1
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Rumker L, Sakaue S, Reshef Y, Kang JB, Yazar S, Alquicira-Hernandez J, Valencia C, Lagattuta KA, Mah-Som A, Nathan A, Powell JE, Loh PR, Raychaudhuri S. Identifying genetic variants that influence the abundance of cell states in single-cell data. Nat Genet 2024:10.1038/s41588-024-01909-1. [PMID: 39327486 DOI: 10.1038/s41588-024-01909-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 08/14/2024] [Indexed: 09/28/2024]
Abstract
Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10-11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.
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Affiliation(s)
- Laurie Rumker
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yakir Reshef
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joyce B Kang
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seyhan Yazar
- Translational Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Jose Alquicira-Hernandez
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Translational Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Cristian Valencia
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kaitlyn A Lagattuta
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Annelise Mah-Som
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph E Powell
- Translational Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Po-Ru Loh
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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2
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Wang QS, Hasegawa T, Namkoong H, Saiki R, Edahiro R, Sonehara K, Tanaka H, Azekawa S, Chubachi S, Takahashi Y, Sakaue S, Namba S, Yamamoto K, Shiraishi Y, Chiba K, Tanaka H, Makishima H, Nannya Y, Zhang Z, Tsujikawa R, Koike R, Takano T, Ishii M, Kimura A, Inoue F, Kanai T, Fukunaga K, Ogawa S, Imoto S, Miyano S, Okada Y. Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance. Nat Genet 2024:10.1038/s41588-024-01896-3. [PMID: 39317738 DOI: 10.1038/s41588-024-01896-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 08/06/2024] [Indexed: 09/26/2024]
Abstract
Studying the genetic regulation of protein expression (through protein quantitative trait loci (pQTLs)) offers a deeper understanding of regulatory variants uncharacterized by mRNA expression regulation (expression QTLs (eQTLs)) studies. Here we report cis-eQTL and cis-pQTL statistical fine-mapping from 1,405 genotyped samples with blood mRNA and 2,932 plasma samples of protein expression, as part of the Japan COVID-19 Task Force (JCTF). Fine-mapped eQTLs (n = 3,464) were enriched for 932 variants validated with a massively parallel reporter assay. Fine-mapped pQTLs (n = 582) were enriched for missense variations on structured and extracellular domains, although the possibility of epitope-binding artifacts remains. Trans-eQTL and trans-pQTL analysis highlighted associations of class I HLA allele variation with KIR genes. We contrast the multi-tissue origin of plasma protein with blood mRNA, contributing to the limited colocalization level, distinct regulatory mechanisms and trait relevance of eQTLs and pQTLs. We report a negative correlation between ABO mRNA and protein expression because of linkage disequilibrium between distinct nearby eQTLs and pQTLs.
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Affiliation(s)
- Qingbo S Wang
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
| | - Takanori Hasegawa
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan.
| | - Ryunosuke Saiki
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kyuto Sonehara
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Shuhei Azekawa
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | | | - Saori Sakaue
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Children's Health and Genetics, Division of Health Science, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuichi Shiraishi
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Kenichi Chiba
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Hiroko Tanaka
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hideki Makishima
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Yasuhito Nannya
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Zicong Zhang
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Rika Tsujikawa
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Ryuji Koike
- Health Science Research and Development Center (HeRD), Tokyo Medical and Dental University, Tokyo, Japan
| | - Tomomi Takano
- Laboratory of Veterinary Infectious Disease, Department of Veterinary Medicine, Kitasato University, Tokyo, Japan
| | - Makoto Ishii
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Fumitaka Inoue
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yukinori Okada
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan.
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3
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Aygün N, Vuong C, Krupa O, Mory J, Le BD, Valone JM, Liang D, Shafie B, Zhang P, Salinda A, Wen C, Gandal MJ, Love MI, de la Torre-Ubieta L, Stein JL. Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis. Am J Hum Genet 2024; 111:1877-1898. [PMID: 39168119 PMCID: PMC11393701 DOI: 10.1016/j.ajhg.2024.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/18/2024] [Accepted: 07/23/2024] [Indexed: 08/23/2024] Open
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 postmortem 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 that genetically mediated post-transcriptional regulation during brain development leads 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
| | - Celine Vuong
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, 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 D 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 M 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
| | - Beck Shafie
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Pan Zhang
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Angelo Salinda
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Cindy Wen
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael J Gandal
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 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
| | - Luis de la Torre-Ubieta
- Intellectual and Developmental Disabilities Research Center, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA 90095, 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.
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4
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Li D, Hsu FC, Palmer ND, Liu L, Choi YA, Murea M, Parks JS, Bowden DW, Freedman BI, Ma L. Multiomics Analyses Identify AKR1A1 as a Biomarker for Diabetic Kidney Disease. Diabetes 2024; 73:1188-1195. [PMID: 38394643 PMCID: PMC11189831 DOI: 10.2337/db23-0540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 02/07/2024] [Indexed: 02/25/2024]
Abstract
Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease. Because many genes associate with DKD, multiomics approaches were used to narrow the list of functional genes, gene products, and related pathways providing insights into the pathophysiological mechanisms of DKD. The Kidney Precision Medicine Project human kidney single-cell RNA-sequencing (scRNA-seq) data set and Mendeley Data on human kidney cortex biopsy proteomics were used. The R package Seurat was used to analyze scRNA-seq data and data from a subset of proximal tubule cells. PathfindR was applied for pathway analysis in cell type-specific differentially expressed genes and the R limma package was used to analyze differential protein expression in kidney cortex. A total of 790 differentially expressed genes were identified in proximal tubule cells, including 530 upregulated and 260 downregulated transcripts. Compared with differentially expressed proteins, 24 genes or proteins were in common. An integrated analysis combining protein quantitative trait loci, genome-wide association study hits (namely, estimated glomerular filtration rate), and a plasma metabolomics analysis was performed using baseline metabolites predictive of DKD progression in our longitudinal Diabetes Heart Study samples. The aldo-keto reductase family 1 member A1 gene (AKR1A1) was revealed as a potential molecular hub for DKD cellular dysfunction in several cross-linked pathways featured by deficiency of this enzyme. ARTICLE HIGHLIGHTS
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Affiliation(s)
- DengFeng Li
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Fang-Chi Hsu
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Nicholette D. Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Liang Liu
- Bioinformatics Shared Resource, Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Young A. Choi
- Section of Nephrology, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Mariana Murea
- Section of Nephrology, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
| | - John S. Parks
- Department of Molecular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Donald W. Bowden
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Barry I. Freedman
- Section of Nephrology, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Lijun Ma
- Section of Nephrology, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
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5
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Sun BB, Suhre K, Gibson BW. Promises and Challenges of populational Proteomics in Health and Disease. Mol Cell Proteomics 2024; 23:100786. [PMID: 38761890 PMCID: PMC11193116 DOI: 10.1016/j.mcpro.2024.100786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024] Open
Abstract
Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.
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Affiliation(s)
- Benjamin B Sun
- Human Genetics, Informatics and Predictive Sciences, Bristol-Myers Squibb, Cambridge, Massachusetts, USA.
