A systems genetics approach provides a bridge from discovered genetic variants to biological pathways in rheumatoid arthritis

Assessing the performance of genome-wide association studies for predicting disease risk

To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS (http://gwasrocs.ca). The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.

m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks

As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m⁶A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m⁶A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m⁶A methylation, we develop here m⁶A-Driver, an algorithm for predicting m⁶A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m⁶A methylation under a specific condition. Specifically, m⁶A-Driver integrates the PPI network and the predicted differential m⁶A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m⁶A-driven network of m⁶A-driven genes. To evaluate the performance, we applied m⁶A-Driver to build the context-specific m⁶A-driven networks for 4 known m⁶A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m⁶A-Driver can robustly and efficiently identify m⁶A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m⁶A methylated genes. Pathway analysis of the constructed context-specific m⁶A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.

Powered by methylated RNA immunoprecipitation sequencing (MeRIP-Seq) technology, recent studies have revealed a new mode of post transcriptional regulation mediated by mRNA N6-methyladenosine (m⁶A). Currently, the analysis of m⁶A focuses mostly on prediction of m⁶A sites as well as differential m⁶A methylation, and systematic approach for predicting m⁶A functions is yet to emerge. We develop here m⁶A-Driver, the first network-based approach, to identify m⁶A-driven genes and their associated networks, whose functional interactions are likely to be actively modulated by m⁶A methylation under a specific condition. Our test results showed that m⁶A-Driver can robustly and efficiently identify m⁶A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m⁶A methylated genes. m⁶A-Driver is an effective and reliable approach to identify functionally relevant m⁶A-driven genes and networks from MeRIP-Seq data.

Aggregation of rare/low-frequency variants of the mitochondria respiratory chain-related proteins in rheumatoid arthritis patients

Exome sequencings were conducted using 59 patients having rheumatoid arthritis (RA) and 93 controls. After stepwise filtering, 107 genes showed less than 0.05 of P-values by gene-burden tests. Among 107 genes, NDUFA7 which is a subunit of the complex I in the mitochondrial respiratory chain was selected for further analysis based on previous reports. A case-control study was performed on the three single-nucleotide variants (SNVs) of NDUFA7 with 432 cases and 432 controls. An association was observed between NDUFA7 and RA with severe erosive arthritis. These results together with previous reports suggested the involvement of reactive oxygen species (ROS) in the pathogenesis of RA. In the next step, four SNVs from three genes related to the mitochondrial respiratory chain were selected, which is a major source of ROS, and conducted a case-control study. An association was observed based on a pathway-burden test comprising NDUFA7, SDHAF2, SCO1 and ATP5O: P=1.56E-04, odds ratio=2.16, 95% confidence interval=1.43-3.28. Previous reports suggested the involvement of ROS in the pathogenesis of RA. The aggregation of SNVs in the mitochondria respiratory chain suggests the pivotal role of those SNVs in the pathogenesis of RA with severe erosive arthritis.

Applications of systems approaches in the study of rheumatic diseases

The complex interaction of molecules within a biological system constitutes a functional module. These modules are then acted upon by both internal and external factors, such as genetic and environmental stresses, which under certain conditions can manifest as complex disease phenotypes. Recent advances in high-throughput biological analyses, in combination with improved computational methods for data enrichment, functional annotation, and network visualization, have enabled a much deeper understanding of the mechanisms underlying important biological processes by identifying functional modules that are temporally and spatially perturbed in the context of disease development. Systems biology approaches such as these have produced compelling observations that would be impossible to replicate using classical methodologies, with greater insights expected as both the technology and methods improve in the coming years. Here, we examine the use of systems biology and network analysis in the study of a wide range of rheumatic diseases to better understand the underlying molecular and clinical features.

VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data

A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.

RAvariome: a genetic risk variants database for rheumatoid arthritis based on assessment of reproducibility between or within human populations

Rheumatoid arthritis (RA) is a common autoimmune inflammatory disease of the joints and is caused by both genetic and environmental factors. In the past six years, genome-wide association studies (GWASs) have identified many risk variants associated with RA. However, not all associations reported from GWASs are reproduced when tested in follow-up studies. To establish a reliable set of RA risk variants, we systematically classified common variants identified in GWASs by the degree of reproducibility among independent studies. We collected comprehensive genetic associations from 90 papers of GWASs and meta-analysis. The genetic variants were assessed according to the statistical significance and reproducibility between or within nine geographical populations. As a result, 82 and 19 single nucleotide polymorphisms (SNPs) were confirmed as intra- and inter-population-reproduced variants, respectively. Interestingly, majority of the intra-population-reproduced variants from European and East Asian populations were not common in two populations, but their nearby genes appeared to be the components of common pathways. Furthermore, a tool to predict the individual’s genetic risk of RA was developed to facilitate personalized medicine and preventive health care. For further clinical researches, the list of reliable genetic variants of RA and the genetic risk prediction tool are provided by open access database RAvariome.

