Working with covariates

Part of the Statistics for Biology and Health book series (SBH)

It is often of interest to take account of a covariate (such as sex or an environmental factor, such as diet) in QTL mapping. If such a covariate has a large effect on the phenotype, its inclusion in the analysis will result in reduced residual variation and so will enhance our ability to detect QTL. It is also of interest to assess possible QTL × covariate interactions. For example, does a QTL have different effects in the two sexes?


Additive Covariates Marker Covariates Hyper Data Binary Phenotype Miss Genotype Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag New York 2009

Authors and Affiliations

  1. 1.Dept. Biostatistics & Medical InformaticsUniversity of Wisconsin-MadisonMadisonUSA
  2. 2.Dept. Epidemiology & BiostatisticsUniversity of California San FranciscoSan FranciscoUSA

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