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Parallel Computing in Interval Mapping of Quantitative Trait Loci
Abstract
Linear regression analysis is considered the least computationally demanding method for mapping quantitative trait loci (QTL). However, simultaneous search for multiple QTL, the use of permutations to obtain empirical significance thresholds, and larger experimental studies significantly increase the computational demand. This report describes an easily implemented parallel algorithm, which significantly reduces the computing time in both QTL mapping and permutation testing. In the example provided, the analysis time was decreased to less than 15% of a single processor system by the use of 18 processors. We indicate how the efficiency of the analysis could be improved by distributing the computations more evenly to the processors and how other ways of distributing the data facilitate the use of more processors. The use of parallel computing in QTL mapping makes it possible to routinely use permutations to obtain empirical significance thresholds for multiple traits and multiple QTL models. It could also be of use to improve the computational efficiency of the more computationally demanding QTL analysis methods.
... Several implementations of regression-based techniques exist, and include but are not limited to SNPAssoc [9], EMMA [8], EMMAX [6], GEMMA [11], and pi-MASS [12]. By parallelizing the computations, total analysis time can be decreased by a large factor, depending upon the number of processors available [13]. ...
... For a historical illustration of the benefits of parallel computing, we consider the 2001 work of Carlborg, Andersson-Eklund and Andersson. They studied the computational gain when using a regression-based method for quantitative trait mapping [13]. In this study, the relative increase in performance (analysis time on one processor divided by analysis time for multiple processors) was as large as 7.04 when the number of processors increased from 1 to 18. ...
... Techniques used in the 2001 study also point to the variation in time and knowledge needed to parallelize an analysis on a multi-core computer [13]. Tutorials such as that of [20,21] have been developed to aid an analyst using R or another coding software in employing multiple cores via free, open-source implementations of parallelized data analysis techniques. ...
... The significance threshold values for genome-wide significance were derived for each trait separately by randomization tests using 1000 random permutations of the data (Churchill and Doerge, 1994 ). The randomization tests were performed using a QTL mapping software implemented for parallel computing on distributed memory platforms (Carlborg et al., 2002). The least squares regression model used for QTL analysis included the fixed effects of sex and batch along with additive and dominance coefficients for the putative QTLs for all traits. ...
... Of course, suggestive QTLs only had a significance level of 20%, and must be treated merely as sources for future studies. The randomization methods used for determining significance levels of genome-wide QTLs are thought to be extremely conservative ; therefore it is common practice to also report the suggestive loci (Carlborg et al., 2002). A regression analysis on the relation between only the two growth QTLs and the behavioral data showed that they affected variables from several of the fear tests employed. ...
The aim of this work was to study fear responses and their relation to production traits in red junglefowl ( Gallus gallus spp.), White Leghorn ( Gallus domesticus ), and their F2-progeny. Quantitative trait locus (QTL) analyses were performed for behavioral traits to gain information about possible genetic links between fear-related behaviors and production. Four behavioral tests were performed that induce different levels of acute fear (open field [OF], exposure to a novel object, tonic immobility, and restraint). Production traits, that is, egg production, sexual maturity (in females), food intake, and growth, were measured individually. A genome scan using 105 microsatellite markers was carried out to identify QTLs controlling the traits studied. In the OF and novel object tests (NO), Leghorns showed less fear behavior than junglefowl, whereas junglefowl behaved less fearfully in the tonic immobility test (TI) and were more active in the restraint test. In the F2 progeny, only weak phenotypic associations were found between production traits and fear behavior. A significant QTL for TI duration was found on chromosome 1 that coincided with a QTL for egg weight and growth in the same animals. Another QTL for NO in males coincided with another major growth QTL. These two known growth QTLs affected a wide range of reactions in different tests. Several other significant and suggestive QTLs for behavioral traits related to fear were found. These QTLs did not coincide with QTLs for production traits, indicating that these fear variables may not be genetically linked to the production traits we measured here. The results show that loci affecting important production traits are located in the same chromosomal region as loci affecting different fear-related behaviors.
