Biology subjects

Gusev, A.

Publications and source records attributed to Gusev, A..

At least 19 recordsLinked to original sources

bioRxiv2026

DPP9-mediated inflammasome repression protects against checkpoint inhibitor lung toxicity

Over one million patients receive cancer immunotherapy annually, yet the mechanisms underlying life-threatening immune-mediated toxicities remain poorly understood. Checkpoint inhibitor pneumonitis (CIP) is the leading cause of immunotherapy-related mortality, with a case fatality rate approaching 10%, and no genetic risk factors have been described to date. We identified Dipeptidyl-peptidase 9 (DPP9) as the first genetic susceptibility gene for CIP in a clinico-genomics cohort of 4,397 patients treated with immune checkpoint inhibitors. Mechanistically, DPP9 suppresses CARD8 inflammasome activation and IL-18 secretion in human monocytes, a pathway which is engaged prior to CIP onset, with IL-18 selectively elevated in the plasma of patients who subsequently develop CIP. Myeloid-restricted ablation of Dpp8 and Dpp9 in mice recapitulated the pulmonary histopathological and immunological hallmarks of CIP, including granuloma formation, accumulation of IFN{gamma}-producing T cells and monocyte-derived macrophages. Each of these phenotypes were driven by excessive IL-18 secretion. Together, these findings establish DPP9 as a genetic determinant of CIP and nominate IL-18 blockade as a mechanistically rational therapeutic strategy.

immunology↗

bioRxiv2026

Breaking the sparsity barrier in clinical targeted-panel sequencing: Mapping the inherited determinants of mutational signatures

Cancer development is shaped by somatic mutational processes that leave characteristic patterns known as mutational signatures. The inherited determinants of variability in signature activity remain largely unknown. Common germline variants that regulate this activity, which we term Signature Quantitative Trait Loci (SigQTLs), are expected to have modest individual effects, requiring cohorts of tens of thousands of samples for reliable detection. Clinical targeted-panel sequencing datasets achieve this scale, but present a fundamental challenge: individual tumors typically harbor too few mutations for stable signature inference. To overcome this sparsity barrier, we introduce GroupSig, a framework that aggregates sparse mutational patterns across samples sharing a germline genotype into information-rich meta-samples, enabling robust signature inference at the population level. We validated GroupSig by recovering the well-established correlations between age and clock-like signatures SBS1 and SBS5 using emulated panel data from The-Cancer-Genome-Atlas. We then applied GroupSig to approximately 32,000 tumor samples from the Dana-Farber Cancer Institute PROFILE cohort in a genome-wide SigQTL scan. We identified 9 genome-wide significant SigQTLs, with the strongest signal at locus 16q24.3, where six variants were associated with increased SBS7 (UV exposure) activity. This association persisted after excluding melanoma samples, arguing against a tumor-type enrichment artifact. Validation in TCGA confirmed 6 SigQTLs, all at 16q24.3, where implicated variants are eQTLs for CDK10 and SPG7 in skin tissue. Beyond genome-wide hits, DNA repair genes were 12.6-fold enriched among sub-threshold signals, supporting a polygenic architecture for mutational process regulation. GroupSig provides a scalable framework for germline-somatic association studies using panel sequencing data.

cancer biology↗

bioRxiv2026

Data-driven RNA phenotyping captures genetically regulated dimensions of the transcriptome

Transcriptomic diversity across individuals arises from multiple modes of RNA regulation--including pre-mRNA expression, splicing, degradation, and other processes--and has been widely leveraged to map quantitative trait loci (xQTLs) and interpret GWAS signals. We recently developed a multimodal framework called Pantry that can extend discovery beyond total expression by integrating multiple transcriptomic modalities. However, Pantry and similar tools remain limited by their reliance on complete gene annotations and the statistical complexity of jointly analyzing correlated modalities. Here, we present LaDDR (Latent Data-Driven RNA phenotyping), a mechanism-agnostic framework that generates orthogonal, latent coverage features per gene, enabling xQTL discovery and GWAS integration without requiring complete gene annotations. Applied to GTEx, LaDDR identified an average of 95% more independent xQTLs per tissue than the six transcriptional regulation modes implemented in Pantry ("knowledge-driven"). Residualizing known modalities prior to LaDDR and combining with knowledge-driven phenotypes increased discovery by an additional 41% per tissue on average, while retaining the interpretability of knowledge-driven signals. In a transcriptome-wide association study (TWAS) of 114 complex traits, using LaDDR-derived phenotypes uncovered an average of 11,790 unique gene-trait pairs per tissue, versus 8,579 from knowledge-driven phenotypes. The newly captured genetic signals exhibit functional and colocalization qualities consistent with known mechanisms, suggesting that LaDDR broadens the detectable landscape of trait-relevant transcriptomic regulation by efficiently recovering regulatory variation missed by current pipelines.

