Identification of RAPD markers linked to genetic factors controlling the milling energy requirement of barley

Doubled haploid (DH) populations of barley have been used in combination with PCR-based polymorphic-assay procedures to identify molecular markers linked to genes controlling the milling energy requirement of the grain. Milling energy (ME) is a quantitative trait and locating individual quantitative trait loci (QTLs) involved the construction of bulks by combining DNA from DH families representing the extreme members of the distribution for ME. In addition, the individuals had alternative alleles at theRrn2 locus that has previously been shown to be linked to an ME QTL. The DNA bulks were screened with Randomly Amplified Polymorphic DNA (RAPD) markers and polymorphic amplification products tested for linkage to genes influencing the expression of ME in a DH population. Several markers were identified which are linked to a QTL controlling ME and the recombination fraction determined by maximum likelihood procedures. The results indicate that DHs in combination with RAPDs and bulked segregant analysis provide an efficient method for locating QTLs in barely. Furthermore, this approach is applicable to mapping other QTLs in a range of organisms from which DH or recombinant inbred lines can be extracted.     

Advances in Statistical Methods to Map Quantitative Trait Loci in Outbred Populations

Statistical methods to map quantitative trait loci (QTL) in outbred populations are reviewed, extensions and applications to human and plant genetic data are indicated, and areas for further research are identified. Simple and computationally inexpensive methods include (multiple) linear regression of phenotype on marker genotypes and regression of squared phenotypic differences among relative pairs on estimated proportions of identity-by-descent at a locus. These methods are less suited for genetic parameter estimation in outbred populations but allow the determination of test statistic distributions via simulation or data permutation; however, further inferences including confidence intervals of QTL location require the use of Monte Carlo or bootstrap sampling techniques. A method which is intermediate in computational requirements is residual maximum likelihood (REML) with a covariance matrix of random QTL effects conditional on information from multiple linked markers. Testing for the number of QTLs on a chromosome is difficult in a classical framework. The computationally most demanding methods are maximum likelihood and Bayesian analysis, which take account of the distribution of multilocus marker-QTL genotypes on a pedigree and permit investigators to fit different models of variation at the QTL. The Bayesian analysis includes the number of QTLs on a chromosome as an unknown.     

A Random Model Approach to Interval Mapping of Quantitative Trait Loci

Mapping quantitative trait loci in outbred populations is important because many populations of organisms are noninbred. Unfortunately, information about the genetic architecture of the trait may not be available in outbred populations. Thus, the allelic effects of genes can not be estimated with ease. In addition, under linkage equilibrium, marker genotypes provide no information about the genotype of a QTL (our terminology for a single quantitative trait locus is QTL while multiple loci are referred to as QTLs). To circumvent this problem, an interval mapping procedure based on a random model approach is described. Under a random model, instead of estimating the effects, segregating variances of QTLs are estimated by a maximum likelihood method. Estimation of the variance component of a QTL depends on the proportion of genes identical-by-descent (IBD) shared by relatives at the locus, which is predicted by the IBD of two markers flanking the QTL. The marker IBD shared by two relatives are inferred from the observed marker genotypes. The procedure offers an advantage over the regression interval mapping in terms of high power and small estimation errors and provides flexibility for large sibships, irregular pedigree relationships and incorporation of common environmental and fixed effects.     

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。     

Mapping quantitative trait loci with dominant markers in four-way crosses

 A common problem in mapping quantitative trait loci (QTLs) is that marker data are often incomplete. This includes missing data, dominant markers, and partially informative markers, arising in outbred populations. Here we briefly present an iteratively re-weighted least square method (IRWLS) to incorporate dominant and missing markers for mapping QTLs in four-way crosses under a heterogeneous variance model. The algorithm uses information from all markers in a linkage group to infer the QTL genotype. Monte Carlo simulations indicate that with half dominant markers, QTL detection is almost as efficient as with all co-dominant markers. However, the precision of the estimated QTL parameters generally decreases as more markers become missing or dominant. Notable differences are observed on the standard deviation of the estimated QTL position for varying levels of marker information content. The method is relatively simple so that more complex models including multiple QTLs or fixed effects can be fitted. Finally, the method can be readily extended to QTL mapping in full-sib families. Received: 16 June 1998 / Accepted: 29 September 1998     

