Links between melanoma germline risk loci, driver genes and comorbidities: insight from a tissue-specific multi-omic analysis

Genome-wide association studies (GWAS) have associated 76 loci with the risk of developing melanoma. However, understanding the molecular basis of such associations has remained a challenge because most of these loci are in non-coding regions of the genome. Here, we integrated data on epigenomic markers, three-dimensional (3D) genome organization, and expression quantitative trait loci (eQTL) from melanoma-relevant tissues and cell types to gain novel insights into the mechanisms underlying melanoma risk. This integrative approach revealed a total of 151 target genes, both near and far away from the risk loci in linear sequence, with known and novel roles in the etiology of melanoma. Using protein-protein interaction networks, we identified proteins that interact-directly or indirectly-with the products of the target genes. The interacting proteins were enriched for known melanoma driver genes. Further integration of these target genes into tissue-specific gene regulatory networks revealed patterns of gene regulation that connect melanoma to its comorbidities. Our study provides novel insights into the biological implications of genetic variants associated with melanoma risk.

De novo network analysis reveals autism causal genes and developmental links to co-occurring traits

Autism is a complex neurodevelopmental condition that manifests in various ways. Autism is often accompanied by other conditions, such as attention-deficit/hyperactivity disorder and schizophrenia, which can complicate diagnosis and management. Although research has investigated the role of specific genes in autism, their relationship with co-occurring traits is not fully understood. To address this, we conducted a two-sample Mendelian randomisation analysis and identified four genes located at the 17q21.31 locus that are putatively causal for autism in fetal cortical tissue (LINC02210, LRRC37A4P, RP11-259G18.1, and RP11-798G7.6). LINC02210 was also identified as putatively causal for autism in adult cortical tissue. By integrating data from expression quantitative trait loci, genes and protein interactions, we identified that the 17q21.31 locus contributes to the intersection between autism and other neurological traits in fetal cortical tissue. We also identified a distinct cluster of co-occurring traits, including cognition and worry, linked to the genetic loci at 3p21.1. Our findings provide insights into the relationship between autism and co-occurring traits, which could be used to develop predictive models for more accurate diagnosis and better clinical management.

Comorbidity genetic risk and pathways impact SARS-CoV-2 infection outcomes

Understanding the genetic risk and mechanisms through which SARS-CoV-2 infection outcomes and comorbidities interact to impact acute and long-term sequelae is essential if we are to reduce the ongoing health burdens of the COVID-19 pandemic. Here we use a de novo protein diffusion network analysis coupled with tissue-specific gene regulatory networks, to examine putative mechanisms for associations between SARS-CoV-2 infection outcomes and comorbidities. Our approach identifies a shared genetic aetiology and molecular mechanisms for known and previously unknown comorbidities of SARS-CoV-2 infection outcomes. Additionally, genomic variants, genes and biological pathways that provide putative causal mechanisms connecting inherited risk factors for SARS-CoV-2 infection and coronary artery disease and Parkinson's disease are identified for the first time. Our findings provide an in depth understanding of genetic impacts on traits that collectively alter an individual's predisposition to acute and post-acute SARS-CoV-2 infection outcomes. The existence of complex inter-relationships between the comorbidities we identify raises the possibility of a much greater post-acute burden arising from SARS-CoV-2 infection if this genetic predisposition is realised.

Juvenile idiopathic arthritis-associated genetic loci exhibit spatially constrained gene regulatory effects across multiple tissues and immune cell types

Juvenile idiopathic arthritis (JIA) is an autoimmune, inflammatory joint disease with complex genetic etiology. Previous GWAS have found many genetic loci associated with JIA. However, the biological mechanism behind JIA remains unknown mainly because most risk loci are located in non-coding genetic regions. Interestingly, increasing evidence has found that regulatory elements in the non-coding regions can regulate the expression of distant target genes through spatial (physical) interactions. Here, we used information on the 3D genome organization (Hi-C data) to identify target genes that physically interact with SNPs within JIA risk loci. Subsequent analysis of these SNP-gene pairs using data from tissue and immune cell type-specific expression quantitative trait loci (eQTL) databases allowed the identification of risk loci that regulate the expression of their target genes. In total, we identified 59 JIA-risk loci that regulate the expression of 210 target genes across diverse tissues and immune cell types. Functional annotation of spatial eQTLs within JIA risk loci identified significant overlap with gene regulatory elements (i.e., enhancers and transcription factor binding sites). We found target genes involved in immune-related pathways such as antigen processing and presentation (e.g., ERAP2, HLA class I and II), the release of pro-inflammatory cytokines (e.g., LTBR, TYK2), proliferation and differentiation of specific immune cell types (e.g., AURKA in Th17 cells), and genes involved in physiological mechanisms related to pathological joint inflammation (e.g., LRG1 in arteries). Notably, many of the tissues where JIA-risk loci act as spatial eQTLs are not classically considered central to JIA pathology. Overall, our findings highlight the potential tissue and immune cell type-specific regulatory changes contributing to JIA pathogenesis. Future integration of our data with clinical studies can contribute to the development of improved JIA therapy.

