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expression analysis and was validated by haplotype analysis of 330 accessions with high-depth (10×) resequencing data (unpublished).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.
References
Ali Z, Raza Q, Atif RM, Aslam U, Ajmal M, Chung G (2019) Genetic and molecular control of floral organ identity in cereals. Int J Mol Sci 20:2743. https://doi.org/10.3390/ijms20112743
Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263–265. https://doi.org/10.1093/bioinformatics/bth457
Barton L, Newsome SD, Chen FH, Wang H, Guilderson TP, Bettinger RL (2009) Agricultural origins and the isotopic identity of domestication in northern China. Proc Natl Acad Sci USA 106:5523–5528. https://doi.org/10.1073/pnas.0809960106
Bradbury PJ, Zhang ZW, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635. https://doi.org/10.1093/bioinformatics/btm308
Diao X, James S, Jeffrey L, Li JY (2014) Initiation of Setaria as a model plant. Front Agric Sci Eng 1:16–20. https://doi.org/10.15302/J-FASE-2014011
Doust AN, Kellogg EA, Devos KM, Bennetzen JL (2009) Foxtail millet: a sequence-driven grass model system. Plant Physiol 149:137–141. https://doi.org/10.1104/pp.108.129627
Gupta PK, Kulwal PL, Jaiswal V (2019) Association mapping in plants in the post-GWAS genomics era. Adv Genet 104:75–154. https://doi.org/10.1016/bs.adgen.2018.12.001
Hanson CH, Robinson HF, Comstock RE (1956) Biometrical studies of yield in segregating populations of Korean Lespedeza. Agron J 48:268–272. https://doi.org/10.2134/agronj1956.00021962004800060008x
Hao H, Margarita MH, Doust AN (2018) Domestication and improvement in the model C4 grass, Setaria. Front Plant Sci 9:719. https://doi.org/10.3389/fpls.2018.00719
He JB, Meng S, Zhao TJ, Xing GN, Yang SP, Li Y, Guan RZ, Lu JJ, Wang YF, Xia QJ, Yang B, Gai JY (2017) An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding. Theor Appl Genet 130:2327. https://doi.org/10.1007/s00122-017-2962-9
He Q, Tang S, Zhi H, Chen JF, Zhang J, Liang HK, Alam O, Li HB, Zhang H, Xing LH, Li XK, Zhang W, Wang HL, Shi JP, Du HL, Wu HP, Wang LW, Yang P, Xing L, Yan HS, Song ZQ, Liu JR, Wang HG, Tian X, Qiao ZJ, Feng GJ, Guo RF, Zhu WJ, Ren YM, Hao HB, Li MZ, Zhang AY, Guo EH, Yan F, Li QQ, Liu YL, Tian BH, Zhao XQ, Jia RL, Feng BL, Zhang JW, Wei JH, Lai JS, Jia GQ, Purugganan M, Diao XM (2023) A graph-based genome and pan-genome variation of the model plant Setaria. Nat Genet 55:1232–1242. https://doi.org/10.1038/s41588-023-01423-w
Hong JP, Byun MY, An K, Yang SJ, An G, Kim WT (2010) OsKu70 is associated with developmental growth and genome stability in rice. Plant Physiol 152:374–387. https://doi.org/10.1104/pp.109.150391
Huang XH, Wei XH, Sang T, Zhao Q, Feng Q, Zhao Y, Li CY, Zhu CR, Lu TT, Zhang ZW, Li M, Fan DL, Guo YL, Wang AH, Wang L, Deng LW, Li WJ, Lu YQ, Weng QJ, Liu KY, Huang T, Zhou TY, Jing YF, Li W, Lin Z, Buckler ES, Qian Q, Zhang QF, Li JY, Han B (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967. https://doi.org/10.1038/ng.695
Hussin SH, Wang HL, Tang S, Zhi H, Tang CJ, Zhang W, Jia GQ, Diao XM (2021) SiMADS34, an E-class MADS-box transcription factor, regulates inflorescence architecture and grain yield in Setaria italica. Plant Mol Biol 105:419–434. https://doi.org/10.1007/s11103-020-01097-6
Jaiswal V, Gupta S, Gahlaut V, Muthamilarasan M, Bandyopadhyay T, Ramchiary N, Prasad M (2019) Genome-wide association study of major agronomic traits in foxtail millet (Setaria italica L.) using ddRAD sequencing. Sci Rep 9:5020. https://doi.org/10.1038/s41598-019-41602-6
