Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma

The worldwide incidence of melanoma is rising faster than any other cancer, and prognosis for patients with metastatic disease is poor. Current targeted therapies are limited in their durability and/or effect size in certain patient populations due to acquired mechanisms of resistance. Thus, the development of synergistic combinatorial treatment regimens holds great promise to improve patient outcomes. We have previously shown that a model for in-silico knowledge discovery, Translational Ontology-anchored Knowledge Discovery Engine (TOKEn), is able to generate valid relationships between bimolecular and clinical phenotypes. In this study, we have aggregated observational and canonical knowledge consisting of melanoma-related biomolecular entities and targeted therapeutics in a computationally tractable model. We demonstrate here that the explicit linkage of therapeutic modalities with biomolecular underpinnings of melanoma utilizing the TOKEn pipeline yield a set of informed relationships that have the potential to generate combination therapy strategies.

Genome-wide genetic interaction analysis of glaucoma using expert knowledge derived from human phenotype networks

The large volume of GWAS data poses great computational challenges for analyzing genetic interactions associated with common human diseases. We propose a computational framework for characterizing epistatic interactions among large sets of genetic attributes in GWAS data. We build the human phenotype network (HPN) and focus around a disease of interest. In this study, we use the GLAUGEN glaucoma GWAS dataset and apply the HPN as a biological knowledge-based filter to prioritize genetic variants. Then, we use the statistical epistasis network (SEN) to identify a significant connected network of pairwise epistatic interactions among the prioritized SNPs. These clearly highlight the complex genetic basis of glaucoma. Furthermore, we identify key SNPs by quantifying structural network characteristics. Through functional annotation of these key SNPs using Biofilter, a software accessing multiple publicly available human genetic data sources, we find supporting biomedical evidences linking glaucoma to an array of genetic diseases, proving our concept. We conclude by suggesting hypotheses for a better understanding of the disease.

Phenome-wide Association Study Relating Pretreatment Laboratory Parameters With Human Genetic Variants in AIDS Clinical Trials Group Protocols

Background.  Phenome-Wide Association Studies (PheWAS) identify genetic associations across multiple phenotypes. Clinical trials offer opportunities for PheWAS to identify pharmacogenomic associations. We describe the first PheWAS to use genome-wide genotypic data and to utilize human immunodeficiency virus (HIV) clinical trials data. As proof-of-concept, we focused on baseline laboratory phenotypes from antiretroviral therapy-naive individuals. Methods.  Data from 4 AIDS Clinical Trials Group (ACTG) studies were split into 2 datasets: Dataset I (1181 individuals from protocol A5202) and Dataset II (1366 from protocols A5095, ACTG 384, and A5142). Final analyses involved 2547 individuals and 5 954 294 imputed polymorphisms. We calculated comprehensive associations between these polymorphisms and 27 baseline laboratory phenotypes. Results.  A total of 10 584 (0.17%) polymorphisms had associations with P < .01 in both datasets and with the same direction of association. Twenty polymorphisms replicated associations with identical or related phenotypes reported in the Catalog of Published Genome-Wide Association Studies, including several not previously reported in HIV-positive cohorts. We also identified several possibly novel associations. Conclusions.  These analyses define PheWAS properties and principles with baseline laboratory data from HIV clinical trials. This approach may be useful for evaluating on-treatment HIV clinical trials data for associations with various clinical phenotypes.

Binning somatic mutations based on biological knowledge for predicting survival: an application in renal cell carcinoma

Enormous efforts of whole exome and genome sequencing from hundreds to thousands of patients have provided the landscape of somatic genomic alterations in many cancer types to distinguish between driver mutations and passenger mutations. Driver mutations show strong associations with cancer clinical outcomes such as survival. However, due to the heterogeneity of tumors, somatic mutation profiles are exceptionally sparse whereas other types of genomic data such as miRNA or gene expression contain much more complete data for all genomic features with quantitative values measured in each patient. To overcome the extreme sparseness of somatic mutation profiles and allow for the discovery of combinations of somatic mutations that may predict cancer clinical outcomes, here we propose a new approach for binning somatic mutations based on existing biological knowledge. Through the analysis using renal cell carcinoma dataset from The Cancer Genome Atlas (TCGA), we identified combinations of somatic mutation burden based on pathways, protein families, evolutionary conversed regions, and regulatory regions associated with survival. Due to the nature of heterogeneity in cancer, using a binning strategy for somatic mutation profiles based on biological knowledge will be valuable for improved prognostic biomarkers and potentially for tailoring therapeutic strategies by identifying combinations of driver mutations.

Measures of exposure impact genetic association studies: an example in vitamin K levels and VKORC1

Studies assessing the impact of gene-environment interactions on common human diseases and traits have been relatively few for many reasons. One often acknowledged reason is that it is difficult to accurately measure the environment or exposure. Indeed, most large-scale epidemiologic studies use questionnaires to assess and measure past and current exposure levels. While questionnaires may be cost-effective, the data may or may not accurately represent the exposure compared with more direct measurements (e.g., self-reported current smoking status versus direct measurement for cotinine levels). Much like phenotyping, the choice in how an exposure is measured may impact downstream tests of genetic association and gene-environment interaction studies. As a case study, we performed tests of association between five common VKORC1 SNPs and two different measurements of vitamin K levels, dietary (n=5,725) and serum (n=348), in the Third National Health and Nutrition Examination Studies (NHANES III). We did not replicate previously reported associations between VKORC1 and vitamin K levels using either measure. Furthermore, the suggestive associations and estimated genetic effect sizes identified in this study differed depending on the vitamin K measurement. This case study of VKORC1 and vitamin K levels serves as a cautionary example of the downstream consequences that the type of exposure measurement choices will have on genetic association and possibly gene-environment studies.

