Search

Abstract Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.

Summary: Background: Consumption of fibre, fruits and vegetables have been linked with lower colorectal cancer (CRC) risk. A genome-wide gene-environment (G × E) analysis was performed to test whether genetic variants modify these associations. Methods: A pooled sample of 45 studies including up to 69,734 participants (cases: 29,896; controls: 39,838) of European ancestry were included. To identify G × E interactions, we used the traditional 1--degree-of-freedom (DF) G × E test and to improve power a 2-step procedure and a 3DF joint test that investigates the association between a genetic variant and dietary exposure, CRC risk and G × E interaction simultaneously. Findings: The 3-DF joint test revealed two significant loci with p-value

Summary: Dinoflagellate genomes often are very large and difficult to assemble, which has until recently precluded their analysis with modern functional genomic tools. Here, we present a protocol for mapping three-dimensional (3D) genome organization in dinoflagellates and using it for scaffolding their genome assemblies. We describe steps for crosslinking, nuclear lysis, denaturation, restriction digest, ligation, and DNA shearing and purification. We then detail procedures sequencing library generation and computational analysis, including initial Hi-C read mapping and 3D-DNA scaffolding/assembly correction.For complete details on the use and execution of this protocol, please refer to Marinov et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract Background Archaea, together with Bacteria, represent the two main divisions of life on Earth, with many of the defining characteristics of the more complex eukaryotes tracing their origin to evolutionary innovations first made in their archaeal ancestors. One of the most notable such features is nucleosomal chromatin, although archaeal histones and chromatin differ significantly from those of eukaryotes, not all archaea possess histones and it is not clear if histones are a main packaging component for all that do. Despite increased interest in archaeal chromatin in recent years, its properties have been little studied using genomic tools. Results Here, we adapt the ATAC-seq assay to archaea and use it to map the accessible landscape of the genome of the euryarchaeote Haloferax volcanii. We integrate the resulting datasets with genome-wide maps of active transcription and single-stranded DNA (ssDNA) and find that while H. volcanii promoters exist in a preferentially accessible state, unlike most eukaryotes, modulation of transcriptional activity is not associated with changes in promoter accessibility. Applying orthogonal single-molecule footprinting methods, we quantify the absolute levels of physical protection of H. volcanii and find that Haloferax chromatin is similarly or only slightly more accessible, in aggregate, than that of eukaryotes. We also evaluate the degree of coordination of transcription within archaeal operons and make the unexpected observation that some CRISPR arrays are associated with highly prevalent ssDNA structures. Conclusions Our results provide the first comprehensive maps of chromatin accessibility and active transcription in Haloferax across conditions and thus a foundation for future functional studies of archaeal chromatin.

Abstract A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research.

Abstract Detecting and mitigating off-target activity is critical to the practical application of CRISPR-mediated genome and epigenome editing. While numerous methods have been developed to map Cas9 binding specificity genome-wide, they are generally time-consuming and/or expensive, and not applicable to catalytically dead CRISPR enzymes. We have developed CasKAS, a rapid, inexpensive, and facile assay for identifying off-target CRISPR enzyme binding and cleavage by chemically mapping the unwound single-stranded DNA structures formed upon binding of a sgRNA-loaded Cas9 protein. We demonstrate this method in both in vitro and in vivo contexts.

Abstract Background 3′-end processing by cleavage and polyadenylation is an important and finely tuned regulatory process during mRNA maturation. Numerous genetic variants are known to cause or contribute to human disorders by disrupting the cis-regulatory code of polyadenylation signals. Yet, due to the complexity of this code, variant interpretation remains challenging. Results We introduce a residual neural network model, APARENT2, that can infer 3′-cleavage and polyadenylation from DNA sequence more accurately than any previous model. This model generalizes to the case of alternative polyadenylation (APA) for a variable number of polyadenylation signals. We demonstrate APARENT2’s performance on several variant datasets, including functional reporter data and human 3′ aQTLs from GTEx. We apply neural network interpretation methods to gain insights into disrupted or protective higher-order features of polyadenylation. We fine-tune APARENT2 on human tissue-resolved transcriptomic data to elucidate tissue-specific variant effects. By combining APARENT2 with models of mRNA stability, we extend aQTL effect size predictions to the entire 3′ untranslated region. Finally, we perform in silico saturation mutagenesis of all human polyadenylation signals and compare the predicted effects of $${>}43$$ > 43 million variants against gnomAD. While loss-of-function variants were generally selected against, we also find specific clinical conditions linked to gain-of-function mutations. For example, we detect an association between gain-of-function mutations in the 3′-end and autism spectrum disorder. To experimentally validate APARENT2’s predictions, we assayed clinically relevant variants in multiple cell lines, including microglia-derived cells. Conclusions A sequence-to-function model based on deep residual learning enables accurate functional interpretation of genetic variants in polyadenylation signals and, when coupled with large human variation databases, elucidates the link between functional 3′-end mutations and human health.

