Fine mapping of trypanosomiasis resistance loci in murine advanced intercross lines

We have previously reported the results of genome-wide searches in two murine F₂ populations for QTLs that influence survival following Trypanosoma congolense infection. Three loci, Tir1, Tir2, and Tir3, were identified and mapped to mouse Chromosomes (Chrs) 17, 5, and 1 respectively, with confidence intervals (CIs) in the range 10–40 cM. The size of these CIs is to a large degree the consequence of limited numbers of recombination events in small chromosomal regions in F₂ populations. A number of population designs have been proposed to increase recombination levels in crosses, one of which is the advanced intercross line (AIL). Here we report fine mapping of Tir1, Tir2, and Tir3 in G6 populations of two independent murine AILs created by crossing the C57BL/6J strain with the A/J and BALB/cJ strains, respectively. Data were analyzed by two methods that gave equally informative and similar results. The three QTLs were confirmed in the A/J × C57BL/6J AIL and in the combined data set, but Tir2 was apparently lost from the BALB/cJ × C57BL/6J AIL. The reduction in CIs for the Tir loci ranged from 2.5 to more than ten-fold in G6 populations by comparison with CIs obtained previously in the equivalent F₂ generations. Mapping in the AILs also resolved the Tir3 locus into three trypanosomiasis resistance QTLs, revealing a degree of complexity not evident in extensive studies at the F₂ level. Received: 16 December 1999 / Accepted: 24 March 2000     

An advanced intercross line resolves Eae18 into two narrow quantitative trait loci syntenic to multiple sclerosis candidate loci

Identification of polymorphic genes regulating inflammatory diseases may unravel crucial pathogenic mechanisms. Initial steps to map such genes using linkage analysis in F(2) intercross or backcross populations, however, result in broad quantitative trait loci (QTLs) containing hundreds of genes. In this study, an advanced intercross line in combination with congenic strains, was used to fine-map Eae18 on rat chromosome 10 in myelin oligodendrocyte glycoprotein-induced experimental autoimmune encephalomyelitis (EAE). Myelin oligodendrocyte glycoprotein-induced EAE is a chronic relapsing disease that closely mimics key features of multiple sclerosis. Congenic DA.ACI rat strains localized Eae18 to an approximately 30-Mb large region. Fine-mapping was then performed in an advanced intercross line consisting of a (DA x PVG.1AV1)F(7) intercross, resulting in two adjacent EAE-regulating QTLs designated Eae18a and Eae18b. The two QTLs span 5.5 and 3 Mb, respectively, and the 3-Mb Eae18b contains as few as 10 genes, including a cluster of chemokine genes (CCL1, CCL2, CCL7, and CCL11). Eae18a and Eae18b are syntenic to human chromosome 17p13 and 17q11, respectively, which both display linkage to multiple sclerosis. Thus, Eae18 consists of at least two EAE-regulating genes, providing additional evidence that clustering of disease-regulating genes in QTLs is an important phenomenon. The overlap between Eae18a and Eae18b with previously identified QTLs in humans and mice further supports the notion that susceptibility alleles in inflammatory disease are evolutionary conserved between species.     

On locating multiple interacting quantitative trait loci in intercross designs

A modified version (mBIC) of the Bayesian Information Criterion (BIC) has been previously proposed for backcross designs to locate multiple interacting quantitative trait loci. In this article, we extend the method to intercross designs. We also propose two modifications of the mBIC. First we investigate a two-stage procedure in the spirit of empirical Bayes methods involving an adaptive (i.e., data-based) choice of the penalty. The purpose of the second modification is to increase the power of detecting epistasis effects at loci where main effects have already been detected. We investigate the proposed methods by computer simulations under a wide range of realistic genetic models, with nonequidistant marker spacings and missing data. In the case of large intermarker distances we use imputations according to Haley and Knott regression to reduce the distance between searched positions to not more than 10 cM. Haley and Knott regression is also used to handle missing data. The simulation study as well as real data analyses demonstrates good properties of the proposed method of QTL detection.     

