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Munro, D.

Publications and source records attributed to Munro, D..

18 recordsLinked to original sources

Genomic SEM reveals shared genetic architecture and expands locus discovery for locomotor activity in heterogeneous stock rats

Locomotor response to a novel environment is a heritable, addiction-relevant behavioral phenotype in rodents. We characterized its genetic basis in 9,341 male and female heterogeneous stock (HS) rats from eight studies. Locomotor activity was heritable across studies, while pairwise genetic correlations were positive but variable, indicating shared yet nonidentical genetic influences. Given nonuniform genetic architecture, we compared three complementary approaches to locus discovery that analyzed SNP effects 1) on a pooled locomotor phenotype (MEGA), 2) across studies under a fixed-effect model (METAL), or 3) on a shared genetic factor (Genomic SEM). To ensure comparability, all three locus-discovery approaches were applied to a common subset of seven studies. Together, these approaches identified 38 loci, with both convergence and method-specific discovery across analyses. Genomic SEM identified 31 loci, compared to 8 for MEGA and 13 for METAL. To interpret the loci, we integrated brain molecular QTLs, predicted coding and splice consequences, and open-field locomotor phenotypes from knockouts of orthologous mouse genes. Our results highlighted established neurobehavioral candidates spanning locomotor and reward-related biology (Negr1 and Lmo4), serotonergic signaling (Htr1b), and GABAergic signaling (Gabrg2), as well as relatively novel genes including Tcerg1l, Pcdh10, and Tmem132e. Our results illustrate how Genomic SEM can integrate genetically correlated yet heterogeneous behavioral measures and enable discovery of loci underlying their shared genetic architecture, highlighting its utility for complex-trait studies in rodents.

genetics↗

Point mutations and complex variants impact gene expression and addiction-related behaviors in Heterogeneous Stock rats

While various variants, including single nucleotide polymorphisms (SNPs), small insertions/deletions, short tandem repeats and structural variants, drive individual genetic differences, their influence on gene expression and complex traits remains unclear. We used short- and long-read sequencing to generate a comprehensive variant catalog in Heterogeneous Stock (HS) rats and performed joint cis-expression quantitative trait loci (cis-eQTL) mapping across five brain regions. We found that non-SNP variants accounted for over 50% of lead regulatory associations, many of which were poorly tagged by nearby SNPs using linkage disequilibrium (LD). Comparison between joint and SNP-only analyses showed that over 46% of shared eQTL genes had a non-SNP lead cis-eQTL, and fewer than half were in strong LD with the corresponding lead eSNP. Linking joint cis-eQTLs to complex trait associations identified mechanisms missed by SNP-only approaches, highlighting the importance of incorporating diverse variant types into genetic studies and providing a foundational resource for the HS rat community.

genetics↗

Trex-QTL: A mixture-model for identification of genetic effects with global effects on molecular phenotypes

While thousands of cis expression quantitative trait loci (cis-eQTLs) have been reliably identified, detecting trans-eQTL effects has proven to be challenging due to insufficient statistical power, lack of comparable tissues and cohorts, and low reproducibility across studies. Here, we present Trex-QTL, a novel trans-eQTL detection method that models eQTL summary statistics as a mixture consisting of both target gene and null associations. Compared to other recently developed methods, Trex-QTL has improved power for trans-eQTL detection and employs a simplified framework, requiring only eQTL association summary statistics as input. We performed extensive simulations to characterize the conditions under which trans-eQTLs are detectable by Trex-QTL across a range of effect sizes and numbers of target genes. We applied Trex-QTL to the Depression Genes and Networks (DGN) dataset and replicated two well-established trans-eQTLs at ARHGEF3 and IKZF1. We then applied Trex-QTL to the deeply characterized heterogeneous stock (HS) rat cohort with matched brain transcriptomic and genomic data, identifying 7 top-scoring, linkage disequilibrium-independent trans-eQTLs. One previously unreported trans-eQTL is at the locus harboring Jag2, a critical ligand for the Notch signaling pathway, which is associated with decreased Jag2 expression and decreased expression of multiple downstream genes including known Notch targets. A second example is a strong trans-eQTL overlapping a cluster of interferon genes associated with interferon-response genes including C4a and Parp14. We show evidence that this signal is mediated by a cis-eQTL for a cluster of interferon ligand genes that operate upstream of interferon receptor signaling. Both of these signals co-localize with association signals for a range of other phenotypes measured in this cohort. Overall, we demonstrate that Trex-QTL represents a powerful method to identify trans-eQTLs with global effects on molecular phenotypes and identify novel biologically compelling examples of such loci.

