数量性状由表型变异到基因发现的研究进展

分子生物学的快速发展为研究数量性状的遗传基础提供了更为有效的途径。我们可以沿着由表型变异去发现基因之路, 更准确地剖析数量性状的遗传基础; 尤其是对作物的许多重要的数量性状进行的QTL研究越来越受到重视。文章对数量遗传发展, QTL作图群体和方法的发展, QTL定位和QTG(quantitative traits genes)的鉴别方面的现状进行了综述。     

与偏分离位点连锁的QTL作图的统计方法

提出了一种统计方法,可以估计与偏分离位点连锁的QTL的位置和效应。该方法利用回交群体中呈现偏分离的分子标记,首先用最大似然法对偏分离位点与标记位点之间的重组率和配子存活率进行估计,然后用区间作图法估计加性-显性模型下QTL的位置和效应参数。该方法可用于对常规作图研究中表现偏分离的标记进行分析,以帮助我们发现新的偏分离基因（或不育基因）和数量性状位点。     

水稻QTL分析的研究进展

水稻许多重要的性状是由多基因控制的数量性状,经典的数量遗传学只能把数量性状作为一个整体进行研究。近年来．高密度分子标记连锁图的构建和有效的生物统计学方法的发展使人们对数量性状遗传基础的研究出现了革命性的变化。通过对不同群体内的个体或品系的分子标记基因型和表型数据的共分离分析,能对QTL进行检测和定位。本文对QTL定位的原理和方法进行了介绍,从QTL的数目和效应、上位性效应、QTL基因型与环境的互作、相关性状的QTL以及个体发育不同阶段的QTL等方面对水稻QTL分析的研究进展进行了综述。水稻基因组测序计划已经完成,本文还对基因组时代水稻QTL精细定位和克隆的方法进行了探讨,对QTL分析在水稻育种中的应用前景进行了展望。     

多基因抗性的QTL作图及其在作物持久性抗病育种上的应用

QTL作图已成为解析生物复杂性状遗传基础和基因座之间互作机制的一种有效的研究工具。多基因抗性没有明显的生理小种特异性,一般表现为数量性状。多基因抗性的QTL作图在植物持久性抗病育种中有重要的应用价值,有助于分离到广谱性抗病基因。从作图群体(F1、F2、DH、RIL、BIL和NIL)构建、抗性表型测定和标记辅助育种等方面论述了多基因抗性QTL作图的最新研究进展。     

植物数量性状基因的定位与克隆

作物的许多重要农艺性状属于数量性状, 鉴定和发掘控制数量性状的基因及其优异的等位变异, 并使之快速应用于育种实践是新时期作物科学家和育种学家所面临的重大课题。本文从QTL作图、QTL的精细定位与图位克隆、QTL近等基因系和染色体片断代换系的建立以及基于LD的关联分析等方面对植物数量性状的研究进展进行了讨论, 提出了以植物基因组学技术为平台, 将QTL作图与关联分析方法相结合, 是进行数量性状遗传机理研究同时服务于作物育种实践的有效途径。     

植物数量性状基因的定位与克隆

作物的许多重要农艺性状属于数量性状,鉴定和发掘控制数量性状的基因及其优异的等位变异,并使之快速应用于育种实践是新时期作物科学家和育种学家所面临的重大课题。本文从QTL作图、QTL的精细定位与图位克隆、QTL近等基因系和染色体片断代换系的建立以及基于LD的关联分析等方面对植物数量性状的研究进展进行了讨论,提出了以植物基因组学技术为平台,将QTL作图与关联分析方法相结合,是进行数量性状遗传机理研究同时服务于作物育种实践的有效途径。     

QTL形态标记定位的一种数学方法

根据家蚕中位于Z染色体上的伴性遗传的双形态标记和假定与其有连锁关系的一个具有一对主基因差异的数量性状在测交世代中,所作的理论分布,本文建立了QTL形态标记定位的数学方法,即频数分布面积法,并给出了相应的检测一对主基因在测交世代中的同分离比例及其与形态标记是否有连锁关系的X2统计量．这种定位方法亦适应于非伴性遗传方式的QTL形态标记定位．与单标记定位的极大似然方法相比,我们的方法所作的双标记定位能显示QTL与形态标记发生重组的交叉干步作用,并且定位结果不受作用于数量性状的环境效应所影响．     