Jia GQ, Huang XH, Zhi H, Zhao Y, Zhao Q, Li WJ, Chai Y, Yang LF, Liu KY, Lu HY, Zhu CR, Lu YQ, Zhou CC, Fan DL, Weng QJ, Guo YL, Huang T, Zhang L, Lu TT, Feng Q, Hao HF, Liu HK, Lu P, Zhang N, Li YH, Guo EH, Wang SJ, Wang SY, Liu JR, Zhang WF, Chen GQ, Zhang BJ, Li W, Wang YF, Li HQ, Zhao BH, Li JY, Diao XM, Han B (2013) A haplotype map of genomic variations and genome-wide association studies of agronomic traits in foxtail millet (Setaria italica). Nat Genet 45:957–961. https://doi.org/10.1038/ng.2673
Jiang H, Fang Y, Yan D, Liu ST, Wei J, Guo FL, Wu XT, Cao H, Yin CB, Lu F, Gao LF, Liu YX (2022) Genome-wide association study reveals a NAC transcription factor TaNAC074 linked to pre-harvest sprouting tolerance in wheat. Theor Appl Genet 135(9):3265–3276. https://doi.org/10.1007/s00122-022-04184-y
Kyozuka J, Tokunaga H, Yoshida A (2014) Control of grass inflorescence form by the fine-tuning of meristem phase change. Curr Opin Plant Biol 17:110–115. https://doi.org/10.1016/j.pbi.2013.11.010
Lata C, Gupta S, Prasad M (2013) Foxtail millet: a model crop for genetic and genomic studies in bioenergy grasses. Crit Rev Biotechnol 33:328–343. https://doi.org/10.3109/07388551.2012.716809
Lou GG, Chen PL, Zhou H, Li PB, Xiong JW, Wan SS, Zheng YY, Mufid A, Liu RJ, Yin H, Yang HY, Tian YH, Bai JJ, Rao WT, Tan Y, Gao HZ, Li YH, Gao GJ, Zhang QL, Li XH, Liu CG, He YQ (2021) FLOURY ENDOSPERM19 encoding a class I glutamine amidotransferase affects grain quality in rice. Mol Breed 41(5):36. https://doi.org/10.1007/s11032-021-01226-z
Li PH, Brutnell TP (2011) Setaria viridis and Setaria italica, model genetic systems for the Panicoid grasses. J Exp Bot 62:3031–3037. https://doi.org/10.1093/jxb/err096
Li SG, Cao YC, He JB, Zhao TJ, Gai JY (2017) Detecting the QTL-allele system conferring flowering date in a nested association mapping population of soybean using a novel procedure. Theor Appl Genet 130:2297–2314. https://doi.org/10.1007/s00122-017-2960-y
Li XK, Gao JH, Song JY, Guo K, Hou SY, Wang XC, He Q, Zhang YY, Zhang YK, Yang YL, Tang JY, Wang HL, Persson S, Huang MQ, Xu LS, Zhong LL, Li DQ, Liu YM, Wu H, Diao XM, Chen P, Wang XW, Han YH (2022) Multi-omics analyses of 398 foxtail millet accessions reveal genomic regions associated with domestication, metabolite traits, and antiinflammatory effects. Mol Plant 15:1367–1383. https://doi.org/10.1016/j.molp.2022.07.003
Li XK, Shi ZY, Gao JH, Wang XC, Guo K (2023) CandiHap: a haplotype analysis toolkit for natural variation study. Mol Breed 43(3):21. https://doi.org/10.1007/s11032-023-01366-4
Liu XQ, Li CY, Cao JQ, Zhang XY, Wang C, He JB, Xing GN, Wang WB, Zhao JM, Gai JY (2021) Growth period QTL-allele constitution of global soybeans and its differential evolution changes in geographic adaptation versus maturity group extension. Plant J 108:1624–1643. https://doi.org/10.1111/tpj.15531
Liu XD, Yang Y, Hou SY, Men YH, Han YH (2022) The integration of genome-wide association study and homology analysis to explore the genomic regions and candidate genes for panicle-related traits in foxtail millet. Int J Mol Sci 23:14735. https://doi.org/10.3390/ijms232314735
Liu YY, Chen J, Yin CB, Wang ZY, Wu H, Shen KC, Zhang ZL, Kang LP, Xu S, Bi A, Zhao XB, Xu DX, He ZH, Zhang XY, Hao CY, Wu JH, Gong Y, Yu XC, Sun ZW, Ye B, Liu DN, Zhang LL, Shen LP, Hao YF, Ma YZ, Lu F, Guo ZF (2023) A high-resolution genotype-phenotype map identifies the TaSPL17 controlling grain number and size in wheat. Genome Biol 24:196. https://doi.org/10.1186/s13059-023-03044-2
