Key message
This study identified candidate genes related to fruit yield for an emerging medicinal crop, weeping forsythia.
Abstract
Background
The genetic basis of crop yield is an agricultural research hotspot. Identifying the genes related to yield traits is the key to increase the yield. Weeping forsythia is an emerging medicinal crop that currently lacks excellent varieties. The genes related to fruit yield in weeping forsythia have not been identified.
Objective
Thus, we aimed to screen the candidate genes related to fruit yield of weeping forsythia by using genome-wide association analysis.
Methods
Here, 60 samples from the same field and source of weeping forsythia were collected to identify its yield-related candidate genes. Association analysis was performed on the variant loci and the traits related to yield, i.e., fruit length, width, thickness, and weight.
Results
Results from admixture, neighbor-joining, and kinship matrix analyses supported the non-significant genetic differentiation of these samples. Significant association was found between 2 variant loci and fruit length, 8 loci and fruit width, 24 loci and fruit thickness, and 13 loci and fruit weight. Further search on the 20 kb up/downstream of these variant loci revealed 1 gene related to fruit length, 16 genes related to fruit width, 12 genes related to fruit thickness, and 13 genes related to fruit weight. Among which, 4 genes, namely, WRKY transcription factor 35, salicylic acid-binding protein, auxin response factor 6, and alpha-mannosidase were highly related to the fruit development of weeping forsythia.
Conclusion
This study identify four candidate genes related to fruit development, which will provide useful information for the subsequent molecular-assisted and genetic breeding of weeping forsythia.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Achary VMM, Sheri V, Manna M, Panditi V, Borphukan B, Ram B, Agarwal A, Fartyal D, Teotia D, Masakapalli SK, Agrawal PK, Reddy MK (2020) Overexpression of improved EPSPS gene results in field level glyphosate tolerance and higher grain yield in rice. Plant Biotechnol J 18:2504–2519
Alemu A, Feyissa T, Tuberosa R, Maccaferri M, Sciara G, Letta T, Abeyo B (2020) Genome-wide association mapping for grain shape and color traits in Ethiopian durum wheat (Triticum turgidum ssp. durum). Crop J 8:757–768
Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664
Bruce RW, Torkamaneh D, Grainger CM, Belzile F, Eskandari M, Rajcan I (2020) Haplotype diversity underlying quantitative traits in Canadian soybean breeding germplasm. Theor Appl Genet 133:1967–1976
Chen S, Zhou Y, Chen Y, Gu J (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34:i884–i890
Cheng Y, Ahammed GJ, Yu J, Yao Z, Ruan M, Ye Q, Li Z, Wang R, Feng K, Zhou G, Yang Y, Diao W, Wan H (2016) Putative WRKYs associated with regulation of fruit ripening revealed by detailed expression analysis of the WRKY gene family in pepper. Sci Rep 6:39000
Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6:80–92
Daba SD, Tyagi P, Brown-Guedira G, Mohammadi M (2020) Genome-wide association study in historical and contemporary U.S. winter wheats identifies height-reducing loci. Crop J 8:243–251
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R, 1000 Genomes Project Analysis Group (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158
De Jong M, Wolters-Arts M, Feron R, Mariani C, Vriezen WH (2009) The Solanum lycopersicum auxin response factor 7 (SlARF7) regulates auxin signaling during tomato fruit set and development. Plant J 57:160–170
De Jong M, Wolters-Arts M, Schimmel BCJ, Stultiens CLM, de Groot PFM, Powers SJ, Tikunov YM, Bovy AG, Mariani C, Vriezen WH, Rieu I (2015) Solanum lycopersicum AUXIN RESPONSE FACTOR 9 regulates cell division activity during early tomato fruit development. J Exp Bot 66:3405–3416
De la Torre AR, Wilhite B, Neale DB (2019) Environmental genome-wide association reveals climate adaptation is shaped by subtle to moderate allele frequency shifts in loblolly pine. Genome Biol Evol 11:2976–2989
Deng B, Wang W, Ruan C, Deng L, Yao S, Zeng K (2020) Involvement of CsWRKY70 in salicylic acid-induced citrus fruit resistance against Penicillium digitatum. Hortic Res 7:20157
Devoghalaere F, Doucen T, Guitton B, Keeling J, Payne W, Ling TJ, Ross JJ, Hallett IC, Gunaseelan K, Dayatilake GA, Diak R, Breen KC, Tustin DS, Costes E, Chagné D, Schaffer RJ, David KM (2012) A genomics approach to understanding the role of auxin in apple (Malus x domestica) fruit size control. BMC Plant Biol 12:7