... Several characteristics such as the rate of survival that are expressed very late in the life may serve as useful criteria of selection. Also, the traditional selection within populations is not very efficient when selection objectives involve several characteristics with unfavorable genetic correlation (Carlborg et al., 2001). The impetus to reveal the underlying mechanisms of complex traits has led to detection of major genes and quantitative trait loci (QTL) for many traits in various species. ...
An F2 Japanese quail population was developed by crossing two strains (wild and white) to map quantitative trait loci (QTL) for performance and carcass traits. A total of 472 F2 birds were reared and slaughtered at 42 days of age. Performance and carcass traits were measured on all of the F2 individuals. Parental (P0), F1, and F2 individuals were genotyped with 3 microsatellites from quail chromosome 5. Based on five quantitative genetic models analyzed, QTL affecting carcass efficiency, breast percentage, femur percentage, back weight and back percentage, head weight, gizzard weight, uropygial weight, liver weight, and liver percentage, and neck percentage were mapped. The results provided an important framework for further genetic mapping and the identification of quantitative trait loci controlling performance carcass traits in the Japanese quail
... QTL mapping is very suitable for parallel computing (Carlborg et al., 2001;Carlborg, 2002) and substantial reduction in elapsed time for computations can be obtained by parallelization of genome scans and randomization tests. When large numbers of traits are analysed, e.g. in QTL mapping using expression data, parallelization can be efficiently implemented across traits as well. ...
Motivation:
Dissection of the genetics underlying gene expression utilizes techniques from microarray analyses as well as quantitative trait loci (QTL) mapping. Available QLT mapping methods are not tailored for the highly automated analyses required to deal with the thousand of gene transcripts encountered in the mapping of QTL affecting gene expression (sometimes referred to as eQTL). This report focuses on the adaptation of QTL mapping methodology to perform automated mapping of QTL affecting gene expression.
Results:
The analyses of expression data on > 12,000 gene transcripts in BXD recombinant inbred mice found, on average, 629 QTL exceeding the genome-wide 5% threshold. Using additional information on trait repeatabilities and QTL location, 168 of these were classified as 'high confidence' QTL. Current sample sizes of genetical genomics studies make it possible to detect a reasonable number of QTL using simple genetic models, but considerably larger studies are needed to evaluate more complex genetic models. After extensive analyses of real data and additional simulated data (altogether > 300,000 genome scans) we make the following recommendations for detection of QTL for gene expression: (1) For populations with an unbalanced number of replicates on each genotype, weighted least squares should be preferred above ordinary least squares. Weights can be based on repeatability of the trait and the number of replicates. (2) A genome scan based on multiple marker information but analysing only at marker locations is a good approximation to a full interval mapping procedure. (3) Significance testing should be based on empirical genome-wide significance thresholds that are derived for each trait separately. (4) The significant QTL can be separated into high and low confidence QTL using a false discovery rate that incorporates prior information such as transcript repeatabilities and co-localization of gene-transcripts and QTL. (5) Including observations on the founder lines in the QTL analysis should be avoided as it inflates the test statistic and increases the Type I error. (6) To increase the computational efficiency of the study, use of parallel computing is advised. These recommendations are summarized in a possible strategy for mapping of QTL in a least squares framework.
Availability:
The software used for this study is available on request from the authors.
... The use of efficient computational algorithms in QTL mapping allows researchers to move from approximate Table 3. Number of QTL pairs identified by a simultaneous mapping strategy for epistatic QTL pairs (SIM) and the number of pairs detected with a marginal effects model including additive and dominance effects (A+D) and an epistatic QTL model (E) were selected. Also, the number of times two, one or none of the QTLs in the detected pair were also detected using forward selection (FS) and a marginal effects model methods to screen for epistasis to true multidimensional searches (Carlborg et al., 2001 ;Carlborg, 2002;Ljungberg et al., 2002). We have previously shown by simulations that simultaneous mapping of multiple epistatic QTLs has the potential to increase the power to map interacting QTLs ( Carlborg et al., 2000;Carlborg & Andersson, 2002). ...
