Biology subjects

Harigaya, Y.

Publications and source records attributed to Harigaya, Y..

5 recordsLinked to original sources

bioRxiv2026

DetectGxT: detecting gene-by-treatment interactions on molecular count phenotypes accounting for allelic additivity

MotivationIdentifying the mechanisms by which genetic variants affect the molecular response to an applied treatment is important across multiple biological fields, and an effective approach to this end is interaction molecular QTL mapping. However, the statistical models commonly used to detect such gene-by-treatment interactions (GxT) are non-trivially misspecified, and this can lead to decreased power. ResultsWe developed an R software package, DetectGxT, that uses nonlinear regression to more accurately model the relationship between the genotype and the transformed molecular count phenotypes. It also optionally models donor or polygenic random effects. Simulations show that nonlinear regression can increase the power to detect interactions. In existing interaction expression QTL mapping data from primary human neural progenitor cells, nonlinear and linear regression approaches identified overlapping but distinct sets of gene-SNP pairs with significant GxT interactions. Overall, our results suggest an advantage of nonlinear regression over linear regression in detecting GxT interactions on molecular phenotypes. AvailabilityThe DetectGxT software is available at https://github.com/yharigaya/detectgxt. Contactmilove@email.unc.edu, william.valdar@unc.edu

genetics↗

bioRxiv2024

Probabilistic classification of gene-by-treatment interactions on molecular count phenotypes

Genetic variation can modulate response to treatment (GxT) or environmental stimuli (GxE), both of which may be highly consequential in biomedicine. An effective approach to identifying GxT signals and gaining insight into molecular mechanisms is mapping quantitative trait loci (QTL) of molecular count phenotypes, such as gene expression and chromatin accessibility, under multiple treatment conditions, which is termed response molecular QTL mapping. Although standard approaches evaluate the interaction between genetics and treatment conditions, they do not distinguish between meaningful interpretations such as whether a genetic effect is only observed in the treated condition or whether a genetic effect is observed but accentuated in the treated condition. To address this gap, we have developed a downstream method for classifying response molecular QTLs into subclasses with meaningful genetic interpretations. Our method uses Bayesian model selection and assigns posterior probabilities to different types of GxT interactions for a given feature-SNP pair. We compare linear and nonlinear regression of log-scale counts, noting that the latter accounts for an expected biological relationship between the genotype and the molecular count phenotype. Through simulation and application to existing datasets of molecular response QTLs, we show that our method provides an intuitive and well-powered framework to report and interpret GxT interactions. We provide a software package, ClassifyGxT, which is available at https://github.com/yharigaya/classifygxt. Author summaryResponses to treatment, such as drug, therapeutic intervention, and infection, can vary across individuals at least in part due to their genetic backgrounds. This phenomenon can be conceptualized as a manifestation of gene-by-treatment (GxT) or gene-by-environment (GxE) interactions, which refer to non-additive effects of genotype and treatment on traits and phenotypes. An understading of GxT or GxE interactions can potentially improve strategies for prevention and treatment of diseases, for example by selecting treatments for which a patient is most likely to respond given their genetic information, or enhanced screening for individuals most susceptible to environmental exposures. An effective approach to GxT discovery is response quantitative trait loci (QTL) mapping, where the effect of the treatment on the association between the genotype and phenotype is examined using a linear regression model including the genotype, treatment, and GxT interaction terms. Despite its effectiveness in identifying a large number of associations, the response QTL mapping relies on hypothesis testing, which does not provide classification of different types of GxT interactions. Herein, we propose a use of Bayesian model selection to classify the GxT types of response QTLs and provide a software package for this method. In addition to standard linear regression, our package provides an option to use nonlinear regression that is suited for molecular count phenotypes, such as gene expression and chromatin accessibility, measured by sequencing-based techniques. It also provides an option to use mixed effect models to accommodate replicate measurements per donor, which are common in data generated in in-vitro cell systems.

genetics↗

bioRxiv2022

Integrated single-cell multiomic analysis of HIV latency reversal reveals novel regulators of viral reactivation.

Despite the success of antiretroviral therapy, HIV cannot be cured because of a reservoir of latently infected cells that evades therapy. To understand the mechanisms of HIV latency, we employed an integrated single-cell RNA-seq/ATAC-seq approach to simultaneously profile the transcriptomic and epigenomic characteristics of ~4000 latently infected cells after reactivation using three different latency-reversing agents (LRAs). Differentially expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor (TF) activities across the cell population. We identify cellular transcripts and TFs whose expression/activity was correlated with viral reactivation and demonstrate that a machine learning model trained on these data was 68% accurate at predicting viral reactivation. Finally, we validate the role of a new candidate HIV-regulating factor, GATA3, in the viral response to prostratin stimulation. These data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.

microbiology↗

bioRxiv2021

Nonparametric Interrogation of Transcriptional Regulation in Single-Cell RNA and Chromatin Accessibility Multiomic Data

Epigenetic control of gene expression is highly cell-type- and context-specific. Yet, despite its complexity, gene regulatory logic can be broken down into modular components consisting of a transcription factor (TF) activating or repressing the expression of a target gene through its binding to a cis-regulatory region. Recent advances in joint profiling of transcription and chromatin accessibility with single-cell resolution offer unprecedented opportunities to interrogate such regulatory logic. Here, we propose a nonparametric approach, TRIPOD, to detect and characterize three-way relationships between a TF, its target gene, and the accessibility of the TFs binding site, using single-cell RNA and ATAC multiomic data. We apply TRIPOD to interrogate cell-type-specific regulatory logic in peripheral blood mononuclear cells and contrast our results to detections from enhancer databases, cis-eQTL studies, ChIP-seq experiments, and TF knockdown/knockout studies. We then apply TRIPOD to mouse embryonic brain data during neurogenesis and gliogenesis and identified known and novel putative regulatory relationships, validated by ChIP-seq and PLAC-seq. Finally, we demonstrate TRIPOD on SHARE-seq data of differentiating mouse hair follicle cells and identify lineage-specific regulation supported by histone marks for gene activation and super-enhancer annotations.

bioinformatics↗

bioRxiv2021

Super interactive promoters provide insight into cell type-specific regulatory networks in blood lineage cell types

Existing studies of chromatin conformation have primarily focused on potential enhancers interacting with gene promoters. By contrast, the interactivity of promoters per se, while equally critical to understanding transcriptional control, has been largely unexplored, particularly in a cell type-specific manner for blood lineage cell types. In this study, we leverage promoter capture Hi-C data across a compendium of blood lineage cell types to identify and characterize cell type-specific super-interactive promoters (SIPs). Notably, promoter-interacting regions (PIRs) of SIPs are more likely to overlap with cell type-specific ATAC-seq peaks and GWAS variants for relevant blood cell traits than PIRs of non-SIPs. Further, SIP genes tend to express at a higher level in the corresponding cell type, and show enriched heritability of relevant blood cell trait(s). Importantly, this analysis shows the potential of using promoter-centric analyses of chromatin spatial organization data to identify biologically important genes and their regulatory regions.

genetics↗