Multiple Complex Diseases Research Articles

Machine Learning for Cancer Drug Combination.

When treating multiple complex diseases, such as cancer, polytherapy may demonstrate efficiency than monotherapy. However, due to the multiplicative relationship between the number of drugs and cell lines vs. the number of combinations, it is impractical to test all drug combinations using high-throughput preclinical approaches. An alternative to experimental tests is predicting drug synergy through computational models. Here, we summarize recent computational approaches for predicting drug synergy, discuss current limitations, and propose future directions.

GWAS of 165,084 Japanese individuals identified nine loci associated with dietary habits.

Dietary habits are important factors in our lifestyle, and confer both susceptibility to and protection from a variety of human diseases. We performed genome-wide association studies for 13 dietary habits including consumption of alcohol (ever versus never drinkers and drinks per week), beverages (coffee, green tea and milk) and foods (yoghurt, cheese, natto, tofu, fish, small whole fish, vegetables and meat) in Japanese individuals (n = 58,610-165,084) collected by BioBank Japan, the nationwide hospital-based genome cohort. Significant associations were found in nine genetic loci (MCL1-ENSA, GCKR, AGR3-AHR, ADH1B, ALDH1B1, ALDH1A1, ALDH2, CYP1A2-CSK and ADORA2A-AS1) for 13 dietary traits (P < 3.8 × 10-9). Of these, ten associations between five loci and eight traits were new findings. Furthermore, a phenome-wide association study revealed that five of the dietary trait-associated loci have pleiotropic effects on multiple human complex diseases and clinical measurements. Our findings provide new insight into the genetics of habitual consumption.

Incorporating Multisource Knowledge To Predict Drug Synergy Based on Graph Co-regularization.

Drug combinations may reduce toxicity and increase therapeutic efficacy, offering a promising strategy to conquer multiple complex diseases. However, due to large-scale combinatorial space, it remains challenging to identify effective combinations. Although many computational methods have focused on predicting drug synergy to reduce combinatorial space, they fail to effectively consider multiple sources of important knowledge. Thus, it is necessary to propose a computational method that can exploit useful information to predict drug synergy. Here, we developed a computational method to predict drug synergy based on graph co-regularization, named DSGCR. By incorporating drug-target network patterns, pharmacological patterns, and prior knowledge of drug combinations, DSGCR performs predictions of synergistic drug combinations. Compared to several existing methods, DSGCR achieves superior performance in predicting drug synergy in terms of various metrics via cross-validation. Additionally, we analyzed the importance of various sources of drug knowledge concerning three DSGCR's scenarios. Finally, the potential of DSGCR to score drug synergy was confirmed by three predicted synergistic drug combinations.

Nutrition, epigenetics and health

Nutrition, epigenetics and health

Apolipoprotein L1, Cardiovascular Disease and Hypertension: More Questions than Answers.

Ethnic disparities in health outcomes exist among multiple complex diseases especially cardiovascular disease, hypertension, and kidney disease. Recent discoveries in genetics have taught us that these disparities go beyond environmental and socioeconomic factors. The discovery of ethnic-specific risk variants in the Apolipoprotein L1 (APOL1) gene on chromosome 22 seen only in individuals of recent African ancestry explains a large proportion of kidney disease disparities. In addition, recent large-scale genotype-phenotype association studies have identified associations with cardiovascular disease and hypertension. This review aims to review the recent literature in this field and point toward future directions for research.

