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A major challenge in human genetics is to identify the molecular mechanisms of trait-associated and disease-associated variants. To achieve this, quantitative trait locus (QTL) mapping of genetic variants with intermediate molecular phenotypes such as gene expression and splicing have been widely adopted(1,2). However, despite successes, the molecular basis for a considerable fraction of trait-associated and disease-associated variants remains unclear(3,4). Here we show that ADAR-mediated adenosine-to-inosine RNA editing, a post-transcriptional event vital for suppressing cellular double-stranded RNA (dsRNA)-mediated innate immune interferon responses(5–11), is an important potential mechanism underlying genetic variants associated with common inflammatory diseases. We identified and characterized 30,319 cis-RNA editing QTLs (edQTLs) across 49 human tissues. These edQTLs were significantly enriched in genome-wide association study signals for autoimmune and immune-mediated diseases. Colocalization analysis of edQTLs with disease risk loci further pinpointed key, putatively immunogenic dsRNAs formed by expected inverted repeat Alu elements as well as unexpected, highly over-represented cis-natural antisense transcripts. Furthermore, inflammatory disease risk variants, in aggregate, were associated with reduced editing of nearby dsRNAs and induced interferon responses in inflammatory diseases. This unique directional effect agrees with the established mechanism that lack of RNA editing by ADAR1 leads to the specific activation of the dsRNA sensor MDA5 and subsequent interferon responses and inflammation(7–9). Our findings implicate cellular dsRNA editing and sensing as a previously underappreciated mechanism of common inflammatory diseases.

Background The vast majority of trait-associated variants identified using genome-wide association studies (GWAS) are noncoding, and therefore assumed to impact gene regulation. However, the majority of trait-associated loci are unexplained by regulatory quantitative trait loci (QTLs). Results We perform a comprehensive characterization of the putative mechanisms by which GWAS loci impact human immune traits. By harmonizing four major immune QTL studies, we identify 26,271 expression QTLs (eQTLs) and 23,121 splicing QTLs (sQTLs) spanning 18 immune cell types. Our colocalization analyses between QTLs and trait-associated loci from 72 GWAS reveals that genetic effects on RNA expression and splicing in immune cells colocalize with 40.4% of GWAS loci for immune-related traits, in many cases increasing the fraction of colocalized loci by two fold compared to previous studies. Notably, we find that the largest contributors of this increase are splicing QTLs, which colocalize on average with 14% of all GWAS loci that do not colocalize with eQTLs. By contrast, we find that cell type-specific eQTLs, and eQTLs with small effect sizes contribute very few new colocalizations. To investigate the 60% of GWAS loci that remain unexplained, we collect H3K27ac CUT&Tag data from rheumatoid arthritis and healthy controls, and find large-scale differences between immune cells from the different disease contexts, including at regions overlapping unexplained GWAS loci. Conclusion Altogether, our work supports RNA splicing as an important mediator of genetic effects on immune traits, and suggests that we must expand our study of regulatory processes in disease contexts to improve functional interpretation of as yet unexplained GWAS loci.

Single-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for many biological and medical applications as it allows users to measure gene expression levels in a cell type–specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq data sets with cell types that may overlap only partially and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering and in terms of avoiding overcorrection. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell type–specific differential gene expression comparisons across biopsy sites and disease conditions and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online.

Recent progress in deep learning has greatly improved the prediction of RNA splicing from DNA sequence. Here, we present Pangolin, a deep learning model to predict splice site strength in multiple tissues. Pangolin outperforms state-of-the-art methods for predicting RNA splicing on a variety of prediction tasks. Pangolin improves prediction of the impact of genetic variants on RNA splicing, including common, rare, and lineage-specific genetic variation. In addition, Pangolin identifies loss-of-function mutations with high accuracy and recall, particularly for mutations that are not missense or nonsense, demonstrating remarkable potential for identifying pathogenic variants.

