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Motivation: Spatial transcriptomics (ST) experiments provide spatially localized measurements of genome-wide gene expression allowing for an unprecedented opportunity to investigate cellular heterogeneity and organization within a tissue. Statistical and computational frameworks exist that implement robust methods for pre-processing and analyzing data in ST experiments. However, the lack of an interactive suite of tools for visualizing ST data and results currently limits the full potential of ST experiments., Results: To fill the gap, we developed SpatialView, an open-source web browser-based interactive application for visualizing data and results from multiple 10× Genomics Visium ST experiments. We anticipate SpatialView will be useful to a broad array of clinical and basic science investigators utilizing ST to study disease., Availability and Implementation: SpatialView is available at https://github.com/kendziorski-lab/SpatialView (and https://doi.org/10.5281/zenodo.10223907); a demo application is available at https://www.biostat.wisc.edu/˜kendzior/spatialviewdemo/., (Published by Oxford University Press 2024.)

Considerable effort has been devoted to refining experimental protocols to reduce levels of technical variability and artifacts in single-cell RNA-sequencing data (scRNA-seq). We here present evidence that equalizing the concentration of cDNA libraries prior to pooling, a step not consistently performed in single-cell experiments, improves gene detection rates, enhances biological signals, and reduces technical artifacts in scRNA-seq data. To evaluate the effect of equalization on various protocols, we developed Scaffold, a simulation framework that models each step of an scRNA-seq experiment. Numerical experiments demonstrate that equalization reduces variation in sequencing depth and gene-specific expression variability. We then performed a set of experiments in vitro with and without the equalization step and found that equalization increases the number of genes that are detected in every cell by 17-31%, improves discovery of biologically relevant genes, and reduces nuisance signals associated with cell cycle. Further support is provided in an analysis of publicly available data., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Motivation: Normalization to remove technical or experimental artifacts is critical in the analysis of single-cell RNA-sequencing experiments, even those for which unique molecular identifiers are available. The majority of methods for normalizing single-cell RNA-sequencing data adjust average expression for library size (LS), allowing the variance and other properties of the gene-specific expression distribution to be non-constant in LS. This often results in reduced power and increased false discoveries in downstream analyses, a problem which is exacerbated by the high proportion of zeros present in most datasets., Results: To address this, we present Dino, a normalization method based on a flexible negative-binomial mixture model of gene expression. As demonstrated in both simulated and case study datasets, by normalizing the entire gene expression distribution, Dino is robust to shallow sequencing, sample heterogeneity and varying zero proportions, leading to improved performance in downstream analyses in a number of settings., Availability and Implementation: The R package, Dino, is available on GitHub at https://github.com/JBrownBiostat/Dino. The Dino package is further archived and freely available on Zenodo at https://doi.org/10.5281/zenodo.4897558., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)

The majority of gene loci that have been associated with type 2 diabetes play a role in pancreatic islet function. To evaluate the role of islet gene expression in the etiology of diabetes, we sensitized a genetically diverse mouse population with a Western diet high in fat (45% kcal) and sucrose (34%) and carried out genome-wide association mapping of diabetes-related phenotypes. We quantified mRNA abundance in the islets and identified 18,820 expression QTL. We applied mediation analysis to identify candidate causal driver genes at loci that affect the abundance of numerous transcripts. These include two genes previously associated with monogenic diabetes ( PDX1 and HNF4A ), as well as three genes with nominal association with diabetes-related traits in humans ( FAM83E , IL6ST , and SAT2 ). We grouped transcripts into gene modules and mapped regulatory loci for modules enriched with transcripts specific for α-cells, and another specific for δ-cells. However, no single module enriched for β-cell-specific transcripts, suggesting heterogeneity of gene expression patterns within the β-cell population. A module enriched in transcripts associated with branched-chain amino acid metabolism was the most strongly correlated with physiological traits that reflect insulin resistance. Although the mice in this study were not overtly diabetic, the analysis of pancreatic islet gene expression under dietary-induced stress enabled us to identify correlated variation in groups of genes that are functionally linked to diabetes-associated physiological traits. Our analysis suggests an expected degree of concordance between diabetes-associated loci in the mouse and those found in human populations, and demonstrates how the mouse can provide evidence to support nominal associations found in human genome-wide association mapping., (Copyright © 2018 by the Genetics Society of America.)