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Bradford W Gibson
- Pharmaceutical Chemistry, University of California, San Francisco, California, USA
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6
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Kalnapenkis A, Jõeloo M, Lepik K, Kukuškina V, Kals M, Alasoo K, Mägi R, Esko T, Võsa U. Genetic determinants of plasma protein levels in the Estonian population. Sci Rep 2024; 14:7694. [PMID: 38565889 PMCID: PMC10987560 DOI: 10.1038/s41598-024-57966-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 03/23/2024] [Indexed: 04/04/2024] Open
Abstract
The proteome holds great potential as an intermediate layer between the genome and phenome. Previous protein quantitative trait locus studies have focused mainly on describing the effects of common genetic variations on the proteome. Here, we assessed the impact of the common and rare genetic variations as well as the copy number variants (CNVs) on 326 plasma proteins measured in up to 500 individuals. We identified 184 cis and 94 trans signals for 157 protein traits, which were further fine-mapped to credible sets for 101 cis and 87 trans signals for 151 proteins. Rare genetic variation contributed to the levels of 7 proteins, with 5 cis and 14 trans associations. CNVs were associated with the levels of 11 proteins (7 cis and 5 trans), examples including a 3q12.1 deletion acting as a hub for multiple trans associations; and a CNV overlapping NAIP, a sensor component of the NAIP-NLRC4 inflammasome which is affecting pro-inflammatory cytokine interleukin 18 levels. In summary, this work presents a comprehensive resource of genetic variation affecting the plasma protein levels and provides the interpretation of identified effects.
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Affiliation(s)
- Anette Kalnapenkis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.
| | - Maarja Jõeloo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Kaido Lepik
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Viktorija Kukuškina
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
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7
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Aherrahrou R, Baig F, Theofilatos K, Lue D, Beele A, Örd T, Kaikkonen MU, Aherrahrou Z, Cheng Q, Ghosh S, Karnewar S, Karnewar V, Finn A, Owens GK, Joner M, Mayr M, Civelek M. Secreted Protein Profiling of Human Aortic Smooth Muscle Cells Identifies Vascular Disease Associations. Arterioscler Thromb Vasc Biol 2024; 44:898-914. [PMID: 38328934 PMCID: PMC10978267 DOI: 10.1161/atvbaha.123.320274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
BACKGROUND Smooth muscle cells (SMCs), which make up the medial layer of arteries, are key cell types involved in cardiovascular disease, the leading cause of mortality and morbidity worldwide. In response to microenvironment alterations, SMCs dedifferentiate from a contractile to a synthetic phenotype characterized by an increased proliferation, migration, production of ECM (extracellular matrix) components, and decreased expression of SMC-specific contractile markers. These phenotypic changes result in vascular remodeling and contribute to the pathogenesis of cardiovascular disease, including coronary artery disease, stroke, hypertension, and aortic aneurysms. Here, we aim to identify the genetic variants that regulate ECM secretion in SMCs and predict the causal proteins associated with vascular disease-related loci identified in genome-wide association studies. METHODS Using human aortic SMCs from 123 multiancestry healthy heart transplant donors, we collected the serum-free media in which the cells were cultured for 24 hours and conducted liquid chromatography-tandem mass spectrometry-based proteomic analysis of the conditioned media. RESULTS We measured the abundance of 270 ECM and related proteins. Next, we performed protein quantitative trait locus mapping and identified 20 loci associated with secreted protein abundance in SMCs. We functionally annotated these loci using a colocalization approach. This approach prioritized the genetic variant rs6739323-A at the 2p22.3 locus, which is associated with lower expression of LTBP1 (latent-transforming growth factor beta-binding protein 1) in SMCs and atherosclerosis-prone areas of the aorta, and increased risk for SMC calcification. We found that LTBP1 expression is abundant in SMCs, and its expression at mRNA and protein levels was reduced in unstable and advanced atherosclerotic plaque lesions. CONCLUSIONS Our results unravel the SMC proteome signature associated with vascular disorders, which may help identify potential therapeutic targets to accelerate the pathway to translation.
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Affiliation(s)
- Rédouane Aherrahrou
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Finland
- Institute for Cardiogenetics, Universität zu Lübeck; DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Germany; University Heart Centre Lübeck, Germany
| | - Ferheen Baig
- King’s British Heart Foundation Centre, King’s College London, London, United Kingdom
| | | | - Dillon Lue
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Alicia Beele
- CVPath Institute, Inc., 19 Firstfield Road, Gaithersburg, MD
| | - Tiit Örd
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Finland
| | - Minna U Kaikkonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Finland
| | - Zouhair Aherrahrou
- Institute for Cardiogenetics, Universität zu Lübeck; DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Germany; University Heart Centre Lübeck, Germany
| | - Qi Cheng
- CVPath Institute, Inc., 19 Firstfield Road, Gaithersburg, MD
| | - Saikat Ghosh
- CVPath Institute, Inc., 19 Firstfield Road, Gaithersburg, MD
| | - Santosh Karnewar
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Vaishnavi Karnewar
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Aloke Finn
- CVPath Institute, Inc., 19 Firstfield Road, Gaithersburg, MD
| | - Gary K. Owens
- Department of Molecular Physiology and Biological Physics, Department of Medicine, Division of Cardiology, Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, United States of America
| | - Michael Joner
- Klinik für Herz- und Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Manuel Mayr
- National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Mete Civelek
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
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8
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Arruda AL, Khandaker GM, Morris AP, Smith GD, Huckins LM, Zeggini E. Genomic insights into the comorbidity between type 2 diabetes and schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:22. [PMID: 38383672 PMCID: PMC10881980 DOI: 10.1038/s41537-024-00445-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/31/2024] [Indexed: 02/23/2024]
Abstract
Multimorbidity represents an increasingly important public health challenge with far-reaching implications for health management and policy. Mental health and metabolic diseases have a well-established epidemiological association. In this study, we investigate the genetic intersection between type 2 diabetes and schizophrenia. We use Mendelian randomization to examine potential causal relationships between the two conditions and related endophenotypes. We report no compelling evidence that type 2 diabetes genetic liability potentially causally influences schizophrenia risk and vice versa. Our findings show that increased body mass index (BMI) has a protective effect against schizophrenia, in contrast to the well-known risk-increasing effect of BMI on type 2 diabetes risk. We identify evidence of colocalization of association signals for these two conditions at 11 genomic loci, six of which have opposing directions of effect for type 2 diabetes and schizophrenia. To elucidate these colocalizing signals, we integrate multi-omics data from bulk and single-cell gene expression studies, along with functional information. We identify putative effector genes and find that they are enriched for homeostasis and lipid-related pathways. We also highlight drug repurposing opportunities including N-methyl-D-aspartate (NMDA) receptor antagonists. Our findings provide insights into shared biological mechanisms for type 2 diabetes and schizophrenia, highlighting common factors that influence the risk of the two conditions in opposite directions and shedding light on the complex nature of this comorbidity.
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Affiliation(s)
- Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Munich School for Data Science, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Technical University of Munich (TUM), TUM School of Medicine and Health, Graduate School of Experimental Medicine, Munich, 81675, Germany
| | - Golam M Khandaker
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Avon and Wiltshire Mental Health Partnership NHS Trust, Bristol, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, M13 9PT, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Laura M Huckins
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany.
- TUM School of Medicine and Health, Technical University of Munich and Klinikum Rechts der Isar, Munich, 81675, Germany.