Comprehensive metabolome analyses reveal N-acetylcysteine-responsive accumulation of kynurenine in systemic lupus erythematosus: implications for activation of the mechanistic target of rapamycin

Systemic lupus erythematosus (SLE) patients exhibit depletion of the intracellular antioxidant glutathione and downstream activation of the metabolic sensor, mechanistic target of rapamycin (mTOR). Since reversal of glutathione depletion by the amino acid precursor, N-acetylcysteine (NAC), is therapeutic in SLE, its mechanism of impact on the metabolome was examined within the context of a double-blind placebo-controlled trial. Quantitative metabolome profiling of peripheral blood lymphocytes (PBL) was performed in 36 SLE patients and 42 healthy controls matched for age, gender, and ethnicity of patients using mass spectrometry that covers all major metabolic pathways. mTOR activity was assessed by western blot and flow cytometry. Metabolome changes in lupus PBL affected 27 of 80 KEGG pathways at FDR p < 0.05 with most prominent impact on the pentose phosphate pathway (PPP). While cysteine was depleted, cystine, kynurenine, cytosine, and dCTP were the most increased metabolites. Area under the receiver operating characteristic curve (AUC) logistic regression approach identified kynurenine (AUC = 0.859), dCTP (AUC = 0.762), and methionine sulfoxide (AUC = 0.708), as top predictors of SLE. Kynurenine was the top predictor of NAC effect in SLE (AUC = 0.851). NAC treatment significantly reduced kynurenine levels relative to placebo in vivo (raw p = 2.8 × 10^-7, FDR corrected p = 6.6 × 10^-5). Kynurenine stimulated mTOR activity in healthy control PBL in vitro. Metabolome changes in lupus PBL reveal a dominant impact on the PPP that reflect greater demand for nucleotides and oxidative stress. The PPP-connected and NAC-responsive accumulation of kynurenine and its stimulation of mTOR are identified as novel metabolic checkpoints in lupus pathogenesis.

Epistasis analysis using artificial intelligence

Here we introduce artificial intelligence (AI) methodology for detecting and characterizing epistasis in genetic association studies. The ultimate goal of our AI strategy is to analyze genome-wide genetics data as a human would using sources of expert knowledge as a guide. The methodology presented here is based on computational evolution, which is a type of genetic programming. The ability to generate interesting solutions while at the same time learning how to solve the problem at hand distinguishes computational evolution from other genetic programming approaches. We provide a general overview of this approach and then present a few examples of its application to real data.

A screening-testing approach for detecting gene-environment interactions using sequential penalized and unpenalized multiple logistic regression

Gene-environment (G × E) interactions are biologically important for a wide range of environmental exposures and clinical outcomes. Because of the large number of potential interactions in genomewide association data, the standard approach fits one model per G × E interaction with multiple hypothesis correction (MHC) used to control the type I error rate. Although sometimes effective, using one model per candidate G × E interaction test has two important limitations: low power due to MHC and omitted variable bias. To avoid the coefficient estimation bias associated with independent models, researchers have used penalized regression methods to jointly test all main effects and interactions in a single regression model. Although penalized regression supports joint analysis of all interactions, can be used with hierarchical constraints, and offers excellent predictive performance, it cannot assess the statistical significance of G × E interactions or compute meaningful estimates of effect size. To address the challenge of low power, researchers have separately explored screening-testing, or two-stage, methods in which the set of potential G × E interactions is first filtered and then tested for interactions with MHC only applied to the tests actually performed in the second stage. Although two-stage methods are statistically valid and effective at improving power, they still test multiple separate models and so are impacted by MHC and biased coefficient estimation. To remedy the challenges of both poor power and omitted variable bias encountered with traditional G × E interaction detection methods, we propose a novel approach that combines elements of screening-testing and hierarchical penalized regression. Specifically, our proposed method uses, in the first stage, an elastic net-penalized multiple logistic regression model to jointly estimate either the marginal association filter statistic or the gene-environment correlation filter statistic for all candidate genetic markers. In the second stage, a single multiple logistic regression model is used to jointly assess marginal terms and G × E interactions for all genetic markers that pass the first stage filter. A single likelihood-ratio test is used to determine whether any of the interactions are statistically significant. We demonstrate the efficacy of our method relative to alternative G × E detection methods on a bladder cancer data set.

Genomics of ecological adaptation in cactophilic Drosophila

Cactophilic Drosophila species provide a valuable model to study gene–environment interactions and ecological adaptation. Drosophila buzzatii and Drosophila mojavensis are two cactophilic species that belong to the repleta group, but have very different geographical distributions and primary host plants. To investigate the genomic basis of ecological adaptation, we sequenced the genome and developmental transcriptome of D. buzzatii and compared its gene content with that of D. mojavensis and two other noncactophilic Drosophila species in the same subgenus. The newly sequenced D. buzzatii genome (161.5 Mb) comprises 826 scaffolds (>3 kb) and contains 13,657 annotated protein-coding genes. Using RNA sequencing data of five life-stages we found expression of 15,026 genes, 80% protein-coding genes, and 20% noncoding RNA genes. In total, we detected 1,294 genes putatively under positive selection. Interestingly, among genes under positive selection in the D. mojavensis lineage, there is an excess of genes involved in metabolism of heterocyclic compounds that are abundant in Stenocereus cacti and toxic to nonresident Drosophila species. We found 117 orphan genes in the shared D. buzzatii–D. mojavensis lineage. In addition, gene duplication analysis identified lineage-specific expanded families with functional annotations associated with proteolysis, zinc ion binding, chitin binding, sensory perception, ethanol tolerance, immunity, physiology, and reproduction. In summary, we identified genetic signatures of adaptation in the shared D. buzzatii–D. mojavensis lineage, and in the two separate D. buzzatii and D. mojavensis lineages. Many of the novel lineage-specific genomic features are promising candidates for explaining the adaptation of these species to their distinct ecological niches.

