Genome Medicine

Tandem repeat expansions cause more than 60 Mendelian disorders but are difficult to detect and size accurately with short-read sequencing because of their length and repetitive structure. Long-read sequencing (LRS) resolves these loci directly, yet analytic tooling for nanopore sequencing data has lagged behind that available for other long-read platforms. Here we present Mahi-mahi, a tandem repeat genotyper designed for nanopore reads that reports allele-level repeat length, motif composition, interruptions, and read-level flags for systematic basecalling artifacts. Across six Genome in a Bottle samples, whole-genome and adaptive sampling (real-time targeted) data returned calls within one repeat unit of each other for 829 of 832 alleles, and Mahi-mahi calls were within one repeat unit of orthogonal HiFi data at 95.6% of compared alleles after excluding three loci at which the two tools define the repeat differently. In 245 samples from the 1000 Genomes Project, Mahi-mahi calls were within one repeat unit of assembly-derived calls for 98.6% of 10,927 alleles at 57 loci, and Mahi-mahi distinguished pathogenic from nonpathogenic RFC1 alleles of overlapping size by motif. Across 14 Coriell reference lines, Mahi-mahi identified the expected expansion in every line and agreed with orthogonal assays to within two repeat units at HTT, ATXN1, DMPK, and PABPN1. Finally, in 103 samples from three laboratories, Mahi-mahi detected an expanded allele in all samples in which prior testing had reported a pathogenic expansion, assigned the same category as the referring laboratory for 98% of 180 genotypes, and identified known ATXN1 interruption structure. Our results demonstrate that Mahi-mahi accurately identifies repeat expansions at disease-associated loci and could serve as a single assay to evaluate repeat expansions.

Background: Short-read whole-genome sequencing (sWGS) has limitations in characterizing tandem-repeat expansions and variants in highly homologous or pseudogene-associated regions. Long-read sequencing can address some of these challenges but often benefits from high-molecular-weight DNA. TruPath Genome incorporates spatial proximity information into a short-read sequencing framework. This study evaluated its performance in characterizing selected complex genomic variants using archived clinical DNA specimens. Methods: Ten archived peripheral blood DNA specimens were analyzed, comprising seven cases with disease-associated short tandem repeat (STR) expansions and three cases involving homologous or pseudogene-associated loci (STRC, SMN1/SMN2, and CYP21A2). TruPath results were compared with previously available orthogonal assay results, including triplet repeat-primed PCR (TP-PCR), multiplex ligation-dependent probe amplification (MLPA), quantitative PCR (qPCR), and sWGS-based ExpansionHunter analysis. Results: TruPath generated repeat-length estimates across the evaluated STR loci. For two FMR1 full-mutation alleles, TruPath estimated 284 and 295 CGG repeats, respectively, while TP-PCR reported expansions exceeding 200 repeats. In a DMPK case, TruPath estimated an expanded allele of 1,203 CTG repeats, compared with a TP-PCR result exceeding 100 repeats. At the STRC and SMN1/SMN2 loci, TruPath findings were consistent with available orthogonal results for deletion and copy-number status. At the CYP21A2 locus, TruPath reconstructed the RCCX gene arrangement and assigned the I2G variant and a chimeric allele to different haplotypes. Conclusions: In this small series of archived clinical DNA specimens, TruPath Genome enabled characterization of selected repeat expansions and homologous or pseudogene-associated variants. The findings support further evaluation of proximity-mapped sequencing as a complementary approach for complex genomic regions, particularly when conventional short-read sequencing is limited.

To evaluate the clinical implementation of long-read genome sequencing (lrGS) and establish the population reference resources needed to support its use in diagnostics, we integrate two complementary initiatives: Genome of Sweden (GoS), Sweden's contribution to Genome of Europe (GoE), and the Genomic Medicine Sweden - Rare Diseases (GMS-RD) long-read sequencing pilot. The initial GoS release of 576 individuals provides population-scale maps of genetic and epigenetic variation. Among 485 GMS-RD probands, 161 (33.2%) receive a molecular diagnosis, with lrGS providing added clinical value in 40 (24.8%). As a first-line test, lrGS could have avoided at least 42 follow-up analyses and an additional testing cycle in 7.2% of probands, reducing costs and the burden on patients. By integrating population and clinical long-read genomics, Sweden establishes a national ecosystem for genomic research and clinical diagnostics. Our findings support the clinical implementation of lrGS and offer a scalable model for national collaboration.

