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A Guide to QTL Mapping with R/qtlNon-normal phenotypes

A Guide to QTL Mapping with R/qtl: Non-normal phenotypes [The methods discussed in Chap. 4 all rely on the assumption that, given QTL genotype, the phenotype follows a normal distribution. This is not the same as to assume that the marginal phenotype distribution is normal—it will follow a mixture of normal distributions. But in the case that no QTL has very large effect, the marginal phenotype distribution would generally be close to normal: unimodal and reasonably symmetric.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Guide to QTL Mapping with R/qtlNon-normal phenotypes

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Publisher
Springer New York
Copyright
© Springer-Verlag New York 2009
ISBN
978-0-387-92124-2
Pages
135 –151
DOI
10.1007/978-0-387-92125-9_5
Publisher site
See Chapter on Publisher Site

Abstract

[The methods discussed in Chap. 4 all rely on the assumption that, given QTL genotype, the phenotype follows a normal distribution. This is not the same as to assume that the marginal phenotype distribution is normal—it will follow a mixture of normal distributions. But in the case that no QTL has very large effect, the marginal phenotype distribution would generally be close to normal: unimodal and reasonably symmetric.]

Published: Jun 5, 2009

Keywords: Interval Mapping; Binary Trait; Survival Package; Argument Model; Interval Mapping Method

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