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9
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Smith D, He B, Shi J, Zhu HJ, Wang X. Novel Independent Trans- and Cis-Genetic Variants Associated with CYP2D6 Expression and Activity in Human Livers. Drug Metab Dispos 2024; 52:143-152. [PMID: 38050015 PMCID: PMC10801631 DOI: 10.1124/dmd.123.001548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/01/2023] [Accepted: 11/21/2023] [Indexed: 12/06/2023] Open
Abstract
Cytochrome P450 2D6 (CYP2D6) is a critical hepatic drug-metabolizing enzyme in humans, responsible for metabolizing approximately 20%-25% of commonly used medications such as codeine, desipramine, fluvoxamine, paroxetine, and tamoxifen. The CYP2D6 gene is highly polymorphic, resulting in substantial interindividual variability in its catalytic function and the pharmacokinetics and therapeutic outcomes of its substrate drugs. Although many functional CYP2D6 variants have been discovered and validated, a significant portion of the variability in the expression and activity of CYP2D6 remains unexplained. In this study, we performed a genome-wide association study (GWAS) to identify novel variants associated with CYP2D6 protein expression in individual human livers, followed by a conditional analysis to control for the effect of functional CYP2D6 star alleles. We also examined their impact on hepatic CYP2D6 activity. Genotyping on a genome-wide scale was achieved using the Illumina Multi-Ethnic Genotyping Array (MEGA). A data-independent acquisition (DIA)-based proteomics method was used to quantify CYP2D6 protein concentrations. CYP2D6 activity was determined by measuring the dextromethorphan O-demethylation in individual human liver s9 fractions. The GWAS identified 44 single nuclear polymorphisms (SNPs) that are significantly associated with CYP2D6 protein expressions with a P value threshold of 5.0 × 10-7 After the conditional analysis, five SNPs, including the cis-variants rs1807493 and rs1062753 and the trans-variants rs4073010, rs729559, and rs80274432, emerged as independent variants significantly correlated with hepatic CYP2D6 protein expressions. Notably, four of these SNPs, except for rs80274432, also exhibited a significant association with CYP2D6 activities in human livers, suggesting their potential as novel and independent cis- and trans-variants regulating CYP2D6. SIGNIFICANT STATEMENT: Using individual human livers, we identified four novel cis- and trans-pQTLs/aQTLs (protein quantitative trait loci/activity quantitative trait loci) of Cytochrome P450 2D6 (CYP2D6) that are independent from known functional CYP2D6 star alleles. This study connects the CYP2D6 gene expression and activity, enhancing our understanding of the genetic variants associated with CYP2D6 protein expression and activity, potentially advancing our insight into the interindividual variability in CYP2D6 substrate medication response.
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Affiliation(s)
- Dylan Smith
- Department of Pharmaceutical Sciences, Northeast Ohio Medical University, Rootstown, Ohio (D.S., X.W.); Department of Clinical Pharmacy, University of Michigan, Ann Arbor, Michigan (H.-J.Z.); Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan (B.H.); and Bristol Myers Squibb, Lawrence Township, New Jersey (J.S.)
| | - Bing He
- Department of Pharmaceutical Sciences, Northeast Ohio Medical University, Rootstown, Ohio (D.S., X.W.); Department of Clinical Pharmacy, University of Michigan, Ann Arbor, Michigan (H.-J.Z.); Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan (B.H.); and Bristol Myers Squibb, Lawrence Township, New Jersey (J.S.)
| | - Jian Shi
- Department of Pharmaceutical Sciences, Northeast Ohio Medical University, Rootstown, Ohio (D.S., X.W.); Department of Clinical Pharmacy, University of Michigan, Ann Arbor, Michigan (H.-J.Z.); Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan (B.H.); and Bristol Myers Squibb, Lawrence Township, New Jersey (J.S.)
| | - Hao-Jie Zhu
- Department of Pharmaceutical Sciences, Northeast Ohio Medical University, Rootstown, Ohio (D.S., X.W.); Department of Clinical Pharmacy, University of Michigan, Ann Arbor, Michigan (H.-J.Z.); Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan (B.H.); and Bristol Myers Squibb, Lawrence Township, New Jersey (J.S.)
| | - Xinwen Wang
- Department of Pharmaceutical Sciences, Northeast Ohio Medical University, Rootstown, Ohio (D.S., X.W.); Department of Clinical Pharmacy, University of Michigan, Ann Arbor, Michigan (H.-J.Z.); Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan (B.H.); and Bristol Myers Squibb, Lawrence Township, New Jersey (J.S.)
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Rumker L, Sakaue S, Reshef Y, Kang JB, Yazar S, Alquicira-Hernandez J, Valencia C, Lagattuta KA, Mah-Som A, Nathan A, Powell JE, Loh PR, Raychaudhuri S. Identifying genetic variants that influence the abundance of cell states in single-cell data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566919. [PMID: 38014313 PMCID: PMC10680752 DOI: 10.1101/2023.11.13.566919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Introductory ParagraphTo understand genetic mechanisms driving disease, it is essential but difficult to map how risk alleles affect the composition of cells present in the body. Single-cell profiling quantifies granular information about tissues, but variant-associated cell states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce GeNA (Genotype-Neighborhood Associations), a statistical tool to identify cell state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of scRNA-seq peripheral blood profiling from 969 individuals,1GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (p=1.96×10-11) associates with increased abundance of NK cells expressing TNF-α response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-TNF treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.
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Affiliation(s)
- Laurie Rumker
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saori Sakaue
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yakir Reshef
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joyce B. Kang
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seyhan Yazar
- Translational Genomics, Garvan Institute of Medical Research, Sydney, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, Australia
| | - Jose Alquicira-Hernandez
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Cristian Valencia
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kaitlyn A Lagattuta
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Annelise Mah-Som
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph E. Powell
- Translational Genomics, Garvan Institute of Medical Research, Sydney, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, Australia
| | - Po-Ru Loh
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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11
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Aherrahrou R, Baig F, Theofilatos K, Lue D, Beele A, Örd T, Kaikkonen MU, Aherrahrou Z, Cheng Q, Ghosh S, Karnewar S, Karnewar V, Finn A, Owens GK, Joner M, Mayr M, Civelek M. Secreted protein profiling of human aortic smooth muscle cells identifies vascular disease associations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.10.23298351. [PMID: 37986932 PMCID: PMC10659471 DOI: 10.1101/2023.11.10.23298351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Background Smooth muscle cells (SMCs), which make up the medial layer of arteries, are key cell types involved in cardiovascular diseases (CVD), the leading cause of mortality and morbidity worldwide. In response to microenvironment alterations, SMCs dedifferentiate from a "contractile" to a "synthetic" phenotype characterized by an increased proliferation, migration, production of extracellular matrix (ECM) components, and decreased expression of SMC-specific contractile markers. These phenotypic changes result in vascular remodeling and contribute to the pathogenesis of CVD, including coronary artery disease (CAD), stroke, hypertension, and aortic aneurysms. Here, we aim to identify the genetic variants that regulate ECM secretion in SMCs and predict the causal proteins associated with vascular disease-related loci identified in genome-wide association studies (GWAS). Methods Using human aortic SMCs from 123 multi-ancestry healthy heart transplant donors, we collected the serum-free media in which the cells were cultured for 24 hours and conducted Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomic analysis of the conditioned media. Results We measured the abundance of 270 ECM and related proteins. Next, we performed protein quantitative trait locus mapping (pQTL) and identified 20 loci associated with secreted protein abundance in SMCs. We functionally annotated these loci using a colocalization approach. This approach prioritized the genetic variant rs6739323-A at the 2p22.3 locus, which is associated with lower expression of LTBP1 in SMCs and atherosclerosis-prone areas of the aorta, and increased risk for SMC calcification. We found that LTBP1 expression is abundant in SMCs, and its expression at mRNA and protein levels was reduced in unstable and advanced atherosclerotic plaque lesions. Conclusions Our results unravel the SMC proteome signature associated with vascular disorders, which may help identify potential therapeutic targets to accelerate the pathway to translation.
