Genetic background influences susceptibility to chemotherapy-induced hematotoxicity.

Hematotoxicity is a life-threatening side effect of many chemotherapy regimens. Although clinical factors influence patient responses, genetic factors may also play an important role. We sought to identify genomic loci that influence chemotherapy-induced hematotoxicity by dosing Diversity Outbred mice with one of three chemotherapy drugs; doxorubicin, cyclophosphamide or docetaxel. We observed that each drug had a distinct effect on both the changes in blood cell subpopulations and the underlying genetic architecture of hematotoxicity. For doxorubicin, we mapped the change in cell counts before and after dosing and found that alleles of ATP-binding cassette B1B (Abcb1b) on chromosome 5 influence all cell populations. For cyclophosphamide and docetaxel, we found that each cell population was influenced by distinct loci, none of which overlapped between drugs. These results suggest that susceptibility to chemotherapy-induced hematotoxicity is influenced by different genes for different chemotherapy drugs.

Gene expression patterns in the hippocampus and amygdala of endogenous depression and chronic stress models.

The etiology of depression is still poorly understood, but two major causative hypotheses have been put forth: the monoamine deficiency and the stress hypotheses of depression. We evaluate these hypotheses using animal models of endogenous depression and chronic stress. The endogenously depressed rat and its control strain were developed by bidirectional selective breeding from the Wistar-Kyoto (WKY) rat, an accepted model of major depressive disorder (MDD). The WKY More Immobile (WMI) substrain shows high immobility/despair-like behavior in the forced swim test (FST), while the control substrain, WKY Less Immobile (WLI), shows no depressive behavior in the FST. Chronic stress responses were investigated by using Brown Norway, Fischer 344, Lewis and WKY, genetically and behaviorally distinct strains of rats. Animals were either not stressed (NS) or exposed to chronic restraint stress (CRS). Genome-wide microarray analyses identified differentially expressed genes in hippocampi and amygdalae of the endogenous depression and the chronic stress models. No significant difference was observed in the expression of monoaminergic transmission-related genes in either model. Furthermore, very few genes showed overlapping changes in the WMI vs WLI and CRS vs NS comparisons, strongly suggesting divergence between endogenous depressive behavior- and chronic stress-related molecular mechanisms. Taken together, these results posit that although chronic stress may induce depressive behavior, its molecular underpinnings differ from those of endogenous depression in animals and possibly in humans, suggesting the need for different treatments. The identification of novel endogenous depression-related and chronic stress response genes suggests that unexplored molecular mechanisms could be targeted for the development of novel therapeutic agents.

Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice.

Mycobacterium tuberculosis, the bacillus that causes tuberculosis (TB), infects 2 billion people across the globe, and results in 8-9 million new TB cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. We investigated the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using clinical indicators of disease, granuloma histopathological features, and immune response traits identified five new loci on mouse chromosomes 1, 2, 4, 16 and three previously identified loci on chromosomes 3 and 17. Quantitative trait loci (QTLs) on chromosomes 1, 16, and 17, associated with multiple correlated traits and had similar patterns of allele effects, suggesting these QTLs contain important genetic regulators of responses to M. tuberculosis. To narrow the list of candidate genes in QTLs, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks, generating functional scores. The scores were then used to rank candidates for each mapped trait in each locus, resulting in 11 candidates: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Importantly, all 11 candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling. Further, all candidates contain single nucleotide polymorphisms (SNPs), and some but not all SNPs were predicted to have deleterious consequences on protein functions. Multiple methods were used for validation including (i) a statistical method that showed Diversity Outbred mice carrying PWH/PhJ alleles on chromosome 17 QTL have shorter survival; (ii) quantification of S100A8 protein levels, confirming predicted allele effects; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and new functionally relevant gene candidates that may be major regulators of granuloma necrosis and acute inflammation in pulmonary TB.

Naive application of permutation testing leads to inflated type I error rates.

Failure to account for family structure within populations or in complex mating designs via uninformed applications of permutation testing will lead to inflated type I error rates. Careful consideration of the design factors is essential since some situations allow several valid permutation strategies, and the choice that maximizes statistical power will not always be intuitive.

Activation of peroxisome proliferator-activated receptor gamma (PPARgamma) by rosiglitazone suppresses components of the insulin-like growth factor regulatory system in vitro and in vivo.

