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Unpublished Paper
Targeted Methods for Finding Quantitative Trait Loci
U.C. Berkeley Division of Biostatistics Working Paper Series
  • Hui Wang, Stanford University
  • Sherri Rose, University of California, Berkeley
  • Mark J. van der Laan, University of California, Berkeley
Date of this Version
7-6-2011
Abstract

Conventional genetic mapping methods typically assume parametric models with Gaussian errors, and obtain parameter estimates through maximum likelihood estimation. We propose a general semiparametric model to map quantitative trait loci (QTL) in experimental crosses. In contrast with widely-used interval mapping (IM) derived methods, our model requires fewer assumptions and also accommodates various machine learning algorithms. Estimation using both targeted maximum likelihood and collaborative targeted maximum likelihood methods is compared to a composite interval mapping (CIM) approach. We demonstrate with simulations and real data analyses that, on average, our semiparametric targeted learning approach produces less biased QTL effect estimates than those from parametric models.

Disciplines
Citation Information
Hui Wang, Sherri Rose and Mark J. van der Laan. "Targeted Methods for Finding Quantitative Trait Loci" (2011)
Available at: http://works.bepress.com/sherri_rose/17/