QTL × genetic background interaction: predicting inbred progeny value

Failures of the additive infinitesimal model continue to provide incentive to study other modes of gene action, in particular, epistasis. Epistasis can be modeled as a QTL by genetic background interaction. Association mapping models lend themselves to fitting such an interaction because they often include both main marker and genetic background factors. In this study, I review a model that fits the QTL by background interaction as an added random effect in the now standard mixed model framework of association analyses. The model is applied to four-generation pedigrees where the objective is to predict the genotypic values of fourth-generation individuals that have not been phenotyped. In particular, I look at how well epistatic effects are estimated under two levels of inbreeding. Interaction detection power was 8% and 65% for pedigrees of 240 randomly mated individuals when the interaction generated 6% and 20% of the phenotypic variance, respectively. Power increased to 21% and 94% for these conditions when evaluated individuals were inbred by selfing four times. The interaction variance was estimated in an unbiased way under both levels of inbreeding, but its mean squared error was reduced by 40% to 70% when estimated in inbred relative to randomly mated individuals. The performance of the epistatic model was also enhanced relative to the additive model by inbreeding. These results are promising for the application of the model to typically self-pollinating crops such as wheat and soybean. The U.S. Government's right to retain a non-exclusive, royalty-free license in and to any copyright is acknowledged.     

Mapping QTLs with epistatic effects and QTL × treatment interactions for salt tolerance at seedling stage of wheat

Quantitative trait loci (QTLs) with additive (a), additive × additive (aa) epistatic effects, and their treatmental interactions (at and aat) were studied under salt stress and normal conditions at seedling stage of wheat (Triticum aestivum L.). A set of 182 recombinant inbred lines (RILs) derived from cross Xiaoyan 54 × Jing 411 were used. A total of 29 additive QTLs and 17 epistasis were detected for 12 traits examined, among which eight and seven, respectively, were identified to have QTL × treatment effects. Physiological traits rather than biomass traits were more likely to be involved in QTL × treatment interactions. Ten intervals on chromosomes 1A, 1D, 2A (two), 2D, 3B, 4B, 5A, 5B and 7D showed overlapping QTLs for different traits; some of them represent a single locus affecting different traits and/or the same trait under both treatments. Eleven pairs of QTLs were detected on seemingly homoeologous positions of six chromosome groups of wheat, showing synteny among the A, B and D genomes. Ten pairs were detected in which each pair was contributed by the same parent, indicating a strong genetic plasticity of the QTLs. The results are helpful for understanding the genetic basis of salt tolerance in wheat and provide useful information for genetic improvement of salt tolerance in wheat by marker-assisted selection.     

Detection of downy and powdery mildew resistance QTL in a ‘Regent’ × ‘RedGlobe’ population

One hundred and eighty six F₁ plants from a ‘Regent’ × ‘RedGlobe’ cross were used to generate a partial linkage map with 139 microsatellite markers spanning all 19 chromosomes. Phenotypic scores for downy mildew, taken over two years, confirmed a major resistance QTL (Rpv3) against downy mildew in the interval VVIN16-cjvh to UDV108 on chromosome 18 of ‘Regent’. This locus explained up to 62 % of the phenotypic variance observed. Additionally a putative minor downy mildew resistance locus was observed on chromosome 1 in one season. A major resistance locus against powdery mildew (Ren3) was also identified on chromosome 15 of ‘Regent’ in the interval UDV116 to VChr15CenGen06. This study established the efficacy of and validated the ‘Regent’-derived downy and powdery mildew major resistance genes/QTL under South African conditions. Closely linked SSR markers for marker-assisted selection and gene pyramiding strategies were identified.     

