How accurate can genetic predictions be?

BACKGROUND: Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution. RESULTS: Here, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk. We present these limits in terms of the best-case receiver-operating characteristic (ROC) curve, consisting of the best-case test sensitivities and specificities, and the AUC (area under the curve) measure of accuracy. We apply our method to genetic prediction of type 2 diabetes and breast cancer, and we additionally show the best possible accuracy that can be obtained from integrated predictors, which can incorporate non-genetic features. CONCLUSION: Knowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified.

Context-specific ontology integration: a bayesian approach.

We introduce a principled computational framework and methodology for automated discovery of context-specific functional links between ontologies. Our model leverages over disparate free-text literature resources to score the model of dependency linking two terms under a context against their model of independence. We identify linked terms as those having a significant bayes factor (p < 0.01). To scale our algorithm over massive ontologies, we propose a heuristic pruning technique as an efficient algorithm for inferring such links.We have applied this method to translationalize Gene Ontology to all other ontologies available at National Center of Biomedical Ontology (NCBO) BioPortal under the context of Human Disease ontology. Our results show that in addition to broadening the scope of hypothesis for researchers, our work can potentially be used to explore continuum of relationships among ontologies to guide various biological experiments.

Transcriptional network predicts viral set point during acute HIV-1 infection.

BACKGROUND: HIV-1-infected individuals with higher viral set points progress to AIDS more rapidly than those with lower set points. Predicting viral set point early following infection can contribute to our understanding of early control of HIV-1 replication, to predicting long-term clinical outcomes, and to the choice of optimal therapeutic regimens. METHODS: In a longitudinal study of 10 untreated HIV-1-infected patients, we used gene expression profiling of peripheral blood mononuclear cells to identify transcriptional networks for viral set point prediction. At each sampling time, a statistical analysis inferred the optimal transcriptional network that best predicted viral set point. We then assessed the accuracy of this transcriptional model by predicting viral set point in an independent cohort of 10 untreated HIV-1-infected patients from Malawi. RESULTS: The gene network inferred at time of enrollment predicted viral set point 24 weeks later in the independent Malawian cohort with an accuracy of 87.5%. As expected, the predictive accuracy of the networks inferred at later time points was even greater, exceeding 90% after week 4. The composition of the inferred networks was largely conserved between time points. The 12 genes comprising this dynamic signature of viral set point implicated the involvement of two major canonical pathways: interferon signaling (p<0.0003) and membrane fraction (p<0.02). A silico knockout study showed that HLA-DRB1 and C4BPA may contribute to restricting HIV-1 replication. CONCLUSIONS: Longitudinal gene expression profiling of peripheral blood mononuclear cells from patients with acute HIV-1 infection can be used to create transcriptional network models to early predict viral set point with a high degree of accuracy.

Glycosaminoglycans and glucose prevent apoptosis in 4-methylumbelliferone-treated human aortic smooth muscle cells.

Smooth muscle cells (SMCs) have a pivotal role in cardiovascular diseases and are responsible for hyaluronan (HA) deposition in thickening vessel walls. HA regulates SMC proliferation, migration, and inflammation, which accelerates neointima formation. We used the HA synthesis inhibitor 4-methylumbelliferone (4-MU) to reduce HA production in human aortic SMCs and found a significant increase of apoptotic cells. Interestingly, the exogenous addition of HA together with 4-MU reduced apoptosis. A similar anti-apoptotic effect was observed also by adding other glycosaminoglycans and glucose to 4-MU-treated cells. Furthermore, the anti-apoptotic effect of HA was mediated by Toll-like receptor 4, CD44, and PI3K but not by ERK1/2.

Inferring cell cycle feedback regulation from gene expression data.

