Silkworm is the basis of sericultural industry and the model organism in insect genetics study. Mapping quantitative trait loci (QTLs) underlying economically important traits of silkworm is of high significance for promoting the silkworm molecular breeding and advancing our knowledge on genetic architecture of the Lepidoptera. Yet, the currently used mapping methods are not well suitable for silkworm, because of ignoring the recombination difference in meiosis between two sexes.
A mixed linear model including QTL main effects, epistatic effects, and QTL × sex interaction effects was proposed for mapping QTLs in an F2 population of silkworm. The number and positions of QTLs were determined by F-test and model selection. The Markov chain Monte Carlo (MCMC) algorithm was employed to estimate and test genetic effects of QTLs and QTL × sex interaction effects. The effectiveness of the model and statistical method was validated by a series of simulations. The results indicate that when markers are distributed sparsely on chromosomes, our method will substantially improve estimation accuracy as compared to the normal chiasmate F2 model. We also found that a sample size of hundreds was sufficiently large to unbiasedly estimate all the four types of epistases (i.e., additive-additive, additive-dominance, dominance-additive, and dominance-dominance) when the paired QTLs reside on different chromosomes in silkworm.
The proposed method could accurately estimate not only the additive, dominance and digenic epistatic effects but also their interaction effects with sex, correcting the potential bias and precision loss in the current QTL mapping practice of silkworm and thus representing an important addition to the arsenal of QTL mapping tools.
The Bayesian shrinkage technique has been applied to multiple quantitative trait loci (QTLs) mapping to estimate the genetic effects of QTLs on quantitative traits from a very large set of possible effects including the main and epistatic effects of QTLs. Although the recently developed empirical Bayes (EB) method significantly reduced computation comparing with the fully Bayesian approach, its speed and accuracy are limited by the fact that numerical optimization is required to estimate the variance components in the QTL model.
We developed a fast empirical Bayesian LASSO (EBLASSO) method for multiple QTL mapping. The fact that the EBLASSO can estimate the variance components in a closed form along with other algorithmic techniques render the EBLASSO method more efficient and accurate. Comparing with the EB method, our simulation study demonstrated that the EBLASSO method could substantially improve the computational speed and detect more QTL effects without increasing the false positive rate. Particularly, the EBLASSO algorithm running on a personal computer could easily handle a linear QTL model with more than 100,000 variables in our simulation study. Real data analysis also demonstrated that the EBLASSO method detected more reasonable effects than the EB method. Comparing with the LASSO, our simulation showed that the current version of the EBLASSO implemented in Matlab had similar speed as the LASSO implemented in Fortran, and that the EBLASSO detected the same number of true effects as the LASSO but a much smaller number of false positive effects.
The EBLASSO method can handle a large number of effects possibly including both the main and epistatic QTL effects, environmental effects and the effects of gene-environment interactions. It will be a very useful tool for multiple QTL mapping.
Industrial production of the edible basidiomycete Pleurotus ostreatus (oyster mushroom) is based on a solid fermentation process in which a limited number of selected strains are used. Optimization of industrial mushroom production depends on improving the culture process and breeding new strains with higher yields and productivities. Traditionally, fungal breeding has been carried out by an empirical trial and error process. In this study, we used a different approach by mapping quantitative trait loci (QTLs) controlling culture production and quality within the framework of the genetic linkage map of P. ostreatus. Ten production traits and four quality traits were studied and mapped. The production QTLs identified explain nearly one-half of the production variation. More interestingly, a single QTL mapping to the highly polymorphic chromosome VII appears to be involved in control of all the productivity traits studied. Quality QTLs appear to be scattered across the genome and to have less effect on the variation of the corresponding traits. Moreover, some of the new hybrid strains constructed in the course of our experiments had production or quality values higher than those of the parents or other commercial strains. This approach opens the possibility of marker-assisted selection and breeding of new industrial strains of this fungus.