Pleiotropic mapping for genome-wide association studies using group variable selection

Introduction Results from genome-wide association studies (GWAS) suggest that complex diseases are often affected by many variants with small effects, known as polygenicity. Bayesian methods provide attractive tools for identifying signal in data where the effects are small but clustered. For example, by incorporating biological pathway membership in the prior they are able to integrate the ideas of gene set enrichment to identify groups of biologically significant genetic variants. Accumulating evidence suggests that genetic variants may affect multiple different complex diseases, a phenomenon known as pleiotropy. Method In this work we propose frequenstist and Bayesian statistical method to leverage pleiotropic effects and incorporate prior pathway knowledge to increase statistical power and identify important risk variants. We offer novel feature selection methods for the group variable selection in multi-task regression problem. We develop methods using both penalised likelihood methods and Bayesian spike and slab priors to induce structured sparsity at a gene and SNP level. We implement Gibbs sampling algorithms for the Bayesian analysis and an alternating direction method of multipliers (ADMM) algorithm for the penalised regression methods. The performances of the proposed approaches are compared to state-of-the-art variable selection strategies on simulated data sets. Results The penalised likelihood approaches are computationally efficient using alternating direction method of multipliers algorithm. These approaches perform reasonably well in variable selection but the reconstructed signal is underestimated. The multivariate Bayesian sparse group selection with spike and slab prior performed the best in terms of signal recovery. The Bayesian method provides a natural method for quantifying the variability of the estimated coefficients. Simulation results suggest that when computationally possible the Bayesian estimators should be used. Conclusion The developed statistical approaches is applied for enriching our insights about the genetic mechanisms of thyroid and breast cancer types. The analysed data come from case-control studies including 6677 SNPs from 618 genes from 10 non-overlapping gene pathways. The thyroid cancer data set includes 482 cases and 463 controls. The breast cancer data set includes 1172 cases and 1125 controls.

Detecting Methylomic Biomarkers of Pediatric Autism in the Peripheral Blood Leukocytes.

Autism was a spectrum of multiple complex diseases that required an interdisciplinary group of experts to make a diagnostic decision. Both genetic and environmental factors play essential roles in causing the onset of Autism. Therefore, this study hypothesized that methylomic biomarkers may facilitate the accurate Autism detection. A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for the Autism detection using the methylomic data of the peripheral blood samples. The best model achieved 99.70% in accuracy with 678 methylomic biomarkers and a tenfold cross validation strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of Autism.

Network-based prediction of drug combinations

Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By quantifying the network-based relationship between drug targets and disease proteins in the human protein–protein interactome, we show the existence of six distinct classes of drug–drug–disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development.

Introducing Heuristic Information Into Ant Colony Optimization Algorithm for Identifying Epistasis.

Epistasis learning, which is aimed at detecting associations between multiple Single Nucleotide Polymorphisms (SNPs) and complex diseases, has gained increasing attention in genome wide association studies. Although much work has been done on mapping the SNPs underlying complex diseases, there is still difficulty in detecting epistatic interactions due to the lack of heuristic information to expedite the search process. In this study, a method EACO is proposed to detect epistatic interactions based on the ant colony optimization (ACO) algorithm, the highlights of which are the introduced heuristic information, fitness function, and a candidate solutions filtration strategy. The heuristic information multi-SURF* is introduced into EACO for identifying epistasis, which is incorporated into ant-decision rules to guide the search with linear time. Two functionally complementary fitness functions, mutual information and the Gini index, are combined to effectively evaluate the associations between SNP combinations and the phenotype. Furthermore, a strategy for candidate solutions filtration is provided to adaptively retain all optimal solutions which yields a more accurate way for epistasis searching. Experiments of EACO, as well as three ACO based methods (AntEpiSeeker, MACOED, and epiACO) and four commonly used methods (BOOST, SNPRuler, TEAM, and epiMODE) are performed on both simulation data sets and a real data set of age-related macular degeneration. Results indicate that EACO is promising in identifying epistasis.

Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders

C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5× 10-8). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.

A clustering linear combination approach to jointly analyze multiple phenotypes for GWAS.

