Biology subjects

Gloyn, A. L.

Publications and source records attributed to Gloyn, A. L..

At least 19 recordsLinked to original sources

bioRxiv2026

PanKbase Integrated Single-Cell Map: A Comprehensive Atlas of Human Pancreatic Islets

Single-cell RNA sequencing (scRNA-seq) of human pancreatic islet tissue is a powerful tool for investigating type 1 diabetes (T1D). However, individual datasets are limited in size and fragmented across donors, laboratories, and experimental conditions. To address this, we constructed a comprehensive, integrated scRNA-seq atlas of isolated human pancreatic islets by collating publicly available data generated from tissue provided by resources including the Human Pancreas Analysis Program, the Integrated Islet Distribution Program, and Prodo Labs. Systematic quality controls were implemented to select high-quality samples, reads, and cells. During integration, we accounted for important variables such as age, sex, body mass index, origin study, treatments, islet distribution resources, and sequencing chemistry. Our single-cell atlas comprises 191 high-quality samples from 140 donors (59 female, 81 male) across five phenotypic groups: no diabetes (controls, n=69), autoantibody positivity without diabetes (n=12), pre-diabetes (n=11), T1D (n=12), and type 2 diabetes (T2D) (n=36). In total, the atlas contains 448,935 cells, capturing 13 distinct populations, including alpha cells (43.3%) and beta cells (26.8%), as well as groups such as immune cells (0.6%). Publicly available at www.pankbase.org, this atlas provides a platform for hypothesis-driven investigation of diabetes pathophysiology and, given rigorous quality control, is well-suited for downstream machine-learning applications. Article HighlightsO_LICurrent scRNA-seq datasets of pancreatic islet tissue are limited in size and scattered across donors, laboratories, and experimental conditions, underscoring the need for a consolidated resource. C_LIO_LIWe harmonized datasets from multiple sources to build a comprehensive single-cell map of isolated human pancreatic islets. C_LIO_LIOur atlas captures 448,935 cells from 191 high-quality samples across 140 donors and multiple phenotypic groups, identifying 13 distinct cell populations. C_LIO_LIAvailable at www.pankbase.org, the atlas provides a scalable, rigorously curated platform to support hypothesis-driven diabetes research and can enable a broad range of downstream computational applications. C_LI

genomics↗

bioRxiv2025

Cell type-specific eQTL detection from single-cell RNA-seq reveals post-transcriptional regulatory mechanisms in human islets

Gene regulatory networks (GRNs) must be robust to maintain cellular identity across individuals yet flexible to accommodate population genetic variants. Comparing expression quantitative trait loci (eQTLs) in health and disease can therefore reveal hidden players in gene regulation. Here, we developed a computational pipeline to infer post-transcriptional regulatory events and their functional consequences from eQTL signals located in the 3' untranslated region of genes using single-cell RNA sequencing. As a case study, we repurposed datasets from human islets of donors with and without type 2 diabetes (T2D). We identified eQTL landscapes differing by cell type and diabetes status, with cell-type specificity associated with gene differential expression and RNA-binding proteins. Integrating eQTLs with GWAS and miRNA hits linked variants in G6PC2, QDPR, and RELL1 to insulin secretion, endoplasmic reticulum stress, and oxidative phosphorylation. Our pipeline provides a framework to unravel post-transcriptional regulatory mechanisms in health and disease at cell type resolution.

genetics↗

bioRxiv2024

Heterogeneous endocrine cell composition defines human islet functional phenotypes

Phenotyping and genotyping initiatives within the Integrated Islet Distribution Program (IIDP), the largest source of human islets for research in the U.S., provide standardized assessment of islet preparations distributed to researchers, enabling the integration of multiple data types. Data from islets of the first 299 organ donors without diabetes, analyzed using this pipeline, highlights substantial heterogeneity in islet cell composition associated with hormone secretory traits, sex, reported race and ethnicity, genetically predicted ancestry, and genetic risk for type 2 diabetes (T2D). While and {beta} cell composition influenced insulin and glucagon secretory traits, the abundance of {delta} cells showed the strongest association with insulin secretion and was also associated with the genetic risk score (GRS) for T2D. These findings have important implications for understanding mechanisms underlying diabetes heterogeneity and islet dysfunction and may provide insight into strategies for personalized medicine and {beta} cell replacement therapy.

