The relationship between the sibling recurrence-risk ratio and genotype relative risk

The recurrence-risk ratio of disease in siblings, lambdaS, is a standard parameter used in genetic analysis to estimate the statistical power for detection of a disease locus. However, the relationship between the underlying risk conferred by a disease-susceptibility allele and lambdaS has not been well described. The former is generally quantified as a genotype relative risk, gamma, and measures the ratio of disease risks between those with and those without the susceptibility genotype(s). We demonstrate that lambdaS varies significantly more with respect to gamma and the disease-allele frequency for two-locus multiplicative models than for other two-locus and for single-locus models. For the single- and two-locus dominant-inheritance models that we studied, when a disease-susceptibility allele had a frequency >/=.2, lambdaS had an upper limit of <10. In general, lambdaS values >10 are possible only under recessive inheritance, dominant inheritance with relatively rare (<5%) disease-susceptibility alleles, or when two or more disease loci have alleles acting either epistatically or multiplicatively. We introduce the idea of a restricted sib recurrence-risk ratio (lambda*S) estimated by restriction of sibships to those ascertained through a proband who already has a putative high-risk allele. A lambda*S larger than the lambdaS value estimated from randomly selected probands can serve as an indirect way of testing whether the posited susceptibility allele increases disease risk. Our results demonstrate that a lambdaS of 2-3 may portend successful mapping for a variety of genetic models but that, for some two-locus models, a lambdaS as high as 10 does not guarantee underlying genes easily mapped by linkage.     

Genetic and molecular basis of quantitative trait loci of arthritis in rat: genes and polymorphisms

Rheumatoid arthritis (RA) is an autoimmune disease, the pathogenesis of which is affected by multiple genetic and environmental factors. To understand the genetic and molecular basis of RA, a large number of quantitative trait loci (QTL) that regulate experimental autoimmune arthritis have been identified using various rat models for RA. However, identifying the particular responsible genes within these QTL remains a major challenge. Using currently available genome data and gene annotation information, we systematically examined RA-associated genes and polymorphisms within and outside QTL over the whole rat genome. By the whole genome analysis of genes and polymorphisms, we found that there are significantly more RA-associated genes in QTL regions as contrasted with non-QTL regions. Further experimental studies are necessary to determine whether these known RA-associated genes or polymorphisms are genetic components causing the QTL effect.     

Detecting shared pathways linked to rheumatoid arthritis with other autoimmune diseases in a in silico analysis

Pathway-based analysis approach has exploded in use during the last several years. It is successful in recognizing additional biological insight of disease and finding groupings of risk genes that represent disease developing processes. Therefore, shared pathways, with pleiotropic effects, are important for understanding similar pathogenesis and indicating the common genetic origin of certain diseases. Here, we present a pathway analysis to reveal the potential disease associations between RA and three potential RA-related autoimmune diseases: psoriasis, diabetes mellitus, type 1 (T1D) and systemic lupus erythematosus (SLE). First, a comprehensive knowledge mining of public databases is performed to discover risk genes associated with RA, T1D, SLE and psoriasis; then by enrichment test of these genes, disease-related risk pathways are detected to recognize the pathways common for RA and three other diseases. Finally, the underlying disease associations are evaluated with the association rules mining method. In total, we identify multiple RA risk pathways with significant pleiotropic effects, the most unsurprising of which are the immunology related pathways. Meanwhile for the first time we highlight the involvement of the viral myocarditis pathway related to cardiovascular disease (CVD) in autoimmune diseases such as RA, psoriasis, T1D and SLE. Further Association rule mining results validate the strong association between RA and T1D and RA and SLE. It is clear that pleiotropy is a common property of pathways associated with disease traits. We provide novel pathway associations among RA and three autoimmune diseases. These results ascertain that there are shared genetic risk profiles that predispose individuals to autoimmune diseases.     

