基于Meta分析的大豆百粒重的QTLs定位

 【目的】百粒重是控制大豆产量性状的主要数量性状,对大豆产量性状进行基因定位具有重要的研究和应用价值。现有百粒重QTL定位结果分散,需选择合适的公共图谱,整合前人的研究结果,使其真正应用到实践中。【方法】以2004年发布大豆公共遗传连锁图谱soymap2为参考图谱,将近20年不同试验中的大豆百粒重的QTLs进行映射整合,构建百粒重QTL综合图谱。利用BioMercator2.1的映射功能将国内外常用的大豆图谱上的百粒重QTLs通过公共标记映射整合到大豆公共遗传连锁图谱soymap2上,并利用Meta分析,通过对比已经报道的QTLs的95%的置信区间来推断QTL位置,从而提取真正有效的QTL标记。【结果】在已经发表的文献中共找到65个百粒重QTLs定位信息,其中有53个QTLs定位区间与公共图谱有共有标记,包括36个增效效应的QTLs和17个减效效应的百粒重QTLs,共得到12个QTL簇,通过Meta分析,发掘出6个增效效应和6个减效效应的百粒重“通用QTLs”及其连锁标记。【结论】本研究得到的“通用QTLs”其置信区间最小可达到1.52 cM,为辅助选择分子标记、QTL精细定位以及数量性状基因的克隆奠定基础。     

大豆粒形性状与百粒重的QTL定位

文章选用东农46和L-100杂交构建的F2:10、F2:11代大豆重组自交系群体127个家系为作图群体,通过全基因组重测序技术,开发bin标记,构建高密度遗传连锁图谱,结合两年四点的大豆粒形性状和百粒重表型数据,利用IciMapping 4.0软件的完备区间作图法作加性QTLs和QTLs间上位性互作检测。结果表明,经粒形性状和百粒重主效QTLs检测,获得81个与大豆粒长、粒宽、粒厚、粒体积和百粒重相关QTLs,分布于18条染色体,贡献率1.66%～30.70%,其中贡献率最高位点分别为qSL-4-2(23.85%)、qSW-1-1(15.40%)、qST-1-2(17.66%)、qSV-15-1(30.70%)和q100-SW-19-1(15.43%);经相关性状加性×加性上位性互作检测,获得43对大豆粒形性状和百粒重加性×加性上位互作效应QTLs,贡献率1.41%～23.19%。     

RTM-GWAS方法应用于大豆RIL群体百粒重QTL检测的功效

【目的】为全面解析大豆重组自交系群体中调控百粒重性状的QTL体系,将限制性两阶段多位点全基因组关联分析方法(RTM-GWAS)和不同定位方法进行比较、优选,为后续候选基因体系探索及分子标记辅助育种设计提供依据。【方法】利用以科丰1号和南农1138-2为亲本衍生的重组自交系群体NJRIKY的427个家系,通过由全基因组39 353个SNP构建的3 683个SNPLDB标记及3个环境下的百粒重表型数据,选用复合区间作图法(CIM)、基于混合线性模型的全基因组关联分析方法(MLM-GWAS)和RTM-GWAS 3种方法检测百粒重QTL,通过QTL数目和总的表型变异解释率比较检测功效,挑选最佳定位结果进行NJRIKY群体中的百粒重遗传体系解析。通过候选基因体系的功能注释,挖掘调控大豆百粒重的生物学途径。【结果】科丰1号与南农1138-2的百粒重差异较大,多环境平均数分别为9.0和17.9 g,遗传变异系数为12.4%,遗传率为85.4%,适用于百粒重性状的遗传解析。比较3种方法定位结果表明RTM-GWAS方法表现最佳,检测QTL数目最多(57个),解释表型变异最多(70.78%)。而CIM仅检...     

