Multiple genetic loci modify susceptibility to plasmacytoma-related morbidity in E(mu)-v-abl transgenic mice

Cox regression model for dissecting genetic architecture of survival time

Common quantitative trait locus (QTL) mapping methods fail to analyze survival traits of skewed normal distributions. As a result, some mapping methods for survival traits have been proposed based on survival analysis. Under a single QTL model, however, those methods perform poorly in detecting multiple QTLs and provide biased estimates of QTL parameters. For sparse oversaturated model used to map survival time loci, the least absolute shrinkage and selection operator (LASSO) for Cox regression model can be employed to efficiently shrink most of genetic effects to zero. Then, a few non-zero genetic effects are re-estimated and statistically tested using the standard maximum Cox partial likelihood method. Simulation shows that the proposed method has higher statistic power for QTL detection than that of the LASSO for logarithmic linear model or the interval mapping based on Cox model, although it somewhat underestimates QTL effects. Especially, computational speed of the method is very fast. An application of this method illustrates mapping main effect and interacting QTLs for heading time in the North American Barley Genome Mapping Project.

Plasmacytomagenesis in Eμ-v-abl transgenic mice is accelerated when apoptosis is restrained

Mice susceptible to plasma cell tumors provide a useful model for human multiple myeloma. We previously showed that mice expressing an Eµ-v-abl oncogene solely develop plasmacytomas. Here we show that loss of the proapoptotic BH3-only protein Bim or, to a lesser extent, overexpression of antiapoptotic Bcl-2 or Mcl-1, significantly accelerated the development of plasmacytomas and increased their incidence. Disease was preceded by an increased abundance of plasma cells, presumably reflecting their enhanced survival capacity in vivo. Plasmacytomas of each genotype expressed high levels of v-abl and frequently harbored a rearranged c-myc gene, probably as a result of chromosome translocation. As in human multiple myelomas, elevated expression of cyclin D genes was common, and p53 deregulation was rare. Our results for plasmacytomas highlight the significance of antiapoptotic changes in multiple myeloma, which include elevated expression of Mcl-1 and, less frequently, Bcl-2, and suggest that closer attention to defects in Bim expression is warranted.

Parametric proportional hazards model for mapping genomic imprinting of survival traits

A number of imprinted genes have been observed in plants, animals and humans. They not only control growth and developmental traits, but may also be responsible for survival traits. Based on the Cox proportional hazards (PH) model, we constructed a general parametric model for dissecting genomic imprinting, in which a baseline hazard function is selectable for fitting the effects of imprinted quantitative trait loci (iQTL) genotypes on the survival curve. The expectation-maximisation (EM) algorithm is derived for solving the maximum likelihood estimates of iQTL parameters. The imprinting patterns of the detected iQTL are statistically tested under a series of null hypotheses. The Bayesian information criterion (BIC) model selection criterion is employed to choose an optimal baseline hazard function with maximum likelihood and parsimonious parameterisation. We applied the proposed approach to analyse the published data in an F(2) population of mice and concluded that, among five commonly used survival distributions, the log-logistic distribution is the optimal baseline hazard function for the survival time of hyperoxic acute lung injury (HALI). Under this optimal model, five QTL were detected, among which four are imprinted in different imprinting patterns.

Characterization of genomic imprinting effects and patterns with parametric accelerated failure time model

Genomic imprinting, a genetic phenomenon of non-equivalent allele expression that depends on parental origins, has been ubiquitously observed in nature. It does not only control the traits of growth and development but also may be responsible for survival traits. Based on the accelerated failure time model, we construct a general parametric model for mapping the imprinted QTL (iQTL). Within the framework of interval mapping, maximum likelihood estimation of iQTL parameters is implemented via EM algorithm. The imprinting patterns of the detected iQTL are statistically tested according to a series of null hypotheses. BIC model selection criterion is employed to choose an optimal baseline hazard function with maximum likelihood and parsimonious parameters. Simulations are used to validate the proposed mapping procedure. A published dataset from a mouse model system was used to illustrate the proposed framework. Results show that among the five commonly used survival distributions, Log-logistic distribution is the optimal baseline hazard function for mapping QTL of hyperoxic acute lung injury (HALI) survival; under the log-logistic distribution, four QTLs were identified, in which only one QTL was inherited in Mendelian fashion, whereas others were imprinted in different imprinting patterns.

