Correct estimation of preferential chromosome pairing in autotetraploids

In recent work, a statistical model was proposed for the purpose of estimating parameters associated with quantitative trait locus (QTL) mapping and preferential pairing within a polyploidy framework. The statistical model contained several parameters that, when estimated from experimental data, supplied information about QTL, including a preferential pairing factor. Among the results reported were estimates of preferential pairing, many of which indicated high levels of preferential pairing (p = 0.60) that were inconsistent with biological expectations. By using the biological inconsistencies as our motivation, we present a reformulated statistical method for estimating preferential pairing, and use this method to reanalyze the same autotetraploid alfalfa data and to conduct a simulation study. Our results directly contradict the current findings of significant preferential pairing and affirm the traditional view of random chromosome segregation in alfalfa.

The nature and identification of quantitative trait loci: a community's view

This white paper by eighty members of the Complex Trait Consortium presents a community's view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?

Transfer of T-DNA and Vir proteins to plant cells by Agrobacterium tumefaciens induces expression of host genes involved in mediating transformation and suppresses host defense gene expression

Agrobacterium tumefaciens is a plant pathogen that incites crown gall tumors by transferring to and expressing a portion of a resident plasmid in plant cells. Currently, little is known about the host response to Agrobacterium infection. Using suppressive subtractive hybridization and DNA macroarrays, we identified numerous plant genes that are differentially expressed during early stages of Agrobacterium-mediated transformation. Expression profiling indicates that Agrobacterium infection induces plant genes necessary for the transformation process while simultaneously repressing host defense response genes, thus indicating successful utilization of existing host cellular machinery for genetic transformation purposes. A comparison of plant responses to different strains of Agrobacterium indicates that transfer of both T-DNA and Vir proteins modulates the expression of host genes during the transformation process.

Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis

BACKGROUND

It has been reported in the quantitative trait locus (QTL) literature that when testing for QTL location and effect, the statistical power supporting methodologies based on two markers and their estimated genetic map is higher than for the genetic map independent methodologies known as single marker analyses. Close examination of these reports reveals that the two marker approaches are more powerful than single marker analyses only in certain cases. Simulation studies are a commonly used tool to determine the behavior of test statistics under known conditions. We conducted a simulation study to assess the general behavior of an intersection test and a two marker test under a variety of conditions. The study was designed to reveal whether two marker tests are always more powerful than intersection tests, or whether there are cases when an intersection test may outperform the two marker approach.We present a reanalysis of a data set from a QTL study of ovariole number in Drosophila melanogaster.

RESULTS

Our simulation study results show that there are situations where the single marker intersection test equals or outperforms the two marker test. The intersection test and the two marker test identify overlapping regions in the reanalysis of the Drosophila melanogaster data. The region identified is consistent with a regression based interval mapping analysis.

CONCLUSION

We find that the intersection test is appropriate for analysis of QTL data. This approach has the advantage of simplicity and for certain situations supplies equivalent or more powerful results than a comparable two marker test.

Understanding mechanisms of novel gene expression in polyploids

Polyploidy has long been recognized as a prominent force shaping the evolution of eukaryotes, especially flowering plants. New phenotypes often arise with polyploid formation and can contribute to the success of polyploids in nature or their selection for use in agriculture. Although the causes of novel variation in polyploids are not well understood, they could involve changes in gene expression through increased variation in dosage-regulated gene expression, altered regulatory interactions, and rapid genetic and epigenetic changes. New research approaches are being used to study these mechanisms and the results should provide a more complete understanding of polyploidy.