Feedback control is an important regulatory process in biological systems, which confers robustness against external and internal disturbances. Genes involved in feedback structures are therefore likely to have a major role in regulating cellular processes. Here we rely on a dynamic Bayesian network approach to identify feedback loops in cell cycle regulation. We analyzed the transcriptional profile of the cell cycle in HeLa cancer cells and identified a feedback loop structure composed of 10 genes. In silico analyses showed that these genes hold important roles in system's dynamics. The results of published experimental assays confirmed the central role of 8 of the identified feedback loop genes in cell cycle regulation. In conclusion, we provide a novel approach to identify critical genes for the dynamics of biological processes. This may lead to the identification of therapeutic targets in diseases that involve perturbations of these dynamics.

A transcriptional network signature characterizes lung cancer subtypes.

BACKGROUND: Transcriptional networks play a central role in cancer development. The authors described a systems biology approach to cancer classification based on the reverse engineering of the transcriptional network surrounding the 2 most common types of lung cancer: adenocarcinoma (AC) and squamous cell carcinoma (SCC). METHODS: A transcriptional network classifier was inferred from the molecular profiles of 111 human lung carcinomas. The authors tested its classification accuracy in 7 independent cohorts, for a total of 422 subjects of Caucasian, African, and Asian descent. RESULTS: The model for distinguishing AC from SCC was a 25-gene network signature. Its performance on the 7 independent cohorts achieved 95.2% classification accuracy. Even more surprisingly, 95% of this accuracy was explained by the interplay of 3 genes (KRT6A, KRT6B, KRT6C) on a narrow cytoband of chromosome 12. The role of this chromosomal region in distinguishing AC and SCC was further confirmed by the analysis of another group of 28 independent subjects assayed by DNA copy number changes. The copy number variations of bands 12q12, 12q13, and 12q12-13 discriminated these samples with 84% accuracy. CONCLUSIONS: These results suggest the existence of a robust signature localized in a relatively small area of the genome, and show the clinical potential of reverse engineering transcriptional networks from molecular profiles.

Is the reduction of dimensionality to a small number of features always necessary in constructing predictive models for analysis of complex diseases or behaviours?

Gene expression and genome wide association data have provided researchers the opportunity to study many complex traits and diseases. When designing prognostic and predictive models capable of phenotypic classification in this area, significant reduction of dimensionality through stringent filtering and/or feature selection is often deemed imperative. Here, this work challenges this presumption through both theoretical and empirical analysis. This work demonstrates that by a proper compromise between structure of the selected model and the number of features, one is able to achieve better performance even in large dimensionality. The inclusion of many genes/variants in the classification rules can help shed new light on the analysis of complex traitstraits that are typically determined by many causal variants with small effect size.

Mapping transcription mechanisms from multimodal genomic data.

BACKGROUND: Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data. RESULTS: We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate. CONCLUSIONS: The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.

Association of linear growth impairment in pediatric Crohn's disease and a known height locus: a pilot study.

The etiology of growth impairment in Crohn's disease (CD) has been inadequately explained by nutritional, hormonal, and/or disease-related factors, suggesting that genetics may be an additional contributor. The aim of this cross-sectional study was to investigate genetic variants associated with linear growth in pediatric-onset CD. We genotyped 951 subjects (317 CD patient-parent trios) for 64 polymorphisms within 14 CD-susceptibility and 23 stature-associated loci. Patient height-for-age Z-score < -1.64 was used to dichotomize probands into growth-impaired and nongrowth-impaired groups. The transmission disequilibrium test (TDT) was used to study association to growth impairment. There was a significant association between growth impairment in CD (height-for-age Z-score < -1.64) and a stature-related polymorphism in the dymeclin gene DYM (rs8099594) (OR = 3.2, CI [1.57-6.51], p = 0.0007). In addition, there was nominal over-transmission of two CD-susceptibility alleles, 10q21.1 intergenic region (rs10761659) and ATG16L1 (rs10210302), in growth-impaired CD children (OR = 2.36, CI [1.26-4.41] p = 0.0056 and OR = 2.45, CI [1.22-4.95] p = 0.0094, respectively). Our data indicate that genetic influences due to stature-associated and possibly CD risk alleles may predispose CD patients to alterations in linear growth. This is the first report of a link between a stature-associated locus and growth impairment in CD.