physiology↗

bioRxiv2024

Single cell multiome profiling of pancreatic islets reveals physiological changes in cell type-specific regulation associated with diabetes risk

Physiological variability in pancreatic cell type gene regulation and the impact on diabetes risk is poorly understood. In this study we mapped gene regulation in pancreatic cell types using single cell multiomic (joint RNA-seq and ATAC-seq) profiling in 28 non-diabetic donors in combination with single cell data from 35 non-diabetic donors in the Human Pancreas Analysis Program. We identified widespread associations with age, sex, BMI, and HbA1c, where gene regulatory responses were highly cell type- and phenotype-specific. In beta cells, donor age associated with hypoxia, apoptosis, unfolded protein response, and external signal-dependent transcriptional regulators, while HbA1c associated with inflammatory responses and gender with chromatin organization. We identified 10.8K loci where genetic variants were QTLs for cis regulatory element (cRE) accessibility, including 20% with lineage- or cell type-specific effects which disrupted distinct transcription factor motifs. Type 2 diabetes and glycemic trait associated variants were enriched in both phenotype- and QTL-associated beta cell cREs, whereas type 1 diabetes showed limited enrichment. Variants at 226 diabetes and glycemic trait loci were QTLs in beta and other cell types, including 40 that were statistically colocalized, and annotating target genes of colocalized QTLs revealed genes with putatively novel roles in disease. Our findings reveal diverse responses of pancreatic cell types to phenotype and genotype in physiology, and identify pathways, networks, and genes through which physiology impacts diabetes risk.

genomics↗

bioRxiv2024

Loss of RREB1 reduces adipogenesis and improves insulin sensitivity in mouse and human adipocytes

There are multiple independent genetic signals at the Ras-responsive element binding protein 1 (RREB1) locus associated with type 2 diabetes risk, fasting glucose, ectopic fat, height, and bone mineral density. We have previously shown that loss of RREB1 in pancreatic beta cells reduces insulin content and impairs islet cell development and function. However, RREB1 is a widely expressed transcription factor and the metabolic impact of RREB1 loss in vivo remains unknown. Here, we show that male and female global heterozygous knockout (Rreb1+/-) mice have reduced body length, weight, and fat mass on high-fat diet. Rreb1+/- mice have sex- and diet-specific decreases in adipose tissue and adipocyte size; male mice on high-fat diet had larger gonadal adipocytes, while males on standard chow and females on high-fat diet had smaller, more insulin sensitive subcutaneous adipocytes. Mouse and human precursor cells lacking RREB1 have decreased adipogenic gene expression and activated transcription of genes associated with osteoblast differentiation, which was associated with Rreb1+/- mice having increased bone mineral density in vivo. Finally, human carriers of RREB1 T2D protective alleles have smaller adipocytes, consistent with RREB1 loss-of-function reducing diabetes risk.

physiology↗

bioRxiv2024

HumanIslets: An integrated platform for human islet data access and analysis

Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.

systems biology↗

bioRxiv2023

Electrophysiological characterisation of iPSC-derived human β-like cells and an SLC30A8 disease model.