Identifying the genetic basis of complex traits is an important problem with the potential to impact a broad range of biological endeavors. A number of effective statistical methods are available for quantitative trait loci (QTL) mapping that allow for the efficient identification of multiple, potentially interacting, loci under a variety of experimental conditions. Although proven useful in hundreds of studies, the majority of these methods assumes a single model common to each subject, which may reduce power and accuracy when genetically distinct subclasses exist. To address this, we have developed an approach to enable latent class QTL mapping. The approach combines latent class regression with stepwise variable selection and traditional QTL mapping to estimate the number of subclasses in a population, and to identify the genetic model that best describes each subclass. Simulations demonstrate good performance of the method when latent classes are present as well as when they are not, with accurate estimation of QTL. Application of the method to case studies of obesity and diabetes in mouse gives insight into the genetic basis of related complex traits., (Copyright © 2017 by the Genetics Society of America.)

Unlabelled: A recent article identified an artifact in multiple single-cell RNA-seq (scRNA-seq) datasets generated by the Fluidigm C1 platform. Specifically, Leng et al. showed significantly increased gene expression in cells captured from sites with small or large plate output IDs. We refer to this artifact as an ordering effect (OE). Including OE genes in downstream analyses could lead to biased results. To address this problem, we developed a statistical method and software called OEFinder to identify a sorted list of OE genes. OEFinder is available as an R package along with user-friendly graphical interface implementations which allows users to check for potential artifacts in scRNA-seq data generated by the Fluidigm C1 platform., Availability and Implementation: OEFinder is freely available at https://github.com/lengning/OEFinder, Contact: rstewart@morgridge.org or lengning1@gmail.com, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press.)

Studies of the genetic loci that contribute to variation in gene expression frequently identify loci with broad effects on gene expression: expression quantitative trait locus hotspots. We describe a set of exploratory graphical methods as well as a formal likelihood-based test for assessing whether a given hotspot is due to one or multiple polymorphisms. We first look at the pattern of effects of the locus on the expression traits that map to the locus: the direction of the effects and the degree of dominance. A second technique is to focus on the individuals that exhibit no recombination event in the region, apply dimensionality reduction (e.g., with linear discriminant analysis), and compare the phenotype distribution in the nonrecombinant individuals to that in the recombinant individuals: if the recombinant individuals display a different expression pattern than the nonrecombinant individuals, this indicates the presence of multiple causal polymorphisms. In the formal likelihood-based test, we compare a two-locus model, with each expression trait affected by one or the other locus, to a single-locus model. We apply our methods to a large mouse intercross with gene expression microarray data on six tissues., (Copyright © 2016 by the Genetics Society of America.)

We surveyed gene expression in six tissues in an F2 intercross between mouse strains C57BL/6J (abbreviated B6) and BTBR T(+) tf/J (abbreviated BTBR) made genetically obese with the Leptin(ob) mutation. We identified a number of expression quantitative trait loci (eQTL) affecting the expression of numerous genes distal to the locus, called trans-eQTL hotspots. Some of these trans-eQTL hotspots showed effects in multiple tissues, whereas some were specific to a single tissue. An unusually large number of transcripts (∼8% of genes) mapped in trans to a hotspot on chromosome 6, specifically in pancreatic islets. By considering the first two principal components of the expression of genes mapping to this region, we were able to convert the multivariate phenotype into a simple Mendelian trait. Fine mapping the locus by traditional methods reduced the QTL interval to a 298-kb region containing only three genes, including Slco1a6, one member of a large family of organic anion transporters. Direct genomic sequencing of all Slco1a6 exons identified a nonsynonymous coding SNP that converts a highly conserved proline residue at amino acid position 564 to serine. Molecular modeling suggests that Pro564 faces an aqueous pore within this 12-transmembrane domain-spanning protein. When transiently overexpressed in HEK293 cells, BTBR organic anion transporting polypeptide (OATP)1A6-mediated cellular uptake of the bile acid taurocholic acid (TCA) was enhanced compared to B6 OATP1A6. Our results suggest that genetic variation in Slco1a6 leads to altered transport of TCA (and potentially other bile acids) by pancreatic islets, resulting in broad gene regulation., (Copyright © 2015 by the Genetics Society of America.)

