Marker-based mapping of quantitative trait loci using replicated progenies

Summary When heritability of the trait under investigation is low, replicated progenies can bring about a major reduction in the number of individuals that need to be scored for marker genotype in determining linkage between marker loci and quantitative trait loci (QTL). Savings are greatest when heritability of the trait is low, but are much reduced when heritability of the quantitative trait is moderate to high. Required numbers for recombinant inbred lines will be greater than those required for a simple F₂ population when heritabilities are moderate to high and the proportion of recombination between marker locus and quantitative trait locus is substantial.Contribution No. 2613-E of the Agricultural Research Organization, 1989 series     

Localization of a quantitative trait locus via a Bayesian approach

A Bayesian approach to the direct mapping of a quantitative trait locus (QTL), fully utilizing information from multiple linked gene markers, is presented in this paper. The joint posterior distribution (a mixture distribution modeling the linkage between a biallelic QTL and N gene markers) is computationally challenging and invites exploration via Markov chain Monte Carlo methods. The parameter's complete marginal posterior densities are obtained, allowing a diverse range of inferences. Parameters estimated include the QTL genotype probabilities for the sires and the offspring, the allele frequencies for the QTL, and the position and additive and dominance effects of the QTL. The methodology is applied through simulation to a half-sib design to form an outbred pedigree structure where there is an entire class of missing information. The capacity of the technique to accurately estimate parameters is examined for a range of scenarios.     

Simultaneous estimation of multiple quantitative trait loci and growth curve parameters through hierarchical Bayesian modeling

A novel hierarchical quantitative trait locus (QTL) mapping method using a polynomial growth function and a multiple-QTL model (with no dependence in time) in a multitrait framework is presented. The method considers a population-based sample where individuals have been phenotyped (over time) with respect to some dynamic trait and genotyped at a given set of loci. A specific feature of the proposed approach is that, instead of an average functional curve, each individual has its own functional curve. Moreover, each QTL can modify the dynamic characteristics of the trait value of an individual through its influence on one or more growth curve parameters. Apparent advantages of the approach include: (1) assumption of time-independent QTL and environmental effects, (2) alleviating the necessity for an autoregressive covariance structure for residuals and (3) the flexibility to use variable selection methods. As a by-product of the method, heritabilities and genetic correlations can also be estimated for individual growth curve parameters, which are considered as latent traits. For selecting trait-associated loci in the model, we use a modified version of the well-known Bayesian adaptive shrinkage technique. We illustrate our approach by analysing a sub sample of 500 individuals from the simulated QTLMAS 2009 data set, as well as simulation replicates and a real Scots pine (Pinus sylvestris) data set, using temporal measurements of height as dynamic trait of interest.     

Trait-based analyses for the detection of linkage between marker loci and quantitative trait loci in crosses between inbred lines

Summary Methods are presented for determining linkage between a marker locus and a nearby locus affecting a quantitative trait (quantitative trait locus=QTL), based on changes in the marker allele frequencies in selection lines derived from the F-2 of a cross between inbred lines, or in the high and low phenotypic classes of an F-2 or BC population. The power of such trait-based (TB) analyses was evaluated and compared with that of methods for determining linkage based on the mean quantitative trait value of marker genotypes in F-2 or BC populations [marker-based (MB) analyses]. TB analyses can be utilized for marker-QTL linkage determination in situations where the MB analysis is not applicable, including analysis of polygenic resistance traits where only a part of the population survives exposure to the Stressor and analysis of marker-allele frequency changes in selection lines. TB analyses may be a useful alternative to MB analyses when interest is centered on a single quantitative trait only and costs of scoring for markers are high compared with costs of raising and obtaining quantitative trait information on F-2 or BC individuals. In this case, a TB analysis will enable equivalent power to be obtained with fewer individuals scored for the marker, but more individuals scored for the quantitative trait. MB analyses remain the method of choice when more than one quantitative trait is to be analyzed in a given population.Contribution from the ARO, Bet Dagan, Israel. No. 1698-E, 1986 series     