Motivation Next-generation sequencing is rapidly improving diagnostic rates in rare Mendelian diseases, but even with whole genome or whole exome sequencing, the majority of cases remain unsolved. Increasingly, RNA sequencing is being used to solve many cases that evade diagnosis through sequencing alone. Specifically, the detection of aberrant splicing in many rare disease patients suggests that identifying RNA splicing outliers is particularly useful for determining causal Mendelian disease genes. However, there is as yet a paucity of statistical methodologies to detect splicing outliers. Results We developed LeafCutterMD, a new statistical framework that significantly improves the previously published LeafCutter in the context of detecting outlier splicing events. Through simulations and analysis of real patient data, we demonstrate that LeafCutterMD has better power than the state-of-the-art methodology while controlling false-positive rates. When applied to a cohort of disease-affected probands from the Mayo Clinic Center for Individualized Medicine, LeafCutterMD recovered all aberrantly spliced genes that had previously been identified by manual curation efforts. Availability and implementation The source code for this method is available under the opensource Apache 2.0 license in the latest release of the LeafCutter software package available online at http://davidaknowles.github.io/leafcutter. Supplementary information Supplementary data are available at Bioinformatics online.

Long introns with short exons in vertebrate genes are thought to require spliceosome assembly across exons (exon definition), rather than introns, thereby requiring transcription of an exon to splice an upstream intron. Here, we developed CoLa-seq (co-transcriptional lariat sequencing) to investigate the timing and determinants of co-transcriptional splicing genome wide. Unexpectedly, 90% of all introns, including long introns, can splice before transcription of a downstream exon, indicating that exon definition is not obligatory for most human introns. Still, splicing timing varies dramatically across introns, and various genetic elements determine this variation. Strong U2AF2 binding to the polypyrimidine tract predicts early splicing, explaining exon definition-independent splicing. Together, our findings question the essentiality of exon definition and reveal features beyond intron and exon length that are determinative for splicing timing.

As splicing is intimately coupled with transcription, understanding splicing mechanisms requires an understanding of splicing timing, which is currently limited. Here, we developed CoLa-seq (co-transcriptionallariatsequencing), a genomic assay that reports splicing timing relative to transcription through analysis of nascent lariat intermediates. In human cells, we mapped 165,282 branch points and characterized splicing timing for over 70,000 introns. Splicing timing varies dramatically across introns, with regulated introns splicing later than constitutive introns. Machine learning-based modeling revealed genetic elements predictive of splicing timing, notably the polypyrimidine tract, intron length, and regional GC content, which illustrate the significance of the broader genomic context of an intron and the impact of co-transcriptional splicing. The importance of the splicing factor U2AF in early splicing rationalizes surprising observations that most introns can splice independent of exon definition. Together, these findings establish a critical framework for investigating the mechanisms and regulation of co-transcriptional splicing.HighlightsCoLa-seq enables cell-type specific, genome-wide branch point annotation with unprecedented efficiency.CoLa-seq captures co-transcriptional splicing for tens of thousands of introns and reveals splicing timing varies dramatically across introns.Modeling uncovers key genetic determinants of splicing timing, most notably regional GC content, intron length, and the polypyrimidine tract, the binding site for U2AF2.Early splicing precedes transcription of a downstream 5’ SS and in some cases accessibility of the upstream 3’ SS, precluding exon definition.

Estrogen receptor (ER) plays important roles in regulating normal development and female reproductive system function. Loss of ER pathway activity is a hallmark of breast cancer progression, associated with accelerated tumor proliferation and resistance to endocrine therapy. How ER loss occurs remains poorly understood. Here, we show that serine starvation, a metabolic stress often found in solid tumors, downregulates estrogen receptor alpha (ERα) expression, represses transcriptional targets such as progesterone receptor (PR), and reduces sensitivity to antiestrogens, suggesting a transition of ER-positive (ER+) breast cancer cells to an ER/PR-negative (ER-/PR-) state. ER downregulation under serine starvation is accompanied by a global loss of histone acetylation. These chromatin changes are driven by metabolic reprogramming triggered by serine starvation, particularly lower glucose flux through glycolysis and the TCA cycle, leading to reduced acetyl-CoA levels and histone hypoacetylation. Supplementation with acetate or glycerol triacetate (GTA), precursors of acetyl-CoA, restores H3K27 acetylation and ERα expression under serine starvation. Therefore, a major consequence of serine starvation in breast cancer could be global chromatin changes that influence lineage-specific gene expression.

