BACKGROUND: Genome-wide association studies (GWAS) aim to identify genetic variants (usually single nucleotide polymorphisms [SNPs]) across the entire human genome that are associated with phenotypic traits such as disease status and drug response. Highly accurate and reproducible genotype calling are paramount since errors introduced by calling algorithms can lead to inflation of false associations between genotype and phenotype. Most genotype calling algorithms currently used for GWAS are based on multiple arrays. Because hundreds of gigabytes (GB) of raw data are generated from a GWAS, the samples are typically partitioned into batches containing subsets of the entire dataset for genotype calling. High call rates and accuracies have been achieved. However, the effects of batch size (i.e., number of chips analyzed together) and of batch composition (i.e., the choice of chips in a batch) on call rate and accuracy as well as the propagation of the effects into significantly associated SNPs identified have not been investigated. In this paper, we analyzed both the batch size and batch composition for effects on the genotype calling algorithm BRLMM using raw data of 270 HapMap samples analyzed with the Affymetrix Human Mapping 500 K array set. RESULTS: Using data from 270 HapMap samples interrogated with the Affymetrix Human Mapping 500 K array set, three different batch sizes and three different batch compositions were used for genotyping using the BRLMM algorithm. Comparative analysis of the calling results and the corresponding lists of significant SNPs identified through association analysis revealed that both batch size and composition affected genotype calling results and significantly associated SNPs. Batch size and batch composition effects were more severe on samples and SNPs with lower call rates than ones with higher call rates, and on heterozygous genotype calls compared to homozygous genotype calls. CONCLUSION: Batch size and composition affect the genotype calling results in GWAS using BRLMM. The larger the differences in batch sizes, the larger the effect. The more homogenous the samples in the batches, the more consistent the genotype calls. The inconsistency propagates to the lists of significantly associated SNPs identified in downstream association analysis. Thus, uniform and large batch sizes should be used to make genotype calls for GWAS. In addition, samples of high homogeneity should be placed into the same batch.

MCBIOS 2008 was held February 23–24, 2008 in Oklahoma City, Oklahoma at the Cox Convention Center in Bricktown. It was the best attended in the series of MCBIOS conferences (140 registrants) with the most participation (68 posters submitted). Informative and engaging keynote talks were delivered by Dr. Bruce Roe and Dr. Edward Dougherty. The full agenda is online at http://www.okbios.org .

BACKGROUND: Comfrey (Symphytum officinale) is a perennial plant and has been consumed by humans as a vegetable, a tea and an herbal medicine for more than 2000 years. It, however, is hepatotoxic and carcinogenic in experimental animals and hepatotoxic in humans. Pyrrolizidine alkaloids (PAs) exist in many plants and many of them cause liver toxicity and/or cancer in humans and experimental animals. In our previous study, we found that the mutagenicity of comfrey was associated with the PAs contained in the plant. Therefore, we suggest that carcinogenicity of comfrey result from those PAs. To confirm our hypothesis, we compared the expression of genes and processes of biological functions that were altered by comfrey (mixture of the plant with PAs) and riddelliine (a prototype of carcinogenic PA) in rat liver for carcinogenesis in this study. RESULTS: Groups of 6 Big Blue Fisher 344 rats were treated with riddelliine at 1 mg/kg body weight by gavage five times a week for 12 weeks or fed a diet containing 8% comfrey root for 12 weeks. Animals were sacrificed one day after the last treatment and the livers were isolated for gene expression analysis. The gene expressions were investigated using Applied Biosystems Rat Whole Genome Survey Microarrays and the biological functions were analyzed with Ingenuity Analysis Pathway software. Although there were large differences between the significant genes and between the biological processes that were altered by comfrey and riddelliine, there were a number of common genes and function processes that were related to carcinogenesis. There was a strong correlation between the two treatments for fold-change alterations in expression of drug metabolizing and cancer-related genes. CONCLUSION: Our results suggest that the carcinogenesis-related gene expression patterns resulting from the treatments of comfrey and riddelliine are very similar, and PAs contained in comfrey are the main active components responsible for carcinogenicity of the plant.