Relevance Epistasis Network of Gastritis for Intra-chromosomes in the Korea Associated Resource (KARE) Cohort Study

Gastritis is a common but a serious disease with a potential risk of developing carcinoma. Helicobacter pylori infection is reported as the most common cause of gastritis, but other genetic and genomic factors exist, especially single-nucleotide polymorphisms (SNPs). Association studies between SNPs and gastritis disease are important, but results on epistatic interactions from multiple SNPs are rarely found in previous genome-wide association (GWA) studies. In this study, we performed computational GWA case-control studies for gastritis in Korea Associated Resource (KARE) data. By transforming the resulting SNP epistasis network into a gene-gene epistasis network, we also identified potential gene-gene interaction factors that affect the susceptibility to gastritis.

Insights from systems pharmacology into cardiovascular drug discovery and therapy

Background

Given the complex nature of cardiovascular disease (CVD), information derived from a systems-level will allow us to fully interrogate features of CVD to better understand disease pathogenesis and to identify new drug targets.

Results

Here, we describe a systematic assessment of the multi-layer interactions underlying cardiovascular drugs, targets, genes and disorders to reveal comprehensive insights into cardiovascular systems biology and pharmacology. We have identified 206 effect-mediating drug targets, which are modulated by 254 unique drugs, of which, 43% display activities across different protein families (sequence similarity < 30%), highlighting the fact that multitarget therapy is suitable for CVD. Although there is little overlap between cardiovascular protein targets and disease genes, the two groups have similar pleiotropy and intimate relationships in the human disease gene-gene and cellular networks, supporting their similar characteristics in disease development and response to therapy. We also characterize the relationships between different cardiovascular disorders, which reveal that they share more etiological commonalities with each other rooted in the global disease-disease networks. Furthermore, the disease modular analysis demonstrates apparent molecular connection between 227 cardiovascular disease pairs.

Conclusions

All these provide important consensus as to the cause, prevention, and treatment of various CVD disorders from systems-level perspective.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-014-0141-z) contains supplementary material, which is available to authorized users.

Time-course human urine proteomics in space-flight simulation experiments

BACKGROUND

Long-term space travel simulation experiments enabled to discover different aspects of human metabolism such as the complexity of NaCl salt balance. Detailed proteomics data were collected during the Mars105 isolation experiment enabling a deeper insight into the molecular processes involved.

RESULTS

We studied the abundance of about two thousand proteins extracted from urine samples of six volunteers collected weekly during a 105-day isolation experiment under controlled dietary conditions including progressive reduction of salt consumption. Machine learning using Self Organizing maps (SOM) in combination with different analysis tools was applied to describe the time trajectories of protein abundance in urine. The method enables a personalized and intuitive view on the physiological state of the volunteers. The abundance of more than one half of the proteins measured clearly changes in the course of the experiment. The trajectory splits roughly into three time ranges, an early (week 1-6), an intermediate (week 7-11) and a late one (week 12-15). Regulatory modes associated with distinct biological processes were identified using previous knowledge by applying enrichment and pathway flow analysis. Early protein activation modes can be related to immune response and inflammatory processes, activation at intermediate times to developmental and proliferative processes and late activations to stress and responses to chemicals.

CONCLUSIONS

The protein abundance profiles support previous results about alternative mechanisms of salt storage in an osmotically inactive form. We hypothesize that reduced NaCl consumption of about 6 g/day presumably will reduce or even prevent the activation of inflammatory processes observed in the early time range of isolation. SOM machine learning in combination with analysis methods of class discovery and functional annotation enable the straightforward analysis of complex proteomics data sets generated by means of mass spectrometry.

Integrating omics: the future of IBD?

The complexity of IBD is well recognized as are the putative four major components of its pathogenesis, i.e. environment, genetic makeup, gut microbiota and mucosal immune response. Each of these components is extremely complex on its own, and at present should be more appropriately defined by the terms 'exposome', 'genome', 'microbiome' and 'immunome', respectively, based on the 'ome' suffix that refers to a totality of some sort. None of these 'omes' is apparently capable of causing IBD by itself; it is instead the intricate and reciprocal interaction among them, through the so-called 'IBD interactome', that results in the emergence of IBD, or more appropriately the 'IBD integrome'. To deal with and understand such overwhelming biological complexity, new approaches and tools are needed, and these are represented by 'omics', defined as the study of related sets of biological molecules in a comprehensive fashion, such as genomics, transcriptomics, proteomics, metabolomics, and so on. Numerous bioinformatics-based tools are available to explore and take advantage of the massive amount of information that can be generated by the analysis of the various omes and their interactions, aiming at identifying the molecular interactome underlying any particular status of health and disease. These novel approaches are fully applicable to IBD and allow us to achieve the ultimate goal of developing and applying personalized medicine and far more effective therapies to individual patients with Crohn's disease and ulcerative colitis. For the practicing gastroenterologist, an omics-based delivery of healthcare may be intimidating, but it must be accepted and implemented if he or she is to provide the best possible care to IBD patients.

Biological mechanism analysis of acute renal allograft rejection: integrated of mRNA and microRNA expression profiles

OBJECTIVES

Renal transplantation is the preferred method for most patients with end-stage renal disease, however, acute renal allograft rejection is still a major risk factor for recipients leading to renal injury. To improve the early diagnosis and treatment of acute rejection, study on the molecular mechanism of it is urgent.

METHODS

MicroRNA (miRNA) expression profile and mRNA expression profile of acute renal allograft rejection and well-functioning allograft downloaded from ArrayExpress database were applied to identify differentially expressed (DE) miRNAs and DE mRNAs. DE miRNAs targets were predicted by combining five algorithm. By overlapping the DE mRNAs and DE miRNAs targets, common genes were obtained. Differentially co-expressed genes (DCGs) were identified by differential co-expression profile (DCp) and differential co-expression enrichment (DCe) methods in Differentially Co-expressed Genes and Links (DCGL) package. Then, co-expression network of DCGs and the cluster analysis were performed. Functional enrichment analysis for DCGs was undergone.