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Affiliation(s)
- Rédouane Aherrahrou
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Finland
- Institute for Cardiogenetics, Universität zu Lübeck; DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Germany; University Heart Centre Lübeck, Germany
| | - Ferheen Baig
- King’s British Heart Foundation Centre, King’s College London, London, United Kingdom
| | | | - Dillon Lue
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Alicia Beele
- CVPath Institute, Inc., 19 Firstfield Road, Gaithersburg, MD
| | - Tiit Örd
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Finland
| | - Minna U Kaikkonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Finland
| | - Zouhair Aherrahrou
- Institute for Cardiogenetics, Universität zu Lübeck; DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Germany; University Heart Centre Lübeck, Germany
| | - Qi Cheng
- CVPath Institute, Inc., 19 Firstfield Road, Gaithersburg, MD
| | - Saikat Ghosh
- CVPath Institute, Inc., 19 Firstfield Road, Gaithersburg, MD
| | - Santosh Karnewar
- Department of Molecular Physiology and Biological Physics, Department of Medicine, Division of Cardiology, Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, United States of America
| | - Vaishnavi Karnewar
- Department of Molecular Physiology and Biological Physics, Department of Medicine, Division of Cardiology, Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, United States of America
| | - Aloke Finn
- CVPath Institute, Inc., 19 Firstfield Road, Gaithersburg, MD
| | - Gary K. Owens
- Department of Molecular Physiology and Biological Physics, Department of Medicine, Division of Cardiology, Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, United States of America
| | - Michael Joner
- Klinik für Herz-und Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Manuel Mayr
- King’s British Heart Foundation Centre, King’s College London, London, United Kingdom
| | - Mete Civelek
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
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12
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Smith LS, Wang X, Shi J, He B, Zhu HJ. Genome-Wide Association Study for the Genetic Determinants of Thiopurine Methyltransferase Protein Expression in Human Livers and Racial Differences. Pharm Res 2023; 40:2525-2531. [PMID: 37430149 DOI: 10.1007/s11095-023-03558-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 06/26/2023] [Indexed: 07/12/2023]
Abstract
INTRODUCTION Polymorphisms in the Thiopurine S-Methyltransferase (TPMT) gene are associated with decreased TPMT activity, but little is known about their impact on TPMT protein expression in the liver. This project is to conduct a genome-wide association study (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with altered TPMT protein expression in human livers and to determine if demographics affect hepatic TPMT protein expression. METHODS Human liver samples (n = 287) were genotyped using a whole genome genotyping panel and quantified for TPMT protein expression using a Data-Independent Acquisition proteomics approach. RESULTS AND DISCUSSION Thirty-one SNPs were found to be associated with differential expression of TPMT protein in the human livers. Subsequent analysis, conditioning on rs1142345, a SNP associated with the TPMT*3A and TPMT*3C alleles, showed no additional independent signals. Mean TPMT expression is significantly higher in wildtype donors compared to those carrying the known TPMT alleles, including TPMT*3A, TPMT*3C, and TPMT*24 (0.107 ± 0.028 vs. 0.052 ± 0.014 pmol/mg total protein, P = 2.2 × 10-16). After removing samples carrying the known TPMT variants, European ancestry donors exhibited significantly higher expression than African ancestry donors (0.109 ± 0.026 vs. 0.090 ± 0.041 pmol/mg total protein, P = 0.020). CONCLUSION The GWAS identified 31 SNPs associated with TPMT protein expression in human livers. Hepatic TPMT protein expression was significantly lower in subjects carrying the TPMT*3A, TPMT*3C, and TPMT*24 alleles compared to non-carriers. European ancestry was associated with significantly higher hepatic TPMT protein expression than African ancestry, independent of known TPMT variants.
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Affiliation(s)
- Logan S Smith
- Department of Clinical Pharmacy, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Xinwen Wang
- Department of Pharmaceutical Sciences, Northeast Ohio Medical University, Rootstown, OH, 44272, USA
| | - Jian Shi
- Bristol Myers Squibb, 3401 Princeton Pike, Lawrence Township, NJ, 08648, USA
| | - Bing He
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - Hao-Jie Zhu
- Department of Clinical Pharmacy, University of Michigan, Ann Arbor, MI, 48109, USA.
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Lin C, Wang W, Zhang D, Huang K, Zhang Y, Li X, Zhao Y, Zhao L, Wang J, Zhou B, Cheng J, Xu D, Li W, Zhang X, Zheng W. Analysis of liver miRNA in Hu sheep with different residual feed intake. Front Genet 2023; 14:1113411. [PMID: 37928243 PMCID: PMC10620975 DOI: 10.3389/fgene.2023.1113411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
Feed efficiency (FE), an important economic trait in sheep production, is indirectly assessed by residual feed intake (RFI). However, RFI in sheep is varied, and the molecular processes that regulate RFI are unclear. It is thus vital to investigate the molecular mechanism of RFI to developing a feed-efficient sheep. The miRNA-sequencing (RNA-Seq) was utilized to investigate miRNAs in liver tissue of 6 out of 137 sheep with extreme RFI phenotypic values. In these animals, as a typical metric of FE, RFI was used to distinguish differentially expressed miRNAs (DE_miRNAs) between animals with high (n = 3) and low (n = 3) phenotypic values. A total of 247 miRNAs were discovered in sheep, with four differentially expressed miRNAs (DE_miRNAs) detected. Among these DE_miRNAs, three were found to be upregulated and one was downregulated in animals with low residual feed intake (Low_RFI) compared to those with high residual feed intake (High_RFI). The target genes of DE_miRNAs were primarily associated with metabolic processes and biosynthetic process regulation. Furthermore, they were also considerably enriched in the FE related to glycolysis, protein synthesis and degradation, and amino acid biosynthesis pathways. Six genes were identified by co-expression analysis of DE_miRNAs target with DE_mRNAs. These results provide a theoretical basis for us to understand the sheep liver miRNAs in RFI molecular regulation.