Dysregulated DNA methylation is associated with poor prognosis in cancer patients, promoting tumorigenesis and therapeutic resistance1. DNA methyltransferase inhibitors (DNMTi) reduce DNA methylation and promote cancer cell differentiation, with two DNMTi already approved for cancer treatment2. However, these drugs rely on cell division to dilute existing methylation, thus the ‘demethylation’ effects are achieved in a passive manner, limiting their application in slow-proliferating tumor cells. In this study we use a mitochondrial uncoupler, niclosamide ethanolamine (NEN), to actively achieve global DNA demethylation. NEN treatment promotes DNA demethylation by activating electron transport chain (ETC) to produce α-ketoglutarate (α-KG), a substrate for the DNA demethylase TET. In addition, NEN inhibits reductive carboxylation, a key metabolic pathway to support growth of cancer cells with defective mitochondria or under hypoxia. Importantly, NEN treatment reduces 2-hydroxyglutarate (2-HG) generation and blocks DNA hypermethylation under hypoxia. Together, these metabolic reprogramming effects of NEN actively alter the global DNA methylation landscape and promote neuroblastoma differentiation. These results not only support Warburg’s original hypothesis that inhibition of ETC causes cell de-differentiation and tumorigenesis, but also suggest that mitochondrial uncoupling is an effective metabolic and epigenetic intervention that remodels the tumor epigenome for better prognosis.

Tens of thousands of genotype-phenotype associations have been discovered to date, yet not all of them are easily accessible to scientists. Here, we describe GWASkb, a machine-compiled knowledge base of genetic associations collected from the scientific literature using automated information extraction algorithms. Our information extraction system helps curators by automatically collecting over 6,000 associations from open-access publications with an estimated recall of 60–80% and with an estimated precision of 78–94% (measured relative to existing manually curated knowledge bases). This system represents a fully automated GWAS curation effort and is made possible by a paradigm for constructing machine learning systems called data programming. Our work represents a step towards making the curation of scientific literature more efficient using automated systems., Most databases of genotype-phenotype associations are manually curated. Here, Kuleshov et al. describe a machine curation system that extracts such relationships from the GWAS literature and synthesizes them into a structured knowledge base called GWASkb that can complement manually curated databases.

Genome-wide association studies (GWAS) have identified over 41 susceptibility loci associated with Parkinson’s Disease (PD) but identifying putative causal genes and the underlying mechanisms remains challenging. Here, we leverage large-scale transcriptomic datasets to prioritize genes that are likely to affect PD by using a transcriptome-wide association study (TWAS) approach. Using this approach, we identify 66 gene associations whose predicted expression or splicing levels in dorsolateral prefrontal cortex (DLFPC) and peripheral monocytes are significantly associated with PD risk. We uncover many novel genes associated with PD but also novel mechanisms for known associations such as MAPT, for which we find that variation in exon 3 splicing explains the common genetic association. Genes identified in our analyses belong to the same or related pathways including lysosomal and innate immune function. Overall, our study provides a strong foundation for further mechanistic studies that will elucidate the molecular drivers of PD., GWAS have identified over 41 susceptibility loci for Parkinson’s disease (PD). Here, the authors integrate PD GWAS summary statistics with transcriptome data from monocytes and DLFPC tissue in a TWAS approach and find 66 significant associations with PD risk highlighting lysosomal and innate immune functions.

A major challenge in human genetics is to identify the molecular mechanisms of trait-associated and disease-associated variants. To achieve this, quantitative trait locus (QTL) mapping of genetic variants with intermediate molecular phenotypes such as gene expression and splicing have been widely adopted

Background Alternative cleavage and polyadenylation (APA), an RNA processing event, occurs in over 70% of human protein-coding genes. APA results in mRNA transcripts with distinct 3′ ends. Most APA occurs within 3′ UTRs, which harbor regulatory elements that can impact mRNA stability, translation, and localization. Results APA can be profiled using a number of established computational tools that infer polyadenylation sites from standard, short-read RNA-seq datasets. Here, we benchmarked a number of such tools—TAPAS, QAPA, DaPars2, GETUTR, and APATrap— against 3′-Seq, a specialized RNA-seq protocol that enriches for reads at the 3′ ends of genes, and Iso-Seq, a Pacific Biosciences (PacBio) single-molecule full-length RNA-seq method in their ability to identify polyadenylation sites and quantify polyadenylation site usage. We demonstrate that 3′-Seq and Iso-Seq are able to identify and quantify the usage of polyadenylation sites more reliably than computational tools that take short-read RNA-seq as input. However, we find that running one such tool, QAPA, with a set of polyadenylation site annotations derived from small quantities of 3′-Seq or Iso-Seq can reliably quantify variation in APA across conditions, such asacross genotypes, as demonstrated by the successful mapping of alternative polyadenylation quantitative trait loci (apaQTL). Conclusions We envisage that our analyses will shed light on the advantages of studying APA with more specialized sequencing protocols, such as 3′-Seq or Iso-Seq, and the limitations of studying APA with short-read RNA-seq. We provide a computational pipeline to aid in the identification of polyadenylation sites and quantification of polyadenylation site usages using Iso-Seq data as input.

