Abstract
Key message
Eleven QTLs for agronomic traits were identified by RTM- and MLM-GWAS, putative candidate genes were predicted and two markers for grain weight were developed and validated.
Abstract
Foxtail millet (Setaria italica), the second most cultivated millet crop after pearl millet, is an important grain crop in arid regions. Seven agronomic traits of 408 diverse foxtail millet accessions from 15 provinces in China were evaluated in three environments. They were clustered into two divergent groups based on genotypic data using ADMIXTURE, which was highly consistent with their geographical distribution. Two models for genome-wide association studies (GWAS), namely restricted two-stage multi-locus multi-allele (RTM)-GWAS and mixed linear model (MLM)-GWAS, were used to dissect the genetic architecture of the agronomic traits based on 13,723 SNPs. Eleven quantitative trait loci (QTLs) for seven traits were identified using two models (RTM- and MLM-GWAS). Among them, five were considered stable QTLs that were identified in at least two environments using MLM-GWAS. One putative candidate gene (SETIT_006045mg, Chr4: 744,701–746,852) that can enhance grain weight per panicle was identified based on homologous gene comparison and gene expression analysis and was validated by haplotype analysis of 330 accessions with high-depth (10×) resequencing data (unpublished). In addition, homologous gene comparison and haplotype analysis identified one putative foxtail millet ortholog (SETIT_032906mg, Chr2: 5,020,600–5,029,771) with rice affecting the target traits. Two markers (cGWP6045 and kTGW2906) were developed and validated and can be used for marker-assisted selection of foxtail millet with high grain weight. The results provide a fundamental resource for foxtail millet genetic research and breeding and demonstrate the power of integrating RTM- and MLM-GWAS approaches as a complementary strategy for investigating complex traits in foxtail millet.
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The original contributions presented in this study are included in the supplementary information, the genotype has been deposited in https://figshare.com/articles/dataset/Resequencing_of_408_Setaria_italica_material/24297745.
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Acknowledgements
The authors are grateful to Dr. Hongjie Li, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, for the critical review of this manuscript.
Funding
This work was supported by the National Key R&D Program of Shanxi Province (2022ZDYF107), Shanxi Agricultural University Science and Technology Innovation Fund Project (2021BQ22, 2020BQ60), Award Scientific Program for Excellent Doctors in Shanxi Province (SXBYKY2021077, SXBYKY2021003), Scientific and Technological Innovation Foundation of Higher Education Institutions in Shanxi (2021L157, 2021L120), and Natural Science Foundation of Shanxi Province (20210302124019).
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KLD, JG, WPS and XMD planned and designed the study. XW, PFQ, and WJX performed experiments. HXL performed the sequence analysis and designed the markers. KLD, JG, WPS, and XMD wrote the manuscript. XW, PFQ, and WJX helped revise the manuscript, and all authors reviewed and commented on the manuscript.
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Supplementary Figure S1
(a) Comparison of TGW between contrasting alleles at SETIT_032906mg based on KASP marker. Significant P-value (Wilcoxon-test) is shown on the boxplot. (b) SETIT_032906mg expression in various tissues of foxtail millet variety Jingu21.
Supplementary Table S1
Geographical origins of the 408 foxtail millet accessions used in this study.
Supplementary Table S2
Details of loci associated with the seven agronomic traits identified using RTM-GWAS.
Supplementary Table S3
Details of loci associated with the seven agronomic traits identified using MLM-GWAS.
Supplementary Table S4
Gene annotation and haplotype analysis of 132 genes in five stable QTLs.
Supplementary Table S5
Gene annotations and haplotype analyses of 171 genes in the other six QTLs identified in the two models.
Supplementary Table S6
Primer sequences used in the present study.
Supplementary Table S7
The overlapping QTLs between present and previous studies.
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Dai, K., Wang, X., Liu, H. et al. Efficient identification of QTL for agronomic traits in foxtail millet (Setaria italica) using RTM- and MLM-GWAS. Theor Appl Genet 137, 18 (2024). https://doi.org/10.1007/s00122-023-04522-8
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DOI: https://doi.org/10.1007/s00122-023-04522-8