Biology subjects

Gamazon, E. R.

Publications and source records attributed to Gamazon, E. R..

At least 19 recordsLinked to original sources

bioRxiv2026

A tRNA-derived small RNA orchestrates cardioprotection through distinct cell-type-specific mechanisms

Pathological cardiac fibrosis and hypertrophy, key contributors to adverse remodeling that drives heart failure (HF), are not adequately targeted by current therapies. We identified tRNA-Asp-GTC-3'tDR, an ischemia-induced tRNA-derived small RNA (tDR), as an endogenous regulator of both remodeling processes across cardiac fibroblasts and cardiomyocytes. In murine models of myocardial ischemia- and pressure overload-induced HF, cardiac delivery of tRNA-Asp-GTC-3'tDR reduced fibrosis and hypertrophy, preserved cardiac function, and improved survival, with limited three doses conferring durable protection after ischemia, whereas silencing it worsened both. Strikingly, tRNA-Asp-GTC-3'tDR acts through distinct mechanisms in different cell types: in fibroblasts, it assembles stress granules that sequester ribosome-loaded profibrotic transcripts, whereas in cardiomyocytes it engages pseudouridine synthase PUS7 to activate RNA autophagy. Its regulation and functions were confirmed in human cardiac cells and tissues. These findings demonstrate that a single tDR coordinates cell-type-specific protective programs in the heart, suggesting a new therapeutic axis to mitigate HF progression.

cell biology↗

bioRxiv2026

Aging under immunosuppression reshapes human immune compartments and lowers clinical alloreactivity after heart transplantation

Solid-organ transplantation in aging recipients represents a unique opportunity to study how age-related immunity in the context of non-specific immunosuppression strategies balances infection, malignancy, and rejection. Heart transplantation is an exemplar platform, as routine endomyocardial biopsy for rejection surveillance is the clinical "gold standard" regardless of clinical status. Here, we undertook the largest granular study to date to characterize the association between increasing recipient age at heart transplantation with acute allograft rejection and age-related cell-specific transcriptomic changes in circulating immune cells. This single-center retrospective cohort study evaluated individuals undergoing heart transplantation between July 2013 and December 2023 at Vanderbilt University Medical Center. Eligible participants were aged [≥]18 years. A subset of individuals underwent single-cell RNA-sequencing of circulating immune cells. Among 799 adults, each one standard deviation increase in recipient age was associated with a [~]17% lower odds of allograft rejection (adjusted OR 0.83, 95% CI 0.71-0.98). In 40 individuals who underwent single-cell RNA-sequencing of circulating immune cells, increasing recipient age was associated with increases in CD4+ and CD8+ memory T cell subsets, monocytes, and NK cells. Furthermore, genes upregulated with increasing recipient age were associated with enrichment for pathways involved in immunosenescence and chronic low-grade inflammation while downregulated genes suggested decreased protein synthesis. These findings have clinical implications for an aging transplant population and support a more personalized approach to immunosuppression.

systems biology↗

bioRxiv2025

A role of extracellular vesicle-mediated inter-organ communication in obesity-related arrhythmia

Obesity contributes to the risk of cardiac arrhythmias, but the exact mechanism remains unclear. Here, we show visceral adipose tissue-derived extracellular vesicles (VAT EVs) from individuals with obesity prolong action potential duration (APD) and impair calcium handling in stem cell-derived cardiomyocytes, in addition to activating fibroblasts and macrophages towards a pro-fibrotic/inflammatory state, thereby creating pro-arrhythmic substrate. Adipose-derived EVs target the heart in obese mice, suggesting the potential for direct communication. Transcriptome-wide genetic association (TWAS) and epigenetic studies anchored on genes differentially expressed in cardiomyocytes, fibroblasts, and macrophages after VAT-EV exposure identified genes significantly associated with QT interval and atrial fibrillation. Finally, as a proof-of-principle, we pharmacologically blocked TRPC3 (a VAT-EV-induced ion channel) in cardiomyocytes, restoring the APD towards normality. This molecular genetic evidence supports an EV-mediated direct communication pathway between adipose tissue and the heart in arrhythmogenesis, offering a new paradigm to identify mediators of cardiovascular disease in obesity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=192 SRC="FIGDIR/small/676027v1_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@114b230org.highwire.dtl.DTLVardef@1be7926org.highwire.dtl.DTLVardef@3d6b0org.highwire.dtl.DTLVardef@3ef15_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