Targeted gene panels (TGPs) dominate clinical sequencing, yet most copy-number (CN) signature studies rely on genome-wide assays. Whether signatures can be recovered from TGPs and retain clinical relevance remains unclear. We analyzed two real-world TGP cohorts comprising 1,726 patients across 62 tumor types, including 825 with clinical annotations, and made the underlying data publicly available. TGP-derived signatures recapitulated established biological associations, including links to homologous recombination deficiency and TP53 alterations, concordant with published genome-wide assay-derived CN signatures. Using our detailed clinical data, we found TGP-derived CN signatures associated with overall survival under standard therapies in ovarian, pancreatic, and colorectal cancer. In ovarian cancer, CN2 was associated with CCNE1 amplification and shorter survival under paclitaxel/carboplatin, with the survival association validated in an independent SNP-array cohort. These findings demonstrate that routine TGPs yield biologically and clinically relevant CN signatures with potential as biomarkers for therapy stratification.

Background: The circadian clock regulates cellular homeostasis, and its disruption has been implicated in aggressive neuroblastoma, particularly in tumours harbouring MYCN- amplification. However, it remains unclear whether alterations are restricted to individual clock genes or extend to circadian gene network coordination. We therefore examined circadian clock network disruption in adverse neuroblastoma across multiple biological contexts. Methods: We estimated circadian gene network dysregulation using Delta-CCD in tumours from two neuroblastoma cohorts (SEQC n=498 and Kocak n=649), comparing clinical features associated with outcome across canonical, adrenal-tissue matched, and developmental references. Robustness was assessed by cross-cohort meta-analysis and leave-one-gene-out analyses. Cox proportional hazards models adjusted for clinical covariates assessed association between individual clock gene expression patient outcome. Results: Delta-CCD was highest in tumours classified as high-risk (study-specific definition) across reference contexts in both cohorts. MYCN-amplified tumours showed a more reference-dependent pattern, strongest in adrenal context, while stage 4 tumours showed a similar but weaker pattern. Additional analyses supported the high-risk signal as a distributed network-level alteration rather than a single-gene phenomenon. Conclusions: High-risk neuroblastoma is characterised by robust disruption of coordinated clock gene network organisation across canonical and tissue-matched references, extending beyond individual clock genes. The extent of circadian dysregulation depends on the reference state used.

Host genetic variation influences heterogeneity in infectious disease severity. Genome-wide association studies (GWAS) have identified 69 lead single nucleotide polymorphisms (SNPs) associated with critical COVID-19, but most lack known regulatory mechanisms. To date, the genetic cis-regulatory effects of critical COVID-19 GWAS lead SNPs have not been systematically characterized across acute illness and convalescent phases in a COVID-19 specific cohort. Here, performing severity-stratified, longitudinal cis-expression quantitative trait loci (cis-eQTL) mapping and colocalization with critical COVID-19 GWAS signals, we find that host cis-regulatory effects vary across clinical severity and disease phase. We analyzed peripheral blood mononuclear cells from 1,031 patients in the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC), a prospective multicenter study of adults hospitalized with COVID-19, profiled across 10 timepoints spanning acute illness and convalescence. We identified 23 GWAS lead SNPs that colocalized with cis-eQTLs at 26 candidate causal genes, with 16 detectable only when severity stratification and longitudinal sampling were incorporated. A colocalized, protective allele (chr19:4,717,660) at the TMIGD2 locus upregulated TMIGD2 expression exclusively during the acute phase in the prolonged-critical/fatal cohort, with no detectable effect in the rapid-recovery cohort. Our results show that host genetic effects on critical COVID-19 can be context-dependent, with cis-regulatory effects varying across clinical severity and disease phase. Severity-stratified, longitudinal cis-eQTL designs have greater sensitivity to identify candidate causal genes for critical COVID-19 GWAS associations than single-timepoint cross-sectional studies.