genomics↗

Data-driven RNA phenotyping captures genetically regulated dimensions of the transcriptome

Transcriptomic diversity across individuals arises from multiple modes of RNA regulation--including pre-mRNA expression, splicing, degradation, and other processes--and has been widely leveraged to map quantitative trait loci (xQTLs) and interpret GWAS signals. We recently developed a multimodal framework called Pantry that can extend discovery beyond total expression by integrating multiple transcriptomic modalities. However, Pantry and similar tools remain limited by their reliance on complete gene annotations and the statistical complexity of jointly analyzing correlated modalities. Here, we present LaDDR (Latent Data-Driven RNA phenotyping), a mechanism-agnostic framework that generates orthogonal, latent coverage features per gene, enabling xQTL discovery and GWAS integration without requiring complete gene annotations. Applied to GTEx, LaDDR identified an average of 95% more independent xQTLs per tissue than the six transcriptional regulation modes implemented in Pantry ("knowledge-driven"). Residualizing known modalities prior to LaDDR and combining with knowledge-driven phenotypes increased discovery by an additional 41% per tissue on average, while retaining the interpretability of knowledge-driven signals. In a transcriptome-wide association study (TWAS) of 114 complex traits, using LaDDR-derived phenotypes uncovered an average of 11,790 unique gene-trait pairs per tissue, versus 8,579 from knowledge-driven phenotypes. The newly captured genetic signals exhibit functional and colocalization qualities consistent with known mechanisms, suggesting that LaDDR broadens the detectable landscape of trait-relevant transcriptomic regulation by efficiently recovering regulatory variation missed by current pipelines.

genomics↗

Ivermectin Reduces Withdrawal-Induced Alcohol Intake via CeA GABAergic Enhancement

Although FDA-approved medications for alcohol use disorder (AUD) are available, their efficacy varies across patients, highlighting the need for novel therapeutics that address inter-individual differences in disease etiology and treatment response. Genetic models, particularly heterogeneous stock (HS) rats, recapitulate human-like genetic diversity and behavioral heterogeneity, enabling the dissection of individual differences in vulnerability to AUD and pharmacotherapeutic sensitivity. P2X4 receptors, which are encoded by the gene P2rx4, are ATP-gated ion channels inhibited by ethanol and abundantly expressed in neurons found in reward and stress circuits. P2X4 receptors have emerged as key modulators of ethanol sensitivity and consumption in preclinical models. Here, we genetically predicted P2rx4 expression in whole brain in a cohort of 130 HS rats exposed to chronic intermittent ethanol (CIE) vapor and phenotyped for self-administration during acute abstinence. Rats were dichotomized into high- and low-predicted expression groups. Higher predicted P2rx4 expression was associated with increased post-vapor intake and escalation. In 32 CIE-escalated rats, ivermectin, a positive allosteric modulator of P2X4 receptors, dose-dependently reduced drinking. We stratified rats into three groups: non-responders, mild responders, and high responders. Electrophysiological recordings from CeA slices revealed that ivermectin differentially enhanced GABAergic IPSCs: high-responders exhibited sustained increases in IPSC frequency and selective amplitude reductions, while the two other groups showed transient frequency increases. All groups displayed prolonged rise times, however non-responders showed extended decay times. These findings suggest that P2rx4 upregulation serves as a vulnerability marker for dependence-like behaviors, with ivermectin attenuating withdrawal-driven alcohol consumption by enhancing CeA GABAergic inhibition.