Cross-ancestry information transfer framework improves protein abundance prediction and protein-trait association identification

Genetics-informed proteome-wide association studies (PWAS) provide an effective way to map the complex molecular landscape of biological mechanisms for complex diseases. PWAS relies on an ancestry-matched reference panel to model protein expression using genetic variants as features and determine the proteins impact on phenotype. However, reference panels from underrepresented populations remain relatively limited. In this study, we developed an analytic framework that borrows information from potentially multiple ancestries to boost the protein abundance prediction accuracy in an underrepresented population. We illustrate the frameworks utility and reproducibility through application to PWAS in East Asians: BioBank Japan (BBJ), Korean Genome and Epidemiology Study (KoGES), and Taiwan Biobank (TWB). An ensemble of information-sharing approaches was integrated to build the Multi-Ancestry-based Best-performing Model (MABM). MABM substantially improved the prediction performance with higher performance observed in both cross-validation and an external validation dataset (Tongji-Huaxi-Shuangliu Birth Cohort). Leveraging the BBJ, we identified three times as many significant PWAS associations with MABM as with the baseline Lasso model. Notably, 47.5% of the MABM specific associations were reproduced in independent East Asian datasets with concordant effect sizes. Furthermore, MABM enhanced gene/protein prioritization for downstream functional validation by (1) confirming a greater number of well-established gene/protein-trait associations and (2) identifying previously uncharacterized trait-associated genes. The benefits of MABM were further validated in additional ancestries and demonstrated in brain tissue-based PWAS, underscoring its broad applicability. Our findings close critical gaps in multi-omics research, develop a new reference resource of genetic models of protein abundance, and facilitate trait-relevant protein discovery in underrepresented populations.

genomics↗

bioRxiv2025

Dynamic responses to rejection in the transplanted human heart revealed through spatial transcriptomics

Allograft rejection following solid-organ transplantation is a major cause of graft dysfunction and mortality. Current approaches to diagnosis rely on histology, which exhibits wide diagnostic variability and lacks access to molecular phenotypes that may stratify therapeutic response. Here, we leverage image-based spatial transcriptomics at sub-cellular resolution in longitudinal human cardiac biopsies to characterize transcriptional heterogeneity in 62 adult and pediatric heart transplant (HT) recipients during and following histologically-diagnosed rejection. Across 28 cell types, we identified significant differences in abundance in CD4+ and CD8+ T cells, fibroblasts, and endothelial cells across different biological classes of rejection (cellular, mixed, antibody-mediated). We observed a broad overlap in cellular transcriptional states across histologic rejection severity and biological class and significant heterogeneity within rejection severity grades that would qualify for immunomodulatory treatment. Individuals who had resolved rejection after therapy had a distinct transcriptomic profile relative to those with persistent rejection, including 216 genes across 6 cell types along pathways of inflammation, IL6-JAK-STAT3 signaling, IFN/IFN{gamma} response, and TNF signaling. Spatial transcriptomics also identified genes linked to long-term prognostic outcomes post-HT. These results underscore importance of subtyping immunologic states during rejection to stratify immune-cardiac interactions following HT that are therapeutically relevant to short- and long-term rejection-related outcomes.

genomics↗

bioRxiv2024

Identifying Patient-Specific Root Causal Genes with High Throughput Perturbations

Root causal gene expression levels - or root causal genes for short - correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high throughput perturbations with single cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.

