Multi-QTL mapping for quantitative traits using distorted markers
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Multi-QTL mapping for quantitative traits using distorted markers Jia Wen • Van Toan Can • Yuan-Ming Zhang
Received: 3 August 2012 / Accepted: 1 October 2012 / Published online: 20 October 2012 Ó Springer Science+Business Media Dordrecht 2012
Abstract Marker segregation distortion is a common natural phenomenon. However, relatively little is known about utilizing distorted markers for detecting quantitative trait loci (QTL). Therefore, in this study we proposed a multi-QTL mapping approach that uses distorted markers. First, the information from all markers, including distorted markers, was used to detect segregation distortion loci (SDL). Second, the information from the detected SDL was used to correct the conditional probabilities of the QTL genotypes conditional on marker information, and these corrected probabilities were then incorporated into a multi-QTL mapping methodology. Finally, the proposed approach was validated by both Monte Carlo simulation studies and real data analysis. The results from the simulation studies show that as long as one or two SDL are placed around the simulated QTL, there are no differences between the new method and the ordinary interval
Electronic supplementary material The online version of this article (doi:10.1007/s11032-012-9797-5) contains supplementary material, which is available to authorized users. J. Wen V. T. Can Y.-M. Zhang (&) State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China e-mail: [email protected] V. T. Can Forestry Faculty, Bacgiang Agricultural and Forestry University, Bichson Commune, Vietyen District, Bacgiang Province, Socialist Republic of Vietnam
mapping method in terms of the power of QTL detection or the estimates of the position and dominant effects of the QTL. However, the power of QTL detection is higher under the dominant genetic model of SDL than under the additive genetic model, and the estimate for the additive effect of QTL using the new method is significantly different from the estimate obtained using ordinary interval mapping. The above results were further confirmed by the detection of QTL for dried soymilk in 222 F2:4 families in soybean. Keywords Empirical Bayes Liability model Marker segregation distortion Multi-QTL mapping Quantitative trait locus Soymilk
Introduction The methodologies for quantitative trait loci (QTL) mapping in populations derived from a bi-parental cross are well established (Lander and Botstein 1989; Jansen 1993; Zeng 1993; Kao et al. 1999; Xu 2003; Wang et al. 2005a, b; Zhang and Xu 2005). Among these methods, there are two basic underlying assumptions: (1) the random errors come from a normal distribution; and (2) the population is ‘‘ideal’’ (Zhu et al. 2007). However, these two assumptions are infrequently met. If the first is not met, the Box–Cox transformation (Box and Cox 1964) can be applied. However, if the second is not met, due to marker segregation distortion for example, relatively little is
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