Bayesian analysis for genetic architecture of dynamic traits

The dissection of the genetic architecture of quantitative traits, including the number and locations of quantitative trait loci (QTL) and their main and epistatic effects, has been an important topic in current QTL mapping. We extend the Bayesian model selection framework for mapping multiple epistatic QTL affecting continuous traits to dynamic traits in experimental crosses. The extension inherits the efficiency of Bayesian model selection and the flexibility of the Legendre polynomial model fitting to the change in genetic and environmental effects with time. We illustrate the proposed method by simultaneously detecting the main and epistatic QTLs for the growth of leaf age in a doubled-haploid population of rice. The behavior and performance of the method are also shown by computer simulation experiments. The results show that our method can more quickly identify interacting QTLs for dynamic traits in the models with many numbers of genetic effects, enhancing our understanding of genetic architecture for dynamic traits. Our proposed method can be treated as a general form of mapping QTL for continuous quantitative traits, being easier to extend to multiple traits and to a single trait with repeat records.     

Bayesian joint mapping of quantitative trait loci for Gaussian and categorical characters in line crosses

Methodology for joint mapping of quantitative trait loci (QTL) affecting continuous and binary characters in experimental crosses is presented. The procedure consists of a Bayesian Gaussian-threshold model implemented via Markov chain Monte Carlo, which bypasses bottlenecks due to high-dimensional integrals required in maximum likelihood approaches. The method handles multiple binary traits and multiple QTL. Modeling of ordered categorical traits is discussed as well. Features of the method are illustrated using simulated datasets representing a backcross design, and the data are analyzed using mixed-trait and single-trait models. The mixed-trait analysis provides greater detection power of a QTL than a single-trait analysis when the QTL affects two or more traits. The number of QTL inferred in the mixed-trait analysis does not pertain to a specific trait, but the roles of each QTL on specific traits can be assessed from estimates of its effects. The impacts of varying incidence level and sample size on the mixed-trait QTL mapping analysis are investigated as well.     

Linkage analysis of quantitative trait loci in multiple line crosses

Simple line crosses, for example, backcross and F₂, are commonly used in mapping quantitative trait loci (QTL). However, these simple crosses are rarely used alone in commercial plant breeding; rather, crosses involving multiple inbred lines or several simple crosses but connected by shared inbred lines may be common in plant breeding. Mapping QTL using crosses of multiple lines is more relevant to plant breeding. Unfortunately, current statistical methods and computer programs of QTL mapping are all designed for simple line crosses or multiple line crosses but under a regular mating system. It is not straightforward to extend the existing methods to handle multiple line crosses under irregular and complicated mating designs. The major hurdle comes from irregular inbreeding, multiple generations, and multiple alleles. In this study, we develop a Bayesian method implemented via the Markov chain Monte Carlo (MCMC) algorithm for mapping QTL using complicated multiple line crosses. With the MCMC algorithm, we are able to draw a complete path of the gene flow from founder alleles to their descendents via a recursive process. This has greatly simplified the problem caused by irregular mating and inbreeding in the mapping population. Adopting the reversible jump MCMC algorithm, we are able to simultaneously search for multiple QTL along the genome. We can even infer the posterior distribution of the number of QTL, one of the most important parameters in QTL study. Application of the new MCMC based QTL mapping procedure is demonstrated using two different mating designs. Design I involves two inbred lines and their derived F₁, F₂, and BC populations. Design II is a half-diallel cross involving three inbred lines. The two designs appear different, but can be handled with the same robust computer program.     

Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

ABSTRACT: BACKGROUND: Although many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis. Single trait analysis does not take advantage of possible genetic and environmental correlations between traits. In this paper, we propose a novel statistical method for multiple trait multiple interval mapping (MTMIM) of QTL for inbred line crosses. We also develop a novel score-based method for estimating genome-wide significance level of putative QTL effects suitable for the MTMIM model. The MTMIM method is implemented in the freely available and widely used Windows QTL Cartographer software. RESULTS: Throughout the paper, we provide compelling empirical evidences that: (1) the score-based threshold maintains proper type I error rate and tends to keep false discovery rate within an acceptable level; (2) the MTMIM method can deliver better parameter estimates and power than single trait multiple interval mapping method; (3) an analysis of Drosophila dataset illustrates how the MTMIM method can better extract information from datasets with measurements in multiple traits. CONCLUSIONS: The MTMIM method represents a convenient statistical framework to test hypotheses of pleiotropic QTL versus closely linked nonpleiotropic QTL, QTL by environment interaction, and to estimate the total genotypic variance-covariance matrix between traits and to decompose it in terms of QTL-specific variance-covariance matrices, therefore, providing more details on the genetic architecture of complex traits.     