QTL mapping of complex binary traits in an advanced intercross line

An advanced intercross line (AIL) is an easier and more cost-effective approach compared to recombinant inbred lines for fine mapping of quantitative trait loci (QTL) identified by F(2) designs. In an AIL, a complex binary trait can be mapped through analysis of either continuously distributed proxy traits for the liability of the binary trait or the liability itself, the latter presenting the greater statistical challenge. In another work, we successfully applied both approaches in an AIL to fine map previously identified QTL underlying anatomical parameters of the cardiac inter-atrial septum including patent foramen ovale. Here, we describe the statistical methods that we used to analyse complex binary traits in our AIL design. This is achieved using a likelihood-based method, with the expectation-maximisation algorithm allowing use of standard logistic regression methods for model fitting.     

Fine mapping dissects pleiotropic growth quantitative trait locus into linked loci

A recurring issue in studies of quantitative trait loci (QTLs) is whether QTLs that appear to have pleiotropic effects are indeed caused by pleiotropy at single loci or by linked QTLs. Previous work identified a QTL that affected tail length in mice and the lengths of various bones, including the humerus, ulna, femur, tibia, and mandible. The effect of this QTL on tail length has since been found to be due to multiple linked QTLs and so its apparently pleiotropic effects may have been due to linked QTLs with distinct effects. In the present study we examined a line of mice segregating only for a 0.94-Mb chromosomal region known to contain a subset of the QTLs influencing tail length. We measured a number of skeletal dimensions, including the lengths of the skull, mandible, humerus, ulna, femur, tibia, calcaneus, metatarsus, and a tail bone. The QTL region was found to have effects on the size of the mandible and length of the tail bone, with little or no effect on the other traits. Using a randomization approach, we rejected the null hypothesis that the QTL affected all traits equally, thereby demonstrating that the pleiotropic effects reported earlier were due to linked loci with distinct effects. This result underlines the possibility that seemingly pleiotropic effects of QTLs may frequently be due to linked loci and that high-resolution mapping will often be required to distinguish between pleiotropy and linkage.     

Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci

A multimarker linkage disequilibrium mapping method was developed for the fine mapping of quantitative trait loci (QTL) using a dense marker map. The method compares the expected covariances between haplotype effects given a postulated QTL position to the covariances that are found in the data. The expected covariances between the haplotype effects are proportional to the probability that the QTL position is identical by descent (IBD) given the marker haplotype information, which is calculated using the genedropping method. Simulation results showed that a QTL was correctly positioned within a region of 3, 1.5, or 0.75 cM in 70, 62, and 68%, respectively, of the replicates using markers spaced at intervals of 1, 0.5, and 0.25 cM, respectively. These results were rather insensitive to the number of generations since the QTL occurred and to the effective population size, except that 10 generations yielded rather poor estimates of the QTL position. The position estimates of this multimarker disequilibrium mapping method were more accurate than those from a single marker transmission disequilibrium test. A general approach for identifying QTL is suggested, where several stages of disequilibrium mapping are used with increasingly dense marker spacing.     

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.     

Lineage-specific mapping of quantitative trait loci

We present an approach for quantitative trait locus (QTL) mapping, termed as ‘lineage-specific QTL mapping'', for inferring allelic changes of QTL evolution along with branches in a phylogeny. We describe and analyze the simplest case: by adding a third taxon into the normal procedure of QTL mapping between pairs of taxa, such inferences can be made along lineages to a presumed common ancestor. Although comparisons of QTL maps among species can identify homology of QTLs by apparent co-location, lineage-specific mapping of QTL can classify homology into (1) orthology (shared origin of QTL) versus (2) paralogy (independent origin of QTL within resolution of map distance). In this light, we present a graphical method that identifies six modes of QTL evolution in a three taxon comparison. We then apply our model to map lineage-specific QTLs for inbreeding among three taxa of yellow monkey-flower: Mimulus guttatus and two inbreeders M. platycalyx and M. micranthus, but critically assuming outcrossing was the ancestral state. The two most common modes of homology across traits were orthologous (shared ancestry of mutation for QTL alleles). The outbreeder M. guttatus had the fewest lineage-specific QTL, in accordance with the presumed ancestry of outbreeding. Extensions of lineage-specific QTL mapping to other types of data and crosses, and to inference of ancestral QTL state, are discussed.     