pharmacology and toxicology↗

Allele Specific Expression Quality Control Fills Critical Gap in Transcriptome Assisted Rare Variant Interpretation

Allele-specific expression (ASE) captures the functional impact of genetic variation on transcription, offering a high-resolution view of cis-regulatory effects, but its quality can be diminished by technical, biological, and analysis artifacts. We introduce aseQC, a statistical framework that quantifies sample-level ASE quality in terms of the overall expected extra-binomial variation to exclude uncharacteristically noisy samples in a cohort to improve robustness of downstream analyses. Applying aseQC to a dataset of rare mendelian muscular disorders, successfully identified previously annotated low-quality cases demonstrating clinical genomic utility. When applied to 15,253 samples in extensively quality controlled GTEx project data, aseQC uncovered 563 low-quality samples that exhibit excessive allelic imbalance. We identify these to be associated with specific processing dates but not otherwise described adequately by any other quality control measures and metadata available in GTEx data. We show that these low-quality samples lead to 23.6 and 31.6 -fold increased ASE, and splicing outliers, degrading the performance of transcriptome analysis for rare variant interpretation. In contrast, we did not observe any adverse effect associated with inclusion of these samples in common-variant analysis using quantitative traits loci mapping. By enabling quick and reliable assessment of sample quality, aseQC presents a critical step for identifying subtle quality issues that remain critical for a successful analysis of rare variant effects using transcriptome data.

bioinformatics↗

Multimodal analysis of RNA sequencing data powers discovery of complex trait genetics

Transcriptome data is commonly used to understand genome function via quantitative trait loci (QTL) mapping and to identify the molecular mechanisms driving genome wide association study (GWAS) signals through colocalization analysis and transcriptome-wide association studies (TWAS). While RNA sequencing (RNA-seq) has the potential to reveal many modalities of transcriptional regulation, such as various splicing phenotypes, such studies are often limited to gene expression due to the complexity of extracting and analyzing multiple RNA phenotypes. Here, we present Pantry (Pan-transcriptomic phenotyping), a framework to efficiently generate diverse RNA phenotypes from RNA-seq data and perform downstream integrative analyses with genetic data. Pantry currently generates phenotypes from six modalities of transcriptional regulation (gene expression, isoform ratios, splice junction usage, alternative TSS/polyA usage, and RNA stability) and integrates them with genetic data via QTL mapping, TWAS, and colocalization testing. We applied Pantry to Geuvadis and GTEx data, and found that 4,768 of the genes with no identified expression QTL in Geuvadis had QTLs in at least one other transcriptional modality, resulting in a 66% increase in genes over expression QTL mapping. We further found that QTLs exhibit modality-specific functional properties that are further reinforced by joint analysis of different RNA modalities. We also show that generalizing TWAS to multiple RNA modalities (xTWAS) approximately doubles the discovery of unique gene-trait associations, and enhances identification of regulatory mechanisms underlying GWAS signal in 42% of previously associated gene-trait pairs. We provide the Pantry code, RNA phenotypes from all Geuvadis and GTEx samples, and xQTL and xTWAS results on the web.

genetics↗

Shieldin and CST co-orchestrate DNA polymerase-dependent tailed-end joining reactions independently of 53BP1-governed repair pathway choice

53BP1 regulates DNA end-joining in lymphocytes, diversifying immune antigen receptors. This involves nucleosome-bound 53BP1 at DNA double-stranded breaks (DSBs) recruiting RIF1 and shieldin, a poorly understood DNA-binding complex. The 53BP1-RIF1-shieldin axis is pathological in BRCA1-mutated cancers, blocking homologous recombination (HR) and driving illegitimate non-homologous end-joining (NHEJ). However, how this axis regulates DNA end-joining and HR suppression remains unresolved. We investigated shieldin and its interplay with CST, a complex recently implicated in 53BP1-dependent activities. Immunophenotypically, mice lacking shieldin or CST are equivalent, with class-switch recombination co-reliant on both complexes. ATM-dependent DNA damage signalling underpins this cooperation, inducing physical interactions between these complexes that reveal shieldin as a DSB-responsive CST adaptor. Furthermore, DNA polymerase {zeta} functions downstream of shieldin, establishing DNA fill-in synthesis as the physiological function of shieldin-CST. Lastly, 53BP1 suppresses HR and promotes NHEJ in BRCA1-deficient mice and cells independently of shieldin. These findings showcase the resilience of the 53BP1 pathway, achieved through the collaboration of chromatin-bound 53BP1 complexes and DNA end-processing effector proteins.

