Biology subjects

Hormozdiari, F.

Publications and source records attributed to Hormozdiari, F..

At least 19 recordsLinked to original sources

bioRxiv2025

Orphan Non-Coding RNAs Drive Tumorigenesis and Enable Pre-Diagnostic Detection in HPV-Negative Head and Neck Cancer

HPV-negative head and neck squamous cell carcinoma (HNSCC) lacks reliable biomarkers for early detection and molecular stratification. We recently described orphan non-coding RNAs (oncRNAs) as a class of cancer-emergent small RNAs that arise from cryptic promoters largely silent in normal tissues. Here, we define the comprehensive landscape of oncRNAs in HNSCC. Using TCGA and CPTAC-3 data, we demonstrate that these transcripts are not only tumor-specific but provide digital fingerprints that accurately stratify tumors by anatomical site. Moving from association to function, we investigated whether these emergent transcripts actively drive tumorigenesis. We focused on oncRNA64585, a transcript highly enriched in oral cavity tumors and associated with tumor progression. Its depletion significantly impaired proliferation in vitro and suppressed tumor growth in syngeneic murine models. Mechanistically, transcriptomic and chromatin accessibility profiling revealed that oncRNA64585 regulates transcriptional programs governing cell cycle progression and apoptosis through interaction with the Polycomb Repressive Complex 2 (PRC2). Finally, we demonstrate the clinical utility of these signatures: oncRNA profiles in plasma predicted oral cavity cancer with >90% accuracy up to four years prior to clinical diagnosis. These findings establish oncRNAs as active drivers of HNSCC biology and potent tools for early detection and therapeutic targeting.

cancer biology↗

bioRxiv2025

Anyone can be the best: Impact of diverse methodologies on the evaluation of structural variant callers

Structural variants (SVs) are medium and large-scale genomic alterations that shape phenotypic diversity and disease risk. Numerous methods have been proposed for discovering SVs, however their benchmarking has been inconsistent across studies, often resulting in contradictory findings. One of the main sources of conflicting evaluation re-sults is the lack of consistency in the SV callsets used as ground truth, ranging from curated callsets released by consortia to more recent approaches that construct callsets from high-quality telomere-to-telomere de novo haplotype assemblies. The discrepancies between benchmarks are further compounded by the choice of the reference genome (GRCh37, GRCh38, and T2T-CHM13), where using T2T-CHM13reveals a different deletion/insertion profile, indicating reduced reference bias. We evaluated the performance of several state-of-the-art SV discovery methods from long-read whole-genome sequencing data and observed substantial variation in their performance and rankings, depending on the choice of ground truth, reference genome, and genomic regions used for evaluation. Counter-intuitively, the more complete reference genome T2T-CHM13does not inherently solve the problem of SV benchmarking; instead it reveals the limitations of each detection method in complex genomic regions. The substantial variation in detection accuracy across different genomic regions calls for additional caution in downstream analyses and in drawing conclusions based on predicted SVs. These findings underscore the complexity of evaluating SV detection methods and highlight the need for careful consideration and, ideally, field-standard best practices when reporting performance metrics.

bioinformatics↗

bioRxiv2025

A multi-modal cell-free RNA language model for liquid biopsy applications

Cell-free RNA (cfRNA) profiling has emerged as a powerful tool for non-invasive disease detection, but its application is limited by data sparsity and complexity, especially in settings with constrained sample availability. We introduce Exai-1, a multi-modal, transformer-based generative foundation model that integrates RNA sequence embeddings with cfRNA abundance data to capture biologically meaningful representations of circulating RNAs. By leveraging both sequence and expression modalities, Exai-1 captures a biologically meaningful latent structure of cfRNA profiles. Pre-trained on over 306 billion tokens from 8,339 samples, Exai-1 enhances signal fidelity, reduces technical noise, and improves disease detection by generating synthetic cfRNA profiles. We show that self-attention and variational inference are particularly important for preservation of biological signals and contextual relationships. Additionally, Exai-1 facilitates cross-biofluid translation and assay compatibility through disentangling biological signals from confounders. By uniting sequence-informed embeddings with cfRNA expression patterns, Exai-1 establishes a transfer learning foundation for liquid biopsy, offering a scalable and adaptable framework for next-generation cfRNA-based diagnostics.

