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Lesson Outline 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 Lesson Media Objects QTL Analysis: Single Factor ANOVA in Excel

Quantitative Trait Locus (QTL) Analysis 2 Rate Me 1 2 3 4 5 QTL Analysis 2 - Objectives and Overview Patrick Byrne Department of Soil and Crop Sciences at Colorado State University, USA Welcome to the QTL Analysis 2 on-line lesson! Use the arrow buttons at the bottom of the page to move through the lesson materials. Before starting the lesson, please note these helpful navigation tips.  Lesson Navigation Tips: Click on ’Animations’ button found to the left in order to view the animation which supplements this lesson. You can also click on the animation icon within the text. Click once on figures to see enlarged versions. Click once on words in color to bring up their definitions. 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 I lesson, we recommend that you do so before beginning QTL Analysis II (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. 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 author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture. Comments Be the first to write a comment... Add Comment

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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, adminstered 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 author(s) and do not necessarily reflect the views of the USDA or NSF.
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