Pathway-level Analyses Research Articles

Maternal smoking impacts key biological pathways in newborns through epigenetic modification in Utero.

BackgroundChildren exposed to maternal smoking during pregnancy exhibit increased risk for many adverse health effects. Maternal smoking influences methylation in newborns at specific CpG sites (CpGs). Here, we extend evaluation of individual CpGs to gene-level and pathway-level analyses among 1062 participants in the Norwegian Mother and Child Cohort Study (MoBa) using the Illumina 450 K platform to measure methylation in newborn DNA and maternal smoking in pregnancy, assessed using the biomarker, plasma cotinine. We used novel implementations of bioinformatics tools to collapse epigenome-wide methylation data into gene- and pathway-level effects to test whether exposure to maternal smoking in utero differentially methylated CpGs in genes enriched in biologic pathways. Unlike most pathway analysis applications, our approach allows replication in an independent cohort.ResultsData on 485,577 CpGs, mapping to a total of 20,199 genes, were used to create gene scores that were tested for association with maternal plasma cotinine levels using Sequence Kernel Association Test (SKAT), and 15 genes were found to be associated (q < 0.25). Six of these 15 genes (GFI1, MYO1G, CYP1A1, RUNX1, LCTL, and AHRR) contained individual CpGs that were differentially methylated with regards to cotinine levels (p < 1.06 × 10−7). Nine of the 15 genes (FCRLA, MIR641, SLC25A24, TRAK1, C1orf180, ITLN2, GLIS1, LRFN1, and MIR451) were associated with cotinine at the gene-level (q < 0.25) but had no genome-wide significant individual CpGs (p > 1.06 × 10−7). Pathway analyses using gene scores resulted in 51 significantly associated pathways, which we tested for replication in an independent cohort (q < 0.05). Of those 32 replicated in an independent cohort, which clustered into six groups. The largest cluster consisted of pathways related to cancer, cell cycle, ERα receptor signaling, and angiogenesis. The second cluster, organized into five smaller pathway groups, related to immune system function, such as T-cell regulation and other white blood cell related pathways.ConclusionsHere we use novel implementations of bioinformatics tools to determine biological pathways impacted through epigenetic changes in utero by maternal smoking in 1062 participants in the MoBa, and successfully replicate these findings in an independent cohort. The results provide new insight into biological mechanisms that may contribute to adverse health effects from exposure to tobacco smoke in utero.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3310-1) contains supplementary material, which is available to authorized users.

Abstract 799: Interaction between meat and fish intake and genes involved in hormones, inflammation and energetic factors, and risk of breast cancer among Hispanic and Non-Hispanic White women: The Breast Cancer Health Disparities Study