Mapping multiple quantitative trait loci (QTL) is commonly viewed as a problem of model selection. Various model selection criteria have been proposed, primarily in the non-Bayesian framework. The deviance information criterion (DIC) is the most popular criterion for Bayesian model selection and model comparison but has not been applied to Bayesian multiple QTL mapping. A derivation of the DIC is presented for multiple interacting QTL models and calculation of the DIC is demonstrated using posterior samples generated by Markov chain Monte Carlo (MCMC) algorithms. The DIC measures posterior predictive error by penalizing the fit of a model (deviance) by its complexity, determined by the effective number of parameters. The effective number of parameters simultaneously accounts for the sample size, the cross design, the number and lengths of chromosomes, covariates, the number of QTL, the type of QTL effects, and QTL effect sizes. The DIC provides a computationally efficient way to perform sensitivity analysis and can be used to quantitatively evaluate if including environmental effects, gene-gene interactions, and/or gene-environment interactions in the prior specification is worth the extra parameterization. The DIC has been implemented in the freely available package R/qtlbim, which greatly facilitates the general usage of Bayesian methodology for genome-wide interacting QTL analysis.
complex trait; deviance; DIC; model selection and comparison; quantitative trait loci
Genetic mapping has proven to be powerful for studying the genetic architecture of complex traits by characterizing a network of the underlying interacting quantitative trait loci (QTLs). Current statistical models for genetic mapping were mostly founded on the biallelic epistasis of QTLs, incapable of analyzing multiallelic QTLs and their interactions that are widespread in an outcrossing population.
Here we have formulated a general framework to model and define the epistasis between multiallelic QTLs. Based on this framework, we have derived a statistical algorithm for the estimation and test of multiallelic epistasis between different QTLs in a full-sib family of outcrossing species. We used this algorithm to genomewide scan for the distribution of mul-tiallelic epistasis for a rooting ability trait in an outbred cross derived from two heterozygous poplar trees. The results from simulation studies indicate that the positions and effects of multiallelic QTLs can well be estimated with a modest sample and heritability.
The model and algorithm developed provide a useful tool for better characterizing the genetic control of complex traits in a heterozygous family derived from outcrossing species, such as forest trees, and thus fill a gap that occurs in genetic mapping of this group of important but underrepresented species.
Bone mineral density (BMD) is a heritable trait, and in mice, over 100 quantitative trait loci (QTLs) have been reported, but candidate genes have been identified for only a small percentage. Persistent errors in the mouse genetic map have negatively affected QTL localization, spurring the development of a new, corrected map. In this study, QTLs for BMD were remapped in 11 archival mouse data sets using this new genetic map. Since these QTLs all were mapped in a comparable way, direct comparisons of QTLs for concordance would be valid. We then compared human genome-wide association study (GWAS) BMD loci with the mouse QTLs. We found that 26 of the 28 human GWAS loci examined were located within the confidence interval of a mouse QTL. Furthermore, 14 of the GWAS loci mapped to within 3 cM of a mouse QTL peak. Lastly, we demonstrated that these newly remapped mouse QTLs can substantiate a candidate gene for a human GWAS locus, for which the peak single-nucleotide polymorphism (SNP) fell in an intergenic region. Specifically, we suggest that MEF2C (human chromosome 5, mouse chromosome 13) should be considered a candidate gene for the genetic regulation of BMD. In conclusion, use of the new mouse genetic map has improved the localization of mouse BMD QTLs, and these remapped QTLs show high concordance with human GWAS loci. We believe that this is an opportune time for a renewed effort by the genetics community to identify the causal variants regulating BMD using a synergistic mouse-human approach. © 2010 American Society for Bone and Mineral Research.
genetic linkage; quantitative trait loci; mouse; human
A mathematical approach was developed to model and optimize selection on multiple known quantitative trait loci (QTL) and polygenic estimated breeding values in order to maximize a weighted sum of responses to selection over multiple generations. The model allows for linkage between QTL with multiple alleles and arbitrary genetic effects, including dominance, epistasis, and gametic imprinting. Gametic phase disequilibrium between the QTL and between the QTL and polygenes is modeled but polygenic variance is assumed constant. Breeding programs with discrete generations, differential selection of males and females and random mating of selected parents are modeled. Polygenic EBV obtained from best linear unbiased prediction models can be accommodated. The problem was formulated as a multiple-stage optimal control problem and an iterative approach was developed for its solution. The method can be used to develop and evaluate optimal strategies for selection on multiple QTL for a wide range of situations and genetic models.
selection; quantitative trait loci; optimization; marker assisted selection
Functional mapping is a powerful approach for mapping quantitative trait loci (QTLs) that control biological processes. Functional mapping incorporates mathematical aspects of growth and development into a general QTL mapping framework and has been recently integrated with composite interval mapping to build up a so-called composite functional mapping model, aimed to separate multiple linked QTLs on the same chromosomal region.