Selection bias in quantitative trait loci mapping

A simulation study was performed to see whether selection affected quantitative trait loci (QTL) mapping. Populations under random selection, under selection among full-sib families, and under selection within a full-sib family were simulated each with heritability of 0.3, 0.5, and 0.7. They were analyzed with the marker spacing of 10 cM and 20 cM. The accuracy for QTL detection decreased for the populations under selection within full-sib family. Estimates of QTL effects and positions differed (P < .05) from their input values. The problems could be ignored when mapping a QTL for the populations under selection among full-sib families. A large heritability helped reduction of such problems. When the animals were selected within a full-sib family, the QTL was detected for the populations with heritability of 0.5 or larger using the marker spacing of 10 cM, and with heritability of 0.7 using the marker spacing of 20 cM. This study implied that when selection was introduced, the accuracy for QTL detection decreased and the estimates of QTL effects were biased. A caution was warranted on the decision of data (including selected animals to be genotyped) for QTL mapping.     

Fine mapping of quantitative trait loci for mastitis resistance on bovine chromosome 11

Quantitative trait loci (QTL) affecting clinical mastitis (CM) and somatic cell score (SCS) were mapped on bovine chromosome 11. The mapping population consisted of 14 grandsire families belonging to three Nordic red cattle breeds: Finnish Ayrshire (FA), Swedish Red and White (SRB) and Danish Red. The families had previously been shown to segregate for udder health QTL. A total of 524 progeny tested bulls were included in the analysis. A linkage map including 33 microsatellite and five SNP markers was constructed. We performed combined linkage disequilibrium and linkage analysis (LDLA) using the whole data set. Further analyses were performed for FA and SRB separately to study the origin of the identified QTL/haplotype and to examine if it was common in both populations. Finally, different two-trait models were fitted. These postulated either a pleiotropic QTL affecting both traits; two linked QTL, each affecting one trait; or one QTL affecting a single trait. A QTL affecting CM was fine-mapped. In FA, a haplotype having a strong association with a high negative effect on mastitis resistance was identified. The mapping precision of an earlier detected SCS-QTL was not improved by the LDLA analysis because of lack of linkage disequilibrium between the markers used and the QTL in the region.     

Nonparametric functional mapping of quantitative trait loci

Summary .  Functional mapping is a useful tool for mapping quantitative trait loci (QTL) that control dynamic traits. It incorporates mathematical aspects of biological processes into the mixture model-based likelihood setting for QTL mapping, thus increasing the power of QTL detection and the precision of parameter estimation. However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal. Here we propose to use nonparametric function estimation, typically implemented with B-splines, to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing QTL. Using the representation of a nonparametric regression as a mixed model, the final test statistic is a likelihood ratio test. We consider two types of genetic maps: dense maps and general maps, and the power of nonparametric functional mapping is investigated through simulation studies and demonstrated by examples.     

Precision and high-resolution mapping of quantitative trait loci by use of recurrent selection,backcross or intercross schemes

Dissecting quantitative genetic variation into genes at the molecular level has been recognized as the greatest challenge facing geneticists in the twenty-first century. Tremendous efforts in the last two decades were invested to map a wide spectrum of quantitative genetic variation in nearly all important organisms onto their genome regions that may contain genes underlying the variation, but the candidate regions predicted so far are too coarse for accurate gene targeting. In this article, the recurrent selection and backcross (RSB) schemes were investigated theoretically and by simulation for their potential in mapping quantitative trait loci (QTL). In the RSB schemes, selection plays the role of maintaining the recipient genome in the vicinity of the QTL, which, at the same time, are rapidly narrowed down over multiple generations of backcrossing. With a high-density linkage map of DNA polymorphisms, the RSB approach has the potential of dissecting the complex genetic architecture of quantitative traits and enabling the underlying QTL to be mapped with the precision and resolution needed for their map-based cloning to be attempted. The factors affecting efficiency of the mapping method were investigated, suggesting guidelines under which experimental designs of the RSB schemes can be optimized. Comparison was made between the RSB schemes and the two popular QTL mapping methods, interval mapping and composite interval mapping, and showed that the scenario of genomic distribution of QTL that was unlocked by the RSB-based mapping method is qualitatively distinguished from those unlocked by the interval mapping-based methods.     

