Author
LIPKA, ALEX - University Of Illinois | |
KANDIANIS, CATHERINE - Cornell University | |
HUDSON, MATTHEW - University Of Illinois | |
YU, JIANMING - Iowa State University | |
DRNEVICH, JENNY - University Of Illinois | |
Bradbury, Peter | |
GORE, MICHAEL - Cornell University |
Submitted to: Current Opinion in Plant Biology
Publication Type: Review Article Publication Acceptance Date: 2/27/2015 Publication Date: 4/1/2015 Citation: Lipka, A.E., Kandianis, C.E., Hudson, M.E., Yu, J., Drnevich, J., Bradbury, P., Gore, M. 2015. From association to prediction: statistical methods for the dissection and selection of complex traits in plants. Current Opinion in Plant Biology. 24:110-118. Interpretive Summary: Technical Abstract: 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. |