作物数量性状研究进展

作物主要农艺性状和经济性状大多属于数量性状。传统数量遗传学采用数理统计方法,把控制数量性状的多基因系统作为一个整体进行研究。DNA分子标记技术的出现和发展,为数量性状研究提供了重要工具。自20世纪80年代以来,QTL定位的统计分析方法发展很快,先后提出单标记分析法、区间作图法及复合区间作图法等。目前,作物QTL研究取得了重要进展,一些重要作物、重要农艺性状的主效QTL基因已被相继克隆成功,作物数量性状的研究已经成为一个具有勃勃生机的热门领域。     

植物数量性状变异的分子基础与QTL克隆研究进展

探讨数量性状变异规律以便对其进行遗传操纵一直是植物遗传学的一个重要领域。DNA分子标记和QTL作图技术的发展以及拟南芥和水稻全基因组测序的完成极大地促进了植物数量性状分子基础的研究。现已克隆了拟南芥ED1、水稻Hdl、玉米Tb1、番茄fw2．2和Brii9-2-5等控制目标数量性状的基因。数量性状表型变异不仅源于多个数量性状基因（QTL）的分离．而且还受到内外环境的修饰。QTL等位基因变异与孟德尔基因变异具有类似的分子基础,即基因表达或蛋白质功能发生改变。通过分析已克隆的植物QTL的变异特征及分子基础,讨论了植物QTL克隆技术策略,并对QTL研究所面临的挑战和应用前景进行了展望。     

数量性状基因座的动态定位策略

分子标记辅助数量性状基因（QTL）定位和效应分析技术为深入研究数量性状的遗传基础提供了一个有力手段．但目前的QTL定位策略是静态的,只估计各QTL在某观察时刻的累积效应,无法了解QTL的表达动态．本文提出一种新的QTL定位策略,称为“动态定位”,能够揭示QTI表达的动态过程,并能极大地提高QTL定位的统计功效.     

植物QTL定位方法的研究进展

本文系统地介绍了QTL定位的单一标记分析法、区间作图法以及复合区间作图法、混合显性模型的分析方法,概述了一些主要定位方法的分析原理、存在的主要优缺点。单一标记分析法可以采用方差分析、回归分析或似然比检验的方法分析。区间作图法和复合区间作图法是基于两个相邻标记的QTL定位方法,可采用回归分析或最大似然法分析。复合区间作图法在模型中包括了与其他QTL连锁的标记,可以提高作图的精度和效率。混合线性模型的QTL定位方法可以包括复杂的遗传效应及QTL与环境的互作效应,具有更广阔的应用前景。 Abstract:QTL mapping methods are reviewed for single-marker mapping,interval mapping,composite interval mapping,and mixed-model based method.Statistical approaches along with their properties are discussed for the mapping methods.ANOVA,regression method and likelihood ratio test can be applied in single-marker mapping.Interval mapping and composite interval mapping can be conducted,based on two interval markers,by regression method and maximum likelihood method.Since markers linked with other QTLs are include in the model,composite interval mapping is more precision and powerful.Mapping QTL by mixed-model approaches is more applicable when complicated QTL effects as well as QTL by environment interaction are analyzed.     

Quantitative trait loci for yield components in oil palm (Elaeis guineensis Jacq.)

The development of an oil palm RFLP marker map has enabled marker-based QTL mapping studies to be undertaken. Information from 153 RFLP markers was used in combination with phenotypic data from an F₂ population to estimate the position and effects of quantitative trait loci (QTLs) for traits including yield of fruit and its components and measures of vegetative growth. The mapping population consisted of 84 palms segregating for the major gene influencing shell thickness. Marker data were analysed to produce a linkage map consisting of 22 linkage groups. The QTL mapping analysis was carried out by interval mapping and single-marker analysis for the unlinked markers; significance thresholds were generated by permutation. Using both single-marker and interval-mapping analysis significant marker associated QTL effects were found for 11 of the 13 traits analysed. The results of interval-mapping analysis of fruit weight, petiole cross section and rachis length, and ratios of shell:fruit, mesocarp:fruit and kernel:fruit indicated significant (P<0.05) QTLs at the genome-wide threshold. The putative QTLs were associated with between 8.2% and 44.0% of the phenotypic variation, with an average of 27% for the single-marker analysis and 19% for the interval-mapping analysis. The higher percentage of phenotypic variation explained in the single-marker analysis, when compared to the interval-mapping analysis, is likely to be due to the lower stringency associated with the single-marker analysis. Large dominance deviations were associated with a sizeable proportion of the putative QTLs. The ultimate objective of mapping QTLs in commercial populations is to utilise novel breeding strategies such as marker-assisted selection (MAS). The potential impact of MAS in oil palm breeding programmes is discussed. Received: 26 June 2000 / Accepted: 24 October 2000     