Abstract Background Nucleomorphs are remnants of secondary endosymbiotic events between two eukaryote cells wherein the endosymbiont has retained its eukaryotic nucleus. Nucleomorphs have evolved at least twice independently, in chlorarachniophytes and cryptophytes, yet they have converged on a remarkably similar genomic architecture, characterized by the most extreme compression and miniaturization among all known eukaryotic genomes. Previous computational studies have suggested that nucleomorph chromatin likely exhibits a number of divergent features. Results In this work, we provide the first maps of open chromatin, active transcription, and three-dimensional organization for the nucleomorph genome of the chlorarachniophyte Bigelowiella natans. We find that the B. natans nucleomorph genome exists in a highly accessible state, akin to that of ribosomal DNA in some other eukaryotes, and that it is highly transcribed over its entire length, with few signs of polymerase pausing at transcription start sites (TSSs). At the same time, most nucleomorph TSSs show very strong nucleosome positioning. Chromosome conformation (Hi-C) maps reveal that nucleomorph chromosomes interact with one other at their telomeric regions and show the relative contact frequencies between the multiple genomic compartments of distinct origin that B. natans cells contain. Conclusions We provide the first study of a nucleomorph genome using modern functional genomic tools, and derive numerous novel insights into the physical and functional organization of these unique genomes.

The RNA sequence and secondary structure regulate RNA editing by ADAR. Here the authors employ a CRISPR/Cas9-mediated saturation mutagenesis and machine learning to predict RNA editing efficiency of specific substrates.

Abstract We develop the free and open-source model Multi-tissue Splicing (MTSplice) to predict the effects of genetic variants on splicing of cassette exons in 56 human tissues. MTSplice combines MMSplice, which models constitutive regulatory sequences, with a new neural network that models tissue-specific regulatory sequences. MTSplice outperforms MMSplice on predicting tissue-specific variations associated with genetic variants in most tissues of the GTEx dataset, with largest improvements on brain tissues. Furthermore, MTSplice predicts that autism-associated de novo mutations are enriched for variants affecting splicing specifically in the brain. We foresee that MTSplice will aid interpreting variants associated with tissue-specific disorders.

Background & Aims: The association of genetic variation with tissue-specific gene expression and alternative splicing guides functional characterization of complex trait-associated loci and may suggest novel genes implicated in disease. Here, our aims were as follows: (1) to generate reference profiles of colon mucosa gene expression and alternative splicing and compare them across colon subsites (ascending, transverse, and descending), (2) to identify expression and splicing quantitative trait loci (QTLs), (3) to find traits for which identified QTLs contribute to single-nucleotide polymorphism (SNP)-based heritability, (4) to propose candidate effector genes, and (5) to provide a web-based visualization resource. Methods: We collected colonic mucosal biopsy specimens from 485 healthy adults and performed bulk RNA sequencing. We performed genome-wide SNP genotyping from blood leukocytes. Statistical approaches and bioinformatics software were used for QTL identification and downstream analyses. Results: We provided a complete quantification of gene expression and alternative splicing across colon subsites and described their differences. We identified thousands of expression and splicing QTLs and defined their enrichment at genome-wide regulatory regions. We found that part of the SNP-based heritability of diseases affecting colon tissue, such as colorectal cancer and inflammatory bowel disease, but also of diseases affecting other tissues, such as psychiatric conditions, can be explained by the identified QTLs. We provided candidate effector genes for multiple phenotypes. Finally, we provided the Colon Transcriptome Explorer web application. Conclusions: We provide a large characterization of gene expression and splicing across colon subsites. Our findings provide greater etiologic insight into complex traits and diseases influenced by transcriptomic changes in colon tissue.