Recombinant inbred lines for genetic mapping in tomato

A cross between the cultivated tomato Lycopersicon esculentum and a related wild species L. cheesmanii yielded 97 recombinant inbred lines (RILs) which were used to construct a genetic map consisting of 132 molecular markers. Significant deviation from the expected 1:1 ratio between the two homozygous classes was found in 73% of the markers. In 98% of the deviating markers, L. esculentum alleles were present in greater frequency than the L. cheesmanii alleles. For most of the markers with skewed segregation, the direction of the deviation was maintained from F₂ to F₇ generations. The average heterozygosity in the population was 15%. This value is significantly greater than the 1.5% heterozygosity expected for RILs in the F₇ generation. On average, recombination between linked markers was twice as high in the RILs than in the F₂ population used to derive them. The utility of RILs for the mapping of qualitative and quantitative traits is discussed.     

Mapping quantitative trait loci controlling seed dormancy and heading date in rice, Oryza sativa L., using backcross inbred lines

 To detect quantitative trait loci (QTLs) controlling seed dormancy, 98 BC₁F₅ lines (backcross inbred lines) derived from a backcross of Nipponbare (japonica)/Kasalath (indica)//Nipponbare were analyzed genetically. We used 245 RFLP markers to construct a framework linkage map. Five putative QTLs affecting seed dormancy were detected on chromosomes 3, 5, 7 (two regions) and 8, respectively. Phenotypic variations explained by each QTL ranged from 6.7% to 22.5% and the five putative QTLs explained about 48% of the total phenotypic variation in the BC₁F₅ lines. Except for those of the QTLs on chromosome 8, the Nipponbare alleles increased the germination rate. Five putative QTLs controlling heading date were detected on chromosomes 2, 3, 4, 6 and 7, respectively. The phenotypic variation explained by each QTL for heading date ranged from 5.7% to 23.4% and the five putative QTLs explained about 52% of the total phenotypic variation. The Nipponbare alleles increased the number of days to heading, except for those of two QTLs on chromosomes 2 and 3. The map location of a putative QTL for heading date coincided with that of a major QTL for seed dormancy on chromosome 3, although two major heading-date QTLs did not coincide with any seed dormancy QTLs detected in this study. Received: 10 October 1997 / Accepted: 12 January 1998     

Bayesian versus frequentist analysis of multiple quantitative trait loci with an application to an outbred apple cross

Two methods, following different statistical paradigms for mapping multiple quantitative trait loci (QTLs), were compared: the first is a frequentist, the second a Bayesian approach. Both methods were applied to previously published experimental data from an outbred progeny of a single cross between two apple cultivars (Malus pumila Mill.). These approaches were compared with respect to (1) the models used, (2) the number of putative QTLs, (3) their estimated map positions and accuracies thereof and (4) the choice of cofactor markers. In general, the strongest evidence for QTLs, provided by both methods, was for the same linkage groups and for similar map positions. However, some differences were found with respect to evidence for QTLs on other linkage groups. The effect of using cofactor markers which were selected differently was also somewhat different. Received: 17 July 2000 / Accepted: 13 January 2001     

Bayesian mapping of multiple quantitative trait loci from incomplete inbred line cross data.

A novel fine structure mapping method for quantitative traits is presented. It is based on Bayesian modeling and inference, treating the number of quantitative trait loci (QTLs) as an unobserved random variable and using ideas similar to composite interval mapping to account for the effects of QTLs in other chromosomes. The method is introduced for inbred lines and it can be applied also in situations involving frequent missing genotypes. We propose that two new probabilistic measures be used to summarize the results from the statistical analysis: (1) the (posterior) QTL intensity, for estimating the number of QTLs in a chromosome and for localizing them into some particular chromosomal regions, and (2) the locationwise (posterior) distributions of the phenotypic effects of the QTLs. Both these measures will be viewed as functions of the putative QTL locus, over the marker range in the linkage group. The method is tested and compared with standard interval and composite interval mapping techniques by using simulated backcross progeny data. It is implemented as a software package. Its initial version is freely available for research purposes under the name Multimapper at URL http://www.rni.helsinki.fi/mjs.     