Identification of QTLs for Yield and Associated Traits in F₂ Population of Rice

Identification of quantitative trait loci (QTLs) controlling yield and yield-related traits in rice was performed in the F₂ mapping population derived from parental rice genotypes DHMAS and K343. A total of 30 QTLs governing nine different traits were identified using the composite interval mapping (CIM) method. Four QTLs were mapped for number of tillers per plant on chromosomes 1 (2 QTLs), 2 and 3; three QTLs for panicle number per plant on chromosomes 1 (2 QTLs) and 3; four QTLs for plant height on chromosomes 2, 4, 5 and 6; one QTL for spikelet density on chromosome 5; four QTLs for spikelet fertility percentage (SFP) on chromosomes 2, 3 and 5 (2 QTLs); two QTLs for grain length on chromosomes 1 and 8; three QTLs for grain width on chromosomes1, 3 and 8; three QTLs for 1000-grain weight (TGW) on chromosomes 1, 4 and 8 and six QTLs for yield per plant (YPP) on chromosomes 2 (3 QTLs), 4, 6 and 8. Most of the QTLs were detected on chromosome 2, so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety. Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection (MAS) breeding. Further, the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.     

QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).     

Efficient Method for Analysis of QTL Using F1 Progenies in an Outcrossing Species

In the present paper, we proposed a statistical procedure based on composite interval mapping for accurate analysis of quantitative trait loci (QTL) for individuals sampled from an outcrossing population with two-generation families consisting of the sampled individuals and F1 progenies obtained by crossing them as parental individuals. In the proposed procedure, haplotypes of markers of parental individuals were reconstructed based on the genotypes of F1 progenies and QTL analyses with composite interval mapping were conducted separately for each of parents as well as jointly for both parents. A least squares method was applied to the composite interval mapping, where some of markers were selected as cofactors to absorb the variation induced by QTL located elsewhere in the genome. The procedure was evaluated for the efficiency in detecting QTL and the precision of estimates of locations and effects of QTL using simulations. It was shown that QTL with interaction between paternal and maternal alleles was effectively detected by joint analysis of both parents, while a QTL segregating only in one parent, closely linked to a QTL segregating only in the other parent, was successfully detected by analyzing separately each of the parents with inclusion of markers of both parents. The proposed procedure can provide detailed genetic information useful for marker assisted breeding in an outcrossing species such as forest trees.     

Mapping of QTLs for Oral Alcohol Self-Administration in B6.C and B6.I Quasi-Congenic RQI Strains

One strategy to identify neurochemical pathways of addiction is to map the relevant genes. In the present study we used 43 B6.C and 35 B6.I inbred RQI mouse strains, carrying <3% donor genome on C57BL/6ByJ background, for gene mapping. The strains were phenotyped for consumption of alcohol (12% v/v) in a two-bottle-choice paradigm, and genotyped for 396 microsatellite markers. The current mapping study extends our earlier experiment scanning five mouse chromosomes (Vadasz et al. (2000) Scanning of five chromosomes for alcohol consumption loci. Alcohol 22:25–34) to a whole-genome study, and discusses the differences and limitations. Data were analyzed with composite interval (CIM) and multiple interval (MIM) QTL mapping methods. CIM of B6.C strains detected significant QTLs on chrs. 6 and 12. A suggestive, but not significant, locus was detected in the B6.I strains on chr. 12. The best MIM model for B6.C strains confirmed one QTL on chr. 6 and one QTL on chr. 12, while the MIM model for the B6.I strains confirmed the suggestive locus on chr. 12. Some of the QTLs for alcohol consumption are new, while others confirm previously reported QTLs for alcohol preference, and alcohol acceptance.     

