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Use Of Hidden Markov Models For Qtl Mapping, Karl W. Broman
Use Of Hidden Markov Models For Qtl Mapping, Karl W. Broman
Johns Hopkins University, Dept. of Biostatistics Working Papers
An important aspect of the QTL mapping problem is the treatment of missing genotype data. If complete genotype data were available, QTL mapping would reduce to the problem of model selection in linear regression. However, in the consideration of loci in the intervals between the available genetic markers, genotype data is inherently missing. Even at the typed genetic markers, genotype data is seldom complete, as a result of failures in the genotyping assays or for the sake of economy (for example, in the case of selective genotyping, where only individuals with extreme phenotypes are genotyped). We discuss the use of …
Poor Performance Of Bootstrap Confidence Intervals For The Location Of A Quantitative Trait Loucs, Ani Manichaikul, Josee Dupuis, Saunak Sen, Karl W. Broman
Poor Performance Of Bootstrap Confidence Intervals For The Location Of A Quantitative Trait Loucs, Ani Manichaikul, Josee Dupuis, Saunak Sen, Karl W. Broman
Johns Hopkins University, Dept. of Biostatistics Working Papers
The aim of many genetic studies is to locate the genomic regions (called quantitative trait loci, QTLs) that contribute to variation in a quantitative trait (such as body weight). Confidence intervals for the locations of QTLs are particularly important for the design of further experiments to identify the gene or genes responsible for the effect. Likelihood support intervals are the most widely used method to obtain confidence intervals for QTL location, but the non-parametric bootstrap has also been recommended. Through extensive computer simulation, we show that bootstrap confidence intervals are poorly behaved and so should not be used in this …