Off-target effects in CRISPR screens for essential regulatory elements have not been systematically evaluated. Here the authors find Cas9 nuclease, CRISPRi/a each have distinct off-target effects, and that these can be accurately identified and removed using the GuideScan sgRNA specificity score.

Abstract Background Hi-C is currently the most widely used assay to investigate the 3D organization of the genome and to study its role in gene regulation, DNA replication, and disease. However, Hi-C experiments are costly to perform and involve multiple complex experimental steps; thus, accurate methods for measuring the quality and reproducibility of Hi-C data are essential to determine whether the output should be used further in a study. Results Using real and simulated data, we profile the performance of several recently proposed methods for assessing reproducibility of population Hi-C data, including HiCRep, GenomeDISCO, HiC-Spector, and QuASAR-Rep. By explicitly controlling noise and sparsity through simulations, we demonstrate the deficiencies of performing simple correlation analysis on pairs of matrices, and we show that methods developed specifically for Hi-C data produce better measures of reproducibility. We also show how to use established measures, such as the ratio of intra- to interchromosomal interactions, and novel ones, such as QuASAR-QC, to identify low-quality experiments. Conclusions In this work, we assess reproducibility and quality measures by varying sequencing depth, resolution and noise levels in Hi-C data from 13 cell lines, with two biological replicates each, as well as 176 simulated matrices. Through this extensive validation and benchmarking of Hi-C data, we describe best practices for reproducibility and quality assessment of Hi-C experiments. We make all software publicly available at http://github.com/kundajelab/3DChromatin_ReplicateQC to facilitate adoption in the community.

Summary: Unlike the nuclear genome, the mammalian mitochondrial genome (mtDNA) is thought to be coated solely by mitochondrial transcription factor A (TFAM), whose binding sequence preferences are debated. Therefore, higher-order mtDNA organization is considered much less regulated than both the bacterial nucleoid and the nuclear chromatin. However, our recently identified conserved DNase footprinting pattern in human mtDNA, which co-localizes with regulatory elements and responds to physiological conditions, likely reflects a structured higher-order mtDNA organization. We hypothesized that this pattern emerges during embryogenesis. To test this hypothesis, we analyzed assay for transposase-accessible chromatin sequencing (ATAC-seq) results collected during the course of mouse and human early embryogenesis. Our results reveal, for the first time, a gradual and dynamic emergence of the adult mtDNA footprinting pattern during embryogenesis of both mammals. Taken together, our findings suggest that the structured adult chromatin-like mtDNA organization is gradually formed during mammalian embryogenesis. : Biological Sciences; Developmental Genetics; Molecular Genetics; Developmental Biology Subject Areas: Biological Sciences, Developmental Genetics, Molecular Genetics, Developmental Biology

BackgroundLifestyle interventions to reduce body mass index (BMI) are critical public health strategies for type 2 diabetes prevention. While weight loss interventions have shown demonstrable benefit for high-risk and prediabetic individuals, we aimed to determine whether the same benefits apply to those at lower risk.Methods and findingsWe performed a multi-stratum Mendelian randomization study of the effect size of BMI on diabetes odds in 287,394 unrelated individuals of self-reported white British ancestry in the UK Biobank, who were recruited from across the United Kingdom from 2006 to 2010 when they were between the ages of 40 and 69 years. Individuals were stratified on the following diabetes risk factors: BMI, diabetes family history, and genome-wide diabetes polygenic risk score. The main outcome measure was the odds ratio of diabetes per 1-kg/m2 BMI reduction, in the full cohort and in each stratum. Diabetes prevalence increased sharply with BMI, family history of diabetes, and genetic risk. Conversely, predicted risk reduction from weight loss was strikingly similar across BMI and genetic risk categories. Weight loss was predicted to substantially reduce diabetes odds even among lower-risk individuals: for instance, a 1-kg/m2 BMI reduction was associated with a 1.37-fold reduction (95% CI 1.12-1.68) in diabetes odds among non-overweight individuals (BMI < 25 kg/m2) without a family history of diabetes, similar to that in obese individuals (BMI ≥ 30 kg/m2) with a family history (1.21-fold reduction, 95% CI 1.13-1.29). A key limitation of this analysis is that the BMI-altering DNA sequence polymorphisms it studies represent cumulative predisposition over an individual's entire lifetime, and may consequently incorrectly estimate the risk modification potential of weight loss interventions later in life.ConclusionsIn a population-scale cohort, lower BMI was consistently associated with reduced diabetes risk across BMI, family history, and genetic risk categories, suggesting all individuals can substantially reduce their diabetes risk through weight loss. Our results support the broad deployment of weight loss interventions to individuals at all levels of diabetes risk.