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Affiliation(s)
- Changchun Lin
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
- Institute of Animal Husbandry Quality Standards, Xinjiang Academy of Animal Sciences, Urumqi, Xinjiang, China
| | - Weimin Wang
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Deyin Zhang
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Kai Huang
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Yukun Zhang
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Xiaolong Li
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Yuan Zhao
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Liming Zhao
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Jianghui Wang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Bubo Zhou
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Jiangbo Cheng
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China
| | - Dan Xu
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Wenxin Li
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Xiaoxue Zhang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China
| | - Wenxin Zheng
- Institute of Animal Husbandry Quality Standards, Xinjiang Academy of Animal Sciences, Urumqi, Xinjiang, China
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14
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Arruda AL, Khandaker GM, Morris AP, Smith GD, Huckins LM, Zeggini E. Genomic insights into the comorbidity between type 2 diabetes and schizophrenia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.16.23297073. [PMID: 37905000 PMCID: PMC10615007 DOI: 10.1101/2023.10.16.23297073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Multimorbidity represents an increasingly important public health challenge with far-reaching implications for health management and policy. Mental health and metabolic diseases have a well-established epidemiological association. In this study, we investigate the genetic intersection between type 2 diabetes and schizophrenia. We use Mendelian randomization to examine potential causal relationships between the two conditions and related endophenotypes. We report no compelling evidence that type 2 diabetes genetic liability potentially causally influences schizophrenia risk and vice versa. Our findings show that increased body mass index (BMI) has a protective effect against schizophrenia, in contrast to the well-known risk-increasing effect of BMI on type 2 diabetes risk. We identify evidence of colocalization of association signals for these two conditions at 11 genomic loci, six of which have opposing directions of effect for type 2 diabetes and schizophrenia. To elucidate these colocalizing signals, we integrate multi-omics data from bulk and single-cell gene expression studies, along with functional information. We identify high-confidence effector genes and find that they are enriched for homeostasis and lipid-related pathways. We also highlight drug repurposing opportunities including N-methyl-D-aspartate (NMDA) receptor antagonists. Our findings provide insights into shared biological mechanisms for type 2 diabetes and schizophrenia, highlighting common factors that influence the risk of the two conditions in opposite directions and shedding light on the complex nature of this comorbidity.
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Affiliation(s)
- Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg, 85764, Germany
- Munich School for Data Science, Helmholtz Munich, Neuherberg, 85764, Germany
- Technical University of Munich (TUM), School of Medicine, Graduate School of Experimental Medicine, Munich, 81675, Germ
| | - Golam M. Khandaker
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Avon and Wiltshire Mental Health Partnership NHS Trust, Bristol, UK
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, M13 9PT, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Laura M. Huckins
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg, 85764, Germany
- TUM school of medicine, Technical University Munich and Klinikum Rechts der Isar, Munich, 81675, Germany
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15
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Chen W, Zhang Z, Liu K, Jiang D, Sun X, Mao Y, Li S, Ye D. Circulating Copper and Liver Cancer. Biol Trace Elem Res 2023; 201:4649-4656. [PMID: 36633787 DOI: 10.1007/s12011-023-03554-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 01/02/2023] [Indexed: 01/13/2023]
Abstract
The association between circulating copper and the risk of liver cancer has been investigated by previous studies, while the findings were inconsistent. Thus, we aimed to evaluate the association between circulating copper and liver cancer by using meta-analysis and Mendelian randomization (MR). For meta-analysis, PubMed and Web of Science were searched to identify eligible studies published before April 4, 2022. Standardized mean difference (SMD) with 95% confidence interval (CI) in circulating copper level between liver cancer patients and controls were pooled. Furthermore, we selected genetic instruments for circulating copper from a genome-wide association study (GWAS) to conduct MR analysis. The summary statistics related to liver cancer were obtained from two large independent cohorts, UKBB and FinnGen, respectively. MR analysis was performed mainly by inverse-variance weighted (IVW) approach, followed by maximum-likelihood method as sensitivity analysis. In meta-analysis of eight studies, circulating copper was found to be higher in liver cancer patients (SMD: 1.65; 95% CI: 0.65 to 2.65) with high heterogeneity (I2 = 96.40%, P = 0.001). However, inconsistent findings were observed among subgroups with high evidence. In MR analysis, genetically predicted circulating copper was not significantly associated with the risk of liver cancer by IVW in UKBB (OR: 1.38; 95% CI: 0.72 to 2.65) and FinnGen (OR: 1.10; 95% CI: 0.69 to 1.73) separately, and the pooled results produced similar results (OR: 1.18, 95% CI: 0.81 to 1.72). Moreover, non-significant finding was confirmed by using maximum-likelihood method. There is no sufficient evidence to demonstrate that high levels of circulating copper increase the risks of liver cancer.
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Affiliation(s)
- Weiwei Chen
- School of Public Health, Zhejiang Chinese Medical University, Binwen Road 548, Hangzhou, 310053, Zhejiang, China
| | - Zhiwei Zhang
- The First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, China
| | - Ke Liu
- School of Public Health, Zhejiang Chinese Medical University, Binwen Road 548, Hangzhou, 310053, Zhejiang, China
| | - Die Jiang
- School of Public Health, Zhejiang Chinese Medical University, Binwen Road 548, Hangzhou, 310053, Zhejiang, China
| | - Xiaohui Sun
- School of Public Health, Zhejiang Chinese Medical University, Binwen Road 548, Hangzhou, 310053, Zhejiang, China
| | - Yingying Mao
- School of Public Health, Zhejiang Chinese Medical University, Binwen Road 548, Hangzhou, 310053, Zhejiang, China
| | - Songtao Li
- School of Public Health, Zhejiang Chinese Medical University, Binwen Road 548, Hangzhou, 310053, Zhejiang, China.
| | - Ding Ye
- School of Public Health, Zhejiang Chinese Medical University, Binwen Road 548, Hangzhou, 310053, Zhejiang, China.
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16
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Gao F, Guan X, Zhang W, Han T, Liu X, Shi B. Oxidized Soybean Oil Evoked Hepatic Fatty Acid Metabolism Disturbance in Rats and their Offspring. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:13483-13494. [PMID: 37667911 DOI: 10.1021/acs.jafc.3c02466] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
The oxidation of fats and oils is an undisputed subject of science, given the effect of oxidized fats and oils on food quality and safety. This study aimed to determine whether maternal exposure to oxidized soybean oil (OSO) causes lipid metabolism disorders in the liver and whether this lipid metabolism disorder can be transmitted to offspring or even worsened. A total of 60 female Sprague-Dawley (SD) rats were divided randomly into four groups in this study. Treatment groups received a pure diet of OSO with a peroxide value of 200, 400, or 800 mEqO2/kg, while the control group received fresh soybean oil (FSO). As for our results, OSO affected serum biochemical parameters in the maternal generation (F0) and induced liver histopathology changes, inflammation, and oxidative stress. Moreover, the expression of genes related to the liver X receptor α (LXRα)─sterol regulatory element binding protein-1c (SREBP-1c) signaling pathway was changed. Similar trends were found in the livers of offspring on postnatal days 21 and 56. In conclusion, exposure to OSO during gestation and lactation can affect liver lipid synthesis. Additionally, it is detrimental to the development of the offspring's liver, affecting fatty acid metabolism and causing liver damage.
