Doctor of Philosophy / Department of Plant Pathology / James C. Nelson / The aim of quantitative trait locus (QTL) mapping is to identify association between
DNA marker genotype and trait phenotype in experimental populations. Many QTL mapping
methods have been developed to improve QTL detecting power and estimation of QTL location
and effect. Recently, shrinkage Bayesian and penalized maximum-likelihood estimation
approaches have been shown to give increased power and resolution for estimating QTL main or
epistatic effect. Here I describe a new method, shrinkage interval mapping, that combines the
advantages of these two methods while avoiding the computing load associated with them.
Studies based on simulated and real data show that shrinkage interval mapping provides higher
resolution for differentiating closely linked QTLs and higher power for identifying QTLs of
small effect than conventional interval-mapping methods, with no greater computing time.
A second new method developed in the course of this research toward increasing QTL
mapping efficiency is the extension of multi-trait QTL mapping to accommodate incomplete
phenotypic data. I describe an EM-based algorithm for exploiting all the phenotypic and
genotypic information contained in the data. This method supports conventional hypothesis tests
for QTL main effect, pleiotropy, and QTL-by-environment interaction. Simulations confirm
improved QTL detection power and precision of QTL location and effect estimation in
comparison with casewise deletion or imputation methods.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/374 |
| Date | January 1900 |
| Creators | Guo, Zhigang |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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