Rosiglitazone (Rosi) belongs to the class of thiazolidinediones (TZDs) that are ligands for peroxisome proliferator-activated receptor gamma (PPARgamma). Stimulation of PPARgamma suppresses bone formation and enhances marrow adipogenesis. We hypothesized that activation of PPARgamma down-regulates components of the IGF regulatory system, leading to impaired osteoblast function. Rosi treatment (1 microm) of a marrow stromal cell line (UAMS-33) transfected with empty vector (U-33/c) or with PPARgamma2 (U-33/gamma2) were analyzed by microarray. Rosi reduced IGF-I, IGF-II, IGFBP-4, and the type I and II IGF receptor (IGF1R and IGF2R) expression at 72 h in U-33/gamma2 compared with U-33/c cells (P < 0.01); these findings were confirmed by RT-PCR. Rosi reduced secreted IGF-I from U-33/gamma2 cells by 75% (P < 0.05). Primary marrow stromal cells (MSCs) extracted from adult (8 months) and old (24 months) C57BL/6J (B6) mice were treated with Rosi (1 microm) for 48 h. IGF-I, IGFBP-4, and IGF1R transcripts were reduced in Rosi-treated MSCs compared with vehicle (P < 0.01) and secreted IGF-I was also suppressed (P < 0.05). B6 mice treated with Rosi (20 mg/kg.d) for short duration (i.e. 4 d), and long term (i.e. 7 wk) had reduced serum IGF-I; this was accompanied by markedly suppressed IGF-I transcripts in the liver and peripheral fat of treated animals. To determine whether Rosi affected circulating IGF-I in humans, we measured serum IGF-I, IGFBP-2, and IGFBP-3 at four time points in 50 postmenopausal women randomized to either Rosi (8 mg/d) or placebo. Rosi-treated subjects had significantly lower IGF-I at 8 wk than baseline (-25%, P < 0.05), and at 16 wk their levels were reduced 14% vs. placebo (P = 0.15). We conclude that Rosi suppresses IGF-I expression in bone and liver; these changes could affect skeletal acquisition through endocrine and paracrine pathways.

Multigenic and imprinting control of ovarian granulosa cell tumorigenesis in mice.

Spontaneous juvenile ovarian granulosa cell (GC) tumors that occur in young girls are similar to GC carcinomas that develop in SWR-derived inbred mice. We analyzed female offspring from a series of matings among SWR and SJL inbred mice for chromosomal loci underlying tumor susceptibility. Intercross F2 female mice were produced by reciprocal matings of (SWR x SJL)F1 and (SJL x SWR)F1 parents. Tumorigenesis in these F2 mice as well as in SWXJ recombinant inbred and congenic strains of mice derived from SWR and SJL showed significant (P < 0.001) association with Gct1, a dominant susceptibility locus on chromosome (CHR) 4 and with Gct2 on CHR 12. Suggestive (P < 0.01) association was found with Gct3 on CHR 15. A fourth susceptibility locus, Gct4 on CHR X, was demonstrated with a strong parent-of-origin effect associated with the paternal genotype. Imprinting and complex interactions among these four loci combine to establish the probability for GC tumorigenesis in this mouse model.

Experimental design for gene expression microarrays.

We examine experimental design issues arising with gene expression microarray technology. Microarray experiments have multiple sources of variation, and experimental plans should ensure that effects of interest are not confounded with ancillary effects. A commonly used design is shown to violate this principle and to be generally inefficient. We explore the connection between microarray designs and classical block design and use a family of ANOVA models as a guide to choosing a design. We combine principles of good design and A-optimality to give a general set of recommendations for design with microarrays. These recommendations are illustrated in detail for one kind of experimental objective, where we also give the results of a computer search for good designs.

Empirical threshold values for quantitative trait mapping.

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.

Permutation tests for multiple loci affecting a quantitative character.

The problem of detecting minor quantitative trait loci (QTL) responsible for genetic variation not explained by major QTL is of importance in the complete dissection of quantitative characters. Two extensions of the permutation-based method for estimating empirical threshold values are presented. These methods, the conditional empirical threshold (CET) and the residual empirical threshold (RET), yield critical values that can be used to construct tests for the presence of minor QTL effects while accounting for effects of known major QTL. The CET provides a completely nonparametric test through conditioning on markers linked to major QTL. It allows for general nonadditive interactions among QTL, but its practical application is restricted to regions of the genome that are unlinked to the major QTL. The RET assumes a structural model for the effect of major QTL, and a threshold is constructed using residuals from this structural model. The search space for minor QTL is unrestricted, and RET-based tests may be more powerful than the CET-based test when the structural model is approximately true.