Background and aims. Inflammatory Bowel Disease (IBD) is a complex immune-mediated condition with a strong genetic contribution. The genetics of IBD are highly complex, with inflammation, autoimmunity, and mucosal immunity pathways involved in its pathogenicity. GenePy is a gene-level disease burden metric. The integration of clinical and genetic data and the application of machine learning methods can aid patient stratification for personalised care. Methods. Lists of consensus IBD pathway genes were curated. GenePy scores were computed from whole exome sequencing data. A K-Means algorithm was optimised using clustering quality and stability metrics on the GenePy scores of genes in each IBD pathway, and the results replicated in three independent IBD cohorts (SPARC IBD, MSCCR IBD, and Southampton IBD). Chi-square tests assessed the association between cluster assignment and phenotypic features, and were multiple test corrected via Benjamini-Hochberg FDR. Results. A strong association was replicated for NOD signalling genes driving IBD subtype, Crohns disease behaviour, and IBD surgery across all cohorts, as well as granuloma in Southampton IBD. This was mainly driven by NOD2. Similar genetic clustering patterns were observed across all cohorts, with additional phenotypic enrichment in clusters driven by NLRP1 (surgery in MSCCR IBD), and nominally significant enrichment for disease behaviour linked to BIRC2 (B2 in Southampton IBD and B2B3 in SPARC IBD). Conclusions. This analysis identified a common genetic architecture across NOD signalling genes across IBD cohorts, replicated a signal for NOD2 as a monogenic driver of disease with a strong phenotypic enrichment, and identified other putative genetic drivers of IBD, such as the NLRP1 gene.

Abstract INTRODUCTION Despite major advances in genomic sequencing and analytical pipelines for rare disorders, more than 50% of affected individuals remain without a molecular diagnosis. One challenge is the interpretation of inherited variants exhibiting incomplete penetrance (IP). MATERIALS AND METHODS We developed a pipeline to systematically investigate inherited variants as the cause of rare disease in 4,325 families, consisting of affected probands and unaffected parents, from the Broad Center for Mendelian Genomics, part of the GREGoR Consortium. As a proof-of-concept of further omics follow-up, epigenomic and transcriptomic analyses were performed in one family harboring an inherited disease-causing frameshift variant in CHD8. RESULTS We identified 81 families where the affected proband harbored a putative disease-causing variant inherited from a reportedly unaffected parent. Clinical re-evaluation identified 30 families in which an inherited variant was considered diagnostic, including six previously missed diagnoses. In this cohort, inherited IP variants account for 1.6% of the diagnosed cases, with an overall diagnostic yield of 0.4% in the cohort. Methylation analysis of the CHD8 family confirmed a positive CHD8 clinical episignature in the male proband, but also in the reportedly unaffected mother. Transcriptomic analysis showed that despite similar CHD8 haploinsufficiency, the proband and mother displayed divergent downstream transcriptional effects. DISCUSSION This study highlights the importance of considering parentally inherited IP variants in rare disease, with at least 1 in 60 diagnostic variants inherited from a reportedly unaffected parent, and demonstrates the potential of integrating omics evaluation to improve variant interpretation.

Genetic prediction of clinically defined disease subtypes could advance precision medicine, but polygenic risk scores (PRSs) often show limited ability to distinguish subtypes, either because the subtypes share genetic architecture or because available PRSs are inaccurate. We introduce GenSep, a statistical framework that estimates the oracle case-case AUC attainable if the true additive genetic values of both subtypes were known, and the proportion of this genetic separation recovered by subtype-specific PRSs. Across 18 subtype pairs in UK Biobank, oracle AUCs ranged from 0.605 to 1.000, whereas current PRSs achieved a median observed case-case AUC of only 0.530 (range, 0.510-0.754), corresponding to recovery of a median of 4.1% (range, 0.3-15.5%) of the genetic separation variance available to a perfect predictor. Oracle AUCs were generally consistent across UK Biobank, All of Us, FinnGen and the Million Veteran Program, and across European, African and Admixed American ancestry groups. FinnGen-trained PRSs, despite 2.6-to 3.9-fold larger discovery samples, performed no better than internally trained PRSs, and substantially larger consortium GWAS raised case-case AUC by 0.06 on average (range, 0.003-0.089). GenSep separates subtype pairs limited by intrinsic genetic overlap from those limited by current PRS accuracy, identifying where larger studies and better predictors could improve genetic discrimination.