Abstract Previously, we reported associations between breast cancer (BC) risk and tuna intake in non-Hispanic White (NHW) and Hispanic women, and processed meat intake among Hispanic women only. We also reported associations with SNPs in genes involved in the Convergence of Hormones, Inflammation, and Energy-related Factors (CHIEF) network. Given the multifactorial nature of BC etiology, we tested for interactions (GxE) between meat/fish intake and SNPs in selected CHIEF pathways using a set-based method that aggregates SNPs into groups for GxE association testing.The study population included Hispanic (1046 cases, 1331 controls) and NHW (1234 cases, 1468 controls) women from two US population-based case-control studies that are part of the Breast Cancer Health Disparities Study. Harmonized meat intake variables were dichotomized, and include red meat, processed meat, poultry, fish, and tuna. 734 SNPs were grouped into 107 genes based on loci. Genes were further grouped into 7 pathways based on biological function: CHIEF core, ALOX/COX, Interleukins, JAK/STAT/SOC, TLR/TNF, MAPK, and TGF-â. We performed the analysis in a top-down fashion, sequentially testing for interactions between meat/fish intake and pathways, genes within pathways, and SNPs within genes. To test pathways and genes, we used a set-based GxE approach that extends the adaptive rank truncated product (ARTP), and to test individual SNPs we used logistic regression. At gene and SNP level analyses, we applied Bonferroni corrections for the number of genes or SNPs tested. Analyses were stratified by ethnicity. Models were adjusted for study, age, education, menopausal status, family history of breast cancer, parity, body mass index, physical activity, education, intake of alcohol, fiber, calories, fat, carbohydrates, proteins, and genetic ancestry. We report results that showed statistically significant interactions at both pathway and gene levels. Analyses were conducted using the R programming language. Among NHW women, pathway-level analyses using ARTP identified an interaction between poultry intake and the interleukin/cytokine pathway, which was driven by the IL17A gene (2 SNPs); poultry intake was inversely associated with BC risk among carriers of the minor alleles in this gene. Among Hispanics, ARTP analyses identified an interaction between tuna intake and total fish intake and the Jak/Stat/SOC pathway, which was driven by STAT5A (tuna: 2 SNPs) and STAT5B (total fish and tuna: 3 SNPs). The positive associations between fish or tuna and BC risk were restricted to carriers of minor alleles in these two genes. Overall, our findings suggest a role for poultry and fish intake in chronic inflammation and tumor growth promotion in breast carcinogenesis and highlight specific genes that may play roles in these carcinogenic pathways. Citation Format: Andre E. Kim, Juan Pablo Lewinger, Shirong Zhang, Roger K. Wolff, Laura Fejerman, Esther M. John, Gabriela Torres-Mejia, Jennifer S. Herrick, Stephanie D. Boone, Avonne E. Connor, Lisa M. Hines, Kathy B. Baumgartner, Anna Giuliano, Martha L. Slattery, Mariana C. Stern. Interaction between meat and fish intake and genes involved in hormones, inflammation and energetic factors, and risk of breast cancer among Hispanic and Non-Hispanic White women: The Breast Cancer Health Disparities Study. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 799.

Meta-dimensional data integration identifies critical pathways for susceptibility, tumorigenesis and progression of endometrial cancer.

Endometrial Cancer (EC) is one of the most common female cancers. Genome-wide association studies (GWAS) have been investigated to identify genetic polymorphisms that are predictive of EC risks. Here we utilized a meta-dimensional integrative approach to seek genetically susceptible pathways that may be associated with tumorigenesis and progression of EC. We analyzed GWAS data obtained from Connecticut Endometrial Cancer Study (CECS) and identified the top 20 EC susceptible pathways. To further verify the significance of top 20 EC susceptible pathways, we conducted pathway-level multi-omics analyses using EC exome-Seq, RNA-Seq and survival data, all based on The Cancer Genome Atlas (TCGA) samples. We measured the overall consistent rankings of these pathways in all four data types. Some well-studied pathways, such as p53 signaling and cell cycle pathways, show consistently high rankings across different analyses. Additionally, other cell signaling pathways (e.g. IGF-1/mTOR, rac-1 and IL-5 pathway), genetic information processing pathway (e.g. homologous recombination) and metabolism pathway (e.g. sphingolipid metabolism) are also highly associated with EC risks, diagnosis and prognosis. In conclusion, the meta-dimensional integration of EC cohorts has suggested some common pathways that may be associated from predisposition, tumorigenesis to progression.

Gene and pathway level analyses of germline DNA-repair gene variants and prostate cancer susceptibility using the iCOGS-genotyping array.

Background:Germline mutations within DNA-repair genes are implicated in susceptibility to multiple forms of cancer. For prostate cancer (PrCa), rare mutations in BRCA2 and BRCA1 give rise to moderately elevated risk, whereas two of ∼100 common, low-penetrance PrCa susceptibility variants identified so far by genome-wide association studies implicate RAD51B and RAD23B.Methods:Genotype data from the iCOGS array were imputed to the 1000 genomes phase 3 reference panel for 21 780 PrCa cases and 21 727 controls from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. We subsequently performed single variant, gene and pathway-level analyses using 81 303 SNPs within 20 Kb of a panel of 179 DNA-repair genes.Results:Single SNP analyses identified only the previously reported association with RAD51B. Gene-level analyses using the SKAT-C test from the SNP-set (Sequence) Kernel Association Test (SKAT) identified a significant association with PrCa for MSH5. Pathway-level analyses suggested a possible role for the translesion synthesis pathway in PrCa risk and Homologous recombination/Fanconi Anaemia pathway for PrCa aggressiveness, even though after adjustment for multiple testing these did not remain significant.Conclusions:MSH5 is a novel candidate gene warranting additional follow-up as a prospective PrCa-risk locus. MSH5 has previously been reported as a pleiotropic susceptibility locus for lung, colorectal and serous ovarian cancers.