In a mouse intercross with more than 500 animals and genome-wide gene expression data on six tissues, we identified a high proportion (18%) of sample mix-ups in the genotype data. Local expression quantitative trait loci (eQTL; genetic loci influencing gene expression) with extremely large effect were used to form a classifier to predict an individual's eQTL genotype based on expression data alone. By considering multiple eQTL and their related transcripts, we identified numerous individuals whose predicted eQTL genotypes (based on their expression data) did not match their observed genotypes, and then went on to identify other individuals whose genotypes did match the predicted eQTL genotypes. The concordance of predictions across six tissues indicated that the problem was due to mix-ups in the genotypes (although we further identified a small number of sample mix-ups in each of the six panels of gene expression microarrays). Consideration of the plate positions of the DNA samples indicated a number of off-by-one and off-by-two errors, likely the result of pipetting errors. Such sample mix-ups can be a problem in any genetic study, but eQTL data allow us to identify, and even correct, such problems. Our methods have been implemented in an R package, R/lineup., (Copyright © 2015 Broman et al.)

Motivation: With improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific expression changes. A number of robust statistical methods are available to identify genes showing differential expression among multiple conditions, but most assume conditions are exchangeable and thereby sacrifice power and precision when applied to ordered data., Results: We propose an empirical Bayes mixture modeling approach called EBSeq-HMM. In EBSeq-HMM, an auto-regressive hidden Markov model is implemented to accommodate dependence in gene expression across ordered conditions. As demonstrated in simulation and case studies, the output proves useful in identifying differentially expressed genes and in specifying gene-specific expression paths. EBSeq-HMM may also be used for inference regarding isoform expression., Availability and Implementation: An R package containing examples and sample datasets is available at Bioconductor., Contact: kendzior@biostat.wisc.edu, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press.)

Motivation: Identifying and prioritizing somatic mutations is an important and challenging area of cancer research that can provide new insights into gene function as well as new targets for drug development. Most methods for prioritizing mutations rely primarily on frequency-based criteria, where a gene is identified as having a driver mutation if it is altered in significantly more samples than expected according to a background model. Although useful, frequency-based methods are limited in that all mutations are treated equally. It is well known, however, that some mutations have no functional consequence, while others may have a major deleterious impact. The spatial pattern of mutations within a gene provides further insight into their functional consequence. Properly accounting for these factors improves both the power and accuracy of inference. Also important is an accurate background model., Results: Here, we develop a Model-based Approach for identifying Driver Genes in Cancer (termed MADGiC) that incorporates both frequency and functional impact criteria and accommodates a number of factors to improve the background model. Simulation studies demonstrate advantages of the approach, including a substantial increase in power over competing methods. Further advantages are illustrated in an analysis of ovarian and lung cancer data from The Cancer Genome Atlas (TCGA) project., (© The Author 2015. Published by Oxford University Press.)

Fetoplacental endothelial cells are exposed to oxygen levels ranging from 2% to 8% in vivo. However, little is known regarding endothelial function within this range of oxygen because most laboratories use ambient air (21% O2) as a standard culture condition (SCN). We asked whether human umbilical artery endothelial cells (HUAECs) that were steadily exposed to the physiological chronic normoxia (PCN, 3% O2) for ∼20-25 days differed in their proliferative and migratory responses to FGF2 and VEGFA as well as in their global gene expression compared with those in the SCN. We observed that PCN enhanced FGF2- and VEGFA-stimulated cell proliferation and migration. In oxygen reversal experiments (i.e., when PCN cells were exposed to SCN for 24 h and vice versa), we found that preexposure to 21% O2 decreased the migratory ability, but not the proliferative ability, of the PCN-HUAECs in response to FGF2 and VEGFA. These PCN-enhanced cellular responses were associated with increased protein levels of HIF1A and NOS3, but not FGFR1, VEGFR1, and VEGFR2. Microarray analysis demonstrated that PCN up-regulated 74 genes and down-regulated 86, 14 of which were directly regulated by hypoxia-inducible factors as evaluated using in silico analysis. Gene function analysis further indicated that the PCN-regulated genes were highly related to cell proliferation and migration, consistent with the results from our functional assays. Given that PCN significantly alters cellular responses to FGF2 and VEGFA as well as transcription in HUAECs, it is likely that we may need to reexamine the current cellular and molecular mechanisms controlling fetoplacental endothelial functions, which were largely derived from endothelial models established under ambient O2.

