Biology subjects

Mohlke, K. L.

Publications and source records attributed to Mohlke, K. L..

15 recordsLinked to original sources

bioRxiv2026

Integrated Analysis of Skeletal Muscle Transcriptional Networks Characterizes Dysregulation in Pathways and Trait-Associated Regulatory Regions in Type 2 Diabetes

Skeletal muscle, a primary site of insulin-mediated glucose uptake, plays a central role in the pathogenesis of type 2 diabetes. It is therefore critical to understand the disease-associated alterations in skeletal muscle and identify the underlying drivers of this dysregulation. Here, we characterize type 2 diabetes associated transcriptional dysregulation using 301 skeletal muscle biopsies from living donors with and without diabetes. Using weighted gene co-expression network analysis, we identify 56 distinct gene modules, which we further characterize using single-nucleus RNA-seq-derived cell type signatures and pathway enrichment analysis. We identify numerous cell type-associated dysregulated pathways in skeletal muscle tissue from individuals with diabetes, including muscle fiber-associated mitochondrial function and mRNA splicing and processing; endothelial vascularization and phospholipase D signaling; and macrophage- and T-cell-associated inflammation. Through analysis of module hub genes and transcription factor regulatory network analysis, we further identify candidate driver genes of this dysregulation including ATP5L, ATF2, SIRT1, and THRAP3 in muscle fibers; JAM2 and CLEC14A in endothelial cells; and F13A1 and IRF8 in immune cells. Finally, we integrate our co-expression networks with single-nucleus ATAC-seq data to identify proximal and distal genomic regulatory elements and identify context-specific enrichment for type 2 diabetes and related trait GWAS signals in muscle fiber and endothelial modules. Together, our results reveal dysregulation in pathways in muscle tissue from individuals with diabetes, identify candidate drivers, and connect the genomic drivers of this dysregulation across type 2 diabetes and related metabolic traits.

bioinformatics↗

bioRxiv2025

Opposite directions of association of higher physical activity and higher insulin resistance with human skeletal muscle cell type abundance and fiber type-level gene expression

To investigate the interplay between physical activity and cardiometabolic traits in human skeletal muscle, we characterized gene expression and chromatin accessibility across skeletal muscle cell types in 263 Finnish individuals from the FUSION Tissue Biopsy Study. We analyzed skeletal muscle single-nucleus RNA-seq data (168,309 nuclei, 23,849 genes), ATAC-seq data (242,069 nuclei, 927,588 peaks), and bulk RNA-seq data (22,309 genes). Lower insulin resistance (HOMA-IR) and higher total physical activity were both associated with higher proportions of Type 1 nuclei and lower proportions of Type 2x nuclei. We identified cell-type-level and tissue-level gene expression-trait and gene set-trait associations for cardiometabolic and physical activity traits, and a smaller proportion of cell-type-level chromatin accessibility-trait associations. Traits typically associated with better health--lower trait values of cardiometabolic traits (BMI, HOMA-IR, normal glucose tolerance vs. type 2 diabetes, 2-hour plasma glucose) and higher physical activity levels (total and vigorous)--were associated with higher expression of energy metabolism genes and lower expression of signaling pathway genes across muscle fiber types, total pseudobulk, and to some extent in bulk tissue. For HOMA-IR and physical activity, these directions of association remained when adjusting for both traits in the same model, indicating apparently independent associations in the same pathways.

genomics↗

bioRxiv2025

Genetic integration with cell-specific nucleosome positioning resolves causal relationships underlying chromatin accessibility profiles

Cell type-specific chromatin accessibility QTL (caQTL) mapping is a promising approach to understand genetic control of chromatin landscapes and identify regulatory mechanisms underlying GWAS associations. However, current caQTL studies lack resolution and do not distinguish nucleosome-free regions (NFR) from positioned nucleosomes. Here, we leverage statistical modeling of fragment position and length to decompose ATAC-seq profiles into NFRs and phased nucleosomes. With single nucleus (sn)ATAC-seq from 281 human muscle biopsies, we map cell type-specific genetic effects on NFRs (76,027 nfrQTLs) and nucleosome occupancy (24,623 nucQTLs) across skeletal muscle cell types. Colocalization and causal inference between nucQTLs and nearby nfrQTLs and show that nfrQTLs are substantially more likely to causally influence nucQTLs and phase adjacent nucleosomes, indicating a causal relationship in shaping chromatin profiles. Hundreds of nfrQTLs colocalize with GWAS signals for muscle-related traits, including grip strength, atrial fibrillation, and fasting insulin, and the majority of colocalizing signals mapped to credible sets overlapping the corresponding nfrPeak. This approach adds mechanistic insights for how variants underlying caQTLs and GWAS signals exert their cis regulatory effects by initially modifying NFR accessibility and subsequently shaping broader chromatin landscapes, nucleosome positioning, gene expression, and ultimately higher-level traits and disease.

