Biology subjects

Skelly, D. A.

Publications and source records attributed to Skelly, D. A..

At least 19 recordsLinked to original sources

bioRxiv2026

CellMAGE: cell-type deconvolution for multi-parent population analysis of gene expression

Single-cell RNA-sequencing remains prohibitively expensive for multiparental population (MPP) studies. Existing deconvolution methods treat bulk RNA-seq as genetically anonymous mixtures, but in MPPs, the proportional contribution of each parental strain to each progeny's transcriptome is already known. CellMAGE (Cell-type deconvolution for Multi-parent Analysis of Gene Expression) weights parental cell-type profiles by each progeny's known genetic composition, requiring no model training and no minimum sample size. Validated in 16 Diversity Outbred mice across 12 prefrontal cortex cell types and 23,116 genes, predicted and measured gene expression were statistically equivalent (0.05) in all cell types (pooled Spearman = 0.923, 95% CI: 0.910, 0.934). CIBERSORTx required 96 additional samples to resolve at most 12.4% of genes and only 3 cell types; CellMAGE outperformed it even within this restricted comparison (per-cell-type median : 0.908-0.974 vs. 0.353-0.641). CellMAGE is applicable to any MPP with parental single-cell data, including diploid crop MAGIC populations.

genomics↗

bioRxiv2026

Simplifying Systems Genetics with QTLretrievR: An R Package for Molecular QTL Identification

MotivationOver the past two decades, integrating "-omics" scale molecular profiling with genetic mapping in diverse populations--termed "systems genetics"--has proven powerful for linking complex disease phenotypes to genetic variants and their regulatory effects. However, these combined genomic and genetic analyses remain computationally intensive and often inaccessible to researchers without a strong computational background, a problem compounded in studies with multiple tissues or thousands of molecular -omics phenotypes. ResultsWe developed QTLretrievR, an R package designed to simplify and streamline the identification and downstream analysis of molecular quantitative trait loci in genetically diverse populations. QTLretrievR builds on widely used R packages such as r/qtl2 for quantitative trait locus mapping and r/intermediate for mediation analysis, and incorporates substantial parallelization features to improve computational efficiency. Benchmarking demonstrates that QTLretrievR can reproduce results from previous quantitative trait locus studies while significantly reducing the time required for quantitative trait locus peak detection and downstream analyses. Additionally, QTL-retrievR includes plotting functions to visualize results of the included downstream analyses. AvailabilitySource code is available at https://github.com/deweyhannah/QTLretrievR. ContactSteven Munger, steven.munger@jax.org

genetics↗

bioRxiv2025

Differential Assembly of Mouse and Human Tumor Microenvironments

Mouse models are frequently used to develop treatments for human cancer. Yet, we lack a comprehensive understanding of the comparative organization of mouse and human tumor microenvironments (mu/huTMEs). Through immunoprofiling of commonly used mouse models, we found that the immune composition of most muTMEs resemble poorly infiltrated human tumors extensively biased toward high macrophages densities. Relatedly, we discover species-specific biases of chemokine expression networks, factors which drive TMEs assembly. Further, assessing coarse cellular networks, we find conserved correlations between some immune cell frequencies, while other relationships only appear conserved in the huTMEs matching muTME profiles. Despite this variable alignment, we define robust cell type-specific gene expression programs conserved in TMEs across species and cohorts and identify ones that are coordinated between cell populations in both species. Together, we isolate and offer methods to study the multiple areas of hazard and opportunities for using mice to model human cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=192 HEIGHT=200 SRC="FIGDIR/small/661217v1_ufig1.gif" ALT="Figure 1"> View larger version (71K): org.highwire.dtl.DTLVardef@22bcf7org.highwire.dtl.DTLVardef@a410b7org.highwire.dtl.DTLVardef@1455eb5org.highwire.dtl.DTLVardef@14cc6f5_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Modeling metabolic disease susceptibility and resilience in genetically diverse mice

