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Hansen, K. D.

Publications and source records attributed to Hansen, K. D..

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Compartmap enables inference of higher-order chromatin structure in individual cells from scRNA-seq and scATAC-seq

Single-cell profiling of chromatin structure remains a challenge due to cost, throughput, and resolution. We introduce compartmap to reconstruct higher-order chromatin domains in individual cells from transcriptomic (RNAseq) and epigenomic (ATACseq) assays. In cell lines and primary human samples, compartmap infers higher-order chromatin structure comparable to specialized chromatin capture methods, and identifies clinically relevant structural alterations in single cells. This provides a common lens to integrate transcriptional and epigenomic results, linking higher-order chromatin architecture to gene regulation and to clinically relevant phenotypes in individual cells.

genomics↗

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)

The traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

bioinformatics↗

Universal prediction of cell cycle position using transfer learning

BackgroundThe cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. ResultsHere, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell cycle embedding using a fixed reference dataset and project new data into this reference embedding; an approach that overcomes key limitations of learning a dataset dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species and even sequencing assays. ConclusionsTricycle generalizes across datasets, is highly scalable and applicable to atlas-level single-cell RNA-seq data.

genomics↗

Placenta DNA methylation at ZNF300 is associated with fetal sex and placental morphology

Fetal sex-specific differences in placental morphology and physiology have been associated with sexually dimorphic health outcomes. However, the molecular mechanisms underlying these sex differences are not well understood. We performed whole genome bisulfite sequencing in 133 placenta samples and discovered a significant difference in DNA methylation (DNAm) at the ZNF300 gene locus between male and female offspring and replicated this result in 6 independent datasets. Additionally, the sex-specific pattern appears to be placenta-specific, is robust to a wide range of gestational ages and adverse health outcomes and is present in sorted placenta villous cytotrophoblast cells. Integration of DNAm, genetic, and placental morphology data from the same individuals revealed ZNF300 methylation is also associated with placenta area, perimeter, and max diameter, genetic variants on chromosomes 5 and X, and may mediate the effects of genetic variation on placental area.

genomics↗

A mammalian methylation array for profiling methylation levels at conserved sequences

Infinium methylation arrays are not available for the vast majority of non-human mammals. Moreover, even if species-specific arrays were available, probe differences between them would confound cross-species comparisons. To address these challenges, we developed the mammalian methylation array, a single custom array that measures 36k CpGs that are well conserved across mammalian species. We designed a set of probes on the array that can tolerate specific cross-species mutations. We annotate the array in over 200 species and report CpG island status and chromatin states in select species. Calibration experiments demonstrate the high fidelity in humans, rats, and mice. The mammalian methylation array has several strengths: it applies to all mammalian species even those that have not yet been sequenced, it provides deep coverage of conserved cytosines facilitating the development epigenetic biomarkers, and it increases the probability that biological insights gained in one species will translate to others.

bioinformatics↗

Human brain region-specific variably methylated regions (VMRs) are enriched for heritability of distinct neuropsychiatric traits

BACKGROUNDDNA methylation dynamics in the brain are associated with normal development and neuropsychiatric disease and differ across functionally distinct brain regions. Previous studies of genome-wide methylation differences among human brain regions focused on limited numbers of individuals and one to two brain regions. RESULTSUsing GTEx samples, we have generated a resource of DNA methylation in purified neuronal nuclei from 8 brain regions as well as lung and thyroid tissues from 12-23 donors. We identified differentially methylated regions between brain regions (DMRs) among neuronal nuclei in both CpG (181,146) and non-CpG (264,868) contexts, few of which were unique to a single pair-wise comparison. This significantly expands the knowledge of differential methylation across the brain by 10-fold. In addition, we present the first differential methylation analysis among neuronal nuclei from basal ganglia tissues and identified 2,295 unique CpG DMRs, many associated with ion transport. Consistent with prior studies, CpG DMRs were enriched in regulatory regions while non-CpG DMRs were enriched in intergenic regions. We also identified 81,130 regions of variably CpG methylated regions (VMRs), i.e. variable methylation among individuals in the same brain region, which were enriched in regulatory regions and in CpG DMRs. Many VMRs were unique to a specific brain region, with only 202 common across all brain regions, as well as lung and thyroid. VMRs identified in the amygdala, anterior cingulate cortex, and hippocampus were enriched for heritability of schizophrenia. CONCLUSIONSThese data suggest that epigenetic variation in these particular human brain regions could be associated with the risk for this neuropsychiatric disorder.

genomics↗

Human methylome variation across Infinium 450K data on the Gene Expression Omnibus

