Linking disease associations with regulatory information in the human genome.

Genome-wide association studies have been successful in identifying single nucleotide polymorphisms (SNPs) associated with a large number of phenotypes. However, an associated SNP is likely part of a larger region of linkage disequilibrium. This makes it difficult to precisely identify the SNPs that have a biological link with the phenotype. We have systematically investigated the association of multiple types of ENCODE data with disease-associated SNPs and show that there is significant enrichment for functional SNPs among the currently identified associations. This enrichment is strongest when integrating multiple sources of functional information and when highest confidence disease-associated SNPs are used. We propose an approach that integrates multiple types of functional data generated by the ENCODE Consortium to help identify "functional SNPs" that may be associated with the disease phenotype. Our approach generates putative functional annotations for up to 80% of all previously reported associations. We show that for most associations, the functional SNP most strongly supported by experimental evidence is a SNP in linkage disequilibrium with the reported association rather than the reported SNP itself. Our results show that the experimental data sets generated by the ENCODE Consortium can be successfully used to suggest functional hypotheses for variants associated with diseases and other phenotypes.

Annotation of functional variation in personal genomes using RegulomeDB.

As the sequencing of healthy and disease genomes becomes more commonplace, detailed annotation provides interpretation for individual variation responsible for normal and disease phenotypes. Current approaches focus on direct changes in protein coding genes, particularly nonsynonymous mutations that directly affect the gene product. However, most individual variation occurs outside of genes and, indeed, most markers generated from genome-wide association studies (GWAS) identify variants outside of coding segments. Identification of potential regulatory changes that perturb these sites will lead to a better localization of truly functional variants and interpretation of their effects. We have developed a novel approach and database, RegulomeDB, which guides interpretation of regulatory variants in the human genome. RegulomeDB includes high-throughput, experimental data sets from ENCODE and other sources, as well as computational predictions and manual annotations to identify putative regulatory potential and identify functional variants. These data sources are combined into a powerful tool that scores variants to help separate functional variants from a large pool and provides a small set of putative sites with testable hypotheses as to their function. We demonstrate the applicability of this tool to the annotation of noncoding variants from 69 full sequenced genomes as well as that of a personal genome, where thousands of functionally associated variants were identified. Moreover, we demonstrate a GWAS where the database is able to quickly identify the known associated functional variant and provide a hypothesis as to its function. Overall, we expect this approach and resource to be valuable for the annotation of human genome sequences.

Sex differences in disease risk from reported genome-wide association study findings.

Men and women differ in susceptibility to many diseases and in responses to treatment. Recent advances in genome-wide association studies (GWAS) provide a wealth of data for associating genetic profiles with disease risk; however, in general, these data have not been systematically probed for sex differences in gene-disease associations. Incorporating sex into the analysis of GWAS results can elucidate new relationships between single nucleotide polymorphisms (SNPs) and human disease. In this study, we performed a sex-differentiated analysis on significant SNPs from GWAS data of the seven common diseases studied by the Wellcome Trust Case Control Consortium. We employed and compared three methods: logistic regression, Woolf's test of heterogeneity, and a novel statistical metric that we developed called permutation method to assess sex effects (PMASE). After correction for false discovery, PMASE finds SNPs that are significantly associated with disease in only one sex. These sexually dimorphic SNP-disease associations occur in Coronary Artery Disease and Crohn's Disease. GWAS analyses that fail to consider sex-specific effects may miss discovering sexual dimorphism in SNP-disease associations that give new insights into differences in disease mechanism between men and women.

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.

A Classifier-based approach to identify genetic similarities between diseases.

MOTIVATION: Genome-wide association studies are commonly used to identify possible associations between genetic variations and diseases. These studies mainly focus on identifying individual single nucleotide polymorphisms (SNPs) potentially linked with one disease of interest. In this work, we introduce a novel methodology that identifies similarities between diseases using information from a large number of SNPs. We separate the diseases for which we have individual genotype data into one reference disease and several query diseases. We train a classifier that distinguishes between individuals that have the reference disease and a set of control individuals. This classifier is then used to classify the individuals that have the query diseases. We can then rank query diseases according to the average classification of the individuals in each disease set, and identify which of the query diseases are more similar to the reference disease. We repeat these classification and comparison steps so that each disease is used once as reference disease. RESULTS: We apply this approach using a decision tree classifier to the genotype data of seven common diseases and two shared control sets provided by the Wellcome Trust Case Control Consortium. We show that this approach identifies the known genetic similarity between type 1 diabetes and rheumatoid arthritis, and identifies a new putative similarity between bipolar disease and hypertension.

Subdivision of large introns in Drosophila by recursive splicing at nonexonic elements.

Many genes with important roles in development and disease contain exceptionally long introns, but special mechanisms for their expression have not been investigated. We present bioinformatic, phylogenetic, and experimental evidence in Drosophila for a mechanism that subdivides many large introns by recursive splicing at nonexonic elements and alternative exons. Recursive splice sites predicted with highly stringent criteria are found at much higher frequency than expected in the sense strands of introns >20 kb, but they are found only at the expected frequency on the antisense strands, and they are underrepresented within introns <10 kb. The predicted sites in long introns are highly conserved between Drosophila melanogaster and Drosophila pseudoobscura, despite extensive divergence of other sequences within the same introns. These patterns of enrichment and conservation indicate that recursive splice sites are advantageous in the context of long introns. Experimental analyses of in vivo processing intermediates and lariat products from four large introns in the unrelated genes kuzbanian, outspread, and Ultrabithorax confirmed that these introns are removed by a series of recursive splicing steps using the predicted nonexonic sites. Mutation of nonexonic site RP3 within Ultrabithorax also confirmed that recursive splicing is the predominant processing pathway even with a shortened version of the intron. We discuss currently known and potential roles for recursive splicing.

Transmission distortion in Crohn's disease risk gene ATG16L1 leads to sex difference in disease association.

BACKGROUND: Crohn's disease (CD), an inflammatory disease of the bowel, affects millions of people around the world. Evidence suggests that disease onset and pathogenesis differ between males and females. Yet no comprehensive efforts exist to assess the sex-specific genetic architecture of CD. METHODS: We used genotyping data from a cohort of 1748 CD cases and 2938 controls to investigate 71 meta-analysis-confirmed CD risk loci for sex differences in disease risk. We further validated the significant results in separate cohorts of 968 CD cases and 2809 controls, and performed a meta-analysis across datasets. RESULTS: The single nucleotide polymorphism (SNP) rs3792106 (C/T) in ATG16L1 showed a significant sex effect with P-value 6.9 × 10(-13) and allelic odds ratio 1.48 in females, and P-value 0.013 and odds ratio 1.22 in males (odds ratio heterogeneity P-value 0.037). Surprisingly, the difference was found to arise from a discrepancy in allele frequencies between male and female controls (P-value 0.0045) rather than cases. We found similar results for this SNP in the separate validation datasets. Using 155 HapMap 3 trios, we detected significant maternal overtransmission of the T allele at rs3792106 (P-value 0.027). CONCLUSIONS: Our results indicate that different transmission patterns between sexes may sustain the disparate allele frequencies at rs3792106 in healthy populations, and furthermore that a virus-risk variant mechanism implicated in CD alters the distribution in diseased patients. To our knowledge, this is the first report of sex-specific CD association in ATG16L1. The possible implications in CD and basic human biology present interesting areas for future investigation.

Qualitative networks: a symbolic approach to analyze biological signaling networks.

BACKGROUND: A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico. RESULTS: We consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 10(86) states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis. CONCLUSION: We introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology.