Biology subjects

Najafabadi, H. S.

Publications and source records attributed to Najafabadi, H. S..

At least 19 recordsLinked to original sources

bioRxiv2026

A high-resolution protein-RNA binding atlas for structure-aware interaction prediction and post-transcriptional regulatory modeling

RNA-binding proteins (RBPs) control post-transcriptional gene expression by regulating RNA processing, localization, stability, and translation. However, our understanding of RNA regulation remains limited by the resolution, scale, and uniformity of available RBP binding maps. Here we introduce eclair CLIP (ecCLIP), a high-throughput CLIP-seq platform that combines endogenous protein tagging for uniform affinity capture with ligation-free library preparation to generate high-resolution RNA-protein binding profiles. We applied ecCLIP to 67 endogenously tagged RBPs in HEK293T cells, generating a high-quality transcriptome-scale atlas that recovered known binding preferences and captured post-transcriptional regulons linked to RNA half-life. To test the utility of the atlas, we incorporated ecCLIP into Borzuki, a Borzoi-derived sequence-to-function model trained on complementary RNA degradation, expression, CLIP, and Perturb-seq data. Inclusion of ecCLIP data improved RNA half-life prediction and assessment of 3' UTR variant effects on RNA stability, demonstrating that our atlas helps capture RNA dynamics. To directly model the cis-regulatory grammar underlying these RBP-RNA interactions, we developed Pythia, a structure-aware neural network architecture that incorporates context-free grammar-inspired fixed convolutions to explicitly encode RNA base-pairing features. Pythia predicted RBP binding from sequence and structural context and decomposed predictions into sequence- and structure-dependent components. We tested this decomposition for five RBPs using massively parallel reporter CLIP (MPRC) binding assays in cells, using synthetic RNA libraries containing mutations designed to preserve or disrupt predicted RNA structures across nominated binding sites. We used the same MPRC strategy to confirm allele-specific binding effects for variants prioritized by Pythia. Together, ecCLIP and Pythia provide a resource for structure-aware decoding of RNA-protein interaction grammar and interpretation of post-transcriptional regulatory variation.

genomics↗

bioRxiv2026

A map of RBP-miRNA regulatory connections for deciphering the mechanisms of transcriptome remodeling in cancer

Micro-RNAs (miRNAs) are key post-transcriptional regulators of gene expression that influence cancer progression and outcome. Whereas transcriptional dysregulation of miRNA abundance underlies abnormal functionality of some miRNAs in cancer, emerging data points to RNA-binding proteins (RBPs) as prominent post-transcriptional regulators of miRNA activity. Here, by modelling the regulon activities of well-characterized miRNA families in RNA-seq data of a comprehensive collection of RBP inhibition experiments, human cell lines and tissues, we report a map of regulatory connections between 34 RBPs and 28 miRNAs. Furthermore, through a global analysis of the abundance as well as the activity of miRNAs in thousands of tumor and control samples from 17 human cancers, we show that RBP-driven regulation of miRNA activity is common in tumors and contributes to the heterogeneity in miRNA activity profiles. We uncover a set of RBPs that regulate the activity of tumor-suppressor and oncogenic miRNAs, including miR-29-3p, miR-17-5p and miR-19-3p. Of particular interest, we describe a pan-cancer negative regulatory role for DDX55 on the activity of the miR-29-3p family, a critical miRNA family with tumor suppressor functions that is involved in most human cancers. Our functional studies confirm that DDX55 inhibition restores the gene-silencing activity of miR-29-3p and reduces cancer cell viability. Overall, our study provides the first pan-cancer investigation of functional regulatory connections between RBPs and miRNAs, and highlights RBPs whose interventional modulation may serve as a new approach to reprogram abnormal miRNA activities in cancer.

genomics↗

bioRxiv2026

Lysyl oxidase drives ccRCC progression by coordinating HIF-2α transcription program with tumor microenvironment

Clear cell renal cell carcinoma (ccRCC) is driven by persistent HIF-2 transcription program initiated by VHL loss, yet molecular mediators sustaining this program are poorly defined. Using single-cell transcriptomics, we identified lysyl oxidase (LOX) as a driver of ccRCC progression, selectively enriched in a hypoxia/epithelial-mesenchymal transition (EMT) gene program associated with poor outcome. While LOX oxidizes and stabilizes HIF-2 by antagonizing HUWE1-mediated ubiquitination and degradation, thereby sustaining HIF-2-driven transcription in cancer cells, it also remodels extracellular matrix (ECM) and promotes angiogenesis in the tumor microenvironment (TME). Genetic or pharmacological inhibition of LOX destabilizes HIF-2, disrupts ECM, inhibits angiogenesis, and suppresses tumor initiation, growth, and metastasis in vivo. LOX inhibition enhances anti-angiogenic therapy response and remains effective in belzutifan-resistant HIF-2 G323E-mutant tumors. Nuclear LOX protein correlates with nuclear HIF-2 in high-grade patient tumors. Together, LOX coordinates HIF-2 transcription program with TME and is a therapeutic target in ccRCC.

cancer biology↗

bioRxiv2026

Non-canonical ribosome organization enables translation stalling in neuronal RNA granules.

Neuronal outgrowth to support cognitive functions relies on a timely supply of proteins to distal sites in the neuron. To meet this demand, neurons transport mRNAs and ribosomes in large assemblies called "neuronal RNA granules". Ribosomes that integrate into these granules initiate protein synthesis in the soma, stall the process, and then become packaged into clusters that are transported to distal sites, where protein synthesis can be reactivated. The exact mechanisms and structure of these ribosome clusters remain unknown. We combined cryo-electron microscopy and cryo-electron tomography to examine these dense ribosome clusters purified from rat brains, which are included in neuronal RNA granules. We found that these clusters contain ribosomes stalled at various stages of elongation and that the mRNA sequences where the ribosomes stall facilitate stalling. The ribosome clusters contain multiple polysomal units, and ribosomes from nearby polysomes interact through rRNA expansion segments, only seen before in disomes of inactive or hibernating ribosomes in neurons. The ribosomes in the clusters form unique three-dimensional arrays that evade detection by the ribosome-associated quality-control and NO-GO decay responses, which normally lead to recycling of 40S and 60S subunits, and degradation of the mRNA and arrested nascent proteins. Overall, this study helps uncover specialized mechanisms in neurons that enable local protein translation at distal sites, supporting proper protein homeostasis, both crucial for neurodevelopment and optimal neuronal function. KEY POINTSO_LIRibosomes in neurons organize into unique clusters that are incorporated into "neuronal RNA granules". C_LIO_LIApproximately half of the ribosomes in these clusters are stalled in elongation. C_LIO_LIThe mRNA sequences read by these ribosomes facilitate stalling. C_LIO_LIRibosomes within the clusters interact through rRNA expansion segments. C_LIO_LIRibosomes in the clusters form unique 3D structures that are not detected by the ribosome-associated quality-control response. C_LI

neuroscience↗

bioRxiv2026

Semantic fragment representations for coordinate-free analysis of genomics data

Many genomic assays begin with individual DNA fragments, but standard analysis quickly collapses those molecules into counts over genomic intervals. Rich information carried by each fragment, including its sequence, fragment body, cleavage boundaries, and local flanking context, is lost in this process. This loss is especially apparent in mixed-source and heterogeneous samples, where individual fragments originate from disparate cell types and can retain information about their cell of origin. To address this, we present LEAF-1, a fragment-level foundation model pre-trained on approximately 58 billion fragments spanning bulk ATAC-seq, single-cell ATAC-seq, and cell-free DNA profiles, representing each DNA molecule as a point in a learned semantic space defined by sequence context, assay modality, and explicit cleavage-boundary tokens. In sparse scATAC-seq datasets, mean-pooled LEAF-1 embeddings readily classify human cell types from as few as [~]1,000 fragments per cell, with high-scoring fragments linked to cell-type-associated transcription-factor programs. Similarly, in cell-free DNA profiling, LEAF-1 outperformed state-of-the-art coordinate-binning strategies and general-purpose DNA language model baselines across cancer detection tasks. Applying attention-based multiple-instance learning to LEAF-1 embeddings further improved cancer detection, reaching an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95. This pan-cancer model generalizes beyond cancer types it is trained on, as we show by profiling plasma samples from clear cell renal cell carcinoma patients and healthy volunteers and applying the frozen classifier without retraining, achieving an AUC of 0.83. These results show that semantic learning over individual DNA fragments preserves biochemical, cell-associated, and disease-associated signals that are otherwise lost during coordinate-based aggregation.