Sex-specific quantitative trait loci govern susceptibility to Theiler's murine encephalomyelitis virus-induced demyelination

Susceptibility to Theiler's murine encephalomyelitis virus-induced demyelination (TMEVD), a mouse model for multiple sclerosis (MS), is genetically controlled. Through a mouse-human comparative mapping approach, identification of candidate susceptibility loci for MS based on the location of TMEVD susceptibility loci may be possible. Composite interval mapping (CIM) identified quantitative trait loci (QTL) controlling TMEVD severity in male and female backcross populations derived from susceptible DBA/2J and resistant BALBc/ByJ mice. We report QTL on chromosomes 1, 5, 15, and 16 affecting male mice. In addition, we identified two QTL in female mice located on chromosome 1. Our results support the existence of three linked sex-specific QTL on chromosome 1 with opposing effects on the severity of the clinical signs of TMEV-induced disease in male and female mice.

Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments

MOTIVATION

We present statistical methods for determining the number of per gene replicate spots required in microarray experiments. The purpose of these methods is to obtain an estimate of the sampling variability present in microarray data, and to determine the number of replicate spots required to achieve a high probability of detecting a significant fold change in gene expression, while maintaining a low error rate. Our approach is based on data from control microarrays, and involves the use of standard statistical estimation techniques.

RESULTS

After analyzing two experimental data sets containing control array data, we were able to determine the statistical power available for the detection of significant differential expression given differing levels of replication. The inclusion of replicate spots on microarrays not only allows more accurate estimation of the variability present in an experiment, but more importantly increases the probability of detecting genes undergoing significant fold changes in expression, while substantially decreasing the probability of observing fold changes due to chance rather than true differential expression.

Interacting quantitative trait loci control loss of peripheral tolerance and susceptibility to autoimmune ovarian dysgenesis after day 3 thymectomy in mice

Day 3 thymectomy (D3Tx) results in a loss of peripheral tolerance mediated by CD4(+)CD25(+) T cells and the development of autoimmune ovarian dysgenesis (AOD) in A/J and (C57BL/6J x A/J)F(1) (B6AF(1)) hybrids but not in C57BL/6J mice. Quantitative trait loci (QTL) linkage analysis using a B6AF(1) x C57BL/6J backcross population verified Aod1 and Aod2 that were previously mapped as qualitative traits. Additionally, three new QTL intervals, Aod3, Aod4, and Aod5, on chromosomes 1, 2, and 7, respectively, influencing specific subphenotypes of AOD were identified. QTL linkage analysis using the A x B and B x A recombinant inbred lines verified Aod3 and confirmed linkage to H2. Aod5 colocalized with Mater, an ovarian-specific autoantigen recognized by anti-ovarian autoantibodies in the sera of D3Tx mice. Sequence analysis of Mater identified allelic, strain-specific splice variants between A/J and C57BL/6J mice making it an attractive candidate gene for Aod5. Interaction analysis revealed significant epistatic effects between Aod1-5 and Gasa2, a locus associated with susceptibility to D3Tx-induced autoimmune gastritis, as well as with H2. These results indicate that the QTL controlling D3Tx-induced autoimmune phenomenon are both organ specific and more generalized in their effects with respect to the genesis and activity of the immunoregulatory mechanisms maintaining peripheral tolerance.

Variable selection in high-dimensional multivariate binary data with application to the analysis of microbial community DNA fingerprints

In order to understand the relevance of microbial communities on crop productivity, the identification and characterization of the rhizosphere soil microbial community is necessary. Characteristic profiles of the microbial communities are obtained by denaturing gradient gel electrophoresis (DGGE) of polymerase chain reaction (PCR) amplified 16S rDNA from soil extracted DNA. These characteristic profiles, commonly called community DNA fingerprints, can be represented in the form of high-dimensional binary vectors. We address the problem of modeling and variable selection in high-dimensional multivariate binary data and present an application of our methodology in the context of a controlled agricultural experiment.

Mapping and analysis of quantitative trait loci in experimental populations

Simple statistical methods for the study of quantitative trait loci (QTL), such as analysis of variance, have given way to methods that involve several markers and high-resolution genetic maps. As a result, the mapping community has been provided with statistical and computational tools that have much greater power than ever before for studying and locating multiple and interacting QTL. Apart from their immediate practical applications, the lessons learnt from this evolution of QTL methodology might also be generally relevant to other types of functional genomics approach that are aimed at the dissection of complex phenotypes, such as microarray assessment of gene expression.

