大豆产量相关性状的遗传与稳定性分析及QTL定位研究
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摘要
高产、稳产始终是大豆育种的主要目标,但产量相关性状遗传复杂、易受环境影响,表型选择效率不高而限制了产量相关性状的遗传改良。分子标记技术的发展使得在分子水平上对大豆产量相关性状进行遗传改良成为可能,但前提是要发现与产量相关性状紧密相关的QTL位点。
目前,国内外有关大豆产量相关性状的QTL定位研究趋向于进行多年多点田间试验,有关大豆产量相关性状稳定性研究主要停留在传统数量遗传学研究阶段,因此,对大豆产量相关性状进行稳定性分析,并深入研究其数量遗传学特征,十分必要。本研究以中豆29和中豆32通过杂交和连续自交形成的大豆重组自交系群体为研究材料,采用植物数量性状新模型和分子标记技术为研究手段,对大豆产量相关性状进行遗传与稳定性分析及QTL定位研究,取得的主要结果如下:
通过4种环境下的田间试验,利用数量性状主基因+多基因混和遗传模型对大豆重组自交系群体的产量相关性状进行遗传分析,结果表明:主茎节数和长分枝符合多基因模型,短分枝符合2～3对主基因+多基因模型,株高、一粒荚、三粒荚、四粒荚、单株荚数、单株粒数和每荚粒数均符合2对主基因+多基因模型,二粒荚符合3对主基因+多基因模型。对各遗传模型的一阶及二阶遗传参数的估算表明:株高、主茎节数、长分枝和短分枝的遗传主要受多基因控制,一粒荚、二粒荚、三粒荚、四粒荚、单株荚数、单株粒数和每荚粒数的遗传主要受主基因控制。
利用SSR、AFLP、SRAP和形态标记构建了一张大豆遗传连锁图谱,该图谱包含27个连锁群,共计227个标记,遗传距离1310.88 cM,平均标记间距为5.77 cM。根据锚定的SSR标记,该图谱中的26个连锁群可以和公共图谱上相应的连锁群对应,且SSR标记在连锁群上的排列顺序和公共图谱一致,距离相当。
采用复合区间作图法,在6种环境下对14个大豆产量相关性状进行了QTL定位,共检测到154个QTL,分布于21个连锁群中。其中位于15号(F)连锁群EA2MC8-2～Satt554标记区间的qPH-15-1; 19号(I)连锁群LS～Sat_268标记区间的qDP-19-1、qTP-19-1、qFP-19-1、qNSP-19-1和qSW-19-1在5～6种环境中均被重复检测到,且解释了较大的遗传变异,为稳定主效QTL。对QTL定位结果与模型分析结果的比较分析表明,控制相关性状的主效基因数基本相似,但QTL定位分析中检测到的主要基因的数目通常大于模型分析所检测到的主基因的数目。
利用6种环境下估算的Shukla稳定性方差对大豆产量相关性状的稳定性进行了QTL定位分析。采用复合区间作图法,共定位到株高、长分枝等7个性状共19个QTL,分布于8、13、15等7个连锁群中。其中与株高稳定性相关的QTL共检测到2个,分布于15和20连锁群;与长分枝稳定性相关的QTL共检测到2个,分布于24号连锁群不同的标记区间;与总分枝稳定性相关的QTL共检测到3个,分布于20和26号连锁群;与三粒荚稳定性相关的QTL共检测到5个,分布于8、19和20号连锁群;与四粒荚稳定性相关的QTL共定位到2个,分布于15号和19号连锁群;与单株荚数稳定性相关的QTL共定位到1个,位于20号连锁群;与百粒重稳定性相关的QTL共定位到4个,位于13、19和26号连锁群。其中位于19号(I)连锁群的LS～Sat_268标记区间聚集了与三粒荚、四粒荚和百粒重稳定性相关的3个QTL,该区间同时也检测到了与三粒荚、四粒荚和百粒重相关的主效QTL,这可能是三粒荚、四粒荚和百粒重相关主效QTL对其性状的稳定性起到了重要作用。
本研究构建的大豆分子连锁图谱为后续相关性状的QTL定位奠定了基础,大豆产量相关性状主效QTL的初步定位为主效QTL精细定位和分子标记辅助育种提供了依据,大豆产量相关性状稳定性QTL的初步定位为大豆的稳产育种提供了一条新的途径和方法。所获得的QTL将对大豆高产、稳产新品种的选育具有重要的理论指导意义。
High-yield and steady-yield is always the main goal of soybean breeding, but yield traits are genetic complex quantitative traits which are vulnerable to the environmental impact, phenotypic selection efficiency is not high enough to limit the yield traits’genetic improvement. With the development of molecular marker technology, it is possible to improve soybean yield traits at molecular level, but the precondition is to discover the quantitative trait loci (QTL) closely related to the yield traits.