Ma B, Zhou Y, Chen H, He SJ, Huang YH, Zhao H, Lu X, Zhang WK, Pang JH, Chen SY, Zhang JS (2018) Membrane protein MHZ3 stabilizes OsEIN2 in rice by interacting with its Nramp-like domain. Proc Natl Acad Sci USA 115:2520–2525. https://doi.org/10.1073/pnas.1718377115
Maung PP, Kim B, Jin Z, Jang S, Lee YK, Koh HJ (2023) Identification and characterization of a novel gene controlling floral organ number in rice (Oryza sativa L.). PLoS ONE 18:e0280022. https://doi.org/10.1371/journal.pone.0280022
Meng S, He JB, Zhao TJ, Xing GN, Li Y, Yang SP, Lu JJ, Wang YF, Gai JY (2016) Detecting the QTL-allele system of seed isoflavone content in Chinese soybean landrace population for optimal cross design and gene system exploration. Theor Appl Genet 129:1557–1576. https://doi.org/10.1007/s00122-016-2724-0
Miao L, Yang SN, Zhang K, He JB, Wu CH, Ren YH, Gai JY, Li Y (2020) Natural variation and selection in GmSWEET39 affect soybean seed oil content. New Phytol 225:1651–1666. https://doi.org/10.1111/nph.16250
Mjomba FM, Zheng Y, Liu HQ, Tang WQ, Hong ZL, Wang F, Wu WR (2016) Homeobox is pivotal for OsWUS controlling tiller development and female fertility in rice. G3 Genes Genomes Genet 6:2013–2021. https://doi.org/10.1534/g3.116.028837
Pan LY, He JB, Zhao TJ, Xing GN, Wang YF, Yu DY, Chen SY, Gai JY (2018) Efficient QTL detection of flowering date in a soybean RIL population using the novel restricted two-stage multi-locus GWAS procedure. Theor Appl Genet 131:2581–2599. https://doi.org/10.1007/s00122-018-3174-7
Pang YL, Liu CX, Wang DF, Amand PS, Bernardo A, Li WH, He F, Li LZ, Wang LM, Yuan XF, Dong L, Su Y, Zhang HR, Zhao M, Liang YL, Jia HZ, Shen XT, Lu Y, Jiang HM, Wu YY, Li AF, Wang HG, Kong LR, Bai GH, Liu SB (2020) High-resolution genome-wide association study identifies genomic regions and candidate genes for important agronomic traits in wheat. Mol Plant 13:1311–1327. https://doi.org/10.1016/j.molp.2020.07.008
Pretini N, Vanzetti LS, Terrile II, Donaire G, González FG (2021) Mapping QTL for spike fertility and related traits in two doubled haploid wheat (Triticum aestivum L.) populations. BMC Plant Biol 21:353. https://doi.org/10.1186/s12870-021-03061-y
Rosegrant MW, Cline SA (2003) Global food security: challenges and policies. Science 302:1917–1919. https://doi.org/10.1126/science.1092958
Shen CQ, Li G, Dreni L, Zhang DB (2021) Molecular control of carpel development in the grass family. Front Plant Sci 12:635500. https://doi.org/10.3389/fpls.2021.635500
Shi CL, Dong NQ, Guo T, Ye WW, Shan JX, Lin HH (2020) A quantitative trait locus GW6 controls rice grain size and yield through the gibberellin pathway. Plant J 103:1174–1188. https://doi.org/10.1111/tpj.14793
Su JJ, Wang CX, Yang DL, Shi CH, Zhang A, Ma Q, Liu JJ, Zhang XL, Huang L, Ma XF (2020a) Decryption of favourable haplotypes and potential candidate genes for five fibre quality properties using a relatively novel genome-wide association study procedure in upland cotton. Ind Crops Prod 158:113004. https://doi.org/10.1016/j.indcrop.2020.113004
Su JJ, Wang CX, Ma Q, Zhang A, Shi CH, Liu JJ, Zhang XL, Yang DL, Ma XF (2020b) An RTM-GWAS procedure reveals the QTL alleles and candidate genes for three yield-related traits in upland cotton. BMC Plant Biol 20:416. https://doi.org/10.1186/s12870-020-02613-y
