Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
Katherine R Lawrence
NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, United States; Quantitative Biology Initiative, Harvard University, Cambridge, United States; Department of Physics, Massachusetts Institute of Technology, Cambridge, United States
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
Shreyas Gopalakrishnan
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Department of Molecular and Cellular Biology, Harvard University, Cambridge, United States
Daniel Temko
Department of Data Science, Dana-Farber Cancer Institute, Boston, United States; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, United States
Franziska Michor
Department of Data Science, Dana-Farber Cancer Institute, Boston, United States; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, United States; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, United States; The Ludwig Center at Harvard, Boston, United States; The Broad Institute of MIT and Harvard, Cambridge, United States
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, United States; Quantitative Biology Initiative, Harvard University, Cambridge, United States; Department of Physics, Harvard University, Cambridge, United States
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.
