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- W2116901749 abstract "Advanced platforms have recently become available for automatic and systematic quantification of plant growth and development. These new techniques can efficiently produce multiple measurements of phenotypes over time, and introduce time as an extra dimension to quantitative trait locus (QTL) studies. Functional mapping utilizes a class of statistical models for identifying QTLs associated with the growth characteristics of interest. A major benefit of functional mapping is that it integrates information over multiple timepoints, and therefore could increase the statistical power for QTL detection. We review the current development of computationally efficient functional mapping methods which provide invaluable tools for analyzing large-scale timecourse data that are readily available in our post-genome era." @default.
- W2116901749 created "2016-06-24" @default.
- W2116901749 creator A5007504195 @default.
- W2116901749 creator A5055050168 @default.
- W2116901749 date "2015-12-01" @default.
- W2116901749 modified "2023-09-30" @default.
- W2116901749 title "Dynamic Quantitative Trait Locus Analysis of Plant Phenomic Data" @default.
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- W2116901749 doi "https://doi.org/10.1016/j.tplants.2015.08.012" @default.
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