Targeted Methods for Finding Quantitative Trait LociU.C. Berkeley Division of Biostatistics Working Paper Series
Date of this Version7-6-2011
AbstractConventional 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.
Citation InformationHui 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/