大豆农艺性状的QTL分析

[目的]分析大豆农艺性状的QTL,为探讨大豆的遗传机制及进行遗传育种提供参考。[方法]应用复合区间作图法对蛋白质含量、脂肪含量、产量、百粒重、生育期等5个数量性状进行QTL定位和遗传效应分析。[结果]控制蛋白质含量、脂肪含量、产量、百粒重、生育期性状的4、4、1、2、5个共16个QTL位点,遗传贡献率在7.4%～33.7%。其中,遗传贡献率较大的主效QTL有分别位于I连锁群上Satt562-Sat_219、Sat_219-Satt496、Sat_219-Satt496区间的3个控制蛋白质含量的QTL位点,其遗传贡献率分别为29.15%、33.70%和31.67%,且均为来自母本合丰25的加效基因,还有位于O连锁群上Satt477-Satt331、Satt331-Satt153区间的2个控制生育期QTL位点,其遗传贡献率分别为24.69%和24.96%,也是来自母本合丰25的加效基因。另外,6个分别距M连锁群Satt175（蛋白质）、A1连锁群Satt684（油分）、F连锁群Satt348（油分）、J连锁群Sat_412（油分）、C1连锁群Sat_416（百粒重）、C1连锁群Sat_416（生育期）标记仅有0.01 cm的QTL位点。[结论]定位了影响蛋白质含量、油分含量、产量、百粒重和生育期等5个重要农艺性状的QTL位点。     

大豆重组自交系群体NJRSXG百粒重超亲分离的遗传解析

【目的】解析大豆重组自交系中百粒重的QTL及其等位变异效应,探究重组自交系中百粒重存在超亲分离的原因,为进一步培育不同类型百粒重大豆提供遗传依据。【方法】利用以先进2号和赶泰2-2为亲本衍生的重组自交系群体NJRSXG为材料,在2009-2011年共5种环境下测定百粒重表型数据,建立具有400个SSR标记的遗传图谱,选用QTLNetwork V2.1软件中混合模型区间作图方法（mixed model based composite interval mapping,MCIM）对表型数据和基因型数据进行大豆百粒重QTL定位研究。在定位结果基础上,分析每个重组自交系群体中每个家系百粒重QTL等位变异类型,建立百粒重QTL-allele矩阵。【结果】5种环境试验的平均结果,亲本先进2号和赶泰2-2的百粒重分别为16.92和14.14g,重组自交系百粒重变幅为12.09-25.01 g,存在超亲分离,多环境下遗传变异系数（genotypic coefficient of variation, GCV）为16.06%,遗传率为96.17%。利用MCIM方法联合5环境原始数据,总共检测到10个加性QTL和9对上位性QTL,10个加性QTL的表型变异解释率变幅为0.69%-14.93%,其中Sw-05-2、Sw-08-1、Sw-12-1和Sw-17-1的表型变异解释率较高,分别为6.91%、14.93%、7.80%和5.01%,Sw-13-3为以往未见报道并兼具加性和上位性效应的位点。上位性QTL的表型变异解释率较小,变幅为0.31%-3.44%,其中Sw-e4的表型变异解释率最高。联合多环境方差分析和QTL定位结果,解析大豆百粒重的遗传结构,发现加性QTL累积贡献了47.91%表型变异,上位性QTL累积贡献13.06%表型变异,未检测出的微效QTL累计解释了35.20%的表型变异。在定位的同时,获得了QTL等位变异的效应,分析重组自交系及其亲本中百粒重QTL等位变异的组成,建立了NJRSXG的百粒重QTL-allele矩阵;两亲本分别具有7对和3对加性增效等位变异,属互补型组合;矩阵中没有一个重组自交家系包含所有减效等位变异或增效等位变异,表明重组自交家系具有进一步改良的潜力;大粒型家系具有较多增效等位变异,小粒型家系具有较多减效等位变异;说明百粒重位点间的重组是产生超亲家系的重要原因。【结论】利用重组自交系群体能够产生超亲分离家系;联合多环境数据检测到10个加性QTL和9对上位性QTL;百粒重QTL位点间的重组是超亲分离的原因;重组自交家系间具有进一步重组的潜力。     

