Guo Wen, Bei Wu, Yi Wang, Ting Wu, Zhenhai Han, Xinzhong Zhang
{"title":"Natural variations in MdNAC18 exert major genetic effect on apple fruit harvest date by regulating ethylene biosynthesis genes","authors":"Guo Wen, Bei Wu, Yi Wang, Ting Wu, Zhenhai Han, Xinzhong Zhang","doi":"10.1111/jipb.13757","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Dissecting the genetic control of apple fruit harvest date (AFHD) into multiple Mendelian factors poses a significant challenge in modern genetics. Here, a quantitative trait locus (QTL) for AFHD was fine-mapped to the NAC transcription factor (TF) <i>MdNAC18</i> within the interval defined by the overlap of QTLs Z03.5/Z03.6 and F03.2/F03.3. One direct target of MdNAC18 is the ethylene biosynthesis gene <i>MdACO1</i>. The single nucleotide polymorphisms (SNPs) SNP517 and SNP958 in the <i>MdNAC18</i> coding sequence modulated activation of <i>MdACO1</i> by MdNAC18. SNP1229 in the <i>MdACO1</i> promoter destroyed the MdNAC18 binding site and thus abolished MdNAC18 binding. SNP517 and SNP958 also affected MdNAC18 activation of the TF gene <i>MdARF5</i>; MdARF5 activates the ethylene biosynthesis gene <i>MdACS1</i>. SNP517 and SNP958 in <i>MdNAC18</i>, SNP1229 and SNP769 (linked to InDel62) in <i>MdACO1</i>, and InDel162 in <i>MdACS1</i> constituted a genetic variation network. The genetic effect of this network on AFHD was estimated as 60.3 d, accounting for 52.6% of the phenotype variation of the training population. The joint effects of these polymorphisms increased the accuracy of a genomics-assisted prediction (GAP) model for AFHD (<i>r</i> = 0.7125). Together, our results suggest that genetic variation in <i>MdNAC18</i> affects AFHD by modulating ethylene biosynthesis and provide an optimized GAP model for apple breeding.</p></div>","PeriodicalId":195,"journal":{"name":"Journal of Integrative Plant Biology","volume":"66 11","pages":"2450-2469"},"PeriodicalIF":9.3000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Integrative Plant Biology","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jipb.13757","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Dissecting the genetic control of apple fruit harvest date (AFHD) into multiple Mendelian factors poses a significant challenge in modern genetics. Here, a quantitative trait locus (QTL) for AFHD was fine-mapped to the NAC transcription factor (TF) MdNAC18 within the interval defined by the overlap of QTLs Z03.5/Z03.6 and F03.2/F03.3. One direct target of MdNAC18 is the ethylene biosynthesis gene MdACO1. The single nucleotide polymorphisms (SNPs) SNP517 and SNP958 in the MdNAC18 coding sequence modulated activation of MdACO1 by MdNAC18. SNP1229 in the MdACO1 promoter destroyed the MdNAC18 binding site and thus abolished MdNAC18 binding. SNP517 and SNP958 also affected MdNAC18 activation of the TF gene MdARF5; MdARF5 activates the ethylene biosynthesis gene MdACS1. SNP517 and SNP958 in MdNAC18, SNP1229 and SNP769 (linked to InDel62) in MdACO1, and InDel162 in MdACS1 constituted a genetic variation network. The genetic effect of this network on AFHD was estimated as 60.3 d, accounting for 52.6% of the phenotype variation of the training population. The joint effects of these polymorphisms increased the accuracy of a genomics-assisted prediction (GAP) model for AFHD (r = 0.7125). Together, our results suggest that genetic variation in MdNAC18 affects AFHD by modulating ethylene biosynthesis and provide an optimized GAP model for apple breeding.
期刊介绍:
Journal of Integrative Plant Biology is a leading academic journal reporting on the latest discoveries in plant biology.Enjoy the latest news and developments in the field, understand new and improved methods and research tools, and explore basic biological questions through reproducible experimental design, using genetic, biochemical, cell and molecular biological methods, and statistical analyses.