The role of admixture in the rare variant contribution to inflammatory bowel disease.

BACKGROUND: Identification of rare variants involved in complex, polygenic diseases like Crohn's disease (CD) has accelerated with the introduction of whole exome/genome sequencing association studies. Rare variants can be used in both diagnostic and therapeutic assessments; however, since they are likely to be restricted to specific ancestry groups, their contributions to risk assessment need to be evaluated outside the discovery population. Prior studies implied that the three known rare variants in NOD2 are absent in West African and Asian populations and only contribute in African Americans via admixture. METHODS: Whole genome sequencing (WGS) data from 3418 African American individuals, 1774 inflammatory bowel disease (IBD) cases, and 1644 controls were used to assess odds ratios and allele frequencies (AF), as well as haplotype-specific ancestral origins of European-derived CD variants discovered in a large exome-wide association study. Local and global ancestry was performed to assess the contribution of admixture to IBD contrasting European and African American cohorts. RESULTS: Twenty-five rare variants associated with CD in European discovery cohorts are typically five-fold lower frequency in African Americans. Correspondingly, where comparisons could be made, the rare variants were found to have a predicted four-fold reduced burden for IBD in African Americans, when compared to European individuals. Almost all of the rare CD European variants were found on European haplotypes in the African American cohort, implying that they contribute to disease risk in African Americans primarily due to recent admixture. In addition, proportion of European ancestry correlates the number of rare CD European variants each African American individual carry, as well as their polygenic risk of disease. Similar findings were observed for 23 mutations affecting 10 other common complex diseases for which the rare variants were discovered in European cohorts. CONCLUSIONS: European-derived Crohn's disease rare variants are even more rare in African Americans and contribute to disease risk mainly due to admixture, which needs to be accounted for when performing cross-ancestry genetic assessments.

Integrative polygenic analysis of the protective effects of fatty acid metabolism on disease as modified by obesity.

Dysregulation of fatty acid metabolites can play a crucial role in the progression of complex diseases, such as cardiovascular disease, digestive diseases, and metabolic diseases. Metabolites can have either protective or risk effects on a disease; however, the details of such associations remain contentious. In this study, we demonstrate an integrative PheWAS approach to establish high confidence, causally suggestive of metabolite-disease associations for three fatty acid metabolites, namely, omega-3 fatty acids, omega-6 fatty acids, and docosahexaenoic acid, for 1,254 disease endpoints. Metabolite-disease associations were established if there was a concordant direction of effect and significance for metabolite level and genetic risk score for the metabolite. There was enrichment for metabolite associations with diseases of the respiratory system for omega-3 fatty acids, diseases of the circulatory system and endocrine system for omega-6 fatty acids, and diseases of the digestive system for docosahexaenoic acid. Upon performing Mendelian randomization on a subset of the outcomes, we identified 3, 6, and 15 significant diseases associated with omega-3 fatty acids, omega-6 fatty acids, and docosahexaenoic acid, respectively. We then demonstrate a class of prevalence-risk relationships indicative of (de)canalization of disease under high and low fatty acid metabolite levels. Finally, we show that the interaction between the metabolites and obesity demonstrates that the degree of protection afforded by fatty acid metabolites is strongly modulated by underlying metabolic health. This study evaluated the disease architectures of three polyunsaturated fatty acids (PUFAs), which were validated by several PheWAS modes of support. Our results not only highlight specific diseases associated with each metabolite but also disease group enrichments. In addition, we demonstrate an integrative PheWAS methodology that can be applied to other components of the human metabolome or other traits of interest. The results of this study can be used as an atlas to cross-compare genetic with non-genetic disease associations for the three PUFAs investigated. The findings can be explored through our R shiny app at https://pufa.biosci.gatech.edu.

PHEVIR: an artificial intelligence algorithm that predicts the molecular role of pathogens in complex human diseases.

