A paternally imprinted QTL for mature body mass on mouse Chromosome 8

Body mass (BM) is a classic polygenic trait that has been extensively investigated to determine the underlying genetic architecture. Many previous studies looking at the genetic basis of variation in BM in murine animal models by quantitative trait loci (QTL) mapping have used crosses between two inbred lines. As a consequence it has not been possible to explore imprinting effects which have been shown to play an important role in the genetic basis of early growth with persistent effects throughout the growth curve. Here we use partially inbred mouse lines to identify QTL for mature BM by applying both Mendelian and Imprinting models. The analysis of an F₂ population (n ≈ 500) identified a number of QTL at 14, 16, and 18 weeks explaining in total 31.5%, 34.4%, and 30.5% of total phenotypic variation, respectively. On Chromosome 8 a QTL of large effect (14% of the total phenotypic variance at 14 weeks) was found to be explained by paternal imprinting. Although Chromosome 8 has not been previously associated with imprinting effects, features of candidate genes within the QTL confidence interval (CpG islands and direct clustered repeats) support the hypothesis that Insulin receptor substrate 2 may be associated with imprinting, but as yet is unidentified as being so.     

The Power of QTL Mapping with RILs

QTL (quantitative trait loci) mapping is commonly used to identify genetic regions responsible to important phenotype variation. A common strategy of QTL mapping is to use recombinant inbred lines (RILs), which are usually established by several generations of inbreeding of an F1 population (usually up to F6 or F7 populations). As this inbreeding process involves a large amount of labor, we are particularly interested in the effect of the number of inbreeding generations on the power of QTL mapping; a part of the labor could be saved if a smaller number of inbreeding provides sufficient power. By using simulations, we investigated the performance of QTL mapping with recombinant inbred lines (RILs). As expected, we found that the power of F4 population could be almost comparable to that of F6 and F7 populations. A potential problem in using F4 population is that a large proportion of RILs are heterozygotes. We here introduced a new method to partly relax this problem. The performance of this method was verified by simulations with a wide range of parameters including the size of the segregation population, recombination rate, genome size and the density of markers. We found our method works better than the commonly used standard method especially when there are a number of heterozygous markers. Our results imply that in most cases, QTL mapping does not necessarily require RILs at F6 or F7 generations; rather, F4 (or even F3) populations would be almost as useful as F6 or F7 populations. Because the cost to establish a number of RILs for many generations is enormous, this finding will cause a reduction in the cost of QTL mapping, thereby accelerating gene mapping in many species.     

MCQTL: multi-allelic QTL mapping in multi-cross design

The aim of the MCQTL software package is to perform QTL mapping in multi-cross designs. It allows the analysis of the usual populations derived from inbred lines and can link the families by assuming that the QTL locations are the same in all of them. Moreover, a diallel modelling of the QTL genotypic effects is allowed in multiple related families. The implemented model is a linear regression model. A composite interval mapping and an iterative QTL mapping are implemented to deal with multiple QTL models. Marker cofactor selections by forward or backward stepwise methods are implemented as well as computation of threshold test value by permutation. AVAILABILITY: The program is available on request after signing a licence agreement; free of charge for academic and non-profit organizations at http://www.genoplante.org (Bioinformatics products).     