Abstract 2794: Genetic variations in vitamin D-related pathways and breast cancer risk in African American women

Abstract Background: Experimental studies support anti-tumor properties of vitamin D for breast cancer (BC), which have yet to be conclusively demonstrated in epidemiological studies. Common genetic variations in the vitamin D-related pathways have also been studied with BC risk, mostly in populations of European ancestry. Studies in populations of African ancestry (AA), known for a high prevalence of vitamin D deficiency due to high skin pigmentation, have been sparse. Methods: We examined 69 genes from three vitamin D-related pathways: vitamin D metabolism and signaling, pigmentation synthesis and metabolism, and UV exposure response in the African American Breast Cancer Epidemiology and Risk (AMBER) Consortium. Tagging single nucleotide polymorphisms (SNPs) were selected and genotyped in a customized Illumina Exome Array, followed by systematic imputation using 1000 Genomes haplotypes. The analytical dataset included 25,295 variants with a minor allele frequency (MAF) of 0.006 or above from the targeted genetic regions in 3,663 breast cancer cases and 4,687 controls. Odds ratios (OR) were derived from multivariable logistic regression models for risk associated with overall BC as well as by estrogen receptor (ER) status (1,983 ER positive and 1,098 ER negative). Case-case analyses were also performed. Results: Among the three vitamin D-related pathways, genes involved in UV exposure response appear to be the top ones associated with BC risk overall and by ER status. The most significant genes in this pathway included: REV1 for overall BC (OR for the T allele of rs9308822 = 0.86, p = 1e-4), similar for both ER+ and ER- BC; ERCC6 for ER-positive BC (OR for the T allele of rs114723899 = 0.62, p = 4e-5); and DDB2 for ER-negative BC (OR for the G allele of rs4647707 = 1.26, p = 4e-5). For the skin pigmentation pathway, the most significant genes were SLC24A2 with ER-negative BC (top SNP rs4900134 p = 1e-4), and OCA2 associated with ER-negative vs. ER-positive status (top SNP rs144465989 p = 6e-5). For the vitamin D metabolism and signaling pathway, the most significant gene was CASR associated with ER-negative vs. ER-positive status (top SNP rs112594756 p = 7e-5). Only one of the commonly studied VDR SNPs, ApaI or rs7975232, was marginally associated with ER-positive BC (OR = 1.11, p = 0.05) in this study of AA women. Gene-level and pathway-level analyses and for multiple testing correction are ongoing. Conclusion: In a comprehensive study of vitamin D-related genetic variations in AA women, we found several genes involved in UV exposure response most significantly associated with BC risk overall and by ER status. Because those genes are also involved in DNA damage and repair pathways, findings should be interpreted with caution due to their roles in cutaneous synthesis of vitamin D. Citation Format: Song Yao, Chi-Chen Hong, Kathryn Lunetta, Stephen Haddad, Edward Ruiz-Narvaez, Qiang Hu, Qianqian Zhu, Song Liu, Lara Sucheston-Campbell, Christopher Haiman, Andrew Olshan, Julie Palmer, Christine Ambrosone. Genetic variations in vitamin D-related pathways and breast cancer risk in African American women. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2794. doi:10.1158/1538-7445.AM2015-2794

Genomic profiling of thousands of candidate polymorphisms predicts risk of relapse in 778 Danish and German childhood acute lymphoblastic leukemia patients.

