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Jiang Z, Sullivan PF, Li T, Zhao B, Wang X, Luo T, Huang S, Guan PY, Chen J, Yang Y, Stein JL, Li Y, Liu D, Sun L, Zhu H. The pivotal role of the X-chromosome in the genetic architecture of the human brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.08.30.23294848. [PMID: 37693466 PMCID: PMC10491353 DOI: 10.1101/2023.08.30.23294848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Genes on the X-chromosome are extensively expressed in the human brain. However, little is known for the X-chromosome's impact on the brain anatomy, microstructure, and functional network. We examined 1,045 complex brain imaging traits from 38,529 participants in the UK Biobank. We unveiled potential autosome-X-chromosome interactions, while proposing an atlas outlining dosage compensation (DC) for brain imaging traits. Through extensive association studies, we identified 72 genome-wide significant trait-locus pairs (including 29 new associations) that share genetic architectures with brain-related disorders, notably schizophrenia. Furthermore, we discovered unique sex-specific associations and assessed variations in genetic effects between sexes. Our research offers critical insights into the X-chromosome's role in the human brain, underscoring its contribution to the differences observed in brain structure and functionality between sexes.
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Aygün N, Krupa O, Mory J, Le B, Valone J, Liang D, Love MI, Stein JL. Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.30.555019. [PMID: 37693528 PMCID: PMC10491258 DOI: 10.1101/2023.08.30.555019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
The function of some genetic variants associated with brain-relevant traits has been explained through colocalization with expression quantitative trait loci (eQTL) conducted in bulk post-mortem adult brain tissue. However, many brain-trait associated loci have unknown cellular or molecular function. These genetic variants may exert context-specific function on different molecular phenotypes including post-transcriptional changes. Here, we identified genetic regulation of RNA-editing and alternative polyadenylation (APA), within a cell-type-specific population of human neural progenitors and neurons. More RNA-editing and isoforms utilizing longer polyadenylation sequences were observed in neurons, likely due to higher expression of genes encoding the proteins mediating these post-transcriptional events. We also detected hundreds of cell-type-specific editing quantitative trait loci (edQTLs) and alternative polyadenylation QTLs (apaQTLs). We found colocalizations of a neuron edQTL in CCDC88A with educational attainment and a progenitor apaQTL in EP300 with schizophrenia, suggesting genetically mediated post-transcriptional regulation during brain development lead to differences in brain function.
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Affiliation(s)
- Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jessica Mory
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Brandon Le
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jordan Valone
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dan Liang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Michael I. Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Lead contact
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Han S, DiBlasi E, Monson ET, Shabalin A, Ferris E, Chen D, Fraser A, Yu Z, Staley M, Callor WB, Christensen ED, Crockett DK, Li QS, Willour V, Bakian AV, Keeshin B, Docherty AR, Eilbeck K, Coon H. Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes. Mol Psychiatry 2023; 28:3909-3919. [PMID: 37794117 PMCID: PMC10730410 DOI: 10.1038/s41380-023-02282-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 09/14/2023] [Accepted: 09/20/2023] [Indexed: 10/06/2023]
Abstract
Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e-06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.
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Affiliation(s)
- Seonggyun Han
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA.