genomics↗

bioRxiv2025

Cancers modulate p53 truncal neoantigen display to evade T cell detection

Summary paragraphTP53 mutations are early truncal events across cancers1,2. These are perceived to encode tumour-specific neoantigens representing prime cytotoxic T lymphocyte (CTL) targets3,4. However, studies systematically examining the physical cell surface display of p53 peptides bound to major histocompatibility complex molecules (pMHC), their relative antigenicity, and resultant immunogenicity have yet to be conducted. Here, we develop an epitope discovery platform using p53-reconstituted lung cancer cells as well as various tumour cells as pMHC sources. Combining data-independent acquisition mass spectrometry (MS), nanoscale chromatography, and peptide detection based on probabilistic measure and three-dimensional ion visualization techniques allows attomole sensitivity identification of pMHCs. This approach excluded [~]97% of algorithm-based virtual p53 immunopeptidomes, highlighting that only a few p53 pMHCs can be presented by common human MHC (human leukocyte antigen, [HLA]) alleles. Strikingly, surface expressed neoantigens are restricted to the corresponding set of such limited self-p53 peptide arrays and unaffected by enhancing p53 proteasomal turnover. Further curtailment of MS-validated, high affinity p53 neoepitopes that are structurally deviant from self-pMHC occurs in established tumours due to immune selection against the antigen presenting MHC allele or by a novel mechanism involving p53 neoepitope destruction by endoplasmic reticulum aminopeptidase 1 (ERAP1). In contrast, given the extremely weak MHC affinity and resultant short-lived cell surface pMHC expression, the common p53 neoepitope R175H/HLA-A*02:01 escapes immune selection despite CTL with high quality T-cell receptors. Rigorous tumour-protective immunoediting makes effective truncal neoepitope targeting a challenge, requiring attentive MS analysis and functional vetting to focus protective cytolytic responses.

immunology↗

bioRxiv2025

Circulating chromatin reveals the effects of disease-associated variants on gene regulation

A fundamental challenge in human genetics is determining how variation in regulatory DNA shapes complex traits and disease risk. Chromatin quantitative trait loci (cQTLs) can address this challenge by revealing the effects of disease-linked genetic variants on regulatory element activity. Discovering cQTLs in disease-relevant tissues at scale remains challenging, however. To address this limitation, we leveraged advances in epigenomic liquid biopsy. We profiled histone modifications in circulating chromatin from patients with cancer to identify cell-free chromatin QTLs (cfcQTLs). By sampling cancer-derived chromatin in plasma, we captured cfcQTLs affecting regulatory elements from diverse non-hematologic tissues, as well as developmentally restricted elements that are reactivated in cancer (enriched 16-fold). Applying a cistrome-wide association study (CWAS), we linked 4,891 cfcQTLs to 1,011 traits and diseases. Developmentally restricted cfcQTLs that were not found in white blood cells were associated with 22.7 traits per 100 QTLs, compared to 0.58 for WBC-restricted cQTLs, underscoring the power of cfcQTLs for capturing genetic variation that shapes phenotypes. We extended our approach beyond germline variants to non-coding somatic mutations in cancer by measuring the activating effects of TERT promoter mutations from circulating chromatin. This work provides a path to scalable discovery of cQTLs across tissues, diseases, and populations to dissect the genetics of common diseases, enabled by the ease of sampling blood.

genomics↗

bioRxiv2025

Clinico-genomic features predict distinct metastatic phenotypes in cutaneous melanoma

Metastasis drives mortality and morbidity in cancer. While some patients develop broad metastatic disease across multiple organs, others exhibit organ-specific spread. To identify mechanisms underlying metastatic organotropism, we analyzed clinico-genomic data from over 7,000 patients with metastatic cutaneous melanoma in three independent cohorts (one primary discovery and two validation cohorts including a nationwide electronic health record-derived deidentified database), leveraging machine learning approaches to clinical data. We found that female sex and increased tumor mutational burden associate with decreased metastatic potential, while older age associates with increased lung and adrenal metastases. Using unsupervised analyses, patients clustered into five metastatic patterns: a "highly metastatic" cluster characterized by involvement of many organs, a "low metastatic" cluster characterized by few metastatic sites (mostly lymph node metastases), and three additional clusters each characterized by metastasis to specific sites (brain, lung, liver). Mutations in B2M and PTEN associated with increased overall metastatic potential. PTEN mutations were also associated with brain metastases but were enriched only in the "highly metastatic" cluster and not the brain-specific cluster. Mutations in GNAQ or GNA11 (GNA) associated with increased liver metastasis. To validate this association, we tested and demonstrated liver tropism in two GNA-mutant genetically engineered cutaneous melanoma mouse models of metastasis. Overall, our study elucidates distinct phenotypes of metastasis in patients with melanoma and identifies novel clinical and genomic associations that illuminate the drivers of clinical metastatic organotropism.

