Identification of cotton fiber quality quantitative trait loci using intraspecific crosses derived from two near-isogenic lines differing in fiber bundle strength

Cotton fiber properties are very important to the yarn quality. Modern high-speed textile operations around the world require long, strong and fine cotton fibers. The objective of this research was to identify stable fiber quantitative trait loci (QTLs) that could be used in cotton breeding through marker-assisted selection (MAS). Two cotton lines, MD90ne and MD52ne, are near-isogenic with significant differences in fiber properties, especially strength. Fiber samples from 734 progeny plants of two F2 populations (A and B) derived from crosses between MD90ne and MD52ne were collected at Stoneville, MS, USA in 2012. Fiber quality attributes were measured using a High Volume Instrument 1000. A simple sequence repeat (SSR) genetic linkage map with 165 loci covering 632.53 cM was constructed using population A, consisting of 356 F2 individuals and used for identifying QTLs related to fiber bundle strength (FBS), short fiber index (SFI) and upper-half mean fiber length (UHML). One QTL for FBS originating from the stronger fiber parent MD52ne was identified on chromosome (Chr.) 3. Three QTLs each for SFI and UHML were identified on Chrs. 3, 4 and 14 and Chrs. 3, 11 and 24, respectively. Population B, consisting of 378 F2 progeny, was used to confirm these QTLs by analyzing 57 SSR markers mapped on Chrs. 3, 14 and 24. Three QTLs—qFBS-c3, qSFI-c14 and qUHML-c24—were confirmed and appeared stable. These three QTLs could potentially be used in breeding to improve cotton fiber quality through a MAS strategy.     

Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines

Dominant phenotype of a genetic marker provides incomplete information about the marker genotype of an individual. A consequence of using this incomplete information for mapping quantitative trait loci (QTL) is that the inference of the genotype of a putative QTL flanked by a marker with dominant phenotype will depend on the genotype or phenotype of the next marker. This dependence can be extended further until a marker genotype is fully observed. A general algorithm is derived to calculate the probability distribution of the genotype of a putative QTL at a given genomic position, conditional on all observed marker phenotypes in the region with dominant and missing marker information for an individual. The algorithm is implemented for various populations stemming from two inbred lines in the context of mapping QTL. Simulation results show that if only a proportion of markers contain missing or dominant phenotypes, QTL mapping can be almost as efficient as if there were no missing information in the data. The efficiency of the analysis, however, may decrease substantially when a very large proportion of markers contain missing or dominant phenotypes and a genetic map has to be reconstructed first on the same data as well. So it is important to combine dominant markers with codominant markers in a QTL mapping study. This revised version was published online in July 2006 with corrections to the Cover Date.     

Marker-assisted introgression of favorable alleles at quantitative trait loci between maize elite lines

This article reports the marker-assisted introgression of favorable alleles at three quantitative trait loci (QTL) for earliness and grain yield among maize elite lines. The QTL were originally detected in 1992 by means of ANOVA in a population of 96 recombinant inbred lines (RILs). Introgression started from a selected RIL, which was crossed three times to one of the original parents and then self-fertilized, leading to BC(3)S(1) progenies. Markers were used to assist both foreground and background selection at each generation. At the end of the program, the effect of introgression was assessed phenotypically in agronomic trials, and QTL detection was performed by composite interval mapping among BC(3)S(1) progenies. The marker-assisted introgression proved successful at the genotypic level, as analyzed by precision graphical genotypes, although no emphasis was put on the reduction of linkage drag around QTL. Also, QTL positions were generally sustained in the introgression background. For earliness, the magnitude and sign of the QTL effects were in good agreement with those expected from initial RIL analyses. Conversely, for yield, important discrepancies were observed in the magnitude and sign of the QTL effects observed after introgression, when compared to those expected from initial RIL analyses. These discrepancies are probably due to important genotype-by-environment interactions.     