玉米雄穗颜色QTL分析

雄穗是玉米的重要生殖器官,不同品种间玉米的雄穗外观差异明显。对玉米雄穗的颜色进行遗传分析和QTL定位,筛选与雄穗颜色紧密连锁的分子标记,可以作为玉米的品种保护和品种鉴别的有用工具。同时,紫色雄穗中花色苷类色素含量较高,与玉米雄穗的抗虫性密切相关。本研究利用一个黑玉米自交系SDM为共同父本,分别与白玉米自交系木6和黄玉米自交系Mo17杂交,构建2个相关F2∶3群体,分别命名为MuS(木6×SDM)和MoS(Mo17×SDM),在云南和重庆两个不同的环境中种植,对玉米花药颜色(COAn)和花药护颖颜色(COCa)2个性状进行QTL定位。结果表明:玉米花药和花药护颖的颜色均为数量性状,受主效基因和微效基因共同控制。2个群体在2个环境中共检测到7个与花药颜色相关的QTL,位于第2、3、6和10染色体上,其中位于第10染色体标记区间umc1196a-IDP8526内的QTL在重庆和云南同时表达,对表型的贡献率分别为23.17%和19.98%;2个群体在2个环境中共检测到9个与花药护颖颜色相关的QTL,位于第3、6、9和10染色体上,其中3个QTL为环境钝感QTL(在2个环境中均表达,且至少在1个环境中贡献率大于10%),分别位于第6染色体标记区间umc1979-umc1796、mmc0523-umc2006内和第10染色体标记区间umc1196a-umc2043内,对表型的贡献率为10.69%～59.30%。2个群体检测到的主效QTL的位置和效应高度一致,且控制花药颜色和花药护颖颜色2个性状的主效QTL有连锁分布的现象,主要表现在bins 6.04处的标记mmc0523和bins 10.04处的标记IDP8526附近。位于第6和第10染色体上的在不同环境和遗传背景下稳定的QTL可以作为进一步精细定位的靶位点,也可以为玉米雄穗颜色的分子标记辅助选择提供有价值的参考。     

Advanced Intercross Lines, an Experimental Population for Fine Genetic Mapping

An advanced intercrossed line (AIL) is an experimental population that can provide more accurate estimates of quantitative trait loci (QTL) map location than conventional mapping populations. An AIL is produced by randomly and sequentially intercrossing a population that initially originated from a cross between two inbred lines or some variant thereof. This provides increasing probability of recombination between any two loci. Consequently, the genetic length of the entire genome is stretched, providing increased mapping resolution. In this way, for example, with the same population size and QTL effect, a 95% confidence interval of QTL map location of 20 cM in the F(2) is reduced fivefold after eight additional random mating generations (F(10)). Simulation results showed that to obtain the anticipated reduction in the confidence interval, breeding population size of the AIL in all generations should comprise an effective number of >/=100 individuals. It is proposed that AILs derived from crosses between known inbred lines may be a useful resource for fine genetic mapping.     

植物QTL分析的理论研究进展

数量性状的表型是由数量性状基因座 ( Quantitative trait locus,QTL)和环境效应共同作用的结果。传统的数量遗传学采用统计学的方法由一级统计量和二级统计量描述处理 QTL的复合作用 ,估计各种遗传参数 (例如遗传力、遗传相关、遗传进度、有效因子数等 ) ,用于指导遗传育种实践。然而 ,在传统的数量遗传学分析中 ,往往假设数量性状受微效多基因控制 ,这些基因具有相同的并且是较微小的效应 ,所估计的遗传参数反映的是数量性状多基因系统的整体特征 ,其理论方法不能用于追踪研究和描述单个数量性状基因的作用。近年来 ,由于分子生物学技…     

Quantitative trait loci (QTL) detection in multicross inbred designs: recovering QTL identical-by-descent status information from marker data

Mapping quantitative trait loci in plants is usually conducted using a population derived from a cross between two inbred lines. The power of such QTL detection and the parameter estimates depend largely on the choice of the two parental lines. Thus, the QTL detected in such populations represent only a small part of the genetic architecture of the trait. In addition, the effects of only two alleles are characterized, which is of limited interest to the breeder, while common pedigree breeding material remains unexploited for QTL mapping. In this study, we extend QTL mapping methodology to a generalized framework, based on a two-step IBD variance component approach, applicable to any type of breeding population obtained from inbred parents. We then investigate with simulated data mimicking conventional breeding programs the influence of different estimates of the IBD values on the power of QTL detection. The proposed method would provide an alternative to the development of specifically designed recombinant populations, by utilizing the genetic variation actually managed by plant breeders. The use of these detected QTL in assisting breeding would thus be facilitated.     

