Functional Genetic Variants Revealed by Massively Parallel Precise Genome Editing

Eilon Sharon; Shi-An A Chen; Neil M Khosla; Justin D Smith; Jonathan K Pritchard; Hunter B Fraser

doi:10.1016/j.cell.2018.08.057

Functional Genetic Variants Revealed by Massively Parallel Precise Genome Editing

Cell. 2018 Oct 4;175(2):544-557.e16. doi: 10.1016/j.cell.2018.08.057. Epub 2018 Sep 20.

Authors

Eilon Sharon¹, Shi-An A Chen², Neil M Khosla², Justin D Smith³, Jonathan K Pritchard⁴, Hunter B Fraser⁵

Affiliations

¹ Department of Biology, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA. Electronic address: eilon@stanford.edu.
² Department of Biology, Stanford University, Stanford, CA 94305, USA.
³ Department of Genetics, Stanford University, Stanford, CA 94305, USA.
⁴ Department of Biology, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
⁵ Department of Biology, Stanford University, Stanford, CA 94305, USA. Electronic address: hbfraser@stanford.edu.

Abstract

A major challenge in genetics is to identify genetic variants driving natural phenotypic variation. However, current methods of genetic mapping have limited resolution. To address this challenge, we developed a CRISPR-Cas9-based high-throughput genome editing approach that can introduce thousands of specific genetic variants in a single experiment. This enabled us to study the fitness consequences of 16,006 natural genetic variants in yeast. We identified 572 variants with significant fitness differences in glucose media; these are highly enriched in promoters, particularly in transcription factor binding sites, while only 19.2% affect amino acid sequences. Strikingly, nearby variants nearly always favor the same parent's alleles, suggesting that lineage-specific selection is often driven by multiple clustered variants. In sum, our genome editing approach reveals the genetic architecture of fitness variation at single-base resolution and could be adapted to measure the effects of genome-wide genetic variation in any screen for cell survival or cell-sortable markers.

Keywords: CRISPR; Cas9; QTL; evolution; fitness; genetic variation; genome editing; yeast.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

CRISPR-Cas Systems
Chromosome Mapping
Clustered Regularly Interspaced Short Palindromic Repeats / genetics
Gene Editing / methods*
Genetic Variation / genetics
Genetic Vectors
Genome
High-Throughput Nucleotide Sequencing / methods*
Saccharomyces cerevisiae / genetics*
Yeasts / genetics

Abstract

Publication types

MeSH terms

Grants and funding