Genome-wide association for smoking cessation success in a trial of precessation nicotine replacement.

Abilities to successfully quit smoking display substantial evidence for heritability in classic and molecular genetic studies. Genome-wide association (GWA) studies have demonstrated single-nucleotide polymorphisms (SNPs) and haplotypes that distinguish successful quitters from individuals who were unable to quit smoking in clinical trial participants and in community samples. Many of the subjects in these clinical trial samples were aided by nicotine replacement therapy (NRT). We now report novel GWA results from participants in a clinical trial that sought dose/response relationships for "precessation" NRT. In this trial, 369 European-American smokers were randomized to 21 or 42 mg NRT, initiated 2 wks before target quit dates. Ten-week continuous smoking abstinence was assessed on the basis of self-reports and carbon monoxide levels. SNP genotyping used Affymetrix 6.0 arrays. GWA results for smoking cessation success provided no P value that reached "genome-wide" significance. Compared with chance, these results do identify (a) more clustering of nominally positive results within small genomic regions, (b) more overlap between these genomic regions and those identified in six prior successful smoking cessation GWA studies and (c) sets of genes that fall into gene ontology categories that appear to be biologically relevant. The 1,000 SNPs with the strongest associations form a plausible Bayesian network; no such network is formed by randomly selected sets of SNPs. The data provide independent support, based on individual genotyping, for many loci previously nominated on the basis of data from genotyping in pooled DNA samples. These results provide further support for the idea that aid for smoking cessation may be personalized on the basis of genetic predictors of outcome.

A systems biology approach to modeling vibrio cholerae gene expression under virulence-inducing conditions.

Vibrio cholerae is a Gram-negative bacillus that is the causative agent of cholera. Pathogenesis in vivo occurs through a series of spatiotemporally controlled events under the control of a gene cascade termed the ToxR regulon. Major genes in the ToxR regulon include the master regulators toxRS and tcpPH, the downstream regulator toxT, and virulence factors, the ctxAB and tcpA operons. Our current understanding of the dynamics of virulence gene expression is limited to microarray analyses of expression at selected time points. To better understand this process, we utilized a systems biology approach to examine the temporal regulation of gene expression in El Tor V. cholerae grown under virulence-inducing conditions in vitro (AKI medium), using high-resolution time series genomic profiling. Results showed that overall gene expression in AKI medium mimics that of in vivo studies but with less clear temporal separation between upstream regulators and downstream targets. Expression of toxRS was unaffected by growth under virulence-inducing conditions, but expression of toxT was activated shortly after switching from stationary to aerating conditions. The tcpA operon was also activated early during mid-exponential-phase growth, while the ctxAB operon was turned on later, after the rise in toxT expression. Expression of ctxAB continued to rise despite an eventual decrease in toxT. Cluster analysis of gene expression highlighted 15 hypothetical genes and six genes related to environmental information processing that represent potential new members of the ToxR regulon. This study applies systems biology tools to analysis of gene expression of V. cholerae in vitro and provides an important comparator for future studies done in vivo.

Genomic characterization of Wilms' tumor suppressor 1 targets in nephron progenitor cells during kidney development.

The Wilms' tumor suppressor 1 (WT1) gene encodes a DNA- and RNA-binding protein that plays an essential role in nephron progenitor differentiation during renal development. To identify WT1 target genes that might regulate nephron progenitor differentiation in vivo, we performed chromatin immunoprecipitation (ChIP) coupled to mouse promoter microarray (ChIP-chip) using chromatin prepared from embryonic mouse kidney tissue. We identified 1663 genes bound by WT1, 86% of which contain a previously identified, conserved, high-affinity WT1 binding site. To investigate functional interactions between WT1 and candidate target genes in nephron progenitors, we used a novel, modified WT1 morpholino loss-of-function model in embryonic mouse kidney explants to knock down WT1 expression in nephron progenitors ex vivo. Low doses of WT1 morpholino resulted in reduced WT1 target gene expression specifically in nephron progenitors, whereas high doses of WT1 morpholino arrested kidney explant development and were associated with increased nephron progenitor cell apoptosis, reminiscent of the phenotype observed in Wt1(-/-) embryos. Collectively, our results provide a comprehensive description of endogenous WT1 target genes in nephron progenitor cells in vivo, as well as insights into the transcriptional signaling networks controlled by WT1 that might direct nephron progenitor fate during renal development.