Presently, the researches on mapping QTLs associated with soybean yield related traits tend to conduct multi-environment field trials, while studies on stability of yield related traits stay at the traditional quantitative genetic analysis level. Therefore, it is necessary to conduct researches on stability of yield related traits and their quantitative characters. In this study, a recombinant inbred lines (RIL) population derived from the Zhongdou 29 and Zhongdou 32 cross was used, and new developed inheritance models and molecular marker technology were used to study the above aspects. The main results were as follows:
A mixed major gene plus polygene inheritance model was used to perform the genetic analysis of twelve yield related traits in the RIL population under four environments. The joint segregation analysis results showed that: For number of nodes on main stem(NM) and number of long-branch per plant(NLB), the best fitting genetic models were polygene model; For number of short-branch per plant(NSB), the best fitting model was two or three major genes plus ploygenes model; For plant height(PH), number of one-seed pods(OP), number of three-seed pods(TP), number of four-seed pods(FP), number of pods(NP), number of seeds per plant(NS), number of seeds per pod(NSP), all of the best fitting models were two major genes plus polygenes; For number of double-seed pods(DP), the best fitting model was three major genes plus polygenes. At the same time, the 1st order parameters and the 2nd order parameters of the genetic models for the yield related traits were estimated, the results showed that PH, NM, NLB and NSB were mainly controlled by polygenes, while OP, DP, TP, FP, NP, NS and NSP were mainly controlled by major genes.
Based on the RIL population consisting of 255 lines, a genetic linkage map of soybean genome was constructed, which consisted of 27 linkage groups with 131 simple sequence repeat (SSR) markers, 96 amplified fragment length polymorphism (AFLP) markers, 14 sequence-related amplified polymorphism (SRAP) markers and 2 classical markers. The map covered 1310.88 cM and the average distance between markers was 5.77 cM. Twenty-six linkage groups (LG) of the map fit the“Consensus Linkage Map”well both in the order of arrangement and the distances of the SSR markers.
A total of 154 QTLs for 16 yield related traits under 6 different environments were identified by using the composite interval mapping (CIM) method. One QTL, qPH-15-1, mapped in the interval of EA2MC8-2～Satt554 on LG F, together with five QTL, qDP-19-1, qTP-19-1, qFP-19-1, qNSP-19-1 and qSW-19-1, mapped in the interval of LS～Sat_268 on LG I, were detected in at least five environments and explained most of the variation. Compared to the results in inheritance analysis, it was found that QTL analysis always detected more major genes than those of model analysis.
Shukla’s stability variance of the RIL population and the parents under six different environments were estimated for the stability of soybean yield traits. Using CIM method, nineteen QTLs associated with stability of yield related traits were detected and located on seven linkage groups. Among which, two QTLs related to PH stability were located on LG F and LG G; two QTLs associated with NLB stability were mapped in different marker intervals on LG N; three QTLs contributed to NB stability were identified on LGG and LG O; five QTLs related to TP stability were located on LG C2, LG I and LG G; two QTLs associated to FP stability were mapped on LG F and LG I; one QTL contributed to NP stability was identified on LG G; four QTLs related to weight of 100-seeds(SW) stability were located on LG E, LG I and LG O. The marker interval LS～Sat_268 on LG I clustered three QTLs associated with TP, FP, SW stability and three major QTLs related to TP, FP, SW, the possible reason might be that the major effect QTLs for TP, FP and SW played important roles in the stability of yield related traits.
In this study, the construction of soybean molecular linkage map established the foundation for the follow-up QTL mapping of the relevant traits, the primary QTL mapping of yield related traits provided basis for the further fine mapping of major QTLs and molecular marker-assisted breeding of the related traits. At the same time, mapping of QTLs associated with the stability of soybean yield related traits provided a new approach and methodology for soybean steady-yielding breeding. The genetic information about soybean yield related traits would provide important theoretical guide the high-yield and steady-yield soybean breeding.