A General Monte Carlo Method for Mapping Multiple Quantitative Trait Loci

In this paper we address the mapping of multiple quantitative trait loci (QTLs) in line crosses for which the genetic data are highly incomplete. Such complicated situations occur, for instance, when dominant markers are used or when unequally informative markers are used in experiments with outbred populations. We describe a general and flexible Monte Carlo expectation-maximization (Monte Carlo EM) algorithm for fitting multiple-QTL models to such data. Implementation of this algorithm is straightforward in standard statistical software, but computation may take much time. The method may be generalized to cope with more complex models for animal and human pedigrees. A practical example is presented, where a three-QTL model is adopted in an outbreeding situation with dominant markers. The example is concerned with the linkage between randomly amplified polymorphic DNA (RAPD) markers and QTLs for partial resistance to Fusarium oxysporum in lily.     

Identification and stability of QTLs for fruit quality traits in apple

Breeding for fruit quality traits is complex due to the polygenic (quantitative) nature of the genetic control of these traits. Therefore, to improve the speed and efficiency of genotype selection, attention in recent years has focused on the identification of quantitative trait loci (QTLs) and molecular markers associated with these QTLs. However, despite the huge potential of molecular markers in breeding programmes, their implementation in practice has been limited by the lack of information on the stability of QTLs across different environments and within different genetic backgrounds. Here, we present the results from a comprehensive analysis of the inheritance of fruit quality traits within a population derived from a cross between the apple cultivars ‘Telamon’ and ‘Braeburn’ over two successive seasons. A total of 74 different QTLs were identified for all the major fruit physiological traits including fruit height, diameter, weight and stiffness, flesh firmness, rate of flesh browning, acidity, the oBrix content and harvest date. Seventeen of these QTLs were ‘major’ QTLs, accounting for over 20% of the observed population variance of the trait. However, only one third (26) of the identified QTLs were stable over both harvest years, and of these year-stable QTLs only one was a major QTL. A direct comparison with published QTL results obtained using other populations (King et al., Theor Appl Genet 102:1227–1235, 2001; Liebhard et al., Plant Mol Biol 52:511–526, 2003) is difficult because the linkage maps do not share a sufficient number of common markers and due to differences in the trait evaluation protocols. Nonetheless, our results suggest that for the six fruit quality traits which were measured in all populations, nine out of a total of 45 QTLs were common or stable across all population × environments combinations. These results are discussed in the framework of the development and application of molecular markers for fruit quality trait improvement.     

A high-density consensus map of barley to compare the distribution of QTLs for partial resistance to Puccinia hordei and of defence gene homologues

A consensus map of barley was constructed based on three reference doubled haploid (DH) populations and three recombinant inbred line (RIL) populations. Several sets of microsatellites were used as bridge markers in the integration of those populations previously genotyped with RFLP or with AFLP markers. Another set of 61 genic microsatellites was mapped for the first time using a newly developed fluorescent labelling strategy, referred to as A/T labelling. The final map contains 3,258 markers spanning 1,081 centiMorgans (cM) with an average distance between two adjacent loci of 0.33 cM. This is the highest density of markers reported for a barley genetic map to date. The consensus map was divided into 210 BINs of about 5 cM each in which were placed 19 quantitative trait loci (QTL) contributing to the partial resistance to barley leaf rust (Puccinia hordei Otth) in five of the integrated populations. Each parental barley combination segregated for different sets of QTLs, with only few QTLs shared by any pair of cultivars. Defence gene homologues (DGH) were identified by tBlastx homology to known genes involved in the defence of plants against microbial pathogens. Sixty-three DGHs were located into the 210 BINs in order to identify candidate genes responsible for the QTL effects. Eight BINs were co-occupied by a QTL and DGH(s). The positional candidates identified are receptor-like kinase, WIR1 homologues and several defence response genes like peroxidases, superoxide dismutase and thaumatin. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