| | - Emily DiBlasi
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Eric T Monson
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Andrey Shabalin
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Elliott Ferris
- Department of Neurobiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Danli Chen
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Alison Fraser
- Pedigree & Population Resource, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Zhe Yu
- Pedigree & Population Resource, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Michael Staley
- Office of the Medical Examiner, Utah Department of Health and Human Services, Salt Lake City, UT, USA
| | - W Brandon Callor
- Office of the Medical Examiner, Utah Department of Health and Human Services, Salt Lake City, UT, USA
| | - Erik D Christensen
- Office of the Medical Examiner, Utah Department of Health and Human Services, Salt Lake City, UT, USA
| | - David K Crockett
- Clinical Analytics, Intermountain Health, Salt Lake City, UT, USA
| | - Qingqin S Li
- Neuroscience Therapeutic Area, Janssen Research & Development, LLC, Titusville, NJ, USA
| | - Virginia Willour
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Amanda V Bakian
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Brooks Keeshin
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Anna R Docherty
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Hilary Coon
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
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Vijh D, Imam MA, Haque MMU, Das S, Islam A, Malik MZ. Network pharmacology and bioinformatics approach reveals the therapeutic mechanism of action of curcumin in Alzheimer disease. Metab Brain Dis 2023; 38:1205-1220. [PMID: 36652025 DOI: 10.1007/s11011-023-01160-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 01/04/2023] [Indexed: 01/19/2023]
Abstract
Curcumin is a natural anti-inflammatory and antioxidant substance which plays a major role in reducing the amyloid plaques formation, which is the major cause of Alzheimer's disease (AD). Consequently, a methodical approach was used to select the potential protein targets of curcumin in AD through network pharmacology. In this study, through integrative methods, AD targets of curcumin through SwissTargetPrediction database, STITCH database, BindingDB, PharmMapper, Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM) database were predicted followed by gene enrichment analysis, network construction, network topology, and docking studies. Gene ontology analysis facilitated identification of a list of possible AD targets of curcumin (74 targets genes). The correlation of the obtained targets with AD was analysed by using gene ontology (GO) pathway enrichment analyses and Kyoto Encyclopaedia of Genes and Genomes (KEGG). We have incorporated the applied network pharmacological approach to identify key genes. Furthermore, we have performed molecular docking for analysing the mechanism of curcumin. In order to validate the temporospatial expression of key genes in human central nervous system (CNS), we searched the Human Brain Transcriptome (HBT) dataset. We identified top five key genes namely, PPARγ, MAPK1, STAT3, KDR and APP. Further validated the expression profiling of these key genes in publicly available brain data expression profile databases. In context to a valuable addition in the treatment of AD, this study is concluded with novel insights into the therapeutic mechanisms of curcumin, will ease the treatment of AD with the clinical application of curcumin.
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Affiliation(s)
- Deepanshi Vijh
- Agriculture Plant Biotechnology Lab (ARL-316), University School of Biotechnology, Guru Gobind Singh Indraprastha University, Sector 16-C, Dwarka, New Delhi, 110078, India
| | - Md Ali Imam
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | | | - Subhajit Das
- National Centre for Cell Science, Pune, Maharashtra, India, 411007
| | - Asimul Islam
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Md Zubbair Malik
- Department of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, P.O. Box 1180, 15462, Dasman, Kuwait.
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Andirkó A, Boeckx C. Brain region-specific effects of nearly fixed sapiens-derived alleles. BMC Genom Data 2022; 23:36. [PMID: 35546225 PMCID: PMC9097168 DOI: 10.1186/s12863-022-01048-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 04/05/2022] [Indexed: 11/10/2022] Open
Abstract
The availability of high-coverage genomes of our extinct relatives, the Neanderthals and Denisovans, and the emergence of large, tissue-specific databases of modern human genetic variation, offer the possibility of probing the effects of modern-derived alleles in specific tissues, such as the brain, and its specific regions. While previous research has explored the effects of introgressed variants in gene expression, the effects of Homo sapiens-specific gene expression variability are still understudied. Here we identify derived, Homo sapiens-specific high-frequency (≥90%) alleles that are associated with differential gene expression across 15 brain structures derived from the GTEx database. We show that regulation by these derived variants targets regions under positive selection more often than expected by chance, and that high-frequency derived alleles lie in functional categories related to transcriptional regulation. Our results highlight the role of these variants in gene regulation in specific regions like the cerebellum and pituitary.
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Affiliation(s)
- Alejandro Andirkó
- University of Barcelona, Barcelona, Spain.,University of Barcelona Institute of Complex Systems, Barcelona, Spain
| | - Cedric Boeckx
- University of Barcelona, Barcelona, Spain. .,University of Barcelona Institute of Complex Systems, Barcelona, Spain. .,ICREA, Barcelona, Spain.
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Robins C, Liu Y, Fan W, Duong DM, Meigs J, Harerimana NV, Gerasimov ES, Dammer EB, Cutler DJ, Beach TG, Reiman EM, De Jager PL, Bennett DA, Lah JJ, Wingo AP, Levey AI, Seyfried NT, Wingo TS. Genetic control of the human brain proteome. Am J Hum Genet 2021; 108:400-410. [PMID: 33571421 PMCID: PMC8008492 DOI: 10.1016/j.ajhg.2021.01.012] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/19/2021] [Indexed: 12/30/2022] Open
Abstract
We generated an online brain pQTL resource for 7,376 proteins through the analysis of genetic and proteomic data derived from post-mortem samples of the dorsolateral prefrontal cortex of 330 older adults. The identified pQTLs tend to be non-synonymous variation, are over-represented among variants associated with brain diseases, and replicate well (77%) in an independent brain dataset. Comparison to a large study of brain eQTLs revealed that about 75% of pQTLs are also eQTLs. In contrast, about 40% of eQTLs were identified as pQTLs. These results are consistent with lower pQTL mapping power and greater evolutionary constraint on protein abundance. The latter is additionally supported by observations of pQTLs with large effects' tending to be rare, deleterious, and associated with proteins that have evidence for fewer protein-protein interactions. Mediation analyses using matched transcriptomic and proteomic data provided additional evidence that pQTL effects are often, but not always, mediated by mRNA. Specifically, we identified roughly 1.6 times more mRNA-mediated pQTLs than mRNA-independent pQTLs (550 versus 341). Our pQTL resource provides insight into the functional consequences of genetic variation in the human brain and a basis for novel investigations of genetics and disease.
