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Activity Number:
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520
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Type:
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Contributed
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Date/Time:
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Thursday, August 7, 2008 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #302300 |
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Title:
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Computationally Efficient Estimation of False Discovery Rate in eQTL Studies Using Sequential Permutation P-Values
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Author(s):
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Timothy Bancroft*+ and Dan Nettleton
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Companies:
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Iowa State University and Iowa State University
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Address:
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, Ames, IA, 50011,
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Keywords:
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eQTL mapping ; Permutation testing ; False discovery rate ; Multiple testing ; Sequential analysis
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Abstract:
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This talk describes a computationally efficient method for permutation testing in quantitative trail loci (QTL) mapping studies involving multiple traits. For each trait, the sequential procedure of Besag and Clifford (1991) is applied to the permutation distribution of the maximum test statistic across all tested loci. The resulting permutation p-value can be used to test the null hypothesis of no association between the trait and the identified locus while controlling the genomewise type I error rate for each trait. To account for multiple testing across thousands of traits - as encountered in expression QTL (eQTL) studies - we develop an extension of the approach of Nettleton et al. (2006) and apply it to the collection of sequential permutation p-values to obtain approximate control of the false discovery rate. We demonstrate the method using data from an eQTL study.
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- Authors who are presenting talks have a * after their name.
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