Model organisms have provided critical insights into the basic biology of metabolic disorders, however, one of the greatest limitations to translation has been the absence of the genetic heterogeneity that is characteristic of human populations. We examined metabolic health across three genetically diverse mouse strains fed control (low fat, no sucrose) or unhealthy (high fat, high sucrose) diets and observed a wide range of metabolic responses from overt type 2 diabetes in NZO/HlLtJ mice, to obesity and glucose intolerance in C57BL/6J mice, to complete resilience in CAST/EiJ mice. Analysis of multi-tissue gene expression revealed strain- and tissue-specific responses to diet, with strongest responses in white adipose tissue and pancreatic islets. In pancreatic islets, diet response was limited to just NZO mice, which showed high levels of inflammation and associated {beta} cell dysfunction. Adipose tissue was responsive to diet across all three strains and revealed both common and strain-specific changes in inflammatory and metabolic pathways. Using a complementary outbred mouse resource, we mapped genetic loci associated with strain-specific diet responses, including a monocyte regulatory locus on mChr19. This multi-strain approach to modeling metabolic disease revealed a prominent role of white adipose tissue and lipid-associated inflammation in the determination of individual disease risk in response to unhealthy diets.

physiology↗

bioRxiv2025

Cross cell-type systems genetics reveals the influence of eQTL at multiple points in the developmental trajectory of mouse neural progenitor cells

Genetic variation leads to phenotypic variability in pluripotent stem cells that presents challenges for regenerative medicine. Although recent studies have investigated the impact of genetic variation on pluripotency maintenance and differentiation capacity, less is known about how genetic variants affecting the pluripotent state influence gene regulation in later stages of development. Here, we characterized expression of more than 12,000 genes in 127 donor-matched Diversity Outbred (DO) mouse embryonic stem cell (mESC) and neural progenitor cell (mNPC) lines. Quantitative trait locus (QTL) mapping identified 2,947 expression QTL (eQTL) unique to DO mNPCs and 1,113 eQTL observed in both mNPCs and mESCs with highly concordant allele effects. We mapped three eQTL hotspots on Chromosomes (Chrs) 1, 10, and 11 that were unique to mNPCs. Target genes of the Chr 1 hotspot were overrepresented for those involved in mRNA processing, DNA repair, chromatin organization, protein degradation, and cell cycle. Mediation analysis of the Chr 1 hotspot identified Rnf152 as the best candidate mediator expressed in mNPCs, while cross-cell type mediation using mESC gene expression along with partial correlation analysis strongly implicated genetic variant(s) affecting Pign expression in the mESC state as regulating the mNPC Chr 1 eQTL hotspot. Together these findings highlight that many local eQTL confer similar effects on gene expression in multiple cell states; distant eQTL in DO mNPCs are numerous and largely unique to that cell state, with many co-localizing to mNPC-specific hotspots; and mediation analysis across cell types suggests that expression of Pign early in development (mESCs) shapes the transcriptome of the more specialized mNPC state.

genetics↗

bioRxiv2024

Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans

Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy has been experimentally challenging because of variations in both host and tumor genomes, differences in the microbiome, and patient life exposures. Utilizing the Collaborative Cross (CC) multi-parent mouse genetic resource population, we developed an approach that fixes the tumor genomic configuration while varying host genetics. With this approach, we discovered that response to anti-PD-1 (aPD1) immunotherapy was significantly heritable in four distinct murine tumor models (H2 between 0.18-0.40). For the MC38 colorectal carcinoma system (H2 = 0.40), we mapped four significant ICI response quantitative trait loci (QTL) localized to mouse chromosomes (mChr) 5, 9, 15 and 17, and identified significant epistatic interactions between specific QTL pairs. Differentially expressed genes within these QTL were highly enriched for immune genes and pathways mediating allograft rejection and graft vs host disease. Using a cross species analytical approach, we found a core network of 48 genes within the four QTLs that showed significant prognostic value for overall survival in aPD1 treated human cohorts that outperformed all other existing validated immunotherapy biomarkers, especially in human tumors of the previously defined immune subtype 4. Functional blockade of two top candidate immune targets within the 48 gene network, GM-CSF and high affinity IL-2/IL-15 signaling, completely abrogated the MC38 tumor transcriptional response to aPD1 therapy in vivo. Thus, we have established a powerful cross species in vivo platform capable of uncovering host genetic factors that establish the tumor immune microenvironment configuration propitious for ICI response.

cancer biology↗

bioRxiv2024

Chromosome X Dosage Modulates Development of Aneuploidy in Genetically Diverse Mouse Embryonic Stem Cells