Humans vary in their susceptibility to infectious disease, partly due to variation in the immune response following infection. Here, we used single-cell RNA-sequencing to quantify genetic contributions to this variation in peripheral blood mononuclear cells, focusing specifically on the transcriptional response to influenza infection. We find that monocytes are the most responsive to influenza infection, but that all cell types mount a conserved interferon response, which is stronger in individuals with increased European ancestry. By comparing European American and African American individuals, we show that genetic ancestry effects on expression are common, influencing 29% of genes, but highly cell type-specific. Further, we demonstrate that much of this population-associated expression variation is explained by cis expression quantitative trait loci, which are enriched for signatures of recent positive selection. Our findings establish common cis-regulatory variants—including those that are differentiated by genetic ancestry—as important determinants of the antiviral immune response.

Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent–child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average contig N50: 26 Mbp) integrate all forms of genetic variation across even complex loci such as the major histocompatibility complex. We focus on 107,590 structural variants (SVs), of which 68% are inaccessible by short-read sequencing. We identify new SV hotspots (spanning megabases of gene-rich sequence), characterize 130 of the most active mobile element source elements, and find that 63% of all SVs arise by homology-mediated mechanisms—a twofold increase from previous studies. Our resource now enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1,525 expression quantitative trait loci (SV-eQTLs) as well as SV candidates for adaptive selection within the human population.

Resolving genomic structural variationMany human genomes have been reported using short-read technology, but it is difficult to resolve structural variants (SVs) using these data. These genomes thus lack comprehensive comparisons among individuals and populations. Ebertet al.used long-read structural variation calling across 64 human genomes representing diverse populations and developed new methods for variant discovery. This approach allowed the authors to increase the number of confirmed SVs and to describe the patterns of variation across populations. From this dataset, they identified quantitative trait loci affected by these SVs and determined how they may affect gene expression and potentially explain genome-wide association study hits. This information provides insights into patterns of normal human genetic variation and generates reference genomes that better represent the diversity of our species.Science, this issue p.eabf7117

The effects of trait-associated variants are often studied in a single relevant cell-type or context. However, for many complex traits, multiple cell-types are involved. This applies particularly to immune-related traits, for which many immune cell-types and contexts play a role. Here, we studied the impact of immune gene regulatory variants on complex traits to better understand genetic risk mediated through immune cell-types. We identified 26,271 expression quantitative trait loci (QTLs) and 23,121 splicing QTLs in 18 immune cell-types, and analyzed their overlap with trait-associated loci from 72 genome-wide association studies (GWAS). We showed that effects on RNA expression and splicing in immune cells colocalize with an average of 40.4% and 27.7% GWAS loci for immune-related and non-immune traits, respectively. Notably, we found that a large number of loci (mean: 14%) colocalize with splicing QTLs but not expression QTLs. The 60% GWAS loci without colocalization harbor genes that have lower expression levels, are less tolerant to loss-of-function mutations, and more enhancerrich than genes at colocalized loci. To further investigate the 60% GWAS loci not explained by our regulatory QTLs, we collected H3K27ac CUT&Tag data from rheumatoid arthritis (RA) and healthy controls. We found several unexplained GWAS hits lying within regions with higher H3K27ac activity in RA patients. We also observed that enrichment of RA GWAS heritability is greater in H3K27ac regions in immune cell-types from RA patients compared to healthy controls. Our study paves the way for future QTL studies to elucidate the mechanisms of as yet unexplained GWAS loci.