This article reports the principle of using composite functional mapping to estimate the effects of QTL-environment interactions on growth trajectories by parametrically modeling the tested QTL in a marker interval and nonparametrically modeling the markers outside the interval as co-factors. With this new model, we can characterize the dynamic patterns of the genetic effects of QTLs governing growth trajectories, estimate the global effects of the underlying QTLs during the course of growth and development, and test the differentiation in the shapes of QTL genotype-specific growth curves between different environments. By analyzing a real example from a soybean genome project, our model detects several QTLs that cause significant genotype-environment interactions for plant height growth processes.
The model provides a basis for deciphering the genetic architecture of trait expression adjusted to different biotic and abiotic environments for any organism.
Quantitative phenotypic variation of agronomic characters in crop plants is controlled by environmental and genetic factors (quantitative trait loci = QTL). To understand the molecular basis of such QTL, the identification of the underlying genes is of primary interest and DNA sequence analysis of the genomic regions harboring QTL is a prerequisite for that. QTL mapping in potato (Solanum tuberosum) has identified a region on chromosome V tagged by DNA markers GP21 and GP179, which contains a number of important QTL, among others QTL for resistance to late blight caused by the oomycete Phytophthora infestans and to root cyst nematodes.
To obtain genomic sequence for the targeted region on chromosome V, two local BAC (bacterial artificial chromosome) contigs were constructed and sequenced, which corresponded to parts of the homologous chromosomes of the diploid, heterozygous genotype P6/210. Two contiguous sequences of 417,445 and 202,781 base pairs were assembled and annotated. Gene-by-gene co-linearity was disrupted by non-allelic insertions of retrotransposon elements, stretches of diverged intergenic sequences, differences in gene content and gene order. The latter was caused by inversion of a 70 kbp genomic fragment. These features were also found in comparison to orthologous sequence contigs from three homeologous chromosomes of Solanum demissum, a wild tuber bearing species. Functional annotation of the sequence identified 48 putative open reading frames (ORF) in one contig and 22 in the other, with an average of one ORF every 9 kbp. Ten ORFs were classified as resistance-gene-like, 11 as F-box-containing genes, 13 as transposable elements and three as transcription factors. Comparing potato to Arabidopsis thaliana annotated proteins revealed five micro-syntenic blocks of three to seven ORFs with A. thaliana chromosomes 1, 3 and 5.
Comparative sequence analysis revealed highly conserved collinear regions that flank regions showing high variability and tandem duplicated genes. Sequence annotation revealed that the majority of the ORFs were members of multiple gene families. Comparing potato to Arabidopsis thaliana annotated proteins suggested fragmented structural conservation between these distantly related plant species.
Higher seed yield is one of the objectives of jatropha breeding. However, genetic analysis of the yield traits has not been done in jatropha. Quantitative trait loci (QTL) mapping was conducted to identify genetic factors controlling growth and seed yield in jatropha, a promising biofuel crop.
A linkage map was constructed consisting of 105 SSR (simple sequence repeat) markers converged into 11 linkage groups. With this map, we identified a total of 28 QTLs for 11 growth and seed traits using a population of 296 backcrossing jatropha trees. Two QTLs qTSW-5 and qTSW-7 controlling seed yield were mapped on LGs 5 and 7 respectively, where two QTL clusters controlling yield related traits were detected harboring five and four QTLs respectively. These two QTL clusters were critical with pleiotropic roles in regulating plant growth and seed yield. Positive additive effects of the two QTLs indicated higher values for the traits conferred by the alleles from J. curcas, while negative additive effects of the five QTLs on LG6, controlling plant height, branch number (in the 4th and 10th months post seed germination), female flower number and fruit number respectively, indicated higher values conferred by the alleles from J. integerrima. Therefore favored alleles from both the parents could be expected to be integrated into elite jatropha plant by further backcrossing and marker assisted selection. Efficient ways to improve the seed yield by applying the two QTL clusters are discussed.
This study is the first report on genetic analysis of growth and seed traits with molecular markers in jatropha. An approach for jatropha improvement is discussed using pleiotropic QTLs, which will be likely to lead to initiation of molecular breeding in jatropha by integrating more markers in the QTL regions.
The identity-by-descent (IBD) based variance component analysis is an important method for mapping quantitative trait loci (QTL) in outbred populations. The interval-mapping approach and various modified versions of it may have limited use in evaluating the genetic variances of the entire genome because they require evaluation of multiple models and model selection. In this study, we developed a multiple variance component model for genome-wide evaluation using both the maximum likelihood (ML) method and the MCMC implemented Bayesian method. We placed one QTL in every few cM on the entire genome and estimated the QTL variances and positions simultaneously in a single model. Genomic regions that have no QTL usually showed no evidence of QTL while regions with large QTL always showed strong evidence of QTL. While the Bayesian method produced the optimal result, the ML method is computationally more efficient than the Bayesian method. Simulation experiments were conducted to demonstrate the efficacy of the new methods.