Linkage and Association Mapping for Two Major Traits Used in the Maritime Pine Breeding Program: Height Growth and Stem Straightness

BackgroundIncreasing our understanding of the genetic architecture of complex traits, through analyses of genotype-phenotype associations and of the genes/polymorphisms accounting for trait variation, is crucial, to improve the integration of molecular markers into forest tree breeding. In this study, two full-sib families and one breeding population of maritime pine were used to identify quantitative trait loci (QTLs) for height growth and stem straightness, through linkage analysis (LA) and linkage disequilibrium (LD) mapping approaches.ResultsThe populations used for LA consisted of two unrelated three-generation full-sib families (n = 197 and n = 477). These populations were assessed for height growth or stem straightness and genotyped for 248 and 217 markers, respectively. The population used for LD mapping consisted of 661 founders of the first and second generations of the breeding program. This population was phenotyped for the same traits and genotyped for 2,498 single-nucleotide polymorphism (SNP) markers corresponding to 1,652 gene loci. The gene-based reference genetic map of maritime pine was used to localize and compare the QTLs detected by the two approaches, for both traits. LA identified three QTLs for stem straightness and two QTLs for height growth. The LD study yielded seven significant associations (P ≤ 0.001): four for stem straightness and three for height growth. No colocalisation was found between QTLs identified by LA and SNPs detected by LD mapping for the same trait.ConclusionsThis study provides the first comparison of LA and LD mapping approaches in maritime pine, highlighting the complementary nature of these two approaches for deciphering the genetic architecture of two mandatory traits of the breeding program.     

A general mixture model for mapping quantitative trait loci by using molecular markers

Summary In a segregating population a quantitative trait may be considered to follow a mixture of (normal) distributions, the mixing proportions being based on Mendelian segregation rules. A general and flexible mixture model is proposed for mapping quantitative trait loci (QTLs) by using molecular markers. A method is discribed to fit the model to data. The model makes it possible to (1) analyse non-normally distributed traits such as lifetimes, counts or percentages in addition to normally distributed traits, (2) reduce environmental variation by taking into account the effects of experimental design factors and interaction between genotype and environment, (3) reduce genotypic variation by taking into account the effects of two or more QTLs simultaneously, (4) carry out a (combined) analysis of different population types, (5) estimate recombination frequencies between markers or use known marker distances, (6) cope with missing marker observations, (7) use markers as covariables in detection and mapping of QTLs, and finally to (8) implement the mapping in standard statistical packages.     

Quantitative trait loci for low aflatoxin production in two related maize populations

Aflatoxin B(1) formed by Aspergillus flavus Fr:Link has been associated with animal disease and liver cancer in humans. We performed genetic studies in progenies derived from maize inbred Tex6, associated with relatively low levels of aflatoxin production, crossed with the historically important inbred B73. (Tex6 x B73) x B73 BC(1)S(1) and Tex6 x B73 F(2:3) mapping populations were produced and evaluated in 1996 and 1997 in Champaign, Ill. Ears were inoculated 20 to 24 days after midsilk using a pinboard method and a mixture of conidia of A. flavus Link:Fr. isolates. Aflatoxin B(1) levels in harvested ears were determined using an indirect competitive ELISA. Molecular markers were assayed on the populations and used to generate maps. Molecular marker - QTL associations for lower levels of aflatoxin production were determined using multiple regression (MR) and composite interval analysis with multiple regression (CIM MR). MR revealed sets of markers associated with lower aflatoxin production in 1996 and 1997, and CIM MR detected a smaller subset of loci significant in 1997. QTLs for lower aflatoxin were attributed to both Tex6 and B73 parental sources. Environment strongly influenced the detection of QTLs for lower aflatoxin production in different years. There were very few chromosome regions associated with QTLs in more than 1 year or population with MR analysis, and none with CIM MR analysis. In 1997, QTLs for lower aflatoxin were detected with CIM MR in bins 5.01-2 and 5.04-5 in the BC(1)S(1) population, and in bins 3.05-6, 4.07-8 and 10.05-10.07 in the F(2:3) population. These QTL associations appear the most promising for further study.     