水稻第6染色体短臂产量性状QTL簇的分解

【目的】将水稻第6染色体短臂上产量性状QTL分解到更小的区间中。【方法】从珍汕97B/密阳46重组自交系群体筛选到针对第6染色体短臂RM587-RM19784区间的剩余杂合体,衍生了一个由221个株系组成的F2:3群体,种植于海南和浙江两地,考察每株穗数、每穗实粒数、每穗总粒数、千粒重、结实率和单株产量,建立SSR标记连锁图,应用Windows QTL Cartographer 2.5检测QTL。【结果】在所分析的6个性状中,除穗数外在第6染色体短臂上的目标区间均检测到QTL,分别座落于目标区域中3个以上的不同区间中,单个QTL对群体性状表型变异的贡献率为6.3%～35.2%;控制产量构成因子的QTL基本以加性作用为主,但3个单株产量QTL的显性度分别为1.65、0.84和0.42。【结论】目标区间存在3个以上的产量性状QTL,且同一区间控制不同性状的QTL、不同区间控制同一个性状的QTL在遗传作用模式、效应方向和效应大小上存在一定差异。     

Dissection of QTLs for Yield Traits on the Short Arm of Rice Chromosome 6

This study was undertaken to dissect quantitative trait loci （QTLs） controlling yield traits on the short arm of rice chromosome 6. A residual heterozygous line that carries a heterozygous segment extending from RM587 to RM19784 on the short arm of rice chromosome 6 was selected from an F7 population of the indica rice cross Zhenshan 97B/Milyang 46. An F2：3 population consisting of 221 lines was derived and grown in two trial sites. Six yield traits including number of panicles per plant, number of filled grains per panicle, total number of spikelets per panicle, spikelet fertility, 1 000-grain weight, and grain yield per plant were measured. An SSR marker linkage map was constructed and employed to determine QTLs for yield traits with Windows QTL Cartographer 2.5. QTLs were detected in the target interval for all the traits analyzed except NP, with phenotypic variance explained by a single QTL ranging between 6.3% and 35.2%. Most of the QTLs for yield components acted as additive QTLs, while the three QTLs for grain yield had dominance degrees of 1.65, 0.84, and -0.42, respectively. It was indicated that three or more QTLs for yield traits were located in the target region. The genetic action mode, the direction of the QTL effect, and the magnitude of the QTL effect varied among different QTLs for a given trait, and among QTLs for different traits that were located in the same interval.     

An Integrated QTL Map of Fungal Disease Resistance in Soybean(Glycine max L.Merr):A Method of Meta-Analysis for Mining R Genes

Diseases caused by fungal pathogens account for approximately 50% of all soybean disease losses around the world.Conflicting results of fungal disease resistance QTLs from different populations often occurred.The objectives of this study were to:(i)evaluate evidence for reported fungal disease resistance QTLs associations in soybean and(ii)extract relatively reliable and useful information from the "real" QTLs and mine putative genes in soybean.An integrated map of fungal disease resistance QTLs in soybean was established with soymap 2 published in 2004 as a reference map.QTLs of fungal disease resistance developed from each of separate populations in recent 10 years were integrated into a combinative map for gene cloning and marker assisted selection in soybean.107 QTLs from different maps were integrated and projected to the reference map with the software BioMercator 2.1.A method of meta-analysis was used to narrow down the confidence interval,and 23 "real" QTLs and their corresponding markers were obtained from 12 linkage groups(LG),respectively.Two published R genes were found in these "real" QTLs intervals.Sequences in the "real" QTLs intervals were predicted by GENSCAN,and these predicted genes were annotated in Goblet.228 resistance gene analogs(RGAs)in 12 different terms were mined.The results will lay the foundation for a bioinformatics platform combining abundant QTLs,and offer the basis for marker assisted selection and gene cloning in soybean.     

Gene Mapping of Brachytic Stem and Its Effect on Main Agronomic Traits in Soybean

Brachytic stem is a major trait in plant type .of soybean and its yield potential may be higher under high population when compared with normal stem. In the present investigation, 152 recombinant inbred line （RIL） families derived from the cross of Bogao （normal stem） and Nannong 94-156 （brachytic stem） were used to map genes and QTLs of three plant type traits and to identify the effects of brachytic stem on agronomic traits such as yield. The primary results indicated that brachytic stem （sb） and determinate growth habit （drl） were mapped on linkage groups B2 and L, three major QTLs related to plant height were detected and mapped on linkage group L near drl, another minor QTL was mapped near sb on linkage group B2-1. Lines with brachytic stem had shorter plant height, lower biomass, yield, harvest index and pods per plant, and essentially no differences in days to maturity and 100-seed weight when compared with normal stem lines. It was obvious that the effect of brachytic stem on yield was due to the decreased height, biomass and harvest index.     

