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Acta Agron Sin ›› 2010, Vol. 36 ›› Issue (07): 1100-1107.doi: 10.3724/SP.J.1006.2010.01100


Simulation Comparisons of Effectiveness among QTL Mapping Procedures of Different Statistical Genetic Models

SU Cheng-Fu,ZHAO Tuan-Jie,GAI Jun-Yi   

  1. Soybean Research Institute,Nanjing Agricultural University,National Center for Soybean Improvement,National Key Laboratory for Crop Genetics and Germplasm Enhancement,Nanjing 210095,China
  • Received:2009-12-28 Revised:2010-03-19 Online:2010-07-12 Published:2010-04-28
  • Contact: GAI Jun-Yi,E-mail:sri@njau.edu.cn; Tel: 025-84395405


QTL mapping has emerged based on the development and integration of molecular genetics and quantitative genetics. Along with the establishment and improvement of QTL mapping procedures, a great number of studies of QTL mapping in various crop species have been carried out. QTLs detected with high accuracy can be used for marker-assisted selection and map-based cloning, while the false-positive QTLs are meaningless, even mislead their usefulness. In the present study, the recombinant inbred line (RIL) populations were simulated based on four kinds of genetic models, including Model I, additive QTL; Model II, additive and epistatic QTLs; Model III, additive QTL and QTL×Environment interaction, and Model IV, additive, epistatic QTLs and QTL×Environment interaction. Two sets of RIL data for each of the four models were obtained, in a total of eight sets of RIL data designated as M-1~M-8. Six QTL mapping procedures, i.e. CIM (Composite interval mapping), MIMF (forward search of multiple interval mapping) and MIMR (regression forward selection of multiple interval mapping) of WinQTL Cartographer Version 2.5, ICIM (Inclusive composite interval mapping) of IciMapping Version 2.0, MQM (multiple-QTL model) of MapQTL Version 5.0, and NWIM (interval mapping) of QTLnetwork Version 2.0 were used for detecting QTLs of the eight sets of RIL data. The results showed: (1) Different mapping procedures fit different genetic models. CIM and MQM were only suitable for Model I data. MIMR, MIMF and ICIM were only suitable for Model I and Model II data. Only NWIM was suitable for all four models’ data. Therefore, the data from different genetic models corresponded to different optimal QTL mapping procedures. (2) Since the genetic model of the practical experimental data was unknown, a multiple model mapping strategy should be taken, i.e. a full model scanning with complex model procedure, such as QTLnetwork 2.0, followed by verification with another procedure corresponding to the scanning results.

Key words: QTL mapping, Genetic model, Mapping procedure, Pertinence between mapping method and data model

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