欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2010, Vol. 36 ›› Issue (07): 1100-1107.doi: 10.3724/SP.J.1006.2010.01100

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

不同统计遗传模型QTL定位方法应用效果的模拟比较

苏成付,赵团结,盖鈞镒*   

  1. 南京农业大学大豆研究所/国家大豆改良中心/作物遗传与种质创新国家重点实验室,江苏南京210095
  • 收稿日期:2009-12-28 修回日期:2010-03-19 出版日期:2010-07-12 网络出版日期:2010-04-28
  • 通讯作者: 盖钧镒, E-mail: sri@njau.edu.cn; Tel: 025-84395405
  • 基金资助:

    本研究由国家重点基础研究发展规划(973计划)项目(2006CB1017, 2009CB1184, 2010CB1259)，国家高技术研究发展计划(863计划)项目(2006AA100104, 2009AA1011)，国家农业部公益性行业专项(200803060)和教育部高等学校创新引智计划项目(B08025)资助。

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 Published:2010-07-12 Published online:2010-04-28
  • Contact: GAI Jun-Yi,E-mail:sri@njau.edu.cn; Tel: 025-84395405

PDF

3137

被引次数

45

摘要：

分子遗传和数量遗传的结合，发展了QTL定位研究。随着定位方法与软件的建立和完善，QTL定位的研究越来越多。准确定位的QTL可用于分子标记辅助选择和图位克隆，而假阳性QTL将误导定位信息的应用。本文研究迄今主要定位方法(软件)对于各种遗传模型数据的适用性。应用计算机模拟4类遗传模型不同的重组自交系群体(RIL)，第一类只包含加性QTL；第二类包含加性和上位性互作QTL；第三类包含加性QTL和QTL与环境互作效应；第四类包含加性、上位性互作QTL和QTL与环境互作效应。每类按模拟QTL个数不同设两种情况，共分为8种数据模型(下称M-1~M-8)。选用WinQTLCart 2.5的复合区间作图(下称CIM)、多区间作图前进搜索(MIMF)、多区间作图回归前进选择(MIMR)，IciMapping 2.0的完备复合区间作图(ICIM)，MapQTL 5.0的多QTL模型(MQM)以及QTLnetwork 2.0的区间作图(NWIM)等6种程序对8种不同遗传模型的RIL进行QTL检测。结果表明，不同程序适用的遗传模型范围不同。CIM和MQM只适于检测第一类模型；MIMR、MIMF和ICIM只适于检测第一类和第二类模型；只有NWIM适于检测所有四类遗传模型；因而不同遗传模型数据的最适用检测程序不同。由于未知实际数据的遗传模型，应采用在复杂模型程序，如QTLnetwork 2.0，扫描基础上的多模型QTL定位策略，对所获模型用相应模型软件进行验证。

关键词: QTL定位, 遗传模型, 定位程序, 定位方法与数据模型的适配性

Abstract:

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


[1] Lander E S, Bostein D R. Mapping Mendelian factors underlying quantitative traits using RFLP linkage map. Genetics, 1989, 121: 185-189


[2] Basten C J, Weir B S, Zeng Z B. Zmap-a QTL cartographer, In: Smith C, Gavora J S, Benkel B, Chesnais J, Fairfull W, Gibson J P, Kennedy B W, Burnside E B eds. Proceedings of the 5th World Congress on Genetics Applied to Livestock Production: Computing Strategies and Software. The Organizing Committee, 5th World Congress on Genetics Applied to Livestock Production, Guelph, Ontario, Canada, 1994, Vol 22, pp 65-66


[3] Utz H F, Melchinger A E. PLABQTL: A program for composite interval mapping of QTL. J Agric Genomics, 1996 2: 1-5


[4] Manly K F, Cudmore J R, Meer J M. Map Manager QTX, cross-platform software for genetic mapping. Mammalian Genome, 2001, 12: 930-932


[5] Nelson J C. QGENE: Software for marker-based genomic analysis and breeding. Mol Breed, 1997, 3: 239-245


[6] Van Ooijen J W, Maliepaard C. MapQTL version 3.0: Software for the calculation of QTL positions on genetic maps. Abstract of Plant Genome IV Conference(http://www.intl-pag.org/4/abstracts/p316.html), San Diego, CA, 1996


[7] Lu Y Y, Liu B H. A new computer package for genomic research: PGRI (Plant Genome Research Initiative). Abstract of Plant Genome III Conference (http://www.intl-pag.org/3/abstracts/201pg3.html), San Diego, CA, 1995


[8] Wang D L, Zhu J, Li Z K, Paterson A H. User Manual for QTLMapper Version 1.6-A Computer Software for Mapping Quantitative Trait Loci (QTLs) with Main Effects, Epistatic Effects and QTL × Environment Interactions. Department of Agronomy, Zhejiang University, Hangzhou, China (http://ibi.zju.edu.cn/ software/qtlmapper/), 2003


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


[10] Yang J, Zhu J, Williams R W. Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics, 2007, 23: 1527-1536


[11] Yang J, Hu C C, Hu H, Yu R D, Xia Z, Ye X Z, Zhu J-723. QTLNetwork: Mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics, 2008, 24: 721