Electronic supplementary material
The online version of this article (doi:10.1007/s10709-010-9497-1) contains supplementary material, which is available to authorized users.
Bayesian analysis; Genome selection; Markov chain Monte Carlo; Maximum likelihood
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 model selection; dynamic trait; QTL; epistatic; Legendre polynomial
Meta-analysis of information from quantitative trait loci (QTL) mapping experiments was used to derive distributions of the effects of genes affecting quantitative traits. The two limitations of such information, that QTL effects as reported include experimental error, and that mapping experiments can only detect QTL above a certain size, were accounted for. Data from pig and dairy mapping experiments were used. Gamma distributions of QTL effects were fitted with maximum likelihood. The derived distributions were moderately leptokurtic, consistent with many genes of small effect and few of large effect. Seventeen percent and 35% of the leading QTL explained 90% of the genetic variance for the dairy and pig distributions respectively. The number of segregating genes affecting a quantitative trait in dairy populations was predicted assuming genes affecting a quantitative trait were neutral with respect to fitness. Between 50 and 100 genes were predicted, depending on the effective population size assumed. As data for the analysis included no QTL of small effect, the ability to estimate the number of QTL of small effect must inevitably be weak. It may be that there are more QTL of small effect than predicted by our gamma distributions. Nevertheless, the distributions have important implications for QTL mapping experiments and Marker Assisted Selection (MAS). Powerful mapping experiments, able to detect QTL of 0.1σp, will be required to detect enough QTL to explain 90% the genetic variance for a quantitative trait.
distribution of gene effects; quantitative trait loci; genetic variance; marker assisted selection
The ultimate goal of genetic mapping of quantitative trait loci (QTL) is the positional cloning of genes involved in any agriculturally or medically important phenotype. However, only a small portion (≤ 1%) of the QTL detected have been characterized at the molecular level, despite the report of hundreds of thousands of QTL for different traits and populations.
We develop a statistical model for detecting and characterizing the nucleotide structure and organization of haplotypes that underlie QTL responsible for a quantitative trait in an F2 pedigree. The discovery of such haplotypes by the new model will facilitate the molecular cloning of a QTL. Our model is founded on population genetic properties of genes that are segregating in a pedigree, constructed with the mixture-based maximum likelihood context and implemented with the EM algorithm. The closed forms have been derived to estimate the linkage and linkage disequilibria among different molecular markers, such as single nucleotide polymorphisms, and quantitative genetic effects of haplotypes constructed by non-alleles of these markers. Results from the analysis of a real example in mouse have validated the usefulness and utilization of the model proposed.
The model is flexible to be extended to model a complex network of genetic regulation that includes the interactions between different haplotypes and between haplotypes and environments.
Conventional multiple-trait quantitative trait locus (QTL) mapping methods must discard cases (individuals) with incomplete phenotypic data, thereby sacrificing other phenotypic and genotypic information contained in the discarded cases. Under standard assumptions about the missing-data mechanism, it is possible to exploit these cases.
We present an expectation-maximization (EM) algorithm, derived for recombinant inbred and F2 genetic models but extensible to any mating design, that supports conventional hypothesis tests for QTL main effect, pleiotropy, and QTL-by-environment interaction in multiple-trait analyses with missing phenotypic data. We evaluate its performance by simulations and illustrate with a real-data example.
The EM method affords improved QTL detection power and precision of QTL location and effect estimation in comparison with case deletion or imputation methods. It may be incorporated into any least-squares or likelihood-maximization QTL-mapping approach.
Motivation: R/qtl is free and powerful software for mapping and exploring quantitative trait loci (QTL). R/qtl provides a fully comprehensive range of methods for a wide range of experimental cross types. We recently added multiple QTL mapping (MQM) to R/qtl. MQM adds higher statistical power to detect and disentangle the effects of multiple linked and unlinked QTL compared with many other methods. MQM for R/qtl adds many new features including improved handling of missing data, analysis of 10 000 s of molecular traits, permutation for determining significance thresholds for QTL and QTL hot spots, and visualizations for cis–trans and QTL interaction effects. MQM for R/qtl is the first free and open source implementation of MQM that is multi-platform, scalable and suitable for automated procedures and large genetical genomics datasets.