Genetic control of susceptibility to experimental Lyme arthritis is polygenic and exhibits consistent linkage to multiple loci on chromosome 5 in four independent mouse crosses

C3H/He mice infected with Borrelia burgdorferi develop severe arthritis and are high antibody responders, while infected C57BL/6 and BALB/c mice develop mild arthritis and less robust humoral responses. Genetic analysis using composite interval mapping (CIM) on reciprocal backcross populations derived from C3H/HeN and C57BL/6N or C3H/HeJ and BALB/cAnN mice identified 12 new quantitative trait loci (QTL) linked to 10 murine Lyme disease phenotypes. These QTL reside on chromosomes 1, 2, 4, 6, 7, 9, 10, 12, 14, 15, 16, and 17. A reanalysis of an F(2) intercross between C57BL/6N and C3H/HeN mice using CIM identified two new QTL on chromosomes 4 and 15 and confirmed the location of seven previously identified loci. Two or more experimental crosses independently verified six QTL controlling phenotypes after B. burgdorferi infection. Additionally, Bb2 on chromosome 5 was reproduced in four experimental populations and was linked to the candidate locus Cora1. Evidence of four distinct QTL residing within the 30-cM region of chromosome 5 encompassing the previously mapped Bb2 and Bb3 loci was shown by CIM. Interestingly, some alleles contributing to susceptibility to Lyme arthritis were derived from C57BL/6N and BALB/cAnN mice, showing that disease-resistant strains harbor susceptibility alleles.

Detection and localization of a single binary trait locus in experimental populations

The advancements made in molecular technology coupled with statistical methodology have led to the successful detection and location of genomic regions (quantitative trait loci; QTL) associated with quantitative traits. Binary traits (e.g. susceptibility/resistance), while not quantitative in nature, are equally important for the purpose of detecting and locating significant associations with genomic regions. Existing interval regression methods used in binary trait analysis are adapted from quantitative trait analysis and the tests for regression coefficients are tests of effect, not detection. Additionally, estimates of recombination that fail to take into account varying penetrance perform poorly when penetrance is incomplete. In this work a complete probability model for binary trait data is developed allowing for unbiased estimation of both penetrance and recombination between a genetic marker locus and a binary trait locus for backcross and F2 experimental designs. The regression model is reparameterized allowing for tests of detection. Extensive simulations were conducted to assess the performance of estimation and testing in the proposed parameterization. The proposed parameterization was compared with interval regression via simulation. The results indicate that our parameterization shows equivalent estimation capabilities, requires less computational effort and works well with only a single marker.

Identification of genetic loci controlling the characteristics and severity of brain and spinal cord lesions in experimental allergic encephalomyelitis

Experimental allergic encephalomyelitis (EAE) is the principal genetically determined animal model for multiple sclerosis (MS), the major inflammatory disease of the central nervous system (CNS). Although genetics clearly play a role in susceptibility to MS, attempts to identify the underlying genes have been disappointing. Considerable variation exists between MS patients with regard to the severity of clinical signs, mechanism of demyelination, and location of CNS lesions, confounding the interpretation of genetic data. A mouse-human synteny mapping approach may allow the identification of candidate susceptibility loci for MS based on the location of EAE susceptibility loci. To date, 16 regions of the mouse genome have been identified that control susceptibility or clinical signs of EAE. In this work, we examined the genetic control of histopathological lesions of EAE in an F2 intercross population generated from the EAE susceptible SJL/J and EAE resistant B10.S/DvTe mouse strains. Composite interval mapping was used to identify 10 quantitative trait loci (QTL), including seven newly identified loci controlling the distribution and severity of CNS lesions associated with murine EAE. QTL on chromosome 10 control lesions in the brain, whereas QTL on chromosomes 3, 7, and 12 control lesions in the spinal cord. Furthermore, sexually dimorphic QTL on chromosomes 2, 9, and 11 control CNS lesions in females, whereas QTL on chromosomes 10, 11, 12, 16, and 19 control lesions in males. Our results suggest that the severity and location of CNS lesions in EAE are genetically controlled, and that the genetic component controlling the character and severity of the lesions can be influenced by sex.

