AUTHOR=Lai Ruiqiang , Ikram Muhammad , Li Ronghua , Xia Yanshi , Yuan Qinghua , Zhao Weicai , Zhang Zhenchen , Siddique Kadambot H. M. , Guo Peiguo TITLE=Identification of Novel Quantitative Trait Nucleotides and Candidate Genes for Bacterial Wilt Resistance in Tobacco (Nicotiana tabacum L.) Using Genotyping-by-Sequencing and Multi-Locus Genome-Wide Association Studies JOURNAL=Frontiers in Plant Science VOLUME=Volume 12 - 2021 YEAR=2021 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.744175 DOI=10.3389/fpls.2021.744175 ISSN=1664-462X ABSTRACT=

Tobacco bacterial wilt (TBW) is a devastating soil-borne disease threatening the yield and quality of tobacco. However, its genetic foundations are not fully understood. In this study, we identified 126,602 high-quality single-nucleotide polymorphisms (SNPs) in 94 tobacco accessions using genotyping-by-sequencing (GBS) and a 94.56 KB linkage disequilibrium (LD) decay rate for candidate gene selection. The population structure analysis revealed two subpopulations with 37 and 57 tobacco accessions. Four multi-locus genome-wide association study (ML-GWAS) approaches identified 142 quantitative trait nucleotides (QTNs) in E1–E4 and the best linear unbiased prediction (BLUP), explaining 0.49–22.52% phenotypic variance. Of these, 38 novel stable QTNs were identified across at least two environments/methods, and their alleles showed significant TBW-DI differences. The number of superior alleles associated with TBW resistance for each accession ranged from 4 to 24; eight accessions had more than 18 superior alleles. Based on TBW-resistant alleles, the five best cross combinations were predicted, including MC133 × Ruyuan No. 1 and CO258 × ROX28. We identified 52 candidate genes around 38 QTNs related to TBW resistance based on homologous functional annotation and KEGG enrichment analysis, e.g., CYCD3;2, BSK1, Nitab4.5_0000641g0050, Nitab4.5_0000929g0030. To the best of our knowledge, this is the first comprehensive study to identify QTNs, superior alleles, and their candidate genes for breeding TBW-resistant tobacco varieties. The results provide further insight into the genetic architecture, marker-assisted selection, and functional genomics of TBW resistance, improving future breeding efforts to increase crop productivity.