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Rare DNA alterations that cause heritable diseases are only partially resolvable by clinical next-generation sequencing due to the difficulty of detecting structural variation (SV) in all genomic contexts. Long-read, high fidelity genome sequencing (HiFi-GS) detects SVs with increased sensitivity and enables assembling personal and graph genomes. We leverage standard reference genomes, public assemblies ( n  = 94) and a large collection of HiFi-GS data from a rare disease program (Genomic Answers for Kids, GA4K, n  = 574 assemblies) to build a graph genome representing a unified SV callset in GA4K, identify common variation and prioritize SVs that are more likely to cause genetic disease (MAF < 0.01). Using graphs, we obtain a higher level of reproducibility than the standard reference approach. We observe over 200,000 SV alleles unique to GA4K, including nearly 1000 rare variants that impact coding sequence. With improved specificity for rare SVs, we isolate 30 candidate SVs in phenotypically prioritized genes, including known disease SVs. We isolate a novel diagnostic SV in KMT2E , demonstrating use of personal assemblies coupled with pangenome graphs for rare disease genomics. The community may interrogate our pangenome with additional assemblies to discover new SVs within the allele frequency spectrum relevant to genetic diseases. A pangenomic approach, where genome sequences are related to each other in a graph, facilitates analysis of genomic variation between individuals. Here, the authors explore the benefits of using such an approach to characterize structural variation (e.g., deletions or duplications of more than 50 base pairs) in a rare disease cohort.

The complex relationship between metabolic disease risk and body fat distribution in humans involves cellular characteristics that are specific to body fat compartments. Here we show depot-specific differences in the stromal vascular fraction of visceral and subcutaneous adipose tissue by performing single-cell RNA sequencing of tissue specimens from obese individuals. We characterize multiple immune cells, endothelial cells, fibroblasts, adipose and haematopoietic stem cell progenitors. Subpopulations of adipose-resident immune cells are metabolically active and associated with metabolic disease status, including a population of potential dysfunctional CD8 +  T cells that express metallothioneins. We identify multiple types of adipocyte progenitors that are common across depots, including a subtype enriched in individuals with type 2 diabetes. Depot-specific analysis reveals a class of adipocyte progenitors unique to visceral adipose tissue, which shares common features with beige pre-adipocytes. Our human single-cell transcriptome atlas across fat depots provides a resource to dissect the functional genomics of metabolic disease. Adipose tissue varies depending on localization. Vijay et al. perform single-cell RNA sequencing in multiple adipose tissue depots from obese individuals and identify distinct subpopulations of endothelial cells, immune cells and pre-adipocytes.

The extravillous trophoblast cell lineage is a key feature of placentation and successful pregnancy. Knowledge of transcriptional regulation driving extravillous trophoblast cell development is limited. Here, we map the transcriptome and epigenome landscape as well as chromatin interactions of human trophoblast stem cells and their transition into extravillous trophoblast cells. We show that integrating chromatin accessibility, long-range chromatin interactions, transcriptomic, and transcription factor binding motif enrichment enables identification of transcription factors and regulatory mechanisms critical for extravillous trophoblast cell development. We elucidate functional roles for TFAP2C , SNAI1 , and EPAS1 in the regulation of extravillous trophoblast cell development. EPAS1 is identified as an upstream regulator of key extravillous trophoblast cell transcription factors, including ASCL2 and SNAI1 and together with its target genes, is linked to pregnancy loss and birth weight. Collectively, we reveal activation of a dynamic regulatory network and provide a framework for understanding extravillous trophoblast cell specification in trophoblast cell lineage development and human placentation. Invasive extravillous trophoblast cells are a key feature of placentation and successful pregnancy. Here, the authors identify transcription factors and regulatory mechanisms critical for extravillous trophoblast cell lineage development.

