Yosuke Tanigawa

Advancing precision medicine requires accurate prediction of disease liability across populations and contexts. A major challenge is the limited transferability of polygenic scores (PGS) across genetic ancestry groups. We present GeneSIS (GENE and Sex Interaction Score), a supervised statistical learning framework for jointly modeling additive and context-dependent genetic effects at single-variant resolution directly from individual-level data. We analyze 406,659 individuals, including admixed individuals, in the UK Biobank and 1.3 million variants to develop predictive models for 99 complex traits. We report that ∼8% of selected variables capture gene-by-sex (GxS) effects, validated by sex-stratified analyses. Modeling GxS effects improves prediction across 32 traits in non-European individuals. For predicting hip circumference in Africans, GeneSIS achieves a 3.7-fold improvement (p=8.0×10-7) over linear-only PGS and highlights biologically plausible hypotheses, such as pleiotropic GxS effects of GCKR (rs1260326) on anthropometry and menopause, as well as GxS pathway enrichments for interleukin-4 regulation. Overall, our results highlight the benefits of integrating context-dependent effects in human genetics studies.

Polygenic risk scores (PRS) assume additive SNP effects, yet genetic risk also arises from interactions between loci and environmental factors that contribute to broad-sense heritability. We developed an extended PRS (ePRS) framework for type 2 diabetes (T2D) that incorporates locus-by-locus non-additive effects beyond those captured by additive single-locus PRS or linkage disequilibrium (LD) tagging. These were modelled as interaction-informed cumulative burden (G+G; summed allele counts), epistasis-screened (GxG; allele count products), and gene-environment effects derived from cardiometabolic variables in electronic health records. Across 235,000 UK Biobank participants, five complementary ePRS models captured largely non-overlapping high-risk individuals, suggesting that a key to individual risk predictions comprises the inclusion of multiple interaction-driven biological components rather than a single signal. A composite score was predictive over traditional additive PRS and predictive of T2D among individuals whose clinical symptoms fall within normal ranges.

The sequence of the human genome provides a foundation for understanding cellular processes in health and disease. The organisation of this primary genetic information into cell-specific structure and function is critical to understanding the cell type-specific interpretation and execution of the genome. Epigenetic processes are essential for packaging and higher-level functional organisation of the genome, and changes therein are increasingly recognised as contributors to human disease. Building on primary data generated by multinational consortia, the International Human Epigenome Consortium (IHEC) has uniformly processed a collection of more than 2000 comprehensive human reference epigenomes, collectively referred to as EpiATLAS. This effort involved the development of standardised molecular and bioinformatics protocols, metadata models, and analytical tools to manage, integrate, display, and share vast amounts of epigenomic data. This includes the creation of a publicly available Epigenome Reference Registry, which provides a system for accessing protected human subject datasets and facilitates open searching of de-identified samples and experimental data. The integrated EpiATLAS ecosystem and its comprehensive human reference epigenome maps provide an unprecedented resource for the biosciences, expanding the annotated epigenomic landscape while uncovering previously unappreciated relationships among regulatory layers and revealing how epigenetic inputs underpin fundamental cellular functions and disease associations.

Mapping the genetic basis of inter-individual heterogeneity in multifactorial diseases opens the door to mechanistic insights and opportunities for targeted intervention. In Alzheimer’s disease (AD), clinical and pathological heterogeneity is well recognized, but genetic dissection is limited by a lack of well-powered cohorts with deep phenotypic characterization. Here, we introduce a polygenic score (PGS) analysis strategy to address these limitations by leveraging the inherent pleiotropy in complex trait genetics. We perform a cross-cohort, cross-trait application of pre-trained PGS, integrating 713 UK Biobank-derived PGS with 36 deep AD phenotypes across 1678 ROSMAP participants. We identify 268 statistically significant (FDRAPOE -excluded PGS. Predictive models trained on multiple prioritized PGS outperform the AD PGS or APOE alone for predicting amyloid and cognition. Lastly, our approach identifies six individual-level AD polygenic subtypes supported by distinct pathological patterns. Overall, we combine large-scale biobank resources and deeply-phenotyped cohorts using PGS, reveal genetic features underlying AD heterogeneity, and provide a general model for stratifying heterogeneous disease-focused cohorts using genomics.

Immune dysfunction has been increasingly implicated in bipolar disorder, but the underlying mechanisms remain unclear. To address this, we profile 833 genome-wide chromatin immunoprecipitation sequencing datasets spanning five histone marks in peripheral blood immune cells from 88 Type I bipolar disorder patients and 92 controls, integrating them with whole-genome sequencing and clinical data. We identify disease-associated cis -regulatory elements and genetically influenced regulatory elements, revealing immune signatures and pathways involving calcium signaling and endoplasmic reticulum transport. By integrating genetic risk variants, differential and genetically influenced regulatory elements, and regulatory element—gene links, we prioritize 39 driver genes, 28 of which are exclusively supported by blood evidence. We further stratify patients into five epigenomic subtypes with distinct clinical features and genetic risk profiles and identify compounds that reverse disease-associated epigenomic signatures. Here, we combine immune epigenomics with genetics and clinical traits to identify driver genes, patient subtypes, and therapeutic candidates, highlighting immune contributions to type I bipolar disorder pathogenesis.