cancer biology↗

bioRxiv2024

Discovery of disease-associated cellular states using ResidPCA in single-cell RNA and ATAC sequencing data

To enhance understanding of cellular heterogeneity and disease from single-cell sequencing data, we introduce ResidPCA, a robust method for cell state identification that models cell type heterogeneity. Simulations demonstrate ResidPCAs efficacy, particularly in complex scenarios, with its accuracy more than four times higher than conventional Principal Component Analysis (PCA) and over three times higher than Non-negative Matrix Factorization (NMF)-based methods in identifying states expressed across multiple cell types. In scRNA-seq data from light-stimulated mouse visual cortex cells, ResidPCA captures stimulus-driven variability with an accuracy more than five times higher than NMF methods. In single nucleus data from an Alzheimers disease cohort, ResidPCA identified 44 snATAC-based and 42 snRNA-based states. 30 snATAC states were significantly enriched for Alzheimers disease heritability and were often more significantly enriched than established cell types such as microglia. The ResidPCA-based snATAC state most significantly enriched for Alzheimers disease heritability further elucidates a recently identified mechanism involving the neuron-ODC-microglial axis. This state links early amyloid production in neurons and oligodendrocytes with later-stage microglial activation and immune response, driving Alzheimers disease progression. These results demonstrate ResidPCAs ability to reveal additional biological variation in single-cell data and uncover disease-relevant cell states.

genomics↗

bioRxiv2024

Multimodal analysis of RNA sequencing data powers discovery of complex trait genetics

Transcriptome data is commonly used to understand genome function via quantitative trait loci (QTL) mapping and to identify the molecular mechanisms driving genome wide association study (GWAS) signals through colocalization analysis and transcriptome-wide association studies (TWAS). While RNA sequencing (RNA-seq) has the potential to reveal many modalities of transcriptional regulation, such as various splicing phenotypes, such studies are often limited to gene expression due to the complexity of extracting and analyzing multiple RNA phenotypes. Here, we present Pantry (Pan-transcriptomic phenotyping), a framework to efficiently generate diverse RNA phenotypes from RNA-seq data and perform downstream integrative analyses with genetic data. Pantry currently generates phenotypes from six modalities of transcriptional regulation (gene expression, isoform ratios, splice junction usage, alternative TSS/polyA usage, and RNA stability) and integrates them with genetic data via QTL mapping, TWAS, and colocalization testing. We applied Pantry to Geuvadis and GTEx data, and found that 4,768 of the genes with no identified expression QTL in Geuvadis had QTLs in at least one other transcriptional modality, resulting in a 66% increase in genes over expression QTL mapping. We further found that QTLs exhibit modality-specific functional properties that are further reinforced by joint analysis of different RNA modalities. We also show that generalizing TWAS to multiple RNA modalities (xTWAS) approximately doubles the discovery of unique gene-trait associations, and enhances identification of regulatory mechanisms underlying GWAS signal in 42% of previously associated gene-trait pairs. We provide the Pantry code, RNA phenotypes from all Geuvadis and GTEx samples, and xQTL and xTWAS results on the web.

genetics↗

bioRxiv2024

Functional dissection of complex and molecular trait variants at single nucleotide resolution

Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.

genetics↗

bioRxiv2023

Compressed Perturb-seq: highly efficient screens for regulatory circuits using random composite perturbations

Pooled CRISPR screens with single-cell RNA-seq readout (Perturb-seq) have emerged as a key technique in functional genomics, but are limited in scale by cost and combinatorial complexity. Here, we reimagine Perturb-seqs design through the lens of algorithms applied to random, low-dimensional observations. We present compressed Perturb-seq, which measures multiple random perturbations per cell or multiple cells per droplet and computationally decompresses these measurements by leveraging the sparse structure of regulatory circuits. Applied to 598 genes in the immune response to bacterial lipopolysaccharide, compressed Perturb-seq achieves the same accuracy as conventional Perturb-seq at 4 to 20-fold reduced cost, with greater power to learn genetic interactions. We identify known and novel regulators of immune responses and uncover evolutionarily constrained genes with downstream targets enriched for immune disease heritability, including many missed by existing GWAS or trans-eQTL studies. Our framework enables new scales of interrogation for a foundational method in functional genomics.