Previous studies focusing on the age disparity in COVID-19 severity have suggested that younger individuals mount a more robust innate immune response in the nasal mucosa after infection with SARS-CoV-2. However, it is unclear if this reflects increased immune activation or increased immune residence in the nasal mucosa. We hypothesized that immune residency in the nasal mucosa of healthy individuals may differ across the age range. We applied single-cell RNA-sequencing and measured the cellular composition and transcriptional profile of the nasal mucosa in 35 SARS-CoV-2 negative children and adults, ranging in age from 4 months to 65 years. We analyzed in total of ~ 30,000 immune and epithelial cells and found that age and immune cell proportion in the nasal mucosa are inversely correlated, with little evidence for structural changes in the transcriptional state of a given cell type across the age range. Orthogonal validation by epigenome sequencing indicate that it is especially cells of the innate immune system that underlie the age-association. Additionally, we characterize the predominate immune cell type in the nasal mucosa: a resident T cell like population with potent antiviral properties. These results demonstrate fundamental changes in the immune cell makeup of the uninfected nasal mucosa over the lifespan. The resource we generate here is an asset for future studies focusing on respiratory infection and immunization strategies.

Long-read HiFi genome sequencing allows for accurate detection and direct phasing of single nucleotide variants, indels, and structural variants. Recent algorithmic development enables simultaneous detection of CpG methylation for analysis of regulatory element activity directly in HiFi reads. We present a comprehensive haplotype resolved 5-base HiFi genome sequencing dataset from a rare disease cohort of 276 samples in 152 families to identify rare (~0.5%) hypermethylation events. We find that 80% of these events are allele-specific and predicted to cause loss of regulatory element activity. We demonstrate heritability of extreme hypermethylation including rare cis variants associated with short (~200 bp) and large hypermethylation events (>1 kb), respectively. We identify repeat expansions in proximal promoters predicting allelic gene silencing via hypermethylation and demonstrate allelic transcriptional events downstream. On average 30–40 rare hypermethylation tiles overlap rare disease genes per patient, providing indications for variation prioritization including a previously undiagnosed pathogenic allele in DIP2B causing global developmental delay. We propose that use of HiFi genome sequencing in unsolved rare disease cases will allow detection of unconventional diseases alleles due to loss of regulatory element activity. HiFi genome sequencing accesses DNA methylation and nucleotide variation in long sequence reads. Here, the authors apply this approach in a rare disease cohort to identify DNA hypermethylation linked to genetic variants including rare disease alleles.

Emerging evidence implicates common genetic variation - aggregated into polygenic scores (PGS) - in the onset and phenotypic presentation of rare diseases. Here, we comprehensively map individual polygenic liability for 1102 open-source PGS in a cohort of 3059 probands enrolled in the Genomic Answers for Kids (GA4K) rare disease study, revealing widespread associations between rare disease phenotypes and PGSs for common complex diseases and traits, blood protein levels, and brain and other organ morphological measurements. Using this resource, we demonstrate increased polygenic liability in probands with an inherited candidate disease variant (VUS) compared to unaffected carrier parents. Further, we show an enrichment for large-effect rare variants in putative core PGS genes for associated complex traits. Overall, our study supports and expands on previous findings of complex trait associations in rare diseases, implicates polygenic liability as a potential mechanism underlying variable penetrance of candidate causal variants, and provides a framework for identifying novel candidate rare disease genes. Polygenic scores aggregate the effects of common genetic variants and have been shown to underly certain rare diseases. Here, the authors conduct a large-scale association analysis of rare disease phenotypes and open-source polygenic scores and apply this information to rare variant interpretation and disease gene discovery

