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Statistical Genetics of Quantitative TraitsComposite QTL Mapping

Statistical Genetics of Quantitative Traits: Composite QTL Mapping [Lander and Botstein’s interval method has an advantage for mapping QTLs genomewide by scanning for the position of a QTL throughout the genome. But this method can lead to biased estimates of QTL positions and effects when multiple QTLs occur on the same linkage group because it makes use of one single-marker interval at a time and has no mechanism to alleviate the impact of other QTLs outside the interval. For this reason, if a real QTL is located near a marker interval with no QTL, interval mapping may still detect a “ghost” QTL due to the linkage between the real QTL and the interval being tested (Martinez and Curnow 1992). Although a simultaneous search for multiple QTLs on different intervals can overcome this problem, this will bring about new difficulties in parameter estimation and model identifiability.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Statistical Genetics of Quantitative TraitsComposite QTL Mapping

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Publisher
Springer New York
Copyright
© Springer Science + Business Media, LLC 2007
ISBN
978-0-387-20334-8
Pages
287 –302
DOI
10.1007/978-0-387-68154-2_13
Publisher site
See Chapter on Publisher Site

Abstract

[Lander and Botstein’s interval method has an advantage for mapping QTLs genomewide by scanning for the position of a QTL throughout the genome. But this method can lead to biased estimates of QTL positions and effects when multiple QTLs occur on the same linkage group because it makes use of one single-marker interval at a time and has no mechanism to alleviate the impact of other QTLs outside the interval. For this reason, if a real QTL is located near a marker interval with no QTL, interval mapping may still detect a “ghost” QTL due to the linkage between the real QTL and the interval being tested (Martinez and Curnow 1992). Although a simultaneous search for multiple QTLs on different intervals can overcome this problem, this will bring about new difficulties in parameter estimation and model identifiability.]

Published: Jan 1, 2007

Keywords: Double Haploid; Interval Mapping; Composite Interval Mapping; Partial Regression; Marker Interval

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