systems biology↗

bioRxiv2022

Polygenic Prediction of Molecular Traits using Large-Scale Meta-analysis Summary Statistics

IntroductionTranscriptome-wide association study (TWAS) integrates expression quantitative trait loci (eQTL) data with genome-wide association study (GWAS) results to infer differential expression. TWAS uses multi-variant models trained using individual-level genotype-expression datasets, but methodological development is required for TWAS to utilise larger eQTL summary statistics. MethodsTWAS models predicting gene expression were derived using blood-based eQTL summary statistics from eQTLGen, the Young Finns Study (YFS), and MetaBrain. Summary statistic polygenic scoring methods were used to derive TWAS models, evaluating their predictive utility in GTEx v8. We investigated gene inclusion criteria and omnibus tests for aggregating TWAS associations for a given gene. We performed a schizophrenia TWAS using summary statistic-based TWAS models, comparing results to existing resources and methods. ResultsTWAS models derived using eQTL summary statistics performed comparably to models derived using individual-level data. Multi-variant TWAS models significantly improved prediction over single variant models for 8.6% of genes. TWAS models derived using eQTLGen summary statistics significantly improved prediction over models derived using a smaller individual-level dataset. The eQTLGen-based schizophrenia TWAS, using the ACAT omnibus test to aggregate associations for each gene, identified novel significant and colocalised associations compared to summary-based mendelian randomisation (SMR) and SMR-multi. ConclusionsUsing multi-variant TWAS models and larger eQTL summary statistic datasets can improve power to detect differential expression associations. We provide TWAS models based on eQTLGen and MetaBrain summary statistics, and software to easily derive and apply summary statistic-based TWAS models based on eQTL and other molecular QTL datasets released in the future.

genomics↗

bioRxiv2022

Germline determinants of the prostate tumor genome

A persons germline genome strongly influences their risk of developing cancer. Yet the molecular mechanisms linking the host genome to the specific somatic molecular phenotypes of individual cancers are largely unknown. We quantified the relationships between germline polymorphisms and somatic mutational features in prostate cancer. Across 1,991 prostate tumors, we identified 23 co-occurring germline and somatic events in close 2D or 3D spatial genomic proximity, affecting 10 cancer driver genes. These driver quantitative trait loci (dQTLs) overlap active regulatory regions, and shape the tumor epigenome, transcriptome and proteome. Some dQTLs are active in multiple cancer types, and information content analyses imply hundreds of undiscovered dQTLs. Specific dQTLs explain at least 16.7% ancestry-biases in rates of TMPRSS2-ERG gene fusions and 67.3% of ancestry-biases in rates of FOXA1 point mutations. These data reveal extensive influences of common germline variation on somatic mutational landscapes.

cancer biology↗

bioRxiv2022

The lingering effects of Neanderthal introgression on human complex traits

The mutations introduced into the ancestors of modern humans from interbreeding with Neanderthals have been suggested to contribute an unexpected extent to complex human traits. However, testing this hypothesis has been challenging due to the idiosyncratic population genetic properties of introgressed mutations. We developed rigorous methods to assess the contribution of introgressed Neanderthal mutations to heritable trait variation relative to that of modern human variants. We applied these methods to analyze 235,592 introgressed Neanderthal mutations and 96 distinct phenotypes measured in about 300,000 unrelated white British individuals in the UK Biobank. Introgressed Neanderthal mutations have a significant contribution to trait variation consistent with the polygenic architecture of complex phenotypes (contributing 0.1% of heritable variation averaged across phenotypes; p = 9.59x10-9). However, the contribution of introgressed mutations tends to be significantly depleted relative to modern human mutations matched for allele frequency and linkage disequilibrium (about 57% depletion on average), consistent with purifying selection on introgressed mutations. Different from previous studies (McArthur 2021), we find no evidence for elevated heritability across the phenotypes examined. We identified 348 independent significant associations of introgressed Neanderthal mutations with 64 phenotypes (p < 1 x10-10). Previous work (Skov 2021) has suggested that a majority of such associations are likely driven by statistical association with nearby modern human variants that are the true causal variants. We therefore developed a customized statistical fine-mapping methodology for introgressed mutations that led us to identify 112 regions (at a false discovery proportion of 16%) across 47 phenotypes containing 4,303 unique genetic variants where introgressed mutations are highly likely to have a phenotypic effect. Examination of these mutations reveal their substantial impact on genes that are important for the immune system, development, and metabolism. Our results provide the first rigorous basis for understanding how Neanderthal introgression modulates complex trait variation in present-day humans.