基于SNP遗传图谱定位甘蓝型油菜千粒重QTL位点

【目的】甘蓝型油菜籽粒重量是构成油菜单株产量的三大因素之一（单株有效角果数、每角果粒数、粒重）,是重要的育种目标。通过对5种环境下甘蓝型油菜千粒重进行QTL定位分析,寻找甘蓝型油菜千粒重的QTL及影响本甘蓝型油菜群体千粒重的候选基因。【方法】利用重组自交系群体在德国吉森、重庆北碚5种不同的环境下,测定各株系天然种子千粒重。利用重庆市油菜工程技术研究中心实验室构建的SNP高密度遗传图谱扫描5种环境中的千粒重QTL。该遗传图谱包括2 795个SNP位点,覆盖甘蓝型油菜基因组1 832.9 cM,标记之间的平均距离为0.66 cM。利用Windows QTL Cartographer2.5复合区间作图法对千粒重进行QTL定位。将49个拟南芥粒重相关基因与QTL对应置信区间序列进行同源比较分析（E值<1E^–21）,找出可能与甘蓝型油菜千粒重关联的候选基因。【结果】5种环境中千粒重变异范围较大,且均呈现正态分布,符合QTL定位要求。在5种环境之间千粒重均表现出正相关,其中,2013北碚与2012北碚、2008年吉森达到极显著水平,相关系数分别为0.248和0.249;2012年北碚与2010年北碚、2011年北碚及2008年吉森达到显著相关,相关系数分别为0.226、0.397和0.190。5种环境中共检测到14个QTL,分布在9条染色体,其中,C03染色体3个,A06、A07和C01各有2个,A03、A05、A08、A10和C02染色体上各有1个,LOD值在2.57-6.05,单个QTL解释的表型变异为4.64%-14.13%。与拟南芥粒重基因进行同源性分析,有16个粒重相关基因落在8个QTL置信区间,匹配E值介于0-2E^-21。其中QTL qTSWA07-2区间内筛出7个粒重基因。粒重基因TTG2在qTSWA03-1与qTSWC02-1 2个QTL区间内均被检测到。AHK3在qTSWA07-2、qTSWA08-1与qTSWC01-1区间内被检测到。【结论】利用该套油菜60K芯片准确定位了5种环境条件千粒重的QTL位点,与拟南芥粒重基因比对出该群体油菜粒重基因,该结果有利于不同材料在使用该套SNP芯片分析及对千粒重QTL位点的比对和候选基因的分析。     

An Integrated QTL Map of Fungal Disease Resistance in Soybean (Glycine max L. Merr):A Method of Meta-Analysis for Mining R Genes

Diseases caused by fungal pathogens account for approximately 50% of all soybean disease losses around the world. Conflicting results of fungal disease resistance QTLs from different populations often occurred. The objectives of this study were to: (i) evaluate evidence for reported fungal disease resistance QTLs associations in soybean and (ii) extract relatively reliable and useful information from the "real" QTLs and mine putative genes in soybean. An integrated map of fungal disease resistance QTLs in soybean was established with soymap 2 published in 2004 as a reference map. QTLs of fungal disease resistance developed from each of separate populations in recent 10 years were integrated into a combinative map for gene cloning and marker assisted selection in soybean. 107 QTLs from different maps were integrated and projected to the reference map with the software BioMercator 2.1. A method of meta-analysis was used to narrow down the confidence interval, and 23 "real" QTLs and their corresponding markers were obtained from 12 linkage groups (LG), respectively. Two published R genes were found in these "real" QTLs intervals. Sequences in the "real" QTLs intervals were predicted by GENSCAN, and these predicted genes were annotated in Goblet. 228 resistance gene analogs (RGAs) in 12 different terms were mined. The results will lay the foundation for a bioinformatics platform combining abundant QTLs, and offer the basis for marker assisted selection and gene cloning in soybean.     