Little is known about co-transcriptional or post-transcriptional regulatory mechanisms linking noncoding variation to variation in organismal traits. To begin addressing this gap, we used 3’ Seq to study the impact of genetic variation on alternative polyadenylation (APA) in the nuclear and total mRNA fractions of 52 HapMap Yoruba human lymphoblastoid cell lines. We mapped 602 APA quantitative trait loci (apaQTLs) at 10% FDR, of which 152 were nuclear specific. Effect sizes at intronic apaQTLs are negatively correlated with eQTL effect sizes. These observations suggest genetic variants can decrease mRNA expression levels by increasing usage of intronic PAS. We also identified 24 apaQTLs associated with protein levels, but not mRNA expression. Finally, we found that 19% of apaQTLs can be associated with disease. Thus, our work demonstrates that APA links genetic variation to variation in gene expression, protein expression, and disease risk, and reveals uncharted modes of genetic regulation.

BackgroundSingle-cell RNA-sequencing (scRNA-seq) is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution. Despite the explosive growth in the number of cells that can be assayed by a single experiment, scRNA-seq still has several limitations, including high rates of dropouts, which result in a large number of genes having zero read count in the scRNA-seq data, and complicate downstream analyses.MethodsTo overcome this problem, we treat zeros as missing values and develop nonparametric deep learning methods for imputation. Specifically, our LATE (Learning with AuToEncoder) method trains an autoencoder with random initial values of the parameters, whereas our TRANSLATE (TRANSfer learning with LATE) method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter estimates.ResultsOn both simulated and real data, LATE and TRANSLATE outperform existing scRNA-seq imputation methods, achieving lower mean squared error in most cases, recovering nonlinear gene-gene relationships, and better separating cell types. They are also highly scalable and can efficiently process over 1 million cells in just a few hours on a GPU.ConclusionsWe demonstrate that our nonparametric approach to imputation based on autoencoders is powerful and highly efficient.

Single-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for most biological and medical applications as it allows users to measure gene expression levels in a cell-type-specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell-type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq datasets with cell-types that may overlap only partially, and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering, and in terms of avoiding over-correction. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell-type-specific differential gene expression comparisons across biopsy sites and disease conditions, and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online (https://qzhan321.github.io/dmatch/).

In hepatitis B virus (HBV)-related hepatocellular carcinoma (HBV+ HCC), the mechanisms by virus-derived antigens shape the T cell immune response remains unclear. Here, we employed bulk and single-cell multi-omics sequencing technologies to study the T cell receptor (TCR) repertoire, antigen specificity and epigenomic profiles in HBV+ HCC patients. We found that HBV is a major driver of T cells, with HBV-specific recurrent and expanding TCR clonotypes from CD8+ T cells, mucosal-associated invariant T (MAIT) cells and regulatory T cells (Tregs). HBV virions can directly activate MAIT cells in a MR1-dependent manner, indicating that these cells are not only sensors of metabolic products, but could participate directly in pathogen defense. And increased numbers of intratumoral Tregs, many of which are HBV specific, correlated with both increased viral titers and tumor size, while more a robust expansion of virus specific CD8+ T cells had better clinical prognosis after surgery. Integrating TCR and ATAC sequences also allowed us to track the epigenomic changes of specific T cell clonal lineages, with HBV-specific conventional CD8+ T cells expressing an effector phenotype when adjacent to the tumor, but cells of the same lineage exhibiting an “exhausted” phenotype in the tumor itself.

Most complex traits, including diseases, have a large genetic component. Identifying the genetic variants and genes underlying phenotypic variation remains one of the most important objectives of current biomedical research. Unlike Mendelian or familial diseases, which are usually caused by mutations in the coding regions of individual genes, complex diseases are thought to result from the cumulative effects of a large number of variants, of which, the vast majority are noncoding. Therefore, to discern the genetic underpinnings of a complex trait, we must first understand the impact of noncoding variation, which presumably affects gene regulation. In this chapter, we outline the recent progress made and methods used to discover putative regulatory regions associated with complex traits. We will specifically focus on mapping splicing quantitative trait loci (sQTL) using Yoruba samples from GEUVADIS as a motivating example.

