Advances in the genetic classification of amyotrophic lateral sclerosis

Purpose of review Amyotrophic lateral sclerosis (ALS) is an archetypal complex disease wherein disease risk and severity are, for the majority of patients, the product of interaction between multiple genetic and environmental factors. We are in a period of unprecedented discovery with new large-scale genome-wide association study (GWAS) and accelerating discovery of risk genes. However, much of the observed heritability of ALS is undiscovered and we are not yet approaching elucidation of the total genetic architecture, which will be necessary for comprehensive disease subclassification. Recent findings We summarize recent developments and discuss the future. New machine learning models will help to address nonlinear genetic interactions. Statistical power for genetic discovery may be boosted by reducing the search-space using cell-specific epigenetic profiles and expanding our scope to include genetically correlated phenotypes. Structural variation, somatic heterogeneity and consideration of environmental modifiers represent significant challenges which will require integration of multiple technologies and a multidisciplinary approach, including clinicians, geneticists and pathologists. Summary The move away from fully penetrant Mendelian risk genes necessitates new experimental designs and new standards for validation. The challenges are significant, but the potential reward for successful disease subclassification is large-scale and effective personalized medicine

Hominin-specific regulatory elements selectively emerged in oligodendrocytes and are disrupted in autism patients

Speciation is associated with substantial rewiring of the regulatory circuitry underlying the expression of genes. Determining which changes are relevant and underlie the emergence of the human brain or its unique susceptibility to neural disease has been challenging. Here we annotate changes to gene regulatory elements (GREs) at cell type resolution in the brains of multiple primate species spanning most of primate evolution. We identify a unique set of regulatory elements that emerged in hominins prior to the separation of humans and chimpanzees. We demonstrate that these hominin gains perferentially affect oligodendrocyte function postnatally and are preferentially affected in the brains of autism patients. This preference is also observed for human-specific GREs suggesting this system is under continued selective pressure. Our data provide a roadmap of regulatory rewiring across primate evolution providing insight into the genomic changes that underlie the emergence of the brain and its susceptibility to neural disease

Recently Evolved Enhancers Emerge with High Interindividual Variability and Less Frequently Associate with Disease

Mutations in non-coding regulatory DNA such as enhancers underlie a wide variety of diseases including developmental disorders and cancer. As enhancers rapidly evolve, understanding their function and configuration in non-human disease models can have important clinical applications. Here, we analyze enhancer configurations in tissues isolated from the common marmoset, a widely used primate model for human disease. Integrating these data with human and mouse data, we find that enhancers containing trait-associated variants are preferentially conserved. In contrast, most human-specific enhancers are highly variable between individuals, with a subset failing to contact promoters. These are located further away from genes and more often reside in inactive B-compartments. Our data show that enhancers typically emerge as instable elements with minimal biological impact prior to their integration in a transcriptional program. Furthermore, our data provide insight into which trait variations in enhancers can be faithfully modeled using the common marmoset.Modeling diseases in non-human species is complicated as many enhancers that regulate expression are species specific. Castelijns et al. demonstrate that species-specific enhancers are highly variable and poorly integrated in the regulatory network, suggesting their biological impact is low. Instead, disease-associated enhancers are typically conserved and can therefore be modeled

Multiomic analysis reveals cell-type-specific molecular determinants of COVID-19 severity

The determinants of severe COVID-19 in healthy adults are poorly understood, which limits the opportunity for early intervention. We present a multiomic analysis using machine learning to characterize the genomic basis of COVID-19 severity. We use single-cell multiome profiling of human lungs to link genetic signals to cell-type-specific functions. We discover >1,000 risk genes across 19 cell types, which account for 77% of the SNP-based heritability for severe disease. Genetic risk is particularly focused within natural killer (NK) cells and T cells, placing the dysfunction of these cells upstream of severe disease. Mendelian randomization and single-cell profiling of human NK cells support the role of NK cells and further localize genetic risk to CD56bright NK cells, which are key cytokine producers during the innate immune response. Rare variant analysis confirms the enrichment of severe-disease-associated genetic variation within NK-cell risk genes. Our study provides insights into the pathogenesis of severe COVID-19 with potential therapeutic targets