We used simultaneous mapping of interacting quantitative trait locus (QTL) pairs to study various growth traits in a chicken F2 intercross. The method was shown to increase the number of detected QTLs by 30 % compared with a traditional method detecting QTLs by their marginal genetic effects. Epistasis was shown to be an important contributor to the genetic variance of growth, with the largest impact on early growth (before 6 weeks of age). There is also evidence for a discrete set of interacting loci involved in early growth, supporting the previous findings of different genetic regulation of early and late growth in chicken. The genotype-phenotype relationship was evaluated for all interacting QTL pairs and 17 of the 21 evaluated QTL pairs could be assigned to one of four clusters in which the pairs in a cluster have very similar genetic effects on growth. The genetic effects of the pairs indicate commonly occurring dominance-by-dominance, heterosis and multiplicative interactions. The results from this study clearly illustrate the increase in power obtained by using this novel method for simultaneous detection of epistatic QTL, and also how visualization of genotype-phenotype relationships for epistatic QTL pairs provides new insights to biological mechanisms underlying complex traits.
... The significance threshold values for genome-wide significance were derived for each trait separately by randomization tests using 1000 random permutations of the data [26]. The randomization tests were performed using a QTL mapping software implemented for parallel computing on distributed memory platforms [27]. The least squares regression model used for QTL-analysis included the fixed effects of sex and batch along with additive and dominance coefficients for the putative QTLs. ...
Feather pecking (FP) is a detrimental behaviour in chickens, which is performed by only some individuals in a flock. FP was studied in 54 red junglefowl (ancestor of domestic chickens), 36 White Leghorn laying hens, and 762 birds from an F(2)-intercross between these two lines. From all F(2)-birds, growth and feed consumption were measured. Age at sexual maturity and egg production in females, and corticosterone levels in males were also measured. From 333 F(2)-birds of both sexes, and 20 parental birds, body composition with respect to bone mineral content, muscle and fat was obtained by post-mortem examinations using Dual X-Ray Absorptiometry (DXA). In femurs of the same birds, the bone density and structure were analysed using DXA and Peripheral Quantitative Computerized Tomography (pQCT), and a biomechanical analysis of bone strength was performed. Furthermore, plumage condition was determined in all birds as a measure of being exposed to feather pecking. Using 105 DNA-markers in all F(2)-birds, a genome-wide scan for Quantitative Trait Loci (QTL), associated with the behaviour in the F(2)-generation was performed. FP was at least as frequent in the red junglefowl as in the White Leghorn strain studied here, and significantly more common among females both in the parental strains and in the F(2)-generation. In the F(2)-birds, FP was phenotypically linked to early sexual maturation, fast growth, weak bones, and, in males, also high fat accumulation, indicating that feather peckers have a different resource allocation pattern. Behaviourally, F(2) feather peckers were more active in an open field test, in a novel food/novel object test, and in a restraint test, indicating that feather pecking might be genetically linked to a proactive coping strategy. Only one suggestive QTL with a low explanatory value was found on chromosome 3, showing that many genes, each with a small effect, are probably involved in the causation of feather pecking. There were significant effects of sire and dam on the risk of being a victim of feather pecking, and victims grew faster pre- and post-hatching, had lower corticosterone levels and were less active in a restraint test. Hence, a wide array of behavioural and developmental traits were genetically linked to FP.