An Integrated QTL Map of Fungal Disease Resistance in Soybean （G/ycine max L. Merr）： A Method of Meta-Analysis for Mining R Genes

Diseases caused by fungal pathogens account for approximately 50% of all soybean disease losses around the world. Conflicting results of fungal disease resistance QTLs from different populations often occurred. The objectives of this study were to： （i） evaluate evidence for reported fungal disease resistance QTLs associations in soybean and （ii） extract relatively reliable and useful information from the ＂real＂ QTLs and mine putative genes in soybean. An integrated map of fungal disease resistance QTLs in soybean was established with soymap 2 published in 2004 as a reference map. QTLs of fungal disease resistance developed from each of separate populations in recent 10 years were integrated into a combinative map for gene cloning and marker assisted selection in soybean. 107 QTLs from different maps were integrated and projected to the reference map with the software BioMercator 2.1. A method of meta-analysis was used to narrow down the confidence interval, and 23 ＂real＂ QTLs and their corresponding markers were obtained from 12 linkage groups （LG）, respectively. Two published R genes were found in these ＂real＂ QTLs intervals. Sequences in the ＂real＂ QTLs intervals were predicted by GENSCAN, and these predicted genes were annotated in Goblet. 228 resistance gene analogs （RGAs） in 12 different terms were mined. The results will lay the foundation for a bioinformatics platform combining abundant QTLs, and offer the basis for marker assisted selection and gene cloning in soybean.     

夏大豆重组自交系群体遗传图谱构建及开花期QTL分析

【背景】开花期是大豆重要的生育期性状,不仅决定了大豆品种的适种范围,而且对大豆的产量和品质有重要影响。江淮地区是中国重要的大豆产区,目前对该地区夏大豆开花期性状遗传基础研究相对较少。【目的】利用2个夏大豆材料杂交衍生的重组自交系群体对开花期进行QTL定位,为分子标记辅助选择育种和基因克隆提供依据。【方法】以科丰35(KF35)和南农1138-2(NN1138-2)为亲本,构建了含91个家系(F2:8)的重组自交系群体(NJK3N-RIL),在6个环境下调查开花期性状数据。利用限制位点相关DNA测序(restriction-site associated DNA sequencing,RAD-seq)技术对群体亲本及家系材料进行SNP标记分型,并利用窗口滑动法进行bin标记划分。利用bin标记构建该群体的遗传图谱,结合多年多点的表型数据,使用QTL Network 2.2软件中的基于混合线性模型的复合区间作图法(mixed-model based composite interval mapping,MCIM)和Windows QTL Cartographer V2.5₀     

基于Meta分析的猪肌内脂肪QTL整合定位

【目的】通过Meta分析,用数学模型分析与优化定位分散的猪肌内脂肪QTL,提高QTL定位的准确度和有效性,为猪肌内脂肪相关基因的精细定位和基因挖掘奠定基础。【方法】收集猪肌内脂肪QTL及其相关信息,以美国肉畜研究中心（USDA-MARC 2.0）公布的猪遗传连锁图谱为参考图谱,利用BioMercator2.1将各QTL映射到参考图谱上,构建新的整合图谱,得到QTL簇。对得到的QTL簇进行Meta分析,缩短置信区间,定位“真实”QTL(MQTL),减少QTL的定位误差。【结果】收集了67个猪肌内脂肪QTL及相关信息,经比对、映射,构建新的整合图谱,发现了12个QTL簇。通过Meta分析,得到12个MQTL(MQTL1～MQTL12),其图距比原平均图距缩小29.16%～87.40%,其中,MQTL3、MQTL5、MQTL6、MQTL7、MQTL9、MQTL12图距较原平均图距缩小比例均超过50%,其图距分别为7.76,6.72,5.20,19.45,15.61,9.37 cM。【结论】得到了12个猪IMF的MQTL,其图距比原平均图距均有不同程度缩小,最小仅5.20 cM,图距缩小比例最大可达87.40%,提高了QTL定位的准确度和有效性。     