Mapping Quantitative Trait Loci in Crosses between Outbred Lines Using Least Squares

The use of genetic maps based upon molecular markers has allowed the dissection of some of the factors underlying quantitative variation in crosses between inbred lines. For many species crossing inbred lines is not a practical proposition, although crosses between genetically very different outbred lines are possible. Here we develop a least squares method for the analysis of crosses between outbred lines which simultaneously uses information from multiple linked markers. The method is suitable for crosses where the lines may be segregating at marker loci but can be assumed to be fixed for alternative alleles at the major quantitative trait loci (QTLs) affecting the traits under analysis (e.g., crosses between divergent selection lines or breeds with different selection histories). The simultaneous use of multiple markers from a linkage group increases the sensitivity of the test statistic, and thus the power for the detection of QTLs, compared to the use of single markers or markers flanking an interval. The gain is greater for more closely spaced markers and for markers of lower information content. Use of multiple markers can also remove the bias in the estimated position and effect of a QTL which may result when different markers in a linkage group vary in their heterozygosity in the F(1) (and thus in their information content) and are considered only singly or a pair at a time. The method is relatively simple to apply so that more complex models can be fitted than is currently possible by maximum likelihood. Thus fixed effects and effects of background genotype can be fitted simultaneously with the exploration of a single linkage group which will increase the power to detect QTLs by reducing the residual variance. More complex models with several QTLs in the same linkage group and two-locus interactions between QTLs can similarly be examined. Thus least squares provides a powerful tool to extend the range of crosses from which QTLs can be dissected whilst at the same time allowing flexible and realistic models to be explored.     

Genome‐wide analyses of rice root development QTLs and development of an online resource, Rootbrowse

Genetic control of root development in rice is complex and the underlying mechanisms (constitutive and adaptive) are poorly understood. Lowland and upland varieties of indica and japonica rice with contrasting root development characteristics have been crossed, mapping populations developed and a number of QTLs in different chromosomes were identified. As these studies have used different sets of markers and many of the QTLs identified are long, it is difficult to exploit the varietal difference for improved root traits by marker assisted selection and for identification of concerned alleles. Intensive data mining of literature resulted in the identification 861 root development QTLs and associated microsatellite markers located on different chromosomes. The QTL and marker data generated and the genome sequence of rice were used for construction of a relational database, Rootbrowse, using MySQL relational database management system and Bio::DB::GFF schema. The data is viewed using GBrowse visualization tool. It graphically displays a section of the genome and all features annotated on it including the QTLs. The QTLs can be displayed along with SSR markers, protein coding genes and/or known root development genes for prediction of probable candidate genes.     

Genetic map construction and QTL mapping of resistance to blackleg (Leptosphaeria maculans) disease in Australian canola (Brassica napus L.) cultivars

Genetic map construction and identification of quantitative trait loci (QTLs) for blackleg resistance were performed for four mapping populations derived from five different canola source cultivars. Three of the populations were generated from crosses between single genotypes from the blackleg-resistant cultivars Caiman, Camberra and ^AVSapphire and the blackleg-susceptible cultivar Westar₁₀. The fourth population was derived from a cross between genotypes from two blackleg resistant varieties (Rainbow and ^AVSapphire). Different types of DNA-based markers were designed and characterised from a collection of 20,000 EST sequences generated from multiple Brassica species, including a new set of 445 EST-SSR markers of high value to the international community. Multiple molecular genetic marker systems were used to construct linkage maps with locus numbers varying between 219 and 468, and coverage ranging from 1173 to 1800 cM. The proportion of polymorphic markers assigned to map locations varied from 70 to 89% across the four populations. Publicly available simple sequence repeat markers were used to assign linkage groups to reference nomenclature, and a sub-set of mapped markers were also screened on the Tapidor × Ningyou (T × N) reference population to assist this process. QTL analysis was performed based on percentage survival at low and high disease pressure sites. Multiple QTLs were identified across the four mapping populations, accounting for 13–33% of phenotypic variance (V _p). QTL-linked marker data are suitable for implementation in breeding for disease resistance in Australian canola cultivars. However, the likelihood of shifts in pathogen race structure across different geographical locations may have implications for the long-term durability of such associations.     