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Bhattacharya A, Li Y, Love MI. MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies. PLoS Genet 2021; 17:e1009398. [PMID: 33684137 PMCID: PMC7971899 DOI: 10.1371/journal.pgen.1009398] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 03/18/2021] [Accepted: 02/04/2021] [Indexed: 02/06/2023] Open
Abstract
Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or other molecular effects underlying the SNP-gene association. Here, we outline multi-omics strategies for transcriptome imputation from germline genetics to allow more powerful testing of gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final predictive model of gene expression, along with local SNPs. In the second extension, we assess distal-eQTLs (SNPs associated with genes not in a local window around it) for their mediation effect through mediating biomarkers local to these distal-eSNPs. Distal-eSNPs with large indirect mediation effects are then included in the transcriptomic prediction model with the local SNPs around the gene of interest. Using simulations and real data from ROS/MAP brain tissue and TCGA breast tumors, we show considerable gains of percent variance explained (1-2% additive increase) of gene expression and TWAS power to detect gene-trait associations. This integrative approach to transcriptome-wide imputation and association studies aids in identifying the complex interactions underlying genetic regulation within a tissue and important risk genes for various traits and disorders.
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Affiliation(s)
- Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, University of California-Los Angeles, Los Angeles, California, United States of America
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Michael I. Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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Author Correction: Genome-wide human brain eQTLs: In-depth analysis and insights using the UKBEC dataset. Sci Rep 2020; 10:16603. [PMID: 32999326 PMCID: PMC7527956 DOI: 10.1038/s41598-020-73067-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Sng LM, Thomson PC, Trabzuni D. Comparison Between Expression Microarrays and RNA-Sequencing Using UKBEC Dataset Identified a trans-eQTL Associated with MPZ Gene in Substantia Nigra. FRONTIERS IN NEUROLOGY AND NEUROSCIENCE RESEARCH 2020; 1:100001. [PMID: 34322689 PMCID: PMC7611373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
In recent years, the advantages of RNA-sequencing (RNA-Seq) have made it the platform of choice for measuring gene expression over traditional microarrays. However, RNA-Seq comes with bioinformatical challenges and higher computational costs. Therefore, this study set out to assess whether the increased depth of transcriptomic information facilitated by RNA-Seq is worth the increased computation over microarrays, specifically at three levels: absolute expression levels, differentially expressed genes identification, and expression QTL (eQTL) mapping in regions of the human brain. Using the United Kingdom Brain Expression Consortium (UKBEC) dataset, there is high agreement of gene expression levels measured by microarrays and RNA-seq when quantifying absolute expression levels and when identifying differentially expressed genes. These findings suggest that depending on the aims of a study, the relative ease of working with microarray data may outweigh the computational time and costs of RNA-Seq pipelines. On the other, there was low agreement when mapping eQTLs. However, a number of eQTLs associated with genes that play important roles in the brain were found in both platforms. For example, a trans-eQTL was mapped that is associated with the MPZ gene in the substantia nigra. These eQTLs that we have highlighted are extremely promising candidates that merit further investigation.
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Affiliation(s)
- Letitia M.F. Sng
- The University of Sydney, School of Life and Environmental Sciences, Australia
| | - Peter C. Thomson
- The University of Sydney, School of Life and Environmental Sciences, Australia
| | - Daniah Trabzuni
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, United Kingdom,Department of Genetics, King Faisal Specialist Hospital and Research Centre, Saudi Arabia,Corresponding author: Daniah Trabzuni, Department of Neurodegenerative Disease, Wing 1.2 (first floor) Cruciform Building, Gower Street, London, WC1E 6BT, UK, Tel: +447872608992;
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