Most complex traits, including diseases, have a large genetic component. Identifying the genetic variants and genes underlying phenotypic variation remains one of the most important objectives of current biomedical research. Unlike Mendelian or familial diseases, which are usually caused by mutations in the coding regions of individual genes, complex diseases are thought to result from the cumulative effects of a large number of variants, of which, the vast majority are noncoding. Therefore, to discern the genetic underpinnings of a complex trait, we must first understand the impact of noncoding variation, which presumably affects gene regulation. In this chapter, we outline the recent progress made and methods used to discover putative regulatory regions associated with complex traits. We will specifically focus on mapping splicing quantitative trait loci (sQTL) using Yoruba samples from GEUVADIS as a motivating example.

With the exception of mRNA splicing, little is known about co-transcriptional or post-transcriptional regulatory mechanisms that link noncoding variation to variation in organismal traits. To begin addressing this gap, we used 3’ Seq to characterize alternative polyadenylation (APA) in the nuclear and total RNA fractions of 52 HapMap Yoruba lymphoblastoid cell lines, which we have studied extensively in the past. We identified thousands of polyadenylation sites that are differentially detected in nuclear mRNA and whole cell mRNA, and found that APA is an important mediator of genetic effects on gene regulation and complex traits. Specifically, we mapped 602 apaQTLs at 10% FDR, of which 152 were found only in the nuclear fraction. Nuclear-specific apaQTLs are highly enriched in introns and are also often associated with changes in steady-state expression levels, suggesting a widespread mechanism whereby genetic variants decrease mRNA expression levels by increasing usage of intronic PAS. We identified 24 apaQTLs associated with protein expression levels, but not mRNA expression, and found that eQTLs that are not associated with chromatin QTLs are enriched in apaQTLs. These findings support multiple independent pathways through which genetic effects on APA can impact gene regulation. Finally, we found that 19% of apaQTLs were also previously associated with disease. Thus, our work demonstrates that APA links genetic variation to variation in gene expression levels, protein expression levels, and disease risk, and reveals uncharted modes of genetic regulation.

A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome-including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an "omnigenic" model.

Early genome-wide association studies (GWAS) led to the surprising discovery that, for typical complex traits, the most significant genetic variants contribute only a small fraction of the estimated heritability. Instead, it has become clear that a huge number of common variants, each with tiny effects, explain most of the heritability. Previously, we argued that these patterns conflict with standard conceptual models, and that new models are needed. Here we provide a formal model in which genetic contributions to complex traits can be partitioned into direct effects from core genes, and indirect effects from peripheral genes acting as trans-regulators. We argue that the central importance of peripheral genes is a direct consequence of the large contribution of trans-acting variation to gene expression variation. In particular, we propose that if the core genes for a trait are co-regulated – as seems likely – then the effects of peripheral variation can be amplified by these co-regulated networks such that nearly all of the genetic variance is driven by peripheral genes. Thus our model proposes a framework for understanding key features of the architecture of complex traits.

The molecular mechanisms that govern the maturation of oligodendrocyte lineage cells remain unclear. Emerging studies have shown that N(6)-methyladenosine (m(6)A), the most common internal RNA modification of mammalian mRNA, plays a critical role in various developmental processes. Here, we demonstrate that oligodendrocyte lineage progression is accompanied by dynamic changes in m(6)A modification on numerous transcripts. In vivo conditional inactivation of an essential m(6)A writer component, METTL14, results in decreased oligodendrocyte numbers and CNS hypomyelination, although oligodendrocyte precursor cell (OPC) numbers are normal. In vitro Mettl14 ablation disrupts post-mitotic oligodendrocyte maturation and has distinct effects on OPC and oligodendrocyte transcriptomes. Moreover, the loss of Mettl14 in oligodendrocyte lineage cells causes aberrant splicing of myriad RNA transcripts, including that which encodes the essential paranodal component neurofascin 155 (NF155). Together, our findings indicate that dynamic RNA methylation plays an important regulatory role in oligodendrocyte development and CNS myelination.

