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Quantitative Trait Loci: A Meta-analysis
Abstract
This article presents a method to combine QTL results from different independent analyses. This method provides a modified Akaike criterion that can be used to decide how many QTL are actually represented by the QTL detected in different experiments. This criterion is computed to choose between models with one, two, three, etc., QTL. Simulations are carried out to investigate the quality of the model obtained with this method in various situations. It appears that the method allows the length of the confidence interval of QTL location to be consistently reduced when there are only very few “actual” QTL locations. An application of the method is given using data from the maize database available online at http://www.agron.missouri.edu/.
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Cited by
Rice Root Genetic Architecture: Meta-analysis from a Drought QTL Database
Courtois, B., Ahmadi, N., Khowaja, F., Price, A. H., Rami, J.-F., Frouin, J., Hamelin, C., & Ruiz, M. (2009) · Rice · doi:10.1007/s12284-009-9028-9 · Cited by 284
Marginal Lands to Grow Novel Bio-Based Crops: A Plant Breeding Perspective
Pancaldi, F., & Trindade, L. M. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00227 · Cited by 78
Advances in mechanisms of drought tolerance in crops, with emphasis on barley
Kebede, A., Kang, M. S., & Bekele, E. (2019) · Advances in Agronomy · doi:10.1016/bs.agron.2019.01.008 · Cited by 79
Metadata
- Authors
- Goffinet, B., & Gerber, S.
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2000-05-01
- Volume
- 155
- Issue
- 1
- Pages
- 463-473
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- OpenAlex
- W1930133311
- Cited by
- 525
- References
- 0
- FWCI
- 0.15
- Type
- journal article
- Language
- en
- Open access
- closed
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- Goffinet, B., & Gerber, S. (2000). Quantitative Trait Loci: A Meta-analysis. Genetics. https://doi.org/10.1093/genetics/155.1.463
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