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Affiliation(s)
- Feng Gao
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Xin Guan
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Wentao Zhang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Tingting Han
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Xinyu Liu
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Baoming Shi
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
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17
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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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18
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Chella Krishnan K, El Hachem EJ, Keller MP, Patel SG, Carroll L, Vegas AD, Gerdes Gyuricza I, Light C, Cao Y, Pan C, Kaczor-Urbanowicz KE, Shravah V, Anum D, Pellegrini M, Lee CF, Seldin MM, Rosenthal NA, Churchill GA, Attie AD, Parker B, James DE, Lusis AJ. Genetic architecture of heart mitochondrial proteome influencing cardiac hypertrophy. eLife 2023; 12:e82619. [PMID: 37276142 PMCID: PMC10241513 DOI: 10.7554/elife.82619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 05/18/2023] [Indexed: 06/07/2023] Open
Abstract
Mitochondria play an important role in both normal heart function and disease etiology. We report analysis of common genetic variations contributing to mitochondrial and heart functions using an integrative proteomics approach in a panel of inbred mouse strains called the Hybrid Mouse Diversity Panel (HMDP). We performed a whole heart proteome study in the HMDP (72 strains, n=2-3 mice) and retrieved 848 mitochondrial proteins (quantified in ≥50 strains). High-resolution association mapping on their relative abundance levels revealed three trans-acting genetic loci on chromosomes (chr) 7, 13 and 17 that regulate distinct classes of mitochondrial proteins as well as cardiac hypertrophy. DAVID enrichment analyses of genes regulated by each of the loci revealed that the chr13 locus was highly enriched for complex-I proteins (24 proteins, P=2.2E-61), the chr17 locus for mitochondrial ribonucleoprotein complex (17 proteins, P=3.1E-25) and the chr7 locus for ubiquinone biosynthesis (3 proteins, P=6.9E-05). Follow-up high resolution regional mapping identified NDUFS4, LRPPRC and COQ7 as the candidate genes for chr13, chr17 and chr7 loci, respectively, and both experimental and statistical analyses supported their causal roles. Furthermore, a large cohort of Diversity Outbred mice was used to corroborate Lrpprc gene as a driver of mitochondrial DNA (mtDNA)-encoded gene regulation, and to show that the chr17 locus is specific to heart. Variations in all three loci were associated with heart mass in at least one of two independent heart stress models, namely, isoproterenol-induced heart failure and diet-induced obesity. These findings suggest that common variations in certain mitochondrial proteins can act in trans to influence tissue-specific mitochondrial functions and contribute to heart hypertrophy, elucidating mechanisms that may underlie genetic susceptibility to heart failure in human populations.
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Affiliation(s)
- Karthickeyan Chella Krishnan
- Department of Pharmacology and Systems Physiology, University of Cincinnati College of MedicineCincinnatiUnited States
| | - Elie-Julien El Hachem
- Department of Integrative Biology and Physiology, Field Systems Biology, Sciences Sorbonne UniversitéParisFrance
| | - Mark P Keller
- Biochemistry Department, University of Wisconsin-MadisonMadisonUnited States
| | - Sanjeet G Patel
- Department of Surgery/Division of Cardiac Surgery, University of Southern California Keck School of MedicineLos AngelesUnited States
| | - Luke Carroll
- Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Alexis Diaz Vegas
- Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | | | - Christine Light
- Cardiovascular Biology Research Program, Oklahoma Medical Research FoundationOklahoma CityUnited States
| | - Yang Cao
- Department of Medicine/Division of Cardiology, University of California, Los AngelesLos AngelesUnited States
| | - Calvin Pan
- Department of Medicine/Division of Cardiology, University of California, Los AngelesLos AngelesUnited States
| | - Karolina Elżbieta Kaczor-Urbanowicz
- Division of Oral Biology and Medicine, UCLA School of DentistryLos AngelesUnited States
- UCLA Institute for Quantitative and Computational BiosciencesLos AngelesUnited States
| | - Varun Shravah
- Department of Chemistry, University of CaliforniaLos AngelesUnited States
| | - Diana Anum
- Department of Integrative Biology and Physiology, University of CaliforniaLos AngelesUnited States
| | - Matteo Pellegrini
- UCLA Institute for Quantitative and Computational BiosciencesLos AngelesUnited States
| | - Chi Fung Lee
- Cardiovascular Biology Research Program, Oklahoma Medical Research FoundationOklahoma CityUnited States
- Department of Physiology, University of Oklahoma Health Sciences CenterOklahoma CityUnited States
| | - Marcus M Seldin
- Center for Epigenetics and MetabolismIrvineUnited States
- Department of Biological Chemistry, University of CaliforniaIrvineUnited States
| | | | | | - Alan D Attie
- Biochemistry Department, University of Wisconsin-MadisonMadisonUnited States
| | - Benjamin Parker
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia
| | - David E James
- Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Aldons J Lusis
- Department of Medicine/Division of Cardiology, University of California, Los AngelesLos AngelesUnited States
- Department of Human Genetics, University of CaliforniaLos AngelesUnited States
- Department of Microbiology, Immunology and Molecular Genetics, University of CaliforniaLos AngelesUnited States
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19
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Fabo T, Khavari P. Functional characterization of human genomic variation linked to polygenic diseases. Trends Genet 2023; 39:462-490. [PMID: 36997428 PMCID: PMC11025698 DOI: 10.1016/j.tig.2023.02.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/30/2023]
Abstract
The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.
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Affiliation(s)
- Tania Fabo
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Graduate Program in Genetics, Stanford University, Stanford, CA, USA; Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Paul Khavari
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Graduate Program in Genetics, Stanford University, Stanford, CA, USA; Stanford University School of Medicine, Stanford University, Stanford, CA, USA; Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA.
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20
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Zhao Q, Han B, Xu Q, Wang T, Fang C, Li R, Zhang L, Pei Y. Proteome and genome integration analysis of obesity. Chin Med J (Engl) 2023; 136:910-921. [PMID: 37000968 PMCID: PMC10278747 DOI: 10.1097/cm9.0000000000002644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Indexed: 04/03/2023] Open
Abstract
ABSTRACT The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.
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Affiliation(s)
- Qigang Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215213, China
| | - Baixue Han
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215213, China
| | - Qian Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215213, China
| | - Tao Wang
- Department of Endocrinology, The Second Affiliated Hospital, Soochow University, Suzhou, Jiangsu 215004, China
| | - Chen Fang
- Department of Endocrinology, The Second Affiliated Hospital, Soochow University, Suzhou, Jiangsu 215004, China
| | - Rui Li
- Department of Gastroenterology, The First Affiliated Hospital, Soochow University, Suzhou, Jiangsu 215006, China
| | - Lei Zhang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215213, China
- Center for Genetic Epidemiology and Genomics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, China
| | - Yufang Pei
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215213, China
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21
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Messner CB, Demichev V, Wang Z, Hartl J, Kustatscher G, Mülleder M, Ralser M. Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology. Proteomics 2023; 23:e2200013. [PMID: 36349817 DOI: 10.1002/pmic.202200013] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022]
Abstract
There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.
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Affiliation(s)
- Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Vadim Demichev
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ziyue Wang
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Hartl
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh, Scotland, UK
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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22
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Yang G, Mishra M, Perera MA. Multi-Omics Studies in Historically Excluded Populations: The Road to Equity. Clin Pharmacol Ther 2023; 113:541-556. [PMID: 36495075 PMCID: PMC10323857 DOI: 10.1002/cpt.2818] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022]
Abstract
Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.