Mapping quantitative trait loci in noninbred mosquito crosses

The identification of genes that affect quantitative traits has been of great interest to geneticists for many decades, and many statistical methods have been developed to map quantitative trait loci (QTL). Most QTL mapping studies in experimental organisms use purely inbred lines, where the two homologous chromosomes in each individual are identical. As a result, many existing QTL mapping methods developed for experimental organisms are applicable only to genetic crosses between inbred lines. However, it may be difficult to obtain inbred lines for certain organisms, e.g., mosquitoes. Although statistical methods for QTL mapping in outbred populations, e.g., humans, can be applied for such crosses, these methods may not fully take advantage of the uniqueness of these crosses. For example, we can generally assume that the two grandparental lines are homozygous at the QTL of interest, but such information is not be utilized through methods developed for outbred populations. In addition, mating types and phases can be relatively easy to establish through the analysis of adjacent markers due to the large number of offspring that can be collected, substantially simplifying the computational need. In this article, motivated by a mosquito intercross experiment involving two selected lines that are not genetically homozygous across the genome, we develop statistical methods for QTL mapping for genetic crosses involving noninbred lines. In our procedure, we first infer parental mating types and use likelihood-based methods to infer phases in each parent on the basis of genotypes of offspring and one parent. A hidden Markov model is then employed to estimate the number of high-risk alleles at marker positions and putative QTL positions between markers in each offspring, and QTL mapping is finally conducted through the inferred QTL configuration across all offspring in all crosses. The performance of the proposed methods is assessed through simulation studies, and the usefulness of this method is demonstrated through its application to a mosquito data set.     

Mapping of epistatic quantitative trait loci in four-way crosses

Four-way crosses (4WC) involving four different inbred lines often appear in plant and animal commercial breeding programs. Direct mapping of quantitative trait loci (QTL) in these commercial populations is both economical and practical. However, the existing statistical methods for mapping QTL in a 4WC population are built on the single-QTL genetic model. This simple genetic model fails to take into account QTL interactions, which play an important role in the genetic architecture of complex traits. In this paper, therefore, we attempted to develop a statistical method to detect epistatic QTL in 4WC population. Conditional probabilities of QTL genotypes, computed by the multi-point single locus method, were used to sample the genotypes of all putative QTL in the entire genome. The sampled genotypes were used to construct the design matrix for QTL effects. All QTL effects, including main and epistatic effects, were simultaneously estimated by the penalized maximum likelihood method. The proposed method was confirmed by a series of Monte Carlo simulation studies and real data analysis of cotton. The new method will provide novel tools for the genetic dissection of complex traits, construction of QTL networks, and analysis of heterosis.     

Joint tests for quantitative trait loci in experimental crosses

Selective genotyping is common because it can increase the expected correlation between QTL genotype and phenotype and thus increase the statistical power of linkage tests (i.e., regression-based tests). Linkage can also be tested by assessing whether the marginal genotypic distribution conforms to its expectation, a marginal-based test. We developed a class of joint tests that, by constraining intercepts in regression-based analyses, capitalize on the information available in both regression-based and marginal-based tests. We simulated data corresponding to the null hypothesis of no QTL effect and the alternative of some QTL effect at the locus for a backcross and an F2 intercross between inbred strains. Regression-based and marginal-based tests were compared to corresponding joint tests. We studied the effects of random sampling, selective sampling from a single tail of the phenotypic distribution, and selective sampling from both tails of the phenotypic distribution. Joint tests were nearly as powerful as all competing alternatives for random sampling and two-tailed selection under both backcross and F2 intercross situations. Joint tests were generally more powerful for one-tailed selection under both backcross and F2 intercross situations. However, joint tests cannot be recommended for one-tailed selective genotyping if segregation distortion is suspected.     