Model selection for quantitative trait locus analysis in polyploids

Over the years, substantial gains have been made in locating regions of agricultural genomes associated with characteristics, diseases, and agroeconomic traits. These gains have relied heavily on the ability to statistically estimate the association between DNA markers and regions of a genome (quantitative trait loci or QTL) related to a particular trait. The majority of these advances have focused on diploid species, even though many important agricultural crops are, in fact, polyploid. The purpose of our work is to initiate an algorithmic approach for model selection and QTL detection in polyploid species. This approach involves the construction of all possible chromosomal configurations (models) that may result in a gamete, model reduction based on estimation of marker dosage from progeny data, and lastly model selection. While simplified for initial explanation, our approach has demonstrated itself to be extendible to many breeding schemes and less restricted settings.

Genetic analysis of the influence of pertussis toxin on experimental allergic encephalomyelitis susceptibility: an environmental agent can override genetic checkpoints

Pertussis toxin (PTX) is a potent ancillary adjuvant used to elicit several different autoimmune diseases, including experimental allergic encephalomyelitis (EAE). To delineate the genetics of PTX effect in EAE, we mapped EAE-modifying (eae-m) loci in cohorts of backcross mice immunized with and without PTX. In this study, we analyzed the genetic basis of EAE susceptibility and severity and the intermediate phenotypes of mononuclear cell infiltration, suppuration, and demyelination. In animals immunized with PTX, one major locus, eae9, controls disease susceptibility and severity. Eae9 also regulates the extent of mononuclear cell infiltration of the spinal cord in male mice. Without PTX, five eae-m loci were noted, including three new loci in intervals on chromosomes 8 (eae14), 10 (eae17), and 18 (eae18). Taken together, these results suggest that eae9 controls the effects of PTX in EAE susceptibility, and is capable of overriding the other genetic checkpoints in the pathogenesis of this disease.

Accounting for variability in the use of permutation testing to detect quantitative trait loci

Locating quantitative trait loci (QTL), or genomic regions associated with known molecular markers, is of increasing interest in a wide variety of applications ranging from human genetics to agricultural genetics. The hope of locating QTL (or genes) affecting a quantitative trait is that it will lead to characterization and possible manipulations of these genes. However, the complexity of both statistical and genetic issues surrounding the location of these regions calls into question the asymptotic statistical results supplying the distribution of the test statistics employed. Coupled with the power of current-day computing, permutation theory was reintroduced for the purpose of estimating the distribution of any test statistic used to test for the location of QTL. Permutation techniques have offered an attractive alternative to significance measures based on asymptotic theory. The ideas of permutation testing are extended in this application to include confidence intervals for the thresholds and p-values estimated in permutation testing procedures. The confidence intervals developed account for the Monte Carlo error associated with practical applications of permutation testing and lead to an effective method of determining an efficient permutation sample size.

Sequence polymorphisms in the chemokines Scya1 (TCA-3), Scya2 (monocyte chemoattractant protein (MCP)-1), and Scya12 (MCP-5) are candidates for eae7, a locus controlling susceptibility to monophasic remitting/nonrelapsing experimental allergic encephalomyelitis