Infectious diseases are known to cause a wide variety of post-infection complications. However, it's been challenging to identify which diseases are most associated with a given pathogen infection. Using the recently developed LeMeDISCO approach that predicts comorbid diseases associated with a given set of putative mode of action (MOA) proteins and pathogen-human protein interactomes, we developed PHEVIR, an algorithm which predicts the corresponding human disease comorbidities of 312 viruses and 57 bacteria. These predictions provide an understanding of the molecular bases of complications and means of identifying appropriate drug targets to treat them. As an illustration of its power, PHEVIR is applied to identify putative driver pathogens and corresponding human MOA proteins for Type 2 diabetes, atherosclerosis, Alzheimer's disease, and inflammatory bowel disease. Additionally, we explore the origins of the oncogenicity/oncolyticity of certain pathogens and the relationship between heart disease and influenza. The full PHEVIR database is available at https://sites.gatech.edu/cssb/phevir/ .

Mendelian Randomization Indicates a Causal Role for Omega-3 Fatty Acids in Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal system. Omega-3 (ω3) fatty acids are polyunsaturated fatty acids (PUFAs) that are largely obtained from diet and have been speculated to decrease the inflammatory response that is involved in IBD; however, the causality of this association has not been established. A two-sample Mendelian randomization (MR) was used to assess genetic associations between 249 circulating metabolites measured in the UK Biobank as exposures and IBD as the outcome. The genome-wide association study summary level data for metabolite measurements and IBD were derived from large European ancestry cohorts. We observed ω3 fatty acids as a significant protective association with IBD, with multiple modes of MR evidence replicated in three IBD summary genetic datasets. The instrumental variables that were involved in the causal association of ω3 fatty acids with IBD highlighted an intronic SNP, rs174564, in FADS2, a protein engaged in the first step of alpha-linolenic acid desaturation leading to anti-inflammatory EPA and thence DHA production. A low ratio of ω3 to ω6 fatty acids was observed to be a causal risk factor, particularly for Crohn's disease. ω3 fatty acid supplementation may provide anti-inflammatory responses that are required to attenuate inflammation that is involved in IBD.

LeMeDISCO is a computational method for large-scale prediction & molecular interpretation of disease comorbidity.

To understand the origin of disease comorbidity and to identify the essential proteins and pathways underlying comorbid diseases, we developed LeMeDISCO (Large-Scale Molecular Interpretation of Disease Comorbidity), an algorithm that predicts disease comorbidities from shared mode of action proteins predicted by the artificial intelligence-based MEDICASCY algorithm. LeMeDISCO was applied to predict the occurrence of comorbid diseases for 3608 distinct diseases. Benchmarking shows that LeMeDISCO has much better comorbidity recall than the two molecular methods XD-score (44.5% vs. 6.4%) and the SAB score (68.6% vs. 8.0%). Its performance is somewhat comparable to the phenotype method-based Symptom Similarity Score, 63.7% vs. 100%, but LeMeDISCO works for far more cases and its large comorbidity recall is attributed to shared proteins that can help provide an understanding of the molecular mechanism(s) underlying disease comorbidity. The LeMeDISCO web server is available for academic users at: http://sites.gatech.edu/cssb/LeMeDISCO .

Prediction of severe adverse events, modes of action and drug treatments for COVID-19's complications.

Following SARS-CoV-2 infection, some COVID-19 patients experience severe host driven adverse events. To treat these complications, their underlying etiology and drug treatments must be identified. Thus, a novel AI methodology MOATAI-VIR, which predicts disease-protein-pathway relationships and repurposed FDA-approved drugs to treat COVID-19's clinical manifestations was developed. SARS-CoV-2 interacting human proteins and GWAS identified respiratory failure genes provide the input from which the mode-of-action (MOA) proteins/pathways of the resulting disease comorbidities are predicted. These comorbidities are then mapped to their clinical manifestations. To assess each manifestation's molecular basis, their prioritized shared proteins were subject to global pathway analysis. Next, the molecular features associated with hallmark COVID-19 phenotypes, e.g. unusual neurological symptoms, cytokine storms, and blood clots were explored. In practice, 24/26 of the major clinical manifestations are successfully predicted. Three major uncharacterized manifestation categories including neoplasms are also found. The prevalence of neoplasms suggests that SARS-CoV-2 might be an oncovirus due to shared molecular mechanisms between oncogenesis and viral replication. Then, repurposed FDA-approved drugs that might treat COVID-19's clinical manifestations are predicted by virtual ligand screening of the most frequent comorbid protein targets. These drugs might help treat both COVID-19's severe adverse events and lesser ones such as loss of taste/smell.