The Sorghum QTL Atlas: a powerful tool for trait dissection, comparative genomics and crop improvement
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.
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.
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.
We would like to acknowledge funding support for this activity from the University of Queensland and the Department of Agriculture and Fisheries.
Compliance with ethical standards
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
- 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 CrossRefPubMedGoogle Scholar
- 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 CrossRefGoogle Scholar
- 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 250–266Google Scholar
- 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 CrossRefPubMedPubMedCentralGoogle Scholar
- 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 CrossRefPubMedPubMedCentralGoogle Scholar
- Buckler ES et al (2018) Practical haplotype graph. https://bitbucket.org/bucklerlab/practicalhaplotypegraph/overview. Accessed 20 Aug 2018
- 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 1:1–11Google Scholar
- 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 CrossRefPubMedPubMedCentralGoogle Scholar
- 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 CrossRefGoogle Scholar
- 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 CrossRefPubMedPubMedCentralGoogle Scholar
- 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 CrossRefGoogle Scholar
- 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 CrossRefPubMedPubMedCentralGoogle Scholar
- 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 148:479–494PubMedPubMedCentralGoogle Scholar
- 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 CrossRefPubMedGoogle Scholar
- 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 CrossRefPubMedGoogle Scholar
- 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 CrossRefPubMedGoogle Scholar
- 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 CrossRefGoogle Scholar
- 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 CrossRefPubMedPubMedCentralGoogle Scholar
- 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 CrossRefPubMedPubMedCentralGoogle Scholar
- 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 CrossRefPubMedGoogle Scholar
- 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 CrossRefPubMedGoogle Scholar
- 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 18:249–257Google Scholar
- 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 CrossRefPubMedPubMedCentralGoogle Scholar
- 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 CrossRefPubMedGoogle Scholar
- 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 CrossRefPubMedGoogle Scholar
- 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
- 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 CrossRefPubMedPubMedCentralGoogle Scholar