Experimental allergic encephalomyelitis (EAE), the principal animal model of multiple sclerosis, is genetically controlled. To date, 13 disease-modifying loci have been identified in the mouse by whole genome scanning using an F2 intercross between EAE-susceptible SJL/J and EAE-resistant B10.S/DvTe mice. Two quantitative trait loci (QTL), eae6 and eae7, on chromosome 11 were identified by classical marker-specific linkage analysis and interval mapping. Both QTL were reported to be associated with severity and duration of clinical signs. eae7 was subsequently shown to be a unique locus controlling the development of monophasic remitting/nonrelapsing EAE. In this study, composite interval mapping resolved eae6 into two linked QTL: eae6a at 0-13 cM is associated with disease severity, and eae6b at 19-28 cM associated with the duration of clinical signs. Additionally, composite interval mapping significantly refined the locations of eae6a, eae6b, and eae7, thereby facilitating systematic candidate gene screening by cDNA sequencing of SJL/J and B10.S/DvTe alleles. Sequence polymorphisms were not seen in Lif and IL12 beta, candidate genes for eae6a and eae6b, respectively. Similarly, cDNA sequence polymorphisms in Nos2, Scya3, Scya4, Scya5, Scya6, Scya7, Scya9, Scya10, and Scya11 were excluded as candidates for eae7. However, multiple sequence polymorphisms resulting in significant amino acid substitutions were identified in Scya1 (TCA-3), Scya2 (monocyte chemoattractant protein (MCP)-1), and Scya12 (MCP-5). Given the role of chemokines in EAE, these sequence polymorphisms are promising candidates for eae7, a locus associated with severity of clinical signs and susceptibility to the shorter, less severe monophasic remitting/nonrelapsing form of disease.

A genetic model and molecular markers for wild oat (Avena fatua L.) seed dormancy

Seed dormancy allows weed seeds to persist in agricultural soils. Wild oat (Avena fatua L.) is a major weed of cereal grains and expresses a range of seed dormancy phenotypes. Genetic analysis of wild oat dormancy has been complicated by the difficulty of phenotypic classification in segregating populations. Therefore, little is known about the nature of the genes that regulate dormancy in wild oat. The objectives of our studies were to develop methods to classify the germination responses of segregating wild oat populations and to find molecular markers linked to quantitative trait loci (QTL) that regulate seed dormancy in wild oat. RAPD markers OPX-06 and OPT-04 explained 12.6% and 6.8% respectively, of the F(2) phenotypic variance. OPF-17 was not significant in a simple regression model, but it was linked in repulsion to OPT-04. A three-locus model of seed dormancy in wild oat is presented based on the 41-day germination profiles of F(1), F(2), F(3), BC(1)P(1)F(1), BC(1)P(1)F(2), and BC(1)P(2)F(1) generations, and the 113 day germination profile of 126 F(7) recombinant inbred lines. Loci G (1) and G (2) promote early germination, and the D locus promotes late germination. If at least one copy of the dominant G (1) or G (2 )alleles are present regardless of the genotype at D locus, then the individual will be nondormant. If the genotype is g (1) g (1) g (2) g (2) D_, then the phenotype will be dormant.

Genetic analysis of disease subtypes and sexual dimorphisms in mouse experimental allergic encephalomyelitis (EAE): relapsing/remitting and monophasic remitting/nonrelapsing EAE are immunogenetically distinct

Experimental allergic encephalomyelitis (EAE) is the principal animal model of multiple sclerosis (MS), the major inflammatory disease of the central nervous system. Murine EAE is generally either an acute monophasic or relapsing disease. Because the clinical spectrum of MS is more diverse, the limited range of disease subtypes observed in EAE has raised concern regarding its relevance as a model for MS. During the generation of a large F2 mapping population between the EAE-susceptible SJL/J and EAE-resistant B10.S/DvTe inbred lines, we identified four distinct subtypes of murine EAE resembling clinical subtypes seen in MS. We observed acute progressive, chronic/nonremitting, remitting/relapsing, and monophasic remitting/nonrelapsing EAE. An additional subtype, benign EAE, was identified after histologic examination revealed that some mice had inflammatory infiltrates of the central nervous system, but did not show clinical signs of EAE. Genome exclusion mapping was performed to identify the loci controlling susceptibility to each disease subtype. We report three novel EAE-modifying loci on chromosomes 16, 7, and 13 (eae11-13, respectively). Additionally, unique loci with gender-specific effects govern susceptibility to remitting/relapsing (eae12) and monophasic remitting/nonrelapsing (eae7 and 13) EAE.