bioinformatics↗

bioRxiv2025

Pangenome graph augmentation from unassembled long reads

Pangenomes are becoming increasingly popular data structures for genomics analyses due to their ability to compactly represent the genetic diversity within populations. Constructing a pangenome graph, however, is still a time-consuming and expensive process. A promising approach for pangenome construction consists of progressively augmenting a pangenome graph with additional high-quality assemblies. Currently, there is no method for augmenting a pangenome graph with unassembled reads from newly sequenced samples without first aligning the reads to a reference genome and performing variant calling and genotyping on the new individuals. In this work, we present the first assembly-free and mapping-free approach for augmenting an existing pangenome graph using unassembled long reads from an individual not already present in the pangenome. Our approach consists of finding sample specific sequences in reads using efficient indexes, clustering reads corresponding to the same novel variant(s), and then building a consensus sequence to be added to the pangenome graph for each variant separately. Using simulated reads based on Human Pangenome Reference Consortium (HPRC) assemblies, we demonstrate the effectiveness of the proposed approach for progressively augmenting the pangenome with long reads, without the need for de novo assembly or predicting genetic variants of the new sample. The software is freely available at https://github.com/ldenti/palss.

bioinformatics↗

bioRxiv2023

Identification of critical cell-types using genetic modules: A case study of neurodevelopmental disorders

Identifying the critical cell-types impacted by various diseases is crucial for understanding disease mechanisms and developing targeted therapeutics. Recent advances in disease genetic module discovery and single-cell technologies provide a unique opportunity to study critical cell-types based on functional pathways and modules. Disease genetic modules are defined as sets of genes with correlated expression that are part of the same biological pathways and are disrupted in the disease. Critical cell-types for a biological function are defined as clusters of similar cells most "active" or "involved" in that biological function. In this paper, we provide a formal problem definition for the critical cell discovery problem using the recently introduced local correlation concept, and show that the proposed problem is intractable in theory. We propose a novel method, MoToCC (Module To Critical Cell-types), to find sets of similar cells with local correlated gene expression activity for input modules. We evaluated MoToCC on four neurodevelopmental disorder modules using single-cell expression data from the developing human cortex. Finally, we demonstrate that the objective value returned by MoToCC for the tested modules is an acceptable approximation to the optimal solution. Overall, our work provides a valuable tool for studying critical cell-types and their role in disease mechanisms, which could lead to the development of more effective targeted therapeutics. The MoToCC package is available at https://github.com/jchow32/MoToCC

bioinformatics↗

bioRxiv2022

Improved structural variant discovery in hard-to-call regions using sample-specific string detection from accurate long reads

Structural variants (SVs) account for a large amount of sequence variability across genomes and play an important role in human genomics and precision medicine. Despite intense efforts over the years, the discovery of SVs in individuals remains challenging due to the diploid and highly repetitive structure of the human genome, and by the presence of SVs that vastly exceed sequencing read lengths. However, the recent introduction of low-error long-read sequencing technologies such as PacBio HiFi may finally enable to overcome these barriers. Here we present SVDSS, a novel hybrid method for discovery of SVs from long-read sequencing technologies (e.g., PacBio HiFi) that combines and effectively leverages mapping-free, mapping-based and assembly-based methodologies for overall superior SV discovery performance. Our experiments on several human samples show that SVDSS outperforms state-of-the-art mapping-based methods for discovery of insertion and deletion SVs in PacBio HiFi reads and achieves significant improvements in calling SVs in repetitive regions of the genome. SVDSS is open source and publicly available at: https://github.com/Parsoa/SVDSS

bioinformatics↗

bioRxiv2021

MAGI-MS: Multiple seed-centric module discovery

Complex disorders manifest by the interaction of multiple genetic and environmental factors. Through the construction of genetic modules that consist of highly co-expressed genes, it is possible to identify genes that participate in common biological pathways relevant to specific phenotypes. We have previously developed tools MAGI and MAGI-S for genetic module discovery by incorporating co-expression and protein-interaction networks. Here we introduce an extension to MAGI-S, denoted as Merging Affected Genes into Integrated Networks - Multiple Seeds (MAGI-MS), that permits the user to further specify a disease pathway of interest by selecting multiple seed genes likely to function in the same molecular mechanism. By providing MAGI-MS with pairs of seed genes involved in processes underlying certain classes of neurodevelopmental disorders, such as epilepsy, we demonstrate that MAGI-MS can reveal modules enriched in genes relevant to chemical synaptic transmission, glutamatergic synapse, and other functions associated with the provided seed genes. Availability and implementationMAGI-MS is free and is available at: https://github.com/jchow32/MAGI-MS

bioinformatics↗

bioRxiv2021

DeepNull: Modeling non-linear covariate effects improves phenotype prediction and association power

Genome-wide association studies (GWAS) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n=370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average).

genetics↗

bioRxiv2021

Comparative genome analysis using sample-specific string detection in accurate long reads

