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We adopt a variance-components method to model complex traits in outbred populations (e.g., humans).
Recognition of these problems resulted in the construction of the Collaborative Cross (CC), a general-purpose mouse resource to model complex traits [ 8, 9].
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Most immediately, these data will provide a foundation for modeling complex traits such as metabolic regulation, a dynamic phenotype that varies between these populations (Greenberg et al. 2010, 2011; Scheitz et al. 2013), and which is amendable to increasingly numerous and sensitive "omic" assays.
Here we have shown that in addition to serving as a model for complex traits a panel of 104 genotyped and expression-profiled yeast strains may also serve as a model for personalized medicine.
June: release of our perspective piece on the 'omnigenic' model of complex traits, with lead authors Evan Boyle and Yang Li [PDF Link].
We build a polygenic model for complex traits that distinguishes candidate trait-relevant variants from the rest of the genome.
This is consistent with Fishers classical infinitesimal model of complex traits, where many genes are involved, each with small but additive effects [ 58].
With genetic information and biochemical understanding incorporated, crop modelling also generates new insights and concepts that can in turn be used to improve genetic analysis and biochemical modelling of complex traits.
A similar forward-genetics approach that most often is used for studying animal models of complex traits is called quantitative trait locus (QTL) mapping.
In the future, integration networks using different types of 'omic' data will be combined to allow more thorough and comprehensive modeling of complex traits [ 14].
Realistic models relating complex traits (i.e., phenotypes) to genotypes may exhibit nonlinearity (epistasis), allowing distinct regions of DNA to interact with one another.
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CEO of Professional Science Editing for Scientists @ prosciediting.com