RESULTS

A total of 1270 miRNA targets were predicted and 698 DE mRNAs were obtained. While overlapping miRNA targets and DE mRNAs, 59 common genes were gained. We obtained 103 DCGs and 5 transcription factors (TFs) based on regulatory impact factors (RIF), then built the regulation network of miRNA targets and DE mRNAs. By clustering the co-expression network, 5 modules were obtained. Thereinto, module 1 had the highest degree and module 2 showed the most number of DCGs and common genes. TF CEBPB and several common genes, such as RXRA, BASP1 and AKAP10, were mapped on the co-expression network. C1R showed the highest degree in the network. These genes might be associated with human acute renal allograft rejection.

CONCLUSIONS

We conducted biological analysis on integration of DE mRNA and DE miRNA in acute renal allograft rejection, displayed gene expression patterns and screened out genes and TFs that may be related to acute renal allograft rejection.

How stemlike are sphere cultures from long-term cancer cell lines? Lessons from mouse glioma models

Cancer stem cells may mediate therapy resistance and recurrence in various types of cancer, including glioblastoma. Cancer stemlike cells can be isolated from long-term cancer cell lines, including glioma lines. Using sphere formation as a model for cancer cell stemness in vitro, we derived sphere cultures from SMA-497, SMA-540, SMA-560, and GL-261 glioma cells. Gene expression and proteomics profiling demonstrated that sphere cultures uniformly showed an elevated expression of stemness-associated genes, notably including CD44. Differences in neural lineage marker expression between nonsphere and sphere cultures were heterogeneous except for a uniform reduction of β-III-tubulin in sphere cultures. All sphere cultures showed slower growth. Self-renewal capacity was influenced by medium conditions but not nonsphere versus sphere culture phenotype. Sphere cultures were more resistant to irradiation, whereas both nonsphere and sphere cultures were highly resistant to temozolomide. Nonsphere cells formed more aggressive tumors in syngeneic mice than sphere cells in all models except SMA-560. There were no major differences in vascularization or infiltration by T cells or microglia/macrophages between nonsphere and sphere cell-derived tumors implanted in syngeneic hosts. Together, these data indicate that mouse glioma cell lines may be induced in vitro to form spheres that acquire features of stemness, but they do not exhibit a uniform biologic phenotype, thereby challenging the view that they represent a superior model system.

A Comprehensive Profile of ChIP-Seq-Based PU.1/Spi1 Target Genes in Microglia

Microglia are resident mononuclear phagocytes that play a principal role in the maintenance of normal tissue homeostasis in the central nervous system (CNS). Microglia, rapidly activated in response to proinflammatory stimuli, are accumulated in brain lesions of neurodegenerative diseases, such as Alzheimer’s disease and Parkinson’s disease. The E26 transformation-specific (ETS) family transcription factor PU.1/Spi1 acts as a master regulator of myeloid and lymphoid development. PU.1-deficient mice show a complete loss of microglia, indicating that PU.1 plays a pivotal role in microgliogenesis. However, the comprehensive profile of PU.1/Spi1 target genes in microglia remains unknown. By analyzing a chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) dataset numbered SRP036026 with the Strand NGS program, we identified 5,264 Spi1 target protein-coding genes in BV2 mouse microglial cells. They included Spi1, Irf8, Runx1, Csf1r, Csf1, Il34, Aif1 (Iba1), Cx3cr1, Trem2, and Tyrobp. By motif analysis, we found that the PU-box consensus sequences were accumulated in the genomic regions surrounding ChIP-Seq peaks. By using pathway analysis tools of bioinformatics, we found that ChIP-Seq-based Spi1 target genes show a significant relationship with diverse pathways essential for normal function of monocytes/macrophages, such as endocytosis, Fc receptor-mediated phagocytosis, and lysosomal degradation. These results suggest that PU.1/Spi1 plays a crucial role in regulation of the genes relevant to specialized functions of microglia. Therefore, aberrant regulation of PU.1 target genes might contribute to the development of neurodegenerative diseases with accumulation of activated microglia.

Verification and validation of bioinformatics software without a gold standard: a case study of BWA and Bowtie

Background

Bioinformatics software quality assurance is essential in genomic medicine. Systematic verification and validation of bioinformatics software is difficult because it is often not possible to obtain a realistic "gold standard" for systematic evaluation. Here we apply a technique that originates from the software testing literature, namely Metamorphic Testing (MT), to systematically test three widely used short-read sequence alignment programs.

Results

MT alleviates the problems associated with the lack of gold standard by checking that the results from multiple executions of a program satisfy a set of expected or desirable properties that can be derived from the software specification or user expectations. We tested BWA, Bowtie and Bowtie2 using simulated data and one HapMap dataset. It is interesting to observe that multiple executions of the same aligner using slightly modified input FASTQ sequence file, such as after randomly re-ordering of the reads, may affect alignment results. Furthermore, we found that the list of variant calls can be affected unless strict quality control is applied during variant calling.

Conclusion

Thorough testing of bioinformatics software is important in delivering clinical genomic medicine. This paper demonstrates a different framework to test a program that involves checking its properties, thus greatly expanding the number and repertoire of test cases we can apply in practice.

Hierarchical closeness efficiently predicts disease genes in a directed signaling network

BACKGROUND

Many structural centrality measures were proposed to predict putative disease genes on biological networks. Closeness is one of the best-known structural centrality measures, and its effectiveness for disease gene prediction on undirected biological networks has been frequently reported. However, it is not clear whether closeness is effective for disease gene prediction on directed biological networks such as signaling networks.

