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

Functional Genetic Variants Revealed by Massively Parallel Precise Genome Editing.

Author information

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

Comment in

PMID:
30245013
PMCID:
PMC6563827
DOI:
10.1016/j.cell.2018.08.057
[Indexed for MEDLINE]
Free PMC Article

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