LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Identification of Novel Marker–Trait Associations for Lint Yield Contributing Traits in Upland Cotton (Gossypium hirsutum L.) Using SSRs

Photo by markusspiske from unsplash

Improving the yield of lint is the main objective for most of the cotton crop improvement programs throughout the world as it meets the demand of fiber for textile industries.… Click to show full abstract

Improving the yield of lint is the main objective for most of the cotton crop improvement programs throughout the world as it meets the demand of fiber for textile industries. In the current study, 96 genotypes of Gossypium hirsutum were used to find novel simple sequence repeat marker-based associations for lint yield contributing traits by linkage disequilibrium. Extensive phenotyping of 96 genotypes for various agronomic traits was done for two consecutive years (2018 and 2019) in early, normal, and late sown environments. Out of 168 SSR markers screened over the 96 genotypes, a total of 97 polymorphic markers containing 293 alleles were used for analysis. Three different models, i.e., mixed linear model (MLM), compressed mixed linear model (CMLM), and multiple locus mixed linear model (MLMM), were used to detect the significant marker–trait associations for six different environments separately. A total of 38 significant marker–trait associations that were common to at least two environments were considered as promising associations and detailed annotation of the significant markers has been carried out. Twenty-two marker–trait associations were found to be novel in the current study. These results will be very useful for crop improvement programs using marker-assisted cotton breeding.

Keywords: trait associations; yield; marker trait; marker; cotton

Journal Title: Frontiers in Plant Science
Year Published: 2021

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.