Heterogeneity in rates of recombination across the mouse genome.

If loci are randomly distributed on a physical map, the density of markers on a genetic map will be inversely proportional to recombination rate. First, proposed by Mary Lyon, we have used this idea to estimate recombination rates from the Drosophila melanogaster linkage map. These results were compared with results of two other studies that estimated regional recombination rates in D. melanogaster using both physical and genetic maps. The three methods were largely concordant in identifying large-scale genomic patterns of recombination. The marker density method was then applied to the Mus musculus microsatellite linkage map. The distribution of microsatellites provided evidence for heterogeneity in recombination rates. Centromeric regions for several mouse chromosomes had significantly greater numbers of markers than expected, suggesting that recombination rates were lower in these regions. In contrast, most telomeric regions contained significantly fewer markers than expected. This indicates that recombination rates are elevated at the telomeres of many mouse chromosomes and is consistent with a comparison of the genetic and cytogenetic maps in these regions. The density of markers on a genetic map may provide a generally useful way to estimate regional recombination rates in species for which genetic, but not physical, maps are available.

A statistical framework for quantitative trait mapping.

We describe a general statistical framework for the genetic analysis of quantitative trait data in inbred line crosses. Our main result is based on the observation that, by conditioning on the unobserved QTL genotypes, the problem can be split into two statistically independent and manageable parts. The first part involves only the relationship between the QTL and the phenotype. The second part involves only the location of the QTL in the genome. We developed a simple Monte Carlo algorithm to implement Bayesian QTL analysis. This algorithm simulates multiple versions of complete genotype information on a genomewide grid of locations using information in the marker genotype data. Weights are assigned to the simulated genotypes to capture information in the phenotype data. The weighted complete genotypes are used to approximate quantities needed for statistical inference of QTL locations and effect sizes. One advantage of this approach is that only the weights are recomputed as the analyst considers different candidate models. This device allows the analyst to focus on modeling and model comparisons. The proposed framework can accommodate multiple interacting QTL, nonnormal and multivariate phenotypes, covariates, missing genotype data, and genotyping errors in any type of inbred line cross. A software tool implementing this procedure is available. We demonstrate our approach to QTL analysis using data from a mouse backcross population that is segregating multiple interacting QTL associated with salt-induced hypertension.

Properties of statistical tests of neutrality for DNA polymorphism data.

A class of statistical tests based on molecular polymorphism data is studied to determine size and power properties. The class includes Tajima's D statistic as well as the D* and F* tests proposed by Fu and Li. A new method of constructing critical values for these tests is described. Simulations indicate that Tajima's test is generally most powerful against the alternative hypotheses of selective sweep, population bottleneck, and population subdivision, among tests within this class. However, even Tajima's test can detect a selective sweep or bottleneck only if it has occurred within a specific interval of time in the recent past or population subdivision only when it has persisted for a very long time. For greatest power against the particular alternatives studied here, it is better to sequence more alleles than more sites.

Quantitative trait loci for femoral and lumbar vertebral bone mineral density in C57BL/6J and C3H/HeJ inbred strains of mice.