BACKGROUND: Pyrrolizidine alkaloids (PAs) are probably the most common plant constituents that poison livestock, wildlife, and humans worldwide. Riddelliine is isolated from plants grown in the western United States and is a prototype of genotoxic PAs. Riddelliine was used to investigate the genotoxic effects of PAs via analysis of gene expression in the target tissue of rats in this study. Previously we observed that the mutant frequency in the liver of rats gavaged with riddelliine was 3-fold higher than that in the control group. Molecular analysis of the mutants indicated that there was a statistically significant difference between the mutational spectra from riddelliine-treated and control rats. RESULTS: Riddelliine-induced gene expression profiles in livers of Big Blue transgenic rats were determined. The female rats were gavaged with riddelliine at a dose of 1 mg/kg body weight 5 days a week for 12 weeks. Rat whole genome microarray was used to perform genome-wide gene expression studies. When a cutoff value of a two-fold change and a P-value less than 0.01 were used as gene selection criteria, 919 genes were identified as differentially expressed in riddelliine-treated rats compared to the control animals. By analysis with the Ingenuity Pathway Analysis Network, we found that these significantly changed genes were mainly involved in cancer, cell death, tissue development, cellular movement, tissue morphology, cell-to-cell signaling and interaction, and cellular growth and proliferation. We further analyzed the genes involved in metabolism, injury of endothelial cells, liver abnormalities, and cancer development in detail. CONCLUSION: The alterations in gene expression were directly related to the pathological outcomes reported previously. These results provided further insight into the mechanisms involved in toxicity and carcinogenesis after exposure to riddelliine, and permitted us to investigate the interaction of gene products inside the signaling networks.

BACKGROUND: Aristolochic acid (AA) is the active component of herbal drugs derived from Aristolochia species that have been used for medicinal purposes since antiquity. AA, however, induced nephropathy and urothelial cancer in people and malignant tumors in the kidney and urinary tract of rodents. Although AA is bioactivated in both kidney and liver, it only induces tumors in kidney. To evaluate whether microarray analysis can be used for distinguishing the tissue-specific carcinogenicity of AA, we examined gene expression profiles in kidney and liver of rats treated with carcinogenic doses of AA. RESULTS: Microarray analysis was performed using the Rat Genome Survey Microarray and data analysis was carried out within ArrayTrack software. Principal components analysis and hierarchical cluster analysis of the expression profiles showed that samples were grouped together according to the tissues and treatments. The gene expression profiles were significantly altered by AA treatment in both kidney and liver (p < 0.01; fold change > 1.5). Functional analysis with Ingenuity Pathways Analysis showed that there were many more significantly altered genes involved in cancer-related pathways in kidney than in liver. Also, analysis with Gene Ontology for Functional Analysis (GOFFA) software indicated that the biological processes related to defense response apoptosis and immune response were significantly altered by AA exposure in kidney, but not in liver. CONCLUSION: Our results suggest that microarray analysis is a useful tool for detecting AA exposure; that analysis of the gene expression profiles can define the differential responses to toxicity and carcinogenicity of AA from kidney and liver; and that significant alteration of genes associated with defense response, apoptosis and immune response in kidney, but not in liver, may be responsible for the tissue-specific toxicity and carcinogenicity of AA.

BACKGROUND: DNA microarrays, which have been increasingly used to monitor mRNA transcripts at a global level, can provide detailed insight into cellular processes involved in response to drugs and toxins. This is leading to new understandings of signaling networks that operate in the cell, and the molecular basis of diseases. Custom printed oligonucleotide arrays have proven to be an effective way to facilitate the applications of DNA microarray technology. A successful microarray experiment, however, involves many steps: well-designed oligonucleotide probes, printing, RNA extraction and labeling, hybridization, and imaging. Optimization is essential to generate reliable microarray data. RESULTS: Hybridization and washing steps are crucial for a successful microarray experiment. By following the hybridization and washing conditions recommended by an oligonucleotide provider, it was found that the expression ratios were compressed greater than expected and data analysis revealed a high degree of non-specific binding. A series of experiments was conducted using rat mixed tissue RNA reference material (MTRRM) and other RNA samples to optimize the hybridization and washing conditions. The optimized hybridization and washing conditions greatly reduced the non-specific binding and improved the accuracy of spot intensity measurements. CONCLUSION: The results from the optimized hybridization and washing conditions greatly improved the reproducibility and accuracy of expression ratios. These experiments also suggested the importance of probe designs using better bioinformatics approaches and the need for common reference RNA samples for platform performance evaluation in order to fulfill the potential of DNA microarray technology.