The genetic integrity of pluripotent stem cells (PSC) is critical to their applications in research and therapy, but it is compromised by frequent development of structural chromosome variants associated with malignancy. Many cell lines exhibit remarkable genetic stability, but little is known about the basis of the known variation in genomic integrity amongst different PSC isolates. Here we identify aneuploidies using RNA-seq and proteomics data from a panel of mouse embryonic stem cell (mESC) lines derived from 170 Diversity Outbred mice. We found 62 lines with detectable aneuploid subpopulations and a subset of originally XX lines that lost one Chromosome X (XO). Strikingly, a much lower proportion of XX lines were aneuploid, compared to XY or XO lines. Two single-cell RNA-seq data sets demonstrated that aneuploid XY DO mESC also show lower Chromosome X gene expression, and a prospective study confirmed that XY mESC accumulate higher aneuploid proportions in culture than isogenic XX lines. We identify potential mechanisms for this protective effect of X chromosome dosage, including our findings that the lines with two active X Chromosomes have a higher proportion of 2-cell-like cells, a state associated with maintenance of genetic integrity of mESC, and that they show differential expression of X-linked tumor suppressor genes associated with the DNA damage response. HighlightsO_LIFirst genetic analysis of predisposition to aneuploidy in pluripotent stem cell cultures C_LIO_LIChromosomal regions duplicated in aneuploid mouse embryonic stem cells are syntenic with regions overrepresented in human pluripotent stem cell lines bearing recurrent genetic abnormalities C_LIO_LIX-Chromosome dosage strongly influences susceptibility to aneuploidy in mouse embryonic stem cells and to a lesser degree in human pluripotent stem cells C_LIO_LIXX mouse embryonic stem cell lines show a higher proportion of cells in 2 cell-like state and higher expression of tumor suppressor genes associated with DNA damage response C_LI

genetics↗

bioRxiv2024

Genetic context modulates aging and degeneration in the murine retina

BackgroundAge is the principal risk factor for neurodegeneration in both the retina and brain. The retina and brain share many biological properties; thus, insights into retinal aging and degeneration may shed light onto similar processes in the brain. Genetic makeup strongly influences susceptibility to age-related retinal disease. However, studies investigating retinal aging have not sufficiently accounted for genetic diversity. Therefore, examining molecular aging in the retina across different genetic backgrounds will enhance our understanding of human-relevant aging and degeneration in both the retina and brain--potentially improving therapeutic approaches to these debilitating conditions. MethodsTranscriptomics and proteomics were employed to elucidate retinal aging signatures in nine genetically diverse mouse strains (C57BL/6J, 129S1/SvlmJ, NZO/HlLtJ, WSB/EiJ, CAST/EiJ, PWK/PhK, NOD/ShiLtJ, A/J, and BALB/cJ) across lifespan. These data predicted human disease-relevant changes in WSB and NZO strains. Accordingly, B6, WSB and NZO mice were subjected to human-relevant in vivo examinations at 4, 8, 12, and/or 18M, including: slit lamp, fundus imaging, optical coherence tomography, fluorescein angiography, and pattern/full-field electroretinography. Retinal morphology, vascular structure, and cell counts were assessed ex vivo. ResultsWe identified common molecular aging signatures across the nine mouse strains, which included genes associated with photoreceptor function and immune activation. Genetic background strongly modulated these aging signatures. Analysis of cell type-specific marker genes predicted age-related loss of photoreceptors and retinal ganglion cells (RGCs) in WSB and NZO, respectively. Fundus exams revealed retinitis pigmentosa-relevant pigmentary abnormalities in WSB retinas and diabetic retinopathy (DR)-relevant cotton wool spots and exudates in NZO retinas. Profound photoreceptor dysfunction and loss were confirmed in WSB. Molecular analyses indicated changes in photoreceptor-specific proteins prior to loss, suggesting photoreceptor-intrinsic dysfunction in WSB. In addition, age-associated RGC dysfunction, loss, and concomitant microvascular dysfunction was observed in NZO mice. Proteomic analyses revealed an early reduction in protective antioxidant processes, which may underlie increased susceptibility to DR-relevant pathology in NZO. ConclusionsGenetic context is a strong determinant of retinal aging, and our multi-omics resource can aid in understanding age-related diseases of the eye and brain. Our investigations identified and validated WSB and NZO mice as improved preclinical models relevant to common retinal neurodegenerative diseases.