Purpose A significant proportion of cases with rare inherited retinal disease (IRD) remain genetically unresolved following short-read whole genome sequencing (WGS). This study aimed to increase the diagnostic yield of two cohorts in a research setting. One cohort was previously screened by short-read WGS (n=120 cases), while the second cohort (n=28 cases) was not included in any previous short-read WGS studies. In contrast to the larger cohort, these cases only underwent either exome sequencing (ES) or single-molecule molecular inversion probe (smMIPs) sequencing as pre-screening in earlier studies. For all cases, we performed a stepwise, case by case reanalysis in which short-read WGS was generated (n=28 cases) and (re)analyzed short-read WGS data using an optimized approach. For a subset of cases (n=20) that remained unresolved, this was followed by long read WGS. Together, these steps aimed to improve the diagnostic yield in these cohorts. Methods Short-read WGS data of 148 IRD-cases were (re)analyzed using updated allele frequency databases, new variant caller and variant predictor tools, as well as updated and extended gene-panels to identify causal single nucleotide variants (SNVs) and structural variants (SVs). For 20 genetically unresolved cases with sufficient high-quality DNA available, long-read WGS was performed. Results The (re)analysis of short-read WGS data in the 120 previously WGS-screened unresolved cases resulted in a diagnostic yield of 15% (18/120). For the 28 individuals without prior WGS, performing WGS analysis within this study provided an additional yield of 32% (9/28). Furthermore, long-read WGS contributed two genetic diagnoses among the 20 long-read WGS-sequenced probands. Amongst other reasons, new genetic diagnoses were attributed to pathogenic variants in genes newly associated with IRDs and in genes that were not included in the gene panels applied in previous WGS studies. The Mobile Element Locator Tool facilitated the identification of two pathogenic Alu insertions in short-read WGS. Long-read WGS identified two additional pathogenic variants. Conclusion These results support periodic reanalysis of existing short-read WGS data as a key component of IRD genetic diagnostics, with selective long-read WGS representing a valuable additional approach for unresolved cases. Together, these complementary strategies offer a practical framework for improving molecular diagnosis and narrowing the remaining diagnostic gap in IRDs.

Objectives: Genotype-phenotype studies in rare epilepsies typically relate a pathogenic DNA sequence change to clinical outcome, without considering the broader molecular context of a given variant. Here we ask whether clinical heterogeneity in SCN8A-related disorders (SCN8A-RD) is patterned along molecular dimensions that go beyond the specific genetic alteration, to include position in the linear channel topology, proximity to post-translational modification (PTM) and structural sites, and predicted effects on mRNA splicing. Using the International SCN8A Registry, we map these features against seizure, developmental, and treatment-response phenotypes to identify the regions and molecular feature classes with which clinical variability is associated. Methods: We identify "hot-spots" across the coding sequence for seizure types and severity of developmental disability (DD). We then test for enrichment of clinical features near sites related to PTM and protein structure, identify variants that may alter mRNA splicing, and evaluate the interaction between these features and clinical subgroups. Results: We expand on previous work identifying regions of coding DNA sequence that are pathogenic or benign "hot-spots". We show regional enrichment of numerous seizure types and severities of DD. Enrichment analysis of PTM and protein structural sites shows distinct enrichment profiles for developmental, seizure, and medication-response features. Finally, we identify 14 variants that are likely to alter RNA splicing. Significance: We provide a higher-resolution map of pathogenic variation across SCN8A and show that seizure types, developmental severity, and medication response are regionally organized along the coding sequence. Proximity to structural and glycosylation sites distinguishes phenotypic subgroups is associated with differential drug response, including a glycosylation-gabapentin relationship, and 14 missense variants are flagged as likely splice-altering, nominating targets for splice-directed therapy and providing candidates for experimental validation of dual pathogenic mechanisms.