genomics↗

bioRxiv2025

Liver single-nucleus multiome profiling reveals cell-type mechanisms for cardiometabolic traits

The liver is a central regulator of cardiometabolic physiology, coordinating processes such as lipid and glucose metabolism, protein synthesis, and detoxification. Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with cardiometabolic traits, yet their molecular mechanisms in liver cell types remain unclear. Using multiome single-nucleus RNA and ATAC sequencing on liver samples from 39 individuals, we profiled gene expression and chromatin accessibility in 68,398 nuclei across six primary liver cell types. We identified 306,706 accessible chromatin regions, including 70,884 regions undetected in bulk tissue analyses and that predominantly represent less abundant cell types. To identify genetic effects on gene regulation in liver cell types, we mapped quantitative trait loci (QTLs) and detected 1,885 chromatin accessibility QTLs (caQTLs) and 67 expression QTLs (eQTLs). We integrated cell-type QTLs with GWAS signals and revealed cell types, genes, and chromatin regulatory elements involved in cardiometabolic traits, such as liver enzyme and cholesterol levels. Non-hepatocyte cell-type QTL analyses exposed previously obscured mechanisms, such as an eQTL for ADAMTS12 in liver sinusoidal endothelial cells potentially involved in liver fibrosis, demonstrating that single-nucleus approaches can capture regulatory events missed in bulk analyses. Furthermore, we predicted cell type of action for bulk liver caQTLs colocalized with GWAS signals, enhancing mechanistic insights for complex trait associations. Our findings provide a high-resolution map of the hepatic regulatory landscape and advance the understanding of cellular contexts and molecular mechanisms underlying cardiometabolic traits.

genomics↗

bioRxiv2025

Higher eQTL power reveals signals that boost GWAS colocalization

Expression quantitative trait locus (eQTL) studies in human cohorts typically detect at least one regulatory signal per gene, and have been proposed as a way to explain mechanisms of genetic liability for other traits, as discovered in genome-wide association studies (GWAS). In particular, eQTL signals may colocalize with GWAS signals, suggesting gene expression as a possible mediator. However, recent studies have noted colocalization occurs infrequently, even when expression is measured in biologically relevant tissues. Most eQTL studies to date include only hundreds of individuals, and are underpowered to discover distal regulatory signals explaining smaller fractions of gene expression variance. We integrate evidence from recent eQTL studies and demonstrate that limited statistical power due to sample size skews the detection of eQTL signals identified at various signal strengths. We estimate that a sample size of 500 detects <0.1 to 60% of eQTL for a range of signal strengths and that a sample size of 2,000 would detect 36.8% of all eQTL. We show that eQTL signals that can only be discovered in larger studies exhibit characteristics more similar to those of GWAS signals, including greater distance to the regulated gene and higher probability of loss intolerance. Finally, using results from recent eQTL studies and meta-analyses, we observe a large increase in detected colocalizations with GWAS signals compared to previous studies. These findings caution against overinterpreting the absence of colocalization in underpowered studies and provide guidance for designing future eQTL experiments, to improve power and complement perturbation-based approaches in characterizing gene-trait mechanisms.

genetics↗

bioRxiv2024

Response eQTLs, chromatin accessibility, and 3D chromatin structure in chondrocytes provide mechanistic insight into osteoarthritis risk

Osteoarthritis (OA) poses a significant healthcare burden with limited treatment options. While genome-wide association studies (GWAS) have identified over 100 OA-associated loci, translating these findings into therapeutic targets remains challenging. Integrating expression quantitative trait loci (eQTL), 3D chromatin structure, and other genomic approaches with OA GWAS data offers a promising approach to elucidate disease mechanisms; however, comprehensive eQTL maps in OA-relevant tissues and conditions remain scarce. We mapped gene expression, chromatin accessibility, and 3D chromatin structure in primary human articular chondrocytes in both resting and OA-mimicking conditions. We identified thousands of differentially expressed genes, including those associated with differences in sex and age. RNA-seq in chondrocytes from 101 donors across two conditions uncovered 3782 unique eGenes, including 420 that exhibited strong and significant condition-specific effects. Colocalization with OA GWAS signals revealed 13 putative OA risk genes, 10 of which have not been previously identified. Chromatin accessibility and 3D chromatin structure provided insights into the mechanisms and conditional specificity of these variants. Our findings shed light on OA pathogenesis and highlight potential targets for therapeutic development. Highlights{circ} Comprehensive analysis of sex- and age-related global gene expression in human chondrocytes revealed differences that correlate with osteoarthritis {circ}First response eQTLs in chondrocytes treated with an OA-related stimulus {circ}Deeply sequenced Hi-C in resting and activated chondrocytes helps connect OA risk variants to their putative causal genes {circ}Colocalization analysis reveals 13 (including 10 novel) putative OA risk genes

