RT Journal Article SR Electronic T1 QTL-guided metabolic engineering of a complex trait JF bioRxiv FD Cold Spring Harbor Laboratory SP 079764 DO 10.1101/079764 A1 Matthew J. Maurer A1 Lawrence Sutardja A1 Dominic Pinel A1 Stefan Bauer A1 Amanda L. Muehlbauer A1 Tyler D. Ames A1 Jeffrey M. Skerker A1 Adam P. Arkin YR 2016 UL http://biorxiv.org/content/early/2016/10/07/079764.abstract AB Engineering complex phenotypes for industrial and synthetic biology applications is difficult and often confounds rational design. Bioethanol production from lignocellulosic feedstocks is a complex trait that requires multiple host systems to utilize, detoxify, and metabolize a mixture of sugars and inhibitors present in plant hydrolysates. Here, we demonstrate an integrated approach to discovering and optimizing host factors that impact fitness of Saccharomyces cerevisiae during fermentation of a Miscanthus x giganteus plant hydrolysate. We first used high-resolution Quantitative Trait Loci (QTL) mapping and systematic Bulk Reciprocal Hemizygosity analysis (bRHA) to discover 17 loci that differentiate hydrolysate tolerance between an industrially related (JAY291) and a laboratory (S288C) strain. We then used this data to identify a subset of favorable allelic loci that were most amenable for strain engineering. Guided by this “genetic blueprint”, and using a dual-guide Cas9-based method to efficiently perform multi-kilobase locus replacements, we engineered an S288C strain with superior hydrolysate tolerance than JAY291. Our methods should be generalizable to engineering any complex trait in S. cerevisiae, as well as other organisms.