Usefulness of the cloned and fine-mapped genes/QTL for grain yield and related traits in indica rice breeding for irrigated ecosystems

Abstract Many genes/QTL for grain yield (GY) and yield related traits in rice have been cloned or fine-mapped in the last three decades. A collection of indica elite breeding lines and cultivars assembled in IRRI was used to test the usefulness of 39 well characterized yield related genes/QTL. The population of lines was phenotyped for GY and ten yield related traits under eight environments of three locations including Jiangxi, and Sichuan in China, and six season (2) and nitrogen rate (3) combinations in IRRI and genotyped using 46 markers tightly linked to the 39 target genes/QTL and 53 SSR markers evenly distributed on the genome. Using the 53 random SSR markers identified two major subpopulations. Association analyses were separately carried out for the whole population and the two subpopulations. All the 39 target genes/QTL were associated with two or more measured traits including traits not previously reported. GW6 and Gn1a were associated with nine and eight traits, respectively. Ghd7 , qSPP7 , SCM2 , and SPP1 were associated with seven traits. GIF1 and Ltn were associated with six traits. GS3 , GW2 , gw3.1 , htd1 , Nop(t) , qGY2-1 , and qPH6-1 were associated with five traits. D10 , d27 , DEP2 , DWL1 , Gnp4 , Gw1-1 , GW3 , gw5 , MOC1 , PAP2 , qGL7 , qGL7-2 and qGN4-1 were associated with four traits, D88 , Ghd8 , GS5 , Gw1-2 , IPA1 , qSH3 and RPH were associated with three traits. ep3 , gw8.1 , gw9.1 and qPDS3 were associated with two traits. A total of 16 genes/QTL were found to be associated with GY. GS3 , GW1-1 and d27 were associated with GY in two testing environments and the others were only in one environment. Sixteen, six and ten genes/QTL were associated with panicle number per plant, grain number per panicle and thousand grain weight, respectively. Significant gene-by-environment interaction was present for all the studied genes/QTL. GY could not be well predicted using the markers significantly associated with the measured traits or all target markers based on stepwise multiple linear regression analysis. The adjusted coefficient of determination ranged from 0.024 to 0.191 for the final selected models considering the associated markers only and from 0.039 to 0.261 for the final selected models considering all target markers. Nevertheless the known genes might be explicitly utilized in developing more efficient selection criteria for enhancing selection accuracy.