Dou J, Zhao S, Lu X, He N, Zhang L, Ali A, Kuang H, Liu W (2018) Genetic mapping reveals a candidate gene (ClFS1) for fruit shape in watermelon (Citrullus lanatus L.). Theor Appl Genet 131:947–958
Doyle JJ, Doyle JL (1987) A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull 19:11–15
Dreccer MF, Molero G, Rivera-Amado C, John-Bejai C, Wilson Z (2019) Yielding to the image: how phenotyping reproductive growth can assist crop improvement and production. Plant Sci 282:73–82
Felsenstein J (2005) PHYLIP (Phylogeny Inference Package) version 36. Distributed by the author. Department of Genome Sciences, University of Washington, Seattle
García-Pastor ME, Zapata PJ, Castillo S, Martínez-Romero D, Guillén F, Valero D, María S (2020) The effects of salicylic acid and its derivatives on increasing pomegranate fruit quality and bioactive compounds at harvest and during storage. Front Plant Sci 11:668
Gu QS, Ke HF, Liu ZW, Lv X, Sun ZW, Zhang M, Chen LT, Yang J, Zhang Y, Wu LQ, Li ZK, Wu JH, Wang GN, Meng CS, Zhang GY, Wang XF, Ma ZY (2020) A high-density genetic map and multiple environmental tests reveal novel quantitative trait loci and candidate genes for fibre quality and yield in cotton. Theor Appl Genet 133:3395–3408
Guan C, Wang C, Li Q, Ji J, Wang G, Jin C, Tong Y (2019) LcSABP2, a salicylic acid binding protein 2 gene from Lycium chinense, confers resistance to triclosan stress in Nicotiana tabacum. Ecotoxicol Environ Saf 183:109516
Hu K, Guan W, Bi Y, Zhang W, Li L, Zhang B, Liu Q, Song Y, Li X, Duan Z, Zheng Q, Yang Z, Liang J, Han M, Ruan L, Wu C, Zhang Y, Jia Z, Zhong N (2020) Efficacy and safety of Lianhuaqingwen capsules, a repurposed Chinese herb, in patients with coronavirus disease 2019: a multicenter, prospective, randomized controlled trial. Phytomedicine 85:153242
Irfan M, Ghosh S, Meli VS, Kumar A, Kumar V, Chakraborty N, Chakraborty S, Datta A (2016) Fruit ripening regulation of α-mannosidase expression by the MADS box transcription factor RIPENING INHIBITOR and ethylene. Front Plant Sci 7:10
Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760
Li LF, Cushman SA, He YX, Li Y (2020a) Genome sequencing and population genomics modeling provide insights into local adaptation of weeping forsythia. Hortic Res 7:130
Li ZW, Liu XG, Xu XJ, Liu JC, Sang ZQ, Yu KC, Yang YX, Dai WS, Jin X, Xu YB (2020b) Favorable haplotypes and associated genes for flowering time and photoperiod sensitivity identified by comparative selective signature analysis and GWAS in temperate and tropical maize. Crop J 8:227–242
Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D (2011) FaST linear mixed models for genome-wide association studies. Nat Methods 8:833–835
Liu S, Zhang Y, Feng Q, Qin L, Pan C, Lamin-Samu AT, Lu G (2018) Tomato AUXIN RESPONSE FACTOR 5 regulates fruit set and development via the mediation of auxin and gibberellin signaling. Sci Rep 8:2971
Luo QL, Zheng Q, Hu P, Liu LQ, Yang GT, Li HW, Li B, Li ZS (2021) Mapping QTL for agronomic traits under two levels of salt stress in a new constructed RIL wheat population. Theor Appl Genet 34:171–189
Ma FY, Du J, Wang OC, Wang H, Zhao BB, He GH, Yang ZL, Zhang T, Wu RH, Zhao FM (2020) Identification of long-grain chromosome segment substitution line Z744 and QTL analysis for agronomic traits in rice. J Integr Agric 19:1163–1169
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303
Parry MAJ, Hawkesford MJ (2012) An integrated approach to crop genetic improvement. J Integr Plant Biol 54:250–259
Phan NT, Trinh LT, Rho MY, Park TS, Kim OR, Zhao J, Kim HM, Sim SC (2019) Identification of loci associated with fruit traits using genome-wide single nucleotide polymorphisms in a core collection of tomato (Solanum lycopersicum L.). Sci Hortic 243:567–574
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575
Qiao Y, Cao Y, Jia M, Wang Y, He J, Zhang X, Wang W, Song Y (2020) Research on flower buds growth development and pollination habits of Forsythia suspensa heterostyly. Acta Hortic Sin 47:699–707
Rehman F, Gong HG, Li Z, Zeng SH, Yang TS, Ai PY, Pan LZ, Huang HW, Wang Y (2020) Identification of fruit size associated quantitative trait loci featuring SLAF based high-density linkage map of goji berry (Lycium spp.). BMC Plant Biol 20:474
Ren Y, McGregor C, Zhang Y, Gong G, Zhang H, Guo S, Sun H, Cai W, Jie Zhang J, Xu Y (2014) An integrated genetic map based on fourmapping populations and quantitative trait loci associated with economically important traits in watermelon (Citrullus lanatus). BMC Plant Biol 14:33
Sahu H, Amadabade J, Dhiri N (2015) Association mapping: a useful tool. Int J Plant Sci 10:85–94