The causes and consequences of metabolic reprogramming during human breast cancer metastasis remain largely unknown. We performed metabolomics in the human triple-negative breast cancer cell line MDA-MB-231 (231-Parental) and its metastatic subclones that display tissue-tropism towards the brain (831-BrM), the bone (1833-BoM), and the lung (4175-LM). Using molecular and metabolic flux analysis, we uncovered that the mitochondrial serine and one-carbon (1C) unit pathway is upregulated in the metastatic subclones. Inhibition of the first rate-limiting enzyme of the pathway, serine hydroxymethyltransferase (SHMT2), potently suppresses proliferation of metastatic subclones in culture and impairs growth of lung metastatic subclones at both primary and metastatic sites in mice. In addition to breast cancer, a few other cancer types, such as adrenocortical carcinoma (ACC) and kidney chromophobe cell carcinoma (KICH), also display increased SHMT2 expression during disease progression. Moreover, we found that metastatic cells are exquisitely adapted to low serine conditions by synthesizing serine de novo from glucose to generate 1C units, and blocking serine catabolism abrogates metastatic growth under serine deprivation. How do metastatic breast cancer cells acquire new metabolic properties such as de novo serine synthesis? We observed that 231-Parental cells grown in media lacking serine for up to ten passages exhibited metastatic cell-like metabolic properties. Subsequent RNA-sequencing revealed dramatic global gene expression changes during serine starvation in more primary tumor-like cells. Several pathways involved in metastasis such as TNFα signaling, mTOR1 signaling, and Ras signaling were modulated upon serine starvation. These data suggest that the availability of the nonessential amino acid serine may play a key regulatory role in breast cancer progression and metastasis, and highlight the potential for metabolite supplementation to block or halt these transformations. Citation Format: Albert Mao Li, Gregory S. Ducker, Yang Li, Jose A. Seoane, Yiren Xiao, Stavros Melemenidis, Yiren Zhou, Ling Liu, Sakari Vanharanta, Edward E. Graves, Erinn B. Rankin, Christina Curtis, Joan Massague, Joshua D. Rabinowitz, Craig B. Thompson, Jiangbin Ye. Reprogramming of serine metabolism during breast cancer progression [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5713.

The Warburg effect is a metabolic hallmark of all cancer cells, characterized by increased glucose uptake and glycolysis for lactate generation. The generation and excretion of lactate would appear be a waste of carbon backbone and energy that is needed for proliferation. It was proposed by Warburg that the cause and consequence of the Warburg effect were the injury of respiration and cell dedifferentiation, respectively. One common factor that damages mitochondrial respiration is hypoxia, which is a metabolic stress that blocks cell differentiation and promotes cancer progression. The underlying mechanism by which this occurs is poorly understood, and no effective therapeutic strategy has been developed to overcome this resistance to differentiation. Using a neuroblastoma (NB) differentiation model, we have discovered that hypoxia represses the differentiation induced by retinoic acid (RA) as demonstrated by loss of neuron differentiation markers and changes in cell morphology, associated with reduction of global histone acetylation, that are caused by the induction of pyruvate dehydrogenase kinases (PDKs). PDKs phosphorylate pyruvate dehydrogenase (PDH), thereby blocking pyruvate entry into the TCA cycle, reducing acetyl-CoA generation, and promoting the Warburg effect. Genetic and pharmaceutical inhibition of PDK restores histone acetylation and NB cell differentiation morphology. Acetate supplementation restores histone acetylation, along with differentiation markers expression and neuron differentiation. In addition, ATAC-Seq analysis demonstrated that hypoxia treatment significantly reduces chromatin accessibility at RAR/RXR binding sites, which can be restored by acetate supplementation. These findings suggest that (1) combining RA and acetate supplementation represents a potentially effective therapeutic strategy for neuroblastoma treatment; (2) diverting pyruvate away from acetyl-CoA generation is a key mechanism by which the Warburg effect blocks cell differentiation. Citation Format: Jiangbin Ye, Yang Li, Joshua J. Gruber, Ulrike M. Litzenburger, Yiren Zhou, Yu R. Miao, Edward L. LaGory, Albert M. Li, Zhen Hu, Lori S. Hart, John M. Maris, Howard Y. Chang, Amato J. Giaccia. Deciphering Warburg effect: hypoxia inhibits tumor cell differentiation through reducing acetyl-CoA generation and chromatin accessibility [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5708.