De novo discovery of traits co-occurring with chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a heterogeneous group of chronic lung conditions. Genome-wide association studies have identified single-nucleotide polymorphisms (SNPs) associated with COPD and the co-occurring conditions, suggesting common biological mechanisms underlying COPD and these co-occurring conditions. To identify them, we have integrated information across different biological levels (i.e., genetic variants, lung-specific 3D genome structure, gene expression and protein-protein interactions) to build lung-specific gene regulatory and protein-protein interaction networks. We have queried these networks using disease-associated SNPs for COPD, unipolar depression and coronary artery disease. COPD-associated SNPs can control genes involved in the regulation of lung or pulmonary function, asthma, brain region volumes, cortical surface area, depressed affect, neuroticism, Parkinson's disease, white matter microstructure and smoking behaviour. We describe the regulatory connections, genes and biochemical pathways that underlay these co-occurring trait-SNP-gene associations. Collectively, our findings provide new avenues for the investigation of the underlying biology and diverse clinical presentations of COPD. In so doing, we identify a collection of genetic variants and genes that may aid COPD patient stratification and treatment.

Establishing gene regulatory networks from Parkinson's disease risk loci

The latest meta-analysis of genome-wide association studies identified 90 independent variants across 78 genomic regions associated with Parkinson's disease, yet the mechanisms by which these variants influence the development of the disease remains largely elusive. To establish the functional gene regulatory networks associated with Parkinson's disease risk variants, we utilized an approach combining spatial (chromosomal conformation capture) and functional (expression quantitative trait loci) data. We identified 518 genes subject to regulation by 76 Parkinson's variants across 49 tissues, whicih encompass 36 peripheral and 13 CNS tissues. Notably, one-third of these genes were regulated via trans-acting mechanisms (distal; risk locus-gene separated by >1 Mb, or on different chromosomes). Of particular interest is the identification of a novel trans-expression quantitative trait loci-gene connection between rs10847864 and SYNJ1 in the adult brain cortex, highlighting a convergence between familial studies and Parkinson's disease genome-wide association studies loci for SYNJ1 (PARK20) for the first time. Furthermore, we identified 16 neurodevelopment-specific expression quantitative trait loci-gene regulatory connections within the foetal cortex, consistent with hypotheses suggesting a neurodevelopmental involvement in the pathogenesis of Parkinson's disease. Through utilizing Louvain clustering we extracted nine significant and highly intraconnected clusters within the entire gene regulatory network. The nine clusters are enriched for specific biological processes and pathways, some of which have not previously been associated with Parkinson's disease. Together, our results not only contribute to an overall understanding of the mechanisms and impact of specific combinations of Parkinson's disease variants, but also highlight the potential impact gene regulatory networks may have when elucidating aetiological subtypes of Parkinson's disease.

Assigning function to SNPs: Considerations when interpreting genetic variation

Assigning function to single nucleotide polymorphisms (SNPs) to understand the mechanisms that link genetic and phenotypic variation and disease is an area of intensive research that is necessary to contribute to the continuing development of precision medicine. However, despite the apparent simplicity that is captured in the name SNP - 'single nucleotide' changes are not easy to functionally characterize. This complexity arises from multiple features of the genome including the fact that function is development and environment specific. As such, we are often fooled by our terminology and underlying assumptions that there is a single function for a SNP. Here we discuss some of what is known about SNPs, their functions and how we can go about characterizing them.

Unravelling the Shared Genetic Mechanisms Underlying 18 Autoimmune Diseases Using a Systems Approach

Autoimmune diseases (AiDs) are complex heterogeneous diseases characterized by hyperactive immune responses against self. Genome-wide association studies have identified thousands of single nucleotide polymorphisms (SNPs) associated with several AiDs. While these studies have identified a handful of pleiotropic loci that confer risk to multiple AiDs, they lack the power to detect shared genetic factors residing outside of these loci. Here, we integrated chromatin contact, expression quantitative trait loci and protein-protein interaction (PPI) data to identify genes that are regulated by both pleiotropic and non-pleiotropic SNPs. The PPI analysis revealed complex interactions between the shared and disease-specific genes. Furthermore, pathway enrichment analysis demonstrated that the shared genes co-occur with disease-specific genes within the same biological pathways. In conclusion, our results are consistent with the hypothesis that genetic risk loci associated with multiple AiDs converge on a core set of biological processes that potentially contribute to the emergence of polyautoimmunity.

