From association to prediction: statistical methods for the dissection and selection of complex traits in plants
Current Opinion in Plant Biology, 2015•Elsevier
Highlights•Controlling for spurious associations in statistical models is essential.•
Computationally efficient approaches are critical for large data sets.•Statistical genetic
models that predict phenotypes help accelerate breeding cycles.•Co-evolution between
statistical models and sequencing and phenotyping advances is ongoing.Quantification of
genotype-to-phenotype associations is central to many scientific investigations, yet the
ability to obtain consistent results may be thwarted without appropriate statistical analyses …
Computationally efficient approaches are critical for large data sets.•Statistical genetic
models that predict phenotypes help accelerate breeding cycles.•Co-evolution between
statistical models and sequencing and phenotyping advances is ongoing.Quantification of
genotype-to-phenotype associations is central to many scientific investigations, yet the
ability to obtain consistent results may be thwarted without appropriate statistical analyses …
Highlights
- Controlling for spurious associations in statistical models is essential.
- Computationally efficient approaches are critical for large data sets.
- Statistical genetic models that predict phenotypes help accelerate breeding cycles.
- Co-evolution between statistical models and sequencing and phenotyping advances is ongoing.
Quantification of genotype-to-phenotype associations is central to many scientific investigations, yet the ability to obtain consistent results may be thwarted without appropriate statistical analyses. Models for association can consider confounding effects in the materials and complex genetic interactions. Selecting optimal models enables accurate evaluation of associations between marker loci and numerous phenotypes including gene expression. Significant improvements in QTL discovery via association mapping and acceleration of breeding cycles through genomic selection are two successful applications of models using genome-wide markers. Given recent advances in genotyping and phenotyping technologies, further refinement of these approaches is needed to model genetic architecture more accurately and run analyses in a computationally efficient manner, all while accounting for false positives and maximizing statistical power.
Elsevier