RESULTS

In this paper, we first show that closeness does not significantly outperform other well-known centrality measures such as Degree, Betweenness, and PageRank for disease gene prediction on a human signaling network. In addition, we observed that prediction accuracy by the closeness measure was worse than that by a reachability measure, but closeness could efficiently predict disease genes among a set of genes with the same reachability value. Based on this observation, we devised a novel structural measure, hierarchical closeness, by combining reachability and closeness such that all genes are first ranked by the degree of reachability and then the tied genes are further ranked by closeness. We discovered that hierarchical closeness outperforms other structural centrality measures in disease gene prediction. We also found that the set of highly ranked genes in terms of hierarchical closeness is clearly different from that of hub genes with high connectivity. More interestingly, these findings were consistently reproduced in a random Boolean network model. Finally, we found that genes with relatively high hierarchical closeness are significantly likely to encode proteins in the extracellular matrix and receptor proteins in a human signaling network, supporting the fact that half of all modern medicinal drugs target receptor-encoding genes.

CONCLUSION

Taken together, hierarchical closeness proposed in this study is a novel structural measure to efficiently predict putative disease genes in a directed signaling network.

A Five-Level Design Framework for Bicluster Visualizations

Analysts often need to explore and identify coordinated relationships (e.g., four people who visited the same five cities on the same set of days) within some large datasets for sensemaking. Biclusters provide a potential solution to ease this process, because each computed bicluster bundles individual relationships into coordinated sets. By understanding such computed, structural, relations within biclusters, analysts can leverage their domain knowledge and intuition to determine the importance and relevance of the extracted relationships for making hypotheses. However, due to the lack of systematic design guidelines, it is still a challenge to design effective and usable visualizations of biclusters to enhance their perceptibility and interactivity for exploring coordinated relationships. In this paper, we present a five-level design framework for bicluster visualizations, with a survey of the state-of-the-art design considerations and applications that are related or that can be applied to bicluster visualizations. We summarize pros and cons of these design options to support user tasks at each of the five-level relationships. Finally, we discuss future research challenges for bicluster visualizations and their incorporation into visual analytics tools.

An update on epigenetics and childhood respiratory diseases

Epigenetic mechanisms, defined as changes in phenotype or gene expression caused by mechanisms other than changes in the underlying DNA sequence, have been proposed to constitute a link between genetic and environmental factors that affect complex diseases. Recent studies show that DNA methylation, one of the key epigenetic mechanisms, is altered in children exposed to air pollutants and environmental tobacco smoke early in life. Several candidate gene studies on epigenetics have been published to date, but it is only recently that global methylation analyses have been performed for respiratory disorders such as asthma and chronic obstructive pulmonary disease. However, large-scale studies with adequate power are yet to be presented in children, and implications for clinical use remain to be evaluated. In this review, we summarize the recent advances in epigenetics and respiratory disorders in children, with a main focus on methodological challenges and analyses related to phenotype and exposure using global methylation approaches.

Genetic variants associated with serum thyroid stimulating hormone (TSH) levels in European Americans and African Americans from the eMERGE Network

Thyroid stimulating hormone (TSH) hormone levels are normally tightly regulated within an individual; thus, relatively small variations may indicate thyroid disease. Genome-wide association studies (GWAS) have identified variants in PDE8B and FOXE1 that are associated with TSH levels. However, prior studies lacked racial/ethnic diversity, limiting the generalization of these findings to individuals of non-European ethnicities. The Electronic Medical Records and Genomics (eMERGE) Network is a collaboration across institutions with biobanks linked to electronic medical records (EMRs). The eMERGE Network uses EMR-derived phenotypes to perform GWAS in diverse populations for a variety of phenotypes. In this report, we identified serum TSH levels from 4,501 European American and 351 African American euthyroid individuals in the eMERGE Network with existing GWAS data. Tests of association were performed using linear regression and adjusted for age, sex, body mass index (BMI), and principal components, assuming an additive genetic model. Our results replicate the known association of PDE8B with serum TSH levels in European Americans (rs2046045 p = 1.85×10⁻¹⁷, β = 0.09). FOXE1 variants, associated with hypothyroidism, were not genome-wide significant (rs10759944: p = 1.08×10⁻⁶, β = −0.05). No SNPs reached genome-wide significance in African Americans. However, multiple known associations with TSH levels in European ancestry were nominally significant in African Americans, including PDE8B (rs2046045 p = 0.03, β = −0.09), VEGFA (rs11755845 p = 0.01, β = −0.13), and NFIA (rs334699 p = 1.50×10⁻³, β = −0.17). We found little evidence that SNPs previously associated with other thyroid-related disorders were associated with serum TSH levels in this study. These results support the previously reported association between PDE8B and serum TSH levels in European Americans and emphasize the need for additional genetic studies in more diverse populations.

Detection of pleiotropy through a Phenome-wide association study (PheWAS) of epidemiologic data as part of the Environmental Architecture for Genes Linked to Environment (EAGLE) study

We performed a Phenome-wide association study (PheWAS) utilizing diverse genotypic and phenotypic data existing across multiple populations in the National Health and Nutrition Examination Surveys (NHANES), conducted by the Centers for Disease Control and Prevention (CDC), and accessed by the Epidemiological Architecture for Genes Linked to Environment (EAGLE) study. We calculated comprehensive tests of association in Genetic NHANES using 80 SNPs and 1,008 phenotypes (grouped into 184 phenotype classes), stratified by race-ethnicity. Genetic NHANES includes three surveys (NHANES III, 1999-2000, and 2001-2002) and three race-ethnicities: non-Hispanic whites (n = 6,634), non-Hispanic blacks (n = 3,458), and Mexican Americans (n = 3,950). We identified 69 PheWAS associations replicating across surveys for the same SNP, phenotype-class, direction of effect, and race-ethnicity at p<0.01, allele frequency >0.01, and sample size >200. Of these 69 PheWAS associations, 39 replicated previously reported SNP-phenotype associations, 9 were related to previously reported associations, and 21 were novel associations. Fourteen results had the same direction of effect across more than one race-ethnicity: one result was novel, 11 replicated previously reported associations, and two were related to previously reported results. Thirteen SNPs showed evidence of pleiotropy. We further explored results with gene-based biological networks, contrasting the direction of effect for pleiotropic associations across phenotypes. One PheWAS result was ABCG2 missense SNP rs2231142, associated with uric acid levels in both non-Hispanic whites and Mexican Americans, protoporphyrin levels in non-Hispanic whites and Mexican Americans, and blood pressure levels in Mexican Americans. Another example was SNP rs1800588 near LIPC, significantly associated with the novel phenotypes of folate levels (Mexican Americans), vitamin E levels (non-Hispanic whites) and triglyceride levels (non-Hispanic whites), and replication for cholesterol levels. The results of this PheWAS show the utility of this approach for exposing more of the complex genetic architecture underlying multiple traits, through generating novel hypotheses for future research.

