Key message
We describe the development and application of the Sorghum QTL Atlas, a high-resolution, open-access research platform to facilitate candidate gene identification across three cereal species, sorghum, maize and rice.
Abstract
The mechanisms governing the genetic control of many quantitative traits are only poorly understood and have yet to be fully exploited. Over the last two decades, over a thousand QTL and GWAS studies have been published in the major cereal crops including sorghum, maize and rice. A large body of information has been generated on the genetic basis of quantitative traits, their genomic location, allelic effects and epistatic interactions. However, such QTL information has not been widely applied by cereal improvement programs and genetic researchers worldwide. In part this is due to the heterogeneous nature of QTL studies which leads QTL reliability variation from study to study. Using approaches to adjust the QTL confidence interval, this platform provides access to the most updated sorghum QTL information than any database available, spanning 23 years of research since 1995. The QTL database provides information on the predicted gene models underlying the QTL CI, across all sorghum genome assembly gene sets and maize and rice genome assemblies and also provides information on the diversity of the underlying genes and information on signatures of selection in sorghum. The resulting high-resolution, open-access research platform facilitates candidate gene identification across 3 cereal species, sorghum, maize and rice. Using a number of trait examples, we demonstrate the power and resolution of the resource to facilitate comparative genomics approaches to provide a bridge between genomics and applied breeding.
This is a preview of subscription content, log in via an institution to check access.
Figure modified with permission from Mace and Jordan (2010) (color figure online)
Figure modified with permission from Mace et al. (2013) (color figure online)
Similar content being viewed by others
References
Andorf CM, Lawrence CJ, Harper LC, Schaeffer ML, Campbell DA, Sen TZ (2010) The Locus Lookup tool at MaizeGDB: identification of genomic regions in maize by integrating sequence information with physical and genetic maps. Bioinformatics 26:434–436. https://doi.org/10.1093/bioinformatics/btp556
Andorf CM, Cannon EK, Portwood JL, Gardiner JM, Harper LC, Schaeffer ML, Braun BL, Campbell DA, Vinnakota AG, Sribalusu VV, Huerta M, Cho KT, Wimalanathan K, Richter JD, Mauch ED, Rao BS, Birkett SM, Sen TZ, Lawrence-Dill CJ (2016) MaizeGDB update: new tools, data and interface for the maize model organism database. Nucleic Acids Res 44:1195–1201. https://doi.org/10.1093/nar/gkv1007
Arcade A, Labourdette A, Falque M, Mangin B, Chardon F, Charcosset A, Joets J (2004) BioMercator: integrating genetic maps and QTL towards discovery of candidate genes. Bioinformatics 14:2324–2326. https://doi.org/10.1093/bioinformatics/bth230
Bandillo N, Jarquin D, Song Q, Nelson R, Cregan P, Specht J, Lorenz A (2015) A population structure and genome-wide association analysis on the USDA soybean germplasm collection. Plant Genome 8:3. https://doi.org/10.3835/plantgenome2015.04.0024
Beavis WD (1994) The power and deceit of QTL experiments: lessons from comparative QTL studies. In: Wilkinson DB (ed) Proceedings 49th annual corn and sorghum research conference, American Seed Trade Association, Chicago, IL, pp 250–266
