Date of Graduation

12-2012

Document Type

Thesis

Degree Name

Master of Science in Crop, Soil & Environmental Sciences (MS)

Degree Level

Graduate

Department

Crop, Soil & Environmental Sciences

Advisor/Mentor

Mason, Richard E.

Committee Member

Pereira, Andy

Second Committee Member

Miller, David M.

Third Committee Member

Bluhm, Burton H.

Keywords

Biological sciences; Abiotic stress; Drought; Heat; Meta-analysis; Triticum aestivum I; Wheat qtl

Abstract

Heat and drought are the two most important environmental constraints to wheat production globally, are often present simultaneously and will become more severe with global climate change. This presents a unique challenge to wheat scientists who must work to develop wheat cultivars that are productive and adapted to future environmental conditions. A number of recent studies have reported quantitative trait loci (QTL) associated with heat and drought tolerance, as well as QTL for stress adaptive traits such as the availability of stem carbohydrates or crop canopy temperature. The objective of this study was to perform a meta-analysis of these QTL to identify regions of the wheat genome that are consistently associated with tolerance to heat and drought. To identify Meta-QTL (MQTL), a QTL database was developed from 30 studies targeted at heat and drought stressed environments. The positions of individual QTL were projected onto a consensus genetic map based on the presence of common molecular markers and a 95% confidence interval (CI) was calculated for each QTL. After positioning the individual QTL, the software `Biomercator v2.1' was used to predict the location and CI of MQTL based on maximum likelihood.

In total, 854 QTL were reported for 80 different traits. This included 502 for drought stress, 234 for heat stress, and 118 adaptive trait QTL in non-stressed environments. These QTL were grouped into 66 MQTL regions distributed throughout the wheat genome. Most regions co-localized for both heat and drought stress, although both drought and heat stress specific MQTL regions were also identified. Using the traits present within MQTL it was possible to genetically model Stress Trait Expression Pathways (STEPs) that can be used to identify target alleles and physiological traits for improvement through breeding.

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