DOI: 10.5061/dryad.1rn8pk0vd
title: Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

description: Mapping the genetic basis of complex traits is critical to uncovering the
 biological mechanisms that underlie disease and other phenotypes.
 Genome-wide association studies (GWAS) in humans and quantitative trait
 locus (QTL) mapping in model organisms can now explain much of the
 observed heritability in many traits, allowing us to predict phenotype
 from genotype. However, constraints on power due to statistical
 confounders in large GWAS and smaller sample sizes in QTL studies still
 limit our ability to resolve numerous small-effect variants, map them to
 causal genes, identify pleiotropic effects across multiple traits, and
 infer non-additive interactions between loci (epistasis). Here, we
 introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which
 allows us to construct, genotype, and phenotype 100,000 offspring of a
 budding yeast cross, two orders of magnitude larger than the previous
 state of the art. We use this panel to map the genetic basis of eighteen
 complex traits, finding that the genetic architecture of these traits
 involves hundreds of small-effect loci densely spaced throughout the
 genome, many with widespread pleiotropic effects across multiple traits.
 Epistasis plays a central role, with thousands of interactions that
 provide insight into genetic networks. By dramatically increasing sample
 size, BB-QTL mapping demonstrates the potential of natural variants in
 high-powered QTL studies to reveal the highly polygenic, pleiotropic, and
 epistatic architecture of complex traits.
description: Dataset accompanying the research article "Barcoded Bulk QTL
 mapping reveals highly polygenic and epistatic architecture of complex
 traits in yeast" by Alex N. Nguyen Ba, Katherine R. Lawrence, Artur
 Rego-Costa, Shreyas Gopalakrishnan, Daniel Temko, Franziska Michor, and
 Michael M. Desai (2021). The dataset contains genotype
 and phenotype information for a panel of approximately  100,000 F1
 offspring from a cross between strains BY and RM of Saccharomyces
 cerevisiae. The genotype is encoded at the approximately 42,000
 single-nucleotide polymorphisms between the two parental strains as an
 inferred posterior probability of that strain having the RM allele at that
 locus. Phenotypic values comprise inferred relative fitness in liquid
 fed-batch culture condition under eighteen different growth media. This
 dataset was created from raw sequencing data stored in the NCBI SRA
 repository.
description: &gt; Dataset description<br>|-
 SNP_list.txt<br>|    Table with index, genome position, and sequence
 information of the approximately 42,000<br>|    single-nucleotide
 polymorphisms between the two parental strains. The genome reference
 sequence<br>|    on which this dataset is based is
 S288C_reference_genome_R64-3-1_20210421 by the Saccharomyces<br>|  
  Genome Database Project. The RM alleles were inferred from sequencing of
 the parental RM strain.<br>|- segregant_info.txt<br>|  
  Unique indeces, library location (Batch, Set, Plate, Well) and barcode
 sequence of each of the<br>|    approximately 100,000 F1 strains in
 our panel.<br>|- geno_data_*.txt.gz<br>|    Inferred posterior
 probability of each strain in our panel having the RM allele at each of
 the<br>|    approximately 42,000 single-nucleotide polymorphisms
 considered. Each row contains the genotype<br>|    of one strain.
 The first column contains the identifying number of that strain, as in the
 'Number'<br>|    column of segregant_info.txt. Each file
 contains the genotype of 20,000 strains.<br>|-
 pheno_data_*.txt.gz<br>|    Each file contain the inferred fitness
 with its associated standard error for each strain in our
 panel<br>|    in a given environment, identified in the file name.
 Missing data from strains for which we could<br>|    not infer
 fitness is coded as 'nan'.<br> 
 &gt; Additional information See readme.txt
 for general dataset information. See manuscript for
 more information on data generation, processing, and access.