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ARS Home » Southeast Area » Stuttgart, Arkansas » Dale Bumpers National Rice Research Center » Research » Publications at this Location » Publication #430151
Research Project: Broadening and Strengthening the Genetic Base of Rice for Adaptation to a Changing Climate, Crop Production Systems, and Markets

Location: Dale Bumpers National Rice Research Center

Title: High-Resolution introgression mapping of rice chromosome segment substitution lines (CSSLs) using skim sequencing

Author
item DANAO, JAIRAM - Purdue University
item Edwards, Jeremy
item Eizenga, Georgia
item WANG, DIANE - Purdue University

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 11/14/2025
Publication Date: 11/14/2025
Citation: Danao, J., Edwards, J., Eizenga, G.C., Wang, D.R. 2025. High-Resolution introgression mapping of rice chromosome segment substitution lines (CSSLs) using skim sequencing. Meeting Abstract. Salt Lake City, Utah. November 9-12, 2025.

Interpretive Summary:

Technical Abstract: Rice is a vital staple crop, providing essential calories and nutrients to over half of the world’s population, with global consumption reaching approximately 534 million metric tons in 2023–2024. To advance rice breeding, Chromosome Segment Substitution Lines (CSSLs), near-isogenic lines developed through backcrossing and marker-assisted selection (MAS), were developed by crossing the elite indica cultivar, IR64 and the japonica cultivar, Cybonnet with three diverse wild ancestral rice (Oryza sp.) accessions. The wild donors were strategically selected based on their genetic and geographic origins, as well as their potential to introduce novel traits. Earlier genomic characterization of these CSSLs relied on the Cornell-IR LD Rice Array (C7AIR) with 7,170 SNPs, offering limited resolution across the ~450 Mb rice genome. In this study, we employed skim sequencing to achieve greater marker density and a more comprehensive view of genome-wide variation. Raw reads were mapped to the MSU v7 rice reference genome and filtered using cutadopt V 3.7, trimmomatic V0.39 and bcftools V 1.17. A probabilistic model was then applied to accurately infer the regions of introgression, particularly addressing challenges posed by high genome similarity between certain parental line combinations. By using skim sequencing, we were also able to recover regions that were previously SNP-sparse. This work provides an enhanced genetic resource for future gene discovery, validation of genome-wide association study (GWAS) results, and targeted breeding efforts in rice.