genomics↗

bioRxiv2026

Recursive mutational robustness in cancer through intra- and inter-genic compensation

Mutational robustness is a fundamental property of biological systems allowing phenotypic stability against genotypic perturbation. At the gene level, this is primarily attributed to inter-genic functional redundancy driven by paralogs. Here, we show that intra-genic functional redundancy, driven by alternatively spliced isoforms, is another prevalent source of mutational robustness. Integrating isoform-resolved pan-cancer genomics and transcriptomics data across tumors and cancer cell lines, we show that alternative isoforms often bypass deleterious somatic mutations. Mutation-skipping isoforms exhibit compensatory upregulation indicative of robustness against deleterious effects, especially in highly expressed genes. We found that intra-genic robustness is strongly associated with perturbation tolerance, is highly context-specific and, notably, can be complemented and augmented recursively by inter-genic robustness. Non-tumour-suppressor genes generally show higher robustness than tumour suppressor genes, and intra-genic compensation of their mutations is associated with worse disease outcome in most cancer types. Furthermore, intra-genic compensation by mutation-skipping isoforms associates with differential isoform usage among proximal interactors, suggesting restorative protein interaction rewiring at isoform-level. Mechanistically, self-transcriptional adaptation (self-TA) following nonsense-mediated decay of perturbed isoforms provides an explanation for the observed up-regulation of mutation-skipping isoforms. Overall, the proposed recursive mutational robustness framework opens new avenues for deeper mechanistic investigation into cancer evolution and identification of selective vulnerabilities in cancer cells. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/727768v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1b0f79aorg.highwire.dtl.DTLVardef@14fa261org.highwire.dtl.DTLVardef@12da16aorg.highwire.dtl.DTLVardef@c03dee_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2026

A latent space thermodynamic model of cell differentiation

Inferring the governing dynamics of differentiation that capture cell state evolution remains a central challenge in single-cell biology. We present Latent Space Dynamics (LSD), a thermodynamics-inspired framework that models cell differentiation as evolution on a learned Waddington landscape in latent space. LSD jointly infers a low-dimensional cell state, a differentiable potential function governing developmental flow, and a local entropy term that quantifies cellular plasticity. Using a neural ordinary differential equation, LSD reconstructs continuous differentiation trajectories from time-ordered single-cell data. Across diverse developmental systems, LSD accurately recovers lineage hierarchies, predicts fate commitment for unseen cell types, and outperforms existing trajectory inference approaches in directional accuracy. Moreover, in silico gene perturbations reveal how individual regulators reshape the landscape, and entropy provides a quantitative measure of plasticity in development and cancer.

bioinformatics↗

bioRxiv2026

A Multiomic Analysis of Cachectic Mice Reveals Cancer Driven Suppression of Muscle Stem Cell Differentiation

Cancer cachexia affects a large proportion of cancer patients, inducing a rapid decline in muscle mass. Patients with cachexia have a worse prognosis and are less responsive to cancer therapies. The exact cause of cachexia remains unknown, nor are there any effective treatments for the condition. In this study, we use the C26 adenocarcinoma cell line to determine how cancer cells affect myofiber and muscle stem cell function. We determined that C26 cancer cells adapt to the host environment, in both male and female mice, greatly altering their transcriptome to promote their survival and growth. C26 cells directly communicate with muscle stem cells via GDF15 and MMP9. These circulatory factors cause the muscle stem cells to upregulate the EMT pathway and become less capable of undergoing differentiation and contributing to muscle regeneration. Muscle stem cells from tumor bearing mice are less proliferative and less prone to differentiation, Chromatin accessibility data shows that there are fewer accessible myogenic regulatory binding sites. Cytokine array determined that circulating GDF15 and MMP9 were highly upregulated and were derived form C26 tumor cells. However, blocking tumor derived GDF15 is not sufficient to prevent the onset of cachexia and rescue the loss of muscle stem cell function. Together, these findings establish a new conceptual paradigm in which cancer orchestrates muscle wasting through coordinated transcriptional, metabolic, and epigenetic suppression of muscle stem cell differentiation.

