Finding needles in a haystack: Using geo-references to enhance the selection and utilization of landraces in breeding for climate-resilient cultivars of upland cotton (gossypium hirsutum l.)

Junghyun Shim, Nonoy B. Bandillo, Rosalyn B. Angeles-Shim

Research output: Contribution to journalReview articlepeer-review

Abstract

The genetic uniformity of cultivated cotton as a consequence of domestication and modern breeding makes it extremely vulnerable to abiotic challenges brought about by major climate shifts. To sustain productivity amidst worsening agro-environments, future breeding objectives need to seriously consider introducing new genetic variation from diverse resources into the current germplasm base of cotton. Landraces are genetically heterogeneous, population complexes that have been primarily selected for their adaptability to specific localized or regional environments. This makes them an invaluable genetic resource of novel allelic diversity that can be exploited to enhance the resilience of crops to marginal environments. The utilization of cotton landraces in breeding programs are constrained by the phenology of the plant and the lack of phenotypic information that can facilitate efficient selection of potential donor parents for breeding. In this review, the genetic value of cotton landraces and the major challenges in their utilization in breeding are discussed. Two strategies namely Focused Identification of Germplasm Strategy and Environmental Association Analysis that have been developed to effectively screen large germplasm collections for accessions with adaptive traits using geo-reference-based, mathematical modelling are highlighted. The potential applications of both approaches in mining available cotton landrace collections are also presented.

Original languageEnglish
Article number1300
JournalPlants
Volume10
Issue number7
DOIs
StatePublished - Jul 2021

Keywords

  • Core collections
  • Environmental association analysis
  • Focused identification of germplasm strategy
  • Landraces
  • Landscape genomics

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