Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

Abstract

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.

Synthetic genotype networks

“…A third aspect important for evolution is the existence and prevalence of epistatic interactions; small sample sizes make the latter difficult to estimate in vivo , but a recent analysis in yeast showed a prevalence of ∼3% (Nguyen Ba et al, 2022). To relate to genotype networks, we focused on one type of epistasis, namely where two sequential changes (‘A’ and ‘B’) allow genotype network exploration without a phenotype loss, while the same changes in the reverse order (first ‘B’ then ‘A’) goes along with a loss of the phenotype after the first change ( Fig.…”

TIME-Seq Enables Scalable and Inexpensive Epigenetic Age Predictions

“…Tn5 transposase was purified according to a protocol originally described in Nguyen Ba et al (pre-print) 41 , which is based on published protocols 42 with several adjustments to increase yield. Briefly, 1 mL of overnight culture containing pTXB1-Tn5 (Addgene plasmid #60240) was used to inoculate 1 L of ZYM-505 growth media containing 100 µg/mL ampicillin and 0.001% polypropylene glycol (L14699-AE, Alfa Aesar).…”

Resolving deleterious and near-neutral effects requires different pooled fitness assay designs

“…This typically involves uniquely tagging each strain with a DNA barcode and tracking changes in the frequency of the barcodes over time using deep sequencing (Smith et al, 2009, 2010). Applications of such sequencing based fitness measurements and phenotyping range from CRISPR (Shalem et al, 2014; Wang et al, 2014) and transposon mutagenesis screening for essential genes (van Opijnen and Camilli, 2013; Wetmore et al, 2015), genetic interaction screens (Du et al, 2017; Jaffe et al, 2017), deep mutational scanning of proteins (Fowler and Fields, 2014; Fowler et al, 2010; Stiffler et al, 2015), codon usage (Kelsic et al, 2016), fitness measurements of thousands of adaptive mutations from evolution experiments (Venkataram et al, 2016), genetic crosses (Nguyen Ba et al, 2022) and natural variants (Carrasquilla et al, 2022).…”