Bayesian LASSO for quantitative trait loci mapping

The mapping of quantitative trait loci (QTL) is to identify molecular markers or genomic loci that influence the variation of complex traits. The problem is complicated by the facts that QTL data usually contain a large number of markers across the entire genome and most of them have little or no effect on the phenotype. In this article, we propose several Bayesian hierarchical models for mapping multiple QTL that simultaneously fit and estimate all possible genetic effects associated with all markers. The proposed models use prior distributions for the genetic effects that are scale mixtures of normal distributions with mean zero and variances distributed to give each effect a high probability of being near zero. We consider two types of priors for the variances, exponential and scaled inverse-chi(2) distributions, which result in a Bayesian version of the popular least absolute shrinkage and selection operator (LASSO) model and the well-known Student's t model, respectively. Unlike most applications where fixed values are preset for hyperparameters in the priors, we treat all hyperparameters as unknowns and estimate them along with other parameters. Markov chain Monte Carlo (MCMC) algorithms are developed to simulate the parameters from the posteriors. The methods are illustrated using well-known barley data.     

Exploring multiple quantitative trait loci models of hepatic fibrosis in a mouse intercross

Most common diseases are attributed to multiple genetic variants, and the feasibility of identifying inherited risk factors is often restricted to the identification of alleles with high or intermediate effect sizes. In our previous studies, we identified single loci associated with hepatic fibrosis (Hfib1–Hfib4). Recent advances in analysis tools allowed us to model loci interactions for liver fibrosis. We analysed 322 F2 progeny from an intercross of the fibrosis-susceptible strain BALB/cJ and the resistant strain FVB/NJ. The mice were challenged with carbon tetrachloride (CCl₄) for 6 weeks to induce chronic hepatic injury and fibrosis. Fibrosis progression was quantified by determining histological fibrosis stages and hepatic collagen contents. Phenotypic data were correlated to genome-wide markers to identify quantitative trait loci (QTL). Thirteen susceptibility loci were identified by single and composite interval mapping, and were included in the subsequent multiple QTL model (MQM) testing. Models provided evidence for susceptibility loci with strongest association to collagen contents (chromosomes 1, 2, 8 and 13) or fibrosis stages (chromosomes 1, 2, 12 and 14). These loci contained the known fibrosis risk genes Hc, Fasl and Foxa2 and were incorporated in a fibrosis network. Interestingly the hepatic fibrosis locus on chromosome 1 (Hfib5) connects both phenotype networks, strengthening its role as a potential modifier locus. Including multiple QTL mapping to association studies adds valuable information on gene–gene interactions in experimental crosses and human cohorts. This study presents an initial step towards a refined understanding of profibrogenic gene networks.     

Fragmentation of two quantitative trait loci controlling collagen-induced arthritis reveals a new set of interacting subloci

Linkage analysis of F(2) crosses has led to identification of large numbers of quantitative trait loci (QTL) for complex diseases, but identification of the underlying genes has been more difficult. Reasons for this could be complications that arise from separation of interacting or neighboring loci. We made a partial advanced intercross (PAI) to characterize and fine-map linkage to collagen-induced arthritis in two chromosomal regions derived from the DBA/1 strain and crossed into the B10.Q strain: Cia7 on chromosome 7 and a locus on chromosome 15. Only Cia7 was detected by a previous F(2) cross. Linkage analysis of the PAI revealed a different linkage pattern than the F(2) cross, adding multiple loci and strong linkage to the previously unlinked chromosome 15 region. Subcongenic strains derived from animals in the PAI confirmed the loci and revealed additional subloci. In total, no less than seven new loci were identified. Several loci interacted and three loci were protective, thus partly balancing the effect of the disease-promoting loci. Our results indicate that F(2) crosses do not reveal the full complexity of identified QTLs, and that detection is more dependent on the genetic context of a QTL than the potential effect of the underlying gene.     

