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ARS Home » Southeast Area » Florence, South Carolina » Coastal Plain Soil, Water and Plant Conservation Research » Research » Publications at this Location » Publication #227845
Title: Multiple-trait QTL mapping using a structural equation model

Author
item MI, XIAOJUAN - UNIV. NEBRASKA
item ESKRIDGE, KENT - UNIV. NEBRASKA
item WANG, DONG - UNIV. NEBRASKA
item BAENZIGER, P - UNIV. NEBRASKA
item Campbell, Benjamin - Todd

Submitted to: Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 7/1/2008
Publication Date: 10/5/2008
Citation: Mi, X., Eskridge, K., Wang, D., Baenziger, P.S., Campbell, B.T. 2008. Multiple-trait QTL mapping using a structural equation model [abstract]. American Society of Agronomy-Crop Science Society of America-Soil Science Society of America Annual Meeting, November 5-9, Houston, Texas. 2008 CDROM

Interpretive Summary:

Technical Abstract: Many agronomic experiments on mapping quantitative trait loci (QTL) result in data on a number of traits that have well established causal relationships such as wheat yield components. Common multiple-trait QTL mapping methods, taking into account the correlation among the multiple traits, have been developed and provide better estimates of QTL effects than single trait analysis. However none of these methods are capable of incorporating the causal structure among the traits with the consequence that biased estimates can result. In this paper, we develop a method for multi-trait composite interval mapping using a structural equation model (SEM) to take into account the causal relationships among traits and QTL. The method is applied to a mapping data set of chromosome 3A recombinant inbred chromosome lines (RICLs) from a wheat genetics experiment on four traits: grain yield, kernel weight (TKW), spikes per square meter (SPSM), and kernels per spike (KPS). Based on the result, three regions are identified containing QTLs. Results show that our proposed method has several advantages compared with single trait analysis and the multi-trait least squares analysis. The method improves understanding of genetic functions by providing insight into how QTLs regulate traits directly and indirectly through other traits and it improves the power to detect QTL and the precision of the parameter estimates.