The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms.

The field of synthetic biology holds an inspiring vision for the future; it integrates computational analysis, biological data and the systems engineering paradigm in the design of new biological machines and systems. These biological machines are built from basic biomolecular components analogous to electrical devices, and the information flow among these components requires the augmentation of biological insight with the power of a formal approach to information management. Here we review the informatics challenges in synthetic biology along three dimensions: in silico, in vitro and in vivo. First, we describe state of the art of the in silico support of synthetic biology, from the specific data exchange formats, to the most popular software platforms and algorithms. Next, we cast in vitro synthetic biology in terms of information flow, and discuss genetic fidelity in DNA manipulation, development strategies of biological parts and the regulation of biomolecular networks. Finally, we explore how the engineering chassis can manipulate biological circuitries in vivo to give rise to future artificial organisms.

Integrative predictive model of coronary artery calcification in atherosclerosis.

BACKGROUND: Many different genetic and clinical factors have been identified as causes or contributors to atherosclerosis. We present a model of preclinical atherosclerosis based on genetic and clinical data that predicts the presence of coronary artery calcification in healthy Americans of European descent 45 to 84 years of age in the Multi-Ethnic Study of Atherosclerosis (MESA). METHODS AND RESULTS: We assessed 712 individuals for the presence or absence of coronary artery calcification and assessed their genotypes for 2882 single-nucleotide polymorphisms. With the use of these single-nucleotide polymorphisms and relevant clinical data, a Bayesian network that predicts the presence of coronary calcification was constructed. The model contained 13 single-nucleotide polymorphisms (from genes AGTR1, ALOX15, INSR, PRKAB1, IL1R2, ESR2, KCNK1, FBLN5, PPARA, VEGFA, PON1, TDRD6, PLA2G7, and 1 ancestry informative marker) and 5 clinical variables (sex, age, weight, smoking, and diabetes mellitus) and achieved 85% predictive accuracy, as measured by area under the receiver operating characteristic curve. This is a significant (P<0.001) improvement on models that use just the single-nucleotide polymorphism data or just the clinical variables. CONCLUSIONS: We present an investigation of joint genetic and clinical factors associated with atherosclerosis that shows predictive results for both cases, as well as enhanced performance for their combination.

Dynamic gene expression analysis links melanocyte growth arrest with nevogenesis.

Like all primary cells in vitro, normal human melanocytes exhibit a physiologic decay in proliferative potential as it transitions to a growth-arrested state. The underlying transcriptional program(s) that regulate this phenotypic change is largely unknown. To identify molecular determinants of this process, we performed a Bayesian-based dynamic gene expression analysis on primary melanocytes undergoing proliferative arrest. This analysis revealed several related clusters whose expression behavior correlated with the melanocyte growth kinetics; we designated these clusters the melanocyte growth arrest program (MGAP). These MGAP genes were preferentially represented in benign melanocytic nevi over melanomas and selectively mapped to the hepatocyte fibrosis pathway. This transcriptional relationship between melanocyte growth stasis, nevus biology, and fibrogenic signaling was further validated in vivo by the demonstration of strong pericellular collagen deposition within benign nevi but not melanomas. Taken together, our study provides a novel view of fibroplasia in both melanocyte biology and nevogenesis.

Predicting response to short-acting bronchodilator medication using Bayesian networks.

