Biology subjects

Chhetri, S. B.

Publications and source records attributed to Chhetri, S. B..

10 recordsLinked to original sources

bioRxiv2026

Integrated inference of cancer gene expression from cell-free plasma chromatin

Gene expression is a defining determinant of tumor identity, behavior, and therapeutic response, yet remains challenging to measure noninvasively. Here, we introduce APEX (Associating Plasma Epigenomic features with eXpression), a framework for inferring expression from circulating cell-free chromatin. Trained on [~]270,000 gene-sample pairs from matched tumor RNA-seq and plasma cfChIP-seq across multiple cancers and validated on >15 unseen cancer subtypes, APEX accurately infers cancer gene expression across a range of tumor fractions and outperforms existing plasma-based approaches by integrating positional histone mark and DNA fragmentation patterns across promoters and gene bodies. Using plasma alone, APEX enables classification of prognostically relevant basal and classical pancreatic cancer subtypes and identifies plasma-inferred NECTIN4 expression as a biomarker of response to enfortumab vedotin in metastatic bladder cancer. Together, these findings establish APEX as a biopsy-free approach for profiling tumor transcriptional states and extend liquid biopsy beyond genomic alterations to clinically relevant gene expression programs.

genomics↗

bioRxiv2025

SNAP: Streamlined Nextflow Analysis Pipeline for Immunoprecipitation-Based Epigenomic Profiling of Circulating Chromatin

Epigenomic profiling of circulating chromatin is a powerful and minimally invasive approach for detecting and monitoring disease, but there are no bioinformatics pipelines tailored to the unique characteristics of cell-free chromatin. We present SNAP (Streamlined Nextflow Analysis Pipeline), a reproducible, scalable, and modular workflow specifically designed for immunoprecipitation-based methods for profiling cell-free chromatin. SNAP incorporates quality control metrics optimized for circulating chromatin, including enrichment score and fragment count thresholds, as well as direct estimation of circulating tumor DNA (ctDNA) content from fragment length distributions. It also includes SNP fingerprinting to enable sample identity verification. When applied to cfChIP-seq and cfMeDIP-seq data across multiple cancer types, SNAPs quality filters significantly improved classification performance while maintaining high data retention. Independent validation using plasma from patients with osteosarcoma confirmed the detection of tumor-associated epigenomic signatures that correlated with ctDNA levels and reflected disease biology. SNAPs modular architecture enables straightforward extension to additional cell-free immunoprecipitation-based assays, providing a robust framework to support studies of circulating chromatin broadly. SNAP is compatible with cloud and high-performance computing environments and is publicly available at https://github.com/prc992/SNAP/. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/694452v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@47c734org.highwire.dtl.DTLVardef@674af8org.highwire.dtl.DTLVardef@16ae938org.highwire.dtl.DTLVardef@1f57a01_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Integrative multi-omic analysis identifies key transcription factors and target proteins in renal cell carcinoma and its subtypes

To characterize key transcription factors (TFs) whose differential DNA binding can be altered by genetic variants associated with risk for renal cell carcinoma (RCC), we conducted a series of mixed model-based analyses integrating 449 TF ChIP-seq profiles across 9 kidney-related cell lines and summary statistics from a multi-ancestry genome-wide association study of RCC. We identified 96 unique TFs for which presence of SNPs in a neighborhood of TF ChIP-seq peaks are significantly associated (p-value <1x10-4) with their effect on RCC, including EPAS1, ARNT, PAX8 and PBRM1, previously implicated in RCC pathogenesis. Most TFs overlapped active promoters/enhancers in RCC tumors but remained significant after adjusting for tumor chromatin accessibility. Further, we found the co-occupancy of 220 pairs of RCC-related TFs to be associated with RCC risk (FDR<5%) beyond effects of individual TFs, highlighting synergistic regulation between pairs of TFs. To further investigate distal (trans) regulation of TF-binding disruption at RCC associated loci on the proteome, we used a set-based regression to aggregate the trans-effects of multiple loci overlapping with TF binding sites. Across 2,732 proteins profiled in UKB-PPP, identified 169 trans-associated (p-value<1.6x10-7) proteins, nominating specific targets for each TF. For example, we identified TLR3 and ZP3 to be associated with EPAS1, ARNT, and PBRM1, indicating these proteins are likely affected by RCC-related variants disrupting binding sites of the corresponding TFs. These results characterize the landscape of RCC-related TFs and implicate TF-mediated proteomic mechanisms in RCC pathogenesis, nominating testable targets for laboratory studies.

