Dynamic Quantitative Trait Locus Analysis of Plant Phenomic Data.
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.
Biocenter Oulu, Oulu, Finland; Department of Mathematical Sciences and Department of Biology, University of Oulu, 90014 Oulu, Finland.
Zitong
Z
Li
Biocenter Oulu, Oulu, Finland; Department of Mathematical Sciences and Department of Biology, University of Oulu, 90014 Oulu, Finland. Electronic address: Mikko.Sillanpaa@oulu.fi.
Mikko J
MJ
Sillanpää
D002874:Chromosome Mapping / Q000379:methods; D023281:Genomics / Q000379:methods*; D057166:High-Throughput Screening Assays / Q000379:methods; D008957:Models, Genetic; D010641:Phenotype; D063245:Plant Development / Q000235:genetics*; D010944:Plants / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci
functional mapping; high-throughput phenotyping; multiple-locus method; plant growth and development; quantitative trait loci; timecourse