Significant differences in vertebral (9%) and femoral (50%) adult bone mineral density (BMD) between the C57BL/6J (B6) and C3H/HeJ (C3H) inbred strains of mice have been subjected to genetic analyses for quantitative trait loci (QTL). Nine hundred eighty-six B6C3F2 females were analyzed to gain insight into the number of genes that regulate peak BMD and their locations. Femurs and lumbar vertebrae were isolated from 4-month-old B6C3F2 females at skeletal maturity and then BMD was determined by peripheral quantitative computed tomography (pQCT). Estimates of BMD heritability were 83% for femurs and 72% for vertebrae. Genomic DNA from F2 progeny was screened for 107 polymerase chain reaction (PCR)-based markers discriminating B6 and C3H alleles on all 19 autosomes. The regression analyses of markers on BMD revealed ten chromosomes (1, 2, 4, 6, 11, 12, 13, 14, 16, and 18) carrying QTLs for femurs and seven chromosomes (1, 4, 7, 9, 11, 14, and 18) carrying QTLs for vertebrae, each with log10 of the odds ratio (LOD) scores of 2.8 or better. The QTLs on chromosomes (Chrs) 2, 6, 12, 13, and 16 were unique to femurs, whereas the QTLs on Chrs 7 and 9 were unique to vertebrae. When the two bone sites had a QTL on the same chromosome, the same marker had the highest, although different, LOD score. A pairwise comparison by analysis of variance (ANOVA) did not reveal significant gene x gene interactions between QTLs for either bone site. BMD variance accounted for by individual QTLs ranged from 1% to 10%. Collectively, the BMD QTLs for femurs accounted for 35.1% and for vertebrae accounted for 23.7 % of the F2 population variances in these bones. When mice were homozygous c3/c3 in the QTL region, 8 of the 10 QTLs increased, while the remaining two QTLs on Chrs 6 and 12 decreased, femoral BMD. Similarly, when mice were homozygous c3/c3 in the QTL region for the vertebrae, five of the seven QTLs increased, while two QTLs on Chrs 7 and 9 decreased, BMD. These findings show the genetic complexity of BMD with multiple genes participating in its regulation. Although 5 of the 12 QTLs are considered to be skeleton-wide loci and commonly affect both femurs and vertebrae, each of the bone sites also exhibited unique QTLs. Thus, the BMD phenotype can be partitioned into its genetic components and the effects of these loci on normal bone biology can be determined. Importantly, the BMD QTLs that we have identified are in regions of the mouse genome that have known human homology, and the QTLs will become useful experimental tools for mechanistic and therapeutic analyses of bone regulatory genes.

Statistical aspects of genetic mapping in autopolyploids.

Many plant species of agriculture importance are polyploid, having more than two copies of each chromosome per cell. In this paper, we describe statistical methods for genetic map construction in autopolyploid species with particular reference to the use of molecular markers. The first step is to determine the dosage of each DNA fragment (electrophoretic band) from its segregation ratio. Fragments present in a single dose can be used to construct framework maps for individual chromosomes. Fragments present in multiple doses can often be used to link the single chromosome maps into homologous groups and provide additional ordering information. Marker phenotype probabilities were calculated for pairs of markers arranged in different configurations among the homologous chromosomes. These probabilities were used to compute a maximum likelihood estimator of the recombination fraction between pairs of markers. A likelihood ratio test for linkage of multidose markers was derived. The information provided by each configuration and power and sample size considerations are also discussed. A set of 294 RFLP markers scored on 90 plants of the species Saccharum spontaneum L. was used to illustrate the construction of an autopolyploid map. Previous studies conducted on the same data revealed that this species of sugar cane is an autooctaploid with 64 chromosomes arranged into eight homologous groups. The methodology described permitted consolidation of 54 linkage groups into ten homologous groups.

Mapping quantitative trait loci for serum insulin-like growth factor-1 levels in mice.

Serum insulin-like growth factor-1 (IGF-1) and femoral bone mineral density (BMD) differ between two inbred strains of mice, C3H/HeJ (C3H) and C57BL/6J (B6), by approximately 30% and 50%, respectively. Similarly, skeletal IGF-1 content, bone formation, mineral apposition, and marrow stromal cell numbers are higher in C3H than in B6 mice. Because IGF-1 and several bone parameters cosegregate, we hypothesize that the serum IGF-1 phenotype has a strong heritable component and that genetic determinants for serum IGF-1 are involved in the regulation of bone mass. We intercrossed (B6 x C3H)F1 hybrids and analyzed 682 F2 female offspring at 4 months of age for serum IGF-1 by radioimmunoassay and femoral BMD by peripheral quantitative computerized tomography (pQCT). Genomic DNA was assayed by polymerase chain reaction (PCR) to determine alleles for 114 Mit markers inherited in F2 mice at average distances of 14 centimorgans (cM) along each chromosome (Chr). Serum IGF-1 levels in the F2 progeny were relatively normal in distribution, but showed a greater range than either progenitor, indicating that serum IGF-1 level is a polygenic trait with an estimated heritability of 52%. Serum IGF-1 correlated with femoral length (r = 0.266, p < 0.0001) and femoral BMD (r = 0.267, p < 0.0001). Whole genome scans for main effects associated with serum IGF-1 levels revealed three significant QTLs (in order of significance) on mouse Chrs 6, 15, and 10. The QTL on Chr 6 showed a significant reduction in IGF-1 associated with increasing C3H allele number, whereas the Chr 15 and Chr 10 loci showed additive effects with increasing C3H allele number. A genome-wide search for interacting marker pairs identified a significant interaction between the Chr 6 QTL and a locus on Chr 11. This interactive effect suggested that when the Chr 11 locus was homozygous for C3H, there was no effect of the Chr 6 locus on serum IGF-1; however, the combination of C3H alleles on Chr 6 with B6 alleles on Chr 11 was associated with reduced serum IGF-1 concentrations. To test this in vivo, we tested congenic mice carrying the Chr 6 QTL region from C3H on a B6 background (B6.C3H-6). Both serum IGF-1 and femoral BMD were significantly lower in female congenic than progenitor B6 mice. In summary, we identified three major QTLs on mouse Chrs 6, 10, and 15, and noted a major locus-locus interaction between Chrs 6 and 11. We named these QTLs IGF-1 serum levels (Igf1sl1 to Igf1sl4). Functional isolation of the Igf1sl1 QTL on Chr 6 for IGF-1 in B6.C3H-6 congenic mice demonstrated effects on both the IGF-1 and BMD phenotypes. The genetic determinants of these Igf1sl QTLs will provide much insight into the regulation of IGF-1 and the subsequent acquisition of peak bone mass.

