Emmanuèle Délot

Sequence-to-omics (S2O) predictions and molecular outliers provide complementary evidence to prioritize functional rare variants and facilitate rare disease diagnoses. However, current approaches do not integrate both lines of evidence. We present Watershed-DL, a framework that integrates S2O predictions of variant effects from DNA and protein sequence with multi-omic outliers to identify large-effect rare variants. We applied Watershed-DL to 1000 Genomes samples with RNA-seq (n=1,030) to prioritize rare variants underlying expression, splicing, and allele-specific expression outliers, and, using a separate peak-level model, to samples with ATAC-seq (n=174) for chromatin accessibility outliers. Compared to a model without S2O predictions, Watershed-DL increased the number of prioritized variants by 61.4%, and S2O features were weighted highly in the model across all molecular signals. Prioritized variants were over 60-fold enriched for ClinVar pathogenic classifications. Matching prioritized variants to known pathogenic variants in the same gene with a concordant molecular effect identified 331 additional candidate recessive disease alleles. Applying this framework to the GREGoR rare disease cohort with RNA-seq (n=677) prioritized previously reported causal variants across 10 solved cases and identified 451 candidate variants in unsolved cases, including a diagnostic variant in PPP1R12A and a novel candidate disease gene, ATP5PF .

Background: Congenital hypogonadotropic hypogonadism (CHH) is a genetically heterogeneous disorder characterized by absent or delayed puberty and infertility due to gonadotropin-releasing hormone deficiency. While loss-of-function variants in FGFR1 are the most commonly known genetic cause of CHH, diagnostic screening typically focuses on coding exons, potentially overlooking pathogenic variants in noncoding regions. Methods: We investigated 4 unrelated families with CHH. Family 1 included a mother and 3 daughters presenting with variable reproductive phenotypes and incomplete penetrance of anosmia; Family 3 consisted of a girl with CHH and anosmia; and Families 2 and 4 consisted of male patients who presented with CHH and anosmia. Genetic investigations were done using either exome or genome sequencing. Experimental characterization of the identified intronic variants was conducted using RNA extracted from peripheral blood mononuclear cells. Exome or genome sequencing–based haplotyping was performed for the recurring variant. Results: In Families 1 to 3, we identified a recurring heterozygous intronic variant in FGFR1: c.92-19G > A. RNA analysis confirmed that this variant generates a cryptic splice acceptor site, leading to the inclusion of 17 intronic nucleotides, resulting in a premature stop codon. Haplotype analysis revealed the variant occurred independently in each family. In Family 4, a distinct heterozygous intronic variant was identified in FGFR1, which was shown to cause intron 15 retention with premature termination. Conclusion: Our findings demonstrate that intronic variants in FGFR1 are a possible cause of CHH and can lead to diverse clinical phenotypes ranging from normosmic CHH to severe Kallmann syndrome, including significant presentation in female patients.

Congenital adrenal hyperplasia (CAH) can result from variants in several genes but is most frequently caused by deletions and gene conversions in the segmentally duplicated RCCX module, which contains the CYP21A2 gene and its pseudogene. Current genetic tests vary greatly by laboratory, method, and consequently diagnostic ability. Less severe forms of CAH are frequently underdiagnosed or misdiagnosed, and phenotype/genotype correlations remain incomplete, hampering genetic counseling and prediction of long-term outcomes. Emerging technologies using long-read sequencing (LRS) have transformed molecular testing for CAH, accurately identifying pathogenic single-nucleotide variants (SNVs), full-gene deletions, gene conversions, fusions creating nonfunctional hybrids between the gene and pseudogene (“30-kb deletion”), and phasing variants, even in the absence of parental samples. This has led to updated reports of previously unrecognized complex alleles in large cohorts. This is a transformative breakthrough, which offers the promise of consolidated testing for all forms of CAH and other conditions under the differences of sex development (DSDs) umbrella. DSDs are notoriously difficult to disambiguate, resulting in a considerable diagnostic gap. The large array of known genetic etiology, ranging from SNVs in coding or noncoding regions in dozens of genes to sex chromosome aneuploidies, associated with overlapping phenotypes, make precise DSD diagnosis one of the most difficult in medicine. We report on our experience with LRS tools for disambiguation of CAH alleles and discuss some of the technical aspects to be considered prior to the deployment of LRS to ensure accurate and equitable results for the diagnosis of rare conditions such as DSDs.