大豆产量有关性状QTL的检测

 【目的】研究大豆产量和生物量、叶面积指数、冠层以及产量构成因素间的相关性,定位控制这些性状的QTL。【方法】以地理和遗传来源均有较大差异的北方亲本科丰1号和南方亲本南农1138-2所衍生的184个重组自交家系2年有重复的田间试验结果进行产量有关性状的QTL分析。【结果】（1）产量与地上部生物量、叶面积指数、根重、冠层宽和高等均有极显著正相关,相关系数0.5～0.7。（2）地上部生物量检测到7个QTL,贡献率6.2%～21.1%,其中2年重复检出1个（qSBO-1）;根重8个QTL,贡献率5.2%～20.1%,重复检出1个（qRTB1-1）。（3）开花期叶面积指数5个QTL,贡献率6.4%～17.2%;结荚期叶面积指数5个QTL,贡献率7.3%～26.2%,重复检出1个（qLAIR3A2）;冠层宽4个QTL,贡献率6.3%～13.1%,重复检出1个（qCWD1b-2）;冠层高11个QTL,贡献率5.2%～9.2%,重复检出4个（qCHH-1、qCHO-1、qCHO-2和qCHO-3）。（4）百粒重6个,荚粒数2个,荚数1个QTL,贡献率6.9%～15.7%;分枝荚数5个,主茎荚数3个QTL,贡献率6.3%～11.1%;主茎节数8个QTL,有效分枝数3个QTL,贡献率4.7%～15.2%。（5）根重和地上部生物量各有1个,R1(始花期)和R3(始荚期)叶面积指数各有2个,冠层宽和高各有2个,产量与荚数各有1个,百粒重和分枝荚数各有1个,荚粒数和主茎节数各有1个,分枝荚数与有效分枝数各有1个共享的QTL。【结论】大豆产量有关的13个性状共检测到68个QTL;年份间有重复检出的,但不多,其表达较大程度上与环境有关;尽管性状间普遍有相关、有共享的QTL,但不多,各有其遗传体系;产量有关性状中很少有贡献率大的主效QTL,产量育种要考虑多数基因聚合的技术。     

水稻产量因子的遗传主效应及基因型和环境互作效应的QTL分析(英文)

在构建DH群体RFLP图谱的基础上定位了产量因子的数量性状位点(QTL).在杭州和海南两地分别种植包括123个DH原的DH群体及其亲本IR64和Azucena,并对产量因子性状进行了测定.运用调整无偏预测(AUP)法预测遗传主效应值和GE互作效应值,并用于QTL定位.结果表明,一些有主效应的QTL同时具有QTL×环境(QE)互作效应,而一些没有主效应的QTL也可以有QE互作效应.研究还表明,QTL对环境的敏感性不同,有的QTL只能在一个环境中检测到,而另一些QTL能在二个环境中都检测到.产量因子包括总粒数和实粒数的QTL无论是主效还是QE互作效应均具有较大的加性效应值,这些QTL在两个环境中起主基因的作用.     

利用BSA法发掘野生大豆种子硬实性相关QTL

【目的】野生大豆的硬实性是大豆遗传改良利用中的重要限制因素。利用BSA法发掘与大豆种子硬实性相关的QTL,为野生大豆在大豆遗传改良中的合理利用奠定基础。【方法】利用栽培大豆中黄39与野生大豆NY27-38杂交构建F₂和F₇分离群体,从每个单株选取整齐一致的种子,取30粒种子置于铺有一层滤纸的培养皿中,加入30 mL蒸馏水,25℃培养箱中暗处理4 h,设3次重复,分别统计每个培养皿中正常吸胀和硬实种子数。在F₂群体中,选取22个正常吸胀单株（吸胀率＞90%）和16个硬实单株（吸胀率＜10%）;在F₇群体中,选取20个完全吸胀单株（吸胀率=100%）和20个完全硬实单株（吸胀率=0%）,单株DNA等量混合,分别构建2个吸胀和2个硬实DNA池。利用259对在亲本间有多态性的SSR标记对吸胀和硬实DNA池进行检测,筛选在吸胀和硬实DNA池间表现多态性的SSR标记;用192个SSR标记检测F₇分离群体,构建遗传图谱,利用复合区间作图法定位大豆硬实相关QTL。【结果】利用F₂个体构建的吸胀和硬实DNA池,在第2染色体16.3 Mb区间和第6染色体23.4 Mb区间分别检测到10个和8个在两池间有差异的SSR标记。利用这些标记检测F₂群体,将第2染色体的QTL定位于Satt274与Sat_198间的276.0 kb区间,该区间包括已克隆的大豆硬实基因GmHs1-1,解释17.2%的表型变异。第6染色体的QTL位于标记BARCSOYSSR_06_0993与BARCSOYSSR_06_1068间,可解释17.8%的表型变异。利用F₇株系构建的吸胀和硬实DNA池,在第2（27.4 Mb区间）、6（27.8 Mb 区间）和3染色体（18.2 Mb区间）分别检测到11个、9个和4个在两池间有多态性的SSR标记。利用F₇群体构建包括192个SSR标记、覆盖2 390.2 cM的遗传图谱,共检测到3个硬实相关QTL,其中第2染色体定位到的QTL位于标记Satt274与Sat_198间,可解释23.3%的遗传变异。第6染色体定位到的QTL位于标记Sat_402与Satt557之间,可解释20.4%的表型变异。在第3染色体标记Sat_266与Sat_236间发现一个可以解释4.9%表型变异的QTL,与BSA法检测的结果相符。【结论】利用BSA法可以检测到传统遗传作图定位的所有与硬实性相关的QTL,证明BSA法发掘大豆种子硬实性主要QTL的高效性。     

