The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)
Open accessAbstract
Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance–covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities.
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Cites
Linear Mixed Models for Longitudinal Data
Verbeke, G. (1997) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2294-1_3 · Cited by 3,506
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Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix
Li, J., & Ji, L. (2005) · Heredity · doi:10.1038/sj.hdy.6800717 · Cited by 1,458
SMITH, A. B., CULLIS, B. R., & THOMPSON, R. (2005) · The Journal of Agricultural Science · doi:10.1017/s0021859605005587 · Cited by 623
Smith, A., Cullis, B., & Thompson, R. (2001) · Biometrics · doi:10.1111/j.0006-341x.2001.01138.x · Cited by 566
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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
Analyzing genotype-environment data by mixed models with multiplicative terms
Piepho, H.-P. (1997) · Biometrics · doi:10.2307/2533976 · Cited by 234
Spatial analysis of multi-environment early generation variety trials
Cullis, B., Gogel, B., Verbyla, A., & Thompson, R. (1998) · Biometrics · doi:10.2307/2533991 · Cited by 168 · cited 2×
“The literature shows many examples of the use of mixed models for complicated genotype by environment data (Denis et al. 1997; Piepho 1997; Cullis et al. 1998; Smith et al. 2001), but is less prolific with respect to the analysis of genotype by trait by environment data (Smith et al. 2007).”
Mixed models including environmental covariables for studying QTL by environment interaction
Malosetti, M., Voltas, J., Romagosa, I., Ullrich, S. E., & van Eeuwijk, F. A. (2004) · Euphytica · doi:10.1023/b:euph.0000040511.46388.ef · Cited by 167
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Cited by
A reaction norm model for genomic selection using high-dimensional genomic and environmental data
Jarquín, D., Crossa, J., Lacaze, X., Du Cheyron, P., Daucourt, J., Lorgeou, J., Piraux, F., Guerreiro, L., Pérez, P., Calus, M., et al. (2014) · Theoretical and Applied Genetics · doi:10.1007/s00122-013-2243-1 · Cited by 687
Self-citationChenu, K., Chapman, S. C., Tardieu, F., McLean, G., Welcker, C., & Hammer, G. L. (2009) · Genetics · doi:10.1534/genetics.109.105429 · Cited by 237
van Eeuwijk, F. A., Bustos-Korts, D., Millet, E. J., Boer, M. P., Kruijer, W., Thompson, A., Malosetti, M., Iwata, H., Quiroz, R., Kuppe, C., et al. (2019) · Plant Science · doi:10.1016/j.plantsci.2018.06.018 · Cited by 217
Self-citationGenetic architecture of plant stress resistance: Multi-trait genome-wide association mapping
Thoen, M. P. M., Davila Olivas, N. H., Kloth, K. J., Coolen, S., Huang, P., Aarts, M. G. M., Bac‐Molenaar, J. A., Bakker, J., Bouwmeester, H. J., Broekgaarden, C., et al. (2017) · New Phytologist · doi:10.1111/nph.14220 · Cited by 188
“Our multi-trait GWA approach closely follows the modeling framework developed by Zhou & Stephens (2014) and generalizes the use of MTMMs as described previously (Boer et al. , 2007; Malosetti et al. , 2008; Alimi et al. , 2013) for classical biparental offspring populations to association panels.”
Self-citationGenome-wide analysis of yield in Europe: allelic effects as functions of drought and heat scenarios
Millet, E., Welcker, C., Kruijer, W., Negro, S., Nicolas, S., Praud, S., Ranc, N., Presterl, T., Tuberosa, R., Bedo, Z., et al. (2016) · Plant Physiology · doi:10.1104/pp.16.00621 · Cited by 160
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Metadata
- Authors
- Malosetti, M., Ribaut, J. M., Vargas, M., Crossa, J., & van Eeuwijk, F. A.
- Venue
- Euphytica
- Published
- 2007-12-07
- Volume
- 161
- Issue
- 1-2
- Pages
- 241-257
- ISSN
- 0014-2336 (ISSN-L)
- 1573-5060
- OpenAlex
- W2119433042
- Cited by
- 178
- References
- 10
- FWCI
- 3.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Malosetti, M., Ribaut, J. M., Vargas, M., Crossa, J., & van Eeuwijk, F. A. (2008). A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.). Euphytica. https://doi.org/10.1007/s10681-007-9594-0
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