/Summary: Background: Collaborative research programs increasingly require infrastructure capable of integrating heterogeneous participant, sample, and experimental data while meeting evolving research needs. Existing tools, including clinical EHRs, REDCap, generic research information management systems, and bespoke database builds, were not designed to operationalize externally governed, versioned consortium data models across database structure, APIs, validation, web forms, and reproducible exports. The ICTS Dashboard, developed at the University of California, Irvine Institute for Clinical and Translational Science, fills this need by providing a general purpose research information management system. Methods: The ICTS Dashboard was built as an open-source, schema-driven platform in which database structure, server-side validation, REST APIs, web-based forms, and reproducible exports are derived from a versioned JavaScript Object Notation (JSON) Schema set. The backend is implemented in Django, Django REST Framework, and PostgreSQL; the frontend in React. We instantiate the platform with the Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Data Model and extend it with two case studies: a locally developed biobank table for biospecimen logistics, and an embedded adaptation of the RAG-HPO retrieval-augmented phenotype curation tool. Results: The ICTS Dashboard deployed at the UCI-GREGoR site supports 37 schema-derived tables and 250 documented API endpoints. It holds metadata for 2,563 participants, 1,238 families, 5,517 biobank entries, 2,593 sequenced experiments, and 289 genetic findings. It supports quarterly external data submissions regenerated directly from the database. The biobank extension adds entities the consortium does not standardize while preserving foreign-key linkage to rare disease records; the RAG-HPO module adds curator-mediated phenotype normalization against 19,389 indexed HPO terms. Both were integrated without modifying the GREGoR Data Model. Conclusion: The Dashboard’s architecture is not limited to GREGoR or to rare disease research. A version-controlled, machine-readable data model can serve not only as a data sharing standard but as the operational backbone of a research program when paired with schema-governed tooling. Any collaborative research program with a structured, versioned model can adopt the same pattern to reduce implementation overhead and improve reproducibility, harmonization, and findable, accessible, interoperable, and reusable (FAIR)-aligned data management.

Rare disease research and diagnosis rely on the integration of genomic and phenotypic data generated across diverse clinical sites; however, the absence of widely adopted standards for representing genomic data and associated metadata has limited data interoperability, reuse, and cross-study analysis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was established to investigate challenging rare disease cases and evaluate emerging multi-omic technologies for clinical translation. To support coordinated data integration across distributed research sites, we developed a common Consortium Data Model in partnership with domain experts to standardize the capture of participant-, family-, phenotype- and assay-level metadata, with a particular emphasis on using a modular architecture to support linking of multiple data versions from multiple omic technologies to a single individual and attribution of a genetic finding to the specific technology used for its initial discovery. Adoption of the GREGoR Data Model has enabled continued generation and public release of a harmonized, analysis-ready Consortium Dataset. The most recent release includes phenotypic, family and multi-omic data from 12,292 participants in 5,029 families. Other rare disease data sharing efforts are beginning to adopt this data model which will facilitate cross consortium analyses and empower rare disease research. This work demonstrates that a collaborative, flexible, and scalable data model can enable large-scale rare disease research, facilitate cross-center data harmonization, and enable data interoperability.

1 We previously introduced Giraffe, a short-read-to-pangenome graph mapper available in the vg pangenomics toolkit. Giraffe was fast and accurate for mapping short reads to human-scale pangenomes, but struggled with long reads. Long reads present a unique challenge to pangenome mapping algorithms due to their length and error profile, which allow them to take more topologically complex paths through the pangenome graph and increase the possible search space for the algorithm. We present updates to Giraffe that allow it to quickly and accurately map long reads to pangenome graphs. For both short and long reads, Giraffe mapping to a pangenome containing data from more than 450 human haplotypes, generated by the Human Pangenome Reference Consortium, is comparable in speed to linear mappers to human reference genomes; Giraffe is also over an order of magnitude faster than GraphAligner, the current state-of-the-art long-read-to-pangenome mapper. Its alignments produce similar or improved small and structural variant calling results, compared to those from commonly used graph-based and linear mappers. We additionally demonstrate using Giraffe’s long read alignments in a pangenome-guided assembly workflow, which is capable of producing more contiguous local assemblies than Hifiasm in our test regions.