Backcross and partial advanced intercross analysis of nonobese diabetic gene-mediated effects on collagen-induced arthritis reveals an interactive effect by two major loci

Genetic segregation analysis between NOD and C57BL strains have been used to identify loci associated with autoimmune disease. Only two loci (Cia2 and Cia9) had earlier been found to control development of arthritis, whereas none of the previously identified diabetes loci was of significance for arthritis. We have now made a high-powered analysis of a backcross of NOD genes on to the B10.Q strain for association with collagen-induced arthritis. We could confirm relevance of both Cia2 and Cia9 as well as the interaction between them, but we did not identify any other significant arthritis loci. Immune cellular subtyping revealed that Cia2 was also associated with the number of blood macrophages. Congenic strains of the Cia2 and Cia9 loci on the B10.Q background were made and used to establish a partial advanced intercross (PAI). Testing the PAI mice for development of collagen-induced arthritis confirmed the loci and the interactions and also indicated that at least two genes contribute to the Cia9 locus. Furthermore, it clearly showed that Cia2 is dominant protective but that the protection is not complete. Because these results may indicate that the Cia2 effect on arthritis is not only due to the deficiency of the complement C5, we analyzed complement functions in the Cia2 congenics as well as the PAI mice. These data show that not only arthritis but also C5-dependent complement activity is dominantly suppressed, confirming that C5 is one of the major genes explaining the Cia2 effect.     

Fine mapping and replication of QTL in outbred chicken advanced intercross lines

Background

Linkage mapping is used to identify genomic regions affecting the expression of complex traits. However, when experimental crosses such as F₂ populations or backcrosses are used to map regions containing a Quantitative Trait Locus (QTL), the size of the regions identified remains quite large, i.e. 10 or more Mb. Thus, other experimental strategies are needed to refine the QTL locations. Advanced Intercross Lines (AIL) are produced by repeated intercrossing of F₂ animals and successive generations, which decrease linkage disequilibrium in a controlled manner. Although this approach is seen as promising, both to replicate QTL analyses and fine-map QTL, only a few AIL datasets, all originating from inbred founders, have been reported in the literature.

Methods

We have produced a nine-generation AIL pedigree (n = 1529) from two outbred chicken lines divergently selected for body weight at eight weeks of age. All animals were weighed at eight weeks of age and genotyped for SNP located in nine genomic regions where significant or suggestive QTL had previously been detected in the F₂ population. In parallel, we have developed a novel strategy to analyse the data that uses both genotype and pedigree information of all AIL individuals to replicate the detection of and fine-map QTL affecting juvenile body weight.

Results

Five of the nine QTL detected with the original F₂ population were confirmed and fine-mapped with the AIL, while for the remaining four, only suggestive evidence of their existence was obtained. All original QTL were confirmed as a single locus, except for one, which split into two linked QTL.

Conclusions

Our results indicate that many of the QTL, which are genome-wide significant or suggestive in the analyses of large intercross populations, are true effects that can be replicated and fine-mapped using AIL. Key factors for success are the use of large populations and powerful statistical tools. Moreover, we believe that the statistical methods we have developed to efficiently study outbred AIL populations will increase the number of organisms for which in-depth complex traits can be analyzed.     

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.     

Accuracy of mapping quantitative trait loci in autogamous species

Summary The development of linkage maps with large numbers of molecular markers has stimulated the search for methods to map genes involved in quantitative traits (QTLs). A promising method, proposed by Lander and Botstein (1989), employs pairs of neighbouring markers to obtain maximum linkage information about the presence of a QTL within the enclosed chromosomal segment. In this paper the accuracy of this method was investigated by computer simulation. The results show that there is a reasonable probability of detecting QTLs that explain at least 5% of the total variance. For this purpose a minimum population of 200 backcross or F₂ individuals is necessary. Both the number of individuals and the relative size of the genotypic effect of the QTL are important factors determining the mapping precision. On the average, a QTL with 5% or 10% explained variance is mapped on an interval of 40 or 20 centiMorgans, respectively. Of course, QTLs with a larger genotypic effect will be located more precisely. It must be noted, however, that the interval length is rather variable.