cell biology↗

bioRxiv2025

Decoy-seq unlocks scalable genetic screening for regulatory small noncoding RNAs

Small noncoding RNAs (smRNAs) play critical roles in regulating various cellular processes, including development, stress response, and disease pathogenesis. However, functional characterization of smRNAs remains limited by the scale and simplicity of phenotypic readouts. Recently, single-cell perturbation screening methods, which link CRISPR-mediated genetic perturbations to rich transcriptomic profiling, have emerged as foundational and scalable approaches for understanding gene functions, mapping regulatory networks, and revealing genetic interactions. However, a comparable approach for probing the regulatory consequences of smRNA perturbations is lacking. Here, we present Decoy-seq as an extension of this approach for high-content, single-cell perturbation screening of smRNAs. This method leverages U6-driven tough decoys (TuD), which form stable duplexes with their target smRNAs, for inhibition in the cell. Lentiviral-encoded TuDs are compatible with conventional single-cell RNA-sequencing (scRNA-seq) technologies, allowing joint identification of the smRNA perturbation in each cell and its associated transcriptomic profile. We applied Decoy-seq to 336 microRNAs (miRNAs) and 196 tRNA-derived fragments (tRFs) in a human breast cancer cell line, demonstrating its ability to uncover complex regulatory pathways and novel functions of these smRNAs. Notably, we show that tRFs influence mRNA polyadenylation and regulate key cancer-associated processes, such as cell cycle progression and proliferation. Therefore, Decoy-seq provides a powerful framework for exploring the functional roles of smRNAs in normal physiology and disease, and holds promise for accelerating future discoveries.

systems biology↗

bioRxiv2024

GHT-SELEX demonstrates unexpectedly high intrinsic sequence specificity and complex DNA binding of many human transcription factors

Precise identification of transcription factor (TF) binding sites is a long standing challenge in human regulatory genomics: TF binding motifs are short and degenerate, while the genome is large. Motif scans, therefore, often produce excessive binding site predictions. By surveying 179 TFs across 25 families using >1,500 cyclic in vitro selection experiments with fragmented, naked, and unmodified genomic DNA - a method we term GHT-SELEX (Genomic HT-SELEX) - we find that many human TFs possess much higher sequence specificity than anticipated. Moreover, genomic binding regions from GHT-SELEX are often surprisingly similar to those obtained in vivo (i.e., ChIP-seq peaks). Contrary to conventional wisdom, we find that high specificity can also be obtained from motif scans, but performance is highly dependent on the derivation and use of the motifs, including accounting for multiple local matches. We also observe alternative engagement of multiple DNA-binding domains within the same protein: long C2H2 zinc finger proteins often utilize modular DNA recognition, engaging different subsets of their DNA-binding domain (DBD) arrays to recognize multiple types of distinct target sites, frequently evolving via internal duplication and divergence of one or more DBDs. Thus, it is common for TFs to possess sufficient intrinsic specificity to delineate a large fraction of in vivo genomic targets, independently of other cellular factors.

genomics↗

bioRxiv2024

Extensive binding of uncharacterized human transcription factors to genomic dark matter

The functional impact of a large portion of the human genome known as "dark matter DNA", which is composed mainly of repeat sequences, remains enigmatic. The genome also encodes hundreds of putative and poorly characterized transcription factors (TFs). Here, we determined genomic binding locations of 166 poorly characterized human TFs in living cells. Nearly half of them associate strongly with known regulatory regions such as promoters and enhancers, frequently co-localizing with each other at conserved motif matches. The other half often associate with genomic dark matter, however, at largely non-overlapping (i.e., unique) sites, via intrinsic sequence recognition. Fifty-four of the latter half, which we term "Dark TFs", mainly bind within regions of closed chromatin, with each recognizing a unique set of repeat sequences. The Dark TFs include many KZNFs, which are known to bind and silence TEs, and other TFs with apparent repressive functions. By contrast, some may be pioneers: we find that induction of TPRX1, a known regulator of zygotic preimplantation, leads to chromatin opening at many of its binding sites in the dark matter genome. Altogether, our results shed light on a large fraction of poorly characterized human TFs and simultaneously illuminate the diversity of function within the dark matter genome.

