Biology subjects

Arking, D. E.

Publications and source records attributed to Arking, D. E..

16 recordsLinked to original sources

bioRxiv2025

cpiVAE: Robust and Interpretable Cross-Platform Proteomics Imputation

Large-scale plasma proteomic studies often use different high-throughput affinity platforms, and measurements of the same protein across platforms are often discordant. Discordance hinders cross-study integration. Improving proteomics data integration would enable more powerful meta-analyses, improve statistical power for biomarker discovery, and provide a better understanding of proteome-phenotype relationships. Here we present a cross-platform proteomics imputation variational autoencoder (cpiVAE), a deep generative model for bidirectional imputation of protein abundances between two widely used platforms: Olink and SomaScan. Using a training cohort of paired measurements from the China Kadoorie Biobank (CKB), cpiVAE learns a joint latent representation that enables cross-platform imputation. The cpiVAE method improves benchmarks provided by established methods, k-nearest neighbors (KNN) Weighted Nearest Neighbors (WNN, from Seurat v4). The cpiVAE method achieves up to 30% higher correlation between imputed and true values than KNN and WNN. The cpiVAE method also generalizes well to an independent cohort from the Atherosclerosis Risk in Communities Study (ARIC). Without retraining, cpiVAE maintains high performance compared to benchmarks. Associations of imputed protein levels with clinical phenotypes closely mirror results using the actual measurements and increases power in a meta-analysis scenario. A post-hoc feature importance matrix enables interpretation of this AI model. Protein pair features extracted from cpiVAE have significant overlap with known associations in the Search Tool for the Retrieval of Interacting Genes (STRING) database. In summary, cpiVAE offers an accurate, generalizable, and interpretable solution for cross-platform proteomic imputation, enabling integrated analyses across studies with proteomics measured on different platforms. This user-friendly framework and pre-trained model weights are available under a BSD2 open source license at https://github.com/joelbaderlab/cpiVAE_v1.

bioinformatics↗

bioRxiv2024

Mitochondrial DNA copy number reduction via in vitro TFAM knockout remodels the nuclear epigenome and transcriptome

Mitochondrial DNA copy number (mtDNA-CN) is associated with several age-related chronic diseases and is a predictor of all-cause mortality. Here, we examine site-specific differential nuclear DNA (nDNA) methylation and differential gene expression resulting from in vitro reduction of mtDNA-CN to uncover shared genes and biological pathways mediating the effect of mtDNA-CN on disease. Epigenome and transcriptome profiles were generated for three independent human embryonic kidney (HEK293T) cell lines harbouring a mitochondrial transcription factor A (TFAM) heterozygous knockout generated via CRISPR-Cas9, and matched control lines. We identified 4,242 differentially methylated sites, 228 differentially methylated regions, and 179 differentially expressed genes associated with mtDNA-CN. Integrated analysis uncovered 381 Gene-CpG pairs. GABAA receptor genes and related pathways, the neuroactive ligand receptor interaction pathway, ABCD1/2 gene activity, and cell signalling processes were overrepresented, providing insight into the underlying biological mechanisms facilitating these associations. We also report evidence implicating chromatin state regulatory mechanisms as modulators of mtDNA-CN effect on gene expression. We demonstrate that mitochondrial DNA variation signals to the nuclear DNA epigenome and transcriptome and may lead to nuclear remodelling relevant to development, aging, and complex disease.

genomics↗

bioRxiv2023

Polygenic Risk Score Associates with Atherosclerotic Plaque Characteristics at Autopsy

