High resolution of quantitative traits into multiple loci via interval mapping.
Open accessAbstract
A very general method is described for multiple linear regression of a quantitative phenotype on genotype [putative quantitative trait loci (QTLs) and markers] in segregating generations obtained from line crosses. The method exploits two features, (a) the use of additional parental and F1 data, which fixes the joint QTL effects and the environmental error, and (b) the use of markers as cofactors, which reduces the genetic background noise. As a result, a significant increase of QTL detection power is achieved in comparison with conventional QTL mapping. The core of the method is the completion of any missing genotypic (QTL and marker) observations, which is embedded in a general and simple expectation maximization (EM) algorithm to obtain maximum likelihood estimates of the model parameters. The method is described in detail for the analysis of an F2 generation. Because of the generality of the approach, it is easily applicable to other generations, such as backcross progenies and recombinant inbred lines. An example is presented in which multiple QTLs for plant height in tomato are mapped in an F2 progeny, using additional data from the parents and their F1 progeny.
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The CIM approach was simultaneously proposed by Jansen and Stam (1994) and Zeng (1994).
“This is the principle of the CIM approach, simultaneously proposed by Jansen and Stam (1994) and Zeng (1994).”
From The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis (2013) · cites this paper as Jansen and Stam (1994)
The paper’s Abstract describes the marker-cofactor interval-mapping approach, so it supports identifying Jansen and Stam’s work with the approach. It says the method follows Jansen (1993b), however, and does not address Zeng (1994) or establish that the two 1994 papers proposed it simultaneously. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe power of QTL detection can be improved by reducing the background noise caused by QTLs outside the region under test.
“The power of QTL detection can be improved by reducing the background noise caused by QTLs outside the region under test.”
From The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis (2013) · cites this paper as Jansen and Stam (1994)
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Cites
Haley, C. S., & Knott, S. A. (1992) · Heredity · doi:10.1038/hdy.1992.131 · Cited by 1,991 · see citing works
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Research works printed but not linked: 2
- BECKMANN, J. S., and M. SOUR, 1989 Backcross inbred lines for map ping and cloning of loci of interest, pp. 117-122 in Development and Application of Molecular Markers to Problems in Plant Ge- netics, edited by B. BumandT. HELENTJARIS. Brookhaven National Laboratory. GENSTAT 5 COMMI'ITEE, 1987 Genstat 5 Reference Manual. Clarendon Press, Oxford.
- STAM, P., 1991 Some aspects of QTL mapping, in Proceedings of the Eighth Meeting of the Eucarpia Section Biometrics in Plant Breed- ing. Brno, July 1991.
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The Genetic Architecture of Quantitative Traits
Mackay, T. F. C. (2001) · Annual Review of Genetics · doi:10.1146/annurev.genet.35.102401.090633 · Cited by 1,106 · see citing works
Malosetti, M., Ribaut, J.-M., & van Eeuwijk, F. A. (2013) · Frontiers in Physiology · doi:10.3389/fphys.2013.00044 · Cited by 539 · see citing works
QTL analysis in plants; where are we now?
Kearsey, M. J., & Farquhar, A. G. L. (1998) · Heredity · doi:10.1046/j.1365-2540.1998.00500.x · Cited by 492 · see citing works
TUBEROSA, R. (2002) · Annals of Botany · doi:10.1093/aob/mcf134 · Cited by 397 · see citing works
Boer, M. P., Wright, D., Feng, L., Podlich, D. W., Luo, L., Cooper, M., & van Eeuwijk, F. A. (2007) · Genetics · doi:10.1534/genetics.107.071068 · Cited by 264 · see citing works
Borevitz, J. O., Maloof, J. N., Lutes, J., Dabi, T., Redfern, J. L., Trainer, G. T., Werner, J. D., Asami, T., Berry, C. C., Weigel, D., et al. (2002) · Genetics · doi:10.1093/genetics/160.2.683 · Cited by 128 · see citing works
Quantitative Trait Loci Mapping in Plants: Concepts and Approaches
Sehgal, D., Singh, R., & Rajpal, V. R. (2016) · Sustainable Development and Biodiversity · doi:10.1007/978-3-319-27090-6_2 · Cited by 50 · see citing works
- Authors
- Jansen, R. C., & Stam, P.
- Affiliations
- Wageningen University & Research (NL)
- ORCID
- Stam, P
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 1994-04-01
- Volume
- 136
- Issue
- 4
- Pages
- 1447-1455
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- OpenAlex
- W2168558754
- Cited by
- 1,046
- References
- 1
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 10 · 2023: 13 · 2022: 17 · 2021: 19 · 2020: 21 · 2019: 25 · 2018: 23 · 2017: 20 · 2016: 29 · 2015: 47 · 2014: 39 · 2013: 44 · 2012: 50
- FWCI
- 17.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- quantitative trait loci, interval mapping, multiple linear regression, F2 progeny, expectation-maximization algorithm, plant height, tomato, backcross progeny, recombinant inbred lines
- MeSH terms
- Chromosome Mapping, Genotype, Models, Genetic, Phenotype
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