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Activity Number:
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32
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Type:
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Contributed
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Date/Time:
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Sunday, August 3, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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| Abstract - #302609 |
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Title:
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A Flexible Semiparametric Test To Detect Associations Between Quantitative Traits and Candidate Genes in Structured Populations with Censored Data
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Author(s):
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Meijuan Li*+
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Companies:
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The University of Minnesota
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Address:
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A460 Mayo Building, 420 Delaware St SE, Minneapolis, MN, 55455,
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Keywords:
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Population structure ; Censoring ; Association mapping ; Mixture of polya tree
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Abstract:
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Several statistical methods for detecting associations between quantitative traits and candidate genes in structured populations have been developed for fully observed phenotypes. However, many experiments are concerned with failure-time phenotypes, which are usually subject to censoring. In this paper, we develop semiparametric statistical methods for detecting association between a censored quantitative trait and candidate genes in the structured population. Our method corrects for population stratification and then models the relationship between trait values, genotypic scores at a candidate marker, and genetic background variables through a semiparametric model, where the error distribution is modeled as a mixture of Polya trees. The proposed method was applied to a real data set of 95 Arabidopsis lines and simulated data to demonstrate power and type I error rate.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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