... The interest in epistasis has increased recently as automated techniques for large-scale genotyping have allowed researchers to design and collect data from populations that are large enough for powerful epistatic QTL mapping experiments. Several new methods for mapping epistatic QTL based on genomic searches for epistatic QTL have been introduced (Kao et al. 1999; Carlborg et al. 2000; Jannink and Jansen 2001; Sen and Churchill 2001; Boer et al. 2002; Carlborg and Andersson 2002). Several of the methods have been used to analyze experimental populations, and epistasis has been shown to be an important contributor to the genetics of complex traits (Zeng et al. 2000; Shimomura et al. 2001; Leamy et al. 2002; Peripato et al. 2002; Carlborg et al. 2003). ...
We have mapped epistatic quantitative trait loci (QTL) in an F2 cross between DU6i x DBA/2 mice. By including these epistatic QTL and their interaction parameters in the genetic model, we were able to increase the genetic variance explained substantially (8.8%-128.3%) for several growth and body composition traits. We used an analysis method based on a simultaneous search for epistatic QTL pairs without assuming that the QTL had any effect individually. We were able to detect several QTL that could not be detected in a search for marginal QTL effects because the epistasis cancelled out the individual effects of the QTL. In total, 23 genomic regions were found to contain QTL affecting one or several of the traits and eight of these QTL did not have significant individual effects. We identified 44 QTL pairs with significant effects on the traits, and, for 28 of the pairs, an epistatic QTL model fit the data significantly better than a model without interactions. The epistatic pairs were classified by the significance of the epistatic parameters in the genetic model, and visual inspection of the two-locus genotype means identified six types of related genotype-phenotype patterns among the pairs. Five of these patterns resembled previously published patterns of QTL interactions.
Owing to massive jump in DNA technology, large-scale genomic datasets, including valuable information, have become available. While this is a prodigious opportunity, and it can also be a big challenge because analysing these large datasets with current computers and software tools is very difficult and may take days or even weeks to complete. Novel approaches such as parallel computing have been suggested to deal with these large datasets. Here, the effect of parallel computing on the performance of random forest (RF) algorithm for imputation of missing genotypes was studied. To this end, the genotypic matrices were simulated for, respectively, 500, 1000, 2000, and 3000 single-nucleotide polymorphism (SNP) for 500, 1000 and 2000 individuals, respectively. Then, 50% of genotypic information was masked and imputed by RF. The per cent of genotypes correctly imputed was used to measure accuracy of genotype imputation. Serial and parallel computing were applied to the data. In comparison to serial computing, parallel computing did not affect the accuracy of imputation, and the accuracy was the same in both scenarios. However, regarding computational time, parallel computing accelerated the analyses significantly in a way that it reduced the running time up to 63%. This was due to the fact that in the serial computing, only 10% of the processing power of the central processing unit (CPU) of the machine was used by the RF, while in the parallel computing, 55% of the processing power of the CPU was utilized. Therefore, as parallel computing significantly reduced the computing time and does not affect the accuracy of the results, this approach should be exploited by researchers to analyse large genomic datasets.
Several studies have shown that the tendency to feather peck is influenced by events early in life and preventive measures should therefore be introduced at hatching. Separating inactive chicks from active chicks by providing dark electrical brooders was predicted to reduce the risk of chicks developing pecking preferences for conspecifics.Twelve groups of 15 layer hen chicks (Lohmann Tradition) were reared in pens (2.55m2); during the first 5 weeks after hatching six pens were provided with dark brooders and six pens with heating lamps. All pens were observed continuously for 30min per pen once a week until the chickens were 23 weeks old, and each bout of severe feather pecks was recorded. The chickens were observed several times daily, and all injured individuals were removed from the experiment. Faecal samples were collected from the pens when the chicks were 16, 17 and 18 days old and analysed for corticosterone metabolites. At the end of the experiment, the plumage and skin damage were scored. Data were analysed using repeated measures ANOVA.The dark brooders completely prevented severe feather pecking in the dark brooder pens, whereas the frequency of severe feather pecking rose with age in the heating lamp pens (treatment×age: P