BackgroundPolygenic risk scores (PRS) for coronary artery disease (CAD) potentially improve cardiovascular risk prediction. However, their relationship with histopathologic features of CAD has never been examined systematically. MethodsFrom 4,327 subjects referred to CVPath by the State of Maryland Office Chief Medical Examiner (OCME) for sudden death between 1994 and 2015, 2,455 cases were randomly selected for genotyping. We generated PRS from 291 known CAD risk loci. Detailed histopathologic examination of the coronary arteries was performed in all subjects. The primary study outcome measurements were histopathologic plaque features determining severity of atherosclerosis, including %stenosis, calcification, thin-cap fibroatheromas (TCFA), and thrombotic CAD. ResultsAfter exclusion of cases with insufficient DNA sample quality or with missing data, 954 cases (mean age 48.8{+/-}14.7; 75.7% men) remained in the final study cohort. Subjects in the highest PRS quintile exhibited more severe atherosclerosis compared to subjects in the lowest quintile, with greater %stenosis (80.3%{+/-}27.0% vs. 50.4%{+/-}38.7%; adjusted p<0.001) and a higher frequency of calcification (69.6% vs. 35.8%; adjusted p=0.004) and TCFAs (26.7% vs. 9.5%; adjusted p=0.007). Even after adjustment for traditional CAD risk factors subjects within the highest PRS quintile had higher odds of severe atherosclerosis (i.e., [≥]75% stenosis; adjusted OR 3.77; 95%CI 2.10-6.78; p<0.001) and plaque rupture (adjusted OR 4.05; 95%CI 2.26-7.24; p<0.001). Moreover, subjects within the highest quintile had higher odds of CAD-associated cause of death, especially among those aged 50 years and younger (adjusted OR 4.08; 95%CI 2.01-8.30; p<0.001). No associations were observed with plaque erosion. ConclusionsThis is the first autopsy study investigating associations between PRS and atherosclerosis severity at the histopathologic level in subjects with sudden death. Our pathological analysis suggests PRS correlates with plaque burden and features of advanced atherosclerosis and may be useful as a method for CAD risk stratification, especially in younger subjects. HighlightsO_LIIn this autopsy study including 954 subjects within the CVPath Sudden Death Registry, high PRS correlated with plaque burden and atherosclerosis severity. C_LIO_LIThe PRS showed differential associations with plaque rupture and plaque erosion, suggesting different etiologies to these two causes of thrombotic CAD. C_LIO_LIPRS may be useful for risk stratification, particularly in the young. Further examination of individual risk loci and their association with plaque morphology may help understand molecular mechanisms of atherosclerosis, potentially revealing new therapy targets of CAD. C_LI Graphic AbstractA polygenic risk score, generated from 291 known CAD risk loci, was assessed in 954 subjects within the CVPath Sudden Death Registry. Histopathologic examination of the coronary arteries was performed in all subjects. Subjects in the highest PRS quintile exhibited more severe atherosclerosis as compared to subjects in the lowest quintile, with a greater plaque burden, more calcification, and a higher frequency of plaque rupture.

genetics↗

bioRxiv2022

Quantifying constraint in human mitochondrial DNA

Mitochondrial DNA (mtDNA) has an important, yet often overlooked, role in health and disease. Constraint models quantify the removal of deleterious variation from the population by selection, representing a powerful tool for identifying genetic variation underlying human phenotypes1-4. However, a constraint model for the mtDNA has not been developed, due to its unique features. Here we describe the development of a mitochondrial constraint model and its application to the Genome Aggregation Database (gnomAD), a large-scale population dataset reporting mtDNA variation across 56,434 humans5. Our results demonstrate strong depletion of expected variation, suggesting most deleterious mtDNA variants remain undiscovered. To aid their identification, we compute constraint metrics for every mitochondrial protein, tRNA, and rRNA gene, revealing a spectrum of intolerance to variation. We characterize the most constrained regions within genes via regional constraint, and positions across the entire mtDNA via local constraint, showing their enrichment in pathogenic variation and functionally critical sites, including topological clustering in 3D protein and RNA structures. Notably, we identify constraint at often overlooked sites, such as rRNAs and non-coding regions. Lastly, we demonstrate how these metrics can improve the discovery of mtDNA variation underlying rare and common human phenotypes.

genetics↗

bioRxiv2022

The functional impact of rare variation across the regulatory cascade

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis (MESA) which included several hundred individuals with whole genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, ten years apart. We evaluated each multi-omic phenotypes ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62x and rare frameshift variants 216x as frequently as controls, compared to 13x to 27x for expression or protein effects alone. We developed a Bayesian hierarchical model to prioritize specific rare variants underlying multi-omic signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimers disease.

genomics↗

bioRxiv2021

Rare instances of non-random dropout with the monochrome multiplex qPCR assay for mitochondrial DNA copy number

