Degree Type

Dissertation

Date of Award

2003

Degree Name

Doctor of Philosophy

Department

Animal Science

First Advisor

Jack Dekkers

Second Advisor

Kenneth Koehler

Abstract

Selective DNA pooling is an efficient method to identify chromosomal regions that harbor quantitative trait loci (QTL) by comparing marker allele frequencies in pooled DNA from phenotypically extreme individuals. The currently used single marker analysis can detect linkage of markers to a QTL, however, it does not provide separate estimates of QTL position and effect, nor does it utilize the joint information from multiple linked markers. In this thesis, two interval mapping methods for analysis of selective DNA pooling data were developed. One was based on least squares regression (LS-pool) and the other on approximate maximum likelihood (ML-pool). Both methods simultaneously utilize information from multiple markers and multiple families and both are easily applied to different family structures (half-sib, F2 cross and backcross). Simulation was used to compare these two DNA pooling interval mapping methods with single marker analysis and with selective genotyping analysis of individual genotypes. Results indicated that both LS-pool and ML-pool provided greater power to detect the QTL than the single marker analysis and separate estimates of QTL location and effect. With large family size, both LS-pool and ML-pool provided similar power and estimates of QTL location and effect as selective genotyping. The LS-pool method, however, resulted in severely biased estimates of QTL location with small family size and distal QTL but the bias was reduced with ML-pool. Both interval mapping methods were also applied to two real data sets, a dairy cattle data set from a half-sib design and a layer chicken data set from an F2 cross. In the dairy cattle data application, both LS-pool and ML-pool solved problems of single marker analysis when missing marker genotypes were present by utilizing joint information from multiple markers. In the chicken data application, both LS-pool and ML-pool provided similar power to detect the QTL and similar estimates of QTL location and effect as analysis of individual genotypes. In conclusion, both LS-pool and ML-pool methods provide powerful tests for QTL detection and accurate estimates of QTL parameters while substantially saving genotyping costs through the use of DNA pooling. In addition, both methods are readily applied to practical situations.

DOI

https://doi.org/10.31274/rtd-180813-10933

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu

Copyright Owner

Jing Wang

Language

en

Proquest ID

AAI3118263

File Format

application/pdf

File Size

175 pages

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