Interacting quantitative trait loci control phenotypic variation in murine estradiol-regulated responses

The steroid hormone estradiol (E2) elicits a spectrum of systemic and uterotropic responses in vivo. For example, E2 treatment of ovariectomized adult and sexually immature rodents leads to uterine leukocytic infiltration, cell proliferation, and organ growth. E2-regulated growth is also associated with a variety of normal and pathological phenotypes. Historically, the uterine growth response has been used as the key model to understand the molecular and biochemical mechanisms underlying E2-dependent growth. In this study, genome exclusion mapping identified two quantitative trait loci (QTL) in the mouse, Est2 and Est3 on chromosomes 5 and 11, respectively, that control the phenotypic variation in uterine wet weight. Both QTL are linked to a variety of E2-regulated genes, suggesting that they may represent loci within conserved gene complexes that play fundamental roles in mediating the effects of E2. Interaction and multiple trait analyses using the uterine leukocyte response and wet weight suggest that Est4, a QTL on chromosome 10, may encode an interacting factor that influences the quantitative variation in both responses. Our results show that E2-dependent responses can be genetically controlled and that a genetic basis may underlie the variation observed in many E2-dependent phenotypes.

Identification of quantitative trait loci governing arthritis severity and humoral responses in the murine model of Lyme disease

A spectrum of disease severity has been observed in patients with Lyme disease, with approximately 60% of untreated individuals developing arthritis. The murine model of Lyme disease has provided strong evidence that the genetic composition of the host influences the severity of arthritis following infection with Borrelia burgdorferi: infected C3H mice develop severe arthritis while infected C57BL/6N mice develop mild arthritis. Regions of the mouse genome controlling arthritis severity and humoral responses during B. burgdorferi infection were identified in the F2 intercross generation of C3H/HeNCr and C57BL/6NCr mice. Rear ankle swelling measurements identified quantitative trait loci (QTL) on chromosomes 4 and 5, while histopathological scoring identified QTL on a unique region of chromosome 5 and on chromosome 11. The identification of QTL unique for ankle swelling or histopathological severity suggests that processes under distinct genetic control are responsible for these two manifestations of Lyme arthritis. Additional QTL that control the levels of circulating Igs induced by B. burgdorferi infection were identified on chromosomes 6, 9, 11, 12, and 17. Interestingly, the magnitude of the humoral response was not correlated with the severity of arthritis in infected F2 mice. This work defines several genetic loci that regulate either the severity of arthritis or the magnitude of humoral responses to B. burgdorferi infection in mice, with implications toward understanding the host-pathogen interactions involved in disease development.

Autoimmune orchitis, epididymitis, and vasitis are immunogenetically distinct lesions

Experimental allergic orchitis (EAO), the principle animal model of noninfectious testicular inflammatory disease, is a genetically determined phenotype. Classical EAO, induced by inoculation with testicular homogenate and the appropriate adjuvants, is characterized by inflammatory infiltrates in the testis (orchitis), epididymis (epididymitis), and vas deferens (vasitis). In this study, the genetic control of susceptibility and resistance to these three lesions was analyzed in the mouse. The results obtained with independent inbred strains and H2 congenic mice show that the genetic control of all three lesions is complex and involves both H2 and non-H2-linked genes. Whole-genome exclusion mapping was performed on a backcross population segregating for all three phenotypes. Permutation-derived thresholds provided experimentwise, chromosomewise, comparisonwise, and marker-specific chromosomewise thresholds for declaration of significant regions linked to marker loci. Unique loci were identified on chromosome 8 for orchitis, chromosome 16 for epididymitis, and chromosome 1 for vasitis and have been designated as Orch6, Epd1, and Vas1, respectively. These results show that autoimmune orchitis, epididymitis, and vasitis are immunogenetically distinct lesions.