Quantitative trait locus analysis of lateral branch– related traits in cucumber (Cucumis sativus L.) using recombinant inbred lines

A group of 224 recombinant inbred lines (RILs) was derived from a narrow cross between 2 cucumber (Cucumis sativus L.) lines, namely, S94 (Northern China type with weak lateral branch growth potential and early lateral branch sprouting time) and S06 (Northern European type with strong lateral branch growth potential and late lateral branch sprouting time). These lines were then used for investigating lateral branch-related traits. A total of 36 quantitative trait loci (QTLs) were detected for the following 4 lateral branch-related traits: lateral branch average length (LBAL), lateral branch total length (LBTL), lateral branch number (LBN), and first lateral branch node (FLBN). Further, each QTL explained 3.1% (lbtl2.1, spring) to 32.3% (lbn2.3, spring) of the observed phenotypic variance. Eleven QTLs (lbal1.1, lbtl1.1, lbn1.2, flbn1.2, etc.) for different traits were found to be clustered on the e23m18d-ME23EM6c section (7.4 cM) of linkage group (LG) 1; further, 15 QTLs (lbal2.1, lbtl2.1, lbn2.1, flbn2.1, etc.) were found to be clustered on the S94A1-ME4SA4a section (13.9 cM) of LG2. Twenty-one QTLs explained more than 10% of the phenotypic variance. Moreover, lbtl1.3 (autumn, 26.2%, logarithm of odds (LOD) = 17.4; spring, 26.9%, LOD = 17.9) had stable position and contribution in both seasons. Several se-quence-anchor markers (CMBR40, F, CS30, S94A1, CSWTA11B, etc.) were closely linked with some QTLs for LBAL, LBTL, LBN, and FLBN, which can be used for the marker-assisted selection to improve the plant architecture in cucumber breeding.     

Detection of linkage between quantitative trait loci and restriction fragment length polymorphisms using inbred lines

Summary In segregating populations, large numbers of individuals are needed to detect linkage between markers, such as restriction fragment length polymorphisms (RFLPs), and quantitative trait loci (QTL), limiting the potential use of such markers for detecting linkage. Fewer individuals from inbred lines are needed to detect linkage. Simulation data were used to test the utility of two methods to detect linkage: maximum likelihood and comparison of marker genotype means. When there is tight linkage, the two methods have similar power, but when there is loose linkage, maximum likelihood is much more powerful. Once inbred lines have been established, they can be screened rapidly to detect QTL for several traits simultaneously. If there is sufficient coverage of the genome with RFLPs, several QTL for each trait may be detected.     

Combining data from multiple inbred line crosses improves the power and resolution of quantitative trait loci mapping

Rodent inbred line crosses are widely used to map genetic loci associated with complex traits. This approach has proven to be powerful for detecting quantitative trait loci (QTL); however, the resolution of QTL locations, typically approximately 20 cM, means that hundreds of genes are implicated as potential candidates. We describe analytical methods based on linear models to combine information available in two or more inbred line crosses. Our strategy is motivated by the hypothesis that common inbred strains of the laboratory mouse are derived from a limited ancestral gene pool and thus QTL detected in multiple crosses are likely to represent shared ancestral polymorphisms. We demonstrate that the combined-cross analysis can improve the power to detect weak QTL, can narrow support intervals for QTL regions, and can be used to separate multiple QTL that colocalize by chance. Moreover, combined-cross analysis can establish the allelic states of a QTL among a set of parental lines, thus providing critical information for narrowing QTL regions by haplotype analysis.     

Random amplified polymorphic DNA markers in crosses between inbred lines of Rhode Island Red and White Leghorn chickens

Reciprocal crosses and backcrosses were conducted between inbred Rhode Island Red and White Leghorn chickens differentiated for egg production and egg quality traits. Random amplified polymorphic DNA (RAPD) markers distinguishing inbred lines were detected. Twenty-two polymorphic bands were found from screening 120 single 10-mer random primers of which two were consistent with sex-linked markers. Of 90 pairwise two-point linkage analyses completed for the autosomal markers, four close linkages (8·2 cM to 14·9 cM) were significantly different from zero.     