Classical genetic and quantitative trait loci analyses of heterosis in a maize hybrid between two elite inbred lines

The exploitation of heterosis is one of the most outstanding advancements in plant breeding, although its genetic basis is not well understood yet. This research was conducted on the materials arising from the maize single cross B73 x H99 to study heterosis by procedures of classical genetic and quantitative trait loci (QTL) analyses. Materials were the basic generations, the derived 142 recombinant inbred lines (RILs), and the three testcross populations obtained by crossing the 142 RILs to each parent and their F(1). For seedling weight (SW), number of kernels per plant (NK), and grain yield (GY), heterosis was >100% and the average degree of dominance was >1. Epistasis was significant for SW and NK but not for GY. Several QTL were identified and in most cases they were in the additive-dominance range for traits with low heterosis and mostly in the dominance-overdominance range for plant height (PH), SW, NK, and GY. Only a few QTL with digenic epistasis were identified. The importance of dominance effects was confirmed by highly significant correlations between heterozygosity level and phenotypic performance, especially for GY. Some chromosome regions presented overlaps of overdominant QTL for SW, PH, NK, and GY, suggesting pleiotropic effects on overall plant vigor.     

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     

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.     

Recombinant near-isogenic lines: a resource for the mendelization of heterotic QTL in maize

Although heterosis is widely exploited in agriculture, a clear understanding of its genetic bases is still elusive. This work describes the development of maize recombinant near-isogenic lines (NILs) for the mendelization of six heterotic QTL previously identified based on a maize (Zea mays L.) RIL population. The efficient and inexpensive strategy adopted to generate sets of NILs starting from QTL-specific residual heterozygous lines (RHLs) is described and validated. In particular, we produced nine pairs of recombinant NILs for all six QTL starting from RHLs F_4:5 originally obtained during the production of the RIL population mentioned above. Whenever possible, two different NIL pairs were generated for each QTL. The efficiency of this procedure was tested by analyzing two segregating populations for two of the selected heterotic QTL for plant height, yield per plant and ears per plant. Both additive and dominant effects were observed, consistently with the presence of the QTL within the introgressed regions. Refinement of QTL detection was consistent with previous observations in terms of effects and position of the considered QTL. The genetic material developed in this work represents the starting point for QTL fine mapping aimed at understanding the genetic bases of hybrid vigor in maize. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Mapping quantitative trait loci for longitudinal traits in line crosses

Quantitative traits whose phenotypic values change over time are called longitudinal traits. Genetic analyses of longitudinal traits can be conducted using any of the following approaches: (1) treating the phenotypic values at different time points as repeated measurements of the same trait and analyzing the trait under the repeated measurements framework, (2) treating the phenotypes measured from different time points as different traits and analyzing the traits jointly on the basis of the theory of multivariate analysis, and (3) fitting a growth curve to the phenotypic values across time points and analyzing the fitted parameters of the growth trajectory under the theory of multivariate analysis. The third approach has been used in QTL mapping for longitudinal traits by fitting the data to a logistic growth trajectory. This approach applies only to the particular S-shaped growth process. In practice, a longitudinal trait may show a trajectory of any shape. We demonstrate that one can describe a longitudinal trait with orthogonal polynomials, which are sufficiently general for fitting any shaped curve. We develop a mixed-model methodology for QTL mapping of longitudinal traits and a maximum-likelihood method for parameter estimation and statistical tests. The expectation-maximization (EM) algorithm is applied to search for the maximum-likelihood estimates of parameters. The method is verified with simulated data and demonstrated with experimental data from a pseudobackcross family of Populus (poplar) trees.     

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     

Heterogeneous inbred family (HIF) analysis: a method for developing near-isogenic lines that differ at quantitative trait loci

Abtract  Analysis of near-isogenic lines (NILs) that differ at quantitative trait loci (QTL) can be an effective approach for the detailed mapping and characterization of individual loci. Although NILs are useful for genetic and physiological studies, the time and effort required to develop these lines have limited their use. Here we describe a procedure to identify NILs for any region of the genome that can be analyzed with molecular or other genetic markers. The procedure utilizes molecular markers to identify heterogeneous inbred families (HIFs) that segregate for a genomic region of interest. Each HIF is isogenic at the majority of loci in the genome, but NILs differing for markers linked to QTL of interest can be extracted from segregating families. The application of this procedure is described for two QTL associated with seed weight in sorghum. A population of 98 HIFs was screened with two RAPD markers from different linkage groups that were associated with seed weight. Three segregating families were identified for each marker. The progeny of these HIFs were characterized for the segregation of seed weight and other yield components and for markers flanking each QTL. NILs derived from each HIF had significantly different seed weights confirming the presence of at least two loci that influence seed weight in sorghum. Received: 16 September 1996 / Accepted: 25 April 1997     

