Structural biology and phylogenetic estimation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62633/1/388527a0.pd

Tools for efficient epistasis detection in genome-wide association study

<p>Abstract</p> <p>Background</p> <p>Genome-wide association study (GWAS) aims to find genetic factors underlying complex phenotypic traits, for which epistasis or gene-gene interaction detection is often preferred over single-locus approach. However, the computational burden has been a major hurdle to apply epistasis test in the genome-wide scale due to a large number of single nucleotide polymorphism (SNP) pairs to be tested.</p> <p>Results</p> <p>We have developed a set of three efficient programs, FastANOVA, COE and TEAM, that support epistasis test in a variety of problem settings in GWAS. These programs utilize permutation test to properly control error rate such as family-wise error rate (FWER) and false discovery rate (FDR). They guarantee to find the optimal solutions, and significantly speed up the process of epistasis detection in GWAS.</p> <p>Conclusions</p> <p>A web server with user interface and source codes are available at the website <url>http://www.csbio.unc.edu/epistasis/</url>. The source codes are also available at SourceForge <url>http://sourceforge.net/projects/epistasis/</url>.</p

Mapping genetic determinants of host susceptibility to Pseudomonas aeruginosa lung infection in mice.

Background: P. aeruginosa is one of the top three causes of opportunistic human bacterial infections. The remarkable variability in the clinical outcomes of this infection is thought to be associated with genetic predisposition. However, the genes underlying host susceptibility to P. aeruginosa infection are still largely unknown. Results: As a step towards mapping these genes, we applied a genome wide linkage analysis approach to a mouse model. A large F2 intercross population, obtained by mating P. aeruginosa-resistant C3H/HeOuJ, and susceptible A/J mice, was used for quantitative trait locus (QTL) mapping. The F2 progenies were challenged with a P. aeruginosa clinical strain and monitored for the survival time up to 7 days post-infection, as a disease phenotype associated trait. Selected phenotypic extremes of the F2 distribution were genotyped with high-density single nucleotide polymorphic (SNP) markers, and subsequently QTL analysis was performed. A significant locus was mapped on chromosome 6 and was named P. aeruginosa infection resistance locus 1 (Pairl1). The most promising candidate genes, including Dok1, Tacr1, Cd207, Clec4f, Gp9, Gata2, Foxp1, are related to pathogen sensing, neutrophils and macrophages recruitment and inflammatory processes. Conclusions: We propose a set of genes involved in the pathogenesis of P. aeruginosa infection that may be explored to complement human studie

Using Stochastic Causal Trees to Augment Bayesian Networks for Modeling eQTL Datasets

<p>Abstract</p> <p>Background</p> <p>The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. In this manuscript, we present a probabilistic method capable of learning the causal relationships between transcripts at all levels in the network. We use the information provided by our method as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction.</p> <p>Results</p> <p>Using established protocols to synthesize eQTL networks and corresponding data, we show that our method achieves improved performance over existing leading methods. For the goal of gene network reconstruction, our method achieves improvements in recall ranging from 20% to 90% across a broad range of precision levels and for datasets of varying sample sizes. Additionally, we show that the learned networks can be utilized for expression quantitative trait loci mapping, resulting in upwards of 10-fold increases in recall over traditional univariate mapping.</p> <p>Conclusions</p> <p>Using the information from our method as a prior for Bayesian network structure learning yields large improvements in accuracy for the tasks of gene network reconstruction and expression quantitative trait loci mapping. In particular, our method is effective for establishing causal relationships between transcripts located both proximally and distally from genomic loci.</p

Identification of genes and pathways associated with cytotoxic T lymphocyte infiltration of serous ovarian cancer

BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are predictors of disease-specific survival (DSS) in ovarian cancer. It is largely unknown what factors contribute to lymphocyte recruitment. Our aim was to evaluate genes and pathways contributing to infiltration of cytotoxic T lymphocytes (CTLs) in advanced-stage serous ovarian cancer. METHODS: For this study global gene expression was compared between low TIL (n=25) and high TIL tumours (n=24). The differences in gene expression were evaluated using parametric T-testing. Selectively enriched biological pathways were identified with gene set enrichment analysis. Prognostic influence was validated in 157 late-stage serous ovarian cancer patients. Using immunohistochemistry, association of selected genes from identified pathways with CTL was validated. RESULTS: The presence of CTL was associated with 320 genes and 23 pathways (P<0.05). In addition, 54 genes and 8 pathways were also associated with DSS in our validation cohort. Immunohistochemical evaluation showed strong correlations between MHC class I and II membrane expression, parts of the antigen processing and presentation pathway, and CTL recruitment. CONCLUSION: Gene expression profiling and pathway analyses are valuable tools to obtain more understanding of tumour characteristics influencing lymphocyte recruitment in advanced-stage serous ovarian cancer. Identified genes and pathways need to be further investigated for suitability as therapeutic targets