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Affiliation(s)
- Guang Yang
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Mrinal Mishra
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Minoli A. Perera
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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23
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He D, Pan C, Zhao Y, Wei W, Qin X, Cai Q, Shi S, Chu X, Zhang N, Jia Y, Wen Y, Cheng B, Liu H, Feng R, Zhang F, Xu P. Exome-wide screening identifies novel rare risk variants for bone mineral density. Osteoporos Int 2023; 34:965-975. [PMID: 36849660 DOI: 10.1007/s00198-023-06710-0] [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: 08/28/2022] [Accepted: 02/14/2023] [Indexed: 03/01/2023]
Abstract
UNLABELLED Bone mineral density (BMD) is an independent risk factor of osteoporosis-related fractures. We performed gene-based burden tests to assess the association between rare variants and BMD, and identified several BMD candidate genes. PURPOSE BMD is highly heritable and a major predictor of osteoporotic fractures, but its genetic basis remains unclear. We aimed to identify rare risk variants contributing to BMD. METHODS Utilizing the newly released UK Biobank 200,643 exome dataset, we conducted a gene-based exome-wide association study in males and females, respectively. First, 100,639 males and 117,338 females with BMD values were included in the polygenic risk scores (PRS) analysis. Among individuals with lower 30% PRS, cases were individuals with top 10% BMD, and individuals with bottom 10% BMD were the controls. Considering the effects of vitamin D (VD), individuals with the highest 30% VD concentration were selected for VD-BMD analysis. After quality control, 741 males and 697 females were included in the BMD analysis, and 717 males and 708 females were included in the VD-BMD analysis. The variants were annotated by ANNOVAR software, then BMD and VD-BMD qualified variants were imported into the SKAT R-package to perform gene-based burden tests, respectively. RESULTS The gene-based burden test of the exonic variants identified genome-wide candidate associations in ANKRD18A (P = 1.60 × 10-5, PBonferroni adjust = 2.11 × 10-3), C22orf31 (P = 3.49 × 10-4, PBonferroni adjust = 3.17 × 10-2), and SPATC1L (P = 1.09 × 10-5, PBonferroni adjust = 8.80 × 10-3). For VD-BMD analysis, three genes were associated with BMD, such as NIPAL1 (P = 1.06 × 10-3, PBonferroni adjust = 3.91 × 10-2). CONCLUSIONS Our study suggested that rare variants contribute to BMD, providing new sights for broadening the genetic structure of BMD.
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Affiliation(s)
- D He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - C Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Y Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - W Wei
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - X Qin
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Q Cai
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - S Shi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - X Chu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - N Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Y Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Y Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - B Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - H Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - R Feng
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, China
| | - F Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China.
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
| | - P Xu
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, China.
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Boahen CK, Oelen R, Le K, Netea MG, Franke L, van der Wijst MG, Kumar V. Integration of Candida albicans-induced single-cell gene expression data and secretory protein concentrations reveal genetic regulators of inflammation. Front Immunol 2023; 14:1069379. [PMID: 36865558 PMCID: PMC9972217 DOI: 10.3389/fimmu.2023.1069379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 01/23/2023] [Indexed: 02/16/2023] Open
Abstract
Both gene expression and protein concentrations are regulated by genetic variants. Exploring the regulation of both eQTLs and pQTLs simultaneously in a context- and cell-type dependent manner may help to unravel mechanistic basis for genetic regulation of pQTLs. Here, we performed meta-analysis of Candida albicans-induced pQTLs from two population-based cohorts and intersected the results with Candida-induced cell-type specific expression association data (eQTL). This revealed systematic differences between the pQTLs and eQTL, where only 35% of the pQTLs significantly correlated with mRNA expressions at single cell level, indicating the limitation of eQTLs use as a proxy for pQTLs. By taking advantage of the tightly co-regulated pattern of the proteins, we also identified SNPs affecting protein network upon Candida stimulations. Colocalization of pQTLs and eQTLs signals implicated several genomic loci including MMP-1 and AMZ1. Analysis of Candida-induced single cell gene expression data implicated specific cell types that exhibit significant expression QTLs upon stimulation. By highlighting the role of trans-regulatory networks in determining the abundance of secretory proteins, our study serve as a framework to gain insights into the mechanisms of genetic regulation of protein levels in a context-dependent manner.
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Affiliation(s)
- Collins K. Boahen
- Department of Internal Medicine and Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
| | - Roy Oelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Kieu Le
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Mihai G. Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
- Department for Immunology and Metabolism, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Monique G.P. van der Wijst
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Vinod Kumar
- Department of Internal Medicine and Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
- Nitte University Centre for Science Education and Research (NUCSER), Nitte (Deemed to be University), Mangalore, India
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25
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Gedik H, Peterson RE, Riley BP, Vladimirov VI, Bacanu SA. Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals. Complex Psychiatry 2023; 9:130-144. [PMID: 37588130 PMCID: PMC10425719 DOI: 10.1159/000530223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 03/09/2023] [Indexed: 08/18/2023] Open
Abstract
Background The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait. Summary In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs. Key Messages As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.
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Affiliation(s)
- Huseyin Gedik
- Integrative Life Sciences, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Roseann E. Peterson
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Brien P. Riley
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Vladimir I. Vladimirov
- Department of Psychiatry, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, USA
| | - Silviu-Alin Bacanu
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
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26
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Senabouth A, Daniszewski M, Lidgerwood GE, Liang HH, Hernández D, Mirzaei M, Keenan SN, Zhang R, Han X, Neavin D, Rooney L, Lopez Sanchez MIG, Gulluyan L, Paulo JA, Clarke L, Kearns LS, Gnanasambandapillai V, Chan CL, Nguyen U, Steinmann AM, McCloy RA, Farbehi N, Gupta VK, Mackey DA, Bylsma G, Verma N, MacGregor S, Watt MJ, Guymer RH, Powell JE, Hewitt AW, Pébay A. Transcriptomic and proteomic retinal pigment epithelium signatures of age-related macular degeneration. Nat Commun 2022; 13:4233. [PMID: 35882847 PMCID: PMC9325891 DOI: 10.1038/s41467-022-31707-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 06/29/2022] [Indexed: 11/08/2022] Open
Abstract
There are currently no treatments for geographic atrophy, the advanced form of age-related macular degeneration. Hence, innovative studies are needed to model this condition and prevent or delay its progression. Induced pluripotent stem cells generated from patients with geographic atrophy and healthy individuals were differentiated to retinal pigment epithelium. Integrating transcriptional profiles of 127,659 retinal pigment epithelium cells generated from 43 individuals with geographic atrophy and 36 controls with genotype data, we identify 445 expression quantitative trait loci in cis that are asssociated with disease status and specific to retinal pigment epithelium subpopulations. Transcriptomics and proteomics approaches identify molecular pathways significantly upregulated in geographic atrophy, including in mitochondrial functions, metabolic pathways and extracellular cellular matrix reorganization. Five significant protein quantitative trait loci that regulate protein expression in the retinal pigment epithelium and in geographic atrophy are identified - two of which share variants with cis- expression quantitative trait loci, including proteins involved in mitochondrial biology and neurodegeneration. Investigation of mitochondrial metabolism confirms mitochondrial dysfunction as a core constitutive difference of the retinal pigment epithelium from patients with geographic atrophy. This study uncovers important differences in retinal pigment epithelium homeostasis associated with geographic atrophy.
