Nature Genetics

Alzheimers disease genomics and other high-dimensional omics studies demand powerful statistical methods, yet Bayesian inference remains underutilized despite its advantages in small-sample settings, owing to the prohibitive cost of eliciting reliable priors across thousands or millions of parameters. We propose an AI-assisted Bayesian-frequentist hybrid inference framework that couples large language model based prior elicitation with the hybrid inference theory of Yuan (2009). ChatGPT-4o is queried via a standardized prompt to assess the strength of evidence linking each gene to a disease of interest, and the response is mapped to an informative normal prior via a standardized effect-size calibration. Parameters for covariates of secondary interest are treated as frequentist parameters, preserving efficiency and avoiding sensitivity to mis-specified priors. We derive closed-form hybrid estimators under uniform and conjugate normal priors in linear models, establish their asymptotic equivalence to the frequentist and full Bayes estimators, and show in simulations that hybrid inference using unconditional variance estimation leads to high statistical power while accurately controlling the Type I error rate. Applied to single-cell RNA sequencing data from the ROSMAP cohort for Alzheimers disease as an example, the framework identifies biologically coherent pathways (such as gamma-secretase pathways) previously undetected. The proposed framework offers a principled and computationally scalable approach to genome-wide Bayesian analysis, with potential for broad application across omics platforms and disease settings.

Polygenic scores (PGS) have emerged as a useful biomarker for stratification of high-risk individuals in genomic medicine, with prediction intervals arising as a principled approach to incorporate statistical uncertainty in their individual-level predictions. In contrast to recent reports by Xu et al7, we show that CalPred6 provides well-calibrated prediction intervals that contain the trait phenotypes at targeted confidence levels. CalPred maintains calibration when PGS performance varies across contextual factors (e.g., ancestry, age, sex, or socio-economic factors) whereas PredInterval7 - a recently introduced method that focuses on marginal calibration across all individuals - exhibits miscalibration.

Genome-wide association studies (GWAS) have advanced the understanding of germline susceptibility in common cancers, yet rare malignancies remain underexplored due to limited sample sizes. To address this gap, we conducted large-scale GWAS across 20 rare cancer types and meta-analyzed results from three cohorts: two clinically sequenced cancer center cohorts and an independent population biobank, comprising over 480,000 individuals. We identified nine novel genome-wide significant susceptibility loci with moderate to large effect sizes that replicated across cohorts in eight rare malignancies, including myelodysplastic syndromes (MDS), germ cell tumors, gastrointestinal stromal tumor (GIST), gastrointestinal neuroendocrine tumors, anal cancer (ANSC), non-melanoma skin cancer, mesothelioma, and hepatobiliary cancer. Among the strongest associations were loci in MDS near API5 (OR = 2.21, p = 1.06x10-8), in GIST near SLC6A18 and TERT (OR = 1.91, p = 8.20x10-50), and in ANSC near HLA-DQA2 (OR = 1.58, p = 5.50x10-18). The GIST risk variant was enriched in tumors harboring somatic KIT mutations (OR = 2.21, p = 6.5x10-4) and was associated with worse survival among carriers with KIT-mutant tumors (hazard ratio = 4.06, p = 0.015), implicating germline-somatic interplay in tumor initiation and progression. The ANSC risk variant was associated with HPV infection (OR = 1.44, p = 3.19x10-5), supporting a host-viral interaction in HPV-driven tumorigenesis. The MDS risk variant at the API5 locus was associated with altered neutrophil counts, suggesting a role in hematopoietic dysregulation in disease pathogenesis. We further identified novel, independent associations with mesothelioma, GIST, and hepatobiliary cancer at the 5p15.33 locus encompassing TERT, consistent with pleiotropic genetic effects at a core telomere-maintenance gene. Collectively, these findings demonstrate that integrating clinically ascertained sequencing cohorts with population biobanks substantially enhances germline discovery in rare cancers, enabling identification of high-confidence susceptibility loci and facilitating downstream biological interpretation through linked somatic, viral, and clinical data. This framework provides a scalable approach for characterizing inherited susceptibility across diverse rare malignancies.