Su YZ, Zhang ZP, He JB, Zeng WY, Cai ZY, Lai ZG, Pan YP, Hao XS, Xing GN, Wang WB, Zhang JP, Li Y, Sun ZD, Gai JY (2023) Gene-allele system of shade tolerance in southern China soybean germplasm revealed by genome-wide association study using gene-allele sequence as markers. Theor Appl Genet 136:152. https://doi.org/10.1007/s00122-023-04390-2
Suzaki T, Sato M, Ashikari M, Miyoshi M, Nagato Y, Hirano HY (2004) The gene FLORAL ORGAN NUMBER1 regulates floral meristem size in rice and encodes a leucine-rich repeat receptor kinase orthologous to Arabidopsis CLAVATA1. Development 131:5649–5657. https://doi.org/10.1242/dev.01441
Tang S, Zhao ZY, Liu XT, Sui Y, Zhang DD, Zhi H, Gao YZ, Zhang H, Zhang LL, Wang YN, Zhao MC, Li DD, Wang KK, He Q, Zhang RL, Zhang W, Jia GQ, Tang WQ, Ye XG, Wu CY, Diao XM (2023) An E2–E3 pair contributes to seed size control in grain crops. Nat Commun 14:3091. https://doi.org/10.1038/s41467-023-38812-y
Wall JD, Pritchard JK (2003) Haplotype blocks and linkage disequilibrium in the human genome. Nat Rev Genet 4:587–597. https://doi.org/10.1038/nrg1123
Wang YP, Tang HB, Debarry JD, Tan X, Li JP, Wang XL, Lee TH, Jin HZ, Marler B, Guo H, Kissinger JC, Paterson AH (2012) MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res 40(7):e49. https://doi.org/10.1093/nar/gkr1293
Wang CX, Ma Q, Xie XY, Zhang XL, Yang DL, Su JY, Ma XF, Lin H (2022) Identification of favorable haplotypes/alleles and candidate genes for three plant architecture-related traits via a restricted two-stage multilocus genome-wide association study in upland cotton. Ind Crops Prod 177:114458. https://doi.org/10.1016/j.indcrop.2021.114458
Yano KJ, Yamamoto E, Aya K, Takeuchi H, Lo PC, Hu L, Yamasaki M, Yoshida S, Kitano H, Hirano K, Matsuoka M (2016) Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat Genet 48:927–934. https://doi.org/10.1038/ng.3596
Yu JM, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208. https://doi.org/10.1038/ng1702
Zhang YH, He JB, Wang YF, Xing GN, Zhao JM, Li Y, Yang SP, Palmer RG, Zhao TJ, Gai JY (2015) Establishment of a 100-seed weight quantitative trait locus-allele matrix of the germplasm population for optimal recombination design in soybean breeding programmes. J Exp Bot 66:6311–6325. https://doi.org/10.1093/jxb/erv342
Zhang K, Fan GY, Zhang XX, Zhao F, Wei W, Du GH, Feng XL, Wang XM, Wang F, Song GL, Zou HF, Zhang XL, Li SD, Ni XM, Zhang GY, Zhao ZH (2017) Identification of QTLs for 14 agronomically important traits in Setaria italica based on SNPs generated from high-throughput sequencing. G3 Genes Genomes Genet 7:1587–1594. https://doi.org/10.1534/g3.117.041517
Zhang W, Liao XL, Cui YM, Ma WY, Zhang XN, Du HY, Ma YJ, Ning LH, Wang H, Huang F, Yang H, Kan GZ, Yu DY (2019) A cation diffusion facilitator, GmCDF1, negatively regulates salt tolerance in soybean. PLoS Genet 15:e1007798. https://doi.org/10.1371/journal.pgen.1007798
Zhi H, He Q, Tang S, Yang JJ, Zhang W, Liu HF, Jia YC, Jia GQ, Zhang AY, Li YH, Guo EH, Gao M, Li SJ, Li JX, Qin N, Zhu CC, Ma CY, Zhang HJ, Chen GQ, Zhang WF, Wang HG, Qiao ZJ, Li SG, Cheng RH, Xing L, Wang SY, Liu JR, Liu J, Diao XM (2021) Genetic control and phenotypic characterization of panicle architecture and grain yield related traits in foxtail millet (Setaria italica). Theor Appl Genet 134:3023–3036. https://doi.org/10.1007/s00122-021-03875-2
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 Information
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