Improved linkage analysis of Quantitative Trait Loci using bulk segregants unveils a novel determinant of high ethanol tolerance in yeast

Abstract

BackgroundBulk segregant analysis (BSA) coupled to high throughput sequencing is a powerful method to map genomic regions related with phenotypes of interest. It relies on crossing two parents, one inferior and one superior for a trait of interest. Segregants displaying the trait of the superior parent are pooled, the DNA extracted and sequenced. Genomic regions linked to the trait of interest are identified by searching the pool for overrepresented alleles that normally originate from the superior parent. BSA data analysis is non-trivial due to sequencing, alignment and screening errors.ResultsTo increase the power of the BSA technology and obtain a better distinction between spuriously and truly linked regions, we developed EXPLoRA (EXtraction of over-rePresented aLleles in BSA), an algorithm for BSA data analysis that explicitly models the dependency between neighboring marker sites by exploiting the properties of linkage disequilibrium through a Hidden Markov Model (HMM).Reanalyzing a BSA dataset for high ethanol tolerance in yeast allowed reliably identifying QTLs linked to this phenotype that could not be identified with statistical significance in the original study. Experimental validation of one of the least pronounced linked regions, by identifying its causative gene VPS70, confirmed the potential of our method.ConclusionsEXPLoRA has a performance at least as good as the state-of-the-art and it is robust even at low signal to noise ratio’s i.e. when the true linkage signal is diluted by sampling, screening errors or when few segregants are available.

Adaptation to High Ethanol Reveals Complex Evolutionary Pathways

“…VPS70 is putatively involved in sorting of vacuolar carboxypeptidase Y to the vacuole [ 65 ]. A mutation in VPS70 has been recently identified by members of our team as a determinant of ethanol tolerance in a Brazilian bioethanol strain [ 66 ]. Interestingly, the VPS70 mutation in our evolved ethanol-tolerant lineages alters the same amino acid as the mutation present in the industrial ethanol tolerant strain (Goovaerts A. and Thevelein J.M., personal communication).…”

Evidence for a Role for the Plasma Membrane in the Nanomechanical Properties of the Cell Wall as Revealed by an Atomic Force Microscopy Study of the Response of Saccharomyces cerevisiae to Ethanol Stress

“…This powerful approach, which relies on crossing two parents, one inferior and the other superior for a trait of interest, followed by whole-genome sequencing of a large set of recombination segregants, allowed identification of potential genetic loci linked to high ethanol tolerance and, thereby, determination of causative genes. In particular, MKT1, SWS2, and AJP1 (14) were isolated by this approach, as well as the VPS16, VPS28, and VPS70 genes, which encode components of the vacuole protein sorting system (15,16). However, the causative genes identified are apparently dependent on several criteria, including the origin of the parental strains, the culture conditions, the size of the segregant sample, and the performance of the algorithm used to analyze the data.…”

Sequencing of bulks of segregants allows dissection of genetic control of amylose content in rice

“…Studies have indicated that very low coverage in genetic marker sites may lead to sampling error which may cause spurious deviations in allele frequency from the expected 50% segregation pattern in regions not linked to the phenotype (Duitama et al ., ). To weed out false‐positive associations, read coverage was therefore assessed across the genome and also specifically in the identified candidate genetic marker sites.…”

“…Linkage causes markers surrounding the causal loci to show deviations from the 50% inheritance pattern expected of parental alleles, though they may not be linked to the trait (Duitama et al ., ). Genetic linkage around the waxy locus has previously been reported (Heuer and Miézan, ; Sano, ).…”

“…Analysis of coverage in individual candidate marker positions shows that all of them had high coverage which ranged from 21 to 41. This coverage ruled out the possibility of spurious deviations in allele frequency from the expected 50% segregation pattern due to low coverage, which could have led these markers/region being falsely linked to amylose (Duitama et al ., ).…”