EHMT1 and EHMT2 genes encode human euchromatin histone lysine methyltransferase 1 and 2 (EHMT1 alias GLP; EHMT2 alias G9a) that form heteromeric GLP/G9a complexes with essential roles in epigenetic regulation of gene expression. While EHMT1 haploinsufficiency has been established as the cause of Kleefstra syndrome 1, the pathogenesis of G9a dysfunction in human disease remains largely unknown. We identified seven de novo EHMT2 variants in patients with clinical presentation, episignatures, histone modifications and transcriptomic profiles similar to those of Kleefstra syndrome 1. In vitro studies revealed that these variants encode for structurally stable G9a proteins that are catalytically incompetent due to aberrant interactions either with histone H3 tail or with S-adenosylmethionine. Heterozygous mice carrying a patient-derived variant exhibited growth retardation, facial/skull dysmorphia and aberrant behavior. Here we report pathogenic EHMT2 variants that likely exert dominant-negative effect on GLP/G9a complexes and thus genocopy the EHMT1 haploinsufficiency via a distinct molecular mechanism, defining an autosomal dominant EHMT2-related Kleefstra syndrome.

Sex differences are commonly observed in neuroimaging phenotypes and in the risk of brain diseases, yet the underlying genetic mechanisms remain poorly understood. We investigated sex differences in the genetic architecture of 805 neuroimaging phenotypes in 22,950 males and 22,950 females matched for sample size and covariates, and systematically compared sex-stratified with sex-combined genetic analyses. We found eight variant-trait associations with significant sex differences, 235 fine-mapped sex-dominant causal associations, 457 sex-dominant colocalizations with sex hormones, and 96 sex-dominant colocalizations with schizophrenia. Compared with sex-combined analysis, sex-stratified analysis identified 47 new genetic associations, 170 new fine-mapped causal associations, 1,019 new colocalizations with sex hormones, and 191 new colocalizations with schizophrenia. Additionally, sex-stratified analysis improved global heritability and genetic-correlation estimates and enhanced polygenic prediction for certain phenotypes. This work highlights the need to routinely perform sex-stratified genetic association analyses to elucidate sex-specific and sex-shared genetic control of neuroimaging phenotypes and related disorders.

The spinocerebellar ataxias (SCAs) are a clinically heterogenous group of neurodegenerative disorders that affect movement, vision, speech and balance. Here, we reassign the linkage of SCA30 to 14q32.13 based on a cumulative LOD score >12. Within this interval we identified a 331 kb duplication, absent in population controls and not observed in >800 unrelated individuals with genetically unresolved cerebellar ataxia. RNASeq analysis of patient-derived lymphoblastoid cell lines revealed a splice-mediated chimeric transcript resulting from the duplication event. This transcript joined exon 1 of CLMN to exon 2 of SYNE3. In silico translation predicted that this chimeric transcript would produce a short N-terminal peptide corresponding to exon 1 of CLMN and the usually untranslated region of exon 2 of SYNE3 fused to the complete and in-frame SYNE3 protein. Transient overexpression of SYNE3 or the CLMN::SYNE3 fusion protein, in both HeLa cells and mouse primary cortical neurons, resulted in equivalent cellular outcomes including altered nuclear morphology and chromosomal DNA fragmentation. SYNE3 forms part of the linker of nucleoskeleton and cytoskeleton complex and is not usually expressed in cerebellar Purkyn[e] neurons while, CLMN has a Purkyn[e] specific expression pattern within the brain. Our data suggests that ectopic expression of SYNE3 in cerebellar Purkyn[e] neurons, mediated by the CLMN promoter, leads to cerebellar atrophy and causes spinocerebellar ataxia in the SCA30 family. This is an example of Mendelian disease arising from a novel, chimeric transcript with a likely dominant negative effect. Chimeric transcripts are commonly associated with cancers, but they are not often associated with monogenic disorders. Detection of chimeric transcripts as part of structural variant analysis could increase the genetic diagnostic yield of Mendelian disorders.

Optical pooled screens (OPS) are bottlenecked by labor-intensive in situ sequencing and analysis protocols. Here we present OttoSeq, an automated OPS platform combining the Otto2 fluid handling system with the Brieflow analysis pipeline. We utilized OttoSeq to complete a genome-wide cell painting screen in eight days, sampling more than 5 million high-quality cells across 21,732 gene knockout perturbations (224 cells per gene) and interpreting 320 functional gene clusters.

Reconstructing complete and accurate lineage trees remains a long-standing challenge in biology. Here, we introduce PALINCODE (Palindromic Coding and Decoding), a system that utilizes ternary CRISPR bits (cBits) to stochastically write one of three possible states over time, permanently embedding lineage relationships in the genome. We demonstrate PALINCODEs lineage-recording potential through simulations and establish palindromic CRISPR editing in cell culture models. We show that truncated Cas9 guide sequences yield ternary outcomes at high efficiency when compared to conventional guides. Using PALINCODE, we derived lineage-recording cell lines with a theoretical coding capacity of up to 10^25 bits, enabling the generation of lineage trees 32 cell divisions deep in single-cell sequencing of 293T cells. Furthermore, we applied PALINCODE using an in vivo melanoma model to jointly read out lineage history and gene expression, enabling in vivo reconstruction of clonal evolution within tumor cell clonal populations. PALINCODE circumvents several limitations of prior CRISPR-based systems while increasing the information potential at individual CRISPR sites, creating a lineage-recording platform with higher density than many competing approaches.

