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Thus, Harushima et al. ([2002]) used multiple crosses to investigate the diverse variation of reproductive barriers in rice.
We describe a formal method for combining multiple crosses to infer the location of a QTL on a tree.
We have described a formal approach for the joint analysis of multiple crosses to map the origin of QTL alleles to a position on a phylogenetic tree.
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The LOD score is the log10 likelihood comparing the hypothesis of a single QTL at that location to the null hypothesis of no QTL but with the multiple crosses allowed to have separate phenotypic means, that is, y ij ∼ normal (μ i, σ).
And he cleaned out multiple crosses thanks to solid positioning and good anticipation.
The goal of the work in Li et al. (2005) was to combine multiple related crosses to more precisely map QTL.
If a compound had multiple cross-references to a single database, each cross-reference was investigated independently.
Because multiple alleles with different effects are likely segregating within natural populations (Mauricio 2001), QTL mapping with multiple crosses is necessary to understand the genetic variation underlying phenotypic variation in natural populations (Barnwell and Noor 2008).
This process is space- and time-consuming (because it requires complex breeding schemes that involve multiple crosses), inefficient (due to unavoidable generation of offspring with unwanted genotypes), and expensive.
With data on multiple crosses, the simplest approach to identifying the location on the tree at which a QTL arose is to compare the pattern of presence and absence of the QTL in the individual crosses and match that to the ideal (see the table in Figure 1).
Our results highlight the importance of using multiple crosses and robust behavioral assays to uncover the genetic basis of behavioral variation in natural populations.
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