genetics↗

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↗

bioRxiv2024

Sparse matrix factorization of GWAS summary statistics robust to sample sharing improves detection and interpretation of factors with diverse genetic architectures

Complex trait-associated genetic variation is highly pleiotropic. This extensive pleiotropy implies that multi-phenotype analyses are informative for characterizing genetic associations, as they facilitate the discovery of trait-shared and trait-specific variants and pathways ("genetic factors"). Previous efforts have estimated genetic factors using matrix factorization (MF) applied to numerous GWAS. However, existing methods are susceptible to spurious factors arising from residual confounding due to sample-sharing in biobank GWAS. Furthermore, MF approaches have historically estimated dense factors, loaded on most traits and variants, that are challenging to map onto interpretable biological pathways. To address these shortcomings, we introduce "GWAS latent embeddings accounting for noise and regularization" (GLEANR), a MF method for detection of sparse genetic factors from summary statistics. GLEANR accounts for sample sharing between studies and uses regularization to estimate a data-driven number of interpretable factors. GLEANR is robust to confounding induced by shared samples and improves the replication of genetic factors derived from distinct biobanks. We used GLEANR to evaluate 137 diverse GWAS from the UK Biobank, identifying 58 factors that decompose the genetic architecture of input traits and have distinct signatures of negative selection and degrees of polygenicity. These sparse factors can be interpreted with respect to disease, cell-type, and pathway enrichment. We highlight three such factors capturing platelet measure phenotypes and enriched for disease-relevant markers corresponding to distinct stages of platelet differentiation. Overall, GLEANR is a powerful tool for discovering both trait-specific and trait-shared pathways underlying complex traits from GWAS summary statistics.

genomics↗

bioRxiv2023

Sources of gene expression variation in a globally diverse cohort

Abstract/SummaryGenetic variation influencing gene expression and splicing is a key source of phenotypic diversity. Though invaluable, studies investigating these links in humans have been strongly biased toward participants of European ancestries, diminishing generalizability and hindering evolutionary research. To address these limitations, we developed MAGE, an open-access RNA-seq data set of lymphoblastoid cell lines from 731 individuals from the 1000 Genomes Project spread across 5 continental groups and 26 populations. Most variation in gene expression (92%) and splicing (95%) was distributed within versus between populations, mirroring variation in DNA sequence. We mapped associations between genetic variants and expression and splicing of nearby genes (cis-eQTLs and cis-sQTLs, respective), identifying >15,000 putatively causal eQTLs and >16,000 putatively causal sQTLs that are enriched for relevant epigenomic signatures. These include 1310 eQTLs and 1657 sQTLs that are largely private to previously underrepresented populations. Our data further indicate that the magnitude and direction of causal eQTL effects are highly consistent across populations and that apparent "population-specific" effects observed in previous studies were largely driven by low resolution or additional independent eQTLs of the same genes that were not detected. Together, our study expands understanding of gene expression diversity across human populations and provides an inclusive resource for studying the evolution and function of human genomes.

genomics↗

bioRxiv2023

Validation of human telomere length trans-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as novel human telomere length regulation genes

Telomere length genome-wide association studies (GWAS) have become well-powered to detect novel genes in telomere length regulation. However, no prior work has validated these putative novel genes to confirm the contribution of GWAS loci to telomere length regulation. We conducted a trans-ancestry meta-analysis of 211,369 individuals. Through enrichment analyses of chromatin state and cell-type heritability we identified blood and immune cells as the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6, a component of an E3 ubiquitin ligase complex, and POP5, a component of the Ribonuclease P/MRP complex, and demonstrating that both lengthened telomeres as predicted by our statistical analyses. CRISPR/Cas9 deletion of the predicted causal regions of these association peaks in K562 immortalized blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5, respectively. Together our results demonstrate the utility of telomere length GWAS in the identification of novel telomere length regulation mechanisms and highlight the importance of the proteasome-ubiquitin pathway in telomere length regulation.