Evidence that Tmevd2 and eae3 may represent either a common locus or members of a gene complex controlling susceptibility to immunologically mediated demyelination in mice

Theiler's murine encephalomyelitis virus (TMEV)-induced demyelination and experimental allergic encephalomyelitis are the principal immunologically mediated, genetically controlled models of multiple sclerosis. Previous studies using different mapping techniques identified susceptibility loci for both diseases on chromosomes 3, 6, and 17. To more precisely map these TMEV and experimental allergic encephalomyelitis loci relative to each other, linkage analysis using microsatellite markers and a (BALB/cByJ x DBA/2J) x BALB/cByJ backcross population segregating for TMEV-induced disease was conducted. Comparisonwise and chromosomewise critical values based on permutation theory were estimated for each chromosome. Evidence for linkage to markers on chromosome 17 was not seen. Chromosomewise linkage (p = 0.13) was detected with D6 Mit36 and D6 Mit149 (marker-specific chromosomewise p values = 0.02) at 40.4 cM on chromosome 6. Chromosomewise linkage (p < 0.01) (marker-specific chromosomewise p value = 0.0) and comparisonwise linkage (p < < 0.0001) to D3 Mit156 at 33.9 cM on chromosome 3 were observed along with chromosomewise linkage (p < 0.05) and comparisonwise linkage (p < < 0.0001) to D3 Mit29, D3 Mit311, D3 Mit28, and D3 Mit11 from 33.9 to 37.2 cM, respectively. Significant linkage to D3 Mit156 places Tmevd2 1.1 cM proximal of D3 Mit101 (35 cM), the maximally linked marker to the eae3 susceptibility gene. Maximum likelihood estimates conducted by multilocus linkage analysis localized Tmevd2 within a 95% confidence interval bordered by D3 Mit29 and D3 Mit10, at 33.9 and 37.2 cM, respectively. Taken together these results suggest that Tmevd2 and eae3 may represent either a single, common susceptibility gene or members of a gene complex involved in central nervous system immunopathology.

Evidence that Tmevd2 and eae3 may represent either a common locus or members of a gene complex controlling susceptibility to immunologically mediated demyelination in mice

Theiler's murine encephalomyelitis virus (TMEV)-induced demyelination and experimental allergic encephalomyelitis are the principal immunologically mediated, genetically controlled models of multiple sclerosis. Previous studies using different mapping techniques identified susceptibility loci for both diseases on chromosomes 3, 6, and 17. To more precisely map these TMEV and experimental allergic encephalomyelitis loci relative to each other, linkage analysis using microsatellite markers and a (BALB/cByJ x DBA/2J) x BALB/cByJ backcross population segregating for TMEV-induced disease was conducted. Comparisonwise and chromosomewise critical values based on permutation theory were estimated for each chromosome. Evidence for linkage to markers on chromosome 17 was not seen. Chromosomewise linkage (p = 0.13) was detected with D6 Mit36 and D6 Mit149 (marker-specific chromosomewise p values = 0.02) at 40.4 cM on chromosome 6. Chromosomewise linkage (p < 0.01) (marker-specific chromosomewise p value = 0.0) and comparisonwise linkage (p < < 0.0001) to D3 Mit156 at 33.9 cM on chromosome 3 were observed along with chromosomewise linkage (p < 0.05) and comparisonwise linkage (p < < 0.0001) to D3 Mit29, D3 Mit311, D3 Mit28, and D3 Mit11 from 33.9 to 37.2 cM, respectively. Significant linkage to D3 Mit156 places Tmevd2 1.1 cM proximal of D3 Mit101 (35 cM), the maximally linked marker to the eae3 susceptibility gene. Maximum likelihood estimates conducted by multilocus linkage analysis localized Tmevd2 within a 95% confidence interval bordered by D3 Mit29 and D3 Mit10, at 33.9 and 37.2 cM, respectively. Taken together these results suggest that Tmevd2 and eae3 may represent either a single, common susceptibility gene or members of a gene complex involved in central nervous system immunopathology.