HYST: A Hybrid Set-Based Test for Genome-wide Association Studies, with Application to Protein-Protein Interaction-Based Association Analysis

The extended Simes’ test (known as GATES) and scaled chi-square test were proposed to combine a set of dependent genome-wide association signals at multiple single-nucleotide polymorphisms (SNPs) for assessing the overall significance of association at the gene or pathway levels. The two tests use different strategies to combine association p values and can outperform each other when the number of and linkage disequilibrium between SNPs vary. In this paper, we introduce a hybrid set-based test (HYST) combining the two tests for genome-wide association studies (GWASs). We describe how HYST can be used to evaluate statistical significance for association at the protein-protein interaction (PPI) level in order to increase power for detecting disease-susceptibility genes of moderate effect size. Computer simulations demonstrated that HYST had a reasonable type 1 error rate and was generally more powerful than its parents and other alternative tests to detect a PPI pair where both genes are associated with the disease of interest. We applied the method to three complex disease GWAS data sets in the public domain; the method detected a number of highly connected significant PPI pairs involving multiple confirmed disease-susceptibility genes not found in the SNP- and gene-based association analyses. These results indicate that HYST can be effectively used to examine a collection of predefined SNP sets based on prior biological knowledge for revealing additional disease-predisposing genes of modest effects in GWASs.     

A new methodology to associate SNPs with human diseases according to their pathway related context

Genome-wide association studies (GWAS) with hundreds of żthousands of single nucleotide polymorphisms (SNPs) are popular strategies to reveal the genetic basis of human complex diseases. Despite many successes of GWAS, it is well recognized that new analytical approaches have to be integrated to achieve their full potential. Starting with a list of SNPs, found to be associated with disease in GWAS, here we propose a novel methodology to devise functionally important KEGG pathways through the identification of genes within these pathways, where these genes are obtained from SNP analysis. Our methodology is based on functionalization of important SNPs to identify effected genes and disease related pathways. We have tested our methodology on WTCCC Rheumatoid Arthritis (RA) dataset and identified: i) previously known RA related KEGG pathways (e.g., Toll-like receptor signaling, Jak-STAT signaling, Antigen processing, Leukocyte transendothelial migration and MAPK signaling pathways); ii) additional KEGG pathways (e.g., Pathways in cancer, Neurotrophin signaling, Chemokine signaling pathways) as associated with RA. Furthermore, these newly found pathways included genes which are targets of RA-specific drugs. Even though GWAS analysis identifies 14 out of 83 of those drug target genes; newly found functionally important KEGG pathways led to the discovery of 25 out of 83 genes, known to be used as drug targets for the treatment of RA. Among the previously known pathways, we identified additional genes associated with RA (e.g. Antigen processing and presentation, Tight junction). Importantly, within these pathways, the associations between some of these additionally found genes, such as HLA-C, HLA-G, PRKCQ, PRKCZ, TAP1, TAP2 and RA were verified by either OMIM database or by literature retrieved from the NCBI PubMed module. With the whole-genome sequencing on the horizon, we show that the full potential of GWAS can be achieved by integrating pathway and network-oriented analysis and prior knowledge from functional properties of a SNP.     

Integrative Omics Analysis of Rheumatoid Arthritis Identifies Non-Obvious Therapeutic Targets

Identifying novel therapeutic targets for the treatment of disease is challenging. To this end, we developed a genome-wide approach of candidate gene prioritization. We independently collocated sets of genes that were implicated in rheumatoid arthritis (RA) pathogenicity through three genome-wide assays: (i) genome-wide association studies (GWAS), (ii) differentially expression in RA fibroblast-like synoviocytes (FLS), and (iii) differentially methylation in RA FLS. Integrated analysis of these complementary data sets identified a significant enrichment of multi-evidence genes (MEGs) within pathways relating to RA pathogenicity. One MEG is Engulfment and Cell Motility Protein-1 (ELMO1), a gene not previously considered as a therapeutic target in RA FLS. We demonstrated in RA FLS that ELMO1 is: (i) expressed, (ii) promotes cell migration and invasion, and (iii) regulates Rac1 activity. Thus, we created links between ELMO1 and RA pathogenicity, which in turn validates ELMO1 as a potential RA therapeutic target. This study illustrated the power of MEG-based approaches for therapeutic target identification.     