Widespread genomic sequencing efforts have characterized the molecular foundations of the different cancers. By combining these genomic data in a manner proportional to the population-level abundances of these different cancers, we estimate the overall abundances of each observed missense and nonsense mutation within the U.S. cancer patient population. We find BRAF V600E (5.2%) is the most common mutation in the cancer patient population, TP53 R175H (1.5%) is the most common tumor suppressor mutation, and APC R876X (0.4%) is the most common nonsense mutation. These values differ largely and significantly from what would be found in a typical pan-cancer analysis, where different cancer types are included out of proportion to population level incidence. We present the full ordered lists of population-level abundances for specific missense and nonsense mutations, and we demonstrate the value of these data by further analyzing high priority genes (e.g., TP53, KRAS, BRAF) and pathways (e.g., RTK/RAS, PI3K, and WNT/{beta}-catenin). Overall, this information is a resource that should benefit the basic science, translational, and clinical cancer research communities.

Down syndrome (DS), caused by trisomy 21, confers a near-universal risk for Alzheimers disease (AD), yet individuals exhibit marked variability in cognitive decline, suggesting the presence of cellular mechanisms that modulate vulnerability and resilience. However, these mechanisms remain poorly defined in the human brain. Here, we integrate matched single-nucleus RNA-seq and ATAC-seq profiles from the prefrontal cortex (PFC) and amygdala (AMY) of age-matched individuals with DS with and without AD (DSAD), enabling direct comparison within a shared genetic background. We identify basal astrocytes in the PFC as a selectively vulnerable cell state in DSAD, characterized by both reduced abundance and coordinated transcriptional and regulatory reprogramming. This state exhibits a shift away from homeostatic support functions, with decreased cytokine signaling and lipid-handling programs, alongside increased steroid- and nuclear receptor-associated activity. Concomitantly, chromatin accessibility profiling reveals reduced engagement of immune- and stress-responsive transcription factor programs, including AP-1, STAT, and BACH families, with linked regulatory perturbations at loci such as ABCA1, DAB2IP, and IL1RAP. Together, these findings define a previously unrecognized astrocyte state marked by epigenetic constraint and diminished responsiveness to stress and inflammatory signals, distinguishing it from classical reactive astrocyte phenotypes. Our results nominate PFC basal astrocytes as a key locus of vulnerability in DSAD and suggest that failure to mount appropriate astrocyte responses, rather than overt activation alone, may contribute to neurodegenerative progression.

The Million Veteran Program (MVP) represents the largest and one of the most diverse single cohorts associated with longitudinal Electronic Health Record data (EHR) data. We profiled a subset of samples from MVP using the Illumina Infinium MethylationEPIC Beadchip (EPIC array) to generate one of the largest single cohort methylation dataset to-date. Methylation profiles were analyzed for 45,460 total individuals, with the most populous ancestries composed of 27,455 Europeans, 11,798 African Americans, and 4,859 Admixed Americans. We detail the strict quality control standards implemented to ensure the most robust method of methylation profiling of the MVP cohort. This dataset was then applied to evaluate the effects of smoking exposure on DNA methylation in MVP participants. Ancestry-stratified epigenome-wide association studies (EWAS) of smoking status (ever/never) were performed using over 750,000 probes with certifiable signal. Our multi-ancestry meta-analysis demonstrates replicability with existing EWAS and identifies 3,207 novel probe-smoking associations unlocked via the depth and breadth of data in this cohort.

Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors. Statement of SignificanceTo understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.

Single-cell RNA sequencing (scRNA-seq) has transformed our ability to analyse cellular heterogeneity, enabling detailed mapping of cellular progression. Trajectory inference tools construct trajectories from scRNA-seq data, facilitating the tracing of cellular progression through developmental pathways. PathPinpointR (PPR) is a lightweight and user-friendly R package developed to predict and compare the positions of scRNA-seq samples along reference biological trajectories, such as those created from large cell atlas projects. PPR utilises sets of switching-gene events from reference trajectories as indicators of cellular progression. By applying these positional indicators to query datasets, each cell can be accurately assigned a pseudo-time value, providing predictive insight into its position along a trajectory. This information can be used to stage cells within an established developmental process, or to evaluate how different patient samples compare when mapped onto reference disease or drug response trajectories. AvailabilityPathPinpointR is available at https://github.com/moi-taiga/PathPinpointR. Contacto.j.l.rackham@soton.ac.uk

