Project properties
Title GWAS Analysis in peanut
Group Plant Breeding, Laboratory of
Project type thesis
Credits 36-39
Supervisor(s) Yanlin Liao and Chris Maliepaard
Examiner(s) Yanlin Liao and Chris Maliepaard
Contact info chris.maliepaard@wur.nl yanlin.liao@wur.nl
Begin date 2023/07/01
End date 2026/07/01
Description Genome-wide association studies (GWAS) have revealed numerous links between traits and chromosome positions of crops plants, making this method an effective means of genetic analysis of phenotypic traits in diversity panels. In peanut, a diversity panel of 400 individuals has been re-sequenced with a read depth of 20, and various traits have been phenotyped, such as flower and plant type, yield, oil content, shell thickness, and rust resistance. Using GAPIT, more than 10 small regions were identified as significant in the current study. However, many of the 400 individuals in the panel are related, so incorporating a model that accounts for IBD could enhance the power and precision of identifying Quantitative Trait Loci (QTL) through GWAS analysis. Moreover, so far only single nucleotide polymorphisms (SNPs) were utilized in the current GWAS analysis, while haplotypes, which provide more information, may be more effective in decreasing false-positive results.
Used skills This is a topic that does not involve phenotyping in field or greenhouse or lab work, but only development of suitable data analysis scripts (in R, preferably) and carrying out these analyses, interpretation of the results and finding leads for subsequent research. This project is a collaboration with the Henan Academy of Agricultural Sciences, the data for this project is from this institute. The project is carried out in Wageningen.
Requirements knowledge of R and skills to write R scripts are required for this topic. The MSc thesis student will also need a good understanding of statistical methods and quantitative genetics (linkage mapping, QTL analysis, GWAS studies) for this topic. The student must have an interest in further developing R scripting skills and statistical analysis skills. Courses: R for statistics; modern statistics for the life sciences; data science for genetics and plant breeding.