There is an increasing interest in joint analysis of multiple phenotypes for genome-wide association studies (GWASs) based on the following reasons. First, cohorts usually collect multiple phenotypes and complex diseases are usually measured by multiple correlated intermediate phenotypes. Second, jointly analyzing multiple phenotypes may increase statistical power for detecting genetic variants associated with complex diseases. Third, there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. In this paper, we develop a clustering linear combination (CLC) method to jointly analyze multiple phenotypes for GWASs. In the CLC method, we first cluster individual statistics into positively correlated clusters and then, combine the individual statistics linearly within each cluster and combine the between-cluster terms in a quadratic form. CLC is not only robust to different signs of the means of individual statistics, but also reduce the degrees of freedom of the test statistic. We also theoretically prove that if we can cluster the individual statistics correctly, CLC is the most powerful test among all tests with certain quadratic forms. Our simulation results show that CLC is either the most powerful test or has similar power to the most powerful test among the tests we compared, and CLC is much more powerful than other tests when effect sizes align with inferred clusters. We also evaluate the performance of CLC through a real case study. R code for implementing our method is available at http://www.math.mtu.edu/∼shuzhang/software.html. Supplementary data are available at Bioinformatics online.

Comparison of 2-Aminobenzamide, Procainamide and RapiFluor-MS as Derivatizing Agents for High-Throughput HILIC-UPLC-FLR-MS N-glycan Analysis.

Rising awareness of the universal importance of protein N-glycosylation governs the development of further advances in N-glycan analysis. Nowadays it is well known that correct glycosylation is essential for proper protein function, which emanates from its important role in many physiological processes. Furthermore, glycosylation is involved in pathophysiology of multiple common complex diseases. In the vast majority of cases, N-glycosylation profiles are analyzed from enzymatically released glycans, which can be further derivatized in order to enhance the sensitivity of the analysis. Techniques wherein derivatized N-glycans are profiled using hydrophilic interaction chromatography (HILIC) with fluorescence (FLR) and mass spectrometry (MS) detection are now routinely performed in a high-throughput manner. Therefore, we aimed to examine the performance of frequently used labeling compounds −2-aminiobenzamide (2-AB) and procainamide (ProA), and the recently introduced RapiFluor-MS (RF-MS) fluorescent tag. In all experiments N-glycans were released by PNGase F, fluorescently derivatized, purified by HILIC solid phase extraction and profiled using HILIC-UPLC-FLR-MS. We assessed sensitivity, linear range, limit of quantification (LOQ), repeatability and labeling efficiency for all three labels. For this purpose, we employed in-house prepared IgG and a commercially available IgG as a model glycoprotein. All samples were analyzed in triplicates using different amounts of starting material. We also tested the performance of all three labels in a high-throughput setting on 68 different IgG samples, all in duplicates and 22 identical IgG standards. In general, ProA labeled glycans had the highest FLR sensitivity (15-fold and 4-fold higher signal intensities compared to 2-AB and RF-MS respectively) and RF-MS had the highest MS sensitivity (68-fold and 2-fold higher signal intensities compared to 2-AB and ProA, respectively). ProA and RF-MS showed comparable limits of quantification with both FLR and MS detection, whilst 2-AB exhibited the lowest sensitivity. All labeling procedures showed good and comparable repeatability. Furthermore, the results indicated that labeling efficiency was very similar for all three labels. In conclusion, all three labels are a good choice for N-glycan derivatization in high-throughput HILIC-UPLC-FLR-MS N-glycan analysis, although ProA and RF-MS are a better option when higher sensitivity is needed.

Regulatory variants: from detection to predicting impact.

Variants within non-coding genomic regions can greatly affect disease. In recent years, increasing focus has been given to these variants, and how they can alter regulatory elements, such as enhancers, transcription factor binding sites and DNA methylation regions. Such variants can be considered regulatory variants. Concurrently, much effort has been put into establishing international consortia to undertake large projects aimed at discovering regulatory elements in different tissues, cell lines and organisms, and probing the effects of genetic variants on regulation by measuring gene expression. Here, we describe methods and techniques for discovering disease-associated non-coding variants using sequencing technologies. We then explain the computational procedures that can be used for annotating these variants using the information from the aforementioned projects, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches. We provide the details of techniques to validate these predictions, by mapping chromatin–chromatin and chromatin–protein interactions, and introduce Clustered Regularly Interspaced Short Palindromic Repeats-Associated Protein 9 (CRISPR-Cas9) technology, which has already been used in this field and is likely to have a big impact on its future evolution.We also give examples of regulatory variants associated with multiple complex diseases. This review is aimed at bioinformaticians interested in the characterization of regulatory variants, molecular biologists and geneticists interested in understanding more about the nature and potential role of such variants from a functional point of views, and clinicians who may wish to learn about variants in non-coding genomic regions associated with a given disease and find out what to do next to uncover how they impact on the underlying mechanisms.

