Year: 2006
Type: article
Abstract: 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). In standard interval mapping, one deals with the missing QTL genotype data by performing maximum likelihood under a mixture model, using a version of the EM algorithm. Central to this approach is the calculation of the distribution of QTL genotypes conditional on the observed multipoint marker data. In the pseudomarker algorithm, which uses a form of multiple imputation, one must be able to simulate from the joint distribution of the genotypes at the pseudomarkers, conditional on the observed marker data. We discuss the use of algorithms developed for hidden Markov models (HMMs) to perform the tasks mentioned above and thus deal with the missing genotype data problem. Simpler approaches
Author: Karl W. Broman
Institution: Johns Hopkins University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2294839069