genetics↗

bioRxiv2023

Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation

Skeletal muscle, the largest human organ by weight, is relevant in several polygenic metabolic traits and diseases including type 2 diabetes (T2D). Identifying genetic mechanisms underlying these traits requires pinpointing cell types, regulatory elements, target genes, and causal variants. Here, we use genetic multiplexing to generate population-scale single nucleus (sn) chromatin accessibility (snATAC-seq) and transcriptome (snRNA-seq) maps across 287 frozen human skeletal muscle biopsies representing nearly half a million nuclei. We identify 13 cell types and integrate genetic variation to discover >7,000 expression quantitative trait loci (eQTL) and >100,000 chromatin accessibility QTLs (caQTL) across cell types. Learning patterns of e/caQTL sharing across cell types increased precision of effect estimates. We identify high-resolution cell-states and context-specific e/caQTL with significant genotype by context interaction. We identify nearly 2,000 eGenes colocalized with caQTL and construct causal directional maps for chromatin accessibility and gene expression. Almost 3,500 genome-wide association study (GWAS) signals across 38 relevant traits colocalize with sn-e/caQTL, most in a cell-specific manner. These signals typically colocalize with caQTL and not eQTL, highlighting the importance of population-scale chromatin profiling for GWAS functional studies. Finally, our GWAS-caQTL colocalization data reveal distinct cell-specific regulatory paradigms. Our results illuminate the genetic regulatory architecture of human skeletal muscle at high resolution epigenomic, transcriptomic, and cell-state scales and serve as a template for population-scale multi-omic mapping in complex tissues and traits.

genomics↗

bioRxiv2023

Adipose tissue eQTL meta-analysis reveals the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits

Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. Here, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34K conditionally distinct expression quantitative trait locus (eQTL) signals in 18K genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared to primary signals, non-primary signals had lower effect sizes, lower minor allele frequencies, and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTL with conditionally distinct genome-wide association study signals for 28 cardiometabolic traits identified 3,605 eQTL signals for 1,861 genes. Inclusion of non-primary eQTL signals increased colocalized signals by 46%. Among 30 genes with [≥]2 pairs of colocalized signals, 21 showed a mediating gene dosage effect on the trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation.

genomics↗

bioRxiv2023

WNT activity reveals context-specific genetic effects on gene regulation in neural progenitors

Gene regulatory effects in bulk-post mortem brain tissues are undetected at many non-coding brain trait-associated loci. We hypothesized that context-specific genetic variant function during stimulation of a developmental signaling pathway would explain additional regulatory mechanisms. We measured chromatin accessibility and gene expression following activation of the canonical Wnt pathway in primary human neural progenitors from 82 donors. TCF/LEF motifs, brain structure-, and neuropsychiatric disorder-associated variants were enriched within Wnt-responsive regulatory elements (REs). Genetically influenced REs were enriched in genomic regions under positive selection along the human lineage. Stimulation of the Wnt pathway increased the detection of genetically influenced REs/genes by 66.2%/52.7%, and led to the identification of 397 REs primed for effects on gene expression. Context-specific molecular quantitative trait loci increased brain-trait colocalizations by up to 70%, suggesting that genetic variant effects during early neurodevelopmental patterning lead to differences in adult brain and behavioral traits.

genetics↗

bioRxiv2022

3D Chromatin Structure in Chondrocytes Identifies Putative Osteoarthritis Risk Genes

Genome-wide association studies (GWAS) have identified over 100 loci associated with osteoarthrtis (OA) risk, but the majority of OA risk variants are non-coding, making it difficult to identify the impacted genes for further study and therapeutic development. To address this need, we used a multi-omic approach and genome editing to identify and functionally characterize potential OA risk genes. Computational analysis of GWAS and ChIP-seq data revealed that chondrocyte regulatory loci are enriched for OA risk variants. We constructed a chondrocyte specific regulatory network by mapping 3D chromatin structure and active enhancers in human chondrocytes. We then intersected these data with our previously collected RNA-seq dataset of chondrocytes responding to fibronectin fragment (FN-f), a known OA trigger. Integration of the three genomic datasets with recently reported OA GWAS variants revealed a refined set of putative causal OA variants and their potential target genes. One of the novel putative target genes identified was SOCS2, which was connected to a putative causal variant by a 170 Kb loop and is differentially regulated in response to FN-f. CRISPR-Cas9-mediated deletion of SOCS2 in primary human chondrocytes from three independent donors led to heightened expression of inflammatory markers after FN-f treatment. These data suggest that SOCS2 plays a role in resolving inflammation in response to cartilage matrix damage and provides a possible mechanistic explanation for its influence on OA risk. In total, we identified 56 unique putative OA risk genes for further research and potential therapeutic development.