Endothelial cells chronically reside in low-O2 environments in vivo (2%-13% O2), which are believed to be critical for cell homeostasis. To elucidate the roles of this physiological chronic normoxia in human endothelial cells, we examined transcriptomes of human umbilical vein endothelial cells (HUVECs), proliferation and migration of HUVECs in response to fibroblast growth factor 2 (FGF2) and vascular endothelial growth factor A (VEGFA), and underlying signaling mechanisms under physiological chronic normoxia. Immediately after isolation, HUVECs were cultured steadily under standard cell culture normoxia (SCN; 21% O2) or physiological chronic normoxia (PCN; 3% O2) up to 25 days. We found that PCN up-regulated 41 genes and down-regulated 21 genes, 90% of which differed from those previously reported from HUVECs cultured under SCN and exposed to acute low O2. Gene ontology analysis indicated that PCN-regulated genes were highly related to cell proliferation and migration, consistent with the results from benchtop assays that showed that PCN significantly enhanced FGF2- and VEGFA-stimulated cell proliferation and migration. Interestingly, preexposing the PCN cells to 21% O2 up to 5 days did not completely diminish PCN-enhanced cell proliferation and migration. These PCN-enhanced cell proliferations and migrations were mediated via augmented activation of MEK1/MEK2/ERK1/ERK2 and/or PI3K/AKT1. Importantly, these PCN-enhanced cellular responses were associated with an increase in activation of VEGFR2 but not FGFR1, without altering their expression. Thus, PCN programs endothelial cells to undergo dramatic changes in transcriptomes and sensitizes cellular proliferative and migratory responses to FGF2 and VEGFA. These PCN cells may offer a unique endothelial model, more closely mimicking the in vivo states.

Motivation: Messenger RNA expression is important in normal development and differentiation, as well as in manifestation of disease. RNA-seq experiments allow for the identification of differentially expressed (DE) genes and their corresponding isoforms on a genome-wide scale. However, statistical methods are required to ensure that accurate identifications are made. A number of methods exist for identifying DE genes, but far fewer are available for identifying DE isoforms. When isoform DE is of interest, investigators often apply gene-level (count-based) methods directly to estimates of isoform counts. Doing so is not recommended. In short, estimating isoform expression is relatively straightforward for some groups of isoforms, but more challenging for others. This results in estimation uncertainty that varies across isoform groups. Count-based methods were not designed to accommodate this varying uncertainty, and consequently, application of them for isoform inference results in reduced power for some classes of isoforms and increased false discoveries for others., Results: Taking advantage of the merits of empirical Bayesian methods, we have developed EBSeq for identifying DE isoforms in an RNA-seq experiment comparing two or more biological conditions. Results demonstrate substantially improved power and performance of EBSeq for identifying DE isoforms. EBSeq also proves to be a robust approach for identifying DE genes., Availability and Implementation: An R package containing examples and sample datasets is available at http://www.biostat.wisc.edu/kendzior/EBSEQ/., Supplementary Information: Supplementary data are available at Bioinformatics online.

Unlabelled: R/EBcoexpress implements the approach of Dawson and Kendziorski using R, a freely available, open source statistical programming language. The approach identifies differential co-expression (DC) by examining the correlations among gene pairs using an empirical Bayesian approach, producing a false discovery rate controlled list of DC pairs. This interrogation of DC gene pairs complements but is distinct from differential expression analyses, under the general goal of understanding differential regulation across biological conditions., Availability and Implementation: R/EBcoexpress is freely available and hosted on Bioconductor; a source file and vignette may be found at http://www.bioconductor.org/packages/release/bioc/html/EBcoexpress.html

Identifying the genetic basis of complex traits remains an important and challenging problem with the potential to affect a broad range of biological endeavors. A number of statistical methods are available for mapping quantitative trait loci (QTL), but their application to high-throughput phenotypes has been limited as most require user input and interaction. Recently, methods have been developed specifically for expression QTL (eQTL) mapping, but they too are limited in that they do not allow for interactions and QTL of moderate effect. We here propose an automated model-selection-based approach that identifies multiple eQTL in experimental populations, allowing for eQTL of moderate effect and interactions. Output can be used to identify groups of transcripts that are likely coregulated, as demonstrated in a study of diabetes in mouse.

