Pitfalls in Single Clone CRISPR-Cas9 Mutagenesis to Fine-map Regulatory Intervals

Genes (Basel). 2020 May 4;11(5):504. doi: 10.3390/genes11050504.

Abstract

The majority of genetic variants affecting complex traits map to regulatory regions of genes, and typically lie in credible intervals of 100 or more SNPs. Fine mapping of the causal variant(s) at a locus depends on assays that are able to discriminate the effects of polymorphisms or mutations on gene expression. Here, we evaluated a moderate-throughput CRISPR-Cas9 mutagenesis approach, based on replicated measurement of transcript abundance in single-cell clones, by deleting candidate regulatory SNPs, affecting four genes known to be affected by large-effect expression Quantitative Trait Loci (eQTL) in leukocytes, and using Fluidigm qRT-PCR to monitor gene expression in HL60 pro-myeloid human cells. We concluded that there were multiple constraints that rendered the approach generally infeasible for fine mapping. These included the non-targetability of many regulatory SNPs, clonal variability of single-cell derivatives, and expense. Power calculations based on the measured variance attributable to major sources of experimental error indicated that typical eQTL explaining 10% of the variation in expression of a gene would usually require at least eight biological replicates of each clone. Scanning across credible intervals with this approach is not recommended.

Keywords: CRISPR-Cas9; eQTL; fine-mapping; power; single-cell clone.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • CRISPR-Cas Systems*
  • Causality
  • Cell Lineage
  • Chromosome Mapping / methods*
  • Clone Cells
  • Gene Deletion
  • Genome-Wide Association Study / methods*
  • HL-60 Cells
  • Humans
  • Leukopoiesis / genetics
  • Multifactorial Inheritance / genetics*
  • Mutagenesis*
  • Neutrophils / cytology
  • Polymorphism, Single Nucleotide*
  • Quantitative Trait Loci / genetics*
  • Quantitative Trait, Heritable
  • RNA-Seq
  • Reproducibility of Results
  • Scientific Experimental Error*
  • Sequence Deletion
  • Single-Cell Analysis / methods*