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Research Article Open access CC BY 4.0

Identification of Contrasting Parents and Development of Mapping Population for Sucking Pest Resistance in Cotton

A. Akash, N. Manikanda Boopathi, R. Ravikesavan, A. Subramanian, K. Thirukumaran

International Journal of Environment and Climate Change · pp. 4119–4141 · Published 26 Sep 2023

10.9734/ijecc/2023/v13i103089

Abstract

The current study was conducted to identify the best parents with good agronomical features and contrasting characteristics for sucking pest resistance to develop a population that can be used for Quantitative Trait Loci mapping of resistance component traits. Initial attempts at this university have enabled to identify of five potential parents (viz., KC3, NDLH 1938, CO18, K12, and RG8) with improved sucking pest resistance and yield characters and they were crossed with the recently released high-yielding variety CO-17, which is also suitable for high-density planting.  All these six parents were evaluated for fiber yield and quality traits besides anatomical characters such as trichomes density, length, width, and sharpness that confers sucking pest resistance and genotyped with 200 Simple Sequence Repeats markers that span the entire genome. Efforts have been dedicated to the careful selection of optimal parents, namely CO17 and KC3, each possessing distinct traits related to resistance against sucking pests and also fiber quality characters in addition. This strategic approach aims to combine their contrasting genetic components for sucking pest resistance, to develop a new generation of plants that exhibit heightened resilience to these types of pests. CIR139, a polymorphic SSR marker of these two parents was used to fix the true hybrids which were advanced to generate an F2 mapping population.  This population is believed to be useful for QTL mapping of anatomical features that confer sucking pest resistance such as trichome length, density, breadth, and sharpness besides fiber yield and quality traits.

Cotton sucking pest trichomes SEM SSR hybrid validation

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