Understanding the impact of SNPs associated with autism spectrum disorder on biological pathways in the human fetal and adult cortex

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by significant and complex genetic etiology. GWAS studies have identified genetic variants associated with ASD, but the functional impacts of these variants remain unknown. Here, we integrated four distinct levels of biological information (GWAS, eQTL, spatial genome organization and protein-protein interactions) to identify potential regulatory impacts of ASD-associated SNPs (p < 5 × 10^-8) on biological pathways within fetal and adult cortical tissues. We found 80 and 58 SNPs that mark regulatory regions (i.e. expression quantitative trait loci or eQTLs) in the fetal and adult cortex, respectively. These eQTLs were also linked to other psychiatric disorders (e.g. schizophrenia, ADHD, bipolar disorder). Functional annotation of ASD-associated eQTLs revealed that they are involved in diverse regulatory processes. In particular, we found significant enrichment of eQTLs within regions repressed by Polycomb proteins in the fetal cortex compared to the adult cortex. Furthermore, we constructed fetal and adult cortex-specific protein-protein interaction networks and identified that ASD-associated regulatory SNPs impact on immune pathways, fatty acid metabolism, ribosome biogenesis, aminoacyl-tRNA biosynthesis and spliceosome in the fetal cortex. By contrast, in the adult cortex they largely affect immune pathways. Overall, our findings highlight potential regulatory mechanisms and pathways important for the etiology of ASD in early brain development and adulthood. This approach, in combination with clinical studies on ASD, will contribute to individualized mechanistic understanding of ASD development.

Genetic variants associated with alcohol dependence co-ordinate regulation of ADH genes in gastrointestinal and adipose tissues

GWAS studies have identified genetic variants associated with Alcohol Dependence (AD), but how they link to genes, their regulation and disease traits, remains largely unexplored. Here we integrated information on the 3D genome organization with expression quantitative loci (eQTLs) analysis, using CoDeS3D, to identify the functional impacts of single nucleotide polymorphisms associated with AD (p < 1 × 10-6). We report that 42% of the 285 significant tissue-specific regulatory interactions we identify were associated with four genes encoding Alcohol Dehydrogenase - ADH1A, ADH1B, ADH1C and ADH4. Identified eQTLs produced a co-ordinated regulatory action between ADH genes, especially between ADH1A and ADH1C within the subcutaneous adipose and gastrointestinal tissues. Five eQTLs were associated with regulatory motif alterations and tissue-specific histone marks consistent with these variants falling in enhancer and promoter regions. By contrast, few regulatory connections were identified in the stomach and liver. This suggests that changes in gene regulation associated with AD are linked to changes in tissues other than the primary sites of alcohol absorption and metabolism. Future work to functionally characterise the putative regulatory regions we have identified and their links to metabolic and regulatory changes in genes will improve our mechanistic understanding of AD disease development and progression.

GWAS SNPs Impact Shared Regulatory Pathways Amongst Multimorbid Psychiatric Disorders and Cognitive Functioning

Background: Epidemiological research has reported that attention-deficit hyperactivity disorder (ADHD), anxiety, bipolar disorder (BD), schizophrenia (SCZ), and unipolar depression (UD) are multimorbid conditions that are typically accompanied by cognitive advantages or deficits, suggesting that common biological mechanisms may underlie these phenotypes. Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) associated with psychiatric disorders and cognitive functioning. However, the mechanisms by which these SNPs contribute to multimorbidities amongst psychiatric and cognitive phenotypes remains largely unknown. Objective: To identify shared regulatory mechanisms amongst multimorbid psychiatric disorders and cognitive functioning. Methods: We integrated data on 3D genome organization, expression quantitative trait loci (eQTLs), and pathway analyses to identify shared and specific regulatory impacts of 2,893 GWAS SNPs (p < 1 × 10^-6) associated with ADHD, anxiety, BD, SCZ, UD, and cognitive functioning on genes and biological pathways. Drug-gene interaction analysis was performed to identify potential pharmacological impacts on these genes and pathways. Results: The analysis revealed 33 genes and 62 pathways that were commonly affected by tissue-specific gene regulatory interactions associated with all six phenotypes despite there being no common SNPs in our original dataset. The analysis of brain-specific regulatory connections revealed similar patterns at eQTL and eGene levels, but no pathways shared by all six phenotypes. Instead, pairwise overlaps and individualized pathways were identified for psychiatric and cognitive phenotypes in brain tissues. Conclusions: This study offers insight into the shared genes and biological pathways that are affected by tissue-specific regulatory impacts resulting from psychiatric- and cognition-associated genetic variants. These results provide limited support for the "p-factor" hypothesis for psychiatric disorders and potential mechanisms that explain drug side-effects. Our results highlight key biological pathways for development of therapies that target single or multiple psychiatric and cognitive phenotypes.