Alcohol is an ancient and enduring component of the human diet, yet it is a dose-dependent cytotoxin and teratogen, raising the possibility that endogenous, state-dependent mechanisms constrain intake. Growth differentiation factor 15 (GDF15) is an endocrine hormone that rises during pregnancy—predominantly via secretion from blastocyst-derived placental trophoblasts into the maternal circulation—and is also induced in other tissues, particularly hepatocytes, by toxins and cellular stress. However, its function in humans remains unclear. Here, we show that circulating GDF15 levels are elevated 5-fold in individuals with alcohol dependence, identify a rare loss-of-function variant in the GDF15 receptor gene GFRAL associated with approximately 2.6 additional UK alcohol units (∼21 g ethanol) per week, and demonstrate that recombinant GDF15 reduces alcohol drinking in mice. Collectively, these findings support a model in which GDF15 acts as an endocrine signal induced by chronic alcohol exposure—and potentially during pregnancy—to limit alcohol intake in humans. Highlights: GDF15 is markedly elevated in humans with alcohol dependence A truncating GFRAL variant associates with higher alcohol intake in UK Biobank GFRAL frameshift disrupts GDF15–RET signaling in vitro Recombinant GDF15 suppresses voluntary alcohol intake in mice Graphical

Approximately 40 percent of Alzheimer’s disease patients develop psychosis (AD+P), yet the molecular and cellular basis of these symptoms remains poorly understood. Here we profiled single-nucleus transcriptomes and epigenomes from 48 postmortem Alzheimer’s brains stratified by psychiatric diagnosis. Across cell types, AD+P was distinguished by transcriptional programs in upper-layer cortical pyramidal neurons consistent with re-engagement of developmental and structural plasticity pathways. These neurons exhibited greater loss in AD+P cortex, indicating that such programs emerge in a context of heightened vulnerability. Integrating these findings with functional perturbation screens in stem-cell-derived brain organoids, we found that activation of these programs alters cortico-cortical network connectivity and can exacerbate network dysfunction. Our data suggest that compensatory neuronal plasticity, shaped by glial inflammatory responses, may paradoxically contribute to circuit instability and selective vulnerability underlying neuropsychiatric symptoms in dementia.

A precise understanding of disease-associated spatio-temporal transcriptional dynamics is critical for nominating therapeutic interventions that drive desired perturbation responses. In disease contexts, pathological processes unfold across diverse cellular states, spatial environments, and timescales; however, current computational approaches have a limited ability to jointly model these complex dynamics or infer cellular trajectories from in silico perturbation experiments. Here, we present sc4D, a biologically interpretable spatio-temporal (4D) analysis framework for single-cell transcriptomics in disease studies, integrating autoencoder embeddings with optimal transport. Applying sc4D to longitudinal spatial transcriptomics samples from Alzheimer’s disease mouse models, we report known disease biology and novel, testable mechanisms, including late-stage microglia-astrocyte syncytium near amyloid-β plaques. In silico perturbations predict the restoration of protective, anti-inflammatory microglia through CAMTA1 activation or Donepezil, as validated by independent experimental findings. Overall, our work highlights the critical benefits of joint spatio-temporal modeling for elucidating disease mechanisms and predicting candidate interventions to improve cellular response.

The limited transferability of polygenic scores (PGS) across populations constrains their clinical utility and risks exacerbating health disparities, given challenges in multi-ancestry training, fine-mapping, and variant prioritization using genomic annotations, particularly when biologically relevant reference resources are sparse or unavailable for the target population. Here, we introduce PRISM, a transfer learning approach that jointly addresses these challenges to enhance PGS transferability. Applying PRISM to 7352 fine-mapped variants, 414 ENCODE annotations, and 406,659 individuals from the UK Biobank, we demonstrate that ancestry-aware integration of tissue-specific annotations yields the largest gains in predictive performance for African ancestry, with an average improvement of 13.10% (p=1.6×10 ⁻⁵ ) over annotation-agnostic multi-ancestry PGS. Notably, the best-performing model uses 102-fold fewer annotations than non-specific models, with contributions from broad categories of annotations. Overall, PRISM complements ongoing data diversification efforts by providing an immediately applicable strategy based on the integration of biologically aligned, best-available resources to address genomic health equity.

Approximately 40% of Alzheimer’s disease (AD) patients develop psychosis, yet the molecular and cellular processes that govern the manifestation of psychotic symptoms in dementia remain poorly understood. To define the neurobiological correlates that distinguish AD patients with psychosis (AD+P) from AD patients that never exhibited psychotic symptoms (AD-P), we performed single-nucleus transcriptome and epigenome profiling from prefrontal cortex and hippocampus of 48 postmortem brains from AD subjects segmented by psychiatric diagnosis. Our snRNA-seq profiling uncovered differentially expressed genes (DEGs) across multiple cell types, including transcriptional signatures of enhanced synaptic transmission in upper-layer pyramidal neurons of the AD+P cortex. Cell fraction analysis and histology both indicate greater loss of upper-layer pyramidal neurons in AD+P in comparison to AD-P cortex. Integrating our snRNA-seq data with functional screens in stem-cell derived brain organoids, we defined how genetic perturbations modify input-output network connectivity in vitro in a model of cortico-cortical communication. We find that differential vulnerability of pyramidal neurons in AD+P is associated with CDK5/p35-associated neurotoxicity and IL6-mediated glial inflammatory expression changes. This neuronal response is associated with microglial exhaustion and astrocytic inflammation signatures triggered by layer-specific neuropathological changes in the brains of AD+P patients. Lastly, we elucidate common and distinct transcriptional signatures between psychosis in AD and several other psychiatric conditions, and found significant enrichment of schizophrenia genetics with AD+P that is most convergent in upper-layer pyramidal neurons. Our work provides novel insight into the pathophysiological role of hyperexcitable circuits in the etiology of neuropsychiatric symptoms of AD. Highlights Cell-type- and brain-region-specific transcriptional changes in AD with psychosis (AD+P) Upper-layer pyramidal dysfunction and metabolic vulnerability marks the pathophysiology of AD+P Circuit wiring programs are evoked in AD+P as maladaptive compensatory responses AD+P-associated IL-6 signaling impairs neuronal network function in brain organoids