Mitochondrial DNA copy number (mtDNA-CN) is a proxy for mitochondrial function and has been of increasing interest to the mitochondrial research community. There are a number of ways to measure mtDNA-CN, ranging from qPCR to whole genome sequencing [1]. A recent article in the Journal of Molecular Diagnostics [2] described a novel method for measuring mtDNA-CN that is both inexpensive and reproducible. After adapting the assay for use in our lab, we have found it to be reproducible and well-correlated with mtDNA-CN derived from whole genome sequencing. However, certain individuals show poor concordance between the two measures, particularly individuals with qPCR mtDNA-CN measurements >3 standard deviations below the sample mean, which corresponds to roughly 1% of assayed individuals (Figure 1). After examining whole genome sequencing data, this seems to be due to specific polymorphisms within the D-loop primer region, at positions MT 338, 340, 452, 457, 458, 460, 461, 466, and 467. All individuals with a variant in at least one of these positions have non-concordant mtDNA-CN measurements. Meanwhile, variants observed at other positions within the primer region do not appear to cause dropout. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/463983v1_fig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@134ca51org.highwire.dtl.DTLVardef@ce9196org.highwire.dtl.DTLVardef@1b82af6org.highwire.dtl.DTLVardef@c9701_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Discrepancy between the monochrome multiplex qPCR mtDNA-CN and the whole genome sequencing mtDNA-CN for 1,732 distinct individuals. Data are centered at 0 and scaled so that the standard deviation = 1. The dotted red line represents 3 standard deviations beneath the sample mean. Individuals in the U, L1, L4, and T haplogroups have a disproportionately higher risk of discordant measures between the two assays. C_FIG

genetics↗

bioRxiv2020

Polygenic Risk Scores for Kidney Function to the Circulating Proteome, and Incident Kidney Diseases: the Atherosclerosis Risk in Communities Study

Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (estimated glomerular filtration rate, eGFR). The relationship of polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known. We developed a genome-wide polygenic risk score (PRS) using a weighted average of 1.2 million SNPs for eGFR using the LDpred algorithm, summary statistics generated by a European-ancestry (EA) meta-analysis of the CKDGen Consortium (N=558,423) and UK Biobank GWAS for eGFR (90% of the cohort; N=289,432), followed by best parameter selection using data from the remaining 10% of the UK Biobank (N=32,159). We then tested the association of the PRS among 8,886 EA participants in the Atherosclerosis Risk in Communities (ARIC) study (mean age: 54{+/-}6 years, 53% female) with incident chronic kidney disease (CKD), end stage kidney disease (ESKD), kidney failure (KF), and acute kidney injury (AKI). We also examined 4,877 plasma proteins measured at two time points (visit 3 (1993-95) and visit 5 (2011-13)) in relation to the PRS and compared associations between the proteome and eGFR itself. All models were adjusted for age, sex, center, and the first 10 principal components of ancestry. The developed PRS had an R2 for eGFR of 0.07 in ARIC. Over 30 years of follow up, the number of incident CKD, ESKD, KF, and AKI were 2,959, 137, 470, and 1,723, respectively. The PRS showed significant associations with all outcomes: hazard ratios (95% CI) per 1 SD lower PRS were 1.33 (1.28, 1.39), 1.20 (1.00, 1.42), 1.17 (1.06, 1.28), and 1.07 (1.02, 1.12) for incident CKD, ESKD, KF, and AKI respectively. The PRS was significantly associated (Bonferroni threshold P<1.02 x 10-5) with 108 proteins at both time points. The strongest associations were with cystatin-C (a marker of kidney function used in clinical practice), collagen alpha-1 (XV) chain, and desmocollin-2. All significant correlations with the PRS were negative, except those of testican-2 and angiostatin. Correlations of proteins with eGFR were much stronger than those with the PRS. Overall, we demonstrated that the PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases as well as broadly influence the plasma proteome.

genetics↗

bioRxiv2020

Blood-derived mitochondrial DNA copy number is associated with gene expression across multiple tissues and is predictive for incident neurodegenerative disease