Benson JM, Poland JA, Benson BM, Stromberg EL, Nelson RJ (2015) Resistance to gray leaf spot of maize: genetic architecture and mechanisms elucidated through nested association mapping and near-isogenic line analysis. PLOS Genet 11(3):e1005045. https://doi.org/10.1371/journal.pgen.1005045
Bigwood DW (1997) Compilation and distribution of data on complex traits. In: Paterson AH (ed) Molecular dissection of complex traits. CRC Press, New York, pp 175–184
Bouchet S, Olatoye MO, Marla SR, Perumal R, Tesso T, Yu J, Tuinstra M, Morris GP (2017) Increased power to dissect adaptive traits in global sorghum diversity using a nested association mapping population. Genetics 206:573–585. https://doi.org/10.1534/genetics.116.198499
Brown PJ, Klein PE, Bortiri E, Acharya CB, Rooney WL, Kresovich S (2006) Inheritance of inflorescence architecture in sorghum. Theor Appl Genet 113:931–942. https://doi.org/10.1007/s00122-006-0352-9
Buckler ES et al (2018) Practical haplotype graph. https://bitbucket.org/bucklerlab/practicalhaplotypegraph/overview. Accessed 20 Aug 2018
Buckler ES et al (2009) The genetic architecture of maize flowering time. Science 325:714–718. https://doi.org/10.1126/science.1174276
Byrne P, Berlyn M, Coe E, Davis G, Polacco M, Hancock D, Letovsky S (1995) Reporting and accessing QTL information in USDA’s Maize Genome Database. J Agric Genomics 1:1–11
Chardon F, Virlon B, Moreau L, Falque M, Joets J, Decousset L, Murigneux A, Charcosset A (2004) Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta-analysis and synteny conservation with the rice genome. Genetics 168:2169–2185. https://doi.org/10.1534/genetics.104.032375
Childs KL, Miller FR, Cordonnier-Pratt MM, Pratt LH, Morgan PW, Mullet JE (1997) The sorghum photoperiod sensitivity gene, Ma 3, encodes a Phytochrome B. Plant Physiol 113:611–619
Cook JP, McMullen MD, Holland JB, Tian F, Bradbury P, Ross-Ibarra J, Buckler ES, Flint-Garcia SA (2012) Genetic architecture of maize kernel composition in the nested association mapping and inbred association panels. Plant Physiol 158:824–834. https://doi.org/10.1104/pp.111.185033
Courtois B, Ahmadi N, Khowaja F, Price AH, Rami JF, Frouin J, Hamelin C, Ruiz M (2009) Rice root genetic architecture: meta-analysis from a drought QTL database. Rice 2:9028. https://doi.org/10.1007/s12284-009-9028-9
Darvasi A, Soller M (1997) A simple method to calculate resolving power and confidence interval of QTL map location. Behav Genet 27:125–132
Draye X, Lin YR, Qian XY, Bowers JE, Burow GB, Morrell PL, Peterson DG, Presting GG, Ren SX, Wing RA, Paterson AH (2001) Toward integration of comparative genetic, physical, diversity, and cytomolecular maps for grasses and grains, using the sorghum genome as a foundation. Plant Physiol 125:1325–1341. https://doi.org/10.1104/pp.125.3.1325
Fragoso CA, Moreno M, Wang Z, Heffelfinger C, LArbelaez LJ, Aguirre JA, Franco N, Romero LE, Labadie K, Zhao H, Dellaporta SL, Lorieux M (2017) Genetic architecture of a rice nested association mapping population. G3: Genes Genomes Genet 7:1913–1926. https://doi.org/10.1534/g3.117.041608
Goff SA et al (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science 296:92–100. https://doi.org/10.1126/science.1068275
Goffinet B, Gerber S (2000) Quantitative trait loci: a meta-analysis. Genetics 155:463–473
Guan Y, Wang H-L, Qin L, Zhang H-W, Yang Y-B, Gao F-J, Li R-Y, Wang H-G (2011) QTL mapping of bio-energy related traits in Sorghum. Euphytica 182:431–440. https://doi.org/10.1007/s10681-011-0528-5