Interval Mapping of Multiple Quantitative Trait Loci

The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in segregating generations derived from crosses between inbred lines. The efficiency of detecting and the accuracy of mapping multiple QTLs by using genetic markers are much increased by employing multiple QTL models instead of the single QTL models (and no QTL models) used in interval mapping. However, the computational work involved with multiple QTL models is considerable when the number of QTLs is large. In this paper it is proposed to combine multiple linear regression methods with conventional interval mapping. This is achieved by fitting one QTL at a time in a given interval and simultaneously using (part of) the markers as cofactors to eliminate the effects of additional QTLs. It is shown that the proposed method combines the easy computation of the single QTL interval mapping method with much of the efficiency and accuracy of multiple QTL models.     

不同发育阶段大豆株高和茎粗QTL的动态分析

利用中豆29×中豆32的重组自交系,以复合区间作图法对不同发育阶段的大豆株高和茎粗同时进行非条件和条件QTL定位,在11个连锁群检测到18个株高QTL,在9个连锁群检测到19个茎粗QTL。不同发育时期影响大豆株高和茎粗QTL的数量、加性效应和贡献率均不相同,QTL表达具有时序性和选择性,有些QTL仅表达1次,有些可多次连续表达。有3个株高QTL和1个茎粗QTL在3个年度重复表达,有6个株高QTL和2个茎粗QTL在2个年度重复表达。F连锁群上株高和茎粗QTL存在共位性,R1～R4期均有株高和茎粗QTL同时表达,但株高和茎粗QTL的增效基因不同,株高QTL表达次数多而茎粗QTL表达次数较少,前期(V4～R3)QTL表达数量多而后期(R4～R5)表达数量较少。株高和茎粗QTL的动态变化与表型相关分析结果一致,对于适期选择粗秆抗倒的高产材料具有指导作用。     

Mapping QTL controlling maize deep-seeding tolerance-related traits and confirmation of a major QTL for mesocotyl length

Deep-seeding tolerant seeds can emerge from deep soil where the moisture is suitable for seed germination. Breeding deep-seeding tolerant cultivars is becoming increasingly important in arid and semi-arid regions. To dissect the quantitative trait loci (QTL) controlling deep-seeding tolerance traits, we selected a tolerant maize inbred line 3681-4 and crossed it with the elite inbred line-X178 to generate an F₂ population and the derivative F_2:3 families. A molecular linkage map composed of 179 molecular markers was constructed, and 25 QTL were detected including 10 QTL for sowing at 10 cm depth and 15 QTL for sowing at 20 cm depth. The QTL analysis results confirmed that deep-seeding tolerance was mainly caused by mesocotyl elongation and also revealed considerable overlap among QTL for different traits. To confirm a major QTL on chromosome 10 for mesocotyl length measured at 20 cm depth, we selected and self-pollinated a BC₃F₂ plant that was heterozygous at the markers around the target QTL and homozygous at other QTL to generate a BC₃F₃ population. We found that this QTL explained more phenotypic variance in the BC₃F₃ population than that in the F₂ population, which laid the foundation for fine mapping and NIL (near-isogenic line) construction.     

A comparison of regression interval mapping and multiple interval mapping for linked QTL

Regression interval mapping and multiple interval mapping are compared with regard to mapping linked quantitative trait loci (QTL) in inbred-line cross experiments. For that purpose, a simulation study was performed using genetic models with two linked QTL. Data were simulated for F(2) populations of different sizes and with all QTL and marker alleles fixed for alternative alleles in the parental lines. The criteria for comparison are power of QTL identification and the accuracy of the QTL position and effect estimates. Further, the estimates of the relative QTL variance are assessed. There are distinct differences in the QTL position estimates between the two methods. Multiple interval mapping tends to be more powerful as compared to regression interval mapping. Multiple interval mapping further leads to more accurate QTL position and QTL effect estimates. The superiority increased with wider marker intervals and larger population sizes. If QTL are in repulsion, the differences between the two methods are very pronounced. For both methods, the reduction of the marker interval size from 10 to 5 cM increases power and greatly improves QTL parameter estimates. This contrasts with findings in the literature for single QTL scenarios, where a marker density of 10 cM is generally considered as sufficient. The use of standard (asymptotic) statistical theory for the computation of the standard errors of the QTL position and effect estimates proves to give much too optimistic standard errors for regression interval mapping as well as for multiple interval mapping.     

