A statistical framework for eQTL mapping using RNA-seq data

RNA-seq may replace gene expression microarrays in the near future. Using RNA-seq, the expression of a gene can be estimated using the total number of sequence reads mapped to that gene, known as the total read count (TReC). Traditional expression quantitative trait locus (eQTL) mapping methods, such as linear regression, can be applied to TReC measurements after they are properly normalized. In this article, we show that eQTL mapping, by directly modeling TReC using discrete distributions, has higher statistical power than the two-step approach: data normalization followed by linear regression. In addition, RNA-seq provides information on allele-specific expression (ASE) that is not available from microarrays. By combining the information from TReC and ASE, we can computationally distinguish cis- and trans-eQTL and further improve the power of cis-eQTL mapping. Both simulation and real data studies confirm the improved power of our new methods. We also discuss the design issues of RNA-seq experiments. Specifically, we show that by combining TReC and ASE measurements, it is possible to minimize cost and retain the statistical power of cis-eQTL mapping by reducing sample size while increasing the number of sequence reads per sample. In addition to RNA-seq data, our method can also be employed to study the genetic basis of other types of sequencing data, such as chromatin immunoprecipitation followed by DNA sequencing data. In this article, we focus on eQTL mapping of a single gene using the association-based method. However, our method establishes a statistical framework for future developments of eQTL mapping methods using RNA-seq data (e.g., linkage-based eQTL mapping), and the joint study of multiple genetic markers and/or multiple genes.     

Comparison of algorithms for the analysis of Affymetrix microarray data as evaluated by co-expression of genes in known operons

Oligonucleotide microarrays are an informative tool to elucidate gene regulatory networks. In order for gene expression levels to be comparable across microarrays, normalization procedures have to be invoked. A large number of methods have been described to correct for systematic biases in microarray experiments. The performance of these methods has been tested only to a limited extend. Here, we evaluate two different types of microarray analyses: (i) the same gene in replicate samples and (ii) different, but co-expressed genes in the same sample. The reliability of the latter analysis needs to be determined for the analysis of regulatory networks and our report is the first attempt to evaluate for the accuracy of different microarray normalization methods in this respect. Consistent with previous results we observed a large effect of the normalization method on the outcome of the expression analyses. Our analyses indicate that different normalization methods should be performed depending on whether a study is aiming to detect differential gene expression between independent samples or whether co-expressed genes should be identified. We make recommendations about the most appropriate method to use.     

A strategy to identify positional candidate genes conferring Marek''s disease resistance by integrating DNA microarrays and genetic mapping

Marker-assisted selection (MAS) to enhance genetic resistance to Marek's disease (MD), a herpesvirus-induced T cell cancer in chicken, is an attractive alternative to augment control with vaccines. Our earlier studies indicate that there are many quantitative trait loci (QTL) containing one or more genes that confer genetic resistance to MD. Unfortunately, it is difficult to sufficiently resolve these QTL to identify the causative gene and generate tightly linked markers. One possible solution is to identify positional candidate genes by virtue of gene expression differences between MD resistant and susceptible chicken using deoxyribonucleic acid (DNA) microarrays followed by genetic mapping of the differentially-expressed genes. In this preliminary study, we show that DNA microarrays containing approximately 1200 genes or expressed sequence tags (ESTs) are able to reproducibly detect differences in gene expression between the inbred ADOL lines 63 (MD resistant) and 72 (MD susceptible) of uninfected and Marek's disease virus (MDV)-infected peripheral blood lymphocytes. Microarray data were validated by quantitative polymerase chain reaction (PCR) and found to be consistent with previous literature on gene induction or immune response. Integration of the microarrays with genetic mapping data was achieved with a sample of 15 genes. Twelve of these genes had mapped human orthologues. Seven genes were located on the chicken linkage map as predicted by the human-chicken comparative map, while two other genes defined a new conserved syntenic group. More importantly, one of the genes with differential expression is known to confer genetic resistance to MD while another gene is a prime positional candidate for a QTL.     

Microarray-based kinetic colorimetric detection for quantitative multiplex protein phosphorylation analysis

Commonly used colorimetric detection applied to protein microarrays with enzymatic signal amplification leads to non‐linear signal production upon increase in analyte concentration, thereby considerably limiting the range and accuracy of quantitative readout interpretation. To extend the detection range, we developed a kinetic colorimetric detection protocol for the analysis of ELISA microarrays designed to measure multiple phosphorylated proteins using the platforms ArrayTube? and ArrayStrip?. With our novel quantification approach, microarrays were calibrated over a broad concentration range spanning four orders of magnitude of analyte concentration with picomolar threshold. We used this design for the simultaneous quantitative measurement of 15 phosphorylated proteins on a single chip.     

