Brian M. Schilder

Background: Rare diseases (RDs) are a highly heterogeneous and underserved group of conditions. Most RDs have a strong genetic basis but their causal pathophysiological mechanisms remain poorly understood, limiting the development of targeted therapies. Methods: We systematically characterised the cell type-specific mechanisms underlying all genetically defined RD phenotypes by integrating the Human Phenotype Ontology (HPO) with whole-body single-cell transcriptomic atlases from embryonic, foetal, and adult samples. Associations were validated against orthogonal biomedical knowledge graphs and then prioritised by strength of supporting evidence, clinical severity, and gene-therapy compatibility. Results: We identified significant associations between 201 cell types and 9,575/11,028 (86.7%) phenotypes across 8,628 RDs, substantially expanding knowledge of phenotype-cell type links. Prioritisation by severity (e.g. lethality, motor or mental impairment) and gene-therapy compatibility (e.g. cell type specificity, postnatal treatability) identified candidate phenotypes and cell types for therapeutic targeting. Conclusions: We present a scalable, reproducible framework for phenome-wide, cell type-specific mechanism prediction in rare diseases, providing a major step toward systematic therapeutic development for patients across a broad spectrum of serious RDs. Software and data availability: Interactive web portal: https://neurogenomics-ukdri.dsi.ic.ac.uk/ . R packages introduced in this study: ( https://github.com/neurogenomics/KGExplorer ), ( https://github.com/neurogenomics/HPOExplorer ), and ( https://github.com/neurogenomics/MSTExplorer ). Manuscript analyses and reproducibility code: https://github.com/neurogenomics/rare_disease_celltyping .

Summary: Many single-cell transcriptomics datasets are stored in the HDF5-backed AnnData (H5AD) file format, as popularised by the Python scverse ecosystem. However, accessing these datasets from R, allowing users to take advantage of the strengths of each language, can be difficult. anndataR facilitates this access by allowing users to natively read and write H5AD files in R, convert them to and from SingleCellExperiment or Seurat objects, or even work with the resulting R AnnData object directly. We perform rigorous testing to ensure compatibility between Python-written and R-written H5AD files, guaranteeing long-term interoperability between languages. Availability: anndataR’s source code is available on GitHub at scverse/anndataR under the MIT license. It is compatible with R version 4.5, has been archived at 10.5281/zenodo.18775712 and included within Bioconductor: 10.18129/B9.bioc.anndataR. Installation instructions and tutorials can be found in the online documentation at anndatar.scverse.org. Issues can be reported at the GitHub repository. Code to reproduce the analyses performed can be found on GitHub at LouiseDck/anndataR-paper, archived at 10.5281/zenodo.18792241.

Predicting the consequences of genetic variants remains a major goal in biomedicine. Conventional approaches typically assess single-nucleotide variants in the context of a single reference genome, without accounting for genetic diversity that can modulate variant effects. Here we introduce the personalized variant effect predictor (pVEP) framework, which quantifies how genetic background across thousands of human genomes from globally diverse populations shapes computational predictions of clinical variant effects. Across deep learning models spanning protein structure, splicing, and noncoding regulation, pVEP reveals that many clinical variants exhibit heterogeneous predicted effects across haplotypes, with the same variant predicted to be pathogenic in some genetic backgrounds and benign in others. We find support for underlying molecular mechanisms, including shifts in predicted protein contacts and changes in splice-site recognition. Overall, personalized genomic context emerges as a systematically underappreciated variable in variant annotation and clinical interpretation, with particular implications for genetically diverse populations.

Craniofacial development is a complex process that involves the specification of diverse and transient cell types. However, our understanding of these processes and the cell-types present during human craniofacial developmental remains limited. We address this gap in knowledge through single-nucleus RNA sequencing of human craniofacial development spanning 4 to 8 post-conception weeks. This resource identifies multiple subtypes of mesenchyme, epithelium, and cranial neural crest, among other functionally distinct cell types. Extensive comparisons to single-nucleus gene expression and spatial transcriptomics of comparable mouse developmental stages reveal functional conservation of most cell types and identification of anatomically distinct cell subtypes. We find distinct contributions of cellular subtypes to normal facial morphology and common risk factors for orofacial clefts. Additionally, we find specific ectodermal and epithelial subtypes whose gene expression networks are most significantly affected by rare de novo protein-altering variants from orofacial cleft patients. Together our data provide an extensive resource for understanding human craniofacial development at the cellular level.