基于分离亚群体QTL定位的模拟研究

在数量性状QTL的精细定位中, 通过数量性状目标QTL的近等基因系可构建分离群体。在目标QTL效应较大的情况下, 数量性状表型值可反映目标QTL的基因型。若目标QTL附近的标记密度大时, 大样本才能定位该QTL。但是, 这增加了试验费用。为节约试验经费, 若只利用QTL纯合隐性基因型植株的分子标记信息, 也可比较准确地定位该QTL。利用极大似然法, 分别推导出F2、BC、DH以及RIL群体中重组率及其标准误的估计公式。Monte Carlo模拟研究表明, 基于定位群体中全部数据或隐性纯合基因型数据所获得的重组率估计值是一致的, 且在相同样本容量条件下, 二者精度相当。     

Joint QTL linkage mapping for multiple-cross mating design sharing one common parent

Background

Nested association mapping (NAM) is a novel genetic mating design that combines the advantages of linkage analysis and association mapping. This design provides opportunities to study the inheritance of complex traits, but also requires more advanced statistical methods. In this paper, we present the detailed algorithm of a QTL linkage mapping method suitable for genetic populations derived from NAM designs. This method is called joint inclusive composite interval mapping (JICIM). Simulations were designed on the detected QTL in a maize NAM population and an Arabidopsis NAM population so as to evaluate the efficiency of the NAM design and the JICIM method.

Principal Findings

Fifty-two QTL were identified in the maize population, explaining 89% of the phenotypic variance of days to silking, and nine QTL were identified in the Arabidopsis population, explaining 83% of the phenotypic variance of flowering time. Simulations indicated that the detection power of these identified QTL was consistently high, especially for large-effect QTL. For rare QTL having significant effects in only one family, the power of correct detection within the 5 cM support interval was around 80% for 1-day effect QTL in the maize population, and for 3-day effect QTL in the Arabidopsis population. For smaller-effect QTL, the power diminished, e.g., it was around 50% for maize QTL with an effect of 0.5 day. When QTL were linked at a distance of 5 cM, the likelihood of mapping them as two distinct QTL was about 70% in the maize population. When the linkage distance was 1 cM, they were more likely mapped as one single QTL at an intermediary position.

Conclusions

Because it takes advantage of the large genetic variation among parental lines and the large population size, NAM is a powerful multiple-cross design for complex trait dissection. JICIM is an efficient and specialty method for the joint QTL linkage mapping of genetic populations derived from the NAM design.     

QTL mapping of complex binary traits in an advanced intercross line

An advanced intercross line (AIL) is an easier and more cost-effective approach compared to recombinant inbred lines for fine mapping of quantitative trait loci (QTL) identified by F(2) designs. In an AIL, a complex binary trait can be mapped through analysis of either continuously distributed proxy traits for the liability of the binary trait or the liability itself, the latter presenting the greater statistical challenge. In another work, we successfully applied both approaches in an AIL to fine map previously identified QTL underlying anatomical parameters of the cardiac inter-atrial septum including patent foramen ovale. Here, we describe the statistical methods that we used to analyse complex binary traits in our AIL design. This is achieved using a likelihood-based method, with the expectation-maximisation algorithm allowing use of standard logistic regression methods for model fitting.     