Gene expression differences in mice divergently selected for methamphetamine sensitivity

In an effort to identify genes that may be important for drug-abuse liability, we mapped behavioral quantitative trait loci (bQTL) for sensitivity to the locomotor stimulant effect of methamphetamine (MA) using two mouse lines that were selectively bred for high MA-induced activity (HMACT) or low MA-induced activity (LMACT). We then examined gene expression differences between these lines in the nucleus accumbens, using 20 U74Av2 Affymetrix microarrays and quantitative polymerase chain reaction (qPCR). Expression differences were detected for several genes, including Casein Kinase 1 Epsilon (Csnkle), glutamate receptor, ionotropic, AMPA1 (GluR1), GABA B1 receptor (Gabbr1), and dopamine- and cAMP-regulated phosphoprotein of 32 kDa (Darpp-32). We used the database to identify QTL that regulate the expression of the genes identified by the microarrays (expression QTL; eQTL). This approach identified an eQTL for Csnkle on Chromosome 15 (LOD=3.8) that comapped with a bQTL for the MA stimulation phenotype (LOD=4.5), suggesting that a single allele may cause both traits. The chromosomal region containing this QTL has previously been associated with sensitivity to the stimulant effects of cocaine. These results suggest that selection was associated with (and likely caused) altered gene expression that is partially attributable to different frequencies of gene expression polymorphisms. Combining classical genetics with analysis of whole-genome gene expression and bioinformatic resources provides a powerful method for provisionally identifying genes that influence complex traits. The identified genes provide excellent candidates for future hypothesis-driven studies, translational genetic studies, and pharmacological interventions.     

Nitric oxide activates diverse signaling pathways to regulate gene expression

Nitric oxide signaling is crucial for effecting long lasting changes in cells, including gene expression, cell cycle arrest, apoptosis, and differentiation. We have determined the temporal order of gene activation induced by NO in mammalian cells and have examined the signaling pathways that mediate the action of NO. Using microarrays to study the kinetics of gene activation by NO, we have determined that NO induces three distinct waves of gene activity. The first wave is induced within 30 min of exposure to NO and represents the primary gene targets of NO. It is followed by subsequent waves of gene activity that may reflect further cascades of NO-induced gene expression. We verified our results using quantitative real time PCR and further validated our conclusions about the effects of NO by using cytokines to induce endogenous NO production. We next applied pharmacological and genetic approaches to determine the signaling pathways that are used by NO to regulate gene expression. We used inhibitors of particular signaling pathways, as well as cells from animals with a deleted p53 gene, to define groups of genes that require phosphatidylinositol 3-kinase, protein kinase C, NF-kappaB, p53, or combinations thereof for activation by NO. Our results demonstrate that NO utilizes several independent signaling pathways to induce gene expression.     

Genome-wide patterns of expression in Drosophila pure species and hybrid males

One of the most fundamental questions for understanding the origin of species is why genes that function to cause fertility in a pure-species genetic background fail to produce fertility in a hybrid genetic background. A related question is why the sex that is most often sterile or inviable in hybrids is the heterogametic (usually male) sex. In this survey, we have examined the extent and nature of differences in gene expression between fertile adult males of two Drosophila species and sterile hybrid males produced from crosses between these species. Using oligonucleotide microarrays and real-time quantitative polymerase chain reaction, we have identified and confirmed that differences in gene expression exist between pure species and hybrid males, and many of these differences are quantitative rather than qualitative. Furthermore, genes that are expressed primarily or exclusively in males, including several involved in spermatogenesis, are disproportionately misexpressed in hybrids, suggesting a possible genetic cause for their sterility.     

Weighted analysis of microarray gene expression using maximum-likelihood

MOTIVATION: The numerical values of gene expression measured using microarrays are usually presented to the biological end-user as summary statistics of spot pixel data, such as the spot mean, median and mode. Much of the subsequent data analysis reported in the literature, however, uses only one of these spot statistics. This results in sub-optimal estimates of gene expression levels and a need for improvement in quantitative spot variation surveillance. RESULTS: This paper develops a maximum-likelihood method for estimating gene expression using spot mean, variance and pixel number values available from typical microarray scanners. It employs a hierarchical model of variation between and within microarray spots. The hierarchical maximum-likelihood estimate (MLE) is shown to be a more efficient estimator of the mean than the 'conventional' estimate using solely the spot mean values (i.e. without spot variance data). Furthermore, under the assumptions of our model, the spot mean and spot variance are shown to be sufficient statistics that do not require the use of all pixel data.The hierarchical MLE method is applied to data from both Monte Carlo (MC) simulations and a two-channel dye-swapped spotted microarray experiment. The MC simulations show that the hierarchical MLE method leads to improved detection of differential gene expression particularly when 'outlier' spots are present on the arrays. Compared with the conventional method, the MLE method applied to data from the microarray experiment leads to an increase in the number of differentially expressed genes detected for low cut-off P-values of interest.     

