Repurposing variance genome-wide association studies (vGWAS) for identifying gene-by-environment interactions

Identifying genotype by environment (GxE) interactions is vital in realizing different plant breeding objectives, such as breeding for responsiveness to new stressors or yield stability and uniformity. While GxE is an important contributor to a complex trait’s overall genetic architecture, a bottleneck of identifying specific GxE loci for use in plant breeding and quantitative genetics is the heavy multiple testing correction involved with testing every genetic marker and environment interaction. One quantitative genetics phenomenon that may provide a shortcut to identifying GxE loci is through variance quantitative trait loci (vQTLs). While vQTLs have been identified in humans, yeast, animal model organisms, and livestock species, this is a relatively new idea in plant breeding. To further investigate using vQTLs to identify GxE loci, this thesis’s three primary objectives are to 1.) conduct a simulation study using publicly available genotypic data from Arabidopsis and maize to assess the potential of two vGWAS models to identify GxE loci and other non-additive genetic loci like pure vQTLs and epistatic loci, 2.) apply the findings from objective 1.) to real flowering-time data in maize to assess how well vGWAS can aid in detecting GxE loci, and 3.) examine the merit of incorporating peak-associated vGWAS signals for obtaining genomic estimated breeding values that are stable across environments. This thesis argues that vGWAS can help identify GxE loci, but more work needs to be done to realize this full potential. I discuss this notion by using simulations and flowering-time traits in maize as a proof of concept for vGWAS and genomic selection.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Crop Sciences

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Murphy, Matthew D.

Contributors dc:contributor

• Lipka , Alexander E
• Moose, Stephen P
• Rutkoski, Jessica E
• Ainsworth, Elizabeth A
• Morota, Gota

• Maize
• Vgwas
• Gxe
• Epistasis, Vqtl, Genomic Selection
• Arabidopsis

Statement dc:rights

• Copyright 2023 Matthew Murphy

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/121432

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F121432