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Mace E, Innes D, Hunt C, Wang X, Tao Y, Baxter J, Hassall M, Hathorn A, Jordan D. The Sorghum QTL Atlas: a powerful tool for trait dissection, comparative genomics and crop improvement. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:751-766. [PMID: 30343386 DOI: 10.1007/s00122-018-3212-5] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 10/11/2018] [Indexed: 05/20/2023]
Abstract
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.
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Affiliation(s)
- Emma Mace
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Warwick, QLD, 4370, Australia.
- Department of Agriculture and Fisheries, Hermitage Research Facility, Warwick, QLD, 4370, Australia.
| | - David Innes
- Department of Agriculture and Fisheries, Ecosciences Precinct, Brisbane, QLD, 4102, Australia
| | - Colleen Hunt
- Department of Agriculture and Fisheries, Hermitage Research Facility, Warwick, QLD, 4370, Australia
| | - Xuemin Wang
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Warwick, QLD, 4370, Australia
| | - Yongfu Tao
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Warwick, QLD, 4370, Australia
| | - Jared Baxter
- Department of Agriculture and Fisheries, Hermitage Research Facility, Warwick, QLD, 4370, Australia
| | - Michael Hassall
- Department of Agriculture and Fisheries, Leslie Research Facility, Toowoomba, QLD, 4350, Australia
| | - Adrian Hathorn
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - David Jordan
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Warwick, QLD, 4370, Australia
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Abstract
Meta-analysis is an important tool for integrating information from multiple quantitative trait loci (QTLs) studies. Pooling of results from several studies allows greater statistical power for QTL detection and more precise estimation of their genetic effects. Hence, a meta-analysis can yield conclusions that are stronger than those of individual studies and can give greater insight into the genetic architecture of complex traits. In this chapter, we present basic theories and methods for meta-analysis of QTL mapping experiments. The meta-analytic procedures are described in a general context. The statistical methods cover both parametric and nonparametric statistical models. Finally, we illustrate the features of these statistical methods using simulated and real datasets.
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Raadsma HW, Jonas E, McGill D, Hobbs M, Lam MK, Thomson PC. Mapping quantitative trait loci (QTL) in sheep. II. Meta-assembly and identification of novel QTL for milk production traits in sheep. Genet Sel Evol 2009; 41:45. [PMID: 19849860 PMCID: PMC2772855 DOI: 10.1186/1297-9686-41-45] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2009] [Accepted: 10/22/2009] [Indexed: 11/29/2022] Open
Abstract
An (Awassi × Merino) × Merino backcross family of 172 ewes was used to map quantitative trait loci (QTL) for different milk production traits on a framework map of 200 loci across all autosomes. From five previously proposed mathematical models describing lactation curves, the Wood model was considered the most appropriate due to its simplicity and its ability to determine ovine lactation curve characteristics. Derived milk traits for milk, fat, protein and lactose yield, as well as percentage composition and somatic cell score were used for single and two-QTL approaches using maximum likelihood estimation and regression analysis. A total of 15 significant (P < 0.01) and additional 25 suggestive (P < 0.05) QTL were detected across both single QTL methods and all traits. In preparation of a meta-analysis, all QTL results were compared with a meta-assembly of QTL for milk production traits in dairy ewes from various public domain sources and can be found on the ReproGen ovine gbrowser http://crcidp.vetsci.usyd.edu.au/cgi-bin/gbrowse/oaries_genome/. Many of the QTL for milk production traits have been reported on chromosomes 1, 3, 6, 16 and 20. Those on chromosomes 3 and 20 are in strong agreement with the results reported here. In addition, novel QTL were found on chromosomes 7, 8, 9, 14, 22 and 24. In a cross-species comparison, we extended the meta-assembly by comparing QTL regions of sheep and cattle, which provided strong evidence for synteny conservation of QTL regions for milk, fat, protein and somatic cell score data between cattle and sheep.
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Affiliation(s)
- Herman W Raadsma
- ReproGen - Animal Bioscience Group, Faculty of Veterinary Science, University of Sydney, Camden NSW 2570, Australia.
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Hu ZL, Fritz ER, Reecy JM. AnimalQTLdb: a livestock QTL database tool set for positional QTL information mining and beyond. Nucleic Acids Res 2006; 35:D604-9. [PMID: 17135205 PMCID: PMC1781224 DOI: 10.1093/nar/gkl946] [Citation(s) in RCA: 149] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The Animal Quantitative Trait Loci (QTL) database (AnimalQTLdb) is designed to house all publicly available QTL data on livestock animal species from which researchers can easily locate and compare QTL within species. The database tools are also added to link the QTL data to other types of genomic information, such as radiation hybrid (RH) maps, finger printed contig (FPC) physical maps, linkage maps and comparative maps to the human genome, etc. Currently, this database contains data on 1287 pig, 630 cattle and 657 chicken QTL, which are dynamically linked to respective RH, FPC and human comparative maps. We plan to apply the tool to other animal species, and add more structural genome information for alignment, in an attempt to aid comparative structural genome studies ().
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Affiliation(s)
- Zhi-Liang Hu
- Department of Animal Science, Center for Integrated Animal Genomics Iowa State University, 2255 Kildee Hall, Ames, IA 50011-3150, USA.
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