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Rapid and robust association mapping of expression quantitative trait loci

By Alex C Lam, Michael Schouten, Yurii S Aulchenko, Chris S Haley and Dirk-Jan de Koning

Abstract

We applied a simple and efficient two-step method to analyze a family-based association study of gene expression quantitative trait loci (eQTL) in a mixed model framework. This two-step method produces very similar results to the full mixed model method, with our method being significantly faster than the full model. Using the Genetic Analysis Workshop 15 (GAW15) Problem 1 data, we demonstrated the value of data filtering for reducing the number of tests and controlling the number of false positives. Specifically, we showed that removing non-expressed genes by filtering on expression variability effectively reduced the number of tests by nearly 50%. Furthermore, we demonstrated that filtering on genotype counts substantially reduced spurious detection. Finally, we restricted our analysis to the markers and transcripts that were closely located. We found five times more signals in close proximity (cis-) to transcripts than in our genome-wide analysis. Our results suggest that careful pre-filtering and partitioning of data are crucial for controlling false positives and allowing detection of genuine effects in genetic analysis of gene expression

Topics: Proceedings
Publisher: BioMed Central
OAI identifier: oai:pubmedcentral.nih.gov:2367564
Provided by: PubMed Central

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Citations

  1. 10-5 is caused by an outlier in genotype class 4/4.
  2. (2002). A direct approach to false discovery rates.
  3. (2004). Cheung VG: Genetic analysis of genome-wide variation in human gene expression. Nature
  4. (2007). Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics
  5. (1986). Sing CF: The use of measured genotype information in the analysis of quantitative phenotypes in man. I. Models and analytical methods. Ann Hum Genet
  6. (2003). Tibshirani R: Statistical significance for genomewide studies.
  7. (2000). WO: A general test of association for quantitative traits in nuclear families.