# High-throughput identification of causal variants underlying quantitative traits in yeast

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $293,688

## Abstract

PROJECT SUMMARY / ABSTRACT
The broad objective of the proposed research is to achieve comprehensive dissection of the genetic basis of
many complex phenotypes in the yeast S. cerevisiae, arguably the most powerful eukaryotic model system due
to its small genome, ease of genetic manipulation, and the ability to generate very large sample sizes.
Evolutionary conservation has also ensured that many yeast traits have direct parallels to biomedically
important human phenotypes. We seek comprehensively identify the DNA sequence variants underlying a
variety of traits, study the distribution of their effect sizes and their frequencies in a population, and build rules
for predicting the functional effects of variants of unknown significance. Success in answering these questions
will provide critical guidance for the design of genotype-phenotype studies in humans and other organisms of
medical, biological, and agricultural interest, and enable improved diagnostic accuracy based on genome
sequencing of patients. Specifically, will use a resource we built that consists of nearly 15,000 genotyped and
phenotyped segregants from crosses between 16 diverse yeast strains to identify causal genes and prioritize
individual putative causal genetic variants. We will then identify specific causal variants by directly engineering
thousands of candidate variants in bulk. We will use methods we have developed for massively parallel
targeted editing by CRISPR/Cas9 to engineer pools of yeast cells, each carrying one of 2000 natural variants.
We will subject the edited pools of yeast cells to selective conditions and track the phenotypic consequences of
the introduced variants over time by short read sequencing of DNA barcodes identifying each edit. We will then
extend massively parallel targeted editing to generate all variants discovered in the panel of 16 diverse yeast
strains. We will assay the effects of single nucleotide polymorphisms (SNPs), small scale insertions or
deletions (indels), and haplotype effects of closely linked variants. We will include non-coding variants to better
understand their effects on fitness. We will extend our engineering toolkit to employ versions of Cas9 and
related enzymes that have different recognition sites, and by using Cas9-based “base editors” that allow
generation of specific classes of mutations. Ultimately, we will edit and measure the phenotypic consequences
of hundreds of thousands of natural genetic variants, which will provide a deep understanding of the genetic
basis of many traits and enable us to develop accurate algorithms that predict the functional effect of any
genetic variant.

## Key facts

- **NIH application ID:** 9977205
- **Project number:** 5R01GM102308-07
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** LEONID KRUGLYAK
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $293,688
- **Award type:** 5
- **Project period:** 2012-09-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9977205

## Citation

> US National Institutes of Health, RePORTER application 9977205, High-throughput identification of causal variants underlying quantitative traits in yeast (5R01GM102308-07). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9977205. Licensed CC0.

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