neuroscience↗

bioRxiv2023

Enhlink infers distal and context-specific enhancer-promoter linkages

Enhancers play a crucial role in regulating gene expression and their functional status can be queried with cell type precision using using single-cell (sc)ATAC-seq. To facilitate analysis of such data, we developed Enhlink, a novel computational approach that leverages single -cell signals to infer linkages between regulatory DNA sequences, such as enhancers and promoters. Enhlink uses an ensemble strategy that integrates cell-level technical covariates to control for batch effects and biological covariates to infer robust condition-specific links and their associated p-values. It can integrate simultaneous gene expression and chromatin accessibility measurements of individual cells profiled by multi-omic experiments for increased specificity. We evaluated Enhlink using simulated and real scATAC-seq data, including those paired with physical enhancer-promoter links enumerated by promoter capture Hi-C and with multi-omic scATAC-/RNA-seq data we generated from the mouse striatum. These examples demonstrated that our method outperforms popular alternative strategies. In conjunction with eQTL analysis, Enhlink revealed a putative super-enhancer regulating key cell type-specific markers of striatal neurons. Taken together, our analyses demonstrate that Enhlink is accurate, powerful, and provides features that can lead to novel biological insights.

bioinformatics↗

bioRxiv2022

Genetic dissection of the pluripotent proteome through multi-omics data integration

Genetic background is a major driver of phenotypic variability in pluripotent stem cells (PSCs). Most studies of variation in PSCs have relied on transcript abundance as the primary molecular readout of cell state. However, little is known about how proteins, the primary functional units in the cell, vary across genetically diverse PSCs, how protein abundance relates to variation in other cell characteristics, and how genetic background confers these effects. Here we present a comprehensive genetic study characterizing the pluripotent proteome of 190 unique mouse embryonic stem cell lines (mESCs) derived from genetically heterogeneous Diversity Outbred (DO) mice. The quantitative proteome is highly variable across DO mESCs, and we identified differentially activated pluripotency-associated pathways in the proteomics data that were not evident in transcriptome data from the same cell lines. Comparisons of protein abundance to transcript levels and chromatin accessibility show broad co-variation across molecular layers and variable correlation across samples, with some lines showing high and others low correlation between these multi-omics datasets. Integration of these three molecular data types using multi-omics factor analysis revealed shared and unique drivers of quantitative variation in pluripotency-associated pathways. QTL mapping localized the genetic drivers of this quantitative variation to a number of genomic hotspots, and demonstrated that multi-omics data integration consolidates the influence of genetic signals shared across molecular traits to increase QTL detection power and overcome the limitations inherent in mapping individual molecular features. This study reveals transcriptional and post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome, and in so doing provides a regulatory map for mouse ESCs that can provide a rational basis for future mechanistic studies, including studies of human PSCs.

genetics↗

bioRxiv2022

Imputation of 3D genome structure by genetic-epigenetic interaction modeling in mice

Gene expression is known to be affected by interactions between local genetic variation and DNA accessibility, with the latter organized into three-dimensional chromatin structures. Analyses of these interactions has previously been limited, obscuring their regulatory context, and the extent to which they occur throughout the genome. Here we undertake a genome-scale analysis of these interactions in a genetically diverse population to systematically identify global genetic-epigenetic interaction, and reveal constraints imposed by chromatin structure. We establish the extent and structure of genotype-by-epigenotype interaction using embryonic stem cells derived from Diversity Outbred mice. This mouse population segregates millions of variants from eight inbred founders, enabling precision genetic mapping with extensive genotypic and phenotypic diversity. With 176 samples profiled for genotype, gene expression, and open chromatin, we used regression modeling to infer genetic-epigenetic interactions on a genome-wide scale. Our results demonstrate that statistical interactions between genetic variants and chromatin accessibility are common throughout the genome. We found that these interactions occur within the local area of the affected gene, and that this locality corresponds to topologically associated domains (TADs). The likelihood of interaction was most strongly defined by the three-dimensional (3D) domain structure rather than linear DNA sequence. We show that stable 3D genome structure is an effective tool to guide searches for regulatory elements and, conversely, that regulatory elements in genetically diverse populations provide a means to infer 3D genome structure. We confirmed this finding with CTCF ChIP-seq that revealed strain-specific binding in the inbred founder mice. In stem cells, open chromatin participating in the most significant regression models demonstrated an enrichment for developmental genes and the TAD-forming CTCF binding complex, providing an opportunity for statistical inference of shifting TAD boundaries operating during early development. These findings provide evidence that genetic and epigenetic factors operate within the context of three- dimensional chromatin structure. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/479436v3_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@14d4221org.highwire.dtl.DTLVardef@130a812org.highwire.dtl.DTLVardef@16a6069org.highwire.dtl.DTLVardef@df9d9c_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

bioRxiv2021

CLARA: A web portal for interactive exploration of the cardiovascular cellular landscape in health and disease