iPSC-derived human {beta}-like cells (BLC) hold promise for both therapy and disease modelling, but their generation remains challenging and their functional analyses beyond transcriptomic and morphological assessments remain limited. Here, we validate an approach using multicellular and single cell electrophysiological tools to evaluate BLCs functions. The Multi-Electrode Arrays (MEAs) measuring the extracellular electrical activity revealed that BLCs are electrically coupled, produce slow potential (SP) signals like primary {beta}-cells that are closely linked to insulin secretion. We also used high-resolution single-cell patch-clamp measurements to capture the exocytotic properties, and characterize voltage-gated sodium and calcium currents. These were comparable to those in primary {beta} and EndoC-{beta}H1 cells. The KATP channel conductance is greater than in human primary {beta} cells which may account for the limited glucose responsiveness observed with MEA. We used MEAs to study the impact of the type 2 diabetes protective SLC30A8 allele (p.Lys34Serfs*50) and found that BLCs with this allele have stronger electrical coupling. Our data suggest that with an adapted approach BLCs from pioneer protocol can be used to evaluate the functional impact of genetic variants on {beta}-cell function and coupling. Article highlightsO_ST_ABSWhy did we undertake this study?C_ST_ABSiPSC-derived beta like cells (BLCs) from pioneering protocols are known for variable {beta}-cell functionality and mixed cell populations which greatly limits downstream functional assessment. To overcome this challenge, we used electrophysiological tools to provide a detailed functional assessment of BLCs. We then wanted to apply this approach to identify additional functional differences from BLCs carrying a protective Type 2 Diabetes SLC30A8 allele. What is the specific question(s) we wanted to answer?Can an electrophysiological approach provide detailed functional characterisation of iPSC-derived BLCs? Is this approach sensitive enough to capture functional differences resulting from SLC30A8 loss of function (lof)? What did we find?We found that BLCs generated from pioneer protocol shared electrophysiological features with human pancreatic {beta}-cells, and that a T2D-protective SLC30A8 lof allele improves the electrical coupling activity of human {beta}-cells. What are the implications of our findings?Our findings validate the use of intra- and extra-cellular electrophysiology to assess and monitor the functions of BLCs. Our approach opens the perspective of using MEAs to live-monitor the differentiation quality of iPSC-derived BLCs and to determine the functional consequences of diabetes-associated variants.

cell biology↗

bioRxiv2023

Multiplexed CRISPR gene editing in primary human islet cells with Cas9 ribonucleoprotein

Successful genome editing in primary human islets could reveal features of the genetic regulatory landscape underlying {beta} cell function and diabetes risk. Here, we describe a CRISPR-based strategy to interrogate functions of predicted regulatory DNA elements using electroporation of a complex of Cas9 ribonucleoprotein (Cas9 RNP) and guide RNAs into primary human islet cells. We successfully targeted coding regions including the PDX1 exon 1, and non-coding DNA linked to diabetes susceptibility. CRISPR/Cas9 RNP approaches revealed genetic targets of regulation by DNA elements containing candidate diabetes risk SNPs, including an in vivo enhancer of the MPHOSPH9 gene. CRISPR/Cas9 RNP multiplexed targeting of two cis-regulatory elements linked to diabetes risk in PCSK1, which encodes an endoprotease crucial for insulin processing, also demonstrated efficient simultaneous editing of PCSK1 regulatory elements, resulting in impaired {beta} cell PCSK1 regulation and insulin secretion. Multiplex CRISPR/Cas9 RNP provides powerful approaches to investigate and elucidate human islet cell gene regulation in health and diabetes.

cell biology↗

bioRxiv2022

Inferring causal genes at type 2 diabetes GWAS loci through chromosome interactions in islet cells

Resolving causal genes for type 2 diabetes at loci implicated by genome-wide association studies (GWAS) requires integrating functional genomic data from relevant cell types. Chromatin features in endocrine cells of the pancreatic islet are particularly informative and recent studies leveraging chromosome conformation capture (3C) with Hi-C based methods have elucidated regulatory mechanisms in human islets. However, these genome-wide approaches are less sensitive and afford lower resolution than methods that target specific loci. To gauge the extent to which targeted 3C further resolves chromatin-mediated regulatory mechanisms at GWAS loci, we generated interaction profiles at 23 loci using next-generation (NG) Capture-C in a human beta cell model (EndoC-{beta}H1) and contrasted these maps with Hi-C maps in EndoC-{beta}H1 cells and human islets and a promoter capture Hi-C map in human islets. We found improvements in assay sensitivity of up to 33-fold and resolved 4.8X more chromatin interactions. At a subset of 18 loci with 25 co-localised GWAS and eQTL signals, NG Capture-C interactions implicated effector transcripts at five additional genetic signals relative to promoter capture Hi-C through physical contact with gene promoters. Therefore, high resolution chromatin interaction profiles at selectively targeted loci can complement genome- and promoter-wide maps.