MotivationComparative genome analysis of two or more whole-genome sequenced (WGS) samples is at the core of most applications in genomics. These include discovery of genomic differences segregating in population, case-control analysis in common diseases, and rare disorders. With the current progress of accurate long-read sequencing technologies (e.g., circular consensus sequencing from PacBio sequencers) we can dive into studying repeat regions of genome (e.g., segmental duplications) and hard-to-detect variants (e.g., complex structural variants). ResultsWe propose a novel framework for addressing the comparative genome analysis by discovery of strings that are specific to one genome ("samples-specific" strings). We have developed an accurate and efficient novel method for discovery of samples-specific strings between two groups of WGS samples. The proposed approach will give us the ability to perform comparative genome analysis without the need to map the reads and is not hindered by shortcomings of the reference genome. We show that the proposed approach is capable of accurately finding samples-specific strings representing nearly all variation (> 98%) reported across pairs or trios of WGS samples using accurate long reads (e.g., PacBio HiFi data). AvailabilityThe proposed tool is publicly available at https://github.com/Parsoa/PingPong.

bioinformatics↗

bioRxiv2020

Identifying Causal Variants by Fine Mapping Across Multiple Studies

Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of "fine mapping" methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. In a trans-ethnic, trans-biobank Type 2 Diabetes analysis, we show that MsCAVIAR returns causal set sizes that are over 20% smaller than those given by current state of the art methods for trans-ethnic fine-mapping.

bioinformatics↗

bioRxiv2019

Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx

BackgroundPopulation structure among study subjects may confound genetic association studies, and lack of proper correction can lead to spurious findings. The Genotype-Tissue Expression (GTEx) project largely contains individuals of European ancestry, but the final release (v8) also includes up to 15% of individuals of non-European ancestry. Assessing ancestry-based adjustments in GTEx provides an opportunity to improve portability of this research across populations and to further measure the impact of population structure on GWAS colocalization.\n\nResultsHere, we identify a subset of 117 individuals in GTEx (v8) with a high degree of population admixture and estimate genome-wide local ancestry. We perform genome-wide cis-eQTL mapping using admixed samples in six tissues, adjusted by either global or local ancestry. Consistent with previous work, we observe improved power with local ancestry adjustment. At loci where the two adjustments produce different lead variants, we observe only 0.8% of tests with GWAS colocalization posterior probabilities that change by 10% or more. Notably, both adjustments produce similar numbers of significant colocalizations. Finally, we identify a small subset of GTEx v8 eQTL-associated variants highly correlated with local ancestry (R2 > 0.7), providing a resource to enhance functional follow-up.\n\nConclusionsWe provide a local ancestry map for admixed individuals in the final GTEx release and describe the impact of ancestry and admixture on gene expression, eQTLs, and GWAS colocalization. While the majority of results are concordant between local and global ancestry-based adjustments, we identify distinct advantages and disadvantages to each approach.

genetics↗

bioRxiv2019

Widespread dose-dependent effects of RNA expression and splicing on complex diseases and traits

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2,519 out of 5,385) of the GWAS loci examined. Our results demonstrate the translational relevance of the GTEx resources and highlight the need to increase their resolution and breadth to further our understanding of the genotype-phenotype link.

genetics↗

bioRxiv2019

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Fine-mapping aims to identify causal variants impacting complex traits. Several recent methods improve fine-mapping accuracy by prioritizing variants in enriched functional annotations. However, these methods can only use information at genome-wide significant loci (or a small number of functional annotations), severely limiting the benefit of functional data. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy using genome-wide functional data for a broad set of coding, conserved, regulatory and LD-related annotations. PolyFun prioritizes variants in enriched functional annotations by specifying prior causal probabilities for fine-mapping methods such as SuSiE or FINEMAP, employing special procedures to ensure robustness to model misspecification and winners curse. In simulations with in-sample LD, PolyFun + SuSiE and PolyFun + FINEMAP were well-calibrated and identified >20% more variants with posterior causal probability >0.95 than their non-functionally informed counterparts (and >33% more fine-mapped variants than previous functionally-informed fine-mapping methods). In simulations with mismatched reference LD, PolyFun + SuSiE remained well-calibrated when reducing the maximum number of assumed causal SNPs per locus, which reduces absolute power but still produces large relative improvements. In analyses of 49 UK Biobank traits (average N=318K) with in-sample LD, PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement vs. SuSiE; 223 variants were fine-mapped for multiple genetically uncorrelated traits, indicating pervasive pleiotropy. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.

genetics↗

bioRxiv2019

The GTEx Consortium atlas of genetic regulatory effects across human tissues

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

genetics↗

bioRxiv2019

Evaluating the informativeness of deep learning annotations for human complex diseases

