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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #322142
Title: From association to prediction: statistical methods for the dissection and selection of complex traits in plants

Author
item LIPKA, ALEX - University Of Illinois
item KANDIANIS, CATHERINE - Cornell University
item HUDSON, MATTHEW - University Of Illinois
item YU, JIANMING - Iowa State University
item DRNEVICH, JENNY - University Of Illinois
item Bradbury, Peter
item 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.