Biases in amino acid replacement matrices and alignment scores due to rate heterogeneity.

Empirically derived amino acid replacement matrices are widely used in sequence comparison and database searches. We consider an extension of the usual Markov process model of protein evolution that admits site to site rate heterogeneity and demonstrates that rate heterogeneity can introduce a bias in estimated replacement probabilities and the corresponding alignment scores derived from these matrices. We suggest an approach to obtain unbiased estimates of replacement probabilities and alignment scores and derive the details for the case where rates are assumed to vary according to a gamma distribution.

Bayesian restoration of a hidden Markov chain with applications to DNA sequencing.

Hidden Markov models (HMMs) are a class of stochastic models that have proven to be powerful tools for the analysis of molecular sequence data. A hidden Markov model can be viewed as a black box that generates sequences of observations. The unobservable internal state of the box is stochastic and is determined by a finite state Markov chain. The observable output is stochastic with distribution determined by the state of the hidden Markov chain. We present a Bayesian solution to the problem of restoring the sequence of states visited by the hidden Markov chain from a given sequence of observed outputs. Our approach is based on a Monte Carlo Markov chain algorithm that allows us to draw samples from the full posterior distribution of the hidden Markov chain paths. The problem of estimating the probability of individual paths and the associated Monte Carlo error of these estimates is addressed. The method is illustrated by considering a problem of DNA sequence multiple alignment. The special structure for the hidden Markov model used in the sequence alignment problem is considered in detail. In conclusion, we discuss certain interesting aspects of biological sequence alignments that become accessible through the Bayesian approach to HMM restoration.

Analysis of variance for gene expression microarray data.

Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for large-scale analysis of gene expression. Microarrays can be used to measure the relative quantities of specific mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent "noise" in microarray data, how does one estimate the error variation associated with an estimated change in expression, i.e., how does one construct the error bars? We demonstrate that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects. This approach establishes a framework for the general analysis and interpretation of microarray data.

The impact of reduced frequency of cage changes on the health of mice housed in ventilated cages.

Our purpose in this investigation was to determine if we could reduce cage changing frequency without adversely affecting the health of mice. We housed mice at three different cage changing frequencies: 7, 14, and 21 days, each at three different cage ventilation rates: 30, 60 and 100 air changes per hour (ACH), for a total of nine experimental conditions. For each condition, we evaluated the health of 12 breeding pairs and 12 breeding trios of C57BL/6J mice for 7 months. Health was assessed by breeding performance, weanling weight and growth, plasma corticosterone levels, immune function, and histological examination of selected organs. Over a period of 4 months, we monitored the cage microenvironment for ammonia and carbon dioxide concentrations, relative humidity, and temperature one day prior to changing the cage. The relative humidity, carbon dioxide concentrations, and temperature of the cages at all conditions were within acceptable levels. Ammonia concentrations remained below 25 ppm (parts per million) in most cages, but, even at higher concentrations, did not adversely affect the health of mice. Frequency of cage changing had only one significant effect; pup mortality with pair matings was greater at the cage changing frequency of 7 days compared with 14 or 21 days. In addition, pup mortality with pair matings was higher at 30 ACH compared with other ventilation rates. In conclusion, under the conditions of this study, cage changes once every 14 days and ventilation rates of 60 ACH provide optimum conditions for animal health and practical husbandry.