Genetics: An integrated genetic analysis of disease.

A genome-wide association study (GWAS) of quantitative traits that incorporated data from GWAS of complex diseases provides clues regarding the relationships between genetic loci, intermediate phenotypes and diseases. Together, the data demonstrate pleiotropy, genetic correlation and cell-type specificity of quantitative traits as predictors of multiple complex diseases.

An integrative functional genomics framework for effective identification of novel regulatory variants in genome\u2013phenome studies

BackgroundGenome–phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases.MethodsIn this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx.ResultsWe found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer’s disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1).ConclusionsThis study offers powerful tools for exploring the functional consequences of variants generated from genome–phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.

Environment, susceptibility windows, development, and child health.

To illustrate the role of the exposome in child health while highlighting unique aspects of this research pertinent to children, such as the time dependency of environmental exposures on fetal programming, as well as the time-dependent nature of child behavior, diet, and motor function, which alter the probability of exposure to different compounds. Future environmental health research will be more hypothesis generating but will also need to heed lessons learned from other 'omic' sciences. The NIH Child Health Environmental Analysis Resource (CHEAR) is a major step toward providing the infrastructure needed to study the exposome and child health. Environmental exposures have overlapping mechanisms such as endocrine disruption and oxidative stress, among others. The nature of the long-term health impact of an exposure is dependent not only on dose, but also on the timing of exposure. Advances in exposure science, toxicology, and biostatistics will create new opportunities to identify and better define windows of susceptibility to environmental exposures. As exposure science matures, we will better understand the role of environment on health. Linking the exposome with genomics will unlock the root origins of multiple complex diseases.

Outcomes of a Randomized Controlled Trial of Genomic Counseling for Patients Receiving Personalized and Actionable Complex Disease Reports

There has been very limited study of patients with chronic disease receiving potentially actionable genomic based results or the utilization of genetic counselors in the online result delivery process. We conducted a randomized controlled trial on 199 patients with chronic disease each receiving eight personalized and actionable complex disease reports online. Primary study aims were to assess the impact of in-person genomic counseling on 1) causal attribution of disease risk, 2) personal awareness of disease risk, and 3) perceived risk of developing a particular disease. Of 98 intervention arm participants (mean age=57.8; 39% female) randomized for in-person genomic counseling, 76 (78%) were seen. In contrast, control arm participants (n=101; mean age=58.5; 54% female) were initially not offered genomic counseling as part of the study protocol but were able to access in-person genomic counseling, if they requested it, 3-months post viewing of at least one test report and post-completion of the study-specific follow-up survey. A total of 64 intervention arm and 59 control arm participants completed follow-up survey measures. We found that participants receiving in-person genomic counseling had enhanced objective understanding of the genetic variant risk contribution for multiple complex diseases. Genomic counseling was associated with lowered participant causal beliefs in genetic influence across all eight diseases, compared to control participants. Our findings also illustrate that for the majority of diseases under study, intervention arm participants believed they knew their genetic risk status better than control arm subjects. Disease risk was modified for the majority during genomic counseling, due to the assessment of more comprehensive family history. In conclusion, for patients receiving personalized and actionable genomic results through a web portal, genomic counseling enhanced their objective understanding of the genetic variant risk contribution to multiple common diseases. These results support the development of additional genomic counseling interventions to ensure a high level of patient comprehension and improve patient-centered health outcomes.