Impact of epistasis and QTL × environmental interaction on the oil filling rate of soybean seed at different developmental stages

The oil accumulation in the developing soybean seed has been shown to be a dynamic process with different rates and activities at different phases affected by both genotype and environment. The objective of the present study was to investigate additive, epistatic and quantitative trait loci (QTL) × environment interaction (QE) effects of the QTL controlling oil filling rate in soybean seed. A total of 143 recombinant inbred lines (RILs) derived from the cross of Charleston and Dongnong 594 were used in this study to obtain 2 years of field data (2004 and 2005). A total of 26 QTL with significantly unconditional and conditional additive (a) effect and/or additive × environment interaction (ae) effect at different filling stages were identified on 14 linkage groups. Among the QTL with significant a effects, 18 QTL showed positive effects and 6 QTL had negative effects on seed filling rate of oil content during seed development. A total of 29 epistatic pairwise QTL underlying seed filling rate were identified at different filling stages. About 28 pairs of the QTL showed additive × additive epistatic (aa) effects and 14 pairs of the QTL showed aa × environment interaction (aae) effects at different filling stages. QTL with aa and aae (additive × additive × environment) effects appeared to vary at different filling stages. Our results demonstrated that oil filling rate in soybean seed were under genetic, developmental and environmental control.     

植物QTL研究进展

摘要：随着分子生物学和基因组学的发展,作物QTL研究方面取得了极大的进展。采用QTL定位方法研究作物数量性状从通常可见表型发展到基因的表达水平,从某一发育阶段的静态QTL定位发展到全生育期的动态QTL定位。并且作物QTL研究也不断深入,近年来已克隆了番茄,水稻和小麦等多种植物重要农艺性状的QTL,并从分子水平上剖析了它们的作用机理。本文就作物QTL分析的现状,发展趋势以及作物QTL图位克隆中精细定位策略等方面进行了介绍和分析。     

Assessing the importance of genotype × environment interaction for root traits in rice using a mapping population III: QTL analysis by mixed models

The phenotypic analysis of field experiments includes information about the experimental design, the randomization structure and a number of putative dependencies of environment and design factors on the trait investigated. In QTL studies, the genetic correlation across environments, which arises when the same set of lines is tested in multiple environments, plays an important role. This paper investigates the effect of model choice on the set and magnitude of detected root QTL in rice. Published studies on QTLs for root traits indicate that different results are obtained if varying genetic populations are used and also if different environmental conditions are included. An experiment was conducted with 168 RILs of the Bala × Azucena mapping population plus parents as checks under four environmental conditions (low light, low nitrogen, drought and a control environment). We propose a model that incorporates all relevant experimental information into a composite interval mapping approach based on a mixed model, which especially considers the correlation of genotypes in different environments. An extensive sequential model selection procedure was applied based on the phenotypic model, using the AIC to determine an appropriate random structure and Type 3 Wald F-tests for selection of fixed effects. In a first step we checked whether any of the fixed effects and random (nested) design effects could be dropped. Secondly, an appropriate covariance structure was chosen for genotype × environment interaction. In a third step Box-Cox transformations were applied based on residual analysis. We compared profiles of composite interval mapping scans with and without the inclusion of genotype × environment interaction and the experimental design information. Some distinct differences in profiles indicate that insufficient modeling of the non-QTL part can lead to an overly optimistic interpretation of QTL main effects in interval mapping. It is concluded that mixed model QTL mapping offers a reasonable way to separate environmental and genetic influences in the evaluation of quantitative genes and especially enables a more realistic assessment of QTL and QTL × environment effects than standard approaches by including all relevant effects.     

利用种子性状QTL定位高油玉米淀粉含量QTL

以普通玉米自交系8984与高油玉米自交系GY220为亲本组配得到284个F2:3家系群体,利用185个SSR标记构建玉米遗传连锁图谱。通过包含母体效应的种子性状QTL作图方法对玉米籽粒淀粉含量进行QTL定位和效应分析,共检测到7个与淀粉含量相关的QTL,分别位于第5,8,10染色体上,除qSTA8-3的遗传作用方式表现为加性外,其余QTL作用方式为部分显性。单个QTL贡献率为5.87%～10.93%,累计贡献率为53.37%。所有QTL的增效基因均来自普通玉米亲本8984。淀粉含量QTL qSTA5-2贡献率较大,可以作为进一步精细定位的主要目标QTL。     

Mapping quantitative trait loci (QTL) for grain size in rice using a RIL population from Basmati × indica cross showing high segregation distortion