BackgroundMitochondrial DNA copy number (mtDNA-CN) can be used as a proxy for mitochondrial function and is associated with a number of aging-related diseases. However, it is unclear how mtDNA-CN measured in blood can reflect risk for diseases that primarily manifest in other tissues. Using the Genotype-Tissue Expression Project, we interrogated the relationships between mtDNA-CN measured in whole blood and gene expression from whole blood as well as 47 additional tissues. ResultsWe evaluated associations between blood-derived mtDNA-CN and gene expression in whole blood for 418 individuals, correcting for known confounders and surrogate variables derived from RNA-sequencing. Using a permutation-derived cutoff (p<2.70e-6), mtDNA-CN was significantly associated with expression for 721 genes in whole blood, including nuclear genes that are required for mitochondrial DNA replication. Significantly enriched pathways included splicing (p=1.03e-8) and ubiquitin-mediated proteolysis (p=2.4e-10). Genes with target sequences for the mitochondrial transcription factor NRF1 were also enriched (p=1.76e-35). In non-blood tissues, there were more significantly associated genes than expected in 30 out of 47 tested tissues, suggesting that global gene expression in those tissues is correlated with mtDNA-CN. Pathways that were associated in multiple tissues included RNA-binding, catalysis, and neurodegenerative disease. We evaluated the association between mtDNA-CN and incident neurodegenerative disease in an independent dataset, the UK Biobank, using a Cox proportional-hazards model. Higher mtDNA-CN was significantly associated with lower risk for incident neurodegenerative disease (HR=0.73, 95% CI= 0.66;0.90). ConclusionsThe observation that mtDNA-CN measured in whole blood is associated with gene expression in other tissues suggests that blood-derived mtDNA-CN can reflect metabolic health across multiple tissues. Key pathways in maintaining cellular homeostasis, including splicing, RNA binding, and catalytic genes were significantly associated with mtDNA-CN, reinforcing the importance of mitochondria in aging-related disease. As a specific example, genes involved in neurodegenerative disease were significantly enriched in multiple tissues. This finding, validated in a large independent cohort study showing an inverse association between mtDNA-CN and neurodegenerative disease, solidifies the link between blood-derived mtDNA-CN, altered gene expression in both blood and non-blood tissues, and aging-related disease.

genetics↗

bioRxiv2019

Mitochondrial DNA Copy Number (mtDNA-CN) Can Influence Mortality and Cardiovascular Disease via Methylation of Nuclear DNA CpGs

BackgroundMitochondrial DNA copy number (mtDNA-CN) has been associated with a variety of aging-related diseases, including all-cause mortality. However, the mechanism by which mtDNA-CN influences disease is not currently understood. One such mechanism may be through regulation of nuclear gene expression via the modification of nuclear DNA (nDNA) methylation. MethodsTo investigate this hypothesis, we assessed the relationship between mtDNA-CN and nDNA methylation in 2,507 African American (AA) and European American (EA) participants from the Atherosclerosis Risk in Communities (ARIC) study. To validate our findings we assayed an additional 2,528 participants from the Cardiovascular Health Study (CHS) (N=533) and Framingham Heart Study (FHS) (N=1,995). We further assessed the effect of experimental modification of mtDNA-CN through knockout of TFAM, a regulator of mtDNA replication, via CRISPR-Cas9. ResultsThirty-four independent CpGs were associated with mtDNA-CN at genome-wide significance (P<5x10-8). Meta-analysis across all cohorts identified six mtDNA-CN associated CpGs at genome-wide significance (P<5x10-8). Additionally, over half of these CpGs were associated with phenotypes known to be associated with mtDNA-CN, including coronary heart disease, cardiovascular disease, and mortality. Experimental modification of mtDNA-CN demonstrated that modulation of mtDNA-CN directly drives changes in nDNA methylation and gene expression of specific CpGs and nearby transcripts. Strikingly, the neuroactive ligand receptor interaction KEGG pathway was found to be highly overrepresented in the ARIC cohort (P= 5.24x10-12), as well as the TFAM knockout methylation (P=4.41x10-4) and expression (P=4.30x10-4) studies. ConclusionsThese results demonstrate that changes in mtDNA-CN influence nDNA methylation at specific loci and result in differential expression of specific genes that may impact human health and disease via altered cell signaling.

