postQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identification
Abstract Objective The determination of the location of quantitative trait loci (QTL) (i.e., QTL mapping) is essential for identifying new genes. Various statistical methods are being incorporated into different QTL mapping functions. However, statistical errors and limitations may often occur in a...
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| Format: | Article |
| Language: | English |
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BMC
2022-05-01
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| Series: | BMC Research Notes |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13104-022-06017-z |
| _version_ | 1817985410969632768 |
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| author | Prashant Bhandari Tong Geon Lee |
| author_facet | Prashant Bhandari Tong Geon Lee |
| author_sort | Prashant Bhandari |
| collection | DOAJ |
| description | Abstract Objective The determination of the location of quantitative trait loci (QTL) (i.e., QTL mapping) is essential for identifying new genes. Various statistical methods are being incorporated into different QTL mapping functions. However, statistical errors and limitations may often occur in a QTL mapping, implying the risk of false positive errors and/or failing to detect a true positive QTL effect. We simulated the power to detect four simulated QTL in tomato using cim() and stepwiseqtl(), widely adopted QTL mapping functions, and QTL.gCIMapping(), a derivative of the composite interval mapping method. While there is general agreement that those three functions identified simulated QTL, missing or false positive QTL were observed, which were prevalent when more realistic data (such as smaller population size) were provided. Results To address this issue, we developed postQTL, a QTL mapping R workflow that incorporates (i) both cim() and stepwiseqtl(), (ii) widely used R packages developed for model selection, and (iii) automation to increase the accuracy, efficiency, and accessibility of QTL mapping. QTL mapping experiments on tomato F2 populations in which QTL effects were simulated or calculated showed advantages of postQTL in QTL detection. |
| first_indexed | 2024-04-13T23:57:55Z |
| format | Article |
| id | doaj.art-d1d28c2073624916a6d11fa301608541 |
| institution | Directory Open Access Journal |
| issn | 1756-0500 |
| language | English |
| last_indexed | 2024-04-13T23:57:55Z |
| publishDate | 2022-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Research Notes |
| spelling | doaj.art-d1d28c2073624916a6d11fa3016085412022-12-22T02:23:50ZengBMCBMC Research Notes1756-05002022-05-011511710.1186/s13104-022-06017-zpostQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identificationPrashant Bhandari0Tong Geon Lee1Horticultural Sciences Department, University of FloridaHorticultural Sciences Department, University of FloridaAbstract Objective The determination of the location of quantitative trait loci (QTL) (i.e., QTL mapping) is essential for identifying new genes. Various statistical methods are being incorporated into different QTL mapping functions. However, statistical errors and limitations may often occur in a QTL mapping, implying the risk of false positive errors and/or failing to detect a true positive QTL effect. We simulated the power to detect four simulated QTL in tomato using cim() and stepwiseqtl(), widely adopted QTL mapping functions, and QTL.gCIMapping(), a derivative of the composite interval mapping method. While there is general agreement that those three functions identified simulated QTL, missing or false positive QTL were observed, which were prevalent when more realistic data (such as smaller population size) were provided. Results To address this issue, we developed postQTL, a QTL mapping R workflow that incorporates (i) both cim() and stepwiseqtl(), (ii) widely used R packages developed for model selection, and (iii) automation to increase the accuracy, efficiency, and accessibility of QTL mapping. QTL mapping experiments on tomato F2 populations in which QTL effects were simulated or calculated showed advantages of postQTL in QTL detection.https://doi.org/10.1186/s13104-022-06017-zQTLMappingModel searchRegularizationR workflow |
| spellingShingle | Prashant Bhandari Tong Geon Lee postQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identification BMC Research Notes QTL Mapping Model search Regularization R workflow |
| title | postQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identification |
| title_full | postQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identification |
| title_fullStr | postQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identification |
| title_full_unstemmed | postQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identification |
| title_short | postQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identification |
| title_sort | postqtl a qtl mapping r workflow to improve the accuracy of true positive loci identification |
| topic | QTL Mapping Model search Regularization R workflow |
| url | https://doi.org/10.1186/s13104-022-06017-z |
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