genetics↗

bioRxiv2022

The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats

Heterogeneous Stock (HS) rats are a genetically diverse outbred rat population that is widely used for studying genetics of behavioral and physiological traits. Mapping Quantitative Trait Loci (QTL) associated with transcriptional changes would help to identify mechanisms underlying these traits. We generated genotype and transcriptome data for five brain regions from 88 HS rats. We identified 21,392 cis-QTLs associated with expression and splicing changes across all five brain regions and validated their effects using allele specific expression data. We identified 80 cases where eQTLs were colocalized with genome-wide association study (GWAS) results from nine physiological traits. Comparing our dataset to human data from the Genotype-Tissue Expression (GTEx) project, we found that the HS rat data yields twice as many significant eQTLs as a similarly sized human dataset. We also identified a modest but highly significant correlation between genetic regulatory variation among orthologous genes. Surprisingly, we found less genetic variation in gene regulation in HS rats relative to humans, though we still found eQTLs for the orthologs of many human genes for which eQTLs had not been found. These data are available from the RatGTEx data portal (RatGTEx.org) and will enable new discoveries of the genetic influences of complex traits.

genetics↗

bioRxiv2022

Optimized high-throughput screening of non-coding variants identified from genome-wide association studies

The vast majority of disease-associated single nucleotide polymorphisms identified from genome-wide association study (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and number of variants tested per loci. Using this strategy, we interrogated 70 of 140 known prostate cancer (PCa) risk-associated loci and demonstrated that 26 (37%) of them harbor 36 SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.

genetics↗

bioRxiv2022

Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) are a powerful approach to identify genes whose expression associates with complex disease risk. However, non-causal genes can exhibit association signals due to confounding by linkage disequilibrium patterns (LD) and eQTL pleiotropy at genomic risk regions which necessitates fine-mapping of TWAS signals. Here, we present MA-FOCUS, a multi-ancestry framework for the improved identification of genes underlying traits of interest. We demonstrate that by leveraging differences in ancestry-specific patterns of LD and eQTL signals, MA-FOCUS consistently outperforms single-ancestry fine-mapping approaches with equivalent total sample size across multiple metrics. We perform 15 blood trait TWAS using genome-wide summary statistics (average NEA=511k, NAA=13k) and lymphoblastoid cell line eQTL data from cohorts of primarily European and African continental ancestries. We recapitulate evidence demonstrating shared genetic architectures for eQTL and blood traits between the two ancestry groups and observe that gene-level effects correlate 20% more strongly across ancestries compared with SNP-level effects. We perform fine-mapping using MA-FOCUS and find evidence that genes at TWAS risk regions are more likely to be shared across ancestries rather than ancestry-specific. Using multiple lines of evidence to validate our findings, we find gene sets produced by MA-FOCUS are more enriched in hematopoietic categories compared to alternative approaches (P = 1.73 x 10-16). Our work demonstrates that including, and appropriately accounting for, genetic diversity can drive deeper insights into the genetic architecture of complex traits.

genetics↗

bioRxiv2021

A novel method to identify cell-type specific regulatory variants and their role in cancer risk

BackgroundExpression quantitative trait loci (eQTLs) have been crucial in providing an understanding of how genetic variants influence gene expression. However, eQTLs are known to exert cell type specific effects, and existing methods to identify cell type specific QTLs in bulk data require large sample sizes. ResultsHere, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. Applying DeCAF to RNA-seq data from TCGA, we identified 3,664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63x increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell type specific eQTL data and were more enriched for cancer risk than conventional eQTLs. The intersection of tumorspecific QTL effects (tsQTLs) with GWAS loci identified rs4765621 and SCARB1, which has been previously linked to renal cell carcinoma (RCC) progression and experimentally validated in tumors. ConclusionsOur new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies. Our study contributes to a better understanding of germline mechanisms underlying the anticancer immune response as well as cfQTLs contributing to cancer risk.

genetics↗

bioRxiv2021

Multi-context genetic modeling of transcriptional regulation resolves novel disease loci

A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We developed CONTENT-- a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We applied CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. Interestingly, we find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increased the number of gene-phenotype associations discovered by over 47% relative to previous methods across 22 complex traits.

genomics↗