Agronomy and Horticulture, Department of
Quantitative Trait Locus (QTL) Analysis 2
Document Type
Learning Object
Date of this Version
2005
Citation
Plant and Soil Sciences eLibrary (PASSeL) Lesson
Abstract
This is the second of a two-part series that describes the methods and uses of QTL analysis.
Objectives and Overview
Quantitative trait locus (QTL) analysis is a methodology that combines DNA marker and phenotypic trait data to locate and characterize genes that influence quantitative traits. This is the second of a two-part series that describes the methods and uses of QTL analysis. If you have not yet reviewed the QTL Analysis 1 lesson, we recommend that you do so before beginning QTL Analysis 2 (this lesson).
Upon completing this lesson you should be able to:
- Compare three methods of QTL analysis: single-factor analysis of variance, simple interval mapping, and composite interval mapping.
- Understand the descriptors used to characterize QTL location and effects.
- Describe the uses of QTL information in genetics and breeding.
- Explain the limitations of QTL analysis.
Modules:
- Lesson home
- QTL Analysis 2 - Objectives and Overview
- Introduction
- Basis of QTL Detection
- Data Structure for QTL Analysis
- QTL Analysis Methods
- Results Obtained from Single Factor ANOVA
- Simple Interval Mapping
- Composite Interval Mapping
- Population sizes for QTL studies
- QTL Analysis II - References
- Test What You’ve Learned
- Glossary
- Videos
COinS
Comments
Copyright © 2005 Patrick Byrne. Used by permission.
JNRLSE approved 2005
This project was supported in part by the National Research Initiative Competitive Grants CAP project 2011-68002-30029 from the USDA National Institute of Food and Agriculture, administered by the University of California-Davis and by the National Science Foundation (NSF), Division of Undergraduate Education, National SMETE Digital Library Program, Award #0938034, administered by the University of Nebraska. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the USDA or NSF.
Development of this lesson was supported in part by USDA Initiative for Future Agriculture and Food Systems (IFAFS) and the Cooperative State Research, Education, & Extension Service, U.S. Dept. of Agriculture under Agreement Number 00-52100-9710. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture.