title: Generalized linear mixed model for segregation distortion analysis.
abstract: BACKGROUND
  Segregation distortion is a phenomenon that the observed genotypic frequencies of a locus fall outside the expected Mendelian segregation ratio. The main cause of segregation distortion is viability selection on linked marker loci. These viability selection loci can be mapped using genome-wide marker information.
  
  
  RESULTS
  We developed a generalized linear mixed model (GLMM) under the liability model to jointly map all viability selection loci of the genome. Using a hierarchical generalized linear mixed model, we can handle the number of loci several times larger than the sample size. We used a dataset from an F(2) mouse family derived from the cross of two inbred lines to test the model and detected a major segregation distortion locus contributing 75% of the variance of the underlying liability. Replicated simulation experiments confirm that the power of viability locus detection is high and the false positive rate is low.
  
  
  CONCLUSIONS
  Not only can the method be used to detect segregation distortion loci, but also used for mapping quantitative trait loci of disease traits using case only data in humans and selected populations in plants and animals.
authors.affiliation: Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.
authors.forename: Haimao
authors.initials: H
authors.lastname: Zhan
authors.forename: Shizhong
authors.initials: S
authors.lastname: Xu
mesh_terms: D000818:Animals; D002874:Chromosome Mapping; D020090:Chromosome Segregation; D014644:Genetic Variation; D016014:Linear Models; D051379:Mice; D008815:Mice, Inbred Strains; D008957:Models, Genetic; D040641:Quantitative Trait Loci
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