Germline pathogenic variants in BRCA1 and BRCA2 confer disproportionately elevated cancer risks in breast and ovarian tissues, yet the basis for this tissue specificity remains incompletely understood. Here, we integrate bulk-tumor aneuploidy analysis across 340,824 cancer cases from three independent cohorts (TCGA, ICGC PCAWG, and FoundationCore) with single-cell whole-genome sequencing from two independent studies to investigate whether tissue-specific patterns of chromosomal deletion contribute to this phenomenon. We find that breast and ovarian cancers are consistently enriched for deletions of chromosome arms 17q and 13q--harboring the BRCA1 and BRCA2 genes, respectively--relative to other solid tumor types, and that mutational timing analysis independently places these deletions among the earliest somatic events in these cancers. Phylogenetic reconstruction of single-cell data reveals that in pre-malignant breast tissue from germline BRCA1/2 carriers, chr17q and chr13q deletions appear as localized subclonal events within small clades against a largely diploid background. In established malignancies, these same deletions are found within dominant clonal lineages accompanied by widespread genomic instability--consistent with clonal sweeps originating from early deletion events. These findings suggest that breast and ovarian cellular environments confer a selective advantage for chr17q and chr13q deletions, providing a mechanism that may contribute to the tissue-specific cancer risk observed in gBRCA1/2 carriers.

Gene expression during mammalian development is orchestrated by non-coding cis-regulatory DNA elements (CREs) such as distal enhancers1-3. Despite their fundamental importance, and notwithstanding recent progress in predictive modeling4-9, many high-level properties of enhancer grammar remain unresolved. How does the length of an autonomously active CRE constrain its activity? How robust are CREs to mutations or rearrangements of transcription factor binding sites (TFBSs)? And how much epistasis exists among these sites? As predictive models solely trained on endogenous CREs are unlikely to resolve these questions10, we subjected several endogenous CREs to intensive sequence-level perturbation. Specifically, we assayed >35,000 variants of 5 parietal endoderm enhancers, with variants organized into four perturbation classes, designed to probe: (i) the functional sufficiency of sub-fragments via dense multi-size tiling, (ii) local epistasis via multi-hit saturation mutagenesis, (iii) activity-size tradeoffs via model-guided compaction, or (iv) functional resilience via sequence derivatization anchored on key TFBSs, including random deposition, reconstitution, and synthetic thripsis. This multi-scale dissection revealed rich phenomena. Sub-tiling uncovered sharp non-additivity between activity and fragment size, highlighting strongly synergistic TFBS clusters. Compaction showed that natural CREs lie far from the activity-size Pareto front, and that model-guided deletions can yield shorter yet stronger elements. Mutational scanning exposed a spectrum of CRE robustness, from tolerant to fragile, together with rare but consequential epistasis between individual TFBSs. Finally, TFBS-anchored derivatization demonstrated that background sequence can influence activity on par with TFBS arrangement. Strikingly, a substantial fraction of CRE derivatives exceeded the activity of their endogenous progenitors. Taken together, these results reveal both soft and stiff directions in regulatory sequence space, advancing a quantitative phenomenology of how enhancer sequences encode function and robustness.

Metabolic remodeling is a hallmark of cardiomyopathy, yet which cell types bear the metabolic burden and how cell-type-specific contributions are disrupted remain unclear. Here, we developed a cell-type-resolved genome-scale metabolic flux inference pipeline optimized for post-mitotic cardiac tissue by maximizing ATP synthesis rather than biomass production and applied it to a single-nucleus transcriptomic atlas of human cardiomyopathies (78 donors, 869,449 nuclei). Metabolic impairment in dilated cardiomyopathy (DCM) was most profound in stromal cells, whereas myeloid cells exhibited opposing metabolic activation. DCM- associated impairment followed a genotype-dependent severity gradient from structural gene mutations to pathogenic variant-negative (PVneg) cases. PVneg hearts uniquely harbored 24 altered metabolic pathways not significant in any other genotype. These PVneg-specific signatures were independent of clinical severity, indicating a genotype-intrinsic metabolic program. Extending the analysis to arrhythmogenic cardiomyopathy and hypertrophic cardiomyopathy showed that ATP depletion is shared across cardiomyopathy subtypes, whereas metabolic remodeling differed across disease subtypes. Additionally, gene regulatory network analysis linked these alterations to broad transcription factor (TF) dysregulation and pervasive TF-metabolic coupling across all cell types. These findings redefine PVneg DCM as a metabolically distinct entity and reveal conserved stromal metabolic remodeling across cardiomyopathies, providing a framework for genotype-informed mechanistic stratification.