QTL Mapping of Hard Seededness in Wild Soybean Using BSA Method

【Objective】 Hard seededness of wild soybean is an important effector that limits the utilization of wild resources in soybean genetic improvement. Bulked segregant analysis (BSA) was employed to identify major quantitative trait loci (QTLs) related with hard seededness in soybean, which laid a foundation for effective utilization of wild soybean germplasm in cultivated soybean improvement. 【Method】 F₂ and F₇ segregation populations were constructed from a cross between cultivated soybean Zhonghuang39 and wild soybean NY27-38. Uniformly sized seeds were selected from each line, and 30 seeds were soaked in a petri dish with 30 mL distilled water for 4 hours at 25℃. The assay was replicated 3 times. The number of permeable and impermeable seeds were counted. In F₂ population, the first DNA pool was constructed from 22 individuals with permeable seeds (imbibition rate ＞90%), and second DNA pool was constructed from 16 individuals with impermeable seeds (imbibition rate ＜10%). In F₇ population, 20 lines with permeable seeds (100% imbibition) and 20 lines with impermeable seeds (no imbibition) were used to construct two DNA pools, respectively. To detect genomic regions associated with hard seededness, these DNA bulks were genotyped with 259 polymorphic SSR markers to identify markers linked to QTL. A linkage map was constructed with 192 SSR markers, QTLs related with hard seededness were identified by composite interval mapping in F₇ segregation population. 【Result】 Out of 259 SSR loci polymorphic between Zhonghuang39 and NY27-38, 10 and eight polymorphic SSR markers between the permeable and impermeable pools were detected in 16.3 Mb interval on chromosome 2 and 23.4 Mb interval on chromosome 6, respectively, in F₂ population. The QTL region (276.0 kb) located between Satt274 and Sat_198 on chromosome 2 contained previously cloned gene GmHs1-1, the QTL explained 17.2% of the total genetic variation. The other QTL was mapped on chromosome 6 flanked by BARCSOYSSR_06_0993 and BARCSOYSSR_06_1068, accounting for 17.8% of the total genetic variation. In F₇ population, eleven, nine and four SSR polymorphic markers between the permeable and impermeable pools were detected in 27.4 Mb interval on chromosome 2, 27.8 Mb interval on chromosome 6, 18.2 Mb interval on chromosome 3, respectively. A linkage map of 192 SSR markers and covering 2 390.2 cM was constructed through composite interval mapping in F₇ population. Three QTLs related with hard seededness were detected. The QTL on chromosome 2 located between Satt274 and Sat_198, explained 23.3% of the total genetic variation; the QTL on chromosome 6 flanked by Sat_402 and Satt557, explained 20.4% of the total genetic variation; the QTL on chromosome 3 flanked by Sat_266 and Sat_236 accounted for 4.9% of the total genetic variation. 【Conclusion】 In this study, three QTLs related to soybean hard seededness were identified by both BSA and traditional linkage mapping, indicating that BSA is an effective strategy for identifying QTLs in soybean.     

