P0463 Genome-Wide Marker-Trait Association Analysis of the USDA Pea (Pisum sativum L.) Core Collection

Soon-Jae Kwon , USDA-ARS Plant Introduction, Pullman, WA
Allan Brown , NC State University, Raleigh, NC
Jinguo Hu , USDA- ARS, Pullman, WA
Rebecca McGee , USDA-ARS, Pullman, WA
Chasity Watt , Department of Crop and Soil Sciences, Washington State University, Pullman, WA
Ted Kisha , USDA-ARS Plant Introduction, Pullman, WA
Gail Timmerman-Vaughan , New Zealand Institute for Plant and Food Research Canterbury Agriculture & Science Centre, Lincoln, New Zealand
Michael A. Grusak , USDA/ARS, Houston, TX
Kevin McPhee , North Dakota State University, Fargo, ND
Clarice J. Coyne , USDA-ARS/Washington State University, Pullman, WA
Genetic diversity, population structure and genome-wide marker-trait association analyses were conducted for the USDA pea (Pisum sativum L.) core collection. The core collection of 285 accessions contains diverse phenotypes and geographic origins. Analyses were carried out using 102 polymorphic fragments amplified by 15 microsatellite primer pairs, 36 RAPD loci and one SCAR (sequence characterized amplified region) marker. A significant amount of variation was revealed by the molecular markers at the DNA level. Three populations were identified that constituted 56.1%, 13.0% and 30.9%, respectively, of the USDA pea core collection. Cluster analysis using a combined dataset comprised of polymorphic markers agreed with the results of the population structure, identifying six clades that corresponded to the bulk of the core collection. Significant marker-trait associations were identified among certain markers with eight important traits such as seed mineral nutrient concentrations and plant morphology. Fifteen pairs of associations were at the significant levels of P≤ 0.01 when tested using the three statistical models. Marker-trait studies in pea germplasm could provide a useful alternative to linkage mapping in the detection of marker-phenotype associations to be used in the implementation of marker-assisted selection and, eventually, in genomic selection for pea crop improvement.