Statistical optimization of parametric accelerated failure time model for mapping survival trait loci

Most existing statistical methods for mapping quantitative trait loci (QTL) are not suitable for analyzing survival traits with a skewed distribution and censoring mechanism. As a result, researchers incorporate parametric and semi-parametric models of survival analysis into the framework of the interval mapping for QTL controlling survival traits. In survival analysis, accelerated failure time (AFT) model is considered as a de facto standard and fundamental model for data analysis. Based on AFT model, we propose a parametric approach for mapping survival traits using the EM algorithm to obtain the maximum likelihood estimates of the parameters. Also, with Bayesian information criterion (BIC) as a model selection criterion, an optimal mapping model is constructed by choosing specific error distributions with maximum likelihood and parsimonious parameters. Two real datasets were analyzed by our proposed method for illustration. The results show that among the five commonly used survival distributions, Weibull distribution is the optimal survival function for mapping of heading time in rice, while Log-logistic distribution is the optimal one for hyperoxic acute lung injury.

QTL mapping in intercross and backcross populations

In the past two decades, various statistical approaches have been developed to identify quantitative trait locus with experimental organisms. In this chapter, we introduce several commonly used QTL mapping methods for intercross and backcross populations. Important issues related to QTL mapping, such as threshold and confidence interval calculations are also discussed. We list and describe five public domain QTL software packages commonly used by biologists.

Blimp1 is limiting for transformation in a mouse plasmacytoma model

Multiple myeloma (MM) and plasmacytomas are cancers of antibody-secreting cells (ASCs). PRDM1/BLIMP1 is an essential regulator of ASC development. Histologic evidence shows that 100% of MM expresses PRDM1/BLIMP1, indicating that PRDM1/BLIMP1 is important for the development or persistence of MM. In contrast, some diffuse large B-cell lymphomas (DLBCLs) lose PRDM1 expression, suggesting that PRDM1 may act as a tumor suppressor in DLBCL. Thus, the role of PRDM1/BLIMP1 in transformation of mature B cells is unclear. We have used a plasmacytoma-prone transgenic mouse model to study the effect of Blimp1 loss on plasmacytoma prevalence, latency, and phenotype. Two possible outcomes could be envisaged: loss of Blimp1 might decrease plasmacytoma prevalence, through reduction of plasma cells, and so the number of susceptible transformation targets. Alternatively, Blimp1 may participate in the transformation process itself. Our results support the latter scenario, showing that decreasing Blimp1 dosage does not change plasma cell number in nontransgenic mice in vivo, but it significantly reduces plasmacytoma prevalence in transgenic mice. Loss of functional Blimp1 completely prevents plasmacytoma formation in this tumor model. These observations suggest that Blimp1 is limiting for plasma cell transformation and thus has potential as a target for new therapies to combat MM.

Genetic loci that control the size of laser-induced choroidal neovascularization

Angiogenesis is controlled by a balance between stimulators and inhibitors. We propose that the balance, as well as the general sensitivity of the endothelium to these factors, varies from individual to individual. Indeed, we have found that individual mouse strains have dramatically different responses to growth factor-induced neovascularization. Quantitative trait loci (QTLs), which influence the extent of corneal angiogenesis induced by vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (FGF2), were previously identified by our laboratory. To investigate the genetic contribution to choroidal neovascularization (CNV), a leading cause of blindness, we have undertaken a similar mapping approach to identify QTLs that influence laser-induced CNV in the BXD series of recombinant inbred mouse strains. Composite interval mapping identified new angiogenic QTLs on chromosomes 2 and 19, in addition to confirming our previous corneal neovascularization QTLs of AngVq1 and AngFq2. The new QTLs are named AngCNVq1 and AngCNVq2. The newly mapped regions contain several candidate genes involved in the angiogenic process, including thrombospondin 1, delta-like 4, BclII modifying factor, phospholipase C, beta 2, adrenergic receptor, beta 1, actin-binding LIM protein 1 and colony stimulating factor 2 receptor, alpha. Differences in these regions may control individual susceptibility to CNV.

Perforin-mediated suppression of B-cell lymphoma

In the present study, we have examined the effect of perforin (pfp) deficiency in 4 models of mouse B-cell lymphomagenesis. We have examined pfp loss on the background of either Mlh1 tumor suppressor allele loss or oncogene expression [Ig heavy chain (Emu)-v-Abl, Emu-myc, and vav-bcl2]. Pfp was shown to act as a suppressor of B-cell malignancies characteristically driven by v-Abl or bcl-2, whereas Mlh loss cooperated in accelerating spontaneous B-cell lymphomas characteristic of pfp loss. No protective role for pfp was observed in the more aggressive Emu-myc model of B-cell lymphoma. These transgenic models have allowed us to distinguish the role of pfp in surveillance of B-cell lymphomagenesis, as opposed to its loss simply driving the onset of a spontaneous lymphoma characteristic of pfp deficiency.