Introduction Methods and Tools for Genetic Linkage Mapping in Plants Statement of the ProblemLocus GroupingLocus OrderingMultilocus Distance EstimationUsing Variant and Mixed Cross Designs Outbreeding SpeciesAutopolyploid SpeciesCombining DatasetsLinkage-mapping Software Availability, Interfaces, and FeaturesMethods and Tools for QTL Mapping in Plants Statement of the ProblemSingle-marker Association Metric TraitsCategorical TraitsInterval Mapping: Simple (SIM) ML MethodsLeast-squares (Regression) and Nonparametric MethodsInterval Mapping: Composite (CIM)Significance TestingInterval Mapping: Multiple-QTL Model Building Stepwise and Exhaustive-search Methods for Building Multiple-QTL ModelsMarkov Chain Monte Carlo (MCMC) MethodsGenetic AlgorithmsMultiple-trait (MT) QTL MappingMultiple-cross (MC) QTL MappingComputational Optimization MethodsFuture Directions in Mapping Methods and Tools Future of Linkage and QTL MappingAdequacy of Software Tools for Plant Mapping Software Merit CriteriaAnalytical ScopeEase of Learning and UseAccessibility and ExtensibilityA Development Model for Public Genetic Mapping SoftwareReferences Methods and Tools for Genetic Linkage Mapping in Plants Statement of the ProblemLocus GroupingLocus OrderingMultilocus Distance EstimationUsing Variant and Mixed Cross Designs Outbreeding SpeciesAutopolyploid SpeciesCombining DatasetsLinkage-mapping Software Availability, Interfaces, and Features Statement of the Problem Outbreeding SpeciesAutopolyploid SpeciesCombining Datasets Statement of the ProblemSingle-marker Association Metric TraitsCategorical TraitsInterval Mapping: Simple (SIM) ML MethodsLeast-squares (Regression) and Nonparametric MethodsInterval Mapping: Composite (CIM)Significance TestingInterval Mapping: Multiple-QTL Model Building Stepwise and Exhaustive-search Methods for Building Multiple-QTL ModelsMarkov Chain Monte Carlo (MCMC) MethodsGenetic AlgorithmsMultiple-trait (MT) QTL MappingMultiple-cross (MC) QTL MappingComputational Optimization Methods Metric TraitsCategorical Traits ML MethodsLeast-squares (Regression) and Nonparametric Methods Stepwise and Exhaustive-search Methods for Building Multiple-QTL ModelsMarkov Chain Monte Carlo (MCMC) Methods
Genetic Algorithms Future of Linkage and QTL MappingAdequacy of Software Tools for Plant Mapping Software Merit CriteriaAnalytical ScopeEase of Learning and UseAccessibility and ExtensibilityA Development Model for Public Genetic Mapping Software Software Merit CriteriaAnalytical ScopeEase of Learning and UseAccessibility and Extensibility
Statistics, agriculture, and genetics share a long successful pre-genomic history that is based on solid principles of experimental
design and analysis of variation. In the era of ‘omics it is essential that statistical and mathematical standards, as well
as guidelines for the experimental design and analysis of biological studies are upheld. The main message of this chapter
recalls past statistical issues, discusses current statistical advances that pertain to understanding complex traits, and
promotes ideas about both the data and statistical genomic models of the future.
The now routine generation of large-scale, high-throughput data in multiple dimensions (genotype, gene expression, and so
on) provides a significant challenge to researchers who desire to integrate data across these dimensions in hopes of painting
a more comprehensive picture of complex system behavior. This type of integration promises to elucidate networks that drive
disease traits associated with common human diseases like obesity, diabetes, and atherosclerosis. However, to effectively
carry out this type of research not only requires the generation of large-scale genotype and molecular profiling data but
also requires the development and application of methods and software in addition to a computing infrastructure capable of
processing the large-scale data sets. Mastery of the methods and tools and having access to an appropriate computing environment
capable of processing large-scale data will be critical to maintaining a competitive advantage, given future successes in
biomedical research will likely demand a more comprehensive view of the complex array of interactions in biological systems
and how such interactions are influenced by genetic background, infection, environmental states, life-style choices, and social
structures more generally. In this chapter, we detail the methodological and computing issues associated with carrying out
large-scale genome-wide association studies on tens of thousands of phenotypes, where the aim is to identify those phenotypes
that are intermediate to DNA variations and disease phenotypes. This type of analysis can provide insights into the molecular
networks that are perturbed by DNA and environmental variations, and as a result, induce changes in disease associated traits,
providing a path to interpret genome-wide association study data as well as uncover networks that drive disease processes.
