Pathway and gene-based analysis of genome wide association studies (GWAS)

My PhD thesis comprises the development and application of novel strategies to analyse genome-wide association studies (GWAS) in the context of functional pathways. I propose pathway and gene-centric methodologies as complementary tools to the conventional singlemarker analyses to mine further the GWAS hidden information. I developed the cumulative trend (CT) test statistic that assesses the cumulative genetic variation of single nucleotide polymorphisms (SNPs) of genes that interact in the same biological pathway and tests the association between a disease and the pathway as an entity. I applied this methodology to the genotypic data of the Wellcome Trust Case Control Consortium (WTCCC) study on Crohn’s disease (CD), type I diabetes (T1D), rheumatoid arthritis (RA), bipolar disorder, hypertension, type II diabetes, coronary artery disease; I identified highly significant associations between the autoimmune diseases (CD, T1D, RA) and inflammatory pathways; almost no association was identified between the same pathways and the non-inflammatory conditions. I extended my approach to a pathway-based gene stability selection methodology, which selects associated genes in the context of associated pathways. This methodology can be used to prioritise genes for follow up studies. I applied it on two GWAS of RA with different ethnic background and typed on different platforms and I demonstrated replication at the pathway, gene and in-silico functional levels. I finally extended my approach on family trios designed GWAS. I applied it on two casecontrol and family trio datasets of Kawasaki disease (KD). I explored the association between the TGF-β pathway and KD susceptibility. The involvement of this pathway in KD was further validated at the gene expression and protein levels. My proposed methodologies were tested on real datasets and provided reproducible results, which indicates rigor and robustness. I would therefore suggest their application to single or multiple GWAS as a complement to conventional single-SNP analysis

A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies

Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses

A systematic SNP selection approach to identify mechanisms underlying disease aetiology: Linking height to post-menopausal breast and colorectal cancer risk

Data from GWAS suggest that SNPs associated with complex diseases or traits tend to co-segregate in regions of low recombination, harbouring functionally linked gene clusters. This phenomenon allows for selecting a limited number of SNPs from GWAS repositories for large-scale studies investigating shared mechanisms between diseases. For example, we were interested in shared mechanisms between adult-attained height and post-menopausal breast cancer (BC) and colorectal cancer (CRC) risk, because height is a risk factor for these cancers, though likely not a causal factor. Using SNPs from public GWAS repositories at p-values < 1 × 10-5 and a genomic sliding window of 1 mega base pair, we identified SNP clusters including at least one SNP associated with height and one SNP associated with either post-menopausal BC or CRC risk (or both). SNPs were annotated to genes using HapMap and GRAIL and analysed for significantly overrepresented pathways using ConsensuspathDB. Twelve clusters including 56 SNPs annotated to 26 genes were prioritised because these included at least one height- and one BC risk- or CRC risk-associated SNP annotated to the same gene. Annotated genes were involved in Indian hedgehog signalling (p-value = 7.78 × 10-7) and several cancer site-specific pathways. This systematic approach identified a limited number of clustered SNPs, which pinpoint potential shared mechanisms linking together the complex phenotypes height, post-menopausal BC and CRC

The genomic basis of mood instability:identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function

Genome-wide association studies (GWAS) of psychiatric phenotypes have tended to focus on categorical diagnoses, but to understand the biology of mental illness it may be more useful to study traits which cut across traditional boundaries. Here, we report the results of a GWAS of mood instability as a trait in a large population cohort (UK Biobank, n = 363,705). We also assess the clinical and biological relevance of the findings, including whether genetic associations show enrichment for nervous system pathways. Forty six unique loci associated with mood instability were identified with a SNP heritability estimate of 9%. Linkage Disequilibrium Score Regression (LDSR) analyses identified genetic correlations with Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizophrenia, anxiety, and Post Traumatic Stress Disorder (PTSD). Gene-level and gene set analyses identified 244 significant genes and 6 enriched gene sets. Tissue expression analysis of the SNP-level data found enrichment in multiple brain regions, and eQTL analyses highlighted an inversion on chromosome 17 plus two brain-specific eQTLs. In addition, we used a Phenotype Linkage Network (PLN) analysis and community analysis to assess for enrichment of nervous system gene sets using mouse orthologue databases. The PLN analysis found enrichment in nervous system PLNs for a community containing serotonin and melatonin receptors. In summary, this work has identified novel loci, tissues and gene sets contributing to mood instability. These findings may be relevant for the identification of novel trans-diagnostic drug targets and could help to inform future stratified medicine innovations in mental health

Traumatic Brain Injury Induces Genome-Wide Transcriptomic, Methylomic, and Network Perturbations in Brain and Blood Predicting Neurological Disorders.