Guo B, Sleper DA, Lu P, Shannon JG, Nguyen HT, Arelli PR (2006) QTLs associated with resistance to soybean cyst nematode in soybean: meta-analysis of QTL locations. Crop Sci 46:595–602. https://doi.org/10.2135/cropsci2005.04-0036-2
Hamblin MT, Salas Fernandez MG, Casa AM, Mitchell SE, Paterson AH, Kresovich S (2005) Equilibrium processes cannot explain high levels of short- and medium-range linkage disequilibrium in the domesticated grass Sorghum bicolor. Genetics 171:1247–1256. https://doi.org/10.1534/genetics.105.041566
Hamelin C, Sempere G, Jouffe V, Ruiz M (2013) TropGeneDB, the multi-tropical crop information system updated and extended. Nucleic Acids Res 41:172–1175. https://doi.org/10.1093/nar/gks1105
Harushima Y, Yano M, Shomura A, Sato M, Shimano T, Kuboki Y, Yamamoto T, Lin SY, Antonio BA, Parco A, Kajiya H, Huang N, Yamamoto K, Nagamura Y, Kurata N, Khush GS, Sasaki T (1998) A high-density rice genetic linkage map with 2275 markers using a single F2 population. Genetics 148:479–494
Hu ZL, Park CA, Wu XL, Reecy JM (2013) Animal QTLdb: an improved database tool for livestock animal QTL/association data dissemination in the post-genome era. Nucleic Acids Res 41:871–879. https://doi.org/10.1093/nar/gks1150
Jordan DR, Klein RR, Sakrewski K, Henzell RG, Klein PE, Mace ES (2011) Mapping and characterization of Rf5: A new loci conditioning pollen fertility restoration in A1 and A2 cytoplasm in sorghum (Sorghum bicolor (L.) Moench). Theor Appl Genet 123:383–396. https://doi.org/10.1007/s00122-011-1591-y
Kawahara Y et al (2013) Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice 6:4. https://doi.org/10.1186/1939-8433-6-4
Kim C-K, Seol Y-J, Lee D-J, Lee J-H, Lee T-H, Park D-S (2014) RiceQTLPro: an integrated database for quantitative trait loci marker mapping in rice plant. Bioinformation 10:664–666. https://doi.org/10.6026/97320630010664
Knoll J, Gunaratna N, Ejeta G (2008) QTL analysis of early-season cold tolerance in sorghum. Theor Appl Genet 116:577–587. https://doi.org/10.1007/s00122-007-0692-0
Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR, Oropeza-Rosas MA, Zwonitzer JC, Kresovich S, McMullen MD, Ware D, Balint-Kurti PJ, Holland JB (2011) Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet 43:163–168. https://doi.org/10.1038/ng.747
Lawrence CJ, Schaeffer ML, Seigfried TE, Campbell DA, LHarper LC (2007) MaizeGDB’s new data types, resources and activities. Nucleic Acids Res 35:895–900. https://doi.org/10.1093/nar/gkl1048
Locke AE et al (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518:197–206. https://doi.org/10.1038/nature14177
Lyons E, Freeling M (2008) How to usefully compare homologous plant genes and chromosomes as DNA sequences. Plant J 53:661–673. https://doi.org/10.1111/j.1365-313X.2007.03326.x
Mace ES, Jordan DR (2010) Location of major effect genes in sorghum [Sorghum bicolor (L.) Moench]. Theor. Appl Genet. 121:1339–1356. https://doi.org/10.1007/s00122-010-1392-8
Mace ES, Jordan DR (2011) Integrating sorghum whole genome sequence information with a compendium of sorghum QTL studies reveals non-random distribution of QTL and of gene rich regions with significant implications for crop improvement. Theor Appl Genet 123:169–191. https://doi.org/10.1007/s00122-011-1575-y