Deep learning models have shown great promise in predicting genome-wide regulatory effects from DNA sequence, but their informativeness for human complex diseases and traits is not fully understood. Here, we evaluate the disease informativeness of allelic-effect annotations (absolute value of the predicted difference between reference and variant alleles) constructed using two previously trained deep learning models, DeepSEA and Basenji. We apply stratified LD score regression (S-LDSC) to 41 independent diseases and complex traits (average N=320K) to evaluate each annotations informativeness for disease heritability conditional on a broad set of coding, conserved, regulatory and LD-related annotations from the baseline-LD model and other sources; as a secondary metric, we also evaluate the accuracy of models that incorporate deep learning annotations in predicting disease-associated or fine-mapped SNPs. We aggregated annotations across all tissues (resp. blood cell types or brain tissues) in meta-analyses across all 41 traits (resp. 11 blood-related traits or 8 brain-related traits). These allelic-effect annotations were highly enriched for disease heritability, but produced only limited conditionally significant results - only Basenji-H3K4me3 in meta-analyses across all 41 traits and brain-specific Basenji-H3K4me3 in meta-analyses across 8 brain-related traits. We conclude that deep learning models are yet to achieve their full potential to provide considerable amount of unique information for complex disease, and that the informativeness of deep learning models for disease beyond established functional annotations cannot be inferred from metrics based on their accuracy in predicting regulatory annotations.

genetics↗

bioRxiv2019

Nebula: Ultra-efficient mapping-free structural variant genotyper

MotivationLarge scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping events in repeat regions. Thus we are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches.\n\nResultsOur method Nebula utilizes the changes in the count of k-mers to predict the genotype of common structural variations. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping deletions and mobile-element insertions, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event.\n\nAvailabilityNebula is publicly available at https://github.com/Parsoa/NebulousSerendipity

bioinformatics↗

bioRxiv2019

Kevlar: a mapping-free framework for accurate discovery of de novo variants

MotivationDiscovery of genetic variants by whole genome sequencing has proven a powerful approach to study the etiology of complex genetic disorders. Elucidation of all variants is a necessary step in identifying causative variants and disease genes. In particular, there is an increased interest in detection of de novo variation and investigation of its role in various disorders. State-of-the-art methods for variant discovery rely on mapping reads from each individual to a reference genome and predicting variants from difference observed between the mapped reads and the reference genome. This process typically results in millions of variant predictions, most of which are inherited and irrelevant to the phenotype of interest. To distinguish between inherited variation and novel variation resulting from de novo germline mutation, whole-genome sequencing of close relatives (especially parents and siblings) is commonly used. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction, which in many cases arises from problems with read mapping. This is a particular challenge in predicting de novo indels and structural variants. ResultsWe have developed a mapping-free method, Kevlar, for de novo variant discovery based on direct comparison of sequence content between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest and retrieves the reads containing these k-mers. These reads are easily partitioned into disjoint sets by shared k-mer content for subsequent locus-by-locus processing and variant calling. Kevlar also utilizes a novel probabilistic approach to score and rank the variant predictions to identify the most likely de novo variants. We evaluated Kevlar on simulated and real pedigrees, and demonstrate its ability to detect both de novo SNVs and indels with high sensitivity and specificity. Availability: https://github.com/dib-lab/kevlar

bioinformatics↗

bioRxiv2018

Functional disease architectures reveal unique biological role of transposable elements

Transposable elements (TE) comprise roughly half of the human genome. Though initially derided as \"junk DNA\", they have been widely hypothesized to contribute to the evolution of gene regulation. However, the contribution of TE to the genetic architecture of diseases and complex traits remains unknown. Here, we analyze data from 41 independent diseases and complex traits (average N=320K) to draw three main conclusions. First, TE are uniquely informative for disease heritability. Despite overall depletion for heritability (54% of SNPs, 39{+/-}2% of heritability; enrichment of 0.72{+/-}0.03; 0.38-1.23 enrichment across four main TE classes), TE explain substantially more heritability than expected based on their depletion for known functional annotations (expected enrichment of 0.35{+/-}0.03; 2.11x ratio of true vs. expected enrichment). This implies that TE acquire function in ways that differ from known functional annotations. Second, older TE contribute more to disease heritability, consistent with acquiring biological function; SNPs inside the oldest 20% of TE explain 2.45x more heritability than SNPs inside the youngest 20% of TE. Third, Short Interspersed Nuclear Elements (SINE; one of the four main TE classes) are far more enriched for blood traits (2.05{+/-}0.30) than for other traits (0.96{+/-}0.09); this difference is far greater than expected based on the weaker depletion of SINEs for regulatory annotations in blood compared to other tissues. Our results elucidate the biological roles that TE play in the genetic architecture of diseases and complex traits.

genetics↗