genomics↗

bioRxiv2024

A long context RNA foundation model for predicting transcriptome architecture

Linking DNA sequence to genomic function remains one of the grand challenges in genetics and genomics. Here, we present a large-scale compendium of single-molecule transcriptome sequencing of diverse cancer cell lines, revealing their isoform diversity and specificity. We used this compendium to build Mach-1, an RNA foundation model that learns how the nucleotide sequence of unspliced pre-mRNA dictates transcriptome architecture--the relative abundances and molecular structures of mRNA isoforms. By using the Striped-Hyena architecture, Mach-1 handles extremely long sequence inputs at nucleotide resolution (64 kilobase pairs), allowing for quantitative, zero-shot prediction of all aspects of transcriptome architecture, spanning isoform abundance, structure, and variant-induced splicing changes. To test both the interpretive and generative capabilities of Mach-1, we experimentally validated its learned regulatory grammar and predictions through perturbation of RNA-binding proteins nominated by Mach-1 to impact targeted splicing, precise CRISPR editing of variants of uncertain significance that the model predicted to alter splicing, and de novo transcript synthesis and expression in human cells. Together, this release establishes a new foundation for sequence-to-transcript modeling. Mach-1s representations can be extended and fine-tuned across a spectrum of biological contexts, from variant interpretation to RNA engineering.

bioinformatics↗

bioRxiv2024

Accurate isoform quantification by joint short- and long-read RNA-sequencing

Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing RNA sequencing methods have significant limitations: short-read (SR) sequencing provides high depth but struggles with isoform deconvolution, whereas long-read (LR) sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. By applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of transcript abundances, we show that untranslated regions (UTRs) are major determinants of isoform proportion and exon usage; this effect is mediated through isoform-specific sequence features embedded in UTRs, which likely interact with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQTs potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.

bioinformatics↗

bioRxiv2023

A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data

Analysis of single cells across multiple samples and/or conditions encompasses a series of interrelated tasks, which range from normalization and inter-sample harmonization to identification of cell state shifts associated with experimental conditions. Other downstream analyses are further needed to annotate cell states, extract pathway-level activity metrics, and/or nominate gene regulatory drivers of cell-to-cell variability or cell state shifts. Existing methods address these analytical requirements sequentially, lacking a cohesive framework to unify them. Moreover, these analyses are currently confined to specific modalities where the biological quantity of interest gives rise to a singular measurement. However, other modalities require joint consideration of dual measurements; for example, modeling the latent space of alternative splicing involves joint analysis of exon inclusion and exclusion reads. Here, we introduce a generative model, called GEDI, to identify latent space variations in multi-sample, multi-condition single cell datasets and attribute them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with the state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states in the form of transcriptomic vector fields, and machine learning-based prediction of sample characteristics from single-cell data. By incorporating gene-level prior knowledge, it can further project pathway and regulatory network activities onto the cellular state space, enabling the computation of the gradient fields of transcription factor activities and their association with the transcriptomic vector fields of sample covariates. Finally, we demonstrate that GEDI surpasses the gene-centric approach by extending all these concepts to the study of alternative cassette exon splicing and mRNA stability landscapes in single cells.

bioinformatics↗

bioRxiv2023

Accounting for genetic effect heterogeneity in fine-mapping and improving power to detect gene-environment interactions with SharePro

BackgroundCharacterizing genetic effect heterogeneity across subpopulations with different environmental exposures is useful for identifying exposure-specific pathways, understanding biological mechanisms underlying disease heterogeneity and further pinpointing modifiable risk factors for disease prevention and management. Classical gene-by-environment interaction (GxE) analysis can be used to characterize genetic effect heterogeneity. However, it can have a high multiple testing burden in the context of genome-wide association studies (GWAS) and requires a large sample size to achieve sufficient power. MethodsWe adapt a colocalization method, SharePro, to account for effect heterogeneity in finemapping and subsequently improve power for GxE analysis. Through joint fine-mapping of exposure stratified GWAS summary statistics, SharePro can greatly reduce multiple testing burden in GxE analysis. ResultsThrough extensive simulation studies, we demonstrated that accounting for effect heterogeneity can improve power for fine-mapping. With efficient joint fine-mapping of exposure stratified GWAS summary statistics, SharePro alleviated multiple testing burden in GxE analysis and demonstrated improved power with well-controlled false discovery rate. Through analyses of smoking status stratified GWAS summary statistics, we identified genetic effects on lung function modulated by smoking status mapped to the genes CHRNA3, ADAM19 and UBR1. Additionally, using sex stratified GWAS summary statistics, we characterized sex differentiated genetic effects on fat distribution and provided biologically plausible candidates for functional follow-up studies. ConclusionsWe have developed an analytical framework to account for effect heterogeneity in finemapping and subsequently improve power for GxE analysis. The SharePro software for GxE analysis is openly available at https://github.com/zhwm/SharePro_gxe.