Mace ES, Rami J-F, Bouchet S, Klein PP, Klein RE, Kilian A, Wenzl P, Xia L, Sakrewski K, Jordan DR (2009) A consensus genetic map of sorghum that integrates multiple component maps and high-throughput Diversity Array Technology (DArT) markers. BMC Plant Biol 9:13. https://doi.org/10.1186/1471-2229-9-13
Mace ES, Tai S, Gilding EK, Li Y, Prentis PJ, Bian L, Campbell BC, Hu W, Innes DJ, Han X, Cruickshank A, Dai C, Frère C, Zhang H, Hunt CH, Wang X, Shatte T, Wang M, Su Z, Li J, Lin X, Godwin ID, Jordan DR, Wang J (2013) Whole genome resequencing reveals untapped genetic potential in Africa’s indigenous cereal crop sorghum. Nat Commun 4:2320. https://doi.org/10.1038/NCOMMS3320
Mace ES, Tai SS, Innes DJ, Godwin ID, Hu WS, Campbell BC, Gilding EK, Cruickshank A, Prentis PJ, Wang J, Jordan DR (2014) The plasticity of NBS resistance genes in sorghum is driven by multiple evolutionary processes. BMC Plant Biol 14:253. https://doi.org/10.1186/s12870-014-0253-z
Martin N (2018) Getting to the genetic and environmental roots of educational inequality. npj Sci Learn 3:4. https://doi.org/10.1038/s41539-018-0023-z
McCormick RF, Truong SK, Sreedasyam A, Jenkins J, Shu S, Sims D, Kennedy M, Amirebrahimi M, Weers BD, McKinley B, Mattison A, Morishige DT, Grimwood J, Schmutz J, Mullet JE (2018) The Sorghum bicolor reference genome: improved assembly, gene annotations, a transcriptome atlas, and signatures of genome organization. Plant J 93:338–354. https://doi.org/10.1111/tpj.13781
Millar AA, Clemens S, Zachgo S, Giblin EM, Taylor DC, Kunst L (1999) CUT1, an Arabidopsis gene required for cuticular wax biosynthesis and pollen fertility, encodes a very-long-chain fatty acid condensing enzyme. Plant Cell 11:825–838
Morris GP, Ramu P, Deshpande SP, Hash CT, Shah T, Upadhyaya HD, Riera-Lizarazu O, Brown PJ, Acharya CB, Mitchell SE, Harriman J, Glaubitz JC, Buckler ES, Kresovich S (2013) Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proc Natl Acad Sci USA 110:453–458. https://doi.org/10.1073/pnas.1215985110
Multani DS, Briggs SP, Chamberlin MA, Blakeslee JJ, Murphy AS, Johal GS (2003) Loss of an MDR transporter in compact stalks of maize br2 and sorghum dw3 mutants. Science 302:81–84. https://doi.org/10.1126/science.1086072
Murray SC, Sharma A, Rooney WL, Klein PE, Mullet JE, Mitchell SE, Kresovich S (2008a) Genetic improvement of sorghum as a biofuel feedstock: I. QTL for stem sugar and grain nonstructural carbohydrates. Crop Sci 48:2165–2179. https://doi.org/10.2135/cropsci2008.01.0016
Murray SC, Rooney WL, Mitchell SE, Sharma A, Klein PE, Mullet JE, Kresovich S (2008b) Genetic improvement of sorghum as a biofuel feedstock: II. QTL for stem and leaf structural carbohydrates. Crop Sci 48:2180–2193. https://doi.org/10.2135/cropsci2008.01.0068
Ni J, Pujar A, Youens-Clark K, Yap I, Jaiswal P, Tecle I, Tung CW, Ren L, Spooner W, Wei X, Avraham S, Ware D, Stein L, McCouch S (2009) Gramene QTL database: development, content and applications. Database. https://doi.org/10.1093/database/bap005
Paterson AH et al (2009) The Sorghum bicolor genome and the diversification of grasses. Nature 457:551–556. https://doi.org/10.1038/nature07723
Pereira MG, Ahnert D, Lee M, Klier K (1995) Genetic-mapping of quantitative trait loci for panicle characteristics and seed weight in sorghum. Braz J Genet 18:249–257
Poland JA, Bradbury PJ, Buckler ES, Nelson RJ (2011) Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proc Natl Acad Sci 108:6893–6898. https://doi.org/10.1073/pnas.1010894108