While DNA methylation (DNAm) is the most-studied epigenetic mark, few recent studies probe the breadth of publicly available DNAm array samples. We collectively analyzed 35,360 Illumina Infinium HumanMethylation450K DNAm array samples published on the Gene Expression Omnibus (GEO). We learned a controlled vocabulary of sample labels by applying regular expressions to metadata and used existing models to predict various sample properties including epigenetic age. We found approximately two-thirds of samples were from blood, one-quarter were from brain, and one-third were from cancer patients. 19% of samples failed at least one of Illuminas 17 prescribed quality assessments; signal distributions across samples suggest modifying manufacturer-recommended thresholds for failure would make these assessments more informative. We further analyzed DNAm variances in seven tissues (adipose, nasal, blood, brain, buccal, sperm, and liver) and characterized specific probes distinguishing them. Finally, we compiled DNAm array data and metadata, including our learned and predicted sample labels, into database files accessible via the recountmethylation R/Bioconductor companion package. Its vignettes walk the user through some analyses contained in this paper.

bioinformatics↗

Leveraging the Mendelian Disorders of the Epigenetic Machinery to Systematically Map Functional Epigenetic Variation

The Mendelian Disorders of the Epigenetic Machinery (MDEMs) have emerged as a class of Mendelian disorders caused by loss-of-function variants in epigenetic regulators. Although each MDEM has a different causative gene, they exhibit several overlapping disease manifestations. Here, we hypothesize that this phenotypic convergence is a consequence of common abnormalities at the epigenomic level, which directly or indirectly lead to downstream convergence at the transcriptomic level. Therefore, we seek to identify abnormalities shared across multiple MDEMs, in order to pinpoint locations where epigenetic variation is causally related to disease phenotypes. To this end, we perform a comprehensive interrogation of chromatin (ATAC-Seq) and expression (RNA-Seq) states in B cells from mouse models of three MDEMs (Kabuki types 1&2 and Rubinstein-Taybi syndromes). We build on recent work in covariate-powered multiple testing to develop a new approach for the overlap analysis, which enables us to find extensive overlap primarily localized in gene promoters. We show that disruption of chromatin accessibility at promoters often leads to disruption of downstream gene expression, and identify 463 loci and 249 genes with shared disruption across all three MDEMs. As an example of how widespread dysregulation leads to specific phenotypes, we show that subtle expression alterations of multiple, IgA-relevant genes, collectively contribute to IgA deficiency in KS1 and RT1. In contrast, we predict that KS2 does not have IgA deficiency, and confirm this observation in vivo. We propose that the joint study of MDEMs offers a principled approach for systematically mapping functional epigenetic variation in mammals.

genetics↗

Purifying selection acts on germline methylation to modify the CpG mutation rate at promoters

In the study of molecular features like epigenetic marks, it is appealing to ascribe observed patterns to the action of natural selection. However, this conclusion requires a test for selection based on a well-defined notion of neutrality. Here, focusing on epigenetic marks at gene loci, we formalize what it means for an epigenetic mark to be neutral, and develop a test for selection. Our test respects the foundational aspect of epigenetics: trans-regulation by transcription factors and chromatin modifiers. It also enables adjustment for confounders. We establish that promoter DNA methylation, promoter H3K4me3 and exonic H3K36me3 are all under selection, and that this is unlikely to be a passive consequence of selection on gene expression. The effect of these epigenetic marks on fitness is arguably partly explained by a causal involvement in gene regulation. However, we also investigate the complementary explanation that DNA methylation and H3K36me3 are under selection in part because they modify the mutation rate of important genomic regions. We show that this explanation is consistent with empirical observations as well as population genetics theory, because of the trans-regulation. Exemplifying the protection of important regions from high mutability, we demonstrate that in humans the more intolerant to loss-of-function mutations a gene is, the lower its coding mutation rate is. Our framework for selection inference is simple but general, and we speculate that its core ideas will be useful for additional molecular features beyond epigenetic marks.

evolutionary biology↗

Promoter CpG density is predictive of downstream gene loss-of-function intolerance