genomics↗

bioRxiv2023

Integrating population-level and cell-based signatures for drug repositioning

Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Drugs with human genetic evidence are more likely to advance successfully through clinical trials towards FDA approval. Single gene-based drug repositioning methods have been implemented, but approaches leveraging a broad spectrum of molecular signatures remain underexplored. We propose a framework called "TReD" (Transcriptome-informed Reversal Distance) that integrates population-level disease signatures robust to reverse causality and cell-based, drug-induced transcriptome response profiles. TReD embeds the disease signature and drug response profiles in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell-based screening. Here, we implemented this framework to identify potential therapeutics relevant to COVID-19 and type 2 diabetes (T2D). For COVID-19, we identified 36 drugs showing potential reversal roles. Notably, nearly 70% (25/36) of the drugs have been linked to COVID-19 from other studies, with seven drugs supported by ongoing/completed clinical trials. For T2D, we observed reversal signals for 16 compounds on multiple disease signatures. Five drugs are supported by published literature, affirming potential therapeutic value. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screening that has the potential to accelerate the search for new therapeutic strategies.

bioinformatics↗

bioRxiv2023

Minimum entropy framework identifies a novel class of genomic functional elements and reveals regulatory mechanisms at human disease loci

We introduce CoRE-BED, a framework trained using 19 epigenomic features in 33 major cell and tissue types to predict cell-type-specific regulatory function. CoRE-BED identifies nine functional classes de-novo, capturing both known and new regulatory categories. Notably, we describe a previously undercharacterized class that we term Development Associated Elements (DAEs), which are highly enriched in cell types with elevated regenerative potential and distinguished by the dual presence of either H3K4me2 and H3K9ac (an epigenetic signature associated with kinetochore assembly) or H3K79me3 and H4K20me1 (a signature associated with transcriptional pause release). Unlike bivalent promoters, which represent a transitory state between active and silenced promoters, DAEs transition directly to or from a non-functional state during stem cell differentiation and are proximal to highly expressed genes. CoRE-BEDs interpretability facilitates causal inference and functional prioritization. Across 70 complex traits, distal insulators account for the largest mean proportion of SNP heritability ([~]49%) captured by the GWAS. Collectively, our results demonstrate the value of exploring non-conventional ways of regulatory classification that enrich for trait heritability, to complement existing approaches for cis-regulatory prediction.

genomics↗

bioRxiv2022

Integrative genetic analysis identifies FLVCR1 as an essential component of choline transport in mammals

Genome-wide association studies (GWAS) of serum metabolites have the potential to uncover genes that influence human metabolism. Here, we combined an integrative genetic analysis associating serum metabolites to membrane transporters with a coessentiality map of metabolic genes. This analysis revealed a connection between feline leukemia virus subgroup C cellular receptor 1 (FLVCR1) - a plasma membrane protein - and phosphocholine, a downstream metabolite of choline metabolism. Loss of FLVCR1 in human cells and in mice strongly impairs choline metabolism due to a block in choline import. Consistently, CRISPR-based genetic screens identified several components of the membrane phospholipid machinery as synthetic lethal with FLVCR1 loss. Finally, cells lacking FLVCR1 exhibit mitochondrial defects and upregulate the integrated stress response (ISR) through heme regulated inhibitors kinase (HRI). Altogether, these findings identify FLVCR1 as a universal mediator of choline transport in mammals and provide a platform to discover substrates for unknown metabolite transporters.

cell biology↗

bioRxiv2022

Mechanistic Deep Learning Framework on Cell Traits Derived from Single-Cell Mass Cytometry Data