Database URL: http://hinv.jp/hinv/rav/

Genomic and epigenomic insights into nutrition and brain disorders

Considerable evidence links many neuropsychiatric, neurodevelopmental and neurodegenerative disorders with multiple complex interactions between genetics and environmental factors such as nutrition. Mental health problems, autism, eating disorders, Alzheimer's disease, schizophrenia, Parkinson's disease and brain tumours are related to individual variability in numerous protein-coding and non-coding regions of the genome. However, genotype does not necessarily determine neurological phenotype because the epigenome modulates gene expression in response to endogenous and exogenous regulators, throughout the life-cycle. Studies using both genome-wide analysis of multiple genes and comprehensive analysis of specific genes are providing new insights into genetic and epigenetic mechanisms underlying nutrition and neuroscience. This review provides a critical evaluation of the following related areas: (1) recent advances in genomic and epigenomic technologies, and their relevance to brain disorders; (2) the emerging role of non-coding RNAs as key regulators of transcription, epigenetic processes and gene silencing; (3) novel approaches to nutrition, epigenetics and neuroscience; (4) gene-environment interactions, especially in the serotonergic system, as a paradigm of the multiple signalling pathways affected in neuropsychiatric and neurological disorders. Current and future advances in these four areas should contribute significantly to the prevention, amelioration and treatment of multiple devastating brain disorders.

Genetics and epigenetics of rheumatoid arthritis

Investigators have made key advances in rheumatoid arthritis (RA) genetics in the past 10 years. Although genetic studies have had limited influence on clinical practice and drug discovery, they are currently generating testable hypotheses to explain disease pathogenesis. Firstly, we review here the major advances in identifying RA genetic susceptibility markers both within and outside of the MHC. Understanding how genetic variants translate into pathogenic mechanisms and ultimately into phenotypes remains a mystery for most of the polymorphisms that confer susceptibility to RA, but functional data are emerging. Interplay between environmental and genetic factors is poorly understood and in need of further investigation. Secondly, we review current knowledge of the role of epigenetics in RA susceptibility. Differences in the epigenome could represent one of the ways in which environmental exposures translate into phenotypic outcomes. The best understood epigenetic phenomena include post-translational histone modifications and DNA methylation events, both of which have critical roles in gene regulation. Epigenetic studies in RA represent a new area of research with the potential to answer unsolved questions.

Systems genetics in "-omics" era: current and future development

The systems genetics is an emerging discipline that integrates high-throughput expression profiling technology and systems biology approaches for revealing the molecular mechanism of complex traits, and will improve our understanding of gene functions in the biochemical pathway and genetic interactions between biological molecules. With the rapid advances of microarray analysis technologies, bioinformatics is extensively used in the studies of gene functions, SNP-SNP genetic interactions, LD block-block interactions, miRNA-mRNA interactions, DNA-protein interactions, protein-protein interactions, and functional mapping for LD blocks. Based on bioinformatics panel, which can integrate "-omics" datasets to extract systems knowledge and useful information for explaining the molecular mechanism of complex traits, systems genetics is all about to enhance our understanding of biological processes. Systems biology has provided systems level recognition of various biological phenomena, and constructed the scientific background for the development of systems genetics. In addition, the next-generation sequencing technology and post-genome wide association studies empower the discovery of new gene and rare variants. The integration of different strategies will help to propose novel hypothesis and perfect the theoretical framework of systems genetics, which will make contribution to the future development of systems genetics, and open up a whole new area of genetics.

What have we learned from six years of GWAS in autoimmune diseases, and what is next?

Genome-wide association studies (GWAS) have discovered hundreds of common genetic variants that predispose humans to autoimmune diseases, opening up unprecedented potential for elucidating the pathways and processes of disease. To understand the role of these variants in susceptibility, we need to derive mechanistic insight by integration of genetic results with other biological data types and also with careful functional studies. In many cases, such studies have highlighted coherent biological processes at a high level and elucidated specific mechanisms that contribute to autoimmunity and inflammation. The understanding of the genetic component of autoimmune etiology will become more complete as fine-mapping and sequencing data become readily available. A comprehensive catalog of human immune phenotypes could provide a functional basis for assessing genetic influence on immune function and variation in response to therapeutic interventions, as well as for rationally designing new targeted therapeutics.

Rheumatoid arthritis and periodontitis - inflammatory and infectious connections. Review of the literature

An association between oral disease/periodontitis and rheumatoid arthritis (RA) has been considered since the early 1820s. The early treatment was tooth eradication. Epidemiological studies suggest that the prevalence of RA and periodontitis may be similar and about 5% of the population are aged 50 years or older. RA is considered as an autoimmune disease whereas periodontitis has an infectious etiology with a complex inflammatory response. Both diseases are chronic and may present with bursts of disease activity. Association studies have suggested odds ratios of having RA and periodontitis varying from 1.8:1 (95% CI: 1.0–3.2, NS) to 8:1 (95% CI: 2.9–22.1, p<0.001). Genetic factors are driving the host responses in both RA and periodontitis. Tumor necrosis factor-α, a proinflammatory cytokine, regulates a cascade of inflammatory events in both RA and periodontitis. Porphyromonas gingivalis is a common pathogen in periodontal infection. P. gingivalis has also been identified in synovial fluid. The specific abilities of P. gingivalis to citrullinate host peptides by proteolytic cleavage at Arg-X peptide bonds by arginine gingipains can induce autoimmune responses in RA through development of anticyclic citrullinated peptide antibodies. In addition, P. gingivalis carries heat shock proteins (HSPs) that may also trigger autoimmune responses in subjects with RA. Data suggest that periodontal therapies combined with routine RA treatments further improve RA status.