Biomedical Data Commons (BMDC) prioritizes B-lymphocyte non-coding genetic variants in Type 1 Diabetes

The transition from genomics to phenomics in personalized population health

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

A human leukocyte antigen imputation study uncovers possible genetic interplay between gut inflammatory processes and autism spectrum disorders

Autism spectrum disorders (ASD) are neurodevelopmental conditions that are for subsets of individuals, underpinned by dysregulated immune processes, including inflammation, autoimmunity, and dysbiosis. Consequently, the major histocompatibility complex (MHC)-hosted human leukocyte antigen (HLA) has been implicated in ASD risk, although seldom investigated. By utilizing a GWAS performed by the EU-AIMS consortium (LEAP cohort), we compared HLA and MHC genetic variants, single nucleotide polymorphisms (SNP), and haplotypes in ASD individuals, versus typically developing controls. We uncovered six SNPs, namely rs9268528, rs9268542, rs9268556, rs14004, rs9268557, and rs8084 that crossed the Bonferroni threshold, which form the underpinnings of 3 independent genetic pathways/blocks that differentially associate with ASD. Block 1 (rs9268528-G, rs9268542-G, rs9268556-C, and rs14004-A) afforded protection against ASD development, whilst the two remaining blocks, namely rs9268557-T, and rs8084-A, associated with heightened risk. rs8084 and rs14004 mapped to the HLA-DRA gene, whilst the four other SNPs located in the BTNL2 locus. Different combinations amongst BTNL2 SNPs and HLA amino acid variants or classical alleles were found either to afford protection from or contribute to ASD risk, indicating a genetic interplay between BTNL2 and HLA. Interestingly, the detected variants had transcriptional and/or quantitative traits loci implications. As BTNL2 modulates gastrointestinal homeostasis and the identified HLA alleles regulate the gastrointestinal tract in celiac disease, it is proposed that the data on ASD risk may be linked to genetically regulated gut inflammatory processes. These findings might have implications for the prevention and treatment of ASD, via the targeting of gut-related processes.

GRHL2 and AP2a coordinate early surface ectoderm lineage commitment during development

Ectodermal dysplasias including skin abnormalities and cleft lip/palate result from improper surface ectoderm (SE) patterning. However, the connection between SE gene regulatory networks and disease remains poorly understood. Here, we dissect human SE differentiation with multiomics and establish GRHL2 as a key mediator of early SE commitment, which acts by skewing cell fate away from the neural lineage. GRHL2 and master SE regulator AP2a balance early cell fate output, with GRHL2 facilitating AP2a binding to SE loci. In turn, AP2a restricts GRHL2 DNA binding away from de novo chromatin contacts. Integration of these regulatory sites with ectodermal dysplasia-associated genomic variants annotated within the Biomedical Data Commons identifies 55 loci previously implicated in craniofacial disorders. These include ABCA4/ARHGAP29 and NOG regulatory regions where disease-linked variants directly affect GRHL2/AP2a binding and gene transcription. These studies elucidate the logic underlying SE commitment and deepen our understanding of human oligogenic disease pathogenesis.

Polygenic autoimmune disease risk alleles impacting B cell tolerance act in concert across shared molecular networks in mouse and in humans

Most B cells produced in the bone marrow have some level of autoreactivity. Despite efforts of central tolerance to eliminate these cells, many escape to periphery, where in healthy individuals, they are rendered functionally non-responsive to restimulation through their antigen receptor via a process termed anergy. Broad repertoire autoreactivity may reflect the chances of generating autoreactivity by stochastic use of germline immunoglobulin gene segments or active mechanisms may select autoreactive cells during egress to the naïve peripheral B cell pool. Likewise, it is unclear why in some individuals autoreactive B cell clones become activated and drive pathophysiologic changes in autoimmune diseases. Both of these remain central questions in the study of the immune system(s). In most individuals, autoimmune diseases arise from complex interplay of genetic risk factors and environmental influences. Advances in genome sequencing and increased statistical power from large autoimmune disease cohorts has led to identification of more than 200 autoimmune disease risk loci. It has been observed that autoantibodies are detectable in the serum years to decades prior to the diagnosis of autoimmune disease. Thus, current models hold that genetic defects in the pathways that control autoreactive B cell tolerance set genetic liability thresholds across multiple autoimmune diseases. Despite the fact these seminal concepts were developed in animal (especially murine) models of autoimmune disease, some perceive a disconnect between human risk alleles and those identified in murine models of autoimmune disease. Here, we synthesize the current state of the art in our understanding of human risk alleles in two prototypical autoimmune diseases - systemic lupus erythematosus (SLE) and type 1 diabetes (T1D) along with spontaneous murine disease models. We compare these risk networks to those reported in murine models of these diseases, focusing on pathways relevant to anergy and central tolerance. We highlight some differences between murine and human environmental and genetic factors that may impact autoimmune disease development and expression and may, in turn, explain some of this discrepancy. Finally, we show that there is substantial overlap between the molecular networks that define these disease states across species. Our synthesis and analysis of the current state of the field are consistent with the idea that the same molecular networks are perturbed in murine and human autoimmune disease. Based on these analyses, we anticipate that murine autoimmune disease models will continue to yield novel insights into how best to diagnose, prognose, prevent and treat human autoimmune diseases.