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Identification of Quantitative Trait Nucleotides and Candidate Genes for Tuber Yield and Mosaic Virus Tolerance in an Elite Population of White Guinea Yam (Dioscorea Rotundata) Using Genome-Wide Association Scan

Article scientifique 2021 Anglais

Résumé

Abstract Background Improvement of tuber yield and tolerance to viruses are priority objectives in white Guinea yam breeding programs. However, phenotypic selection for these traits is quite challenging due to phenotypic plasticity and cumbersome screening of phenotypic-induced variations. This study assessed quantitative trait nucleotides (QTNs) and the underlying candidate genes related to tuber yield per plant (TYP) and yam mosaic virus (YMV) tolerance in a panel of 406 white Guinea yam (Dioscorea rotundata) breeding lines using a genome-wide association study (GWAS). Results Population structure analysis using 5,581 SNPs differentiated the 406 genotypes into four distinct sub-groups (K = 4). Marker-trait association (MTA) analysis using the generalized linear model identified ten QTN regions significant for TYP and five for YMV. We identified variants responsible for predicting higher yield and low virus severity scores in the breeding panel through the marker-effect prediction. Gene annotation for the significant SNP loci identified several essential putative genes associated with the growth and development of tuber yield and those that code for tolerance to mosaic virus. Conclusion Our results provide valuable insight for marker validation and deployment for tuber yield and mosaic virus tolerance in white yam breeding. The information on SNP variants and genes from the present study would fast-track the application of genomics-informed selection decisions in breeding white Guinea yam for rapid introgression of the targeted traits.

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Agre, P., Norman, P., Asiedu, R., Asfaw, A. (2021). Identification of Quantitative Trait Nucleotides and Candidate Genes for Tuber Yield and Mosaic Virus Tolerance in an Elite Population of White Guinea Yam (Dioscorea Rotundata) Using Genome-Wide Association Scan. https://doi.org/10.21203/rs.3.rs-612999/v1

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