Childhood acute lymphoblastic leukemia survival approaches 90%. New strategies are needed to identify the 10–15% who evade cure. We applied targeted, sequencing-based genotyping of 25 000 to 34 000 preselected potentially clinically relevant single-nucleotide polymorphisms (SNPs) to identify host genome profiles associated with relapse risk in 352 patients from the Nordic ALL92/2000 protocols and 426 patients from the German Berlin–Frankfurt–Munster (BFM) ALL2000 protocol. Patients were enrolled between 1992 and 2008 (median follow-up: 7.6 years). Eleven cross-validated SNPs were significantly associated with risk of relapse across protocols. SNP and biologic pathway level analyses associated relapse risk with leukemia aggressiveness, glucocorticosteroid pharmacology/response and drug transport/metabolism pathways. Classification and regression tree analysis identified three distinct risk groups defined by end of induction residual leukemia, white blood cell count and variants in myeloperoxidase (MPO), estrogen receptor 1 (ESR1), lamin B1 (LMNB1) and matrix metalloproteinase-7 (MMP7) genes, ATP-binding cassette transporters and glucocorticosteroid transcription regulation pathways. Relapse rates ranged from 4% (95% confidence interval (CI): 1.6–6.3%) for the best group (72% of patients) to 76% (95% CI: 41–90%) for the worst group (5% of patients, P<0.001). Validation of these findings and similar approaches to identify SNPs associated with toxicities may allow future individualized relapse and toxicity risk-based treatments adaptation.

Gene–environment interactions in genome‐wide association studies: current approaches and new directions

Complex psychiatric traits have long been thought to be the result of a combination of genetic and environmental factors, and gene-environment interactions are thought to play a crucial role in behavioral phenotypes and the susceptibility and progression of psychiatric disorders. Candidate gene studies to investigate hypothesized gene-environment interactions are now fairly common in human genetic research, and with the shift toward genome-wide association studies, genome-wide gene-environment interaction studies are beginning to emerge. We summarize the basic ideas behind gene-environment interaction, and provide an overview of possible study designs and traditional analysis methods in the context of genome-wide analysis. We then discuss novel approaches beyond the traditional strategy of analyzing the interaction between the environmental factor and each polymorphism individually. Two-step filtering approaches that reduce the number of polymorphisms tested for interactions can substantially increase the power of genome-wide gene-environment studies. New analytical methods including data-mining approaches, and gene-level and pathway-level analyses, also have the capacity to improve our understanding of how complex genetic and environmental factors interact to influence psychologic and psychiatric traits. Such methods, however, have not yet been utilized much in behavioral and mental health research. Although methods to investigate gene-environment interactions are available, there is a need for further development and extension of these methods to identify gene-environment interactions in the context of genome-wide association studies. These novel approaches need to be applied in studies of psychology and psychiatry.

Plasma Fatty Acid Ratios Affect Blood Gene Expression Profiles - A Cross-Sectional Study of the Norwegian Women and Cancer Post-Genome Cohort

High blood concentrations of n-6 fatty acids (FAs) relative to n-3 FAs may lead to a “physiological switch” towards permanent low-grade inflammation, potentially influencing the onset of cardiovascular and inflammatory diseases, as well as cancer. To explore the potential effects of FA ratios prior to disease onset, we measured blood gene expression profiles and plasma FA ratios (linoleic acid/alpha-linolenic acid, LA/ALA; arachidonic acid/eicosapentaenoic acid, AA/EPA; and total n-6/n-3) in a cross-section of middle-aged Norwegian women (n = 227). After arranging samples from the highest values to the lowest for all three FA ratios (LA/ALA, AA/EPA and total n-6/n-3), the highest and lowest deciles of samples were compared. Differences in gene expression profiles were assessed by single-gene and pathway-level analyses. The LA/ALA ratio had the largest impact on gene expression profiles, with 135 differentially expressed genes, followed by the total n-6/n-3 ratio (125 genes) and the AA/EPA ratio (72 genes). All FA ratios were associated with genes related to immune processes, with a tendency for increased pro-inflammatory signaling in the highest FA ratio deciles. Lipid metabolism related to peroxisome proliferator-activated receptor γ (PPARγ) signaling was modified, with possible implications for foam cell formation and development of cardiovascular diseases. We identified higher expression levels of several autophagy marker genes, mainly in the lowest LA/ALA decile. This finding may point to the regulation of autophagy as a novel aspect of FA biology which warrants further study. Lastly, all FA ratios were associated with gene sets that included targets of specific microRNAs, and gene sets containing common promoter motifs that did not match any known transcription factors. We conclude that plasma FA ratios are associated with differences in blood gene expression profiles in this free-living population, and that affected genes and pathways may influence the onset and progression of disease.

Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE

BackgroundHuman tissues perform diverse metabolic functions. Mapping out these tissue-specific functions in genome-scale models will advance our understanding of the metabolic basis of various physiological and pathological processes. The global knowledgebase of metabolic functions categorized for the human genome (Human Recon 1) coupled with abundant high-throughput data now makes possible the reconstruction of tissue-specific metabolic models. However, the number of available tissue-specific models remains incomplete compared with the large diversity of human tissues.ResultsWe developed a method called metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE). mCADRE is able to infer a tissue-specific network based on gene expression data and metabolic network topology, along with evaluation of functional capabilities during model building. mCADRE produces models with similar or better functionality and achieves dramatic computational speed up over existing methods. Using our method, we reconstructed draft genome-scale metabolic models for 126 human tissue and cell types. Among these, there are models for 26 tumor tissues along with their normal counterparts, and 30 different brain tissues. We performed pathway-level analyses of this large collection of tissue-specific models and identified the eicosanoid metabolic pathway, especially reactions catalyzing the production of leukotrienes from arachidnoic acid, as potential drug targets that selectively affect tumor tissues.ConclusionsThis large collection of 126 genome-scale draft metabolic models provides a useful resource for studying the metabolic basis for a variety of human diseases across many tissues. The functionality of the resulting models and the fast computational speed of the mCADRE algorithm make it a useful tool to build and update tissue-specific metabolic models.

IRsp1095: A genome-scale reconstruction of the Rhodobacter sphaeroides metabolic network

BackgroundRhodobacter sphaeroides is one of the best studied purple non-sulfur photosynthetic bacteria and serves as an excellent model for the study of photosynthesis and the metabolic capabilities of this and related facultative organisms. The ability of R. sphaeroides to produce hydrogen (H2), polyhydroxybutyrate (PHB) or other hydrocarbons, as well as its ability to utilize atmospheric carbon dioxide (CO2) as a carbon source under defined conditions, make it an excellent candidate for use in a wide variety of biotechnological applications. A genome-level understanding of its metabolic capabilities should help realize this biotechnological potential.ResultsHere we present a genome-scale metabolic network model for R. sphaeroides strain 2.4.1, designated iRsp1095, consisting of 1,095 genes, 796 metabolites and 1158 reactions, including R. sphaeroides-specific biomass reactions developed in this study. Constraint-based analysis showed that iRsp1095 agreed well with experimental observations when modeling growth under respiratory and phototrophic conditions. Genes essential for phototrophic growth were predicted by single gene deletion analysis. During pathway-level analyses of R. sphaeroides metabolism, an alternative route for CO2 assimilation was identified. Evaluation of photoheterotrophic H2 production using iRsp1095 indicated that maximal yield would be obtained from growing cells, with this predicted maximum ~50% higher than that observed experimentally from wild type cells. Competing pathways that might prevent the achievement of this theoretical maximum were identified to guide future genetic studies.ConclusionsiRsp1095 provides a robust framework for future metabolic engineering efforts to optimize the solar- and nutrient-powered production of biofuels and other valuable products by R. sphaeroides and closely related organisms.

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors

IntroductionAs part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints.MethodsWe used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set.ResultsA ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models.ConclusionsWe showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

Gene, region and pathway level analyses in whole‐genome studies

In the setting of genome-wide association studies, we propose a method for assigning a measure of significance to pre-defined sets of markers in the genome. The sets can be genes, conserved regions, or groups of genes such as pathways. Using the proposed methods and algorithms, evidence for association between a particular functional unit and a disease status can be obtained not just by the presence of a strong signal from a SNP within it, but also by the combination of several simultaneous weaker signals that are not strongly correlated. This approach has several advantages. First, moderately strong signals from different SNPs are combined to obtain a much stronger signal for the set, therefore increasing power. Second, in combination with methods that provide information on untyped markers, it leads to results that can be readily combined across studies and platforms that might use different SNPs. Third, the results are easy to interpret, since they refer to functional sets of markers that are likely to behave as a unit in their phenotypic effect. Finally, the availability of gene-level P-values for association is the first step in developing methods that integrate information from pathways and networks with genome-wide association data, and these can lead to a better understanding of the complex traits genetic architecture. The power of the approach is investigated in simulated and real datasets. Novel Crohn's disease associations are found using the WTCCC data.

