Introduction
Most traits of industrial importance in yeast are polygenic, therefore complex and difficult to study. In our laboratory, we have developed a technology called ‘pooled-segregant whole-genome sequence analysis’, which has been successfully applied to complex traits such as high ethanol tolerance and maximal ethanol accumulation capacity. The aim of this study was to identify new genes underlying high ethanol tolerance and maximal ethanol accumulation capacity by combining both pooled-segregant whole-genome sequence analysis and pooled-segregant RNA expression analysis.
Methods
A haploid strain displaying the superior trait of interest was crossed with a haploid inferior lab strain. Segregants of this cross showing the phenotype of the superior parent were selected and pooled together. The genomic DNA of the pool and the parents was extracted and sequenced by Illumina HiSeq2000. The SNP variant frequency of the pooled DNA was plotted against the SNP chromosomal position, to map the quantitative trait loci (QTLs). Reciprocal hemizygosity analysis (RHA) was applied to identify the causative genes in the QTLs. Genome-wide gene expression analysis at a fermentation time-point where the ethanol concentration was 13.8% (v/v) was performed for the superior pool and parent strains via RNA-Seq. The new identified genes are introduced in a second-generation bio-ethanol producing strain with the Crispr/Cas9 method in order to improve the ethanol tolerance and maximal ethanol accumulation.
Results and discussion
Several QTLs were identified for the traits ethanol tolerance and maximal ethanol accumulation. Fine-mapping and RHA identified several specific causative genes for these traits of interest. RNA sequence analysis revealed 37 genes that were overexpressed in the pool of segregants and superior parent in comparison with the inferior parent. Most of these genes have a biological function related to stress tolerance. Our results reveal that a combination of pooled-segregant whole-genome sequence analysis and gene expression analysis is a promising approach to understand the genetic basis of complex traits.