Genetic analysis of resistance to yellow rust in hexaploid wheat using a mixture model for multiple crosses

DNA-based molecular markers have been used in numerous studies for tagging specific genes in wheat for subsequent use in marker-assisted selection. Usually in plant breeding, procedures for mapping genes are based on analysis of a single segregating population. However, breeding programmes routinely evaluate large numbers of progeny derived from multiple-related crosses with some parental lines shared. In most such related crosses, the number of progeny is quite small. Thus, statistical techniques for detecting quantitative trait loci (QTLs) using data from conventional multi-cross breeding programmes are interesting. The objective of this study is to present a mixture model for QTL mapping in crosses of multiple inbred varieties with non-normal phenotype distributions and to use this model to map QTLs for yellow rust resistance in elite wheat breeding material. Three doubled haploid populations consisting of 41, 42 and 55 lines, respectively, originating from four parental varieties were studied. Multi-cross QTL analysis with three specific pathogen isolates of Puccinia striiformis f. sp. tritici and a mixture of the isolates revealed QTLs for resistance at four different genomic locations. These QTLs were found on chromosome 2AL, 2AS, 2BL and 6BL and explained between 21 and 41% of the phenotypic variation. Two of these QTLs, one on the long arm of chromosome 2A and one on the short arm of chromosome 2A were identical to the known yellow rust resistance genes Yr32 and Yr17, respectively, whereas the QTLs located on the long arms of chromosomes 2B and 6B may reflect types of resistance to yellow rust, which have not previously been mapped.     

Inferences regarding the numbers and locations of QTLs under multiple-QTL models using interval mapping and composite interval mapping

This paper examines the properties of likelihood maps generated by interval mapping (IM) and composite interval mapping (CIM), two widely used methods for detecting quantitative trait loci (QTLs). We evaluate the usefulness of interpretations of entire maps, rather than only evaluating summary statistics that consider isolated features of maps. A simulation study was performed in which traits with varying genetic architectures, including 20-40 QTLs per chromosome, were examined with both IM and CIM under different marker densities and sample sizes. IM was found to be an unreliable tool for precise estimation of the number and locations of individual QTLs, although it has greater power for simply detecting the presence of QTLs than CIM. The ability of CIM to resolve the correct number of QTLs and to estimate their locations correctly is good if there are three or fewer QTLs per 100 centiMorgans, but can lead to erroneous inferences for more complex architectures. When the underlying genetic architecture of a trait consists of several QTLs with randomly distributed effects and locations likelihood profiles were often indicative of a few underlying genes of large effect. Studies that have detected more than a few QTLs per chromosome should be interpreted with caution.     

Prediction of additive genetic effects for the QTL-cluster on the basis of data on surrounding markers in outbred populations

This paper described a method for predicting additive effects of a cluster of tightly linked QTLs for outbred populations of animals in the situation where the QTLs are located on a chromosome segment surrounded by multiple linked DNA markers. We present a mixed model method for best linear unbiased prediction (conditional to the marker data) of the additive effects of the QTL-cluster and of the remaining QTLs unlinked to the marker linkage group. This method takes into consideration the identity-by-descent proportion (IBDP) for the particular chromosomal segment, in contrast to some other methods which use IBD probabilities at one specific location. In this method, fully informative data on different flanking markers is used to calculate the values of the expectations of the IBDPs (EIBDPs) between gametes for animals to be evaluated. Then the expected values are used as the elements of the gametic relationship matrix required in the best linear unbiased prediction. Giving a small numerical example, we illustrate how the present method can be used for the prediction of the QTL-cluster effects and for genetic evaluation of animals in outbred populations. A computational strategy is discussed on the basis of the calculation of the EIBDPs and the inverted gametic relationship matrix in complex pedigrees.     

Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between single nucleotide polymorphisms

Effectiveness of marker-assisted selection (MAS) and quantitative trait locus (QTL) mapping using population-wide linkage disequilibrium (LD) between markers and QTLs depends on the extent of LD and how it declines with distance between markers and QTLs in a population. Marker-QTL LD can be predicted from LD between markers. Our previous work evaluated LD measures between multi-allelic markers as predictors of usable LD of multi-allelic markers with QTLs. Since single nucleotide polymorphisms (SNPs) are the current marker of choice for high-density genotyping and LD-mapping of QTLs, the objective of this study was to use LD between multi-allelic markers to predict LD among biallelic SNPs or between SNPs and QTLs. Observable LD between multi-allelic markers was evaluated using nine measures. These included two pooled and standardized measures of LD between pairs of alleles at two markers based on Lewontin's LD measure, two pooled measures of squared correlations between alleles, one standardized measure using Hardy-Weinberg heterozygosities, and four measures based on the chi-square statistic for testing for association between alleles at two loci. The standardized chi-square measure that best predicted usable LD between multi-allelic markers and QTLs, based on our previous work, overestimated usable SNP-SNP or SNP-QTL LD. Instead, three other measures were found to be good predictors of usable SNP-SNP or SNP-QTL LD when LD is generated by drift. Therefore, the LD measure between multi-allelic markers that is best for predicting usable LD in a population depends on the type of markers (i.e. multi-allelic or biallelic) that will eventually be used for QTL mapping or MAS.     

Comparative analysis of QTLs affecting plant height and flowering among closely-related diploid and polyploid genomes.

Quantitative trait loci (QTLs) affecting plant height and flowering were studied in the two Saccharum species from which modern sugarcane cultivars are derived. Two segregating populations derived from interspecific crosses between Saccharum officinarum and Saccharum spontaneum were genotyped with 735 DNA markers. Among the 65 significant associations found between these two traits and DNA markers, 35 of the loci were linked to sugarcane genetic maps and 30 were unlinked DNA markers. Twenty-one of the 35 mapped QTLs were clustered in eight genomic regions of six sugarcane homologous groups. Some of these could be divergent alleles at homologous loci, making the actual number of genes implicated in these traits much less than 35. Four QTL clusters controlling plant height in sugarcane corresponded closely to four of the six plant-height QTLs previously mapped in sorghum. One QTL controlling flowering in sugarcane corresponded to one of three flowering QTLs mapped in sorghum. The correspondence in locations of QTLs affecting plant height and flowering in sugarcane and sorghum reinforce the notion that the simple sorghum genome is a valuable "template" for molecular dissection of the much more complex sugarcane genome.     

Association mapping and meta-analysis: two complementary approaches for the detection of reliable Septoria tritici blotch quantitative resistance in bread wheat (Triticum aestivum L.)

Septoria tritici blotch (STB), caused by the ascomycete Mycosphaerella graminicola, is one of the most ubiquitous and important diseases of bread wheat worldwide. The aim of this study was to identify markers linked to loci conferring resistance to STB from seven biparental populations. Linkage analysis, meta-analysis and association mapping were combined to identify robust quantitative trait loci (QTLs) for resistance. Linkage analysis led to the detection of 115 QTLs for resistance to STB and 66 QTLs linked to plant height and/or earliness. Meta-analysis clustered these 115 QTLs into 27 Meta-QTLs (MQTLs) of pathogen resistance, of which 14 were found to be linked to plant height and/or earliness. Both the relationship between dwarfing and susceptibility to STB and the significant negative correlation between earliness and STB symptoms were confirmed. Eleven loci were linked to STB resistance by association mapping using a general linear model and/or a mixed linear model, of which eight co-located with STB MQTLs and two co-located with individual QTLs. Associated markers located in MQTL regions enhanced the relevance of the results and validated the potential of an association mapping approach. With several biparental populations, meta-analysis is the most relevant form of genetic analysis study, but association mapping can be used as a validation method. Regions linked to resistance in both methods should be relevant for use in breeding programs for improving resistance to STB in wheat varieties. The main interest in comparing both approaches is to detect robust loci that will be functional in many genetic backgrounds rather than just in one or a few specific backgrounds.