For most diseases, whether PRS improves prediction beyond clinical data remains unknown, as existing evidence is concentrated in a handful of conditions with established risk models. Using 900,000 participants from UK Biobank and FinnGen, we developed and validated models for predicting 150 diseases and all-cause mortality. Genetic information provided the largest predictive gains in metabolic, cardiovascular, autoimmune, and neurological disease, but added little for genitourinary and respiratory conditions. All significant PRS-clinical risk interactions were negative, with predictive power gains from genetic data larger at lower clinical risk, suggesting that PRS may reveal susceptibility not yet apparent clinically. For example, in atherosclerotic cardiovascular disease, PRS expanded the clinically identified high-risk subgroup by 40%, and this reclassified subgroup had 4.9-fold higher incidence during the follow-up than those remaining low-risk. Our interpretable clinical-genetic models generalized across three biobanks and are publicly available through PrognosIQ.

Backgroud Breast cancer is a leading cause of cancer-related mortality, and HR+/HER2-; is the predominant subtype. Molecular pathology, including PIK3CA and ESR1 mutation, informs prognosis and treatment selection. Circulating tumor DNA (ctDNA) offers a non-invasive approach for cancer monitoring, but its clinical utility is limited by tissue plasma discordance, reflecting heterogeneous ctDNA release. The cellular and spatial determinants of this release remain poorly understood, particularly in HR+/HER2-; subtype, where heterogeneity across subclones complicates liquid biopsy interpretation. Results To identify the cellular and spatial determinants of ctDNA release, we developed scMASTER, integrating single-cell transcriptomes and mutations, spatial transcriptomics and matched tumor and plasma DNA sequencing. In ten patients with non-metastatic HR+/HER2- breast cancer, eight recurrent malignant epithelial states showed distinct ctDNA release potential. Antigen-presentation, stress, mitosis and mesenchymal-like states showed high release. PIK3CA and ESR1 mutations were linked to distinct functional states and altered ctDNA detectability. Three spatial architectures, EMT-enriched regions, intraductal luminal progenitors and avascular hypoxic zones, showed high release. Tumor-microenvironment interactions were bidirectional: anti-tumoral immunity correlated with reduced release, whereas immunomodulatory components correlated with increased release. A stratification model based on these features identified high-risk, immunosuppressed patients with poor prognosis, validated across external cohorts. Conclusions Our study establishes a single-cell spatial framework for decoding ctDNA release heterogeneity, linking tumor states, somatic mutations, spatial architecture, and immune interactions to ctDNA release. These findings provide mechanistic insights into tissue-plasma discordance, offer a biological rationale for interpreting ctDNA dynamics, refine patient risk stratification and liquid biopsy-based monitoring in HR+/HER2-; breast cancer.

Autism is highly heritable, yet many cases remain genetically unsolved even after short-read sequencing (SRS). Long-read sequencing (LRS) increases sensitivity for pathogenic variant discovery, especially for complex structural variants (SVs) in repetitive regions missed by SRS. However, LRS largely relies on blood-derived DNA; whether saliva can support genome-wide LRS at population scale remains to be seen. We report the first systematic evaluation of saliva-derived DNA for genome-wide LRS in autism, benchmarking 105 new saliva samples against 68 new blood samples and integrating 189 previously published LRS blood samples to expand the autism cohort and improve ultra-rare pathogenic variant discovery. A matched blood-saliva quad provided direct comparisons of variant detection between sample sources. Saliva samples contained substantially more nonhuman DNA than blood (mean 10% versus 0.005% of reads), reducing mean effective genome coverage from an estimated 28X to 24X after removal of nonhuman reads, compared with 34X in blood samples. Despite this reduction, saliva and blood samples yielded comparable numbers of validated (22,591 versus 23,064) and rare (209 versus 213) SVs per sample, which was also observed in the quad when matching coverage. Saliva LRS identified four new pathogenic and likely pathogenic variants missed by SRS. We report variants affecting MECP2, DNMT3A, CHD8, LMBRD2, POGZ, and PHF3; four additional candidates with potential relevance to autism; and an increase of diagnostic yield of 9-18%. Saliva-derived DNA support blood-comparable discovery of pathogenic variants missed by SRS, including SVs, with increased sequencing coverage compensating for nonhuman DNA contamination and reduced assembly contiguity, thus showing saliva DNA collection as a feasible strategy for large-scale LRS studies of patients and population-scale sequencing of cohorts.

