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Regulatory Sites for Splicing in Human Basal Ganglia Are Enriched for Disease-Relevant Information

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Regulatory Sites for Splicing in Human Basal Ganglia Are Enriched for Disease-Relevant Information UCSF UC San Francisco Previously Published Works Title Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information. Permalink https://escholarship.org/uc/item/4zp1w5m4 Journal Nature communications, 11(1) ISSN 2041-1723 Authors Guelfi, Sebastian D'Sa, Karishma Botía, Juan A et al. Publication Date 2020-02-25 DOI 10.1038/s41467-020-14483-x Peer reviewed eScholarship.org Powered by the California Digital Library University of California ARTICLE https://doi.org/10.1038/s41467-020-14483-x OPEN Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information Sebastian Guelfi1,72, Karishma D’Sa1,2,72, Juan A. Botía1,3,72, Jana Vandrovcova1, Regina H. Reynolds 1, David Zhang1, Daniah Trabzuni 1,4, Leonardo Collado-Torres 5, Andrew Thomason 6, Pedro Quijada Leyton6, Sarah A. Gagliano Taliun 7, Mike A. Nalls8,9, International Parkinson’s Disease Genomics Consortium (IPDGC)*, UK Brain Expression Consortium (UKBEC)*, Kerrin S. Small 10, Colin Smith11, ✉ Adaikalavan Ramasamy1,2,12, John Hardy1, Michael E. Weale2,13,72 & Mina Ryten1,72 1234567890():,; Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved under- standing of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, inter- rogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain tran- scriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/. 1 Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, University College London (UCL) Institute of Neurology, London, UK. 2 Department of Medical & Molecular Genetics, School of Medical Sciences, King’s College London, Guy’s Hospital, London, UK. 3 Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain. 4 Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia. 5 Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD, USA. 6 Goldsmiths, University of London, New Cross, London, UK. 7 Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA. 8 Laboratory of Neurogenetics, National Institute on Aging, US National Institutes of Health, Bethesda, MD, USA. 9 Data Tecnica International, Glen Echo, MD, USA. 10 Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK. 11 Department of Neuropathology, MRC Sudden Death Brain Bank Project, University of Edinburgh, Edinburgh, UK. 12 Singapore Institute for Clinical Sciences, Brenner Centre for Molecular Medicine, Singapore, Singapore. 13 Genomics plc, Oxford, UK. 72These authors contributed equally: Sebastian Guelfi,KarishmaD’Sa, Juan A. Botía, Michael E. Weale, Mina Ryten. *A full ✉ list of consortia members appears at the end of the paper. email: [email protected] NATURE COMMUNICATIONS | (2020) 11:1041 | https://doi.org/10.1038/s41467-020-14483-x | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-14483-x he use of genome-wide genotyping in large patient and these paired data, we searched for eQTL associations between Tcontrol populations has resulted in the identification of ~6.5 million genetic variants and ~411,000 RNA expression traits increasing numbers of common variants that impact on in putamen and ~370,000 RNA expression traits in substantia the risk of a wide range of neurological and psychiatric condi- nigra, resulting in ~5.3 billion eQTL tests. We generated RNA tions, including Parkinson’s disease1–3, Alzheimer’s disease4–6, expression traits using both annotation-based (known tran- and schizophrenia7,8. However, the majority of these risk loci are scripts) and annotation-agnostic approaches, noting that both still poorly characterised, and we do not yet fully understand the types of traits distinguished between the brain regions (Fig. 1a, underlying molecular and cellular processes through which they Supplementary Fig. 2). Within annotated regions, RNA quanti- act. As it is reasonable to assume that many causal variants fication was performed with RNA processing in mind (Fig. 1a) to operate by regulating gene expression, several studies have produce four separate measures of transcription, which formed attempted to address this problem through the use of expression the bases of our eQTL analysis. This resulted in the generation of quantitative trait loci (eQTL) and allele-specific expression (ASE) four types of eQTLs (gi-eQTLs, e-eQTLs, ex-ex-eQTLs, and ge- analyses in a wide range of human tissues, with the aim of finding eQTLs), of which two were designed to capture the genetic reg- eQTL and ASE signals that colocalise with disease risk signals9,10. ulation of splicing eQTLs (e-eQTLs and ex-ex-eQTLs). Finally, This approach has had success, but perhaps not as much as we included annotation-independent approaches to quantify might have been expected for all diseases. Although the identi- transcription. We focused specifically on unannotated transcrip- fication of eQTLs in blood has provided insights into auto- tion within intergenic regions (producing i-eQTLs, Methods). immune disorders11,12, the utility of brain-related eQTL and ASE Following stepwise conditional analyses under a false discovery data sets, particularly with regard to neurodegenerative disorders, rate (FDR) of 5%, we identified 19,156 separate significant eQTL has been harder to demonstrate. For example, monocyte eQTL signals (hereafter, eQTLs) genome-wide (Supplementary Data 2–6), data sets appear to provide greater insights for Alzheimer’s dis- of which 359 were secondary eQTLs (i.e., eQTLs with independent ease13, probably because they reflect regulatory processes in effects after conditioning on the primary eQTL in the region). microglia. This would suggest that while relevant eQTL and ASE Although there was a substantial difference in the number of signals are present in the brain, they are currently difficult to eQTLs identified in putamen and substantia nigra, the difference in detect given the constraints on eQTL and ASE analyses in sample size (N = 111 for putamen and N = 69 for substantia nigra) human brain. most likely accounted for this. However, eQTL discovery was not At present, the most easily available sampling method for brain simply driven by the number of features tested. Notably, the rate of tissue is post mortem, making repeat sampling impossible and eQTL discovery, defined as the percentage of all expression features typically leading to smaller sample sizes, particularly for smaller tested with at least one significant associated eQTL, was highest in structures such as the substantia nigra. Furthermore, the brain is a unannotated intronic and intergenic regions (Fig. 1b), suggesting highly complex organ. Not only is it split into many regions with that such eQTLs could be biologically important. known inter-regional differences in expression14, each region is composed of an assemblage of different cell types, which com- plicates the interpretation of transcriptomic data and limits sta- eQTLs and i-eQTL target regions show high replication rates. tistical power. Finally, the brain transcriptome is unusual in We found that 50.6 and 50.4% of the testable eQTLs identified in having a high degree of alternative splicing and a high degree of putamen and substantia nigra, respectively, were also detected non-coding RNA activity15, much of which has yet to be fully using microarray data, generated by the UK Brain Expression characterised16. Consortium17 and based on a common set of RNA samples. We We address the latter of these constraints by conducting total also found that 39.3% of putamen and 50.6% of substantia nigra RNA sequencing in two basal ganglia regions of clinical interest to eQTLs replicated in the GTEx (v7) data resource19, using their human neurodegenerative and neuropsychiatric disorders: the 111 putamen and 80 substantia nigra samples respectively. Fur- substantia nigra and putamen. Using a comprehensive set of thermore, we investigated eQTL replication across all brain analyses to interrogate different stages of RNA processing and regions studied in GTEx (Supplementary Table 1). We demon- uncover novel unannotated transcripts, we seek to identify not strated a replication rate of 19 to 53.3% for putamen, with the only disease-relevant regulatory loci but also the types of analyses highest replication rates observed in cerebellum (53.3%) and and regulatory positions yielding the most brain and disease- caudate (48.2%). In the case of substantia nigra samples, repli- specific information. We find that splicing eQTLs are enriched for cation rates were more similar across all brain tissues neuron-specific
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