Rami JF, Dufour P, Trouche G, Fliedel G, Mestres C, Davrieux F, Blanchard P, Hamon P (1998) Quantitative trait loci for grain quality, productivity, morphological and agronomical traits in sorghum (Sorghum bicolor L. Moench). Theor Appl Genet 97:605–616
Ren X, Pan Z, Zhao H, Zhao J, Cai M, Li J, Zhang Z, Qiu F (2017) EMPTY PERICARP11 serves as a factor for splicing of mitochondrial nad1 intron and is required to ensure proper seed development in maize. J. Exp Bot 68:4571–4581. https://doi.org/10.1093/jxb/erx212
Rhodes DH, Hoffmann L, Rooney WL, Herlad TJ, Bean S, Boyles R, Brenton ZW, Kresovich S (2017) Genetic architecture of kernel composition in global sorghum germplasm. BMC Genomics 18:15. https://doi.org/10.1186/s12864-016-3403-x
Salvi S, Tuberosa R (2015) The crop QTLome comes of age. Curr Opin Biotechnol 32:179–185. https://doi.org/10.1016/j.copbio.2015.01.001
Savage JE et al (2018) Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet 50:912–919. https://doi.org/10.1038/s41588-018-0152-6
Schnable JC (2015) Genome evolution in maize: from genomes back to genes. Annu Rev Plant Biol 66:329–343. https://doi.org/10.1146/annurev-arplant-043014-115604
Schnable PS et al (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326:1112–1115. https://doi.org/10.1126/science.1178534
Schnable JS, Freeling M, Lyson E (2012) Genome-wide analysis of syntenic gene deletion in the grasses. Genome Biol Evol 4:265–277. https://doi.org/10.1093/gbe/evs009
Shakoor N, Ziegler G, Dilkes BP, Brenton Z, Boyles R, Connolly EL, Kresovich S, Ivan R, Baxter IR (2016) Genetic determinants of seed element composition. Plant Physiol 170:1989–1998. https://doi.org/10.1104/pp.15.01971
Singh G, Kuzniar A, van Mulligan EM, Gavai A, Bachem CW, Visser RGF, Finkers R (2018) QTLTableMiner++: semantic mining of QTL tables in scientific articles. BMC Bioinform 19:183. https://doi.org/10.1186/s12859-018-2165-7
Star KV, Song Q, Zhu A, Böttinger EP (2006) QTL MatchMaker: a multi-species quantitative trait loci (QTL) database and query system for annotation of genes and QTL. Nucleic Acids Res 34:586–589. https://doi.org/10.1093/nar/gkj027
Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, Rocheford TR, McMullen MD, Holland JB, Buckler ES (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet 43:159–162. https://doi.org/10.1038/ng.746
Upadhyaya HD, Wang YH, Sharma R, Sharma S (2013) SNP markers linked to leaf rust and grain mold resistance in sorghum. Mol Breed 32:451–462. https://doi.org/10.1007/s11032-013-9883-3
Veyrieras JB, Goffinet B, Charcosset A (2007) MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments. BMC Bioinform 8:49. https://doi.org/10.1186/1471-2105-8-49
Wallace JG, Bradbury PJ, Zhang N, Gibon Y, Stitt M, Buckler ES (2014) Association mapping across numerous traits reveals patterns of functional variation in maize. PLoS Genet 10:e1004845. https://doi.org/10.1371/journal.pgen.1004845
Wang Y, Yao J, Zhang Z, Zheng Y (2006) The comparative analysis based on maize integrated QTL map and meta-analysis of plant height QTLs. Chin Sci Bull 51:2219. https://doi.org/10.1007/s11434-006-2119-8
Wang YH, Upadhyaya HD, Burrell AM, Sahraeian SME, Klein RR, Klein PE (2013) Genetic structure and linkage disequilibrium in a diverse, representative collection of the C4 model plant, Sorghum bicolor. G3: Genes Genomes Genet 3:783–793. https://doi.org/10.1534/g3.112.004861
Wood AR et al (2014) Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet 46:1173–1186. https://doi.org/10.1038/ng.3097