Genomic architecture of pharmacological efficacy and adverse events

The pharmacokinetic and pharmacodynamic disciplines address pharmacological traits, including efficacy and adverse events. Pharmacogenomics studies have identified pervasive genetic effects on treatment outcomes, resulting in the development of genetic biomarkers for optimization of drug therapy. Pharmacogenomics-based tests are already being applied in clinical decision making. However, despite substantial progress in identifying the genetic etiology of pharmacological response, current biomarker panels still largely rely on single gene tests with a large portion of the genetic effects remaining to be discovered. Future research must account for the combined effects of multiple genetic variants, incorporate pathway-based approaches, explore gene-gene interactions and nonprotein coding functional genetic variants, extend studies across ancestral populations, and prioritize laboratory characterization of molecular mechanisms. Because genetic factors can play a key role in drug response, accurate biomarker tests capturing the main genetic factors determining treatment outcomes have substantial potential for improving individual clinical care.

The functional scope of plant microRNA-mediated silencing

Deep sequencing has identified a complex set of plant miRNAs that potentially regulates many target genes of high complementarity. Furthermore, the discovery that many plant miRNAs work through a translational repression mechanism, along with the identification of noncanonical targets, has encouraged bioinformatic searches with less stringent parameters, identifying an even wider range of potential targets. Together, these findings suggest that any given plant miRNA family may regulate a highly diverse set of mRNAs. Here we present evolutionary, genetic, and mechanistic evidence that opposes this idea but instead suggests that families of sequence-related miRNAs regulate very few functionally related targets. We propose that complexities beyond complementarity impact plant miRNA target recognition, possibly explaining the current disparity between bioinformatic prediction and functional evidence.

Empirical study using network of semantically related associations in bridging the knowledge gap

BACKGROUND

The data overload has created a new set of challenges in finding meaningful and relevant information with minimal cognitive effort. However designing robust and scalable knowledge discovery systems remains a challenge. Recent innovations in the (biological) literature mining tools have opened new avenues to understand the confluence of various diseases, genes, risk factors as well as biological processes in bridging the gaps between the massive amounts of scientific data and harvesting useful knowledge.

METHODS

In this paper, we highlight some of the findings using a text analytics tool, called ARIANA--Adaptive Robust and Integrative Analysis for finding Novel Associations.

RESULTS

Empirical study using ARIANA reveals knowledge discovery instances that illustrate the efficacy of such tool. For example, ARIANA can capture the connection between the drug hexamethonium and pulmonary inflammation and fibrosis that caused the tragic death of a healthy volunteer in a 2001 John Hopkins asthma study, even though the abstract of the study was not part of the semantic model.

CONCLUSION

An integrated system, such as ARIANA, could assist the human expert in exploratory literature search by bringing forward hidden associations, promoting data reuse and knowledge discovery as well as stimulating interdisciplinary projects by connecting information across the disciplines.

Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation

Background

Generally, QSAR modelling requires both model selection and validation since there is no a priori knowledge about the optimal QSAR model. Prediction errors (PE) are frequently used to select and to assess the models under study. Reliable estimation of prediction errors is challenging – especially under model uncertainty – and requires independent test objects. These test objects must not be involved in model building nor in model selection. Double cross-validation, sometimes also termed nested cross-validation, offers an attractive possibility to generate test data and to select QSAR models since it uses the data very efficiently. Nevertheless, there is a controversy in the literature with respect to the reliability of double cross-validation under model uncertainty. Moreover, systematic studies investigating the adequate parameterization of double cross-validation are still missing. Here, the cross-validation design in the inner loop and the influence of the test set size in the outer loop is systematically studied for regression models in combination with variable selection.

Methods

Simulated and real data are analysed with double cross-validation to identify important factors for the resulting model quality. For the simulated data, a bias-variance decomposition is provided.

Results

The prediction errors of QSAR/QSPR regression models in combination with variable selection depend to a large degree on the parameterization of double cross-validation. While the parameters for the inner loop of double cross-validation mainly influence bias and variance of the resulting models, the parameters for the outer loop mainly influence the variability of the resulting prediction error estimate.

Conclusions

Double cross-validation reliably and unbiasedly estimates prediction errors under model uncertainty for regression models. As compared to a single test set, double cross-validation provided a more realistic picture of model quality and should be preferred over a single test set.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-014-0047-1) contains supplementary material, which is available to authorized users.

Behavioral and proteomic analysis of stress response in zebrafish (Danio rerio)

The purpose of this study is to determine the behavioral and proteomic consequences of shock-induced stress in zebrafish (Danio rerio) as a vertebrate model. Here we describe the behavioral effects of exposure to predictable and unpredictable electric shock, together with quantitative tandem mass tag isobaric labeling workflow to detect altered protein candidates in response to shock exposure. Behavioral results demonstrate a hyperactivity response to electric shock and a suppression of activity to a stimulus predicting shock. On the basis of the quantitative changes in protein abundance following shock exposure, eight proteins were significantly up-regulated (HADHB, hspa8, hspa5, actb1, mych4, atp2a1, zgc:86709, and zgc:86725). These proteins contribute crucially in catalytic activities, stress response, cation transport, and motor activities. This behavioral proteomic driven study clearly showed that besides the rapid induction of heat shock proteins, other catalytic enzymes and cation transporters were rapidly elevated as a mechanism to counteract oxidative stress conditions resulting from elevated fear/anxiety levels.