molecular biology↗

Y and Mitochondrial Chromosomes in the Heterogeneous Stock Rat Population

Genome-wide association studies typically evaluate the autosomes and sometimes the X Chromosome, but seldom consider the Y or mitochondrial Chromosomes. We genotyped the Y and mitochondrial chromosomes in heterogeneous stock rats (Rattus norvegicus), which were created in 1984 by intercrossing eight inbred strains and have subsequently been maintained as an outbred population for 100 generations. As the Y and mitochondrial Chromosomes do not recombine, we determined which founder had contributed these chromosomes for each rat, and then performed association analysis for all complex traits (n=12,055; intersection of 12,116 phenotyped and 15,042 haplotyped rats). We found the eight founders had 8 distinct Y and 4 distinct mitochondrial Chromosomes, however only two of each were observed in our modern heterogeneous stock rat population (Generations 81-97). Despite the unusually large sample size, the p-value distribution did not deviate from expectations; there were no significant associations for behavioral, physiological, metabolome, or microbiome traits after correcting for multiple comparisons. However, both Y and mitochondrial Chromosomes were strongly associated with expression of a few genes located on those chromosomes, which provided a positive control. Our results suggest that within modern heterogeneous stock rats there are no Y and mitochondrial Chromosomes differences that strongly influence behavioral or physiological traits. These results do not address other ancestral Y and mitochondrial Chromosomes that do not appear in modern heterogeneous stock rats, nor do they address effects that may exist in other rat populations, or in other species. Article SummaryHeterogeneous stock rats were created in 1984 by intercrossing eight inbred strains. This genetically and phenotypically diverse population has been used for numerous genetic studies. We developed a method (leveraging existing data) to identify the founder strain origin of Y and mitochondrial Chromosomes in modern heterogeneous stock rats. We examined effects of these chromosomes genotype on behavioral, physiological, and gene expression traits among 12,055 rats. We found no significant associations, except for expression of genes located on these chromosomes.

bioinformatics↗

Microglia shape hippocampal networks but are dispensable for pruning of synapses during development

Microglia, the brain-resident macrophages, are believed to sculpt developing neural circuits by eliminating excess synapses in a process called synaptic pruning, removing apoptotic neurons, and promoting neuronal survival. To elucidate the role of microglia during embryonic and postnatal brain development, we used a mouse model deficient in microglia throughout life as a consequence of deletion of the fms-intronic regulatory element (FIRE) in the Csf1r locus. Surprisingly, young adult Csf1r{Delta}FIRE/{Delta}FIRE mice displayed no changes in excitatory synapse number and spine density of CA1 hippocampal neurons compared to Csf1r+/+ littermates. However, CA1 neurons were less excitable, received less CA3 Schaffer collateral-mediated excitatory input and showed altered synaptic properties, but this did not affect novel object recognition. Cytokine profiling indicated an anti-inflammatory state along with increases in ApoE levels and astrocyte reactivity in brains of Csf1r{Delta}FIRE/{Delta}FIRE mice. Notably, these morphological and functional changes in Csf1r{Delta}FIRE/{Delta}FIRE mice closely resemble the effects of acute microglial depletion in adult mice that had undergone normal development. Thus, our findings challenge the prevailing view that microglia are indispensable for the establishment of neural networks during development.

neuroscience↗

Genome-wide association study and sequence similarity analysis for unilateral renal agenesis using heterogeneous stock rats undercovers the KIT gene and AHR, ATF3, GATA3, HNF1B, POU2F2, and TFCP2 transcription factors as potential candidates to explain incomplete penetrance