Robust Bayesian mapping of quantitative trait loci using Student-t distribution for residual

In most quantitative trait loci (QTL) mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may affect the accuracy of QTL detection, leading to detection of false positive QTL. To improve the robustness of QTL mapping methods, we replace the normal distribution assumption for residuals in a multiple QTL model with a Student-t distribution that is able to accommodate residual outliers. A Robust Bayesian mapping strategy is proposed on the basis of the Bayesian shrinkage analysis for QTL effects. The simulations show that Robust Bayesian mapping approach can substantially increase the power of QTL detection when the normality assumption does not hold and applying it to data already normally distributed does not influence the result. The proposed QTL mapping method is applied to mapping QTL for the traits associated with physics-chemical characters and quality in rice. Similarly to the simulation study in the real data case the robust approach was able to detect additional QTLs when compared to the traditional approach. The program to implement the method is available on request from the first or the corresponding author.

Bayesian robust analysis for genetic architecture of quantitative traits

Motivation: In most quantitative trait locus (QTL) mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may affect the accuracy of QTL detection and lead to detection of spurious QTLs. To improve the robustness of QTL mapping methods, we replaced the normal distribution for residuals in multiple interacting QTL models with the normal/independent distributions that are a class of symmetric and long-tailed distributions and are able to accommodate residual outliers. Subsequently, we developed a Bayesian robust analysis strategy for dissecting genetic architecture of quantitative traits and for mapping genome-wide interacting QTLs in line crosses.

Results: Through computer simulations, we showed that our strategy had a similar power for QTL detection compared with traditional methods assuming normal-distributed traits, but had a substantially increased power for non-normal phenotypes. When this strategy was applied to a group of traits associated with physical/chemical characteristics and quality in rice, more main and epistatic QTLs were detected than traditional Bayesian model analyses under the normal assumption.

Contact:runqingyang@sjtu.edu.cn; dengh@umkc.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Multigenic control of tuberculosis resistance: analysis of a QTL on mouse chromosome 7 and its synergism with sst1

Tuberculosis remains a significant global health problem: one-third of the human population is infected with Mycobacterium tuberculosis (MTB) and 10% of those are at lifetime risk of developing tuberculosis. In the majority of individuals infected, genetic determinants of susceptibility remain largely unknown due to complex multigenic control and the influence of genes--environment interactions. Genetic variation of host resistance to MTB in animal models reflects heterogeneity among humans. Stepwise dissection of these interactions will permit the deciphering of MTB's complex virulence strategy. Previously, we have characterized a mouse supersusceptibility locus (sst1) controlling antituberculosis immunity. In this study, eight host resistance quantitative trait loci (QTLs) were mapped that counter-balance the devastating effect of sst1, among which a QTL on chromosome 7 (Chr7) was most prominent. The Chr7 and sst1 loci independently control distinct resistance mechanisms to MTB, but their effects apparently converge on macrophages in remarkable synergy. Combining these resistance alleles on a C3HeB/FeJ-susceptible background reduced the lung pathology and improved survival after MTB challenge accounting for half of the difference between susceptible and resistant parental strains. These data reveal novel gene interactions controlling MTB resistance and will enable the identification of resistance gene(s) encoded within Chr7 locus.

Genetic architecture of tuberculosis resistance in a mouse model of infection

Tuberculosis remains a significant public health problem: one-third of the human population is infected with virulent Mycobacterium tuberculosis (MTB) and 10% of those are at risk of developing tuberculosis during their lifetime. In both humans and experimental animal models, genetic variation among infected individuals contributes to the outcome of infection. However, in immunocompetent individuals (the majority of patients), genetic determinants of susceptibility to tuberculosis remain largely unknown. Mouse models of MTB infection, allowing control of exposure and other potential environmental contributors, have proven extremely useful for examining this genetic component. In a cross of C3HeB/FeJ (susceptible) by C57BL/6J (resistant) inbred mouse strains, we have previously identified one major genetic locus, sst1, the susceptible allele of which did not confer an overt immunodeficiency, but rather specifically affected progression of lung tuberculosis. Having generated and tested the sst1 congenic strains, we have observed that this locus only partially explained the difference in susceptibility of the parental strains to MTB. We now present further studies controlling for the effect of the sst1, identify four additional tuberculosis susceptibility loci and characterize their effects by testing an independent cross, knockout or congenic mice.

Mixture cure model with an application to interval mapping of quantitative trait loci

When censored time-to-event data are used to map quantitative trait loci (QTL), the existence of nonsusceptible subjects entails extra challenges. If the heterogeneous susceptibility is ignored or inappropriately handled, we may either fail to detect the responsible genetic factors or find spuriously significant locations. In this article, an interval mapping method based on parametric mixture cure models is proposed, which takes into consideration of nonsusceptible subjects. The proposed model can be used to detect the QTL that are responsible for differential susceptibility and/or time-to-event trait distribution. In particular, we propose a likelihood-based testing procedure with genome-wide significance levels calculated using a resampling method. The performance of the proposed method and the importance of considering the heterogeneous susceptibility are demonstrated by simulation studies and an application to survival data from an experiment on mice infected with Listeria monocytogenes.

