Title of article :
Allelic diversity and association analysis for grain quality traits in exotic rice genotypes
Author/Authors :
Eimer, Solmaz Department of Plant Production - Faculty of Agricultural and Natural Resources Sciences - Gonbad Kavoos University, Iran , Sabouri, Hossein Department of Plant Production - Faculty of Agricultural and Natural Resources Sciences - Gonbad Kavoos University, Iran , Ahangar, Leila Department of Plant Production - Faculty of Agricultural and Natural Resources Sciences - Gonbad Kavoos University, Iran , Gholizadeh, Abdollatif Department of Plant Production - Faculty of Agricultural and Natural Resources Sciences - Gonbad Kavoos University, Iran
Abstract :
The present research aims to study the association and allelic diversity of linked microsatellite markers to
grain quality QTLs of 84 exotic rice genotypes. To this end, 9 microsatellite markers (RM540, RM539, RM587, RM527,
RM216, RM467, RM3188, RM246, RM5461) were used in which a total of 61 alleles were identified with a mean of 6
alleles per locus. The polymorphism information content (PIC) varied from 0.542 (RM540) to 0.812 (RM3188) for SSR
markers. Cluster analysis was performed using UPGMA method and genotypes were divided into five groups.
Furthermore, based on regression analysis, for rice grain quality properties in flooding conditions as long as drought
stresses, 10 alleles were identified. Of these, four alleles with gelatinization temperature, an allele with protein content
under flooding conditions, and three alleles with protein content and three alleles with gelatinization temperature were
related under drought stress. It should be noted that the RM216-C and RM5461-D alleles were commonly identified in
several traits. The presence of common markers for traits is probably due to the consistency of chromosomal locus
controlling these traits or pleiotropy. The results of this study may imply that the important identified alleles for example
RM216-A for gelatinization temperature (R2=30.1 %) can be used in rice quality improvement programs
Keywords :
Association analysis , Genetic variation , Grain physicochemical quality , Polymorphic Information Content (PIC)
Journal title :
Astroparticle Physics