Germline genetic variations can alter cellular differentiation, potentially impacting the response of immune cells to inflammatory challenges. Current variant- and gene-based methods in mouse and human models have established associations with disease phenotypes; however, the underlying mechanisms at the cellular level are less well-understood. Immunophenotyping by multi-parameter flow cytometry, and more recently mass cytometry, has allowed high-resolution identification and characterization of hematopoietic cells. The obtained characterization yields increased dimensionality; however, conventional analysis workflows have been inefficient, incomplete, or unreliable. In this work, we develop a comprehensive machine learning framework - MDL4Cyto - that is tailored to the analysis of mass cytometry data, incorporating statistical, unsupervised learning, and supervised learning models. The statistical modeling can be used to illuminate cell fate decision and cell-type dynamics. The unsupervised learning models along with complementary marker enrichment analyses highlight genetic perturbations that are significantly associated with alterations in cell populations in the hematopoietic system. Furthermore, our supervised learning models, including deep learning and tree-based algorithms, address the bottleneck to data pre-processing that characterizes conventional workflows and generate inferences (e.g., on marker/cell-type interactions) from raw experimental characterization. Notably, we reveal a close relationship among network design, prediction performance, and the underlying biological context. We show that the network architecture extracted from the differentiation cascade of the investigated biological system yields enhanced prediction performance. The presented methodology will enable new insights into hematopoietic differentiation at baseline and following perturbation. HighlightsO_LIAnalysis pipeline on mass cytometry data with high-performance implementation of statistical, unsupervised learning, and supervised learning models C_LIO_LIConcordance of machine learning results with biological contexts C_LIO_LIBiologically-informed neural network designs enhance prediction performance C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/504669v3_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@10e35cborg.highwire.dtl.DTLVardef@1edc8ecorg.highwire.dtl.DTLVardef@220726org.highwire.dtl.DTLVardef@35916_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Towards mechanistic models of mutational effects: Deep Learning on Alzheimer's Aβ peptide

Alzheimers Disease (AD) is a debilitating form of dementia with a high prevalence in the global population and a large burden on the community and health care systems. ADs complex pathobiology consists of extracellular {beta}-amyloid deposition and intracellular hyperphosphorylated tau. Comprehensive mutational analyses can generate a wealth of knowledge about protein properties and enable crucial insights into molecular mechanisms of disease. Deep Mutational Scanning (DMS) has enabled multiplexed measurement of mutational effects on protein properties, including kinematics and self-organization, with unprecedented resolution. However, potential bottlenecks of DMS characterization include experimental design, data quality, and the depth of mutational coverage. Here, we apply Deep Learning to comprehensively model the mutational effect of the AD-associated peptide A{beta}42 on aggregation-related biochemical traits from DMS measurements. Among tested neural network architectures, Convolutional Neural Networks (ConvNets) and Recurrent Neural Networks (RNN) are found to be the most cost-effective models with robust high performance even under insufficiently-sampled DMS studies. While sequence features are essential for satisfactory prediction from neural networks, geometric-structural features further enhance the prediction performance. Notably, we demonstrate how mechanistic insights into phenotype may be extracted from the neural networks themselves suitably designed. This methodological benefit is particularly relevant for biochemical systems displaying a strong coupling between structure and phenotype such as the conformation of A{beta}42 aggregate and nucleation, as shown here using a Graph Convolutional Neural Network (GCN) developed from the protein atomic structure input. In addition to accurate imputation of missing values (which here ranged up to 55% of all phenotype values at key residues), the mutationally-defined nucleation phenotype generated from a GCN shows improved resolution for identifying known disease-causing mutations relative to the original DMS phenotype. Our study suggests that neural network derived sequence-phenotype mapping can be exploited not only to provide direct support for protein engineering or genome editing but also to facilitate therapeutic design with the gained perspectives from biological modeling.