基于Meta分析的猪后腿性状QTL整合定位

【目的】通过Meta分析,利用数学模型整合与优化猪后腿腿臀质量、腿臀肉质量和腿臀比性状的QTL,提高QTL定位的准确度和有效性,为猪后腿性状QTL的精细定位和分子辅助育种奠定基础。【方法】收集猪后腿腿臀质量,腿臀肉质量和腿臀比性状的QTL及其相关信息,利用BioMercator2.1,将原始QTL映射到美国肉畜研究中心（USDA-MARC 2.0）公布的猪遗传连锁图谱,构建新的整合图谱,分析得到QTL簇。进一步对各QTL簇进行Meta分析,定位“真实”QTL（MQTL）,缩短95%置信区间,减少定位误差。【结果】收集了93个猪后腿性状的QTL及其相关信息,经比对、映射,构建了新的整合图谱,发现19个QTL簇。通过Meta分析,得到19个MQTL,其图距比原平均图距缩短16.19%～78.96%,其中,MQTL1、MQTL5、MQTL6、MQTL8、MQTL9、MQTL10、MQTL11、MQTL12和MQTL17等9个MQTL图距的缩短比例均超过50%。【结论】Meta分析得到的MQTL图距均有不同程度缩短,最小的仅1.75 cM,缩短比例最大可达78.96%,提高了QTL定位的准确度和有效性。     

多环境下野生大豆染色体片段代换系群体农艺性状相关QTL/片段的鉴定

【目的】改进染色体片段代换系群体,挖掘野生大豆（Glycine soja Sieb. et Zucc.）中蕴藏的农艺性状优异等位变异,为拓宽栽培大豆（Glycine max (L.) Merr.）的遗传基础提供材料和依据。【方法】通过标记加密和剔除部分单标记型片段的方法,改进以野生大豆N24852为供体,栽培大豆NN1138-2为受体的染色体片段代换系（CSSL）群体SojaCSSLP1;对改进后的群体（SojaCSSLP2）进行3年2点田间试验,通过单标记分析、区间作图、完备复合区间作图和基于混合线性模型的复合区间作图等4种定位方法,结合与轮回亲本有显著差异的染色体片段代换系间相互比对,检测与大豆开花期、株高、主茎节数、单株荚数、百粒重和单株粒重相关的野生片段。【结果】改进后的群体（SojaCSSLP2）由150个CSSL构成,其中,有130个家系与SojaCSSLP1相同;在原遗传图谱上,新增40个SSR标记,相邻标记间平均遗传距离由16.15 cM变为12.91 cM,大于20 cM的区段由32个减少至17个,标记覆盖遗传距离总长度较原图谱（2 063.04 cM）增加103.52 cM;群体NN1138-2背景回复率变幅为79.45%-99.70%,平均为94.62%。利用SojaCSSLP2群体,分别鉴定到与开花期、株高、主茎节数、单株荚数、百粒重和单株粒重相关的4、5、5、7、14和3个工作QTL（working QTL）/片段,其中有15个工作QTL/片段能在多个环境下检测到,属共性工作QTL（joint working QTL）;除片段Sct_190-Sat_293上的主茎节数位点外,野生等位变异具有的加性效应方向与双亲表型差异方向一致;单个位点分别能解释5%-64%的表型变异;同时,分别检测到3、2和2个与地点存在互作的株高、主茎节数和单株荚数QTL/片段,其中与凤阳环境的互作均具有增加表型的效应,这可能与凤阳较南京所处纬度高有关;这些位点/片段分布在26个染色体片段上,其中有7个片段与2个及以上性状相关,可能是性状相关的遗传基础;与前人结果比较,有3个开花期、3个株高、2个主茎节数、2个单株荚数、8个百粒重、2个单株粒重位点能在其他遗传背景栽培大豆中检测到,说明在这些位点上野生大豆和栽培大豆间及栽培大豆间均存在遗传差异;另外18个位点（片段）为本研究利用野生大豆的新发现。【结论】大豆开花期、株高和主茎节数的遗传基础较百粒重简单,前者均存在效应较大位点/片段,后者多由小效应位点控制,遗传基础极为复杂;野生大豆中蕴藏着新的等位变异,能拓宽栽培大豆遗传基础。