中国农业科学院 作物科学研究所
Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China

 

A Library of Real and Simulated Populations for Evaluating

QTL Mapping Methods (PopLib)

 

  • Rationale

    The rapid increase in availability of fine-scale genetic marker maps has led to the intensive use of QTL mapping in the genetic study of quantitative traits in plants, animals, and humans. It is essential to apply the most suitable statistical method for specific mapping populations. Statistical methods are many but not all of them are applicable to everywhere. Therefore the novelty of newly developed QTL mapping approaches (including association mapping) should not be judged by whether the statistical methods have been used in QTL mapping but by the gains compared with other existing methods in terms of mapping results and computing demands.

    QTL mapping is an issue more genetically than statistically. Any QTL mapping method should make sense both in genetics and in statistics. Power analysis through simulations is normally used to evaluate the efficiency of a statistical method. To compare different QTL mapping methods properly, it is important to build a standard population library consisting of various real mapping populations (i.e. backcross, F2, DH, F2 derived RIL, and Backcross derived RIL etc) in different species (i.e. self pollinated and cross pollinated), and simulated mapping populations where the QTL locations and effects are known before conducting the QTL mapping study.

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  • Real Populations

Barley DH population: 146 DH lines, 127 markers

Maize RIL population: 224 RIL, 132 markers

Rice RIL population: 71 RIL, 250 markers

Rice F2 population : 180F2, 137 markers

 

  • Simulated Populations: Each simulated population has three formats: one for Windows QTL Cartographer (CIM, Zeng 1994), one for empirical Bayesian model (Xu and Jia 2007) and one for Windows QTL IciMapping (Li et al. 2007).

Genetic model 1 (Li et al. 2007), 100 Backcross populations

Genetic model 2 (Boer et al. 2002), 100 Backcross populations

Genetic model 3 (Yi et al. 2003), 100 Backcross populations

100 DH populations based on barley genome and QTL for kernel weight identified by ICIM

100 RIL populations based on maize genome and QTL for male flowering time identified by ICIM

100 RIL populations based on maize genome and QTL for male flowering time identified by empirical Bayesian model

400 DH populations for two marker densities and two population sizes

400 RIL populations for two marker densities and two population sizes

 

 

  • References

Boer, M. P., C. J. F. Ter Braak and R. C. Jansen, 2002 A penalized likelihood method for mapping epistatic quantitative trait loci with one-dimensional genome searches. Genetics 162: 951-960.

Li, H., G. Y. Ye and J. K. Wang, 2007 A modified algorithm for the improvement of composite interval mapping. Genetics 175: 361-374.

Yi, N., S. Xu and D. B. Allison, 2003 Bayesian model choice and search strategies for mapping interacting quantitative trait loci. Genetics 165: 867-883.

Zeng, Z.-B., 1994 Precision mapping of quantitative trait loci. Genetics 136: 1457-1468.

Xu, S and Z. Jia, 2007 Genome wide analysis of epistatic effects for quantitative traits in barley. Genetics 176: 611–623.