The aggregation and joint analysis of large numbers of exome sequences has recently made it possible to de-rive estimates of intolerance to loss-of-function (LoF) variation for human genes. Here, we demonstrate strong and widespread coupling between genic LoF-intolerance and promoter CpG density across the human genome. Genes downstream of the most CpG-rich pro-moters (top 10% CpG density) have a 67.2% probability of being highly LoF-intolerant, using the LOEUF metric from gnomAD. This is in contrast to 7.4% of genes downstream of the most CpG-poor (bottom 10% CpG density) promoters. Combining promoter CpG density with exonic and promoter conservation explains 33.4% of the variation in LOEUF, and the contribution of CpG density exceeds the individual contributions of exonic and promoter conservation. We leverage this to train a simple and easily interpretable predictive model that out-performs other existing predictors and allows us to classify 1,760 genes - which currently lack reliable LOEUF estimates - as highly LoF-intolerant or not. These predictions have the potential to aid in the interpretation of novel patient variants. Moreover, our results reveal that high CpG density is not merely a generic feature of human promoters, but is preferentially encountered at the promoters of the most selectively constrained genes, calling into question the prevailing view that CpG islands are not subject to selection.

genetics↗

Co-expression analysis is biased by a mean-correlation relationship

Estimates of correlation between pairs of genes in co-expression analysis are commonly used to construct networks among genes using gene expression data. Here, we show that the distribution of such correlations depend on the expression level of the involved genes, which we refer to this as a mean-correlation relationship in RNA-seq data, both bulk and single-cell. This dependence introduces a bias in co-expression analysis whereby highly expressed genes are more likely to be highly correlated. Such a relationship is not observed in protein-protein interaction data, suggesting that it is not reflecting biology. Ignoring this bias can lead to missing potentially biologically relevant pairs of genes that are lowly expressed, such as transcription factors. To address this problem, we introduce spatial quantile normalization (SpQN), a method for normalizing local distributions in a correlation matrix. We show that spatial quantile normalization removes the mean-correlation relationship and corrects the expression bias in network reconstruction.

genomics↗

Precocious Chondrocyte Differentiation Disrupts Skeletal Growth in Kabuki Syndrome Mice

Kabuki syndrome 1 (KS1) is a Mendelian disorder of the epigenetic machinery caused by mutations in the gene encoding KMT2D, which methylates lysine 4 on histone H3 (H3K4). KS1 is characterized by intellectual disability, postnatal growth retardation, and distinct craniofacial dysmorphisms. A mouse model (Kmt2d+/ {beta} Geo) exhibits features of the human disorder and has provided insight into other phenotypes; however, the mechanistic basis of skeletal abnormalities and growth retardation remains elusive. Using high-resolution micro-computed tomography we show that Kmt2d+/{beta}Geo mice have shortened long bones and ventral bowing of skulls. In vivo expansion of growth plates within both the skull and long bones suggests disrupted endochondral ossification as a common disease mechanism. Stable chondrocyte cell lines harboring inactivating mutations in Kmt2d exhibit increased proliferation and differentiation, which further supports this mechanism. A known inducer of chondrogenesis, SOX9, and its targets show markedly increased expression in Kmt2d-/- chondrocytes. By transcriptome profiling, we identify Shox2 as a putative KMT2D target. We propose that decreased KMT2D-mediated H3K4me3 at Shox2 releases Sox9 inhibition and thereby leads to enhanced chondrogenesis, providing a novel and plausible explanation for precocious chondrocyte differentiation. Our findings not only provide insight into the pathogenesis of growth retardation in KS1, but also suggest novel therapeutic targets to rescue growth retardation in KS1 and related disorders.

genetics↗

Transcriptional suppression from KMT2D loss disrupts cell cycle and hypoxic responses in neurodevelopmental models of Kabuki syndrome

Chromatin modifiers act to coordinate gene expression changes critical to neuronal differentiation from neural stem/progenitor cells (NSPCs). Lysine-specific methyltransferase 2D (KMT2D) encodes a histone methyltransferase that promotes transcriptional activation, and is frequently mutated in cancers and in the majority (>70%) of patients diagnosed with the congenital, multisystem intellectual disability (ID) disorder Kabuki syndrome 1 (KS1). Critical roles for KMT2D are established in various non-neural tissues, but the effects of KMT2D loss in brain cell development have not been described. We conducted parallel studies of proliferation, differentiation, transcription, and chromatin profiling in KMT2D-deficient human and mouse models to define KMT2D-regulated functions in neurodevelopmental contexts, including adult-born hippocampal NSPCs in vivo and in vitro. We report cell-autonomous defects in proliferation, cell cycle, and survival, accompanied by early NSPC maturation in several KMT2D-deficient model systems. Transcriptional suppression in KMT2D-deficient cells indicated strong perturbation of hypoxia-responsive metabolism pathways. Functional experiments confirmed abnormalities of cellular hypoxia responses in KMT2D-deficient neural cells, and accelerated NSPC maturation in vivo. Together, our findings support a model in which loss of KMT2D function suppresses expression of oxygen-responsive gene programs important to neural progenitor maintenance, resulting in precocious neuronal differentiation in a mouse model of KS1.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC=\"FIGDIR/small/484410v3_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (22K):\norg.highwire.dtl.DTLVardef@55dd1aorg.highwire.dtl.DTLVardef@1270c80org.highwire.dtl.DTLVardef@a5bda2org.highwire.dtl.DTLVardef@144df37_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