Salespeople’s Renqing Orientation, Self-esteem, and Selling Behaviors: An Empirical Study in Taiwan

The purpose of this study was to investigate how salespeople’s renqing orientation and self-esteem jointly affect their selling behavior. Data were obtained from a survey of salespeople from 17 pharmaceutical and consumer-goods companies in Taiwan (n = 216). Salespeople’s renqing orientation (i.e., their propensity to adhere to the accepted norm of reciprocity) compensates the negative effect of self-esteem on their selling behaviors, such as adaptive selling and hard work. Our study results underscore the critical role of the character trait of renqing orientation in a culture emphasizing a norm of reciprocity. Therefore, it would be useful to consider a strategy of recruiting salespeople with either a high self-esteem or a combination of high renqing orientation and low self-esteem. The existing literature of industrial/organizational psychology and marketing primarily relies on constructs that are derived from Western cultural contexts. However, the present paper extended these literatures by investigating the possible joint effects of self-esteem with a trait originated from the Chinese culture on salespeople’s selling behaviors

A Population Proportion approach for ranking differentially expressed genes

<p>Abstract</p> <p>Background</p> <p>DNA microarrays are used to investigate differences in gene expression between two or more classes of samples. Most currently used approaches compare mean expression levels between classes and are not geared to find genes whose expression is significantly different in only a subset of samples in a class. However, biological variability can lead to situations where key genes are differentially expressed in only a subset of samples. To facilitate the identification of such genes, a new method is reported.</p> <p>Methods</p> <p>The key difference between the Population Proportion Ranking Method (PPRM) presented here and almost all other methods currently used is in the quantification of variability. PPRM quantifies variability in terms of inter-sample ratios and can be used to calculate the relative merit of differentially expressed genes with a specified difference in expression level between at least some samples in the two classes, which at the same time have lower than a specified variability within each class.</p> <p>Results</p> <p>PPRM is tested on simulated data and on three publicly available cancer data sets. It is compared to the t test, PPST, COPA, OS, ORT and MOST using the simulated data. Under the conditions tested, it performs as well or better than the other methods tested under low intra-class variability and better than t test, PPST, COPA and OS when a gene is differentially expressed in only a subset of samples. It performs better than ORT and MOST in recognizing non differentially expressed genes with high variability in expression levels across all samples. For biological data, the success of predictor genes identified in appropriately classifying an independent sample is reported.</p

Desire and Dread from the Nucleus Accumbens: Cortical Glutamate and Subcortical GABA Differentially Generate Motivation and Hedonic Impact in the Rat

Background: GABAergic signals to the nucleus accumbens (NAc) shell arise from predominantly subcortical sources whereas glutamatergic signals arise mainly from cortical-related sources. Here we contrasted GABAergic and glutamatergic generation of hedonics versus motivation processes, as a proxy for comparing subcortical and cortical controls of emotion. Local disruptions of either signals in medial shell of NAc generate intense motivated behaviors corresponding to desire and/or dread, along a rostrocaudal gradient. GABA or glutamate disruptions in rostral shell generate appetitive motivation whereas disruptions in caudal shell elicit fearful motivation. However, GABA and glutamate signals in NAc differ in important ways, despite the similarity of their rostrocaudal motivation gradients. Methodology/Principal Findings: Microinjections of a GABAA agonist (muscimol), or of a glutamate AMPA antagonist (DNQX) in medial shell of rats were assessed for generation of hedonic ‘‘liking’ ’ or ‘‘disliking’ ’ by measuring orofacial affective reactions to sucrose-quinine taste. Motivation generation was independently assessed measuring effects on eating versus natural defensive behaviors. For GABAergic microinjections, we found that the desire-dread motivation gradient was mirrored by an equivalent hedonic gradient that amplified affective taste ‘‘liking’ ’ (at rostral sites) versus ‘‘disliking’ ’ (at caudal sites). However, manipulation of glutamatergic signals completely failed to alter pleasure-displeasure reactions to sensory hedonic impact, despite producing a strong rostrocaudal gradient of motivation