Grain size is one of the three productivity related traits in rice and hence a major target for genetic improvement. Since understanding genetic variation in grain size between Basmati and indica genotypes is important for rice improvement, a recombinant inbred population was developed from a traditional aromatic cultivar ‘Basmati 370’ and a non-aromatic indica genotype ‘IRBB60’. This population was phenotyped in two locations for grain length (GL), grain breadth (GB), GL/GB ratio (LBR) and grain weight (GW). Though the RIL population reported in the current study exhibited segregation distortion (SD) for 54 % of the markers, they were utilized in analysis using an appropriate statistical package, PROC QTL in the SAS environment. Interval mapping revealed a total of 15 QTLs for GL, seven for GB, 15 for LBR and two for GW. Among them 13 were not reported earlier and thus novel. For a known major QTL identified in the study, GW8 for GB, a PCR based functional marker was designed and validated. This is the first report wherein a very high proportion of markers (>50 %) exhibiting SD have been successfully used for QTL mapping.     

水稻株高QTL分析及其与产量QTL的关系

分别应用具有112和160个标记位点的两个籼/籼交组合的F2群体的连锁图,对控制水稻株高的数量性状基因(QTL)进行了研究.各定位了4个和3个株高QTL,每个QTL的贡献率在5.6%～22.9%之间.在一个群体中,4个QTL都表现为完全显性或超显性;在另一个群体中,3个QTL均表现为部分显性.分别检测到7对和5对影响株高的双基因互作,其中一个群体以     

利用种子性状QTL定位高油玉米蛋白质含量QTL

利用普通玉米自交系8984与高油玉米自交系GY220为亲本构建了284个F2∶3家系群体及含有185个SSR标记的玉米遗传连锁图谱。通过包含母体效应的种子性状QTL作图方法对玉米子粒蛋白质含量进行定位和效应分析,共检测到4个QTL,位于第5和第8染色体上。除qPRO8-2遗传作用方式表现为加性外,其余QTL作用方式均为部分显性。单个QTL贡献率为3.86%~5.17%,累计贡献率为18.54%。所有QTL的增效基因均来自高油亲本GY220。     

大麦籽粒亮氨酸QTL定位

为挖掘大麦籽粒亮氨酸含量QTLs,本研究以‘紫光芒裸二棱’和‘澳选3号’构建的193个重组自交系为试验材料,采用全自动氨基酸分析仪测定了群体籽粒亮氨酸含量,利用完备区间作图法定位了大麦籽粒亮氨酸含量QTL。结果表明:籽粒亮氨酸含量在‘紫光芒裸二棱’和‘澳选3号’中分别为1.70 mg/g、0.87 mg/g,重组自交系群体后代中亮氨酸含量平均值为(1.20±0.01) mg/g,变异系数为16.92%;共检测到3个与亮氨酸含量相关的QTLs,分别位于2HL (Scssr03381～Scssr07759)、4HL (HVBAMMGB84～BMAG0808)和7HL (GMS056～GBM1297)染色体上,表型贡献率分别为7.65%、12.96%、19.74%,加性效应分别为-0.07、0.06和-0.1。本研究结果为大麦籽粒亮氨酸含量QTL精细定位及高亮氨酸含量大麦品种选育提供了理论依据。     

小麦白粉病抗性QTL分析

本研究以国际小麦作图组织提供的(W7984×Opata85)重组近交群体为材料,2001－2002年对其亲本和114个株系进行了人工接种条件下的抗白粉病鉴定,并利用基于混合线性模型的复合区间作图软件QTLMAPER,进行了抗白粉病QTL分析,共检测到3个与小麦白粉病抗性相关的加性QTL,分别位于3B、5D、7D染色体上,可以解释42.8%的表型变异     

小麦赤霉病抗性QTL分析

以小麦赤霉病抗源望水白与感病品种Alondra杂交产生的104个重组自交系为材料，采用JoinMap®3.0软件构建了含有2个RAPD、109个SSR和105个AFLP标记共25个连锁群的遗传连锁图，其中24个连锁群可以确定为相应的染色体；采用自然发病和土表接种方法，对该重组自交系群体在建阳和苏州进行了连续两年赤霉病抗性鉴定，结果表明：     