genetics↗

bioRxiv2022

Loss of RREB1 in pancreatic beta cells reduces cellular insulin content and affects endocrine cell gene expression

Aims/hypothesisGenome-wide studies have uncovered multiple independent signals at the RREB1 locus associated with altered type 2 diabetes risk and related glycemic traits. However, little is known about the function of the zinc finger transcription factor RREB1 in glucose homeostasis or how changes in its expression and/or function influence diabetes risk. MethodsA zebrafish model lacking rreb1a and rreb1b was used to study the effect of RREB1 loss in vivo. Using transcriptomic and cellular phenotyping of a human beta cell model (EndoC-{beta}H1) and human induced pluripotent stem cell (hiPSC)-derived beta-like cells, we investigated how loss of RREB1 expression and activity affects pancreatic endocrine cell development and function. Ex vivo measurements of human islet function were performed in donor islets from carriers of RREB1 T2D-risk alleles. ResultsCRISPR-Cas9-mediated loss of rreb1a and rreb1b function in zebrafish supports an in vivo role for the transcription factor in beta cell mass, beta cell insulin expression, and glucose levels. Loss of RREB1 reduced insulin gene expression and cellular insulin content in EndoC-{beta}H1 cells, and impaired insulin secretion under prolonged stimulation. Transcriptomic analysis of RREB1 knockdown and knockout EndoC-{beta}H1 cells supports RREB1 as a novel regulator of genes involved in insulin secretion. In vitro differentiation of RREB1KO/KO hiPSCs revealed a dysregulation of pro-endocrine cell genes, including RFX family members, suggesting that RREB1 also regulates genes involved in endocrine cell development. Human donor islets from carriers of T2D-risk alleles in RREB1 have altered glucose-stimulated insulin secretion ex vivo, consistent with RREB1 regulating islet cell function. Conclusions/interpretationTogether, our results indicate that RREB1 regulates beta cell function by transcriptionally regulating the expression of genes involved in beta cell development and function. Research in contextO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIHuman genetic variation in RREB1 is associated with altered diabetes risk, variation in glycemic, and anthropometric traits C_LIO_LIRREB1 is a transcription factor that binds to Ras-responsive elements and is expressed in multiple diabetes relevant tissues, including pancreatic islets C_LI What is the key question?O_LIHow does altered expression or function of RREB1 influence diabetes risk? C_LI What are the new findings?O_LIKnockdown and knockout of RREB1 in mature human EndoC-{beta}H1 cells reduces expression of insulin transcript and cellular content, as well as insulin secretion under prolonged stress C_LIO_LICarriers of the T2D-risk RREB1 coding allele trend towards reduced insulin content, but have improved glucose-stimulated insulin secretion C_LIO_LIA loss-of-function zebrafish model suggests that RREB1 is required for insulin expression C_LI How might this impact on clinical practice in the foreseeable future?O_LIRREB1 controls beta cell function and whole-body glucose homeostasis by transcriptionally regulating the development and function of pancreatic beta cells C_LI

physiology↗

bioRxiv2022

Zmiz1 is required for mature β-cell function and mass expansion upon high fat feeding

Genome-wide association studies have identified hundreds of signals for type 2 diabetes (T2D), most of which confer risk through effects on gene expression. We previously identified the transcription factor ZMIZ1 as a probable effector transcript in human islets, but how altered ZMIZ1 expression impacts T2D risk is unknown. We now show that islets from carriers of the T2D-risk alleles have reduced islet insulin content and glucose-stimulated insulin secretion. To elucidate the mechanism for islet-cell dysfunction, we generated {beta}-cell-specific Zmiz1 knockout (Zmiz1{beta}KO) mice. Male and female Zmiz1{beta}KO mice were glucose intolerant with impaired insulin secretion, compared with control littermates. Transcriptomic profiling of Zmiz1{beta}KO islets identified over 500 differentially expressed genes including those involved in {beta}-cell function and maturity which we confirmed at the protein level. After high fat feeding, Zmiz1{beta}KO mice fail to expand {beta}-cell mass and become severely diabetic. Thus, Zmiz1 is required for normal glucose homeostasis and may contribute to T2D risk by maintaining a mature {beta}-cell state and allowing islet mass expansion upon metabolic stress.