Epigenetic Analysis of Endocrine Cell Subtypes from Human Pancreatic Islets.

Epigenetic mechanisms have been proposed to contribute to multiple complex diseases, including diabetes mellitus. Dissecting the epigenomic landscape of normal and disease states has greatly expanded our understanding of the regulation of key players in disease pathogenesis and is thus opening doors to novel therapeutic avenues. The human pancreatic islet is a pivotal micro-organ that maintains global glucose homeostasis. The heterogeneity of the islet impedes meaningful in-depth epigenetic analysis using whole islet tissue, because important marks in one cell type are masked by signals from other cell types. We describe here a detailed protocol for the isolation of highly purified endocrine cell subtypes (beta, alpha, and delta cells) from human cadaveric islets, to perform cell-type-specific epigenomic analysis of histone modifications as well as global gene expression profiling.

Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases.

Genome-wide association (GWA) studies typically lack power to detect genotypes significantly associated with complex diseases, where different causal mutations of small effect may be present across cases. A common, tractable approach for identifying genomic elements associated with complex traits is to evaluate combinations of variants in known pathways or gene sets with shared biological function. Such gene-set analyses require the computation of gene-level P-values or gene scores; these gene scores are also useful when generating hypotheses for experimental validation. However, commonly used methods for generating GWA gene scores are computationally inefficient, biased by gene length, imprecise, or have low true positive rate (TPR) at low false positive rates (FPR), leading to erroneous hypotheses for functional validation. Here we introduce a new method, PEGASUS, for analytically calculating gene scores. PEGASUS produces gene scores with as much as 10 orders of magnitude higher numerical precision than competing methods. In simulation, PEGASUS outperforms existing methods, achieving up to 30% higher TPR when the FPR is fixed at 1%. We use gene scores from PEGASUS as input to HotNet2 to identify networks of interacting genes associated with multiple complex diseases and traits; this is the first application of HotNet2 to common variation. In ulcerative colitis and waist–hip ratio, we discover networks that include genes previously associated with these phenotypes, as well as novel candidate genes. In contrast, existing methods fail to identify these networks. We also identify networks for attention-deficit/hyperactivity disorder, in which GWA studies have yet to identify any significant SNPs.

A Pleiotropic Missense Variant in SLC39A8 Is Associated With Crohn’s Disease and Human Gut Microbiome Composition

Genome-wide association studies have identified 200 inflammatory bowel disease (IBD) loci, but the genetic architecture of Crohn's disease (CD) and ulcerative colitis remain incompletely defined. Here, we aimed to identify novel associations between IBD and functional genetic variants using the Illumina ExomeChip (San Diego, CA). Genotyping was performed in 10,523 IBD cases and 5726 non-IBD controls. There were 91,713 functional single-nucleotide polymorphism loci in coding regions analyzed. A novel identified association was replicated further in 2 independent cohorts. We further examined the association of the identified single-nucleotide polymorphism with microbiota from 338 mucosal lavage samples in the Mucosal Luminal Interface cohort measured using 16S sequencing. We identified an association between CD and a missense variant encoding alanine or threonine at position 391 in the zinc transporter solute carrier family 39, member 8 protein (SLC39A8 alanine 391 threonine, rs13107325) and replicated the association with CD in 2 replication cohorts (combined meta-analysis P= 5.55× 10(-13)). This variant has been associated previously with distinct phenotypes including obesity, lipid levels, blood pressure, and schizophrenia. We subsequently determined that the CD risk allele was associated with altered colonic mucosal microbiome composition in both healthy controls (P= .009) and CD cases(P= .0009). Moreover, microbes depleted in healthycarriers strongly overlap with those reduced in CD patients (P=9.24× 10(-16)) and overweight individuals (P=6.73×10(-16)). Our results suggest that anSLC39A8-dependent shift in the gut microbiome could explain itspleiotropic effects on multiple complex diseases includingCD.