Genomic Dissection of Leaf Angle in Maize (Zea mays L.) Using a Four-Way Cross Mapping Population

Increasing grain yield by the selection for optimal plant architecture has been the key focus in modern maize breeding. As a result, leaf angle, an important determinant of plant architecture, has been significantly improved to adapt to the ever-increasing plant density in maize production over the past several decades. To extend our understanding on the genetic mechanisms of leaf angle in maize, we developed the first four-way cross mapping population, consisting of 277 lines derived from four maize inbred lines with varied leaf angles. The four-way cross mapping population together with the four parental lines were evaluated for leaf angle in two environments. In this study, we reported linkage maps built in the population and quantitative trait loci (QTL) on leaf angle detected by inclusive composite interval mapping (ICIM). ICIM applies a two-step strategy to effectively separate the cofactor selection from the interval mapping, which controls the background additive and dominant effects at the same time. A total of 14 leaf angle QTL were identified, four of which were further validated in near-isogenic lines (NILs). Seven of the 14 leaf angle QTL were found to overlap with the published leaf angle QTL or genes, and the remaining QTL were unique to the four-way population. This study represents the first example of QTL mapping using a four-way cross population in maize, and demonstrates that the use of specially designed four-way cross is effective in uncovering the basis of complex and polygenetic trait like leaf angle in maize.     

Precise mapping Fhb5, a major QTL conditioning resistance to Fusarium infection in bread wheat (Triticum aestivum L.)

Qfhi.nau-5A is a major quantitative trait locus (QTL) against Fusarium graminearum infection in the resistant wheat germplasm Wangshuibai. Genetic analysis using BC(3)F(2) and BC(4)F(2) populations, derived from selfing two near-isogenic lines (NIL) heterozygous at Qfhi.nau-5A that were developed, respectively, with Mianyang 99-323 and PH691 as the recurrent parent, showed that Qfhi.nau-5A inherited like a single dominant gene. This QTL was thus designated as Fhb5. To fine map it, these two backcross populations and a recombinant inbred line (RIL) population derived from Nanda2419?×?Wangshuibai were screened for recombinants occurring between its two flanking markers Xbarc56 and Xbarc100. Nineteen NIL recombinants were identified from the two backcross populations and nine from the RIL population. In the RIL recombinant selection process, selection against Fhb4 present in the RIL population was incorporated. Genotyping these recombinant lines with ten markers mapping to the Xbarc56-Xbarc100 interval revealed four types of Mianyang 99-323-derived NIL recombinants, three types of PH691-derived NIL recombinants, and four types of RIL recombinants. In different field trials, the percentage of infected spikes of these lines displayed a distinct two-peak distribution. The more resistant class had over 55% less infection than the susceptible class. Common to these resistant genotypes, the 0.3-cM interval flanked by Xgwm304 and Xgwm415 or one of these two loci was derived from Wangshuibai, while none of the susceptible recombinants had Wangshuibai chromatin in this interval. This interval harboring Fhb5 was mapped to the pericentromeric C-5AS3-0.75 bin through deletion bin mapping. The precise localization of Fhb5 will facilitate its utilization in marker-assisted wheat breeding programs.     

Mapping QTL for grain yield and other agronomic traits in post-rainy sorghum [Sorghum bicolor (L.) Moench]

Sorghum, a cereal of economic importance ensures food and fodder security for millions of rural families in the semi-arid tropics. The objective of the present study was to identify and validate quantitative trait loci (QTL) for grain yield and other agronomic traits using replicated phenotypic data sets from three post-rainy dry sorghum crop seasons involving a mapping population with 245 F₉ recombinant inbred lines derived from a cross of M35-1 × B35. A genetic linkage map was constructed with 237 markers consisting of 174 genomic, 60 genic and 3 morphological markers. The QTL analysis for 11 traits following composite interval mapping identified 91 QTL with 5–12 QTL for each trait. QTL detected in the population individually explained phenotypic variation between 2.5 and 30.3 % for a given trait and six major genomic regions with QTL effect on multiple traits were identified. Stable QTL across seasons were identified. Of the 60 genic markers mapped, 21 were found at QTL peak or tightly linked with QTL. A gene-based marker XnhsbSFCILP67 (Sb03g028240) on SBI-03, encoding indole-3-acetic acid-amido synthetase GH3.5, was found to be involved in QTL for seven traits. The QTL-linked markers identified for 11 agronomic traits may assist in fine mapping, map-based gene isolation and also for improving post-rainy sorghum through marker-assisted breeding.     

