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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 Data Structure for QTL Analysis A data set used in QTL analysis will consist of a set of molecular marker scores and a set of phenotypic traits for each individual or line of the mapping population. Typically, a QTL data set will consist of 100 or more markers and five or more traits. A partial view of a data set for the Oregon Wolfe Barley population is shown in Table 1.   Marker Scores Trait Values Line ABG704 Wx MWG089 CDO475 plant height days to heading 1 A A A A 73.34 39.3 2 B B A A 40.01 23.7 4 A A A A 59.37 41.0 5 B A . B 81.92 46.3 6 B B A A 37.47 28.0 7 B B B B 71.12 43.0 8 A A B B 39.69 30.4 9 A B B B 78.42 60.8 10 A A A A 48.90 32.0 Table 1. A subset of marker scores and phenotypic traits for 10 lines of the Oregon Wolfe Barley population, http://barleyworld.org/oregonwolfe.php. A and B scores refer to the parental alleles at a marker locus, and a period indicates missing data. In addition to the marker and trait data, a marker linkage map is usually developed for each mapping population. A linkage map is required for simple and composite interval mapping, and is very useful, but not essential, for single-factor analysis of variance. Note the linkage map example (Fig. 7). Fig. 7. An example of a marker linkage map in wheat (after Gupta et al., 2002). 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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