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Title:   Author  item
                                            McClung, Annaitem
                                            Tai, Thomasitem
                                            LINSCOMBE, STEVE - LSU AGRIC. RES. CENTERitem
                                            MOLDENHAUER, KAREN A - UNIV. OF AR RRECitem
                                            KANTER, DWIGHT - MISS. AG & FOREST EXT. STitem
                                            BEIGHLEY, DONN - SE MO ST. UNIV.item
                                            ORDONEZ, JR., SAMUEL - LSUitem
                                            KADARU, SURESH - LSUitem
                                            OARD, JAMES - LSU
  Submitted to: American Society of Agronomy Abstracts
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 9/1/2007
                                    Publication  Date: 11/1/2007
                                    Citation: Mcclung, A.M., Tai, T., Linscombe, S.D., Moldenhauer, K.K., Kanter, D., Beighley, D., Ordonez, Jr., S.A. 2007. Validation of mixed model-regression procedure for association genetics in rice. [abstract] American Society of Agronomy Abstracts. p. 259-5.Interpretive Summary:
                                Technical Abstract: Mixed models for association genetics of outcrossing plant species such as maize have been developed recently, but validation of selected markers associated with agronomic traits in different populations has not been extensively studied. Moreover, the mixed models developed for outcrossing may not be appropriate for inbred plants such as rice. Our first research objective was to evaluate the mixed model for selection of markers that explain phenotypic variance associated with four agronomic traits in two genetically diverse and narrow germplasm collections of inbreds and validate results in separate lines not involved in the original analysis. The results showed that kinship estimates incorporated into the mixed model for the narrow germplasm did reduce Type I errors, but this improvement was not sufficient for high phenotypic prediction rates in a separate validation sample. Our second research objective was to create and evaluate a mixed model-regression procedure that identified main and epistatic effects by standard hypothesis testing and Bayesian information criteria in a multivariate format. The new procedure resulted in increased power and enhanced prediction ability of markers evaluated in validation samples from both diverse and narrow germplasm. Addition of an epistatic component to the procedure improved validation results by ~ 18%. The results indicated that a sequential mixed model-regression approach with epistatic effects coupled with a validation step should be considered for association genetic studies in rice.


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