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ARS Home » Southeast Area » Stuttgart, Arkansas » Dale Bumpers National Rice Research Center » Research » Publications at this Location » Publication #349061

Research Project: Genomic Approaches and Genetic Resources for Improving Rice Yield and Grain Quality

Location: Dale Bumpers National Rice Research Center

Title: Association analysis and marker development for grain quality traits using USDA diverse rice germplasm collections

Author
item Edwards, Jeremy
item Huggins, Trevis
item Chen, Ming-hsuan
item Jackson, Aaron
item Mcclung, Anna
item Mcclung, Anna

Submitted to: Rice Technical Working Group Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 1/19/2018
Publication Date: 10/16/2018
Citation: Edwards, J., Huggins, T.D., Chen, M., Jackson, A.K., McClung, A.M. 2018. Association analysis and marker development for grain quality traits using USDA diverse rice germplasm collections. Rice Technical Working Group Meeting Proceedings, February 19-22, 2018, San Diego, California. Electronic Publication

Interpretive Summary:

Technical Abstract: New molecular markers are being designed and validated for grain quality improvement based on computationally assisted analysis of genome wide association study (GWAS) findings across multiple panels and multiple grain quality traits. The traits include grain dimensions, apparent amylose content (AAC), alkali spreading value (ASV), protein content, and chalk percent. The markers utilize the Kompetitive Allele Specific PCR (KASP) SNP technology. Discovery of SNPs associated with grain quality was accomplished using the USDA rice core subset (RCS) and mini-core (MC) GWAS panels. High density genotyping in these panels comes from public re-sequencing data on the MC and simple sequence repeat (SSR) markers on the RCS. In addition, public single nucleotide polymorphism (SNP) data from the High Density Rice Array (HDRA) genotyping of rice diversity panels 1 and 2 were merged with resequencing data from the MC which included 189 RCS accessions to yield a combined dataset of 383 individuals and 122,102 SNPs. Perl scripts were developed to define significant chromosomal regions from p-values and allele effects of the GWAS and find overlapping segments across traits and panels, and assist in candidate gene identification by summarizing curated known genes in those regions. By comparing GWAS across traits, it was possible to determine if a segment influenced a single grain quality trait or multiple traits, and whether there was a likely candidate gene for that region. From those results, significant SNPs were targeted for KASP marker development. These markers are now being validated in breeding lines and diverse collections.