1Human unilateral renal agenesis is a congenital urinary tract malformation. Affected individuals have only one kidney, which is often an asymptomatic developmental defect. A total of 5,585 male and female HS rats were assessed for unilateral renal agenesis and genotyped for 3513,321 markers. The R package SAIGEgds was used for the association analysis. The adjusted p-value threshold for the association analysis determined by permutation was equal to 5.6 (-log10). Two additional datasets were used as validation tests. Population two included 1,577 rats genotyped for 7,425,889 markers and a case-control imbalance equal to 1:174; population three included 1,407 rats, genotyped for 254,932 markers and case-control ratio equal to 1:38. The python package GxTheta was used to perform a polygenic epistasis analysis for the analyzed HS rat population. A founder haplotype mosaic determination was performed using the R package QTL2. Associated regions were selected for further analysis, including long-read PacBio sequencing for founder individuals and a founder haplotype prediction test. A similarity analysis at a genomic level and for loci encoding transcription factors predicted to interact with selected sequences inside the associated loci were accomplished. A total of 1,181 polymorphisms were associated with URA. All associated polymorphisms were located on chromosome 14 between 32.9 and 36.6 Mb. The most significant polymorphism was chr14:36,411,266, a G/T transversion. The same associated region was identified in population three. Polygenic epistasis was determined as not predominant for the presentation of URA. Based on the haplotype mosaic probability estimation, cases display a higher probability of inheriting the ACI allele. The long-read sequencing analysis showed the presence of an Erv insertion inside the intron one of the KIT gene located inside the associated region. The Erv insertion comprises one Erv sequence and two Ltr sequences located downstream and upstream of the former. No Erv insertion was identified for the founder strain BN. For ACI and HSRA, only one Ltr sequence was identified. One hundred and seven genes encoding TFs that recognize binding sites on the Erv insertion were analyzed for sequence similarity against the reference HSRA. The TF similarity score analysis for the interaction genotype and phenotype showed significance after FDR correction for 20 TFs, including AHR, HNF1B, JUNB, RARG, and RXRA. A mechanism identifying URA as a threshold phenotype is suggested in HS rats. It implies the existence of a minimum threshold for the final number of nephrons and kidney associated structures required for stalling the apoptotic process of the metanephric rudiments. Animals exhibiting a quantitative cumulative defect would express URA, being this malformation identified as a phenotype with decreased penetrance in the assessed population of HS rats. All these processes are described as mediated by KIT and TFs able to interact with sequences of the Erv insertion.

genetics↗

A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats

The seventh iteration of the reference genome assembly for Rattus norvegicus-- mRatBN7.2--corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared to its predecessor. Gene annotations are now more complete, significantly improving the mapping precision of genomic, transcriptomic, and proteomics data sets. We jointly analyzed 163 short-read whole genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined [~]20.0 million sequence variations, of which 18.7 thousand are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

genomics↗

Exponential increase in QTL detection with increased sample size.

Power analyses are often used to determine the number of animals required for a genome wide association analysis (GWAS). These analyses are typically intended to estimate the sample size needed for at least one locus to exceed a genome-wide significance threshold. A related question that is less commonly considered is the number of significant loci that will be discovered with a given sample size. We used simulations based on a real dataset that consisted of 3,173 male and female adult N/NIH heterogeneous stock (HS) rats to explore the relationship between sample size and the number of significant loci discovered. Our simulations examined the number of loci identified in sub-samples of the full dataset. The sub-sampling analysis was conducted for four traits with low (0.15 {+/-} 0.03), medium (0.31 {+/-} 0.03 and 0.36 {+/-} 0.03) and high (0.46 {+/-} 0.03) SNP-based heritabilities. For each trait, we sub-sampled the data 100 times at different sample sizes (500, 1,000, 1,500, 2,000, and 2,500). We observed an exponential increase in the number of significant loci with larger sample sizes. Our results are consistent with similar observations in human GWAS and imply that future rodent GWAS should use sample sizes that are significantly larger than those needed to obtain a single significant result.

genetics↗

Genome-Wide Association Study Finds Multiple Loci Associated with Intraocular Pressure in HS Rats

