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Quantitative genetics of complex traits: solutions for studying the genetic basis of variation in yeast

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thesis
posted on 14.07.2020, 09:12 by Yue Hu
Recent advances in high-throughput techniques for DNA sequencing and phenotyping have greatly facilitated the identification of genetic variants underlying traits at a genomewide level. In this study, a large amount of yeast genetic resources and phenotypic data were collected for the study of natural genetic variation in yeast under different environment conditions. Quantitative trait locus (QTL) analysis and epistasis analysis have been applied to Saccharomyces cerevisiae on 6 groups of 1st generation bi-parental inter-cross segregants and 12th generation multiparental high resolution segregants. Using yeast as model organism, growth under stress conditions of a variety of conventional genotoxic agents was measured. Different QTLs were mapped to causative genes that are related to DNA repair and protein transport. In addition, by comparing the genes identified under 19 different agents, 14 frequently occurring genes producing effect on the growth of yeast, were further analysed. QTL output was clustered through a changepoint model for improving the selection of candidate genes in large gene sets. Furthermore, Temporal QTL analysis was applied to study the dynamic development of yeast growth under X-ray irradiation that expands the phenotype in the time dimension. By comparing the QTL in different time spans, genes that only exhibit effects for a certain period of time rather than continuously through, or at the end of, the experiment were found. One of the major industrial applications of yeast is brewing. In this project, whole genome sequencing analysis were performed on a highly diverse 12th generation de novo hybrid population. Variant calling was applied for these pool sequencing and identification of genetic variants. Pool QTL analysis was applied to compare the allele frequency difference of extreme pools under the same condition. Multiple QTL intervals responding to the brewing environment were identified. This provides useful genetic insights for brewing yeast breeding and improvement.

History

Supervisor(s)

Edward Louis; Alexander Gorban

Date of award

03/04/2020

Author affiliation

Department of Genetics and Genome Biology

Awarding institution

University of Leicester

Qualification level

Doctoral

Qualification name

PhD

Language

en

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