10218202, Capturing the phenotypic landscape of single-nucleotide variation via systematic genome editing (5R01GM121932-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10218202. Licensed CC0.

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