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Acta Agron Sin ›› 2013, Vol. 39 ›› Issue (02): 198-206.doi: 10.3724/SP.J.1006.2013.00198

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Establishment of Segregation Analysis of Mixed Inheritance Model with Four Major Genes Plus Polygenes in Backcross Inbred Lines (BIL) Populations

WANG Jin-She,ZHAO Tuan-Jie,GAI Jun-Yi*   

  1. Soybean Research Institute of Nanjing Agricultural University / National Center for Soybean Improvement / Key Laboratory for Biology and Genetic Improvement of Soybean (General), Minister of Agriculture / National Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing 210095, China
  • Received:2012-05-25 Revised:2012-11-16 Online:2013-02-12 Published:2012-12-11
  • Contact: 盖钧镒, E-mail: sri@njau.edu.cn, Tel: 025-84395405

Abstract:

The segregation analysis of major genes plus polygenes is a statistical method for genetic analysis of quantitative traits. The method is particularly valuable for plant breeders to use their data accumulated from segregation populations to estimate the genetic system of target traits, which is necessary for designing breeding strategies and also useful for validating the results of QTL mapping. The backcross inbred line (BIL) population is one of the permanent populations, which is suitable for genetic analysis of complex traits and can be used in replicated experiments. For BIL population, the analytical procedures of three and less major genes plus polygenes mixed inheritance models have been established. The objective of the present study was to establish the analytical procedures of segregation analysis for four major genes plus polygenes mixed inheritance models in BIL population. Eleven genetic models with four additive and (or) epistatic major genes including those without and with polygenes were established. The component distribution parameters were solved and estimated by using maximum likelihood method based on IECM (Iterative Expectation Conditional Maximization) algorithm. Among the possible models, the best one was chosen according to Akaike’s Information Criterion (AIC) and a set of tests for goodness of fit. Then the genetic parameters of the optimal model were estimated through the least square method. For demonstration of the established procedures, a simulated data set of a randomized block experiment with three replications was analyzed and the estimated genetic parameters showed a relatively high consistency with those fixed for the model. To validate the usefulness of the established genetic models, the data of resistance to race of Cyst Nematode (Heterodera glycines Ichinohe) in soybeans from a BIL population derived from Essex×ZDD2315 along with their P1 and P2 were analyzed. The results show that the four major genes genetic model is better than three major genes genetic model, which illustrate the actual use of these genetic models.

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