Bayesian neural networks for detecting epistasis in genetic association studies

Background

Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions.

Results

A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships.

Conclusions

The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0368-0) contains supplementary material, which is available to authorized users.

Heuristic identification of biological architectures for simulating complex hierarchical genetic interactions

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects.

Bioinformatic prediction of SNPs within miRNA binding sites of inflammatory genes associated with gastric cancer

Polymorphisms in miRNA binding sites have been shown to affect miRNA binding to target genes, resulting in differential mRNA and protein expression and susceptibility to common diseases. Our purpose was to predict SNPs (single nucleotide polymorphisms) within miRNA binding sites of inflammatory genes in relation to gastric cancer. A complete list of SNPs in the 3'UTR regions of all inflammatory genes associated with gastric cancer was obtained from Pubmed. miRNA target prediction databases (MirSNP, Targetscan Human 6.2, PolymiRTS 3.0, miRNASNP 2.0, and Patrocles) were used to predict miRNA target sites. There were 99 SNPs with MAF>0.05 within the miRNA binding sites of 41 genes among 72 inflammation-related genes associated with gastric cancer. NF-κB and JAK-STAT are the two most important signaling pathways. 47 SNPs of 25 genes with 95 miRNAs were predicted. CCL2 and IL1F5 were found to be the shared target genes of hsa-miRNA-624-3p. Bioinformatic methods could identify a set of SNPs within miRNA binding sites of inflammatory genes, and provide data and direction for subsequent functional verification research.

Understanding the relationship between cotton fiber properties and non-cellulosic cell wall polysaccharides

A detailed knowledge of cell wall heterogeneity and complexity is crucial for understanding plant growth and development. One key challenge is to establish links between polysaccharide-rich cell walls and their phenotypic characteristics. It is of particular interest for some plant material, like cotton fibers, which are of both biological and industrial importance. To this end, we attempted to study cotton fiber characteristics together with glycan arrays using regression based approaches. Taking advantage of the comprehensive microarray polymer profiling technique (CoMPP), 32 cotton lines from different cotton species were studied. The glycan array was generated by sequential extraction of cell wall polysaccharides from mature cotton fibers and screening samples against eleven extensively characterized cell wall probes. Also, phenotypic characteristics of cotton fibers such as length, strength, elongation and micronaire were measured. The relationship between the two datasets was established in an integrative manner using linear regression methods. In the conducted analysis, we demonstrated the usefulness of regression based approaches in establishing a relationship between glycan measurements and phenotypic traits. In addition, the analysis also identified specific polysaccharides which may play a major role during fiber development for the final fiber characteristics. Three different regression methods identified a negative correlation between micronaire and the xyloglucan and homogalacturonan probes. Moreover, homogalacturonan and callose were shown to be significant predictors for fiber length. The role of these polysaccharides was already pointed out in previous cell wall elongation studies. Additional relationships were predicted for fiber strength and elongation which will need further experimental validation.

Influence of the pathogen Candidatus Liberibacter solanacearum on tomato host plant volatiles and psyllid vector settlement

Candidatus Liberibacter solanacearum (CLso) is an unculturable bacterium vectored by the tomato potato psyllid (TPP) Bactericera cockerelli and has been associated with Zebra chip disease in potato and with other economically relevant symptoms observed in solanaceous crops. By altering their host and vector's biological system, pathogens are able to induce changes that benefit them by increasing their transmission rate. Understanding these changes can enable better targeting of mechanisms to control pathogen outbreaks. Here, we explored how the CLso infectious status affects the volatile organic compounds (VOCs) of the tomato plant, and whether the CLso infectious status of TPP influences host plant settlement. These chemical and behavioral changes can ultimately affect the rate of encounter between the host and the vector. Results from headspace volatile collection of tomato plants showed that CLso infected tomato plants emitted a qualitatively and quantitatively different blend of VOCs compared to sham-infected plants. By a factorial experiment, we showed that CLso negative (CLso-) TPP preferred to settle 70 % more often on infected tomato plants, while CLso positive (CLso+) TPP were found 68 % more often on sham-infected tomato plants. These results provide new evidence in favor of both host and vector manipulation by CLso.

Genetic simulation tools for post-genome wide association studies of complex diseases

Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.

Nimodipine enhances neurite outgrowth in dopaminergic brain slice co-cultures

Calcium ions (Ca(2+)) play important roles in neuroplasticity and the regeneration of nerves. Intracellular Ca(2+) concentrations are regulated by Ca(2+) channels, among them L-type voltage-gated Ca(2+) channels, which are inhibited by dihydropyridines like nimodipine. The purpose of this study was to investigate the effect of nimodipine on neurite growth during development and regeneration. As an appropriate model to study neurite growth, we chose organotypic brain slice co-cultures of the mesocortical dopaminergic projection system, consisting of the ventral tegmental area/substantia nigra and the prefrontal cortex from neonatal rat brains. Quantification of the density of the newly built neurites in the border region (region between the two cultivated slices) of the co-cultures revealed a growth promoting effect of nimodipine at concentrations of 0.1μM and 1μM that was even more pronounced than the effect of the growth factor NGF. This beneficial effect was absent when 10μM nimodipine were applied. Toxicological tests revealed that the application of nimodipine at this higher concentration slightly induced caspase 3 activation in the cortical part of the co-cultures, but did neither affect the amount of lactate dehydrogenase release or propidium iodide uptake nor the ratio of bax/bcl-2. Furthermore, the expression levels of different genes were quantified after nimodipine treatment. The expression of Ca(2+) binding proteins, immediate early genes, glial fibrillary acidic protein, and myelin components did not change significantly after treatment, indicating that the regulation of their expression is not primarily involved in the observed nimodipine mediated neurite growth. In summary, this study revealed for the first time a neurite growth promoting effect of nimodipine in the mesocortical dopaminergic projection system that is highly dependent on the applied concentrations.

