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

4031

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] 郑玉珍, 齐飞艳, 孙子淇, 刘华, 秦利, 石磊, 王娟, 汪蒙蒙, 韩锁义, 徐静, 苗利娟, 黄冰艳, 董文召, 郑峥, 张新友. 花生籽仁总超长链脂肪酸和7种脂肪酸组分的QTL定位[J]. 作物学报, 2026, 52(6): 1646-1657.
[2] 刘长友, 王珅, 时会影, 沈颖超, 孙蕾, 王彦, 张志肖, 苏秋竹, 田静, 范保杰. 基于饭豆基因资源的小豆远缘杂交群体抗豆象QTL定位[J]. 作物学报, 2026, 52(3): 936-944.
[3] 杨海洋, 吴林宣, 李博纹, 石翰峰, 袁禧龙, 刘金朝, 蔡海荣, 陈诗怡, 郭涛, 王慧. 基于QTL定位发现的OsWRI3调控水稻种子的落粒性[J]. 作物学报, 2025, 51(7): 1712-1724.
[4] 胡蒙, 沙丹, 张晟瑞, 谷勇哲, 张世碧, 李静, 孙君明, 邱丽娟, 李斌. 大豆分枝数QTL定位及候选基因筛选[J]. 作物学报, 2025, 51(7): 1747-1756.
[5] 邵顺伟, 陈卓, 兰振东, 蔡兴奎, 邹华芬, 李晨曦, 唐景华, 朱熙, 张彧, 董建科, 金辉, 宋波涛. 基于BSA-seq技术的块茎芽眼深度QTL定位分析[J]. 作物学报, 2025, 51(7): 1725-1735.
[6] 张金泽, 周庆国, 肖莉晶, 金海润, 欧阳青静, 龙旭, 晏中彬, 田恩堂. 芥菜型油菜不同组织硫苷含量的QTL定位与候选基因分析[J]. 作物学报, 2025, 51(5): 1166-1177.
[7] 郭淑慧, 潘转霞, 赵战胜, 杨六六, 皇甫张龙, 郭宝生, 胡晓丽, 录亚丹, 丁霄, 吴翠翠, 兰刚, 吕贝贝, 谭逢平, 李朋波. 陆地棉D11染色体一个纤维长度主效位点的遗传解析[J]. 作物学报, 2025, 51(2): 383-394.
[8] 王哲, 胡燕灵, 龚方仪, 易睿, 赵书宏, 刘睿琴, 刘雨杭, 张甜, 张亚洲, 郑有良, 刘登才, 黄林, 伍碧华. 基于16K芯片的野生二粒小麦渗入系BAd7-209籽粒蛋白含量QTL定位[J]. 作物学报, 2025, 51(12): 3238-3250.
[9] 徐晓伟, 冯晶, 王凤涛, 童朝阳, 张建周, 李春盈, 蔺瑞明. 小麦地方品种蚕老麦成株抗条锈病QTL定位[J]. 作物学报, 2025, 51(11): 2933-2943.
[10] 杨景发, 余鑫莲, 姚有华, 姚晓华, 王蕾, 吴昆仑, 李新. 青稞分蘖角度的QTL定位[J]. 作物学报, 2025, 51(1): 260-272.
[11] 韩丽, 汤胜胜, 李佳, 胡海斌, 刘龙龙, 吴斌. 燕麦SNP高密度遗传图谱构建及β-葡聚糖含量QTL定位[J]. 作物学报, 2024, 50(7): 1710-1718.
[12] 毕俊鸽, 曾占奎, 李琼, 洪壮壮, 颜群翔, 赵越, 王春平. 两个RIL群体中小麦籽粒品质相关性状QTL定位及KASP标记开发[J]. 作物学报, 2024, 50(7): 1669-1683.
[13] 秦娜, 叶珍言, 朱灿灿, 付森杰, 代书桃, 宋迎辉, 景雅, 王春义, 李君霞. 谷子籽粒类黄酮含量和粒色的QTL定位[J]. 作物学报, 2024, 50(7): 1719-1727.
[14] 郑雪晴, 王兴荣, 张彦军, 龚佃明, 邱法展. 玉米果穗相关性状QTL定位及重要候选基因分析[J]. 作物学报, 2024, 50(6): 1435-1450.
[15] 张月, 王志慧, 淮东欣, 刘念, 姜慧芳, 廖伯寿, 雷永. 花生含油量的遗传基础与QTL定位研究进展[J]. 作物学报, 2024, 50(3): 529-542.
Viewed
Full text


Abstract

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