Yamamoto T, Yonemaru J, Yano Y (2009) Towards the understanding of complex traits in rice: substantially or superficially? DNA Res 16:141–154. https://doi.org/10.1093/dnares/dsp006
Yengo L et al (2018) Meta-analysis of genome-wide association studies for height and body mass index in ~ 700,000 individuals of European ancestry. bioRxiv https://doi.org/10.1101/274654
Yonemaru J, Yamamoto T, Fukuoka S, Uga Y, Hori K, Yano M (2010) Q-TARO: QTL annotation rice online database. Rice 3:194. https://doi.org/10.1007/s12284-010-9041-z
Yu J et al (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 296:79–92. https://doi.org/10.1126/science.1068037
Zeng H, Luo L, Zhang W, Zhou J, Li Z, Liu H, Zhu T, Feng X, Zhong Y (2006) PlantQTL-GE: a database system for identifying candidate genes in rice and Arabidopsis by gene expression and QTL information. Nucleic Acids Res 35:879–882. https://doi.org/10.1093/nar/gkl814
Zhang D, Guo H, Kim C, Lee TH, Li J, Robertson J, Wang X, Wang Z, Paterson A (2013) CSGRqtl, a comparative QTL database for Saccharinae grasses. Plant Physiol 161:594–599. https://doi.org/10.1104/pp.112.206870
Zhang N, Gibon Y, Wallace JG, Lepak N, Li P, Dedow L, Chen C, So YS, Kremling K, Bradbury PJ, Brutnell T, Stitt M, Buckler ES (2015) Genome-wide association of carbon and nitrogen metabolism in the maize nested association mapping population. Plant Physiol 168:575–583. https://doi.org/10.1104/pp.15.00025
Zhao L, Liu HJ, Zhang CX, Wang QY, Li XH (2015) Meta-analysis of constitutive QTLs for disease resistance in maize and its synteny conservation in the rice genome. Genet Mol Res 14:961–970. https://doi.org/10.4238/2015.February.3.3
Zhao J, Mantilla Perez MB, Hu J, Salas Fernandez MG (2016) Genome-wide association study for nine plant architecture traits in sorghum. Plant Genome. https://doi.org/10.3835/plantgenome2015.06.0044
Author Contribution statement
EM collated the QTL and GWAS data and wrote the manuscript, XW and YT projected QTL locations and co-wrote the manuscript, CH and AH undertook statistical analysis of the data, DI, JB and MH developed the web application and DJ provided the original concept and co-wrote the manuscript.
Acknowledgements
We would like to acknowledge funding support for this activity from the University of Queensland and the Department of Agriculture and Fisheries.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Communicated by Jessica Rutkoski.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Fig. S1
QTL density plots along the maize genome. A. On the genetic linkage scale (number of QTL/0.5 cM). B. On the physical scale (number of QTL/0.5Mbp) (DOCX 100 kb)
Fig. S2
QTL density plots (number of QTL/0.5 cM) along the rice genome. A. On the genetic linkage scale (number of QTL/0.5 cM). B. On the physical scale (number of QTL/0.1Mbp) (DOCX 104 kb)
Fig. S3
Visual site map of AusSORGM QTL Atlas (DOCX 35 kb)
Fig. S4
A histogram plot of the distance of 70 height QTL on SBI-07 from dw3 (DOCX 1553 kb)
Table S1
Details of all of the traits within each trait category (XLSX 1465 kb)
Rights and permissions
About this article
Cite this article
Mace, E., Innes, D., Hunt, C. et al. The Sorghum QTL Atlas: a powerful tool for trait dissection, comparative genomics and crop improvement. Theor Appl Genet 132, 751–766 (2019). https://doi.org/10.1007/s00122-018-3212-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00122-018-3212-5