Analysis of quantitative trait loci (QTLs) and quantitative trait alleles (QTAs) for potato tuber yield and starch content

 Using RFLP markers, QTLs for tuber starch-content and tuber yield were mapped in two F₁ populations derived from crossing non-inbred di-haploid potato breeding lines. QTLs were identified and mapped, based on both single-marker tests and interval analyses. A model specifically developed for interval QTL analysis in non-inbred plant species was successfully applied for the first time to experimental data. Results of both methods of QTL analysis were similar but not identical. QTLs for tuber starch-content and tuber yield were analysed in segregating populations K31 and LH in five and two environments, respectively. Population K31 was fully genotyped whereas population LH was selectively genotyped according to high and low tuber-starch content. Eighteen putative QTLs for tuber starch-content were identified on all 12 potato linkage groups and eight putative QTLs for tuber yield were identified on eight linkage groups. Twenty of twenty six putative QTLs were reproducibly detected in at least two environments and/or mapping populations. Few major QTLs for tuber starch-content were highly stable across environments but were detected in only one of the two mapping populations analysed. Most QTLs for tuber yield were linked with QTLs for tuber starch-content suggesting that the effects on both traits are controlled by the same genetic factors. The results are discussed with respect to marker-assisted selection in potato. Received: 9 March 1998 / Accepted: 29 April 1998     

Genetic Relationships Among Panicle Characteristics of Rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) Using Unconditional and Conditional QTL Analyses

Using mixed-model-based composite interval mapping and conditional statistical methods, we studied quantitative trait loci (QTLs) with epistatic effects and QTLs by environment interaction effects for rice seed set percent (SSP), filled grain number per plant (FGP), and panicle length (PL). A population of 241 recombinant inbred lines was used which was derived from a cross between “Zhenshan 97” and “Minghui 63.” Its linkage map included 221 molecular markers. Our QTL analysis detected 28, 25, and 32 QTLs for SSP, FGP, and PL, respectively. Each QTL explained 1.37%∼13.19% of the mean phenotypic variation. A comparison of conventional and conditional mapping provided information about the genetic control system involved in the synthetic process of SSP, FGP, and PL at the level of individual QTLs. Conditional QTLs with reduced (or increased) effects were identified for SSP, which were significantly influenced by FGP or PL. Some QTLs could express independently for the given traits, thereby providing possibilities for simultaneous improvement of SSR and PL, and SSR and FGP. Epistasis was more sensitive to environmental conditions than were additive effects.     

Genomic regions affecting seed shattering and seed dormancy in rice

Non-shattering of the seeds and reduced seed dormancy were selected consciously and unconsciously during the domestication of rice, as in other cereals. Both traits are quantitative and their genetic bases are not fully elucidated, though several genes with relatively large effects have been identified. In the present study, we attempted to detect genomic regions associated with shattering and dormancy using 125 recombinant inbred lines obtained from a cross between cultivated and wild rice strains. A total of 147 markers were mapped on 12 rice chromosomes, and QTL analysis was performed by simple interval mapping and composite interval mapping. For seed shattering, two methods revealed the same four QTLs. On the other hand, for seed dormancy a number of QTLs were estimated by the two methods. Based on the results obtained with the intact and de-hulled seeds, QTLs affecting hull-imposed dormancy and kernel dormancy, respectively, were estimated. Some QTLs detected by simple interval mapping were not significant by composite interval mapping, which reduces the effects of residual variation due to the genetic background. Several chromosomal regions where shattering QTLs and dormancy QTLs are linked with each other were found. This redundancy of QTL associations was explained by ”multifactorial linkages” followed by natural selection favoring these two co-adapted traits. Received: 23 November 1998 / Accepted: 27 August 1999     

QTL analysis of variation in male courtship song characters in Drosophila virilis

We have used a quantitative trait locus (QTL) mapping approach to study the genetic basis of differences between two Drosophila virilis strains representing extreme phenotypes in two song characters, the number of pulses in a pulse train (PN) and the length of a pulse train (PTL). Variation in these characters among 520 F2 males was studied by single-marker analysis and composite interval mapping (CIM) using a recombination linkage map constructed for 26 microsatellite markers. In single-marker analysis, two adjacent microsatellite markers on the third chromosome, msat19 and vir84 explained 13.8 and 12.4% of the variation in PN and 9.9 and 6.5% of the variation in PTL, respectively. CIM analysis revealed significant QTLs affecting PN, located on the X and the second, third and fourth chromosome of D. virilis, while variation in PTL was attributable to QTLs located only on the third chromosome.