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Detecting Cotton Leaf Curl Virus Resistance Quantitative Trait Loci in Gossypium hirsutum and iCottonQTL a New R/Shiny App to Streamline Genetic Mapping

Authors :
Ashley N. Schoonmaker
Amanda M. Hulse-Kemp
Ramey C. Youngblood
Zainab Rahmat
Muhammad Atif Iqbal
Mehboob-ur Rahman
Kelli J. Kochan
Brian E. Scheffler
Jodi A. Scheffler
Source :
Plants, Vol 12, Iss 5, p 1153 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Cotton leaf curl virus (CLCuV) causes devastating losses to fiber production in Central Asia. Viral spread across Asia in the last decade is causing concern that the virus will spread further before resistant varieties can be bred. Current development depends on screening each generation under disease pressure in a country where the disease is endemic. We utilized quantitative trait loci (QTL) mapping in four crosses with different sources of resistance to identify single nucleotide polymorphism (SNP) markers associated with the resistance trait to allow development of varieties without the need for field screening every generation. To assist in the analysis of multiple populations, a new publicly available R/Shiny App was developed to streamline genetic mapping using SNP arrays and to also provide an easy method to convert and deposit genetic data into the CottonGen database. Results identified several QTL from each cross, indicating possible multiple modes of resistance. Multiple sources of resistance would provide several genetic routes to combat the virus as it evolves over time. Kompetitive allele specific PCR (KASP) markers were developed and validated for a subset of QTL, which can be used in further development of CLCuV-resistant cotton lines.

Details

Language :
English
ISSN :
22237747
Volume :
12
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Plants
Publication Type :
Academic Journal
Accession number :
edsdoj.5748ac17393e4ae686ab5c2e25e76f21
Document Type :
article
Full Text :
https://doi.org/10.3390/plants12051153