bioinformatics↗

bioRxiv2021

SLC25A39 is necessary for mitochondrial glutathione import in mammalian cells

Glutathione (GSH) is a small molecule thiol abundantly present in all eukaryotes with key roles in oxidative metabolism1. Mitochondria, as the major site of oxidative reactions, must maintain sufficient levels of GSH to perform protective and biosynthetic functions2. GSH is exclusively synthesized in the cytosol, yet the molecular machinery involved in mitochondrial GSH import remain elusive. Here, using organellar proteomics and metabolomics approaches, we identify SLC25A39, a mitochondrial membrane carrier of unknown function, to regulate GSH transport into mitochondria. SLC25A39 loss reduces mitochondrial GSH import and abundance without impacting whole cell GSH levels. Cells lacking both SLC25A39 and its paralog SLC25A40 exhibit defects in the activity and stability of ironsulfur cluster containing proteins. Moreover, mitochondrial GSH import is necessary for cell proliferation in vitro and red blood cell development in mice. Remarkably, the heterologous expression of an engineered bifunctional bacterial GSH biosynthetic enzyme (GshF) in mitochondria enabled mitochondrial GSH production and ameliorated the metabolic and proliferative defects caused by its depletion. Finally, GSH availability negatively regulates SLC25A39 protein abundance, coupling redox homeostasis to mitochondrial GSH import in mammalian cells. Our work identifies SLC25A39 as an essential and regulated component of the mitochondrial GSH import machinery.

biochemistry↗

bioRxiv2021

Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2

Biochemical phenotypes are major indexes for protein structure and function characterization. They are determined, at least in part, by the intrinsic physicochemical properties of amino acids and may be reflected in the protein three-dimensional structure. Modeling mutational effects on biochemical phenotypes is a critical step for understanding protein function and disease mechanism as well as enabling drug discovery. Deep Mutational Scanning (DMS) experiments have been performed on SARS-CoV-2s spike receptor binding domain and the human ACE2 zinc-binding peptidase domain - both central players in viral infection and evolution and antibody evasion - quantifying how mutations impact binding affinity and protein expression. Here, we modeled biochemical phenotypes from massively parallel assays, using convolutional neural networks trained on protein sequence mutations in the virus and human host. We found that neural networks are significantly predictive of binding affinity, protein expression, and antibody escape, learning complex interactions and higher-order features that are difficult to capture with conventional methods from structural biology. Integrating the intrinsic physicochemical properties of amino acids, including hydrophobicity, solvent-accessible surface area, and long-range non-bonded energy per atom, significantly improved prediction (empirical p<0.01) though there was such a strong dependence on the sequence data alone to yield reasonably good prediction. We observed concordance of the DMS data and our neural network predictions with an independent study on intermolecular interactions from molecular dynamics (multiple 500 ns or 1 s all-atom) simulations of the spike protein-ACE2 interface, with critical implications for the use of deep learning to dissect molecular mechanisms. The mutation- or genetically-determined component of a biochemical phenotype estimated from the neural networks has improved causal inference properties relative to the original phenotype and can facilitate crucial insights into disease pathophysiology and therapeutic design.

bioinformatics↗

bioRxiv2020

Analysis of Genetically Determined Gene Expression Suggests Role of Inflammatory Processes in Exfoliation Syndrome

Exfoliation syndrome (XFS) is an age-related systemic disorder characterized by excessive production and progressive accumulation of abnormal extracellular material, with pathognomonic ocular manifestations. It is the most common cause of secondary glaucoma, resulting in widespread global blindness. We performed Transcriptomic Wide Association Studies (TWAS) using PrediXcan models trained in 48 GTEx tissues to identify genetically- determined gene expression changes associated with XFS risk, leveraging on results from a global GWAS that included 123,457 individuals from 24 countries. We observed twenty-eight genes in a three-Megabase chr15q22-25 region that showed statistically significant associations, which were further whittled down to ten genes after additional statistical validations. In experimental analysist of these ten genes, mRNA transcript levels for ARID3B, CD276, LOXL1, NEO1, SCAMP2, and UBL7 were significantly decreased in iris tissues from XFS patients compared to control samples. Genes with genetically determined expression changes in XFS were significantly enriched for genes associated with inflammatory conditions. We further explored the health consequences of high susceptibility to XFS using a large electronic health record and observed a higher incidence of XFS comorbidity with inflammatory and connective tissue diseases. Our results implicate a role for connective tissues and inflammation in the etiology of XFS. Targeting the inflammatory pathway may be a potential therapeutic option to reduce progression in XFS.

