title: Mapping QTLs for improving grain yield using the USDA rice mini-core collection.
abstract: Yield is the most important and complex trait for genetic improvement in crops, and marker-assisted selection enhances the improvement efficiency. The USDA rice mini-core collection derived from over 18,000 accessions of global origins is an ideal panel for association mapping. We phenotyped 203 O. sativa accessions for 14 agronomic traits and identified 5 that were highly and significantly correlated with grain yield per plant: plant height, plant weight, tillers, panicle length, and kernels/branch. Genotyping with 155 genome-wide molecular markers demonstrated 5 main cluster groups. Linkage disequilibrium (LD) decayed at least 20 cM and marker pairs with significant LD ranged from 4.64 to 6.06% in four main groups. Model comparisons revealed that different dimensions of principal component analysis affected yield and its correlated traits for mapping accuracy, and kinship did not improve the mapping in this collection. Thirty marker-trait associations were highly significant, 4 for yield, 3 for plant height, 6 for plant weight, 9 for tillers, 5 for panicle length and 3 for kernels/branch. Twenty-one markers contributed to the 30 associations, because 8 markers were co-associated with 2 or more traits. Allelic analysis of OSR13, RM471 and RM7003 for their co-associations with yield traits demonstrated that allele 126 bp of RM471 and 108 bp of RM7003 should receive greater attention, because they had the greatest positive effect on yield traits. Tagging the QTLs responsible for multiple yield traits may simultaneously help dissect the complex yield traits and elevate the efficiency to improve grain yield using marker-assisted selection in rice.
authors.affiliation: State Key Lab of Rice Biology, IAEA Collaborating Center, Zhejiang University, Hangzhou, China.
authors.forename: Xiaobai
authors.initials: X
authors.lastname: Li
authors.forename: Wengui
authors.initials: W
authors.lastname: Yan
authors.forename: Hesham
authors.initials: H
authors.lastname: Agrama
authors.forename: Limeng
authors.initials: L
authors.lastname: Jia
authors.forename: Xihong
authors.initials: X
authors.lastname: Shen
authors.forename: Aaron
authors.initials: A
authors.lastname: Jackson
authors.forename: Karen
authors.initials: K
authors.lastname: Moldenhauer
authors.forename: Kathleen
authors.initials: K
authors.lastname: Yeater
authors.forename: Anna
authors.initials: A
authors.lastname: McClung
authors.forename: Dianxing
authors.initials: D
authors.lastname: Wu
mesh_terms: D000704:Analysis of Variance; D001499:Bayes Theorem; D002874:Chromosome Mapping; D032461:Chromosomes, Plant / Q000235:genetics; D018556:Crops, Agricultural / Q000235:genetics; D003433:Crosses, Genetic; D002523:Edible Grain / Q000235:genetics* / Q000254:growth & development; D056726:Genetic Association Studies; D056426:Genetic Loci; D005819:Genetic Markers; D014644:Genetic Variation / Q000235:genetics; D005838:Genotype; D015810:Linkage Disequilibrium; D008954:Models, Biological; D012275:Oryza / Q000235:genetics* / Q000254:growth & development; D010641:Phenotype; D040641:Quantitative Trait Loci / Q000235:genetics*; D014481:United States; D016885:United States Department of Agriculture
chemical_list: D005819:Genetic Markers