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?
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© 2009 Springer-Verlag New York
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Broman, K.W., Sen, Ś. (2009). Working with covariates. In: A Guide to QTL Mapping with R/qtl. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-92125-9_7
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DOI: https://doi.org/10.1007/978-0-387-92125-9_7
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