Mammalian cardiovascular tissues are comprised of complex and diverse collections of cells. Recent advances in single-cell profiling technologies have accelerated our understanding of tissue cellularity and the molecular networks that orchestrate cardiovascular development, maintain homeostasis, and are disrupted in pathological states. Despite the rapid development and application of these technologies, many cardiac single-cell functional genomics datasets remain inaccessible for most cardiovascular biologists. Access to custom visual representations of the data, including querying changes in cellular phenotypes and interactions in diverse contexts, remains unavailable in publicly accessible data portals. Visualizing data is also challenging for scientists without expertise in processing single-cell genomic data. Here we present CLARA--CardiovascuLAR Atlas--a web portal facilitating exploration of the cardiovascular cellular landscape. Using mouse and human single-cell transcriptomic datasets, CLARA enables scientists unfamiliar with single-cell-omic data analysis approaches to examine gene expression patterns and the cell population dynamics of cardiac cells in a range of contexts. The web-application also enables investigation of intercellular interactions that form the cardiac cellular niche. CLARA is designed for ease-of-use and we anticipate that the portal will aid deeper exploration of cardiovascular cellular landscapes in the context of development, homeostasis and disease. CLARA is freely available at https://clara.baker.edu.au.

cell biology↗

bioRxiv2021

Genetic Control of Pluripotency Epigenome Determines Differentiation Bias in Mouse Embryonic Stem Cells

Genetically diverse pluripotent stem cells (PSCs) display varied, heritable responses to differentiation cues in the culture environment. By harnessing these disparities through derivation of embryonic stem cells (ESCs) from the BXD mouse genetic reference panel, along with C57BL/6J (B6) and DBA/2J (D2) parental strains, we demonstrate genetically determined biases in lineage commitment and identify major regulators of the pluripotency epigenome. Upon transition to formative pluripotency using epiblast-like cells (EpiLCs), B6 quickly dissolves naive networks adopting gene expression modules indicative of neuroectoderm lineages; whereas D2 retains aspects of naive pluripotency with little bias in differentiation. Genetic mapping identifies 6 major trans-acting loci co-regulating chromatin accessibility and gene expression in ESCs and EpiLCs, indicating a common regulatory system impacting cell state transition. These loci distally modulate occupancy of pluripotency factors, including TRIM28, P300, and POU5F1, at hundreds of regulatory elements. One trans-acting locus on Chr 12 primarily impacts chromatin accessibility in ESCs; while in EpiLCs the same locus subsequently influences gene expression, suggesting early chromatin priming. Consequently, the distal gene targets of this locus are enriched for neurogenesis genes and were more highly expressed when cells carried B6 haplotypes at this Chr 12 locus, supporting genetic regulation of biases in cell fate. Spontaneous formation of embryoid bodies validated this with B6 showing a propensity towards neuroectoderm differentiation and D2 towards definitive endoderm, confirming the fundamental importance of genetic variation influencing cell fate decisions.

genomics↗

bioRxiv2020

Natural genetic variation determines microglia heterogeneity in wild-derived mouse models of Alzheimer's disease

Microglia are now considered drivers of Alzheimers disease (AD) pathology. However, single-cell RNA-sequencing (scRNA-seq) of microglia in mice, a key preclinical model organsim, have shown mixed results regarding translatability to human studies. To address this, scRNA-seq of microglia from C57BL/6J (B6) and wild-derived strains WSB/EiJ, CAST/EiJ and PWK/PhJ carrying APP/PS1 was performed and demonstrated that genetic diversity significantly altered features and dynamics of microglia in baseline neuroimmune functions and in response to amyloidosis. There was significant variation in abundance of microglial subpopulations, including numbers of disease-associated microglia and interferon-responding microglia across the strains. Further, for each subpopulation, significant gene expression differences were observed between strains, and relative to B6 that included nineteen genes previously associated with human AD including Apoe, Trem2, Bin1 and Sorl1. This resource will be critical in the development of appropriately targeted therapeutics for AD and a range of other neurological diseases.

