Abstract
BackgroundSegregation 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.ResultsWe 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 F2 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.ConclusionsNot 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.
Highlights
Segregation distortion is a phenomenon that the observed genotypic frequencies of a locus fall outside the expected Mendelian segregation ratio
In a recent study [10], we found that segregation distortion is not necessarily harmful to QTL mapping; rather, it can help in some circumstances
There may be some effects not related to genetics, such as age, location and other systematic effects, and these effects are captured by b and the design matrix X
Summary
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. Segregation distortion refers to a phenomenon that the observed genotypic frequencies deviate significantly from the expected Mendelian frequencies [1]. The most promising explanation is viability selection on the distorted markers or loci linked to the markers [8]. In a recent study [10], we found that segregation distortion is not necessarily harmful to QTL mapping; rather, it can help in some circumstances. We can incorporate segregation distortion into existing QTL mapping programs [11]
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