WrapEvoFS provides a deterministic, auditable rule for choosing one feature set from repeated stochastic searches. It retains candidates within a prespecified development-score gap, selects the most representative set by feature overlap, and records the decision before held-out evaluation. A corrected genetic objective preserves score ordering lost through zero truncation. In a fully nested four-class cancer analysis, all 30 locked signatures met the 0.01 tolerance, and 11 differed from the highest-scoring candidate; every changed selection increased mean feature overlap within the eligible pool. Paired differences in area under the receiver operating characteristic curve against recursive feature elimination with cross-validation all had 95% intervals crossing zero. No selector dominated matched comparisons on feature count and predictive performance, and one-time evaluation of frozen signatures at held-out centers yielded heterogeneous effects. WrapEvoFS standardizes selection within a fixed candidate bank under an empirical score-gap constraint, enabling reproducible reporting of performance, compression, and feature-agreement trade-offs without implying predictive superiority or stability under changes in participant composition.

Background: Archival biobanks of formalin-fixed paraffin-embedded (FFPE) specimens represent a vast and underused resource for retrospective molecular studies linked to clinical follow-up. However, fixation-induced chemical modification and nucleic acid fragmentation limit scalable genomic and transcriptomic profiling. FFPE-CUTAC (Cleavage Under Targeted Accessible Chromatin) is an RNA polymerase II-targeted DNA assay specifically designed to profile regulatory activity in FFPE tissue. Here, we assess its archival robustness, information content, compatibility with RNA-seq, and ability to recover copy-number alterations. Results: In meningioma and breast cancer cohorts, we benchmarked FFPE-CUTAC against matched fresh-frozen RNA-seq, FFPE RNA-seq and FFPE whole-genome sequencing. For FFPE specimens spanning up to 25 years, FFPE-CUTAC showed no systematic collection-year-associated decline in library yield or signal-quality metrics. In contrast, FFPE RNA-seq is more affected by the FFPE tissue degradation and specimen age on read composition, gene-body coverage, and gene detection. By measuring RNA polymerase II occupancy on chromatin rather than mature RNA abundance, FFPE-CUTAC is less constrained by transcript half-life, polyadenylation or transcript-capture design. FFPE-CUTAC extends beyond gene regions to non-coding regulatory elements, while the shared gene-level signals supported its integration with existing RNA-seq cohorts for large-scale, long-term clinical association study. FFPE-CUTAC libraries also retain DNA dosage information and can be used to recover chromosome-arm gain, loss and intact states with 96% concordance to matched whole-genome sequencing. Conclusions: By converting routinely preserved pathology sections into integrated regulatory and copy-number profiles, FFPE-CUTAC provides a practical foundation for constructing clinically annotated disease maps to support molecular stratification, prognostic modeling and treatment-association studies.

Motivation: Interpreting noncoding pharmacogenomic variants requires identifying the regulatory program that may be perturbed and the direction of the effect, information not supplied by population frequency or general regulatory annotations. Results: We developed AetherXeno, an allele-resolved atlas that scores all substitutions across 8,691 experimentally supported elements for PXR, FXR and AhR and connects 17.27 million variants to evidence matched by exact allele. Across the six paired comparisons, the gain in Spearman correlation ranged from 0.039 to 0.334. Every regional bootstrap interval excluded zero. Homology exclusion preserved these findings, while motif core and liver expression enrichment supported biological organization of the score landscape. The browser, programmatic interface and bulk files expose the same versioned records from individual variants to cohort analyses. Availability and implementation: The web resource is available at https://aetherxeno.org and the versioned data at doi:10.5281/zenodo.20753827.