Motivation: Mapping genes across identifier systems and species is a routine but critical step in bioinformatics workflows. Despite its ubiquity, gene mapping is frequently handled using bespoke, ad hoc solutions, increasing duplicated effort and introducing opportunities for error. These issues are exacerbated by the prevalence of non-one-to-one homolog relationships and inconsistent handling of gene identifiers across species and databases, which can compromise downstream analyses and reproducibility. Results: We present orthogene, an R/Bioconductor package that simplifies gene mapping within and across hundreds of species. orthogene provides a unified, workflow-oriented framework that integrates automated species and identifier standardization, homolog inference across multiple databases, flexible handling of ambiguous homolog relationships, and transformation of gene lists, tables, and high-dimensional matrices into analysis-ready formats. By abstracting common sources of technical complexity while retaining user control, orthogene enables transparent, reproducible, and scalable gene mapping across a wide range of biological contexts.

Summary: Biomedical knowledge graphs (KGs) aggregate and provide a wealth of information, linking genes and their variants, diseases, phenotypes, and much more. While these data are available in raw and API-hosted form, to date, functionality for working with KGs in the R programming language has been limited. We introduce monarchr, a package for querying and manipulating KG data. Support for the expansive Monarch Initiative KG is built in, and monarchr can accommodate any KG in the Knowledge Graph eXchange (KGX) format. This tidy-inspired interface offers researchers an intuitive, iterative approach to querying and visualizing KG data. Availability and implementation: Source code, documentation, and installation instructions are available at https://github.com/monarch-initiative/monarchr.

Summary: Many single-cell transcriptomics datasets are stored in the HDF5-backed AnnData (H5AD) file format, as popularised by the Python scverse ecosystem. However, accessing these datasets from R, allowing users to take advantage of the strengths of each language, can be difficult. anndataR facilitates this access by allowing users to natively read and write H5AD files in R, convert them to and from SingleCellExperiment or Seurat objects, or even work with the resulting R AnnData object directly. We perform rigorous testing to ensure compatibility between Python-written and R-written H5AD files, guaranteeing long-term interoperability between languages. Availability: anndataR’s source code is available on GitHub at scverse/anndataR under the MIT license. It is compatible with R version 4.5, has been archived at 10.5281/zenodo.18775712 and included within Bioconductor: 10.18129/B9.bioc.anndataR. Installation instructions and tutorials can be found in the online documentation at anndatar.scverse.org. Issues can be reported at the GitHub repository. Code to reproduce the analyses performed can be found on GitHub at LouiseDck/anndataR-paper, archived at 10.5281/zenodo.18792241. Contact: robrecht.cannoodt@gmail.com

Bipolar disorder is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 bipolar disorder risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci and prioritized 17 likely causal SNPs for bipolar disorder. We mapped these SNPs to genes and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci and results from rare variant exome sequencing in bipolar disorder. Convergent lines of evidence supported the roles of genes involved in neurotransmission and neurodevelopment, including SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, FKBP2, RASGRP1, FURIN, FES, MED24 and THRA among others in bipolar disorder. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance of bipolar disorder polygenic risk scores across diverse populations and present a high-throughput fine-mapping pipeline.

Craniofacial development gives rise to the complex structures of the face and involves the interplay of diverse cell types. Despite its importance, our understanding of human-specific craniofacial developmental mechanisms and their genetic underpinnings remains limited. Here, we present a comprehensive single-nucleus RNA sequencing (snRNA-seq) atlas of human craniofacial development from craniofacial tissues of 24 embryos that span six key time points during the embryonic period (4–8 post-conception weeks). This resource resolves the transcriptional dynamics of seven major cell types and uncovers distinct major cell types, including muscle progenitors and cranial neural crest cells (CNCCs), as well as dozens of subtypes of ectoderm and mesenchyme. Comparative analyses reveal substantial conservation of major cell types, alongside human biased differences in gene expression programs. CNCCs, which play a crucial role in craniofacial morphogenesis, exhibit the lowest marker gene conservation, underscoring their evolutionary plasticity. Spatial transcriptomics further localizes cell populations, providing a detailed view of their developmental roles and anatomical context. We also link these developmental processes to genetic variation, identifying cell type-specific enrichments for common variants associated with facial morphology and rare variants linked to orofacial clefts. Intriguingly, Neanderthal-introgressed sequences are enriched near genes with biased expression in cartilage and specialized ectodermal subtypes, suggesting their contribution to modern human craniofacial features. This atlas offers unprecedented insights into the cellular and genetic mechanisms shaping the human face, highlighting conserved and distinctly human aspects of craniofacial biology. Our findings illuminate the developmental origins of craniofacial disorders, the genetic basis of facial variation, and the evolutionary legacy of ancient hominins. This work provides a foundational resource for exploring craniofacial biology, with implications for developmental genetics, evolutionary biology, and clinical research into congenital anomalies.