Cross-platform expression microarray performance in a mouse model of mitochondrial disease therapy

Microarray expression profiling has become a valuable tool in the evaluation of the genetic consequences of metabolic disease. Although 3′-biased gene expression microarray platforms were the first generation to have widespread availability, newer platforms are gradually emerging that have more up-to-date content and/or higher cost efficiency. Deciphering the relative strengths and weaknesses of these various platforms for metabolic pathway-level analyses can be daunting. We sought to determine the practical strengths and weaknesses of four leading commercially available expression array platforms relative to biologic investigations, as well as assess the feasibility of cross-platform data integration for purposes of biochemical pathway analyses. Methods: Liver RNA from B6.Alb/cre,Pdss2loxP/loxP mice having primary coenzyme Q deficiency was extracted either at baseline or following treatment with an antioxidant/antihyperlipidemic agent, probucol. Target RNA samples were prepared and hybridized to Affymetrix 430 2.0, Affymetrix Gene 1.0 ST, Affymetrix Exon 1.0 ST, and Illumina Mouse WG-6 expression arrays. Probes on all platforms were re-mapped to coding sequences in the current version of the mouse genome. Data processing and statistical analysis were performed by R/Bioconductor functions, and pathway analyses were carried out by KEGG Atlas and GSEA. Results: Expression measurements were generally consistent across platforms. However, intensive probe-level comparison suggested that differences in probe locations were a major source of inter-platform variance. In addition, genes expressed at low or intermediate levels had lower inter-platform reproducibility than highly expressed genes. All platforms showed similar patterns of differential expression between sample groups, with ‘steroid biosynthesis’ consistently identified as the most down-regulated metabolic pathway by probucol treatment. Conclusions: This work offers a timely guide for metabolic disease investigators to enable informed end-user decisions regarding choice of expression microarray platform best-suited to specific research project goals. Successful cross-platform integration of biochemical pathway expression data is also demonstrated, especially for well-annotated and highly expressed genes. However, integration of gene-level expression data is limited by individual platform probe design and the expression level of target genes. Cross-platform analyses of biochemical pathway data will require additional data processing and novel computational bioinformatics tools to address unique statistical challenges.

Transcriptional Profiles in Liver from Rats Treated with Tumorigenic and Non-tumorigenic Triazole Conazole Fungicides: Propiconazole, Triadimefon, and Myclobutanil

Conazoles are a class of fungicides used as pharmaceutical and agricultural agents. In chronic bioassays in rats, triadimefon was hepatotoxic and induced follicular cell adenomas in the thyroid gland, whereas, propiconazole and myclobutanil were hepatotoxic but had no effect on the thyroid gland. These conazoles administered in the feed to male Wistar/Han rats were found to induce hepatomegaly, induce high levels of pentoxyresorufin-O-dealkylase, increase cell proliferation in the liver, increase serum cholesterol, decrease serum T3 and T4, and increase hepatic uridine diphosphoglucuronosyl transferase activity. The goal of the present study was to define pathways that explain the biologic outcomes. Male Wistar/Han rats (3 per group), were exposed to the 3 conazoles in the feed for 4, 30, or 90 days of treatment at tumorigenic and nontumorigenic doses. Hepatic gene expression was determined using high-density Affymetrix GeneChips (Rat 230_2). Differential gene expression was assessed at the probe level using Robust Multichip Average analysis. Principal component analysis by treatment and time showed within group sample similarity and that the treatment groups were distinct from each other. The number of altered genes varied by treatment, dose, and time. The greatest number of altered genes was induced by triadimefon and propiconazole after 90 days of treatment, while myclobutanil had minimal effects at that time point. Pathway level analyses revealed that after 90 days of treatment the most significant numbers of altered pathways were related to cell signaling, growth, and metabolism. Pathway level analysis for triadimefon and propiconazole resulted in 71 altered pathways common to both chemicals. These pathways controlled cholesterol metabolism, activation of nuclear receptors, and N-ras and K-ras signaling. There were 37 pathways uniquely changed by propiconazole, and triadimefon uniquely altered 34 pathways. Pathway level analysis of altered gene expression resulted in a more complete description of the associated toxicological effects that can distinguish triadimefon from propiconazole and myclobutanil.