Motivation: The power of a microarray experiment derives from the identification of genes differentially regulated across biological conditions. To date, differential regulation is most often taken to mean differential expression, and a number of useful methods for identifying differentially expressed (DE) genes or gene sets are available. However, such methods are not able to identify many relevant classes of differentially regulated genes. One important example concerns differentially co-expressed (DC) genes., Results: We propose an approach, gene set co-expression analysis (GSCA), to identify DC gene sets. The GSCA approach provides a false discovery rate controlled list of interesting gene sets, does not require that genes be highly correlated in at least one biological condition and is readily applied to data from individual or multiple experiments, as we demonstrate using data from studies of lung cancer and diabetes., Availability: The GSCA approach is implemented in R and available at www.biostat.wisc.edu/ approximately kendzior/GSCA/., Contact: kendzior@biostat.wisc.edu, Supplementary Information: Supplementary data are available at Bioinformatics online.

Network reconstruction is a main goal of many biological endeavors. Graphical Gaussian models (GGMs) are often used since the underlying assumptions are well understood, the graph is readily estimated by calculating the partial correlation (paCor) matrix, and its interpretation is straightforward. In spite of these advantages, GGMs are limited in that interactions are not accommodated as the underlying multivariate normality assumption allows for linear dependencies only. As we show, when applied in the presence of interactions, the GGM framework can lead to incorrect inference regarding dependence. Identifying the exact dependence structure in this context is a difficult problem, largely because an analogue of the paCor matrix is not available and dependencies can involve many nodes. We here present a computationally efficient approach to identify bivariate interactions in networks. A key element is recognizing that interactions have a marginal linear effect and as a result information about their presence can be obtained from the paCor matrix. Theoretical derivations for the exact effect are presented and used to motivate the approach; and simulations suggest that the method works well, even in fairly complicated scenarios. Practical advantages are demonstrated in analyses of data from a breast cancer study.

Background: The C3H/HeJ mouse strain develops severe bladder and kidney infections after receiving intravesical inoculation with uropathogenic Escherichia coli. This susceptibility is genetically determined, but the specific genes involved have not been completely defined. The objective of the present study was to use quantitative trait locus (QTL) mapping to identify chromosomal sites associated with susceptibility to infection in C3H/HeJ mice., Methods: Female mice from a backcross of C3H/HeJ and (BALB/cxC3H/HeJ)F1 mice were inoculated with E. coli, and the number of E. coli colony-forming units present in the bladder and kidneys was quantified 10 days later. Genomic DNA was scanned using microsatellite markers to localize chromosomal segments derived from parental strains. Statistical analyses associated infection phenotypes with chromosomal sites., Results: A highly significant QTL for susceptibility to bladder infection was identified on chromosome 4, and C3H/HeJ alleles at this locus interacted with BALB/c alleles on chromosome 19 to increase the severity of infection. A significant QTL on chromosome 6 was associated with severe kidney infections., Conclusions: Increased susceptibility to E. coli bladder and kidney infections in female C3H/HeJ mice is associated with specific chromosomal sites located near genes contributing to host resistance to infection. The results demonstrate the multigenic nature of susceptibility to urinary tract infections.

In 2001, Sen and Churchill reported a general Bayesian framework for quantitative trait loci (QTL) mapping in inbred line crosses. The framework is a powerful one, as many QTL mapping methods can be represented as special cases and many important considerations are accommodated. These considerations include accounting for covariates, nonstandard crosses, missing genotypes, genotyping errors, multiple interacting QTL, and nonnormal as well as multivariate phenotypes. The dimension of a multivariate phenotype easily handled within the framework is bounded by the number of subjects, as a full-rank covariance matrix describing correlations across the phenotypes is required. We address this limitation and extend the Sen-Churchill framework to accommodate expression quantitative trait loci (eQTL) mapping studies, where high-dimensional gene-expression phenotypes are obtained via microarrays. Doing so allows for the precise comparison of existing eQTL mapping approaches and facilitates the development of an eQTL interval-mapping approach that shares information across transcripts and improves localization of eQTL. Evaluations are based on simulation studies and a study of diabetes in mice.