BACKGROUND: The Thymidine kinase (Tk) mutants generated from the widely used L5178Y mouse lymphoma assay fall into two categories, small colony and large colony. Cells from the large colonies grow at a normal rate while cells from the small colonies grow slower than normal. The relative proportion of large and small colonies after mutagen treatment is associated with a mutagen's ability to induce point mutations and/or chromosomal mutations. The molecular distinction between large and small colony mutants, however, is not clear. RESULTS: To gain insights into the underlying mechanisms responsible for the mutant colony phenotype, microarray gene expression analysis was carried out on 4 small and 4 large colony Tk mutant samples. NCTR-fabricated long-oligonucleotide microarrays of 20,000 mouse genes were used in a two-color reference design experiment. The data were analyzed within ArrayTrack software that was developed at the NCTR. Principal component analysis and hierarchical clustering of the gene expression profiles showed that the samples were clearly separated into two groups based on their colony size phenotypes. The Welch T-test was used for determining significant changes in gene expression between the large and small colony groups and 90 genes whose expression was significantly altered were identified (p < 0.01; fold change > 1.5). Using Ingenuity Pathways Analysis (IPA), 50 out of the 90 significant genes were found in the IPA database and mapped to four networks associated with cell growth. Eleven percent of the 90 significant genes were located on chromosome 11 where the Tk gene resides while only 5.6% of the genes on the microarrays mapped to chromosome 11. All of the chromosome 11 significant genes were expressed at a higher level in the small colony mutants compared to the large colony mutants. Also, most of the significant genes located on chromosome 11 were disproportionally concentrated on the distal end of chromosome 11 where the Tk mutations occurred. CONCLUSION: The results indicate that microarray analysis can define cellular phenotypes and identify genes that are related to the colony size phenotypes. The findings suggest that genes in the DNA segment altered by the Tk mutations were significantly up-regulated in the small colony mutants, but not in the large colony mutants, leading to differential expression of a set of growth regulation genes that are related to cell apoptosis and other cellular functions related to the restriction of cell growth.

BACKGROUND: Comfrey is consumed by humans as a vegetable and a tea, and has been used as an herbal medicine for more than 2000 years. Comfrey, however, is hepatotoxic in livestock and humans and carcinogenic in experimental animals. Our previous study suggested that comfrey induces liver tumors by a genotoxic mechanism and that the pyrrolizidine alkaloids in the plant are responsible for mutation induction and tumor initiation in rat liver. RESULTS: In this study, we identified comfrey-induced gene expression profile in the livers of rats. Groups of 6 male transgenic Big Blue rats were fed a basal diet and a diet containing 8% comfrey roots, a dose that resulted in liver tumors in a previous carcinogenicity bioassay. The animals were treated for 12 weeks and sacrificed one day after the final treatment. We used a rat microarray containing 26,857 genes to perform genome-wide gene expression studies. Dietary comfrey resulted in marked changes in liver gene expression, as well as in significant decreases in the body weight and increases in liver mutant frequency. When a two-fold cutoff value and a P-value less than 0.01 were selected, 2,726 genes were identified as differentially expressed in comfrey-fed rats compared to control animals. Among these genes, there were 1,617 genes associated by Ingenuity Pathway Analysis with particular functions, and the differentially expressed genes in comfrey-fed rat livers were involved in metabolism, injury of endothelial cells, and liver injury and abnormalities, including liver fibrosis and cancer development. CONCLUSION: The gene expression profile provides us a better understanding of underlying mechanisms for comfrey-induced hepatic toxicity. Integration of gene expression changes with known pathological changes can be used to formulate a mechanistic scheme for comfrey-induced liver toxicity and tumorigenesis.

