PhD Defense: Joel Hemingway


Interested Members of the University Community are invited to attend the Final Oral Examination for the Degree of Doctor of Philosophy of Joel Hemingway of the Department of Plant Agriculture.

Monday, January 7 at 2:00 PM
Room 202, Crop Science Building

Thesis Title: Genomic Selection for Seed Oil Concentration in Bi-parental Soybean Populations Derived from Parents Carrying the DP-305423-1 Transgene for High Oleic Acid in the Seed

Advisory Committee                                           

Dr. Istvan Rajcan, Advisor
Dr. Milad Eskandari
Dr. Lewis Lukens
Dr. Steve Schnebly 

Examination Committee

Dr. Dave Wolyn, Chair
Dr. Aaron Lorenz, External Examiner
Dr. Ali Navabi
Dr. Steve Schnebly
Drr. Istvan Rajcan                                            


Soybean (Glycine max [L.] Merrill) oil is an economically important commodity worldwide with many uses.  High levels of polyunsaturated fatty acids cause oxidative instability of the oil; however, the DP-3054231-1 transgene confers elevated oleic acid concentrations resulting in oil with increased oxidative stability.  The first objective of this thesis was to study the effects of the DP-305423-1 transgene on agronomic and seed traits across multiple genetic backgrounds and environments.  An equal number of high oleic (HO) and normal oleic (NO) BC1F4:Fprogeny from four unique populations were grown at four locations in Southern Ontario and two in Northern Iowa.  Overall, the difference in mean yield between the HO and NO progeny varied across populations and locations and generally, the HO progeny had lower mean oil concentration and greater mean protein concentration.  Differences in 100-seed weight were not consistent across populations or locations.  Genomic selection (GS) has been shown to be a valuable tool for performing selection on complex quantitative traits, such as seed oil concentration in soybean.  The second objective of the thesis was to evaluate multiple GS models for seed oil concentration using a low-density marker panel in bi-parental, high oleic soybean populations and compare the prediction accuracies of 6 unique training populations (TPs).  Prediction accuracy was calculated as the Pearson correlation coefficient between the predicted value of an individual and the ‘true’ phenotypic value, as determined through multi location field testing.  Across all populations, models and TPs, GS prediction accuracy was 0.65, 0.59 and 0.65 for BayesA, BayesB and GBLUP models, respectively, and phenotypic selection accuracy was 0.72 for lines grown in Chatham, ON and 0.66 for lines grown at Wallaceburg, ON.  Generally, TPs consisting of more individuals had greater predictability; however, variations were observed across populations and models.  TPs consisting of individuals from a single location had greater predictability of all genotypes than training populations of equal size comprised of individuals from both locations, indicating potential influence of marker x environment effects across training environments.  These results show that context specific bi-parental genomic selection is a valuable tool for increasing oil concentration in high oleic, low linolenic soybeans.