目前,国内外有关大豆产量相关性状的QTL定位研究趋向于进行多年多点田间试验,有关大豆产量相关性状稳定性研究主要停留在传统数量遗传学研究阶段,因此,对大豆产量相关性状进行稳定性分析,并深入研究其数量遗传学特征,十分必要。本研究以中豆29和中豆32通过杂交和连续自交形成的大豆重组自交系群体为研究材料,采用植物数量性状新模型和分子标记技术为研究手段,对大豆产量相关性状进行遗传与稳定性分析及QTL定位研究,取得的主要结果如下:
通过4种环境下的田间试验,利用数量性状主基因+多基因混和遗传模型对大豆重组自交系群体的产量相关性状进行遗传分析,结果表明:主茎节数和长分枝符合多基因模型,短分枝符合2～3对主基因+多基因模型,株高、一粒荚、三粒荚、四粒荚、单株荚数、单株粒数和每荚粒数均符合2对主基因+多基因模型,二粒荚符合3对主基因+多基因模型。对各遗传模型的一阶及二阶遗传参数的估算表明:株高、主茎节数、长分枝和短分枝的遗传主要受多基因控制,一粒荚、二粒荚、三粒荚、四粒荚、单株荚数、单株粒数和每荚粒数的遗传主要受主基因控制。
利用SSR、AFLP、SRAP和形态标记构建了一张大豆遗传连锁图谱,该图谱包含27个连锁群,共计227个标记,遗传距离1310.88 cM,平均标记间距为5.77 cM。根据锚定的SSR标记,该图谱中的26个连锁群可以和公共图谱上相应的连锁群对应,且SSR标记在连锁群上的排列顺序和公共图谱一致,距离相当。
采用复合区间作图法,在6种环境下对14个大豆产量相关性状进行了QTL定位,共检测到154个QTL,分布于21个连锁群中。其中位于15号(F)连锁群EA2MC8-2～Satt554标记区间的qPH-15-1; 19号(I)连锁群LS～Sat_268标记区间的qDP-19-1、qTP-19-1、qFP-19-1、qNSP-19-1和qSW-19-1在5～6种环境中均被重复检测到,且解释了较大的遗传变异,为稳定主效QTL。对QTL定位结果与模型分析结果的比较分析表明,控制相关性状的主效基因数基本相似,但QTL定位分析中检测到的主要基因的数目通常大于模型分析所检测到的主基因的数目。
利用6种环境下估算的Shukla稳定性方差对大豆产量相关性状的稳定性进行了QTL定位分析。采用复合区间作图法,共定位到株高、长分枝等7个性状共19个QTL,分布于8、13、15等7个连锁群中。其中与株高稳定性相关的QTL共检测到2个,分布于15和20连锁群;与长分枝稳定性相关的QTL共检测到2个,分布于24号连锁群不同的标记区间;与总分枝稳定性相关的QTL共检测到3个,分布于20和26号连锁群;与三粒荚稳定性相关的QTL共检测到5个,分布于8、19和20号连锁群;与四粒荚稳定性相关的QTL共定位到2个,分布于15号和19号连锁群;与单株荚数稳定性相关的QTL共定位到1个,位于20号连锁群;与百粒重稳定性相关的QTL共定位到4个,位于13、19和26号连锁群。其中位于19号(I)连锁群的LS～Sat_268标记区间聚集了与三粒荚、四粒荚和百粒重稳定性相关的3个QTL,该区间同时也检测到了与三粒荚、四粒荚和百粒重相关的主效QTL,这可能是三粒荚、四粒荚和百粒重相关主效QTL对其性状的稳定性起到了重要作用。
本研究构建的大豆分子连锁图谱为后续相关性状的QTL定位奠定了基础,大豆产量相关性状主效QTL的初步定位为主效QTL精细定位和分子标记辅助育种提供了依据,大豆产量相关性状稳定性QTL的初步定位为大豆的稳产育种提供了一条新的途径和方法。所获得的QTL将对大豆高产、稳产新品种的选育具有重要的理论指导意义。
High-yield and steady-yield is always the main goal of soybean breeding, but yield traits are genetic complex quantitative traits which are vulnerable to the environmental impact, phenotypic selection efficiency is not high enough to limit the yield traits’genetic improvement. With the development of molecular marker technology, it is possible to improve soybean yield traits at molecular level, but the precondition is to discover the quantitative trait loci (QTL) closely related to the yield traits.