Single-Cell Gene Expression Analysis: Implications for Neurodegenerative and Neuropsychiatric Disorders

Technical and experimental advances in microaspiration techniques, RNA amplification, quantitative real-time polymerase chain reaction (qPCR), and cDNA microarray analysis have led to an increase in the number of studies of single-cell gene expression. In particular, the central nervous system (CNS) is an ideal structure to apply single-cell gene expression paradigms. Unlike an organ that is composed of one principal cell type, the brain contains a constellation of neuronal and noneuronal populations of cells. A goal is to sample gene expression from similar cell types within a defined region without potential contamination by expression profiles of adjacent neuronal subpopulations and noneuronal cells. The unprecedented resolution afforded by single-cell RNA analysis in combination with cDNA microarrays and qPCR-based analyses allows for relative gene expression level comparisons across cell types under different experimental conditions and disease states. The ability to analyze single cells is an important distinction from global and regional assessments of mRNA expression and can be applied to optimally prepared tissues from animal models as well as postmortem human brain tissues. This focused review illustrates the potential power of single-cell gene expression studies within the CNS in relation to neurodegenerative and neuropsychiatric disorders such as Alzheimer's disease (AD) and schizophrenia, respectively.     

Cross-Platform Prediction of Gene Expression Signatures

Gene expression signatures can predict the activation of oncogenic pathways and other phenotypes of interest via quantitative models that combine the expression levels of multiple genes. However, as the number of platforms to measure genome-wide gene expression proliferates, there is an increasing need to develop models that can be ported across diverse platforms. Because of the range of technologies that measure gene expression, the resulting signal values can vary greatly. To understand how this variation can affect the prediction of gene expression signatures, we have investigated the ability of gene expression signatures to predict pathway activation across Affymetrix and Illumina microarrays. We hybridized the same RNA samples to both platforms and compared the resultant gene expression readings, as well as the signature predictions. Using a new approach to map probes across platforms, we found that the genes in the signatures from the two platforms were highly similar, and that the predictions they generated were also strongly correlated. This demonstrates that our method can map probes from Affymetrix and Illumina microarrays, and that this mapping can be used to predict gene expression signatures across platforms.     

功能基因组学的研究方法

基因组学的研究已从结构基因组学转向功能基因组学,功能基因组学时代对于基因功能的研究也由单一基因转向大规模,批量分析,本综述了功能基因组学的研究内容与方法,主要包括：差异显示反转录PCR,基因表达序列分析（SAGE）,微点阵,遗传足迹法,反求遗传学,蛋白质组学和生物信息学等新方法。     

Parent-offspring conflict and co-adaptation: behavioural ecology meets quantitative genetics

The evolution of the complex and dynamic behavioural interactions between caring parents and their dependent offspring is a major area of research in behavioural ecology and quantitative genetics. While behavioural ecologists examine the evolution of interactions between parents and offspring in the light of parent-offspring conflict and its resolution, quantitative geneticists explore the evolution of such interactions in the light of parent-offspring co-adaptation due to combined effects of parental and offspring behaviours on fitness. To date, there is little interaction or integration between these two fields. Here, we first review the merits and limitations of each of these two approaches and show that they provide important complementary insights into the evolution of strategies for offspring begging and parental resource provisioning. We then outline how central ideas from behavioural ecology and quantitative genetics can be combined within a framework based on the concept of behavioural reaction norms, which provides a common basis for behavioural ecologists and quantitative geneticists to study the evolution of parent-offspring interactions. Finally, we discuss how the behavioural reaction norm approach can be used to advance our understanding of parent-offspring conflict by combining information about the genetic basis of traits from quantitative genetics with key insights regarding the adaptive function and dynamic nature of parental and offspring behaviours from behavioural ecology.     

Differential expression of the L-plastin gene in human colorectal cancer progression and metastasis.

To identify molecular alterations in the progression of colorectal carcinoma, we analyzed gene expression profiles of colon cancer cell lines derived from primary and metastatic tumors from a single patient. Of 2280 cDNAs investigated using our in-house microarray, the expression of 6 genes (tumor-associated antigen L6, L-plastin, the human homologue of yeast ribosomal protein S28, the B-cell translocation gene, mitochondrial aspartate-aminotransferase, and HLA-A) increased, while that of 2 genes (keratin 5 and phosphoglucomutase) decreased in metastatic-tumor-derived cells compared with primary-tumor-derived cells. Of these genes, we assessed the L-plastin gene, an actin-bundling protein, at the protein level using a tissue microarray consisting of 58 clinically stratified colorectal cancer specimens. Consistent with our microarray results, the expression of L-plastin was significantly correlated with the progression of cancer staging. Therefore, our results suggest that the L-plastin gene is a potential metastatic marker. In addition, combining cDNA microarrays and tissue arrays, as shown here, is thought to facilitate the rapid characterization of candidate biomarkers.     

Ligand accumulation in autocrine cell cultures

Cell-culture assays are routinely used to analyze autocrine signaling systems, but quantitative experiments are rarely possible. To enable the quantitative design and analysis of experiments with autocrine cells, we develop a biophysical theory of ligand accumulation in cell-culture assays. Our theory predicts the ligand concentration as a function of time and measurable parameters of autocrine cells and cell-culture experiments. The key step of our analysis is the derivation of the survival probability of a single ligand released from the surface of an autocrine cell. An expression for this probability is derived using the boundary homogenization approach and tested by stochastic simulations. We use this expression in the integral balance equations, from which we find the Laplace transform of the ligand concentration. We demonstrate how the theory works by analyzing the autocrine epidermal growth factor receptor system and discuss the extension of our methods to other experiments with cultured autocrine cells.