Searching for genetic factors of fatty liver in SMXA-5 mice by quantitative trait loci analysis under a high-fat diet

Fatty liver is strongly associated with the metabolic syndrome characterized by obesity, insulin resistance, and type 2 diabetes, but the genetic basis and functional mechanisms linking fatty liver with the metabolic syndrome are largely unknown. The SMXA-5 mouse is one of the SMXA recombinant inbred substrains established from SM/J and A/J strains and is a model for polygenic type 2 diabetes, characterized by moderately impaired glucose tolerance, hyperinsulinemia, and mild obesity. SMXA-5 mice also developed fatty liver, and a high-fat diet markedly worsened this trait, although SM/J and A/J mice are resistant to fatty liver development under a high-fat diet. To dissect loci for fatty liver in the A/J regions of the SMXA-5 genome, we attempted quantitative trait loci (QTLs) analysis in (SM/JxSMXA-5)F2 intercross mice fed a high-fat diet. We mapped a major QTL for relative liver weight and liver lipid content near D12Mit270 on chromosome 12 and designated this QTL Fl1sa. The A/J allele at this locus contributes to the increase in these traits. We confirmed the effect of Fl1sa on lipid accumulation in liver using the A/J-Chr12(SM) consomic strain, which showed significantly less accumulation than A/J mice. This suggests that the SM/J and A/J strains, neither of which develops fatty liver, possess loci causing fatty liver and that the coexistence of these loci causes fatty liver in SMXA-5 mice.     

SSR标记用于玉米自交系遗传变异与优势类群划分的研究

采用SSR和杂种优势聚类方法分析我国15个玉米骨干自交系的遗传变异,并初步进行了杂种优势类群划分,从62个SSR引物中筛选的40对有效引物对15个玉米自交系进行了分析,共检测到188个等位基因变异,每个SSR座位的等位基因数2－9个,平均为4．7个,SSR位点的多态信息含量PIC值平均为0．675,分布范围在0．360－0．851之间,根据SSR数据对供试材料进行遗传相似性分析,Nei氏相似性系数分布在0．574－0．777之间,10对多态性高的SSR引物可有效区分15个自交系,应用SSR聚类分析的结果与系谱关系相一致,与杂种优势类法相比较,SSR方法具有效率高,结果可靠,可标准化的特点,对SSR方法在玉米育种实践上的应用进行了初步探讨。     

利用向日葵重组自交系构建遗传图谱

以向日葵自选系K55为母本、K58为父本杂交组合,通过单粒传得到的187个F_5：6代重组自交系群体为作图材料,联合应用SSR和AFLP标记构建遗传连锁图谱。经过78对SSR引物和48对AFLP引物组合选择性扩增,分别得到341和1119条带,共1460条,分别获得多态性条带184条和393条,共577条多态性条带,占所有条带的39.52%。SSR和AFLP标记各有84个和108个多态性标记偏离孟德尔分离比例(P=0.05),共192个偏分离标记。采用JoinMap4.0软件进行连锁分析,构建了1张总长度为2759.4 cM、包含17个连锁群、连锁495个多态性标记的遗传图谱,其中偏分离标记170个,标记间的平均图距为5.57 cM。每个连锁群上分布有5~72个标记,长68.88~250.17 cM。本图谱为向日葵永久性图谱,为向日葵重要性状QTL定位和基因克隆奠定基础。     

Mapping quantitative trait loci using multiple families of line crosses.

To avoid a loss in statistical power as a result of homozygous individuals being selected as parents of a mapping population, one can use multiple families of line crosses for quantitative trait genetic linkage analysis. Two strategies of combining data are investigated: the fixed-model and the random-model strategies. The fixed-model approach estimates and tests the average effect of gene substitution for each parent, while the random-model approach treats each effect of gene substitution as a random variable and directly estimates and tests the variance of gene substitution. Extensive Monte Carlo simulations verify that the two strategies perform equally well, although the random model is preferable in combining data from a large number of families. Simulations also show that there may be an optimal sampling strategy (number of families vs. number of individuals per family) in which QTL mapping reaches its maximum power and minimum estimation error. Deviation from the optimal strategy reduces the efficiency of the method.