genetics↗

bioRxiv2022

Genome-Wide Large-Scale Multi-Trait Analysis Characterizes Global Patterns of Pleiotropy and Unique Trait-Specific Variants

Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, an extension of the method ASSET, to allow computationally efficient detection of variant-level pleiotropic association across a large number of traits. We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the NIH GRASP repository and a number of other large GWAS consortia. We identify a total of 2,293 independent loci at the genome-wide significance level and found that the lead variants in nearly all of these loci ([~]99%) to be associated with to two or more (median = 6) traits. Further, the estimated degree of pleiotropy for the detected variants strongly predicted their degree of pleiotropy across a much larger number of traits (K=4,114) in the UK Biobank Study. Follow-up analyses of 21 unique trait-specific variants suggest that they are often linked to the expression in trait-related tissues for a small number of genes, some of which are well known to be involved in relevant biological processes. Our findings provide deeper insight into the nature of complex trait pleiotropy and leads to, for the first time, identification of highly unique trait-specific susceptibility variants.

genetics↗

bioRxiv2019

Mechanisms of tissue-specific genetic regulation revealed by latent factors across eQTLs

BackgroundGenetic regulation of gene expression, revealed by expression quantitative trait loci (eQTLs), varies across tissues in complex patterns ranging from highly tissue-specific effects to effects shared across many or all tissues. Improved characterization of these patterns may allow us to better understand the biological mechanisms that underlie tissue-specific gene regulation and disease etiology.\n\nResultsWe develop a constrained matrix factorization model to learn patterns of tissue sharing and tissue specificity of eQTLs across 49 human tissues from the Genotype-Tissue Expression (GTEx) project. The learned factors include patterns reflecting tissues with known biological similarity or shared cell types, in addition to a dense factor representing a universal genetic effect across all tissues. To explore the regulatory mechanisms that generate tissue-specific patterns of expression, we evaluate chromatin state enrichment and identify specific transcription factors with binding sites enriched for eQTLs from each factor.\n\nConclusionsOur results demonstrate that matrix factorization can be applied to learn the tissue specificity pattern of eQTLs and that it exhibits better biological interpretability than heuristic methods. We present a framework to characterize the tissue specificity of eQTLs, and we identify examples of tissue-specific eQTLs that may be driven by tissue-specific transcription factor (TF) binding, with relevance to interpretation of disease association.

genomics↗

bioRxiv2018

Occupancy patterns of 208 DNA-associated proteins in a single human cell type

Genome-wide occupancy maps of transcriptional regulators are important for understanding gene regulation and its effects on diverse biological processes, but only a small fraction of the >1,600 transcription factors (TFs) encoded in the human genome has been assayed. Here we present data and analyses of ChIP-seq experiments for 208 DNA-associated proteins (DAPs) in the HepG2 hepatocellular carcinoma line, spanning nearly a quarter of its expressed TFs, transcriptional co-factors, and chromatin regulator proteins. The DAP binding profiles classify into major groups associated predominantly with promoters or enhancers, or with both. We confirm and expand the current catalog of DNA sequence motifs; 77 factors showed similar motifs to those previously described using in vivo and/or in vitro methods, and 17 yielded novel motifs. We also describe motifs corresponding to other TFs that co-enrich with the primary ChIP target. FOX family motifs are, for example, significantly enriched in ChIP-seq peaks of 37 other DAPs. We show that promoters and enhancers can be discriminated based on motif content and occupancy patterns. This large catalog reveals High Occupancy Target (HOT) regions at which many DAPs associate, although each contains motifs for only a minority of the numerous associated DAPs. These analyses provide a deeper and more complete overview of the gene regulatory networks that define this cell type.

genomics↗