Su JJ, Wang CX, Ma Q, Zhang A, Shi CH, Liu JJ, Zhang XL, Yang DL, Ma XF (2020) An RTM-GWAS procedure reveals the QTL alleles and candidate genes for three yield-related traits in upland cotton. BMC Plant Biol 20:416
Tiwari S, Lata C, Chauhan PS, Prasad V, Prasad M (2017) A functional genomic perspective on drought signalling and its crosstalk with phytohormone-mediated signalling pathways in plants. Curr Genomics 18:469–482
Tripathi D, Jiang YL, Kumar D (2010) SABP2, a methyl salicylate esterase is required for the systemic acquired resistance induced by acibenzolar-S-methyl in plants. FEBS Lett 584:3458–3463
Wan J (2018) Genetic crop improvement: a guarantee for sustainable agricultural production. Engineering 4:431–432
Wang MY, Xu SZ (2019) Statistical power in genome-wide association studies and quantitative trait locus mapping. Heredity 123:287–306
Wang L, Zhang XL, Wang L, Tian Y, Jia N, Chen S, Shi NB, Huang X, Zhou C, Yu Y, Zhang ZQ, Pang XQ (2017) Regulation of ethylene-responsive SlWRKYs involved in color change during tomato fruit ripening. Sci Rep 7:16674
Wang TQ, Chen T, Yan HF, Wang Y (2018) TCM treatment of anemopyretic cold rule analysis. J Tianjin Univ Tradit Chin Med 37:113–117
Wang Y, Pang YL, Chen K, Zhai LY, Shen CC, Wang S, Xu JL (2020) Genetic bases of source-, sink-, and yield-related traits revealed by genome-wide association study in Xian rice. Crop J 8:119–131
Wei Q, Fu W, Wang Y, Qin X, Wang J, Li J, Lou Q, Chen J (2016) Rapid identification of fruit length loci in cucumber (Cucumis sativus L.) using next-generation sequencing (NGS)-based QTL analysis. Sci Rep 6:27496
Yan R, Yang YJ, Liu HW, Li XN, Guo BL (2016) Effect of earlier period harvest on content of forsythoside A and phillyrin of forsythiae fructus. Mod Chin Med 18:579–582
Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88:76–82
Yang L, Li D, Li Y, Gu X, Huang S, Garcia-Mas J, Weng Y (2013) A 1681-locus consensus genetic map of cultivated cucumber including 67 NB-LRR resistance gene homolog and ten gene loci. BMC Plant Biol 13:53
Zhang XX, Guan ZR, Li ZL, Liu P, Ma LL, Zhang YC, Pan L, He SJ, Zhang YL, Li P, Ge F, Zou CY, He YC, Gao SB, Pan GT, Shen YO (2020) A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments. Theor Appl Genet 133:2881–2895
Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44:821–824
Zhu DF, Zhang YP, Chen HZ, Xiang J, Zhang YK (2015) Innovation and practice of high-yield rice cultivation technology in China. Sci Agric Sin 48:3404–3414
Acknowledgements
The authors wish to acknowledge the sampling assistance of Xin Sun.
Funding
This work was supported by the National Natural Science Foundation of China (31770225), Henan Science and Technology Project (202102110077), National Key R&D Program of China (2017FY100705), Foundation of Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences (PCU201903), and Henan Agricultural University Science & Technology Innovation Fund (KJCX2016A2).
Author information
Authors and Affiliations
Contributions
YL conceived the research project, NCP and YL wrote the paper and analyzed the data, QW and HLL collected the data, JEQ, QW, HLL, and YXH revised the paper. All authors are agreed with the content of the manuscript. All authors read and approved the final manuscript.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Data availability
Sequence data are archived at the National Center for Biotechnology Information (NCBI SRR12897525-12897584).
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
13258_2021_1186_MOESM1_ESM.docx
Supplementary file1 Fig. S1 All fruit samples of sixty weeping forsythia seedlings. Fig. S2 The optimal clustering at K = 1 were supported based on the cross-validation error rate. Fig. S3 Genotyping of variant loci binding phenotypic data for fruit length. Fig. S4 Genotyping of variant loci binding phenotypic data for fruit width. Fig. S5 Genotyping of variant loci binding phenotypic data for fruit thickness. Fig. S6 Genotyping of variant loci binding phenotypic data for single grain weight (DOCX 921 kb)
13258_2021_1186_MOESM2_ESM.xls
Supplementary file2 Table S1 Phenotypic data of weeping forsythia fruits from sixty weeping forsythia seedlings. Table S2 The variant loci and candidate genes related to fruit length, width, thickness, and single grain weight (XLS 47 kb)
Rights and permissions
About this article
Cite this article
Li, Y., Wu, Q., Liu, HL. et al. Identification of yield-related genes through genome-wide association: case study of weeping forsythia, an emerging medicinal crop. Genes Genom 44, 145–154 (2022). https://doi.org/10.1007/s13258-021-01186-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13258-021-01186-w