P0144
Developing Rice Mapping Populations as a Genetic Resource for Validation of GWAS

Date: Monday, January 14, 2013
Room: Grand Exhibit Hall
Georgia C. Eizenga , USDA-ARS Dale Bumpers National Rice Research Center, Stuttgart, AR
M Liakat Ali , U Nebraska, West Central Research & Extension Center, North Platte, NE
Susan McCouch , Cornell University, Ithaca, NY
Asian rice (Oryza sativa L.) is divided into two major subspecies, Indica and Japonica with the temperate and tropical japonica subpopulations being part of Japonica.  These two subpopulations encompass the majority of the rice grown in the USA.  Recently, a genome-wide association study (GWAS) identified SNP markers associated with 32 traits of agronomic importance.  To validate these associations, four bi-parental recombinant inbred line (RIL) populations are currently under development using Japonica parents that were extreme for several of the traits evaluated.  Two populations (Estrela x NSFTV 199; Norin 20 x Fortuna) were evaluated in the field during summer 2012 in an unreplicated study to observe the phenotypic variation and produce enough seed for replicated field studies.  Transgressive variation was observed for days to heading, plant height and plant type (tiller angle).  Data on panicle and seed traits (panicle length, primary panicle branch number, seeds per panicle, seed size) is currently being collected.  This year, F7 progeny lines will be genotyped using genotyping-by-sequencing (GBS).  Several double cross F1 seeds were obtained from crosses between the bi-parental crosses and the parents representing combinations of tropical japonica & indica; tropical & temperate japonica; and tropical japonica, temperate japonica & indica subpopulations.  Currently crosses are being made between several of these double cross F1 progenies to develop multi-parent advanced generation inter-cross (MAGIC) populations with eight parents as founder lines.  After genotyping by GBS, these MAGIC populations will be used to investigate quantitatively inherited traits of agronomic importance using GWAS and fine mapping to better understand the genetic basis of transgressive variation for traits of interest.