DOI: 10.5061/dryad.xgxd254qb
title: Data for: Raw count data, transcribed variant count data, and reference genomic annotation files for Boocock et al. 2024

description: Expression quantitative trait loci (eQTLs) provide a key bridge between
 noncoding DNA sequence variants and organismal traits. The effects of
 eQTLs can differ among tissues, cell types, and cellular states, but these
 differences are obscured by gene expression measurements in bulk
 populations. We developed a one-pot approach to map eQTLs in Saccharomyces
 cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to
 over 100,000 single cells from three crosses. We used scRNA-seq data to
 genotype each cell, measure gene expression, and classify the cells by
 cell-cycle stage. We mapped thousands of local and distant eQTLs and
 identified interactions between eQTL effects and cell-cycle stages. We
 took advantage of single-cell expression information to identify hundreds
 of genes with allele-specific effects on expression noise. We used
 cell-cycle stage classification to map 20 loci that influence cell-cycle
 progression. One of these loci influenced the expression of genes involved
 in the mating response. We showed that the effects of this locus arise
 from a common variant (W82R) in the gene GPA1, which encodes a signaling
 protein that negatively regulates the mating pathway. The 82R allele
 increases mating efficiency at the cost of slower cell-cycle progression
 and is associated with a higher rate of outcrossing in nature. Our results
 provide a more granular picture of the effects of genetic variants on gene
 expression and downstream traits.
description: ## Description of the data and file structure Includes cell-cycle
 assignments in `cell_cycle_feb02152022.tsv` . Each row contains the
 cell-cycle assignment for cells analyzed in the experiment. Columns
 include the data set, cell barcode, cell-cycle assignment, and
 Seurat-based cluster assignments prior to manual cell-cycle assignment.
 Additionally, output data structures from Cell Ranger and Vartrix are
 included in `processed.tar.gz`. When expanded,
 processed/.*/filtered_feature_bc_matrix/ contains for each single-cell
 experiment (experiments are indicated as the folder names in . * and are
 described in provided R code referenced below at github): 1.
 `barcodes.tsv` are cell barcodes used in analysis, each line indicates a
 cell barcode that was used for downstream analysis 2. `features.tsv.gz`
 are gene features used in analysis, each line indicates a transcript
 (systematic gene name and common gene name are provided) 3.
 `matrix.mtx.gz` are the UMI counts per transcript in sparse matrix format
 as output by Cell Ranger Includes output from Vartrix, quantifying
 allele-specific expression counts in processed/.*/ as `alt_counts.mtx`,
 `ref_counts.mtx` and `out_var.txt` . Each of the mtx files are the UMI
 counts for reference or alternate alleles per variant in sparse matrix
 format as output by Vartrix. `out_var.txt` contains chromosome name and
 position for each variant.  `reference.tar.gz` includes gene and genome
 annotation information when expanded to reference/ :  * `cross.list.RData`
 R structure of genetic maps from Bloom et al. 2019 * `parents.list.RData`
 RData R structure of expected parental variants from Bloom et al. 2019 *
 `genes.gtf` gtf file of gene annotations * `sacCer3.fasta` reference fasta
 file * `saccharomyces_cerevisiae.gff` reference genome gff ##
 Code/Software Analysis code can be found at
 [https://github.com/joshsbloom/single_cell_eQTL/tree/master/yeast/code](https://github.com/joshsbloom/single_cell_eQTL/tree/master/yeast/code)