CRISPR-Cas9 screens are powerful high-throughput tools but can be confounded by nuclease toxicity. Here the authors design a library of variable length gRNAs with thousands of negative controls, including the targeting of ‘safe’ loci to account for on-target site DNA damage toxicity.

The relationship between noncoding DNA sequence and gene expression is not well-understood. Massively parallel reporter assays (MPRAs), which quantify the regulatory activity of large libraries of DNA sequences in parallel, are a powerful approach to characterize this relationship. We present MPRA-DragoNN, a convolutional neural network (CNN)-based framework to predict and interpret the regulatory activity of DNA sequences as measured by MPRAs. While our method is generally applicable to a variety of MPRA designs, here we trained our model on the Sharpr-MPRA dataset that measures the activity of ∼500,000 constructs tiling 15,720 regulatory regions in human K562 and HepG2 cell lines. MPRA-DragoNN predictions were moderately correlated (Spearman ρ = 0.28) with measured activity and were within range of replicate concordance of the assay. State-of-the-art model interpretation methods revealed high-resolution predictive regulatory sequence features that overlapped transcription factor (TF) binding motifs. We used the model to investigate the cell type and chromatin state preferences of predictive TF motifs. We explored the ability of our model to predict the allelic effects of regulatory variants in an independent MPRA experiment and fine map putative functional SNPs in loci associated with lipid traits. Our results suggest that interpretable deep learning models trained on MPRA data have the potential to reveal meaningful patterns in regulatory DNA sequences and prioritize regulatory genetic variants, especially as larger, higher-quality datasets are produced.

An amendment to this paper has been published and can be accessed via the original article.

Biological networks entail important topological features and patterns critical to understanding interactions within complicated biological systems. Despite a great progress in understanding their structure, much more can be done to improve our inference and network analysis. Spectral methods play a key role in many network-based applications. Fundamental to spectral methods is the Laplacian, a matrix that captures the global structure of the network. Unfortunately, the Laplacian does not take into account intricacies of the network's local structure and is sensitive to noise in the network. These two properties are fundamental to biological networks and cannot be ignored. We propose an alternative matrix Vicus. The Vicus matrix captures the local neighborhood structure of the network and thus is more effective at modeling biological interactions. We demonstrate the advantages of Vicus in the context of spectral methods by extensive empirical benchmarking on tasks such as single cell dimensionality reduction, protein module discovery and ranking genes for cancer subtyping. Our experiments show that using Vicus, spectral methods result in more accurate and robust performance in all of these tasks.

BACKGROUND:The evaluation of less frequent genetic variants and their effect on complex disease pose new challenges for genomic research. To investigate whether epigenetic data can be used to inform aggregate rare-variant association methods (RVAM), we assessed whether variants more significantly associated with colorectal cancer (CRC) were preferentially located in non-coding regulatory regions, and whether enrichment was specific to colorectal tissues. METHODS:Active regulatory elements (ARE) were mapped using data from 127 tissues and cell-types from NIH Roadmap Epigenomics and Encyclopedia of DNA Elements (ENCODE) projects. We investigated whether CRC association p-values were more significant for common variants inside versus outside AREs, or 2) inside colorectal (CR) AREs versus AREs of other tissues and cell-types. We employed an integrative epigenomic RVAM for variants with allele frequency

We performed frequency-domain analysis in the genomes of various organisms using tricolor spectrograms, identifying several types of distinct visual patterns characterizing specific DNA regions. We relate patterns and their frequency characteristics to the sequence characteristics of the DNA. At times, the spectrogram patterns could be related to the structure of the corresponding protein region by using various public databases such as GenBank. Some patterns are explained from the biological nature of the corresponding regions, which relate to chromosome structure and protein coding, and some patterns have yet unknown biological significance. We found biologically meaningful patterns, on the scale of millions of base pairs, to a few hundred base pairs. Chromosome-wide patterns include periodicities ranging from 2 to 300. The color of the spectrogram depends on the nucleotide content at specific frequencies, and therefore can be used as a local indicator of CG content and other measures of relative base content. Several smaller-scale patterns were found to represent different types of domains made up of various tandem repeats.

Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included.