Availability: R/qtl is free and open source multi-platform software for the statistical language R, and is made available under the GPLv3 license. R/qtl can be installed from http://www.rqtl.org/. R/qtl queries should be directed at the mailing list, see http://www.rqtl.org/list/.
Gramene is a comparative information resource for plants that integrates data across diverse data domains. In this article, we describe the development of a quantitative trait loci (QTL) database and illustrate how it can be used to facilitate both the forward and reverse genetics research. The QTL database contains the largest online collection of rice QTL data in the world. Using flanking markers as anchors, QTLs originally reported on individual genetic maps have been systematically aligned to the rice sequence where they can be searched as standard genomic features. Researchers can determine whether a QTL co-localizes with other QTLs detected in independent experiments and can combine data from multiple studies to improve the resolution of a QTL position. Candidate genes falling within a QTL interval can be identified and their relationship to particular phenotypes can be inferred based on functional annotations provided by ontology terms. Mutations identified in functional genomics populations and association mapping panels can be aligned with QTL regions to facilitate fine mapping and validation of gene–phenotype associations. By assembling and integrating diverse types of data and information across species and levels of biological complexity, the QTL database enhances the potential to understand and utilize QTL information in biological research.
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.
Two methods (Scheme A and Scheme B) were developed to optimize the relative weights on quantitative trait loci (QTL) and contributions of selected individuals simultaneously to maximize selection response while constraining the rate of inbreeding to the rate observed in gene assisted selection (GAS). In Scheme A, both the relative weights give to QTL and contributions of the selected individuals were optimized using a genetic algorithm. The possible solutions for relative weights of QTL and contributions of the selected individuals were encoded simultaneously. A physical selection population was used to evaluate the fitness of each encoded solution using stochastic simulation with 50 replicates. The fitness of each solution was the mean of all replicates for accumulative discounted sum of genetic means of all generations in physical selection population. In Scheme B, the optimization for relative weights on QTL was similar to Scheme A, and also was implemented based on a genetic algorithm. However, unlike Scheme A, an optimal contribution algorithm (OC) was used to optimize contributions of selection candidates. When compared with GAS, Schemes A and B resulted in up to 15.88 and 22.26% extra discounted sum of genetic value of all generations in a long planning horizon, respectively. Compared GAS+OC and Scheme B, most of the increase (about 78%) in genetic gain was produced by only optimizing contributions of selected individuals. The optimization for relative weight given to QTL just avoided the long-term loss (about 22%) observed in GAS scheme.
Genetic algorithm; Marker assisted selection; Optimization; Quantitative trait loci
Haplotype reconstruction is important in linkage mapping and association mapping of quantitative trait loci (QTL). One widely used statistical approach for haplotype reconstruction is simulated annealing (SA), implemented in SimWalk2. However, the algorithm needs a very large number of sequential iterations, and it does not clearly show if convergence of the likelihood is obtained.
An evolutionary algorithm (EA) is a good alternative whose convergence can be easily assessed during the process. It is feasible to use a powerful parallel-computing strategy with the EA, increasing the computational efficiency. It is shown that the EA can be ~4 times faster and gives more reliable estimates than SimWalk2 when using 4 processors. In addition, jointly updating dependent variables can increase the computational efficiency up to ~2 times. Overall, the proposed method with 4 processors increases the computational efficiency up to ~8 times compared to SimWalk2. The efficiency will increase more with a larger number of processors.
The use of the evolutionary algorithm and the joint updating method can be a promising tool for haplotype reconstruction in linkage and association mapping of QTL.
From an extensive review of public domain information on dairy cattle quantitative trait loci (QTL), we have prepared a draft online QTL map for dairy production traits. Most publications (45 out of 55 reviewed) reported QTL for the major milk production traits (milk, fat and protein yield, and fat and protein concentration (%)) and somatic cell score. Relatively few QTL studies have been reported for more complex traits such as mastitis, fertility and health. The collated QTL map shows some chromosomal regions with a high density of QTL, as well as a substantial number of QTL at single chromosomal locations. To extract the most information from these published records, a meta-analysis was conducted to obtain consensus on QTL location and allelic substitution effect of these QTL. This required modification and development of statistical methodologies. The meta-analysis indicated a number of consensus regions, the most striking being two distinct regions affecting milk yield on chromosome 6 at 49 cM and 87 cM explaining 4.2 and 3.6 percent of the genetic variance of milk yield, respectively. The first of these regions (near marker BM143) affects five separate milk production traits (protein yield, protein percent, fat yield, fat percent, as well as milk yield).
quantitative trait loci; dairy cattle; review; meta-analysis
Body weight and length are economically important traits in foodfish species influenced by quantitative trait loci (QTL) and environmental factors. It is usually difficult to dissect the genetic and environmental effects. Asian seabass (Lates calcarifer) is an important marine foodfish species with a compact genome (~700 Mb). The recent construction of a first generation linkage map of Asian seabass with 240 microsatellites provides a good opportunity to determine the number and position of QTL, and the magnitude of QTL effects with a genome scan.