Background: Hypomagnesaemia is a common side effect of cyclosporin A (CsA) therapy. Animal studies suggest that magnesium (Mg) supplementation inhibits chronic CsA nephropathy., Methods: To determine if low Mg levels correlate with true CsA-induced nephrotoxicity in humans, we examined kidney transplant biopsy records at our centre for all transplant biopsies performed between 1990 and 2002. We simultaneously reviewed the medical records to determine whether serum Mg levels were checked at the time of biopsy. Those individuals with histologically proven CsA nephrotoxicity were studied., Results: Serum total Mg levels were available for 320 patients, 60 of whom were diagnosed with chronic CsA-mediated nephropathy. Patients were divided in two groups, a low Mg [n = 29, 1.8 (1.67-1.9) mg/dl or 0.74 (0.68-0.78) mmol/l] and a normal Mg group [n = 31, 2.2 (2.0-2.4) mg/dl or 0.9 (0.82-0.98) mmol/l, P<0.05] based on the median Mg level in the entire cohort (2 mg/dl or 0.82 mmol/l). Both groups were analysed for disease progression and graft loss using the slope of creatinine clearance (CCR) and multivariate analyses. Although the CCR at the time of biopsy was greater in the low Mg group [44.3 (36.3-64.3) ml/min vs 37.8 (25.2-47.3) ml/min, P<0.05), the decline in graft function was faster in this group (-8.9+/-3.5 vs 1+/-2.7 ml/min/year; P = 0.02) compared with the normal Mg cohort. Using Cox proportional hazards analyses, the adjusted graft survival was significantly reduced in the low Mg group 5 years after biopsy., Conclusions: Our study demonstrates that low serum Mg levels were associated with a faster rate of decline in kidney allograft function and increased rates of graft loss in renal transplant recipients with chronic CsA nephropathy. This suggests that hypomagnesaemia could potentiate CsA-mediated nephropathy.

In a microarray experiment, messenger RNA samples are oftentimes pooled across subjects out of necessity, or in an effort to reduce the effect of biological variation. A basic problem in such experiments is to estimate the nominal expression levels of a large number of genes. Pooling samples will affect expression estimation, but the exact effects are not yet known as the approach has not been systematically studied in this context. We consider how mRNA pooling affects expression estimates by assessing the finite-sample performance of different estimators for designs with and without pooling. Conditions under which it is advantageous to pool mRNA are defined; and general properties of estimates from both pooled and non-pooled designs are derived under these conditions. A formula is given for the total number of subjects and arrays required in a pooled experiment to obtain gene expression estimates and confidence intervals comparable to those obtained from the no-pooling case. The formula demonstrates that by pooling a perhaps increased number of subjects, one can decrease the number of arrays required in an experiment without a loss of precision. The assumptions that facilitate derivation of this formula are considered using data from a quantitative real-time PCR experiment. The calculations are not specific to one particular method of quantifying gene expression as they assume only that a single, normalized, estimate of expression is obtained for each gene. As such, the results should be generally applicable to a number of technologies provided sufficient pre-processing and normalization methods are available and applied.

To gain information about the genetic basis of a complex disease such as hypertension, blood pressure averages are often obtained and used as phenotypes in genetic mapping studies. In contrast, direct measurements of physiological regulatory mechanisms are not often obtained, due in large part to the time and expense required. As a result, little information about the genetic basis of physiological controlling mechanisms is available. Such information is important for disease diagnosis and treatment. In this article, we use a mathematical model of blood pressure to derive phenotypes related to the baroreceptor reflex, a short-term controller of blood pressure. The phenotypes are then used in a quantitative trait loci (QTL) mapping study to identify a potential genetic basis of this controller.

In this study, the Wistar-Kyoto (WKy) rat was genetically characterized for loci that modify susceptibility to mammary carcinogenesis. We used a genetic backcross between resistant WKy and susceptible Wistar-Furth (WF) rats as a panel for linkage mapping to genetically identify mammary carcinoma susceptibility (Mcs) loci underlying the resistance of the WKy rat. Rats were phenotyped for DMBA-induced mammary carcinomas and genotyped using microsatellite markers. To detect quantitative trait loci (QTL), we analyzed the genome scan data under both parametric and nonparametric distributional assumptions and used permutation tests to calculate significance thresholds. A generalized linear model analysis was also performed to test for interactions between significant QTL. This methodology was extended to identify interactions between the significant QTL and other genome locations. Chromosomes 5, 7, 10, and 14 were found to contain significant QTL, termed Mcs5, Mcs6, Mcs7, and Mcs8, respectively. The WKy alleles of Mcs5, -6, and -8 are associated with mammary carcinoma resistance; the WKy allele of Mcs7 is associated with an increased incidence of mammary cancer. In addition, we identified an interaction between Mcs8 and a region on chromosome 6 termed Mcsm1 (modifier of Mcs), which had no significant main effect on mammary cancer susceptibility in this genetic analysis.