title: A novel targeted learning method for quantitative trait loci mapping.
abstract: We present a novel semiparametric method for quantitative trait loci (QTL) mapping in experimental crosses. Conventional genetic mapping methods typically assume parametric models with Gaussian errors and obtain parameter estimates through maximum-likelihood estimation. In contrast with univariate regression and interval-mapping methods, our model requires fewer assumptions and also accommodates various machine-learning algorithms. Estimation is performed with targeted maximum-likelihood learning methods. We demonstrate our semiparametric targeted learning approach in a simulation study and a well-studied barley data set.
authors.affiliation: Palo Alto Veterans Institute for Research, Palo Alto, California 94304 Hui.Wang@va.gov.
authors.forename: Hui
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
authors.forename: Zhongyang
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Health Care Policy, Harvard Medical School, Cambridge, Massachusetts 02115.
authors.forename: Sherri
authors.initials: S
authors.lastname: Rose
authors.affiliation: Division of Biostatistics, University of California, Berkeley School of Public Health, Berkeley, California 94720.
authors.forename: Mark
authors.initials: M
authors.lastname: van der Laan
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D003433:Crosses, Genetic; D066264:Datasets as Topic; D005819:Genetic Markers; D001467:Hordeum / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D040641:Quantitative Trait Loci; D019655:Quantitative Trait, Heritable
chemical_list: D005819:Genetic Markers
keywords: QTL mapping; experimental crosses; semiparametric model; targeted maximum-likelihood estimation