Elevated intraocular pressure (IOP) is influenced by environmental and genetic factors. Increased IOP is a major risk factor for most types of glaucoma, including primary open angle glaucoma (POAG). Investigating the genetic basis of IOP may lead to a better understanding of the molecular mechanisms of POAG. The goal of this study was to identify genetic loci involved in regulating IOP using outbred heterogeneous stock (HS) rats. HS rats are a multigenerational outbred population derived from eight inbred strains that have been fully sequenced. This population is ideal for genome-wide association studies (GWASs) owing to the accumulated recombinations among well-defined haplotypes, the relatively high allele frequencies, the accessibility to a large collection of tissue samples, and the large allelic effect size compared to human studies. Both male and female HS rats (N=1,812) were used in the study. Genotyping-by-sequencing was used to obtain ~3.5 million single nucleotide polymorphisms (SNP) from each individual. SNP heritability for IOP in HS rats was 0.32, which agrees with other studies. We performed a GWAS for the IOP phenotype using a linear mixed model and used permutation to determine a genome-wide significance threshold. We identified three genome-wide significant loci for IOP on chromosomes 1, 5, and 16. Next, we sequenced the mRNA of 51 whole eye samples to find cis-eQTLs to aid in identification of candidate genes. We report 5 candidate genes within those loci: Tyr, Ctsc, Plekhf2, Ndufaf6 and Angpt2. Tyr, Ndufaf6 and Angpt2 genes have been previously implicated by human GWAS of IOP-related conditions. Ctsc and Plekhf2 genes represent novel findings that may provide new insight into the molecular basis of IOP. This study highlights the efficacy of HS rats for investigating the genetics of elevated IOP and identifying potential candidate genes for future functional testing. Contribution to the field statementGlaucoma is the leading cause of irreversible blindness worldwide. Intraocular pressure (IOP) is the only known modifiable risk factor. This study describes results of the genome-wide association study (GWAS) performed in outbred rats that identifies known and novel genes involved in IOP regulation. To our knowledge, this is the first GWAS performed for IOP in a rat model. Identifying novel candidate genes in the rat model provides insight into the risk factors for glaucoma in humans and potential pharmacological targets for regulating IOP. The rat model is advantageous for studying natural variations in IOP, controlling environmental exposures, and providing easier access to tissue that can be used in phenotyping and gene expression in future studies.

genomics↗

Polygenic Transcriptome Risk Scores Can Translate Genetic Results Between Species

Genome-wide association studies (GWAS) have implicated specific alleles and genes as risk factors for numerous complex traits. However, translating GWAS results into biologically and therapeutically meaningful discoveries remains extremely challenging. Most GWAS results identify noncoding regions of the genome, suggesting that differences in gene regulation are the major driver of trait variability. To better integrate GWAS results with gene regulatory polymorphisms, we previously developed PrediXcan (also known as "transcriptome-wide association studies" or TWAS), which maps SNPs to predicted gene expression using GWAS data. In this study, we developed RatXcan, a framework that extends this methodology to outbred heterogeneous stock (HS) rats. RatXcan accounts for the close familial relationships among HS rats by modeling the relatedness with a random effect that encodes the genetic relatedness. RatXcan also corrects for polygenic-driven inflation because of the equivalence between a relatedness random effect and the infinitesimal polygenic model. To develop RatXcan, we trained transcript predictors for 8,934 genes using reference genotype and expression data from five rat brain regions. We found that the cis genetic architecture of gene expression in both rats and humans was sparse and similar across brain tissues. We tested the association between predicted expression in rats and two example traits (body length and BMI) using phenotype and genotype data from 5,401 densely genotyped HS rats and identified a significant enrichment between the genes associated with rat and human body length and BMI. Thus, RatXcan represents a valuable tool for identifying the relationship between gene expression and phenotypes across species and paves the way to explore shared biological mechanisms of complex traits. Author SummaryUnderstanding how genetic variation affects phenotypic variation is critical to leveraging the wealth of genetic studies to make biologically and therapeutically useful discoveries. Since most of the genetic loci associated with complex diseases are regulatory in nature--meaning that they do not alter protein coding but rather subtly affect gene expression--transcriptome-wide association studies have been developed. However, these apply only to human data where large samples of unrelated individuals are available. For animal models, relatedness is much higher, causing higher false-positive rates. We propose a computationally efficient method to address this problem and find shared biology between humans and rats. Taken together, our development paves the way to further explore shared biological mechanisms of complex traits across species.