Presently, the researches on mapping QTLs associated with soybean yield related traits tend to conduct multi-environment field trials, while studies on stability of yield related traits stay at the traditional quantitative genetic analysis level. Therefore, it is necessary to conduct researches on stability of yield related traits and their quantitative characters. In this study, a recombinant inbred lines (RIL) population derived from the Zhongdou 29 and Zhongdou 32 cross was used, and new developed inheritance models and molecular marker technology were used to study the above aspects. The main results were as follows:
A mixed major gene plus polygene inheritance model was used to perform the genetic analysis of twelve yield related traits in the RIL population under four environments. The joint segregation analysis results showed that: For number of nodes on main stem(NM) and number of long-branch per plant(NLB), the best fitting genetic models were polygene model; For number of short-branch per plant(NSB), the best fitting model was two or three major genes plus ploygenes model; For plant height(PH), number of one-seed pods(OP), number of three-seed pods(TP), number of four-seed pods(FP), number of pods(NP), number of seeds per plant(NS), number of seeds per pod(NSP), all of the best fitting models were two major genes plus polygenes; For number of double-seed pods(DP), the best fitting model was three major genes plus polygenes. At the same time, the 1st order parameters and the 2nd order parameters of the genetic models for the yield related traits were estimated, the results showed that PH, NM, NLB and NSB were mainly controlled by polygenes, while OP, DP, TP, FP, NP, NS and NSP were mainly controlled by major genes.
Based on the RIL population consisting of 255 lines, a genetic linkage map of soybean genome was constructed, which consisted of 27 linkage groups with 131 simple sequence repeat (SSR) markers, 96 amplified fragment length polymorphism (AFLP) markers, 14 sequence-related amplified polymorphism (SRAP) markers and 2 classical markers. The map covered 1310.88 cM and the average distance between markers was 5.77 cM. Twenty-six linkage groups (LG) of the map fit the“Consensus Linkage Map”well both in the order of arrangement and the distances of the SSR markers.
A total of 154 QTLs for 16 yield related traits under 6 different environments were identified by using the composite interval mapping (CIM) method. One QTL, qPH-15-1, mapped in the interval of EA2MC8-2～Satt554 on LG F, together with five QTL, qDP-19-1, qTP-19-1, qFP-19-1, qNSP-19-1 and qSW-19-1, mapped in the interval of LS～Sat_268 on LG I, were detected in at least five environments and explained most of the variation. Compared to the results in inheritance analysis, it was found that QTL analysis always detected more major genes than those of model analysis.
Shukla’s stability variance of the RIL population and the parents under six different environments were estimated for the stability of soybean yield traits. Using CIM method, nineteen QTLs associated with stability of yield related traits were detected and located on seven linkage groups. Among which, two QTLs related to PH stability were located on LG F and LG G; two QTLs associated with NLB stability were mapped in different marker intervals on LG N; three QTLs contributed to NB stability were identified on LGG and LG O; five QTLs related to TP stability were located on LG C2, LG I and LG G; two QTLs associated to FP stability were mapped on LG F and LG I; one QTL contributed to NP stability was identified on LG G; four QTLs related to weight of 100-seeds(SW) stability were located on LG E, LG I and LG O. The marker interval LS～Sat_268 on LG I clustered three QTLs associated with TP, FP, SW stability and three major QTLs related to TP, FP, SW, the possible reason might be that the major effect QTLs for TP, FP and SW played important roles in the stability of yield related traits.
In this study, the construction of soybean molecular linkage map established the foundation for the follow-up QTL mapping of the relevant traits, the primary QTL mapping of yield related traits provided basis for the further fine mapping of major QTLs and molecular marker-assisted breeding of the related traits. At the same time, mapping of QTLs associated with the stability of soybean yield related traits provided a new approach and methodology for soybean steady-yielding breeding. The genetic information about soybean yield related traits would provide important theoretical guide the high-yield and steady-yield soybean breeding.
引文
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