Summary The molecular mechanisms that govern the maturation of oligodendrocyte lineage cells remain unclear. Emerging studies have shown that N6-methyladenosine (m6A), the most common internal RNA modification of mammalian mRNA, plays a critical role in various developmental processes. Here, we demonstrate that oligodendrocyte lineage progression is accompanied by dynamic changes in m6A modification on numerous transcripts. In vivo conditional inactivation of an essential m6A writer component, METTL14, results in decreased oligodendrocyte numbers and CNS hypomyelination, although oligodendrocyte precursor cell (OPC) numbers are normal. In vitro Mettl14 ablation disrupts postmitotic oligodendrocyte maturation and has distinct effects on OPC and oligodendrocyte transcriptomes. Moreover, the loss of Mettl14 in oligodendrocyte lineage cells causes aberrant splicing of myriad RNA transcripts, including those that encode the essential paranodal component neurofascin 155 (NF155). Together, our findings indicate that dynamic RNA methylation plays an important regulatory role in oligodendrocyte development and CNS myelination.

Summary The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%–11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.

Here we use deep sequencing to identify sources of variation in mRNA splicing in the dorsolateral prefrontal cortex (DLPFC) of 450 subjects from two aging cohorts. Hundreds of aberrant pre-mRNA splicing events are reproducibly associated with Alzheimer’s disease. We also generate a catalog of splicing quantitative trait loci (sQTL) effects: splicing of 3,006 genes is influenced by genetic variation. We report that altered splicing is the mechanism for the effects of the PICALM, CLU and PTK2B susceptibility alleles. Furthermore, we performed a transcriptome-wide association study and identified 21 genes with significant associations with Alzheimer’s disease, many of which are found in known loci, whereas 8 are in novel loci. These results highlight the convergence of old and new genes associated with Alzheimer’s disease in autophagy–lysosomal-related pathways. Overall, this study of the transcriptome of the aging brain provides evidence that dysregulation of mRNA splicing is a feature of Alzheimer’s disease and is, in some cases, genetically driven.

We use deep sequencing to identify sources of variation in mRNA splicing in the dorsolateral prefrontal cortex (DLFPC) of 450 subjects from two prospective cohort studies of aging. Hundreds of aberrant pre-mRNA splicing events are reproducibly associated with Alzheimer’s Disease (AD). We also generate a catalog of splicing quantitative trait loci (sQTL) effects in the human cortex: splicing of 3,198 genes is influenced by genetic variation. sQTLs are enriched among those variants influencing DNA methylation and histone acetylation. In assessing known AD loci, we report that altered splicing is the mechanism for the effects of the PICALM, CLU, and PTK2B susceptibility alleles. Further, we leverage our sQTL catalog to identify genes whose aberrant splicing is associated with AD and mediated by genetics. This transcriptome-wide association study identified 21 genes with significant associations, many of which are found in AD GWAS loci, but 8 are in novel AD loci, including FUS, which is a known amyotrophic lateral sclerosis (ALS) gene. This highlights an intriguing shared genetic architecture that is further elaborated by the convergence of old and new AD genes in autophagy-lysosomal-related pathways already implicated in AD and other neurodegenerative diseases. Overall, this study of the aging brain’s transcriptome provides evidence that dysregulation of mRNA splicing is a feature of AD and is, in some genetically-driven cases, causal.

Induced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation on gene regulation across different cell types and as models for studies of complex disease. To do so, we established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression levels, chromatin accessibility, and DNA methylation. Our analysis focused on a comparison of inter-individual regulatory variation across cell types. While most cell-type–specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell-type–specific regulatory QTLs are in shared open chromatin. This observation motivated us to develop a deep neural network to predict open chromatin regions from DNA sequence alone. Using this approach, we were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell-type–specific chromatin accessibility.

The excision of introns from pre-mRNA is an essential step in mRNA processing. We developed LeafCutter to study sample and population variation in intron splicing. LeafCutter identifies variable splicing events from short-read RNA-seq data and finds events of high complexity. Our approach obviates the need for transcript annotations and circumvents the challenges in estimating relative isoform or exon usage in complex splicing events. LeafCutter can be used both to detect differential splicing between sample groups and to map splicing quantitative trait loci (sQTLs). Compared with contemporary methods, our approach identified 1.4-2.1 times more sQTLs, many of which helped us ascribe molecular effects to disease-associated variants. Transcriptome-wide associations between LeafCutter intron quantifications and 40 complex traits increased the number of associated disease genes at a 5% false discovery rate by an average of 2.1-fold compared with that detected through the use of gene expression levels alone. LeafCutter is fast, scalable, easy to use, and available online.

Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease. Here we generated peripheral blood RNA-seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.