physiology↗

bioRxiv2022

PAX4 loss of function alters human endocrine cell development and influences diabetes risk

Diabetes is a major chronic disease with an excessive healthcare burden on society1. A coding variant (p.Arg192His) in the transcription factor PAX4 is uniquely and reproducibly associated with an altered risk for type 2 diabetes (T2D) in East Asian populations2-7, whilst rare PAX4 alleles have been proposed to cause monogenic diabetes8. In mice, Pax4 is essential for beta cell formation but neither the role of diabetes-associated variants in PAX4 nor PAX4 itself on human beta cell development and/or function are known. Here, we demonstrate that non-diabetic carriers of either the PAX4 p.Arg192His or a newly identified p.Tyr186X allele exhibit decreased pancreatic beta cell function. In the human beta cell model, EndoC-{beta}H1, PAX4 knockdown led to impaired insulin secretion, reduced total insulin content, and altered hormone gene expression. Deletion of PAX4 in isogenic human induced pluripotent stem cell (hiPSC)-derived beta-like cells resulted in derepression of alpha cell gene expression whilst in vitro differentiation of hiPSCs from carriers of PAX4 p.His192 and p.X186 alleles exhibited increased polyhormonal endocrine cell formation and reduced insulin content. In silico and in vitro studies showed that these PAX4 alleles cause either reduced PAX4 expression or function. Correction of the diabetes-associated PAX4 alleles reversed these phenotypic changes. Together, we demonstrate the role of PAX4 in human endocrine cell development, beta cell function, and its contribution to T2D-risk.

developmental biology↗

bioRxiv2022

A multiplexed assay of human glucokinase reveals thousands of potential disease variants with both decreased and increased activity

Glucokinase (GCK) regulates insulin secretion to maintain appropriate blood glucose levels. Sequence variants can alter GCK activity to cause hyperinsulinemic hypoglycemia (HH) or hyperglycemia associated with GCK-maturity-onset diabetes of the young (GCK-MODY), collectively affecting up to 10 million people worldwide. Patients with GCK-MODY are frequently misdiagnosed and treated unnecessarily. Genetic testing can prevent this but is hampered by the challenge of interpreting novel missense variants. Here we exploited a multiplexed yeast complementation assay to measure both hyper- and hypoactive GCK variation, capturing 97% of all possible missense and nonsense variants. Activity scores correlated with in vitro catalytic efficiency, fasting glucose levels in carriers of GCK variants and with evolutionary conservation. Hypoactive variants were concentrated at buried positions, near the active site, and at a region of known importance for GCK conformational dynamics. Some hyperactive variants shifted the conformational equilibrium towards the active state through a relative destabilization of the inactive conformation. Our comprehensive assessment of GCK variant activity promises to facilitate variant interpretation and diagnosis, expand our mechanistic understanding of hyperactive variants, and inform development of therapeutics targeting GCK.

genetics↗

bioRxiv2022

Single-Cell Multi-Omic Roadmap of Human Fetal Pancreatic Development

The critical cellular transitions that govern human pancreas development are largely unknown. We performed large-scale single-cell RNA-sequencing (scRNA-Seq) to interrogate human fetal pancreas development from 8-20 weeks post conception. We identified 103 distinct cell types, including four novel endocrine progenitor subtypes displaying unique transcriptional features and differentiation potency. Integration with single-nucleus Assay for Transposase Accessible Chromatin Sequencing (snATAC-Seq) identified candidate regulators of human endocrine cell fate and revealed development-specific regulatory annotation at diabetes risk loci. Comparison of in vitro stem cell-derived and endogenous endocrine cells predicted aberrant genetic programs leading to the generation of off-target cells. Finally, knock-out studies revealed that the gene FEV regulates human endocrine differentiation. This work establishes a roadmap of human pancreatic development, highlights previously unappreciated cellular diversity and lineage dynamics, and provides a blueprint for understanding pancreatic disease and physiology, as well as generating human stem cell-derived islet cells in vitro for regenerative medicine purposes.