AO_SCPLOWBSTRACTC_SCPLOWAphids threaten crop productivity through phloem feeding and the transmission of plant viruses. Aphis gossypii, in particular, is a widespread and damaging pest of worldwide cultivated melon. Resistance to its emerging CUC1 clone in Europe remains poorly characterized. Here, we dissected the genetic architecture of melon resistance to CUC1 using complementary traits that capture multiple stages of the aphid-melon interaction: plant attractiveness to aphids, acceptance, aphid colonization, and multiplication. Genome-wide association studies (GWAS) in a diversity panel of 174 accessions identified a quantitative trait locus (QTL) for attractiveness on chromosome 6, while analyses in a complementary panel of 212 accessions revealed QTLs for plant acceptance by aphids on chromosomes 3, 8, and 12. Colonization and multiplication traits further highlighted resistance QTLs on chromosomes 5 and 12, the latter supported by both SNP-based GWAS and bulk-segregant analysis. Pan-NLRome k-mer- and graph- based GWAS, together with Vat presence/absence association analyses, provided allele-level resolution of the QTL on chromosome 5, corresponding to the Vat region. Leveraging allelic diversity at this locus, we functionally characterized 20 Vat homologs with four R65aa motifs within their leucine-rich repeat (LRR) domain and demonstrated the capacity of R65aa-type Vat alleles to confer clone-specific resistance. Resistance-conferring alleles limited virus multiplication, such as Cucumber mosaic virus (CMV), when transmitted by five A. gossypii clones, including CUC1. Together, our results revealed multiple genetic determinants underlying quantitative resistance to the A. gossypii CUC1 clone in melon and highlighted the central role of Vat homologs in resistance to both A. gossypii and the viruses it vectors. These findings provide strategic targets for pyramiding resistance loci acting at different stages of the pest life cycle to enhance durable protection against these biotic threats.

Genome-guided dietary advice is a goal of precision nutrition. However, the contribution of gene-diet interactions (GxDs) to disease risk remains unclear, hindering the identification of diet-outcome pairs more likely amenable to genetic-based recommendations. We thus implemented a two-step approach: first, we comprehensively assessed the contributions of genome-wide GxDs to cardiometabolic outcomes across a broad array of dietary exposures in UK Biobank participants (N = 141,144 to 325,989). Second, we selected the 20 significant diet-outcome pairs from the 713 pairs tested (p < 7.0 x 10-5) and derived GxD polygenic scores. In an independent sample, all scores were nominally associated with their corresponding outcomes, with 12 of 20 polygenic scores Bonferroni significant (p < 0.0025). Further analyses revealed GxD polygenic scores were associated with clinical outcomes such as incident gout, suggesting translational potential. Altogether, these results showcase the promise of GxD scores to inform precision nutrition.

Rheumatoid arthritis (RA) is a heritable and common autoimmune condition. To date, most genetic associations were derived from individuals with either European or East Asian ancestries. Here, we applied a multimodal automated phenotyping strategy to define RA and performed a genome-wide association study (GWAS) of RA in the Million Veteran Program (MVP), including underrepresented African American (AFR) and Admixed American (AMR) populations. Meta-analyses with previous RA cohorts identified 152 autosomal genome-wide significant loci, of which 31 were novel. Inclusion of multi-ancestry data dramatically improved fine-mapping resolution. Functional characterization of these loci using single-cell transcriptomic and chromatin data suggested new RA genes such as CHD7 and CD247. We identified underappreciated functional roles of fine-grained immune cell states other than T cells, such as B cell and myeloid cell states. We observed that multi-ancestry polygenic risk scores using our data demonstrated better predictive ability, especially for AFR and AMR populations.

Hereditary hearing loss is highly genetically heterogeneous, with emerging overlap between genes implicated in early-onset and age-related hearing loss. We report a consanguineous family with autosomal recessive, non-syndromic hearing loss in which the proband harbors a homozygous splice-site variant in PALM3 (NM_001145028.2:c.314+1G>A) and a homozygous missense variant in OTOA. A minigene assay for the PALM3 variant demonstrated aberrant splicing with exon skipping, resulting in a frameshift and a large inframe deletion, both consistent with loss of function and impacting all known transcripts. While the organ of Corti from 12-month-old heterozygous Palm3 mice showed preserved overall architecture, published Palm3 knockout mice exhibit auditory dysfunction, supporting an auditory phenotype with loss of function. Although a dual molecular diagnosis cannot be excluded, the combined genetic, functional, and comparative data support PALM3 as a strong candidate gene for autosomal recessive hearing loss.