genomics↗

bioRxiv2019

The effect of sex and underlying disease on the genetic association of QT interval and sudden cardiac death

BackgroundSudden cardiac death (SCD) accounts for ~300,000 deaths annually in the US. Men have a higher risk of SCD and are more likely to have underlying coronary artery disease (CAD) than women. In contrast, women are more likely to have arrhythmic events in the setting of inherited or acquired QT prolongation. Moreover, there is evidence of sex differences in the underlying genetics of QT interval duration. Using sex- and CAD-stratified analyses, we assess differences in genetic association between prolonged QT interval and SCD risk.\n\nMethodsWe examined 2,282 SCD subjects with autopsy-confirmed underlying disease from the Fingesture cohort and 3,561 Finnish controls. The SCD subjects were stratified by underlying disease (ischemic vs. non-ischemic) and by sex. We used logistic regression to test for association between the top QT interval associated SNP, rs12143842 (in the NOS1AP locus), and SCD risk. We also performed Mendelian randomization to test for causal association of QT interval in the various subgroups.\n\nResultsFemale SCD victims with underlying non-ischemic disease had the strongest association between rs12143842 and SCD risk (OR=1.37; 95% CI, 1.07-1.75) and the strongest causal association, established using Mendelian randomization, between prolonged QT interval and SCD (OR in SCD risk per SD increase in QT, 3.60; 95% CI, 1.22-10.49). Ischemic SCD victims, irrespective of sex, did not show an association between rs12143842 and SCD risk or a causal association for QT interval.\n\nConclusionsThis study provides evidence that the causal effect of QT prolongation on SCD risk differs by sex and underlying disease.

genetics↗

bioRxiv2019

Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways

Liability to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published GWAS in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected (\"Concordant\") direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants demonstrating the counterintuitive (\"Discordant\") relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education and/or schizophrenia at p<5x10-8. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs, and many of these have been validated by larger, more recent single-phenotype GWAS. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms: early neurodevelopmental pathways that characterize concordant allelic variation, and adulthood synaptic pruning pathways that were linked to the paradoxical positive genetic association between education and schizophrenia. Further, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia, but also to the broader biological dimensions that are implicated in both general health outcomes and psychiatric illness.

genetics↗

bioRxiv2018

Discovering patterns of pleiotropy in genome-wide association studies

MotivationGenome-wide association studies have had great success in identifying human genetic variants associated with disease, disease risk factors, and other biomedical phenotypes. Many variants are associated with multiple traits, even after correction for trait-trait correlation. Discovering subsets of variants associated with a shared subset of phenotypes could help reveal disease mechanisms, suggest new therapeutic options, and increase the power to detect additional variants with similar pattern of associations. Here we introduce two methods based on a Bayesian framework, SNP And Pleiotropic PHenotype Organization (SAPPHO), one modeling independent phenotypes (SAPPHO-I) and the other incorporating a full phenotype covariance structure (SAPPHO-C). These two methods learn patterns of pleiotropy from genotype and phenotype data, using identified associations to discover additional associations with shared patterns.\n\nResultsThe SAPPHO methods, along with other recent approaches for pleiotropic association tests, were assessed using data from the Atherosclerotic Risk in Communities (ARIC) study of 8,000 individuals, whose gold-standard associations were provided by meta-analysis of 40,000 to 100,000 individuals from the CHARGE consortium. Using power to detect gold-standard associations at genome-wide significance (0.05 family-wise error rate) as a metric, SAPPHO performed best. The SAPPHO methods were also uniquely able to select the most significant variants in a parsimonious model, excluding other less likely variants within a linkage disequilibrium block. For meta-analysis, the SAPPHO methods implement summary modes that use sufficient statistics rather than full phenotype and genotype data. Meta-analysis applied to CHARGE detected 16 additional associations to the gold-standard loci, as well as 124 novel loci, at 0.05 false discovery rate. Reasons for the superior performance were explored by performing simulations over a range of scenarios describing different genetic architectures. With SAPPHO we were able to learn genetic structures that were hidden using the traditional univariate tests.\n\nAvailabilityhttps://bitbucket.org/baderlab/fast/wiki/Home. SAPPHO software is available under the GNU General Public License, v2.