AIMS: Bronchodilator response tests measure the effect of beta(2)-agonists, the most commonly used short-acting reliever drugs for asthma. We sought to relate candidate gene SNP data with bronchodilator response and measure the predictive accuracy of a model constructed with genetic variants. MATERIALS & METHODS: Bayesian networks, multivariate models that are able to account for simultaneous associations and interactions among variables, were used to create a predictive model of bronchodilator response using candidate gene SNP data from 308 Childhood Asthma Management Program Caucasian subjects. RESULTS: The model found that 15 SNPs in 15 genes predict bronchodilator response with fair accuracy, as established by a fivefold cross-validation area under the receiver-operating characteristic curve of 0.75 (standard error: 0.03). CONCLUSION: Bayesian networks are an attractive approach to analyze large-scale pharmacogenetic SNP data because of their ability to automatically learn complex models that can be used for the prediction and discovery of novel biological hypotheses.

Transcriptional network classifiers.

BACKGROUND: Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biological processes are outcomes of regulatory gene interactions, this paper develops a system biology approach to infer function-dependent transcriptional networks modulating phenotypic traits, which serve as a classifier to identify tissue states. Due to gene interactions taken into account in the analysis, we can achieve higher classification accuracy than existing methods. RESULTS: Our system biology approach is carried out by the Bayesian networks framework. The algorithm consists of two steps: gene filtering by Bayes factor followed by collinearity elimination via network learning. We validate our approach with two clinical data. In the study of lung cancer subtypes discrimination, we obtain a 25-gene classifier from 111 training samples, and the test on 422 independent samples achieves 95% classification accuracy. In the study of thoracic aortic aneurysm (TAA) diagnosis, 61 samples determine a 34-gene classifier, whose diagnosis accuracy on 33 independent samples achieves 82%. The performance comparisons with three other popular methods, PCA/LDA, PAM, and Weighted Voting, confirm that our approach yields superior classification accuracy and a more compact signature. CONCLUSIONS: The system biology approach presented in this paper is able to infer function-dependent transcriptional networks, which in turn can classify biological samples with high accuracy. The validation of our classifier using clinical data demonstrates the promising value of our proposed approach for disease diagnosis.

Links between regimen-related toxicities in patients being treated for colorectal cancer.

PURPOSE OF REVIEW: To summarize the available evidence on cooccurring gastrointestinal toxicities and their potential link with other symptoms in cancer patients. The information obtained from colorectal cancer patient cohorts will be used as an example. RECENT FINDINGS: In recent years, it has become clear that gastrointestinal toxicities do not occur in isolation in cancer patients. Rather, they may link or associate with many other disturbances. Data have emerged that suggest that many of the complications of cancer chemotherapy occur in clusters and seem to support the sharing of common pathogenesis for clustering toxicities. SUMMARY: During the last few years, research in symptom clusters and cooccurring linked toxicities has markedly changed, progressively shifting from a simplistic descriptive picture to more comprehensive and pathogenetically driven analyses. Still, many questions remain to be answered, and whether and how toxicity aggregations vary during the treatment course remains to be elucidated.

Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records.

OBJECTIVE: Identify clinical factors that modulate the risk of progression to COPD among asthma patients using data extracted from electronic medical records. DESIGN: Demographic information and comorbidities from adult asthma patients who were observed for at least 5 years with initial observation dates between 1988 and 1998, were extracted from electronic medical records of the Partners Healthcare System using tools of the National Center for Biomedical Computing "Informatics for Integrating Biology to the Bedside" (i2b2). MEASUREMENTS: A predictive model of COPD was constructed from a set of 9,349 patients (843 cases, 8,506 controls) using Bayesian networks. The model's predictive accuracy was tested using it to predict COPD in a future independent set of asthma patients (992 patients; 46 cases, 946 controls), who had initial observation dates between 1999 and 2002. RESULTS: A Bayesian network model composed of age, sex, race, smoking history, and 8 comorbidity variables is able to predict COPD in the independent set of patients with an accuracy of 83.3%, computed as the area under the Receiver Operating Characteristic curve (AUROC). CONCLUSIONS: Our results demonstrate that data extracted from electronic medical records can be used to create predictive models. With improvements in data extraction and inclusion of more variables, such models may prove to be clinically useful.