Testing the Regulatory Consequences of 1,049 Schizophrenia Associated Variants With a Massively Parallel Reporter Assay

Recent genome-wide association studies (GWAS) identified numerous schizophrenia (SZ) and Alzheimers disease (AD) associated loci, most outside protein-coding regions and hypothesized to affect gene transcription. We used a massively parallel reporter assay (MPRA) to screen, 1,049 SZ and 30 AD variants in 64 and 9 loci respectively for allele differences in driving reporter gene expression. A library of synthetic oligonucleotides assaying each allele 5 times was transfected into K562 chronic myelogenous leukemia lymphoblasts and SK-SY5Y human neuroblastoma cells. 148 variants showed allelic differences in K562 and 53 in SK-SY5Y cells, on average 2.6 variants per locus. Nine showed significant differences in both lines, a modest overlap reflecting different regulatory landscapes of these lines that also differ significantly in chromatin marks. Eight of nine were in the same direction. We observe no preference for risk alleles to increase or decrease expression. We find a positive correlation between the number of SNPs in Linkage Disequilibrium (LD) and the proportion of functional SNPs supporting combinatorial effects that may lead to haplotype selection. Our results prioritize future functional follow up of disease associated SNPs to determine the driver GWAS variant(s), at each locus and enhance our understanding of gene regulation dynamics.

genetics↗

Co-expression patterns define epigenetic regulators associated with neurological dysfunction

Coding variants in genes encoding for epigenetic regulators are an emerging cause of neurological dysfunction and cancer. However, a systematic effort to identify disease candidates within the human epigenetic machinery (EM) has not been performed, and it is unclear whether features exist that distinguish between variation-intolerant and variation-tolerant EM genes, and between EM genes associated with neurological dysfunction versus cancer. Here, we rigorously define a set of 295 human genes with a direct role in epigenetic regulation (writers, erasers, remodelers, readers). Systematic exploration of these genes reveals that while individual enzymatic functions are always mutually exclusive, readers often also exhibit enzymatic activity as well (dual function EM genes). We find that the majority of EM genes are very intolerant to loss-of-function variation, even when compared to the dosage sensitive group of transcription factors. Using this strategy, we identify 103 novel EM disease candidates. We show that the intolerance to loss-of-function variation is driven by the protein domains encoding the epigenetic function, strongly suggesting that disease is caused by a perturbed chromatin state. Unexpectedly, we also describe a large subset of EM genes that are co-expressed within multiple tissues. This subset is almost exclusively populated by extremely variation-intolerant EM genes, and shows enrichment for dual function EM genes. It is also highly enriched for genes associated with neurological dysfunction, even when accounting for dosage sensitivity, but not for cancer-associated EM genes. These findings prioritize novel disease candidate EM genes, and suggest that the co-expression itself may play a functional role in normal neurological homeostasis.

genomics↗

Removing unwanted variation between samples in Hi-C experiments

Hi-C data is commonly normalized using single sample processing methods, with focus on comparisons between regions within a given contact map. Here, we aim to compare contact maps across different samples. We demonstrate that unwanted variation, of likely technical origin, is present in Hi-C data with replicates from different individuals, and that properties of this unwanted variation changes across the contact map. We present BNBC, a method for normalization and batch correction of Hi-C data and show that it substantially improves comparisons across samples, including in a QTL analysis as well as differential enrichment across cell types.

genomics↗

Transient Reduction of DNA Methylation at the Onset of Meiosis in Male Mice

The quality of germ cells depends on successful chromatin organization in meiotic prophase I (MPI). To better understand the epigenetic context of MPI we studied the dynamics of DNA methylation in wild-type male mice. We discovered an extended period of genome-wide transient reduction of DNA methylation (TRDM) during early MPI. Our data show that TRDM arises by passive demethylation in the premeiotic S phase highlighting the abundance of hemimethylated DNA in MPI. Importantly, TRDM unmasks a deficit in retrotransposon LINE-1 DNA methylation contributing to its expression in early MPI. We propose that TRDM facilitates meiosis and gamete quality control.

molecular biology↗