genetics↗

bioRxiv2017

GWAS meta-analysis (N=279,930) identifies new genes and functional links to intelligence

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimers dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

genetics↗

bioRxiv2017

Large-Scale cognitive GWAS Meta-analysis Reveals Tissue-Specific Neural Expression and Potential Nootopic Drug Targets

Neurocognitive ability is a fundamental readout of brain function, and cognitive deficits are a critical component of neuropsychiatric disorders, yet neurocognition is poorly understood at the molecular level. In the present report, we present the largest genome-wide association studies (GWAS) of cognitive ability to date (N=107,207), and further enhance signal by combining results with a large-scale GWAS of educational attainment. We identified 70 independent genomic loci associated with cognitive ability, 34 of which were novel. A total of 350 genes were implicated, and this list showed significant enrichment for genes associated with Mendelian disorders with an intellectual disability phenotype. Competitive pathway analysis of gene results implicated the biological process of neurogenesis, as well as the gene targets of two pharmacologic agents: cinnarizine, a T-type calcium channel blocker; and LY97241, a potassium channel inhibitor. Transcriptome-wide analysis revealed that the implicated genes were strongly expressed in neurons, but not astrocytes or oligodendrocytes, and were more strongly associated with fetal brain expression than adult brain expression. Several tissue-specific gene expression relationships to cognitive ability were observed (for example, DAG1 levels in the hippocampus). Finally, we report novel genetic correlations between cognitive ability and disparate phenotypes such as maternal age at first birth and number of children, as well as several autoimmune disorders.

genomics↗

bioRxiv2017

Towards the human cellular microRNAome

microRNAs are short RNAs that serve as master regulators of gene expression and are essential components of normal development as well as modulators of disease. MicroRNAs generally act cell autonomously and thus their localization to specific cell types is needed to guide our understanding of microRNA activity. Current tissue-level data has caused considerable confusion and comprehensive cell-level data does not yet exist. Here we establish the landscape of human cell-specific microRNA expression. This project evaluated 8 billion small RNA-seq reads from 46 primary cell types, 42 cancer or immortalized cell lines, and 26 tissues. It identified both specific and ubiquitous patterns of expression that strongly correlate with adjacent super-enhancer activity. Analysis of unaligned RNA reads uncovered 207 unknown minor strand (passenger) microRNAs of known microRNA loci and 2,632 novel putative microRNA loci. Although cancer cell lines generally recapitulated the expression patterns of matched primary cells, their isomiR sequence families exhibited increased disorder suggesting Drosha and Dicer-dependent microRNA processing variability. Cell-specific patterns of microRNA expression were used to deconvolute variable cellular composition of adipose tissue samples highlighting one use of this cell-specific microRNA expression data. Characterization of cellular microRNA expression across a wide variety of cell types provides a new understanding of this critical regulatory RNA species.

genomics↗

bioRxiv2016

Cross-tissue integration of genetic and epigenetic data offers insight into autism spectrum disorder

Epigenetics is an emerging area of investigation for Autism Spectrum Disorder (ASD). Integration of epigenetic information with ASD genetic results may elucidate functional insights not possible via either source of information in isolation. We used concurrent genotype and DNA methylation (DNAm) data from cord blood and peripheral blood from preschool-aged children to identify SNPs associated with DNA methylation, or methylation quantitative trait loci (meQTLs), and combined this with publicly available fetal brain and lung meQTL lists to assess enrichment of ASD GWAS results for tissue-specific meQTLs. ASD-associated SNPs were enriched for fetal brain (OR = 3.55; p < 0.001) and peripheral blood meQTLs (OR = 1.58; p < 0.001). The CpG site targets of ASD meQTLs across cord, blood, and brain tissues were enriched for immune-related pathways, consistent with other expression and DNAm results in ASD, and revealing pathways not implicated by genes identified from ASD rare variant work. Further, DNaseI hypersensitive sites and the STAT1 and TAF1 transcription factor binding sites were enriched for meQTL target CpGs of SNPs associated with psychiatric conditions. This joint analysis of genotype and DNAm demonstrates the potential utility of both brain and blood-based DNAm for insights into ASD and psychiatric phenotypes more broadly.

genomics↗