BACKGROUND: The acceptance of microarray technology in regulatory decision-making is being challenged by the existence of various platforms and data analysis methods. A recent report (E. Marshall, Science, 306, 630-631, 2004), by extensively citing the study of Tan et al. (Nucleic Acids Res., 31, 5676-5684, 2003), portrays a disturbingly negative picture of the cross-platform comparability, and, hence, the reliability of microarray technology. RESULTS: We reanalyzed Tan's dataset and found that the intra-platform consistency was low, indicating a problem in experimental procedures from which the dataset was generated. Furthermore, by using three gene selection methods (i.e., p-value ranking, fold-change ranking, and Significance Analysis of Microarrays (SAM)) on the same dataset we found that p-value ranking (the method emphasized by Tan et al.) results in much lower cross-platform concordance compared to fold-change ranking or SAM. Therefore, the low cross-platform concordance reported in Tan's study appears to be mainly due to a combination of low intra-platform consistency and a poor choice of data analysis procedures, instead of inherent technical differences among different platforms, as suggested by Tan et al. and Marshall. CONCLUSION: Our results illustrate the importance of establishing calibrated RNA samples and reference datasets to objectively assess the performance of different microarray platforms and the proficiency of individual laboratories as well as the merits of various data analysis procedures. Thus, we are progressively coordinating the MAQC project, a community-wide effort for microarray quality control.

BACKGROUND: Microarray-based measurement of mRNA abundance assumes a linear relationship between the fluorescence intensity and the dye concentration. In reality, however, the calibration curve can be nonlinear. RESULTS: By scanning a microarray scanner calibration slide containing known concentrations of fluorescent dyes under 18 PMT gains, we were able to evaluate the differences in calibration characteristics of Cy5 and Cy3. First, the calibration curve for the same dye under the same PMT gain is nonlinear at both the high and low intensity ends. Second, the degree of nonlinearity of the calibration curve depends on the PMT gain. Third, the two PMTs (for Cy5 and Cy3) behave differently even under the same gain. Fourth, the background intensity for the Cy3 channel is higher than that for the Cy5 channel. The impact of such characteristics on the accuracy and reproducibility of measured mRNA abundance and the calculated ratios was demonstrated. Combined with simulation results, we provided explanations to the existence of ratio underestimation, intensity-dependence of ratio bias, and anti-correlation of ratios in dye-swap replicates. We further demonstrated that although Lowess normalization effectively eliminates the intensity-dependence of ratio bias, the systematic deviation from true ratios largely remained. A method of calculating ratios based on concentrations estimated from the calibration curves was proposed for correcting ratio bias. CONCLUSION: It is preferable to scan microarray slides at fixed, optimal gain settings under which the linearity between concentration and intensity is maximized. Although normalization methods improve reproducibility of microarray measurements, they appear less effective in improving accuracy.

MOTIVATION: A microarray experiment is a multi-step process, and each step is a potential source of variation. There are two major sources of variation: biological variation and technical variation. This study presents a variance-components approach to investigating animal-to-animal, between-array, within-array and day-to-day variations for two data sets. The first data set involved estimation of technical variances for pooled control and pooled treated RNA samples. The variance components included between-array, and two nested within-array variances: between-section (the upper- and lower-sections of the array are replicates) and within-section (two adjacent spots of the same gene are printed within each section). The second experiment was conducted on four different weeks. Each week there were reference and test samples with a dye-flip replicate in two hybridization days. The variance components included week-to-week, animal-to-animal and between-array and within-array variances. RESULTS: We applied the linear mixed-effects model to quantify different sources of variation. In the first data set, we found that the between-array variance is greater than the between-section variance, which, in turn, is greater than the within-section variance. In the second data set, for the reference samples, the week-to-week variance is larger than the between-array variance, which, in turn, is slightly larger than the within-array variance. For the test samples, the week-to-week variance has the largest variation. The animal-to-animal variance is slightly larger than the between-array and within-array variances. However, in a gene-by-gene analysis, the animal-to-animal variance is smaller than the between-array variance in four out of five housekeeping genes. In summary, the largest variation observed is the week-to-week effect. Another important source of variability is the animal-to-animal variation. Finally, we describe the use of variance-component estimates to determine optimal numbers of animals, arrays per animal and sections per array in planning microarray experiments.