We present a ∞exible parallel implementation of the exhaus- tive grid search algorithm for multidimensional QTL mapping problems. A generic, parallel algorithm is presented and a two-level scheme is in- troduced for partitioning the work corresponding to the independent computational tasks in the algorithm. At the outer level, a static block- cyclic partitioning is used, and at the inner level a dynamic pool-of-tasks model is used. The implementation of the parallelism at the outer level is performed using scripts, while MPI is used at the inner level. By com- paring results from the SweGrid system to those obtained using a shared memory server, we show that this type of application is highly suitable for execution in a grid framework.
Behaviors with high energetic costs may decrease in frequency in domestic animals as a response to selection for increased production. The aim of this study was to quantify production traits, foraging behavior, and social motivation in F2 progeny from a White Leghorn x red junglefowl intercross (n = 751-1046) and to perform QTL analyses on the behavioral traits. A foraging-social maze was used for behavioral testing, which consisted of four identical arms and a central box. In two arms there was ad libitum access to the birds' usual food, and in the other two there was novel food (sunflower seeds) mixed with cat litter. In one arm with each of the two food sources, social stimuli were simulated by the presence of a mirror. Each bird could therefore feed on novel or well known food either alone or in the perceived company of a conspecific. Egg production, sexual maturity (females), food intake, and growth were measured individually, and residual food intake and metabolic body weight were estimated using standard methods. A genome scan using 104 microsatellite markers was carried out to identify QTLs affecting behavioral traits. Phenotypic growth rates at different ages showed weak associations in both sexes. Sexual maturity and egg weight were not strongly correlated to growth, indicating that these traits are not genetically linked. Time spent in each arm and in the central part of the maze was analyzed using principal component analyses. Four principal components (PC) were extracted, each reflecting a pattern of behavior in the maze. Females with early onset of sexual maturity scored higher on the PC1 reflecting preference for free food without social stimuli, and females with higher egg production scored higher on the PC2 reflecting exploration. Males with an overall higher growth rate and higher residual food intake scored higher on the PC3, which possibly reflected fear of the test situation, and tended to score higher on the PC4 reflecting low contrafreeloading. Significant QTLs were found for PC1 and PC4 scores on chromosomes 27 and 7, respectively. The location of the QTLs coincided with known QTLs for growth rate and body weight. The results suggest a trade-off between energy-demanding behavior and high production and that some of this may be caused by genetic linkage or pleiotropic gene effects.
A large intercross between the domestic White Leghorn chicken and the wild ancestor, the red junglefowl, has been used in a Quantitative Trait Loci (QTL) study of growth and egg production. The linkage map based on 105 marker loci was in good agreement with the chicken consensus map. The growth of the 851 F2 individuals was lower than both parental lines prior to 46 days of age and intermediate to the two parental lines thereafter. The QTL analysis of growth traits revealed 13 loci that showed genome-wide significance. The four major growth QTLs explained 50 and 80% of the difference in adult body weight between the founder populations for females and males, respectively. A major QTL for growth, located on chromosome 1 appears to have pleiotropic effects on feed consumption, egg production and behaviour. There was a strong positive correlation between adult body weight and average egg weight. However, three QTLs affecting average egg weight but not body weight were identified. An interesting observation was that the estimated effects for the four major growth QTLs all indicated a codominant inheritance.