小麦品种冀麦24抗麦红吸浆虫QTL分析

麦红吸浆虫是影响小麦产量和品质的重要害虫,研究小麦对吸浆虫抗性的遗传及其连锁分子标记对于提高抗虫品种的选择效率具有重要意义。本研究以小麦感虫品系6218与抗虫品种冀麦24产生的重组近交系(RIL)群体为材料,利用SSR标记和人工虫圃对冀麦24的抗虫性遗传进行了研究。结果表明:6218与冀麦24的抗性差异显著,RIL群体在2年2点的鉴定中抗性稳定;所构建的遗传连锁图谱包含112个SSR位点,形成26个连锁群,图谱全长835.7 cM,标记间平均距离为7.5 cM。利用QTL IciMapping的完备区间作图法,在4A染色体上检测到1个加性效应位点(QSm.hbau-4A),该位点在2个鉴定年度的贡献率分别为9.67%、10.57%。该抗性QTL及其连锁SSR标记的发掘,将有助于提高小麦抗吸浆虫育种的选择效率。     

Fine Mapping QTL for Drought Resistance Traits in Rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) Using Bulk Segregant Analysis

Drought stress is a major limitation to rice (Oryza sativa L.) yields and its stability, especially in rainfed conditions. Developing rice cultivars with inherent capacity to withstand drought stress would improve rainfed rice production. Mapping quantitative trait loci (QTLs) linked to drought resistance traits will help to develop rice cultivars suitable for water-limited environments through molecular marker-assisted selection (MAS) strategy. However, QTL mapping is usually carried out by genotyping large number of progenies, which is labour-intensive, time-consuming and cost-ineffective. Bulk segregant analysis (BSA) serves as an affordable strategy for mapping large effect QTLs by genotyping only the extreme phenotypes instead of the entire mapping population. We have previously mapped a QTL linked to leaf rolling and leaf drying in recombinant inbred (RI) lines derived from two locally adapted indica rice ecotypes viz., IR20/Nootripathu using BSA. Fine mapping the QTL will facilitate its application in MAS. BSA was done by bulking DNA of 10 drought-resistant and 12 drought-sensitive RI lines. Out of 343 rice microsatellites markers genotyped, RM8085 co-segregated among the RI lines constituting the respective bulks. RM8085 was mapped in the middle of the QTL region on chromosome 1 previously identified in these RI lines thus reducing the QTL interval from 7.9 to 3.8 cM. Further, the study showed that the region, RM212–RM302–RM8085–RM3825 on chromosome 1, harbours large effect QTLs for drought-resistance traits across several genetic backgrounds in rice. Thus, the QTL may be useful for drought resistance improvement in rice through MAS and map-based cloning.     

QTL analysis: unreliability and bias in estimation procedures

Several statistical methods which employ multiple marker data are currently available for the analysis of quantitative trait loci (QTL) in experimental populations. Although comparable estimates of QTL location and effects have been obtained by these methods, using simulated and real data sets, their accuracy and reliability have not been extensively investigated. The present study specifically examines the merit of using F₂ and doubled haploid populations for locating QTL and estimating their effects. Factors which may affect accuracy and reliability of QTL mapping, such as the number and position of the markers available, the accuracy of the marker locations and the size of the experimental population used, are considered. These aspects are evaluated for QTL of differing heritabilities and locations along the chromosome.A population of 300 F₂ individuals and 150 doubled haploid lines gave estimates of QTL position and effect which were comparable, albeit extremely unreliable. Even for a QTL of high heritability (10%), the confidence interval was 35 cM. There was little increase in reliability to be obtained from using 300, rather than 200, F₂ individuals and 100 doubled haploid lines gave similar results to 150. QTL estimates were not significantly improved either by using the expected, rather than the observed, marker positions or by using a dense map of markers rather than a sparse map. A QTL which was asymmetrically located in the linkage group resulted in inaccurate estimates of QTL position which were seriously biassed at low heritability of the QTL. In a population of 300 F₂ individuals the bias increased from 4 cM to 20 cM, for a QTL with 10% and 2% heritability respectively.