水稻千粒重QTL定位分析

以粳稻南粳35和耘稻N22杂交得到的F:群体为研究对象,通过构建遗传连锁图谱,对控制水稻千粒重的QTL进行定位。结果表明,千粒重在F:群体中呈正态分布,表现为数量性状特征;利用Win QTLcart2.5软件共检测到3个控制千粒重的QTL,分别位于第3和第6染色体上,其中第3染色体检测到2个QTL, qTGW3a,qTGW3b和qTGW6分别解释千粒重表型变异的15.03%,17.65%和7.87%,qTGW3a增加千粒重的基因来自南粳35,qTGw3b和qTGW6的增效基因来自私稻品种N22;3个位点基因的作用方式均为加性,在育种中应用潜力较大。     

水稻种子贮藏性QTL研究

水稻种子贮藏性对生产用种量、幼苗活力，以及杂交种子制种风险大小等都有影响，在中国南方的潮湿气候条件下更是如此。虽然有些传统方法能改善水稻种子的贮藏性，但是它们都存在成本高、有化学残毒以及小规模农户使用困难等缺点。本研究分析了籼粳交组合‘ZYQ8’／‘JXl7’的127冷花培加倍单倍体品系的种子贮藏性。我们用127个品系的种子在人工气候室中40℃和95％相对湿度条件下处理10天以前和以后的发芽率的差异来评价种子贮藏性。     

大豆百粒重QTL定位

大豆百粒重是产量构成的重要因素之一,与产量呈正相关。本研究以溧水中子黄豆和南农493-1的504个F2正反交单株及其亲本间具有多态性的150个SSR标记信息构建连锁图谱,2008年分别在江苏南京和山东临沂两地种植其衍生的正反交F2:4家系,鉴定其百粒重,应用Win QTL CartographerV2.5复合区间作图法和两地正反交联合分析进行QTL定位。结果表明,复合区间作图法检测到16个主效QTL,联合分析检测到24个主效QTL、环境效应与细胞质效应、1个环境×QTL互作和12个细胞质×QTL互作。其中,两方法共同检测到10个主效QTL,正反交群体在两地中共同检测到3个主效QTL;Meta分析发现与其他研究一致的4个QTL。这些结果为大豆产量遗传与标记辅助育种实践提供理论基础。     

水稻抽穗期的QTL剖析

利用沈农265/丽江新团黑谷的F2群体对抽穗期进行定位分析表明,该群体共定位到7个控制抽穗期的 QTLs,分别位于第2,3,6和7染色体上.单个QTL对性状表型贡献率在9.7%～23%之间.与其他研究结果比较表明,多数控制抽穗期的QTLs在不同的材料、不同遗传背景下重演性较好.同时,发现一个新的QTL位点,这些为北方粳稻育种的生育期选择提供基础性数据.     

玉米抗纹枯病QTL定位

以玉米自交系R15（抗）×掖478（感）的229个F₂单株为作图群体,构建了包含146个SSR标记位点的遗传连锁图谱,全长1 666 cM,平均图距11.4 cM。通过麦粒嵌入法对F2:4群体进行人工接种纹枯病菌,并以相对病斑高为病级划分标准鉴定了玉米纹枯病的抗性。用复合区间作图法分析抗病QTL及遗传效应,共检测到9个抗性QTL,分布于第1、2、3、4、5、6和10条染色体上,单个QTL可解释表型方差的3.72%~7.19%,其中有2个QTL位于染色体6.01抗病基因簇附近。     

稻米垩白QTL定位分析

以高垩白度品种V20B和低垩白度品种CPSLO 17作为亲本,构建150个V 20 B/CPSLO 17重组自交家系(RIL)为作图群体,进行稻米垩白QTL检测及其遗传效应分析.以垩白度为稻米垩白高低的表型数据,结合SLAF标签构建的分子连锁图谱,运用MapQTL 5软件检测到4个稻米垩白QTL,分别命名为qC-5 a、qC-5 6、qC-5 c和qC-5d.这4个QTL的LOD值分别为4.02、4.09、3.94和4.1,对表型变异的解释率分别为11.6％、11.8％、11.2％和11.8％,且这4个QTL抗性等位基因都来自高垩白亲本V20B.