genomics↗

bioRxiv2020

Content and performance of the MiniMUGA genotyping array, a new tool to improve rigor and reproducibility in mouse research

The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well annotated genome, wealth of genetic resources and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost effective, informative and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole genome sequencing. Here we describe the content and performance of a new Mouse Universal Genotyping Array (MUGA). MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: 1) chromosomal sex determination, 2) discrimination between substrains from multiple commercial vendors, 3) diagnostic SNPs for popular laboratory strains, 4) detection of constructs used in genetically engineered mice, and 5) an easy to interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA we genotyped 6,899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived and recombinant inbred lines. Here we also report the detection of a substantial number of XO and XXY individuals across a variety of sample types, the extension of the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. There is preliminary but striking evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the main stock for a significant action of the genotyped inbred samples. We conclude that MiniMUGA is a valuable platform for genetic QC and important new tool to the increase rigor and reproducibility of mouse research.

genetics↗

bioRxiv2019

Assessing exposure effects on gene expression

In observational genomics datasets, there is often confounding of the effect of an exposure on gene expression. To adjust for confounding when estimating the exposure effect, a common approach involves including potential confounders as covariates with the exposure in a regression model of gene expression. However, when the exposure and confounders interact to influence gene expression, the fitted regression model does not necessarily estimate the overall effect of the exposure. Using inverse probability weighting (IPW) or the parametric g-formula in these instances is straightforward to apply and yields consistent effect estimates. IPW can readily be integrated into a genomics data analysis pipeline with upstream data processing and normalization, while the g-formula can be implemented by making simple alterations to the regression model. The regression, IPW, and g-formula approaches to exposure effect estimation are compared herein using simulations; advantages and disadvantages of each approach are explored. The methods are applied to a case study estimating the effect of current smoking on gene expression in adipose tissue.

genomics↗

bioRxiv2019

Influence of genetic variants on gene expression in human pancreatic islets - implications for type 2 diabetes

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

genomics↗

bioRxiv2018

SMUT: multi-SNP Mediation intersection-Union Test

Tens of thousands of reproducibly identified GWAS (Genome-Wide Association Studies) variants, with the vast majority falling in non-coding regions resulting in no eventual protein products, call urgently for mechanistic interpretations. Although numerous methods exist, there are few, if any methods, for simultaneously testing the mediation effects of multiple correlated SNPs via some mediator (for example, the expression of a gene in the neighborhood) on phenotypic outcome. We propose SMUT, multi-SNP Mediation intersection-Union Test to fill in this methodological gap. Our extensive simulations demonstrate the validity of SMUT as well as substantial, up to 92%, power gains over alternative methods. In addition, SMUT confirmed known mediators in a real dataset of Finns for plasma adiponectin level, which were missed by many alternative methods. We believe SMUT will become a useful tool to generate mechanistic hypotheses underlying GWAS variants, facilitating functional follow-up. The R package SMUT is publicly available from CRAN at https://CRAN.R-project.org/package=SMUT.

genetics↗

bioRxiv2017

HUGIn: Hi-C Unifying Genomic Interrogator

MotivationHigh throughput chromatin conformation capture (3C) technologies, such as Hi-C and ChlA-PET, have the potential to elucidate the functional roles of non-coding variants. However, most of published genome-wide unbiased chromatin organization studies have used cultured cell lines, limiting their generalizability.\n\nResultsWe developed a web browser, HUGIn, to visualize Hi-C data generated from 21 human primary tissues and cell liens. HUGIn enables assessment of chromatin contacts both constitutive across and specific to tissue(s) and/or cell line(s) at any genomic loci, including GWAS SNPs, eQTLs and cis-regulatory elements, facilitating the understanding of both GWAS and eQTLs results and functional genomics data.\n\nAvailabilityHUGIn is available at http://yunliweb.its.unc.edu/HUGIn.\n\nContactyunli@med.unc.edu and hum@ccf.org\n\nSupplementary information:

genetics↗