neuroscience↗

bioRxiv2020

Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics

Single-cell RNA sequencing is a powerful tool for characterizing cellular heterogeneity in gene expression. However, high variability and a large number of zero counts present challenges for analysis and interpretation. There is substantial controversy over the origins and proper treatment of zeros and no consensus on whether zero-inflated count distributions are necessary or even useful. While some studies assume the existence of zero inflation due to technical artifacts and attempt to impute the missing information, other recent studies of argue that there is no zero inflation in scRNA-Seq data. We apply a Bayesian model selection approach to unambiguously demonstrate zero inflation in multiple biologically realistic scRNA-Seq datasets. We show that the primary causes of zero inflation are not technical but rather biological in nature. We also demonstrate that parameter estimates from the zero-inflated negative binomial distribution are an unreliable indicator of zero inflation. Despite the existence of zero inflation of scRNA-Seq counts, we recommend the generalized linear model with negative binomial count distribution (not zero-inflated) as a suitable reference model for scRNA-Seq analysis.

bioinformatics↗

bioRxiv2019

High-resolution transcriptomic profiling of the heart during chronic stress reveals cellular drivers of cardiac fibrosis and hypertrophy

BackgroundCardiac fibrosis is a key antecedent to many types of cardiac dysfunction including heart failure. Physiological factors leading to cardiac fibrosis have been recognized for decades. However, the specific cellular and molecular mediators that drive cardiac fibrosis, and the relative impact of disparate cell populations on cardiac fibrosis, remain unclear. MethodsWe developed a novel cardiac single-cell transcriptomics strategy to characterize the cardiac cellulome--the network of cells that forms the heart. This method was utilized to profile the cardiac cellular ecosystem in response to two weeks of continuous administration of Angiotensin II, a pro-fibrotic stimulus which drives pathological cardiac remodeling. ResultsThis analysis uncovered multiple cell populations contributing to pathological remodeling of the extracellular matrix of the heart. Two phenotypically distinct fibroblast populations emerged after induction of tissue stress to promote fibrosis in the absence of smooth muscle actin-expressing myofibroblasts, a key pro-fibrotic cell population. Further, the cellular responses to Angiotensin II and the relative abundance of fibrogenic cells were sexually dimorphic. ConclusionsThese results offer a valuable resource for exploring the cardiac cellular landscape in health and after chronic cardiovascular stress. These data provide insights into the cellular and molecular mechanisms that promote pathological remodeling of the mammalian heart, highlighting early transcriptional changes which precede chronic cardiac fibrosis.

cell biology↗

bioRxiv2019

Genetic variation influences pluripotent ground state stability in mouse embryonic stem cells through a hierarchy of molecular phenotypes

Mouse embryonic stem cells (mESCs) cultured under controlled conditions occupy a stable ground state where pluripotency-associated transcriptional and epigenetic circuitry are highly active. However, mESCs from some genetic backgrounds exhibit metastability, where ground state pluripotency is lost in the absence of ERK1/2 and GSK3 inhibition. We dissected the genetic basis of metastability by profiling gene expression and chromatin accessibility in 185 genetically heterogeneous mESCs. We mapped thousands of loci affecting chromatin accessibility and/or transcript abundance, including eleven instances where distant QTL co-localized in clusters. For one cluster we identified Lifr transcript abundance as the causal intermediate regulating 122 distant genes enriched for roles in maintenance of pluripotency. Joint mediation analysis implicated a single enhancer variant ~10kb upstream of Lifr that alters chromatin accessibility and precipitates a cascade of molecular events affecting maintenance of pluripotency. We validated this hypothesis using reciprocal allele swaps, revealing mechanistic details underlying variability in ground state metastability in mESCs.

genomics↗

bioRxiv2018

Reference trait analysis reveals correlations between gene expression and quantitative traits in disjoint samples

Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTLs. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript-trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative \"reference\" traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint sub-samples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest in order to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the dataset and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait datasets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of the reference trait method for identifying relations between complex traits and their molecular substrates.\n\nAUTHOR SUMMARYSystems genetics exploits natural genetic variation and high-throughput measurements of molecular intermediates to dissect genetic contributions to complex traits. An important goal of this strategy is to correlate molecular features, such as transcript or protein abundance, with complex traits. For practical, technical, or financial reasons, it may be impossible to measure complex traits and molecular intermediates on the same individuals. Instead, in some cases these two sets of traits may be measured on independent cohorts. We outline a method, reference trait analysis, for identifying molecular correlates of complex traits in this scenario. We show that our method powerfully identifies complex trait correlates across a wide range of parameters that are biologically plausible and experimentally practical. Furthermore, we show that reference trait analysis can identify transcripts correlated to a complex trait more accurately than approaches such as TWAS that use genetic variation to predict gene expression. Reference trait analysis will contribute to furthering our understanding of variation in complex traits by identifying molecular correlates of complex traits that are measured in different individuals.

genomics↗