High-grade serous ovarian carcinoma (HGSOC) is the most common form of ovarian carcinoma and accounts for the majority of deaths from the disease, with fewer than half of patients surviving five years after diagnosis. Bulk transcriptomic profiling has identified molecular subtypes and gene-expression signatures associated with shorter or longer survival. However, because bulk profiling measures a composite of tumour and microenvironmental gene expression, the cellular origins of these signatures and their relationship to the tumour microenvironment remain unresolved. To address this, we applied Xenium spatial transcriptomics to treatment-naive HGSOC tumours from 122 patients using a custom 480-gene panel designed to measure tumour-intrinsic gene expression and distinguish immune and stromal cell populations in the surrounding microenvironment. Analysis of tumour-intrinsic gene expression identified 61 genes nominally associated with overall survival, from which Elastic-Net modelling prioritised five genes associated with longer survival and nine associated with shorter survival. Notably, 46 of the 61 associations did not reach the nominal significance threshold when expression was instead aggregated across all cells from the same patients, highlighting how whole-tissue expression can obscure tumour-cell-specific survival associations. Complementary analysis of tumour-proximal cell populations revealed that multiple immune populations were associated with longer survival, whereas two fibroblast populations were associated with shorter survival. Integrating these analyses revealed that survival-associated tumour-intrinsic gene expression was correlated with the abundance of neighbouring immune and stromal populations that were themselves associated with survival. Pairwise Cox regression demonstrated substantial overlap in the prognostic information captured by these features, while analysis of individual gene-cell combinations showed that survival associations differed according to the abundance of neighbouring populations. Consensus clustering further identified four patient groups with distinct combinations of tumour-intrinsic expression and microenvironmental composition, broadly separating patients with longer and shorter survival. Together, this cohort-scale spatial analysis identifies tumour-intrinsic and microenvironmental features associated with HGSOC survival and reveals how their correlated patterns and combined associations relate to patient outcome.

AlphaGenome Atlas, released in September 2026, provides functional predictions for nearly all possible human single-nucleotide variants using the AlphaGenome Variant Impact (AVI) score. However, empirical thresholds relating AVI quantile rank (AVI_QUANTILE) to clinical variant classifications have not been established. We derived AVI_QUANTILE thresholds distinguishing ClinVar pathogenic, likely pathogenic, uncertain, conflicting, likely benign, and benign SNVs using receiver operating characteristic analyses. Pathogenic/likely pathogenic variants were strongly enriched at the upper end of the AVI_QUANTILE distribution, yielding a Youden-optimal ClinVar-derived cutoff of 0.9938. Optimal cutoffs were generally consistent across several major molecular consequence classes, although lower thresholds were observed for selected noncoding categories. Similarly, most well-powered gene-disease groups showed cutoffs close to the global threshold, with modest gene-specific variation. Among 24,989 variants of uncertain significance, 11,357 (45.45%) exceeded the global cutoff, identifying a substantial subset with AlphaGenome-predicted functional effects comparable to those observed among pathogenic/likely pathogenic variants and potentially warranting further evaluation. In a steroid-dependent asthma fine-mapping analysis, AVI_QUANTILE showed little correspondence with SuSiE posterior inclusion probabilities, indicating that AlphaGenome functional predictions capture information distinct from statistical fine-mapping.

Human immune responses remain poorly characterized across Indigenous South American (ISA) populations, limiting our understanding of human immune variation. Here, we generated a single-cell transcriptional atlas of 1.4 million peripheral blood mononuclear cells from 345 individuals representing 13 South American populations, profiled at baseline and following stimulation with lipopolysaccharide, influenza A virus, or Trypanosoma cruzi. Genetic ancestry was associated with the expression of a large fraction of the transcriptome, often only after stimulation, and explained more variation than age or sex. On average, cis-eQTLs accounted for one-quarter of these ancestry-associated differences. Thousands of regulatory variants were common in ISA populations but rare or absent in European reference datasets. Integrating eQTLs with GWAS revealed stimulation-specific disease-associated regulatory variants and an enrichment of ancestry-associated expression differences among genes with colocalizing signals. Our findings highlight the importance of studying diverse populations across immune contexts to understand the genetic architecture of human immune responses.