developmental biology↗

bioRxiv2021

Genetic regulation of RNA splicing in human pancreatic islets

Genetic variants that influence transcriptional regulation in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D). For many susceptibility loci, however, the mechanisms are unknown. We examined splicing QTLs (sQTLs) in islets from 399 donors and observed that genetic variation has a widespread influence on splicing of genes with important functions in islet biology. In parallel, we profiled expression QTLs, and used transcriptome-wide association and co-localization studies to assign islet sQTLs or eQTLs to T2D susceptibility signals that lacked candidate effector genes. We found novel T2D associations, including an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effectors revealed overrepresented pathways, including regulators of G-protein-mediated cAMP production. This data exposes an underappreciated layer of genetic regulation in pancreatic islets, and nominates molecular mediators of T2D susceptibility.

genetics↗

bioRxiv2021

A genome-wide CRISPR screen identifies regulators of beta cell function involved in type 2 diabetes risk

Identification of the genes and processes mediating genetic association signals for complex disease represents a major challenge. Since many of the genetic signals for type 2 diabetes exert their effects through pancreatic islet-cell dysfunction, we performed a genome-wide pooled CRISPR loss-of- function screen in human pancreatic beta cells. We focused on the regulation of insulin content as a disease-relevant readout of beta cell function. We identified 580 genes influencing this phenotype: integration with genetic and genomic data provided experimental support for 20 candidate type 2 diabetes effector transcripts including the autophagy receptor CALCOCO2. Our study highlights how cellular screens can augment existing multi-omic efforts to accelerate biological and translational inference at GWAS loci.

genomics↗

bioRxiv2021

TIGER: The gene expression regulatory variation landscape of human pancreatic islets

GWAS have identified more than 700 genetic signals associated with type 2 diabetes (T2D). To gain insight into the underlying molecular mechanisms, we created the Translational human pancreatic Islet Genotype tissue-Expression Resource (TIGER), aggregating >500 human islet RNA-seq and genotyping datasets. We imputed genotypes using 4 reference panels and meta-analyzed cohorts to improve coverage of expression quantitative trait loci (eQTL) and developed a method to combine allele-specific expression across samples (cASE). We identified >1 million islet eQTLs (56% novel), of which 53 colocalize with T2D signals (60% novel). Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identified 8 novel cASE colocalizations, among which an SLC30A8 T2D associated variant. We make all the data available through the open-access TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translate this into therapeutic insight and precision medicine for T2D.

bioinformatics↗

bioRxiv2020

A multi-omic integrative scheme characterizes tissues of action at loci associated with type 2 diabetes

Resolving the molecular processes that mediate genetic risk remains a challenge as most disease-associated variants are non-coding and functional and bioinformatic characterization of these signals requires knowledge of the specific tissues and cell-types in which they operate. To address this challenge, we developed a framework for integrating tissue-specific gene expression and epigenomic maps (primarily from tissues involved in insulin secretion and action) to obtain tissue-of-action (TOA) scores for each association signal by systematically partitioning posterior probabilities from Bayesian fine-mapping. We applied this scheme to credible set variants for 380 association signals from a recent GWAS meta-analysis of type 2 diabetes (T2D) in Europeans. The resulting tissue profiles underscored a predominant role for pancreatic islets and, to a lesser extent, subcutaneous adipose and liver, that was largely attributable to enhancer elements and transcribed regions, particularly among signals with greater fine-mapping resolution. We incorporated resulting TOA scores into a rule-based classifier, and validated the tissue assignments through comparison with data from cis-eQTL enrichment, functional fine-mapping, RNA co-expression, and patterns of physiological association. In addition to implicating signals with a single tissue-of-action, we also found evidence for signals with shared effects in multiple tissues as well as distinct tissue profiles between independent signals within heterogeneous loci. Lastly, we demonstrated that TOA scores can be directly coupled with eQTL colocalization to further resolve effector transcripts at T2D signals. This framework guides mechanistic inference by directing functional validation studies to the most relevant tissues and can gain power as fine-mapping resolution and cell-specific annotations become richer. This method is generalizable to all complex traits with relevant annotation data and is made available as an R package.

genetics↗