genomics↗

The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats

Heterogeneous Stock (HS) rats are a genetically diverse outbred rat population that is widely used for studying genetics of behavioral and physiological traits. Mapping Quantitative Trait Loci (QTL) associated with transcriptional changes would help to identify mechanisms underlying these traits. We generated genotype and transcriptome data for five brain regions from 88 HS rats. We identified 21,392 cis-QTLs associated with expression and splicing changes across all five brain regions and validated their effects using allele specific expression data. We identified 80 cases where eQTLs were colocalized with genome-wide association study (GWAS) results from nine physiological traits. Comparing our dataset to human data from the Genotype-Tissue Expression (GTEx) project, we found that the HS rat data yields twice as many significant eQTLs as a similarly sized human dataset. We also identified a modest but highly significant correlation between genetic regulatory variation among orthologous genes. Surprisingly, we found less genetic variation in gene regulation in HS rats relative to humans, though we still found eQTLs for the orthologs of many human genes for which eQTLs had not been found. These data are available from the RatGTEx data portal (RatGTEx.org) and will enable new discoveries of the genetic influences of complex traits.

genetics↗

Gene-to-Protein-to-Disease: a segmentation-based approach to identify new cancer prognostic signatures of exome sequence variants

Somatic mutations are early drivers of tumorigenesis and tumor progression. However, the mutations typically occur at variable positions across different individuals, resulting in the data being too sparse to test meaningful associations between variants and phenotypes. To overcome this challenge, we devised a novel approach called Gene-to-Protein-to-Disease (GPD) which accumulates variants into new sequence units as the degree of genetic assault on structural or functional units of each protein. The variant frequencies in the sequence units were highly reproducible between two large cancer cohorts. Survival analysis identified 247 sequence units in which somatic mutations had deleterious effects on overall survival, including consensus driver mutations obtained from multiple calling algorithms. By contrast, around 75% of the survival predictive units had been undetected by conventional gene-level analysis. We demonstrate the ability of these signatures to separate patient groups according to overall survival, therefore providing novel prognostic tools for various cancers. GPD also identified sequence units with somatic mutations whose impact on survival was modified by the occupancy of germline variants in the surrounding regions. The findings indicate that a patients genetic predisposition interacts with the effect of somatic mutations on survival outcome in some cancers.

genomics↗

Joint analysis of gene expression levels and histological images identifies genes associated with tissue morphology

Histological images are used to identify and to characterize complex phenotypes such as tumor stage. Our goal is to associate histological image phenotypes with high-dimensional genomic markers; the limitations to incorporating histological image phenotypes in genomic studies are that the relevant image features are difficult to identify and extract in an automated way, and confounders are difficult to control in this high-dimensional setting. In this paper, we use convolutional autoencoders and sparse canonical correlation analysis (CCA) on histological images and gene expression levels from paired samples to find subsets of genes whose expression values in a tissue sample correlate with subsets of morphological features from the corresponding sample image. We apply our approach, ImageCCA, to three data sets, two from TCGA and one from GTEx v6, and we find three types of biological associations. In TCGA, we find gene sets associated with the structure of the extracellular matrix and cell wall infrastructure, implicating uncharacterized genes in extracellular processes. Across studies, we find sets of genes associated with specific cell types, including muscle tissue and neuronal cells, and with cell type proportions in heterogeneous tissues. In the GTEx v6 data, we find image features that capture population variation in thyroid and in colon tissues associated with genetic variants, suggesting that genetic variation regulates population variation in tissue morphological traits. The software is publicly available at: https://github.com/daniel-munro/imageCCA.

genomics↗