[12] Wang S, Basten C J, Zeng Z B. Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC (http://statgen.ncsu. edu/qtlcart/WQTLCart.htm), 2007


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


[14] Van Ooijen J W, Kyazma B V. MapQTL 5, Software for the Mapping of Quantitative Trait Loci in Experimental Populations. Wageningen, the Netherlands, 2004


[15] Kao C H, Zeng Z B, Teasdale R D. Multiple interval mapping for quantitative trait loci. Genetics, 1999, 152: 1203−1216


[16] Wang J-K(王建康). Inclusive composite interval mapping of quantitative trait genes. Acta Agron Sin (作物学报), 2009, 35(2): 239-245 (in Chinese with English abstract)


[17] He X-H(何小红), Xu C-W(徐辰武), Kuai J-M(蒯建敏), Gu S-L(顾世梁), Li T(李韬). Principal factors affecting the power of detection and accuracy of QTL mapping. Acta Agron Sin (作物学报), 2001, 27(4): 469-475 (in Chinese with English abstract)


[18] Su C F, Lu W G, Zhao T J, Gai J Y. Verification and fine-mapping of QTLs conferring days to flowering in soybean using residual heterozygous lines. Chin Sci Bull, 2010, 55(6): 499-508
 Gai J Y. Segregation analysis on genetic system of quantitative traits in plants. Front Biol China, 2006, 1: 85-92
[1] 于春淼, 张勇, 王好让, 杨兴勇, 董全中, 薛红, 张明明, 李微微, 王磊, 胡凯凤, 谷勇哲, 邱丽娟. 栽培大豆×半野生大豆高密度遗传图谱构建及株高QTL定位[J]. 作物学报, 2022, 48(5): 1091-1102.
[2] 张艳波, 王袁, 冯甘雨, 段慧蓉, 刘海英. 棉籽油分和3种主要脂肪酸含量QTL分析[J]. 作物学报, 2022, 48(2): 380-395.
[3] 张波, 裴瑞琴, 杨维丰, 朱海涛, 刘桂富, 张桂权, 王少奎. 利用单片段代换系鉴定巴西陆稻IAPAR9中的粒型基因[J]. 作物学报, 2021, 47(8): 1472-1480.
[4] 黄冰艳, 孙子淇, 刘华, 房元瑾, 石磊, 苗利娟, 张毛宁, 张忠信, 徐静, 张梦圆, 董文召, 张新友. 花生巢式群体的脂肪含量遗传分析[J]. 作物学报, 2021, 47(6): 1100-1108.
[5] 周新桐, 郭青青, 陈雪, 李加纳, 王瑞. GBS高密度遗传连锁图谱定位甘蓝型油菜粉色花性状[J]. 作物学报, 2021, 47(4): 587-598.
[6] 沈文强, 赵冰冰, 于国玲, 李凤菲, 朱小燕, 马福盈, 李云峰, 何光华, 赵芳明. 优良水稻染色体片段代换系Z746的鉴定及重要农艺性状QTL定位及其验证[J]. 作物学报, 2021, 47(3): 451-461.
[7] 刘代铃,谢俊锋,何乾瑞,陈四维,胡跃,周佳,佘跃辉,刘卫国,杨文钰,武晓玲. 净作和套作下大豆贮藏蛋白11S、7S组分相对含量的QTL分析[J]. 作物学报, 2020, 46(3): 341-353.
[8] 吴海涛, 张勇, 苏伯鸿, Lamlom F Sobhi, 邱丽娟. 大豆分枝数相关分子标记开发及qBN-18位点精细定位[J]. 作物学报, 2020, 46(11): 1667-1677.
[9] 王存虎,刘东,许锐能,杨永庆,廖红. 大豆叶柄角的QTL定位分析[J]. 作物学报, 2020, 46(01): 9-19.
[10] 杨晓梦, 李霞, 普晓英, 杜娟, Muhammad Kazim Ali, 杨加珍, 曾亚文, 杨涛. 大麦重组自交系群体籽粒总花色苷含量和千粒重QTL定位[J]. 作物学报, 2020, 46(01): 52-61.
[11] 王大川,汪会,马福盈,杜婕,张佳宇,徐光益,何光华,李云峰,凌英华,赵芳明. 增加穗粒数的水稻染色体代换系Z747鉴定及相关性状QTL定位[J]. 作物学报, 2020, 46(01): 140-146.
[12] 魏丽娟,刘瑞影,张莉,陈志友,杨鸿,霍强,李加纳. 甘蓝型油菜茎高QTL定位及株高相关位点整合[J]. 作物学报, 2019, 45(6): 818-828.
[13] 闫超,郑剑,段文静,南文斌,秦小健,张汉马,梁永书. 越冬栽培稻产量性状相关QTL定位[J]. 作物学报, 2019, 45(4): 522-537.
[14] 张春宵,李淑芳,金峰学,刘文平,李万军,刘杰,李晓辉. 用3种方法定位玉米萌发期和苗期的耐盐和耐碱相关性状QTL[J]. 作物学报, 2019, 45(4): 508-521.
[15] 刘江宁,王楚鑫,张宏根,缪一栩,高海林,许作鹏,刘巧泉,汤述翥. 水稻黑条矮缩病抗性QTL定位[J]. 作物学报, 2019, 45(11): 1664-1671.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2