genetics↗

bioRxiv2023

SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals

MotivationColocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus. ResultsWe propose SharePro to extend the COLOC framework for colocalization analysis. Share-Pro integrates linkage disequilibrium (LD) modelling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Through a challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 (RSPO3) GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals. Availability and ImplementationThe SharePro software for colocalization analysis is openly available at https://github.com/zhwm/SharePro_coloc and the analysis conducted in this study is available at https://github.com/zhwm/SharePro_coloc_analysis.

genetics↗

bioRxiv2023

The E3 Ubiquitin Ligase Nedd4L Acts as a Checkpoint Against Activation in Quiescent Muscle Stem Cells

Adult stem cells play a critical role in tissue repair and maintenance. In tissues with slow turnover, including skeletal muscle, these cells are maintained in a mitotically quiescent state yet remain poised to re-enter the cell cycle to replenish themselves and regenerate the tissue. Using a multiomics approach we identify the PAX7/NEDD4L axis as a checkpoint against muscle stem cell activation in homeostatic skeletal muscle. Our findings demonstrate that PAX7 transcriptionally activates the E3 ubiquitin ligase Nedd4L and that the conditional genetic deletion of Nedd4L impairs muscle stem cell quiescence, with an upregulation of cell cycle and myogenic differentiation genes. Loss of Nedd4L in muscle stem cells results in the expression of DCX which is only expressed during their in vivo activation. Together, this data establishes that the ubiquitin proteasome system, mediated by Nedd4L, is a key regulator of the muscle stem cell quiescent state in non-injured skeletal muscle. Highlights- General inhibition of the ubiquitin proteasome system with MG132 results in muscle stem cells (MuSCs) breaking quiescence. - The E3 ubiquitin ligase Nedd4L is a transcriptional target of Pax7. - The Pax7/Nedd4l axis restricts MuSC activation in homeostatic skeletal muscle. - Genetic deletion of Nedd4L induces MuSCs transition towards activation.

cell biology↗

bioRxiv2021

SparsePro: an efficient genome-wide fine-mapping method integrating summary statistics and functional annotations

Identifying causal variants from genome-wide association studies (GWAS) is challenging due to widespread linkage disequilibrium (LD) and the possible existence of multiple causal variants in the same genomic locus. Functional annotations of the genome may help to prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. Classical fine-mapping methods conducting an exhaustive search of variant-level causal configurations have a high computational cost, especially when the underlying genetic architecture and LD patterns are complex. SuSiE provided an iterative Bayesian stepwise selection algorithm for efficient fine-mapping. In this work, we build connections between SuSiE and a paired mean field variational inference algorithm through the implementation of a sparse projection, and propose effective strategies for estimating hyperparameters and summarizing posterior probabilities. Moreover, we incorporate functional annotations into fine-mapping by jointly estimating enrichment weights to derive functionally-informed priors. We evaluate the performance of SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved improved power for fine-mapping with reduced computation time. We demonstrate the utility of SparsePro through fine-mapping of five functional biomarkers of clinically relevant phenotypes. In summary, we have developed an efficient fine-mapping method for integrating summary statistics and functional annotations. Our method can have wide utility in understanding the genetics of complex traits and increasing the yield of functional follow-up studies of GWAS. SparsePro software is available on GitHub at https://github.com/zhwm/SparsePro. 1 Author SummaryAccurately identifying causal variants from genome-wide association studies summary statistics is important for understanding genetic architecture of complex traits and identifying therapeutic targets. Functional annotations are commonly used as additional evidence for prioritizing causal variants. In this study, we present SparsePro to integrate summary statistics and functional annotations for accurate identification of causal variants. SparsePro extends the capabilities of a popular fine-mapping method, SuSiE, with important contributions in hyperparameter estimation, posterior summaries and integration of function annotations. Through extensive simulations, we demonstrate that our proposed approach can effectively integrate summary statistics and functional annotation, leading to improved power for identifying causal variants. Furthermore, we evaluate the benefits of incorporating functional annotations through real data analyses of five functional biomarkers. In summary, by improving power and providing valuable insights into complex disease genetics, SparsePro will have wide utility in advancing our knowledge and facilitating follow-up discoveries.

genetics↗