The complexity of the traumatic brain injury (TBI) pathology, particularly concussive injury, is a serious obstacle for diagnosis, treatment, and long-term prognosis. Here we utilize modern systems biology in a rodent model of concussive injury to gain a thorough view of the impact of TBI on fundamental aspects of gene regulation, which have the potential to drive or alter the course of the TBI pathology. TBI perturbed epigenomic programming, transcriptional activities (expression level and alternative splicing), and the organization of genes in networks centered around genes such as Anax2, Ogn, and Fmod. Transcriptomic signatures in the hippocampus are involved in neuronal signaling, metabolism, inflammation, and blood function, and they overlap with those in leukocytes from peripheral blood. The homology between genomic signatures from blood and brain elicited by TBI provides proof of concept information for development of biomarkers of TBI based on composite genomic patterns. By intersecting with human genome-wide association studies, many TBI signature genes and network regulators identified in our rodent model were causally associated with brain disorders with relevant link to TBI. The overall results show that concussive brain injury reprograms genes which could lead to predisposition to neurological and psychiatric disorders, and that genomic information from peripheral leukocytes has the potential to predict TBI pathogenesis in the brain

The Multiscale Backbone of the Human Phenotype Network Based on Biological Pathways

Background: Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes. Results: The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle. Conclusions: We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases’ common biology, and in the elaboration of diagnosis and treatments

A genome-wide association scan on estrogen receptor-negative breast cancer.

INTRODUCTION: Breast cancer is a heterogeneous disease and may be characterized on the basis of whether estrogen receptors (ER) are expressed in the tumour cells. ER status of breast cancer is important clinically, and is used both as a prognostic indicator and treatment predictor. In this study, we focused on identifying genetic markers associated with ER-negative breast cancer risk. METHODS: We conducted a genome-wide association analysis of 285,984 single nucleotide polymorphisms (SNPs) genotyped in 617 ER-negative breast cancer cases and 4,583 controls. We also conducted a genome-wide pathway analysis on the discovery dataset using permutation-based tests on pre-defined pathways. The extent of shared polygenic variation between ER-negative and ER-positive breast cancers was assessed by relating risk scores, derived using ER-positive breast cancer samples, to disease state in independent, ER-negative breast cancer cases. RESULTS: Association with ER-negative breast cancer was not validated for any of the five most strongly associated SNPs followed up in independent studies (1,011 ER-negative breast cancer cases, 7,604 controls). However, an excess of small P-values for SNPs with known regulatory functions in cancer-related pathways was found (global P = 0.052). We found no evidence to suggest that ER-negative breast cancer shares a polygenic basis to disease with ER-positive breast cancer. CONCLUSIONS: ER-negative breast cancer is a distinct breast cancer subtype that merits independent analyses. Given the clinical importance of this phenotype and the likelihood that genetic effect sizes are small, greater sample sizes and further studies are required to understand the etiology of ER-negative breast cancers.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Addiction, Mental Health, and Infectious Disease: A complex web of genetic interactions

Opiate, dopamine and GABA addictions are complex diseases with strong genetic components. These three substance disorders represent significant costs to the global judicial and healthcare systems. The treatment of addiction is further confounded by the co-occurrence of other pathologies that complicate treatment regimes. For example, addiction and mental health are well-characterized co-morbidities. Mental health conditions such as depression, bipolar disorder and schizophrenia have clear genetic synergies between the prevalence of one mental health condition and addiction. This proposal focuses on the characterization of addiction hotspots in the genome, their interplay with mental health genetics and then examines how infectious disease burden is correlated to the rise of immune and addiction variants. Molecular genetics, metabolism analyses, epigenetic and association studies have contributed to current understandings of the genetic components of addiction disorder phenotypes. The resulting literature curated gene sets can be used to identify the modules and pathways mediating shared addiction, mental health and immune disorders. Studying addiction, mental health and immune genes in a geographically diverse sample of human populations is critical to understanding the role that evolutionary factors play in the rise and maintenance of variation potentially underlying addiction phenotypes. These human population comparisons are possible due to the recent expansion of human polymorphism databases, such as the HapMap Project, the Human Genome Diversity Panel and the 1000 genomes datasets. Careful comparisons of allele frequencies in human populations can point to those polymorphisms for which both functional and evolutionary histories converge to either promote or inhibit addiction, mental health, and immune susceptibility. We can project curated addiction genes onto gene ontology categories and cellular pathways to draw a bioinformatics portrayal of addiction and its interplay with mental health and immunity. These addiction genes lists as well as schizophrenia, depression, and bipolar disorder gene sets can be further projected onto the genome to portray the overlap between addiction and mental health disorders. This can also serve as a tool to discover additional genes that play a candidate role in mental illness and addiction. Functionally annotating these regions using existing databases such as the Kyoto Encyclopedia of Genes and Genomes allows for robust characterization of the roles that genes and genomic regions play in modulating addiction phenotypes. This approach enables the identification of candidate genes sitting adjacent to known addiction hotspot genes and the subsequent identification of the candidate polymorphisms in a diverse array of human populations. Finally the addition of new databases of genome wide association studies can inform candidate polymorphisms for addiction, mental health and immune response to infectious disease.Ph.D., Biomedical Engineering -- Drexel University, 201