Affinity purification-mass spectrometry and network analysis to understand protein-protein interactions

By determining protein-protein interactions in normal, diseased and infected cells, we can improve our understanding of cellular systems and their reaction to various perturbations. In this protocol, we discuss how to use data obtained in affinity purification-mass spectrometry (AP-MS) experiments to generate meaningful interaction networks and effective figures. We begin with an overview of common epitope tagging, expression and AP practices, followed by liquid chromatography-MS (LC-MS) data collection. We then provide a detailed procedure covering a pipeline approach to (i) pre-processing the data by filtering against contaminant lists such as the Contaminant Repository for Affinity Purification (CRAPome) and normalization using the spectral index (SIN) or normalized spectral abundance factor (NSAF); (ii) scoring via methods such as MiST, SAInt and CompPASS; and (iii) testing the resulting scores. Data formats familiar to MS practitioners are then transformed to those most useful for network-based analyses. The protocol also explores methods available in Cytoscape to visualize and analyze these types of interaction data. The scoring pipeline can take anywhere from 1 d to 1 week, depending on one's familiarity with the tools and data peculiarities. Similarly, the network analysis and visualization protocol in Cytoscape takes 2-4 h to complete with the provided sample data, but we recommend taking days or even weeks to explore one's data and find the right questions.

Novel multivariate methods for integration of genomics and proteomics data: applications in a kidney transplant rejection study

Multi-omics research is a key ingredient of data-intensive life sciences research, permitting measurement of biological molecules at different functional levels in the same individual. For a complete picture at the biological systems level, appropriate statistical techniques must however be developed to integrate different 'omics' data sets (e.g., genomics and proteomics). We report here multivariate projection-based analyses approaches to genomics and proteomics data sets, using the case study of and applications to observations in kidney transplant patients who experienced an acute rejection event (n=20) versus non-rejecting controls (n=20). In this data sets, we show how these novel methodologies might serve as promising tools for dimension reduction and selection of relevant features for different analytical frameworks. Unsupervised analyses highlighted the importance of post transplant time-of-rejection, while supervised analyses identified gene and protein signatures that together predicted rejection status with little time effect. The selected genes are part of biological pathways that are representative of immune responses. Gene enrichment profiles revealed increases in innate immune responses and neutrophil activities and a depletion of T lymphocyte related processes in rejection samples as compared to controls. In all, this article offers candidate biomarkers for future detection and monitoring of acute kidney transplant rejection, as well as ways forward for methodological advances to better harness multi-omics data sets.

Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization

Our aim was to uncover unrecognized phenomic relationships using force-based network visualization methods, based on observed electronic medical record data. A primary phenotype was defined from actual patient profiles in the Multiparameter Intelligent Monitoring in Intensive Care II database. Network visualizations depicting primary relationships were compared to those incorporating secondary adjacencies. Interactivity was enabled through a phenotype visualization software concept: the Phenomics Advisor. Subendocardial infarction with cardiac arrest was demonstrated as a sample phenotype; there were 332 primarily adjacent diagnoses, with 5423 relationships. Primary network visualization suggested a treatment-related complication phenotype and several rare diagnoses; re-clustering by secondary relationships revealed an emergent cluster of smokers with the metabolic syndrome. Network visualization reveals phenotypic patterns that may have remained occult in pairwise correlation analysis. Visualization of complex data, potentially offered as point-of-care tools on mobile devices, may allow clinicians and researchers to quickly generate hypotheses and gain deeper understanding of patient subpopulations.

Kernel-based whole-genome prediction of complex traits: a review

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

The effect of UGT1A and UGT2B polymorphisms on colorectal cancer risk: haplotype associations and gene–environment interactions

UDP-glucuronosyltransferases (UGTs) play an important role in the phase II metabolism of exogenous and endogenous compounds. As colorectal cancer (CRC) etiology is thought to involve the biotransformation of dietary factors, UGT polymorphisms may affect CRC risk by altering levels of exposure. Genotyping of over 1800 Caucasian subjects was completed to identify the role of genetic variation in nine UGT1A and five UGT2B genes on CRC risk. Unconditional logistic regression and haplotype analyses were conducted to identify associations with CRC risk and potential gene-environment interactions. UGT1A haplotype analysis found that the T-G haplotype in UGT1A10 exon 1 (block 2: rs17864678, rs10929251) decreased cancer risk for the colon [proximal (OR = 0.28, 95% CI = 0.11–0.69) and for the distal colon (OR = 0.32, 95% CI = 0.12–0.91)], and that the C-T-G haplotype in the 3′ region flanking the UGT1A shared exons (block 11: rs7578153, rs10203853, rs6728940) increased CRC risk in males (OR = 2.56, 95% CI = 1.10–5.95). A haplotype in UGT2B15 containing a functional variant (rs4148269, K523T) and an intronic SNP (rs6837575) was found to affect rectal cancer risk overall (OR = 2.57, 95% CI = 1.21–5.04) and in females (OR = 3.08, 95% CI = 1.08–8.74). An interaction was found between high NSAID use and the A-G-T haplotype (block 10: rs6717546, rs1500482, rs7586006) in the UGT1A shared exons that decreased CRC risk. This suggests that UGT genetic variation alters CRC risk differently by anatomical sub-site and gender and that polymorphisms in the UGT1A shared exons may have a regulatory effect on gene expression that allows for the protective effect of NSAIDs on CRC risk.