Coronary atherosclerosis and somatic mutations: an overview of the contributive factors for oxidative DNA damage

Coronary artery disease (CAD) is a multifactorial process that appears to be caused by the interaction of environmental risk factors with multiple predisposing genes. Genetic research on CAD has traditionally focused on investigation aimed at identifying disease-susceptibility genes. Recent evidence suggests that somatically acquired DNA mutations may also contribute significantly to the pathogenesis of the disease, underlining the similarity between atherosclerotic and carcinogenic processes. The generation of oxidative stress has been emphasized as an important cause of DNA damage in atherosclerosis. This review highlights some of the major atherogenic risk factors as likely mediators in the oxidative modification of DNA. It also examines the hypothesis that an increase in oxidative stress may derive from "oxidatively" damaged mitochondria. Accordingly, further research in this field should be given high priority, since increased somatic DNA damage could be an important pathogenic factor and an additional prognostic predictor, as well as a potential target for therapeutic strategies in coronary artery disease.     

Molecular biology and genetics of Alzheimer's disease

Like several other adult onset neurodegenerative diseases, Alzheimer's disease is a multifactorial illness with both genetic and non-genetic causes. Recent genetic studies have identified four genes associated with inherited risk for AD (presenilin 1, presenilin 2, amyloid precursor protein, and apolipoprotein E). These genes account for about half of the total genetic risk for Alzheimer's disease. It is suspected that several other Alzheimer's disease-susceptibility genes exist, and their identification is the subject of ongoing research. Nevertheless, biological studies on the effects of mutations in the four known genes has led to the conclusion that all of these genes cause dysregulation of amyloid precursor protein processing and in particular dysregulation of the handling of a proteolytic derivative termed Abeta. The accumulation of Abeta appears to be an early and initiating event that triggers a series of downstream processes including misprocessing of the tau protein. This cascade ultimately causes neuronal dysfunction and death, and leads to the clinical and pathological features of Alzheimer's disease. Knowledge of this biochemical cascade now provides several potential targets for the development of diagnostics and therapeutics.     

Characterization of histopathology and gene-expression profiles of synovitis in early rheumatoid arthritis using targeted biopsy specimens

The disease category of early rheumatoid arthritis (RA) has been limited with respect to clinical criteria. Pathological manifestations of synovitis in patients whose disease is clinically classified as early RA seem to be heterogeneous, with regular variations. To clarify the relation between the molecular and histopathological features of the synovitis, we analyzed gene-expression profiles in the synovial lining tissues to correlate them with histopathological features. Synovial tissues were obtained from knee joints of 12 patients with early RA by targeted biopsy under arthroscopy. Surgical specimens of long-standing RA (from four patients) were examined as positive controls. Each histopathological parameter characteristic of rheumatoid synovitis in synovial tissues was scored under light microscopy. Total RNAs from synovial lining tissues were obtained from the specimens selected by laser capture microdissection and the mRNAs were amplified by bacteriophage T7 RNA polymerase. Their cDNAs were analyzed in a cDNA microarray with 23,040 cDNAs, and the levels of gene expression in multilayered lining tissues, compared with those of normal-like lining tissues in specimens from the same person, were determined to estimate gene-expression profiles characteristic of the synovial proliferative lesions in each case. Based on cluster analysis of all cases, gene-expression profiles in the lesions in early RA fell into two groups. The groups had different expression levels of genes critical for proliferative inflammation, including those encoding cytokines, adhesion molecules, and extracellular matrices. One group resembled synovitis in long-standing RA and had high scores for some histopathological features – involving accumulations of lymphocytes and plasma cells – but not for other features. Possible differences in the histopathogenesis and prognosis of synovitis between the two groups are discussed in relation to the candidate genes and histopathology.     

Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology

Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein-protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease.     

Plasma metabonomics study of rheumatoid arthritis and its Chinese medicine subtypes by using liquid chromatography and gas chromatography coupled with mass spectrometry

Rheumatoid arthritis (RA) is the most severe type of chronic inflammatory disease and has always been a research hotspot in different fields. In this study, a non-targeted metabonomics approach was carried out to profile metabolic characteristics of RA and its Chinese medicine subtypes by using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Plasma samples of 57 RA patients and 23 healthy controls were collected. On the basis of the traditional Chinese medicine (TCM), RA patients were classified into two main patterns, the cold pattern and the heat pattern. By using univariate and multivariate data analysis, we found that the RA patients presented diverse dysfunctions in inositol phosphate metabolism, lipid metabolism, amino acid metabolism, glucose metabolism, ascorbate metabolism, glyoxylate and dicarboxylate metabolism. The metabolic phenotypes were different between the RA cold pattern and the RA heat pattern. Compared with the RA cold pattern, the RA heat pattern showed elevated plasma concentrations of glycochenodeoxycholate, proline, saturated and mono-unsaturated phosphatidylcholine (PC) but decreased levels of urea, free fatty acid (FFA) and polyunsaturated PC. Our data show that metabonomics is a valuable tool in disease and TCM subtype research.     

Compensatory up-regulation of behavioral disease avoidance in immuno-compromised people with rheumatoid arthritis

Disgust and disease-related cues can activate the immune system. Here, we test whether immuno-suppression is associated with an up-regulation of cognitions and behaviors that assist in disease avoidance. People with rheumatoid arthritis (RA), who have a heightened risk of infection-related morbidity and mortality, were compared to age, gender and demographically matched healthy controls on a range of disease avoidance tasks. People with RA scored higher on reports of behavior likely to control infection, were more accurate in spotting individuals who were sick, and showed disease-specific ethnocentrism, ascribing a greater risk of contracting disease to non-Caucasians, although having no overall propensity for greater racism on the Modern Racism Scale. Contrary to predictions, disgust sensitivity (DS) did not differ between groups, however among people with RA, DS was found to be lower in those taking drugs that can increase infection risk. While more explicit disease avoidance behaviors are clearly up-regulated in people with RA, changes in DS may have a different and perhaps more biological casual basis.     

Autoimmune Disease Classification by Inverse Association with SNP Alleles

With multiple genome-wide association studies (GWAS) performed across autoimmune diseases, there is a great opportunity to study the homogeneity of genetic architectures across autoimmune disease. Previous approaches have been limited in the scope of their analysis and have failed to properly incorporate the direction of allele-specific disease associations for SNPs. In this work, we refine the notion of a genetic variation profile for a given disease to capture strength of association with multiple SNPs in an allele-specific fashion. We apply this method to compare genetic variation profiles of six autoimmune diseases: multiple sclerosis (MS), ankylosing spondylitis (AS), autoimmune thyroid disease (ATD), rheumatoid arthritis (RA), Crohn''s disease (CD), and type 1 diabetes (T1D), as well as five non-autoimmune diseases. We quantify pair-wise relationships between these diseases and find two broad clusters of autoimmune disease where SNPs that make an individual susceptible to one class of autoimmune disease also protect from diseases in the other autoimmune class. We find that RA and AS form one such class, and MS and ATD another. We identify specific SNPs and genes with opposite risk profiles for these two classes. We furthermore explore individual SNPs that play an important role in defining similarities and differences between disease pairs. We present a novel, systematic, cross-platform approach to identify allele-specific relationships between disease pairs based on genetic variation as well as the individual SNPs which drive the relationships. While recognizing similarities between diseases might lead to identifying novel treatment options, detecting differences between diseases previously thought to be similar may point to key novel disease-specific genes and pathways.