genetics↗

bioRxiv2020

Multilayer modelling and analysis of the humantranscriptome

Here, we performed a comprehensive intra-tissue and inter-tissue network analysis of the human transcriptome. We generated an atlas of communities in co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters. Methodologically, integration of the communities into a transcriptome-wide association study of C-reactive protein (CRP) in 361,194 individuals in the UK Biobank identified genetically-determined expression changes associated with CRP and led to considerably improved performance. Furthermore, a deep learning framework applied to the communities in nearly 11,000 tumours profiled by The Cancer Genome Atlas across 33 different cancer types learned biologically-meaningful latent spaces, representing metastasis (p < 2.2 x 10-16) and stemness (p < 2.2 x 10-16). Our study provides a rich genomic resource to catalyse research into inter-tissue regulatory mechanisms and their downstream phenotypic consequences.

genomics↗

bioRxiv2019

An Integrative Network-based Analysis Reveals Gene Networks, Biological Mechanisms, and Novel Drug Targets in Alzheimer's Disease

Alzheimers disease is a highly heritable and severe neuropsychiatric condition. Genome-wide association studies have identified multiple genetic risk factors underlying susceptibility to Alzheimers disease, however their functional impact remains poorly understood. To overcome this shortcoming, we integrated genome-wide association summary statistics (71,880 cases, 338,378 controls) with tissue-specific gene co-expression networks derived from GTEx to identify functional gene co-expression networks underlying the disease. We found genetic variants associated with Alzheimers disease are enriched in gene co-expression networks involved in immune response-related biological processes. The implicated gene co-expression networks are preserved across multiple brain and peripheral tissues, highlighting the potential utility of peripheral tissues in genetic studies of Alzheimers disease. We also performed a computational drug repositioning analysis by integrating gene expression changes within Alzheimers disease modules with drug-gene signatures from the Connectivity Map, and show disease implicated networks retrieve known Alzheimers disease drugs and novel drug repurposing candidates for follow-up functional studies. Our results improve the biological interpretation of recent genetic data for Alzheimers disease and provide a list of potential anti-dementia drug repositioning candidates of which the efficacy should be investigated in functional validation studies.

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↗

bioRxiv2018

On Using Local Ancestry to Characterize the Genetic Architecture of Human Phenotypes: Genetic Regulation of Gene Expression in Multiethnic or Admixed Populations as a Model

BackgroundUnderstanding the nature of the genetic regulation of gene expression promises to advance our understanding of the genetic basis of disease. However, the methodological impact of use of local ancestry on high-dimensional omics analyses, including most prominently expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admixed populations remains critically underexplored.\n\nResultsHere we develop a statistical framework that characterizes the relationships among the determinants of the genetic architecture of an important class of molecular traits. We estimate the trait variance explained by ancestry using local admixture relatedness between individuals. Using National Institute of General Medical Sciences (NIGMS) and Genotype-Tissue Expression (GTEx) datasets, we show that use of local ancestry can substantially improve eQTL mapping and heritability estimation and characterize the sparse versus polygenic component of gene expression in admixed and multiethnic populations respectively. Using simulations of diverse genetic architectures to estimate trait heritability and the level of confounding, we show improved accuracy given individual-level data and evaluate a summary statistics based approach. Furthermore, we provide a computationally efficient approach to local ancestry analysis in eQTL mapping while increasing control of type I and type II error over traditional approaches.\n\nConclusionOur study has important methodological implications on genetic analysis of omics traits across a range of genomic contexts, from a single variant to a prioritized region to the entire genome. Our findings highlight the importance of using local ancestry to better characterize the heritability of complex traits and to more accurately map genetic associations.

bioinformatics↗