基于3种遗传统计模型对粳稻米质性状的QTL分析

刘强明1,江建华1,2,牛付安1,赫英俊1,洪德林1,*

doi:10.3969/j.issn.10017216.2012.03.005

中国水稻科学 >

2012 , Vol. 26 >Issue 3: 283 - 290

DOI: https://doi.org/10.3969/j.issn.10017216.2012.03.005

研究报告

基于3种遗传统计模型对粳稻米质性状的QTL分析

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¹ 南京农业大学作物遗传与种质创新国家重点实验室，江苏南京 210095；² 安徽省农业科学院作物研究所，安徽合肥 230031；

收稿日期: 2011-06-07

修回日期: 2011-06-14

网络出版日期: 2012-05-10

基金资助

国家863计划资助项目(2010AA101301)；农业部948计划资助项目\[2006G8(4)311\]；教育部科技基础条件平台重点资助项目(505005)。

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QTL Analysis for Seven Quality Traits of japonica Rice Based on Three Genetic Statistical Models

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¹ State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China; ² Institute of Crops, Anhui Academy of Agricultural Sciences, Hefei 230031, China;

Received date: 2011-06-07

Revised date: 2011-06-14

Online published: 2012-05-10

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摘要

以粳粳交组合秀水79/C堡衍生的254个重组自交系为材料，利用基于混合线性模型的QTLMapper 2.0软件的复合区间作图法(MCIM)、基于逐步回归线性模型的QTL IciMapping 3.0软件的完备复合区间作图法(ICIM)和基于多元回归分析的Windows QTL Cartographer 2.5软件的多区间作图回归前进选择法(MIMR)等3种定位方法，对整精米的粒长、长宽比、垩白粒率、垩白度、直链淀粉含量、糊化温度和胶稠度等7个米质性状进行了QTL分析。结果表明，3种方法同时检测到的具有加性效应的QTL (AQTL)有5个，2种方法同时检测到的AQTL有2个，仅能在1种方法中检测到的AQTL有23个。MCIM、ICIM和MIMR检测到的AQTL个数分别为5、9和28，单个AQTL贡献率为0.89%~38.07%。MIMR检测到的具有上位性效应的QTL (EQTL)在另2种方法中都未被检测到。MCIM 和ICIM同时检测到的EQTL有14对，仅能在1种方法中检测到的EQTL有142对。MCIM、ICIM和MIMR检测到的EQTL对数分别为25、141和4，单对EQTL贡献率为2.60%~23.78%。在秀堡RIL群体中，粒长和垩白度的变异以上位性效应为主，长宽比则以加性效应为主，而垩白粒率、直链淀粉含量、糊化温度和胶稠度为加性效应和上位性效应同等重要。两种及以上方法同时检测到的QTL可靠性高，可用于改良杂交粳稻米质。

关键词： 粳稻; 米质性状; 遗传统计模型; 数量性状基因座

本文引用格式

刘强明1,江建华1,2,牛付安1,赫英俊1,洪德林1,* . 基于3种遗传统计模型对粳稻米质性状的QTL分析[J]. 中国水稻科学, 2012 , 26(3) : 283 -290 . DOI: 10.3969/j.issn.10017216.2012.03.005

Abstract

QTL mapping for seven quality traits was conducted by using 254 recombinant inbred lines derived from a japonicajaponica rice (Oryza sativa L.) cross of Xiushui 79/C Bao. The seven traits were grain length (GL), grain length to width ratio (LWR), percentage of grains with chalkiness (PGWC), degree of endosperm chalkiness (DEC), gelatinization temperature (GT), amylose content (AC) and gel consistency (GC) of headrice. Three mapping methods employed were composite interval mapping in QTLMapper 2.0 software based on mixed linear model (MCIM), the inclusive composite interval mapping in QTL IciMapping 3.0 software based on stepwise regression linear model (ICIM) and the multiple interval mapping with regression forward selection in Windows QTL Cartographer 2.5 based on multiple regression analysis (MIMR). Five QTLs with additive effect (AQTLs) were detected by all the three methods simultaneously, two by two methods simultaneously, and 23 by only one method. Five AQTLs were detected by MCIM, nine by ICIM and 28 by MIMR. The contribution rate of single AQTL ranged from 0.89% to 38.07%. All the QTLs with epistatic effect (EQTLs) detected by MIMR were not detected by the other two methods. Fourteen pairs of EQTLs were detected by both MCIM and ICIM, and 142 pairs of EQTLs were detected by only one method. Twentyfive pairs of EQTLs were detected by MCIM, 141 pairs by ICIM and four pairs by MIMR. The contribution rate of a single pair of EQTL was from 2.60% to 23.78%. In the XiuBao RIL population, epistatic effect played a major role in the variation of GL and DEC, and additive effect was the dominant in the variation of LWR, while epistatic effect and additive effect had equal importance in the variation of PGWC, AC, GT and GC. QTLs detected by two or more methods simultaneously were highly reliable, and could be applied to improvement of the quality in japonica hybrid rice.

Key words： japonica rice; rice quality trait; genetic statistical models; quantitative trait locus

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