An intercross between wild boar and a domestic Large White pig population was used to map quantitative trait loci (QTL) for body propor- tions, weight of internal organs, carcass composition, and meat quality. The results concerning growth traits and fat deposition traits have been reported elsewhere. In the present study, all 200 F2 animals, their parents, and their grandparents were genotyped for 236 markers. The marker genotypes were used to calculate the additive and dominance coefficients at fixed positions in the genome of each F2 animal, and the trait values were regressed onto these coefficients in intervals of 1 cM. In addition, the effect of proportion of wild boar alleles was tested for each chromosome. Significant QTL effects were found for percentage lean meat and percentage lean meat plus bone in various cuts, proportion of bone in relation to lean meat in ham, muscle area, and carcass length. The significant QTL were located on chromosomes 2, 3, 4, and 8. Each QTL explained 9 to 16% of the residual variance of the traits. Gene action for most QTL was largely additive. For meat quality traits, there were no QTL that reached the significance threshold. However, the average proportion of wild boar alleles across the genome had highly significant effects on reflectance and drip loss. The results show that there are several chromosome regions with a considerable effect on carcass traits in pigs.
The detection of genes that control quantitative characters is a problem of great interest to the genetic mapping community. Methods for locating these quantitative trait loci (QTL) relative to maps of genetic markers are now widely used. This paper addresses an issue common to all QTL mapping methods, that of determining an appropriate threshold value for declaring significant QTL effects. An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand. The method is demonstrated using two real data sets derived from F(2) and recombinant inbred plant populations. An example using simulated data from a backcross design illustrates the effect of marker density on threshold values.
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 quantitative trait locus (QTL) analysis of growth and fatness data from a three generation pig experiment is presented. The population of 199 F2 animals was derived from a cross between wild boar and Large White pigs. Animals were typed for 240 markers spanning 23 Morgans of 18 autosomes and the X chromosome. A series of analyses are presented within a least squares framework. First, these identify chromosomes containing loci controlling trait variation and subsequently attempt to map QTLs to locations within chromosomes. This population gives evidence for a large QTL affecting back fat and another for abdominal fat segregating on chromosome 4. The best locations for these QTLs are within 4 cM of each other and, hence, this is likely to be a single QTL affecting both traits. The allele inherited from the wild boar causes an increase in fat deposition. A QTL for intestinal length was also located in the same region on chromosome 4 and could be the same QTL with pleiotropic effects. Significant effects, owing to multiple QTLs, for intestinal length were identified on chromosomes 3 and 5. A single QTL affecting growth rate to 30 kg was located on chromosome 13 such that the Large White allele increased early growth rate, another QTL on chromosome 10 affected growth rate from 30 to 70 kg and another on chromosome 4 affected growth rate to 70 kg.
We generated an intercross between the European Wild Boar and Large White domestic pigs and used this pedigree to map quantitative trait loci1, 2, 3, 4 (QTL). We previously reported a QTL on pig chromosome 2p with a moderate effect on muscle mass4. The conserved synteny between this region and human chromosome 11p suggested IGF2 as a candidate for the QTL. We isolated a porcine BAC IGF2 clone (253G10) and used it for FISH mapping, resulting in a consistent signal on the distal tip of chromosome 2p (band 2p1.7). A microsatellite is located in the IGF2 3' UTR in mice (GenBank U71085), humans (GenBank S62623) and horse (GenBank AF020598). Direct BAC sequencing of the IGF2 3' UTR revealed a microsatellite 800 bp downstream of the IGF2 stop codon identical to a previously described microsatellite, Swc9 (5). The IGF2 microsatellite was found to be highly polymorphic, with three alleles among the two Wild Boar founders and another two among the eight Large White founders, and was fully informative in the intercross, as the breed and parent of origin could be determined for each allele in F2 animals. Linkage analysis showed that IGF2 maps 25 cM distal to Sw256, the end marker in our previous study4 (Fig. 1). The Swr2516/Sw2443 markers represent the end of the known linkage map; FISH mapping of IGF2 indicated that they are close to the telomere.