Genetic mapping of loci controlling diethylstilbestrol-induced thymic atrophy in the Brown Norway rat

Chronic estrogen administration can lead to thymic atrophy in rodents. In this article we report that the Brown Norway (BN) rat is sensitive to thymic atrophy induced by the estrogen diethylstilbestrol (DES). By contrast, DES does not induce significant thymic atrophy in the August x Copenhagen-Irish (ACI) strain. The sensitivity of the BN rat to DES-induced thymic atrophy appears to segregate as an incompletely dominant trait in crosses between the BN and ACI strains. In a (BN x ACI)F(2) population, we find strong evidence for three major genetic determinants of sensitivity to DES-induced thymic atrophy on rat Chromosome (RNO) 10 and RNO2. Genotypes at these loci, termed Esta1, 2, and 3, do not have a significant impact on the ability of DES to induce pituitary tumorigenesis or inhibit growth of these F(2) rats. These data indicate that the genetic factors that control DES-induced thymic atrophy are distinct from those that control the effects of DES on pituitary mass and body mass. The Esta intervals on RNO10 and RNO2 overlap with loci that control sensitivity to radiation-induced thymocyte apoptosis, as well as susceptibility to a variety of allergic and autoimmune pathologies, including allergic encephalitis, arthritis, and glomerulonephritis in rodents. These observations suggest that common genetic determinants may control sensitivity to estrogen-induced thymic atrophy, maintenance of thymocyte homeostasis, and immune function.

A simple linear regression method for quantitative trait loci linkage analysis with censored observations

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

Mapping quantitative trait loci with censored observations

The existing statistical methods for mapping quantitative trait loci (QTL) assume that the phenotype follows a normal distribution and is fully observed. These assumptions may not be satisfied when the phenotype pertains to the survival time or failure time, which has a skewed distribution and is usually subject to censoring due to random loss of follow-up or limited duration of the experiment. In this article, we propose an interval-mapping approach for censored failure time phenotypes. We formulate the effects of QTL on the failure time through parametric proportional hazards models and develop efficient likelihood-based inference procedures. In addition, we show how to assess genome-wide statistical significance. The performance of the proposed methods is evaluated through extensive simulation studies. An application to a mouse cross is provided.

Semiparametric Methods for Mapping Quantitative Trait Loci with Censored Data

Statistical methods for the detection of genes influencing quantitative traits with the aid of genetic markers are well developed for normally distributed, fully observed phenotypes. Many experiments are concerned with failure-time phenotypes, which have skewed distributions and which are usually subject to censoring because of random loss to follow-up, failures from competing causes, or limited duration of the experiment. In this article, we develop semiparametric statistical methods for mapping quantitative trait loci (QTLs) based on censored failure-time phenotypes. We formulate the effects of the QTL genotype on the failure time through the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model and derive efficient likelihood-based inference procedures. In addition, we show how to assess statistical significance when searching several regions or the entire genome for QTLs. Extensive simulation studies demonstrate that the proposed methods perform well in practical situations. Applications to two animal studies are provided.

Genetic loci that control the angiogenic response to basic fibroblast growth factor

Angiogenesis is controlled by a balance between stimulatory growth factors and endogenous inhibitors. We propose that the balance of stimulators and inhibitors, as well as the general sensitivity of the endothelium to these factors, varies from individual to individual. Indeed, we have found that individual mouse strains have dramatically different responses to growth factor-induced neovascularization. Quantitative trait loci (QTLs), which influence the extent of angiogenesis induced by vascular endothelial growth factor (VEGF), were previously identified by our laboratory. Since genetic susceptibility may vary according to the angiogenic stimulator, we have undertaken a similar mapping approach to identify QTLs that influence basic fibroblast growth factor (FGF2) induced neovascularization in the BXD series of recombinant inbred mouse strains. Composite and multiple interval mapping identified areas of chromosomes 4, 13, 15, and 18. These new angiogenesis QTLs, named AngFq1 through AngFq4 (for angiogenesis due to FGF2), are different from previously identified VEGF QTLs. The mapped regions contain several genes involved in the angiogenic process including matrix metalloproteinase 16, eph receptor A7, angiopoetin 1, endothelial lipase, and autotaxin. Differences in these regions may influence individual susceptibility to angiogenesis related diseases such as cancer, macular degeneration, atherosclerosis, and arthritis.