Background CpG methylation variation is involved in human trait formation and disease susceptibility. Analyses within populations have been biased towards CpG-dense regions through the application of targeted arrays. We generate whole-genome bisulfite sequencing data for approximately 30 adipose and blood samples from monozygotic and dizygotic twins for the characterization of non-genetic and genetic effects at single-site resolution. Results Purely invariable CpGs display a bimodal distribution with enrichment of unmethylated CpGs and depletion of fully methylated CpGs in promoter and enhancer regions. Population-variable CpGs account for approximately 15–20 % of total CpGs per tissue, are enriched in enhancer-associated regions and depleted in promoters, and single nucleotide polymorphisms at CpGs are a frequent confounder of extreme methylation variation. Differential methylation is primarily non-genetic in origin, with non-shared environment accounting for most of the variance. These non-genetic effects are mainly tissue-specific. Tobacco smoking is associated with differential methylation in blood with no evidence of this exposure impacting cell counts. Opposite to non-genetic effects, genetic effects of CpG methylation are shared across tissues and thus limit inter-tissue epigenetic drift. CpH methylation is rare, and shows similar characteristics of variation patterns as CpGs. Conclusions Our study highlights the utility of low pass whole-genome bisulfite sequencing in identifying methylome variation beyond promoter regions, and suggests that targeting the population dynamic methylome of tissues requires assessment of understudied intergenic CpGs distal to gene promoters to reveal the full extent of inter-individual variation.

Sparse profiling of CpG methylation in blood by microarrays has identified epigenetic links to common diseases. Here we apply methylC-capture sequencing (MCC-Seq) in a clinical population of ~200 adipose tissue and matched blood samples (N total ~400), providing high-resolution methylation profiling (>1.3 M CpGs) at regulatory elements. We link methylation to cardiometabolic risk through associations to circulating plasma lipid levels and identify lipid-associated CpGs with unique localization patterns in regulatory elements. We show distinct features of tissue-specific versus tissue-independent lipid-linked regulatory regions by contrasting with parallel assessments in ~800 independent adipose tissue and blood samples from the general population. We follow-up on adipose-specific regulatory regions under (1) genetic and (2) epigenetic (environmental) regulation via integrational studies. Overall, the comprehensive sequencing of regulatory element methylomes reveals a rich landscape of functional variants linked genetically as well as epigenetically to plasma lipid traits. Obesity and related metabolic complications represent an important health burden. Here the authors carry out a methylC-capture sequencing-based epigenome-wide association study to link circulating plasma lipid levels, CpG methylation and cardiometabolic risk across adipose and blood tissues.

Background MEDLINE®/PubMed® indexes over 20 million biomedical articles, providing curated annotation of its contents using a controlled vocabulary known as Medical Subject Headings (MeSH). The MeSH vocabulary, developed over 50+ years, provides a broad coverage of topics across biomedical research. Distilling the essential biomedical themes for a topic of interest from the relevant literature is important to both understand the importance of related concepts and discover new relationships. Results We introduce a novel method for determining enriched curator-assigned MeSH annotations in a set of papers associated to a topic, such as a gene, an author or a disease. We generate MeSH Over-representation Profiles (MeSHOPs) to quantitatively summarize the annotations in a form convenient for further computational analysis and visualization. Based on a hypergeometric distribution of assigned terms, MeSHOPs statistically account for the prevalence of the associated biomedical annotation while highlighting unusually prevalent terms based on a specified background. MeSHOPs can be visualized using word clouds, providing a succinct quantitative graphical representation of the relative importance of terms. Using the publication dates of articles, MeSHOPs track changing patterns of annotation over time. Since MeSHOPs are quantitative vectors, MeSHOPs can be compared using standard techniques such as hierarchical clustering. The reliability of MeSHOP annotations is assessed based on the capacity to re-derive the subset of the Gene Ontology annotations with equivalent MeSH terms. Conclusions MeSHOPs allows quantitative measurement of the degree of association between any entity and the annotated medical concepts, based directly on relevant primary literature. Comparison of MeSHOPs allows entities to be related based on shared medical themes in their literature. A web interface is provided for generating and visualizing MeSHOPs.

Following publication of the original article [1], the authors reported an error in Additional file 1.Following publication of the original article [1], the authors reported an error in Additional file 1.