We describe a parallel implementation of a genetic linkage analysis program that achieves good speedups, even for analyses on a single pedigree and with a single starting recombination fraction vector. Our parallel implementation has been run on three different platforms: an Ethernet network of workstations, a higherbandwidth Asynchronous Transfer Mode (ATM) network of workstations, and a shared-memory multiprocessor. The same program, written in a shared memory programming style, is used on all platforms. On the workstation networks, the hardware does not provide shared memory, so the program executes on a distributed shared memory system that implements shared memory in software. These three platforms represent different points on the price/performance scale. Ethernet networks are cheap and omnipresent, ATM networks are an emerging technology that offers higher bandwidth, and shared-memory multiprocessors offer the best performance because communication is implemented entirely by har...
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 F1 (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 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.
Adequate separation of effects of possible multiple linked quantitative trait loci (QTLs) on mapping QTLs is the key to increasing the precision of QTL mapping. A new method of QTL mapping is proposed and analyzed in this paper by combining interval mapping with multiple regression. The basis of the proposed method is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. This is achieved by fitting other genetic markers in the statistical model as a control when performing interval mapping. Compared with the current QTL mapping method (i.e., the interval mapping method which uses a pair or two pairs of markers for mapping QTLs), this method has several advantages. (1) By confining the test to one region at a time, it reduces a multiple dimensional search problem (for multiple QTLs) to a one dimensional search problem. (2) By conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved. (3) By selectively and simultaneously using other markers in the analysis, the efficiency of QTL mapping can be also improved. The behavior of the test statistic under the null hypothesis and appropriate critical value of the test statistic for an overall test in a genome are discussed and analyzed. A simulation study of QTL mapping is also presented which illustrates the utility, properties, advantages and disadvantages of the method.
The European wild boar was crossed with the domesticated Large White pig to genetically dissect phenotypic differences between
these populations for growth and fat deposition. The most important effects were clustered on chromosome 4, with a single
region accounting for a large part of the breed difference in growth rate, fatness, and length of the small intestine. The
study is an advance in genome analyses and documents the usefulness of crosses between divergent outbred populations for the
detection and characterization of quantitative trait loci. The genetic mapping of a major locus for fat deposition in the
pig could have implications for understanding human obesity.
A general fine-scale Bayesian quantitative trait locus (QTL) mapping method for outcrossing species is presented. It is suitable for an analysis of complete and incomplete data from experimental designs of F2 families or backcrosses. The amount of genotyping of parents and grandparents is optional, as well as the assumption that the QTL alleles in the crossed lines are fixed. Grandparental origin indicators are used, but without forgetting the original genotype or allelic origin information. The method treats the number of QTL in the analyzed chromosome as a random variable and allows some QTL effects from other chromosomes to be taken into account in a composite interval mapping manner. A block-update of ordered genotypes (haplotypes) of the whole family is sampled once in each marker locus during every round of the Markov Chain Monte Carlo algorithm used in the numerical estimation. As a byproduct, the method gives the posterior distributions for linkage phases in the family and therefore it can also be used as a haplotyping algorithm. The Bayesian method is tested and compared with two frequentist methods using simulated data sets, considering two different parental crosses and three different levels of available parental information. The method is implemented as a software package and is freely available under the name Multimapper/outbred at URL http://www.rni.helsinki.fi/mjs/.
Farm animal populations harbour rich collections of mutations with phenotypic effects that have been purposefully enriched by breeding. Most of these mutations do not have pathological phenotypic consequences, in contrast to the collections of deleterious mutations in model organisms or those causing inherited disorders in humans. Farm animals are of particular interest for identifying genes that control growth, energy metabolism, development, appetite, reproduction and behaviour, as well as other traits that have been manipulated by breeding. Genome research in farm animals will add to our basic understanding of the genetic control of these traits and the results will be applied in breeding programmes to reduce the incidence of disease and to improve product quality and production efficiency.