We conducted a genome scan for QTL affecting body weight, standard length and condition factors in an F1 family containing 380 full-sib individuals from a breeding stock by using 97 microsatellites evenly covering 24 chromosomes. Interval mapping and multiple QTL model mapping detected five significant and 27 suggestive QTL on ten linkage groups (LGs). Among the five significant QTL detected, three (qBW2-a, qTL2-a and qSL2-a) controlling body weight, total and standard length respectively, were mapped on the same region near Lca287 on LG2, and explained 28.8, 58.9 and 59.7% of the phenotypic variance. The other two QTL affecting body weight, qBW2-b and qBW3, were located on LG2 and 3, and accounted for 6.4 and 8.8% of the phenotypic variance. Suggestive QTL associated with condition factors are located on six different LGs.
This study presents the first example of QTL detection for growth-related traits in an F1 family of a marine foodfish species. The results presented here will enable further fine-mapping of these QTL for marker-assisted selection of the Asian seabass, eventually identifying individual genes responsible for growth-related traits.
Identification of the genetic basis of common traits may be hindered by underlying complex genetic architectures that are inadequately captured by existing models, including both multiallelic and multilocus modes of inheritance (MOI). One useful approach for localizing genes underlying continuous complex traits is the joint oligogenic linkage and segregation analysis implemented in the package Loki. The method uses reversible jump Markov chain Monte Carlo to eliminate the need to prespecify the number of quantitative trait loci (QTLs) in the trait model, thus providing posterior distributions for the number of QTLs in a Bayesian framework. The current implementation assumes QTLs are diallelic, and therefore can overestimate the number of linked QTLs in the presence of a multiallelic QTL. To address the possibility of multiple alleles, we extended the QTL model to allow for a variable number of additive alleles at each locus. Application to simulated data shows that, under a diallelic MOI, the multiallelic and diallelic analysis models give similar results. Under a multiallelic MOI, the multiallelic analysis model provides better mixing and improved convergence, and leads to a more accurate estimate of the underlying trait MOI and model parameter values, than does the diallelic model. Application to real data shows the multiallelic model results in fewer estimated linked QTLs and that the predominant QTL model is similar to one of two predominant models estimated from the diallelic analysis. Our results indicate that use of a multiallelic analysis model can lead to better understanding of the genetic architecture underlying complex traits.
complex trait; MCMC; pedigree; continuous trait; Bayesian
Many investigations have reported the successful mapping of quantitative trait loci (QTLs) for gene expression phenotypes (eQTLs). Local eQTLs, where expression phenotypes map to the genes themselves, are of especially great interest, because they are direct candidates for previously mapped physiological QTLs. Here we show that many mapped local eQTLs in genetical genomics experiments do not reflect actual expression differences caused by sequence polymorphisms in cis-acting factors changing mRNA levels. Instead they indicate hybridization differences caused by sequence polymorphisms in the mRNA region that is targeted by the microarray probes. Many such polymorphisms can be detected by a sensitive and novel statistical approach that takes the individual probe signals into account. Applying this approach to recent mouse and human eQTL data, we demonstrate that indeed many local eQTLs are falsely reported as “cis-acting” or “cis” and can be successfully detected and eliminated with this approach.
Expression quantitative trait loci (eQTL) mapping is a widely used technique to uncover regulatory relationships between genes. A range of methodologies have been developed to map links between expression traits and genotypes. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) initiative is a community project to objectively assess the relative performance of different computational approaches for solving specific systems biology problems. The goal of one of the DREAM5 challenges was to reverse-engineer genetic interaction networks from synthetic genetic variation and gene expression data, which simulates the problem of eQTL mapping. In this framework, we proposed an approach whose originality resides in the use of a combination of existing machine learning algorithms (committee). Although it was not the best performer, this method was by far the most precise on average. After the competition, we continued in this direction by evaluating other committees using the DREAM5 data and developed a method that relies on Random Forests and LASSO. It achieved a much higher average precision than the DREAM best performer at the cost of slightly lower average sensitivity.