Advances in Bayesian multiple quantitative trait loci mapping in experimental crosses

Many complex human diseases and traits of biological and/or economic importance are determined by interacting networks of multiple quantitative trait loci (QTL) and environmental factors. Mapping QTL is critical for understanding the genetic basis of complex traits, and for ultimate identification of genes responsible. A variety of sophisticated statistical methods for QTL mapping have been developed. Among these developments, the evolution of Bayesian approaches for multiple QTL mapping over the past decade has been remarkable. Bayesian methods can jointly infer the number of QTL, their genomic positions and their genetic effects. Here, we review recently developed and still developing Bayesian methods and associated computer software for mapping multiple QTL in experimental crosses. We compare and contrast these methods to clearly describe the relationships among different Bayesian methods. We conclude this review by highlighting some areas of future research.     

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.     

In silico mapping of quantitative trait loci in maize

Quantitative trait loci (QTL) are most often detected through designed mapping experiments. An alternative approach is in silico mapping, whereby genes are detected using existing phenotypic and genomic databases. We explored the usefulness of in silico mapping via a mixed-model approach in maize (Zea mays L.). Specifically, our objective was to determine if the procedure gave results that were repeatable across populations. Multilocation data were obtained from the 1995–2002 hybrid testing program of Limagrain Genetics in Europe. Nine heterotic patterns comprised 22,774 single crosses. These single crosses were made from 1,266 inbreds that had data for 96 simple sequence repeat (SSR) markers. By a mixed-model approach, we estimated the general combining ability effects associated with marker alleles in each heterotic pattern. The numbers of marker loci with significant effects—37 for plant height, 24 for smut [Ustilago maydis (DC.) Cda.] resistance, and 44 for grain moisture—were consistent with previous results from designed mapping experiments. Each trait had many loci with small effects and few loci with large effects. For smut resistance, a marker in bin 8.05 on chromosome 8 had a significant effect in seven (out of a maximum of 18) instances. For this major QTL, the maximum effect of an allele substitution ranged from 5.4% to 41.9%, with an average of 22.0%. We conclude that in silico mapping via a mixed-model approach can detect associations that are repeatable across different populations. We speculate that in silico mapping will be more useful for gene discovery than for selection in plant breeding programs.     

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.     

Isolate specificity of quantitative trait loci for partial resistance of barley to Puccinia hordei confirmed in mapping populations and near-isogenic lines

Partial resistance is considered race-nonspecific and durable, consistent with the concept of 'horizontal' resistance. However, detailed observations of partial resistance to leaf rust (Puccinia hordei) in barley (Hordeum vulgare) revealed small cultivar x isolate interactions, suggesting a minor-gene-for-minor-gene interaction model, similar to so-called 'vertical' resistance. Three consistent quantitative trait loci (QTLs), labelled Rphq2, Rphq3 and Rphq4, that were detected in the cross susceptible L94 x partially resistant Vada have been incorporated into the L94 background to obtain near-isogenic lines (NILs). Three isolates were used to map QTLs on seedlings of the L94 x Vada population and to evaluate the effect of each QTL on adult plants of the respective NILs under field conditions. Rphq2 had a strong effect in seedlings but almost no effect in adult plants, while Rphq3 was effective in seedlings and in adult plants against all three isolates. However, Rphq4 was effective in seedlings and in adult plants against two isolates but ineffective in both development stages against the third, demonstrating a clear and reproducible isolate-specific effect. The resistance governed by the three QTLs was not associated with a hypersensitive reaction. Those results confirm the minor-gene-for-minor-gene model suggesting specific interactions between QTLs for partial resistance and P. hordei isolates.