Congenital Adrenal Hyperplasia (CAH), one of the most common inherited disorders, is caused by defects in adrenal steroidogenesis. It is potentially lethal if untreated and is associated with multiple comorbidities, including fertility issues, obesity, insulin resistance, and dyslipidemia. CAH can result from variants in multiple genes, but the most frequent cause is deletions and conversions in the segmentally duplicated RCCX module, which contains the CYP21A2 gene and a pseudogene. The molecular genetic test to identify pathogenic alleles is cumbersome, incomplete, and available from a limited number of laboratories. It requires testing parents for accurate interpretation, leading to healthcare inequity. Less severe forms are frequently misdiagnosed, and phenotype/genotype correlations incompletely understood. We explored whether emerging technologies could be leveraged to identify all pathogenic alleles of CAH, including phasing in proband-only cases. We targeted long-read sequencing outputs that would be practical in a clinical laboratory setting. Both HiFi-based and nanopore-based whole-genome long-read sequencing datasets could be mined to accurately identify pathogenic single-nucleotide variants, full gene deletions, fusions creating non-functional hybrids between the gene and pseudogene (“30-kb deletion”), as well as count the number of RCCX modules and phase the resulting multimodular haplotypes. On the Hi-Fi data set of 6 samples, the PacBio Paraphase tool was able to distinguish nine different mono-, bi-, and tri-modular haplotypes, as well as the 30-kb and whole gene deletions. To do the same on the ONT-Nanopore dataset, we designed a tool, Parakit, which creates an enriched local pangenome to represent known haplotype assemblies and map ClinVar pathogenic variants and fusions onto them. With few labels in the region, optical genome mapping was not able to reliably resolve module counts or fusions, although designing a tool to mine the dataset specifically for this region may allow doing so in the future. Both sequencing techniques yielded congruent results, matching clinically identified variants, and offered additional information above the clinical test, including phasing, count of RCCX modules, and status of the other module genes, all of which may be of clinical relevance. Thus long-read sequencing could be used to identify variants causing multiple forms of CAH in a single test.

Introduction Advancements in sequencing technologies have significantly improved clinical genetic testing, yet the diagnostic yield remains around 30-40%. Emerging sequencing technologies are now being deployed in the clinical setting to address the remaining diagnostic gap. Methods We tested whether short-read genome sequencing could increase diagnostic yield in individuals enrolled into the UCI-GREGoR research study, who had suspected Mendelian conditions and prior inconclusive clinical genetic testing. Two other collaborative research cohorts, focused on aortopathy and dilated cardiomyopathy, consisted of individuals who were undiagnosed but had not undergone harmonized prior testing. Results We sequenced 353 families (754 participants) and found a molecular diagnosis in 54 (15.3%) of them. Of these diagnoses, 55.5% were previously missed because the causative variants were in regions not interrogated by the original testing. In 9 cases, they were deep intronic variants, 5 of which led to abnormal splicing and cryptic exon inclusion, as directly shown by RNA sequencing. All 5 of these variants had inconclusive spliceAI scores. In 26% of newly diagnosed cases, the causal variant could have been detected by exome sequencing reanalysis. Conclusion Genome sequencing overcomes multiple limitations of clinical genetic testing, such as inability to call intronic variants and technical limitations. Our findings highlight cryptic exon inclusion as a common mechanism via which deep intronic variants cause Mendelian disease. However, they also reinforce that reanalysis of exome datasets can be a fruitful approach.

More than 50% of families with suspected rare monogenic diseases remain unsolved after whole genome analysis by short read sequencing (SRS). Long-read sequencing (LRS) could help bridge this diagnostic gap by capturing variants inaccessible to SRS, facilitating long-range mapping and phasing, and providing haplotype-resolved methylation profiling. To evaluate LRS’s additional diagnostic yield, we sequenced a rare disease cohort of 98 samples, including 41 probands and some family members, using nanopore sequencing, achieving per sample ∼36x average coverage and 32 kilobase (kb) read N50 from a single flow cell. Our Napu pipeline generated assemblies, phased variants, and methylation calls. LRS covered, on average, coding exons in ∼280 genes and ∼5 known Mendelian disease genes that were not covered by SRS. In comparison to SRS, LRS detected additional rare, functionally annotated variants, including SVs and tandem repeats, and completely phased 87% of protein-coding genes. LRS detected additional de novo variants, and could be used to distinguish postzygotic mosaic variants from prezygotic de novos . Eleven probands were solved, with diverse underlying genetic causes including de novo and compound heterozygous variants, large-scale SVs, and epigenetic modifications. Our study demonstrates LRS’s potential to enhance diagnostic yield for rare monogenic diseases, implying utility in future clinical genomics workflows.