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Affiliation(s)
- Anne Senabouth
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Maciej Daniszewski
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Grace E Lidgerwood
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Helena H Liang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Damián Hernández
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Mehdi Mirzaei
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Stacey N Keenan
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Ran Zhang
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Xikun Han
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Drew Neavin
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Louise Rooney
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | | | - Lerna Gulluyan
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Linda Clarke
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Lisa S Kearns
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | | | - Chia-Ling Chan
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Uyen Nguyen
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Angela M Steinmann
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Rachael A McCloy
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Nona Farbehi
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Vivek K Gupta
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - David A Mackey
- Lions Eye Institute, Centre for Vision Sciences, University of Western Australia, Perth, WA, 6009, Australia
- School of Medicine, University of Tasmania, Hobart, TAS, 7005, Australia
| | - Guy Bylsma
- Lions Eye Institute, Centre for Vision Sciences, University of Western Australia, Perth, WA, 6009, Australia
| | - Nitin Verma
- School of Medicine, University of Tasmania, Hobart, TAS, 7005, Australia
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Matthew J Watt
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Robyn H Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
- Department of Surgery, Ophthalmology, Royal Victorian Eye and Ear Hospital, The University of Melbourne, East Melbourne, VIC, 3002, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, 2052, Australia.
| | - Alex W Hewitt
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia.
- School of Medicine, University of Tasmania, Hobart, TAS, 7005, Australia.
- Department of Surgery, Ophthalmology, Royal Victorian Eye and Ear Hospital, The University of Melbourne, East Melbourne, VIC, 3002, Australia.
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, 7000, Australia.
| | - Alice Pébay
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia.
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia.
- Department of Surgery, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, 3010, Australia.
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Nova A, Baldrighi GN, Fazia T, Graziano F, Saddi V, Piras M, Beecham A, McCauley JL, Bernardinelli L. Heritability Estimation of Multiple Sclerosis Related Plasma Protein Levels in Sardinian Families with Immunochip Genotyping Data. LIFE (BASEL, SWITZERLAND) 2022; 12:life12071101. [PMID: 35888189 PMCID: PMC9317284 DOI: 10.3390/life12071101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 11/25/2022]
Abstract
This work aimed at estimating narrow-sense heritability, defined as the proportion of the phenotypic variance explained by the sum of additive genetic effects, via Haseman–Elston regression for a subset of 56 plasma protein levels related to Multiple Sclerosis (MS). These were measured in 212 related individuals (with 69 MS cases and 143 healthy controls) obtained from 20 Sardinian families with MS history. Using pedigree information, we found seven statistically significant heritable plasma protein levels (after multiple testing correction), i.e., Gc (h2 = 0.77; 95%CI: 0.36, 1.00), Plat (h2 = 0.70; 95%CI: 0.27, 0.95), Anxa1 (h2 = 0.68; 95%CI: 0.27, 1.00), Sod1 (h2 = 0.58; 95%CI: 0.18, 0.96), Irf8 (h2 = 0.56; 95%CI: 0.19, 0.99), Ptger4 (h2 = 0.45; 95%CI: 0.10, 0.96), and Fadd (h2 = 0.41; 95%CI: 0.06, 0.84). A subsequent analysis was performed on these statistically significant heritable plasma protein levels employing Immunochip genotyping data obtained in 155 healthy controls (92 related and 63 unrelated); we found a meaningful proportion of heritable plasma protein levels’ variability explained by a small set of SNPs. Overall, the results obtained, for these seven MS-related proteins, emphasized a high additive genetic variance component explaining plasma levels’ variability.
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Affiliation(s)
- Andrea Nova
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (G.N.B.); (T.F.); (L.B.)
- Correspondence:
| | - Giulia Nicole Baldrighi
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (G.N.B.); (T.F.); (L.B.)
| | - Teresa Fazia
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (G.N.B.); (T.F.); (L.B.)
| | - Francesca Graziano
- Centre of Biostatistics for Clinical Epidemiology, University of Milano-Bicocca, 20900 Monza, Italy;
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy
| | - Valeria Saddi
- Divisione di Neurologia, Presidio Ospedaliero S. Francesco, ASL Numero 3 Nuoro, 08100 Nuoro, Italy; (V.S.); (M.P.)
| | - Marialuisa Piras
- Divisione di Neurologia, Presidio Ospedaliero S. Francesco, ASL Numero 3 Nuoro, 08100 Nuoro, Italy; (V.S.); (M.P.)
| | - Ashley Beecham
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33146, USA; (A.B.); (J.L.M.)
- Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Jacob L. McCauley
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33146, USA; (A.B.); (J.L.M.)
- Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Luisa Bernardinelli
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (G.N.B.); (T.F.); (L.B.)
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Mendelian randomization study for the roles of IL-18 and IL-1 receptor antagonist in the development of inflammatory bowel disease. Int Immunopharmacol 2022; 110:109020. [PMID: 35843146 DOI: 10.1016/j.intimp.2022.109020] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/17/2022] [Accepted: 06/30/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND AND AIMS IL-1 and IL-18 play important roles in intestine barrier integrity maintenance and inflammatory response. However, their net effects on the risk of IBD are still inconclusive. Here, we used Mendelian randomization (MR) approaches to investigate the causal associations of IL-18 and IL-1Ra (receptor antagonist) on the risks of IBD and subtypes. METHODS For IL-18, both three-sample and two-sample MR approaches were used for the causal inferences. In three-sample MR, three single nucleotide polymorphisms (SNPs) and the effect values were extracted from two quantitative trait loci (pQTL) datasets with non-overlapping populations. In two-sample MR, we extracted genetic instruments information from the same larger pQTL dataset. For IL-1Ra, we applied the two-sample MR method with summary-statistics from the larger pQTL dataset. Summary-level results of three large IBD/CD/UC genome-wide association studies in European ancestry were employed. Inverse-variance weighted method, various sensitivity analyses and meta-analysis were performed to give causal estimates, detect heterogeneity and correct for outliers. RESULTS We observed consistent positive causal effects of IL-18 on all three major outcomes using three-sample MR, with meta-analyses odds ratios (ORs) equal to 1.240 (IBD), 1.199 (CD) and 1.274 (UC) respectively. The two-sample MR demonstrated similar results. Moreover, genetically predicted IL-1Ra is inversely associated with the risk of IBD/UC/CD with ORs equal to 0.915 (IBD), 0.902 (CD) and 0.899 (UC) respectively in meta-analyses. CONCLUSIONS This study suggested genetically predicted IL-18 and IL-1Ra level are causally associated with an increased and decreased risk of IBD and subtypes.
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Pott J, Garcia T, Hauck SM, Petrera A, Wirkner K, Loeffler M, Kirsten H, Peters A, Scholz M. Genetically regulated gene expression and proteins revealed discordant effects. PLoS One 2022; 17:e0268815. [PMID: 35604899 PMCID: PMC9126407 DOI: 10.1371/journal.pone.0268815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 05/10/2022] [Indexed: 11/29/2022] Open
Abstract
Background Although gene-expression (GE) and protein levels are typically strongly genetically regulated, their correlation is known to be low. Here we investigate this phenomenon by focusing on the genetic background of this correlation in order to understand the similarities and differences in the genetic regulation of these omics layers. Methods and results We performed locus-wide association studies of 92 protein levels measured in whole blood for 2,014 samples of European ancestry and found that 66 are genetically regulated. Three female- and one male-specific effects were detected. We estimated the genetically regulated GE for all significant genes in 49 GTEx v8 tissues. A total of 7 proteins showed negative correlations with their respective GE across multiple tissues. Finally, we tested for causal links of GE on protein expression via Mendelian Randomization, and confirmed a negative causal effect of GE on protein level for five of these genes in a total of 63 gene-tissue pairs: BLMH, CASP3, CXCL16, IL6R, and SFTPD. For IL6R, we replicated the negative causal effect on coronary-artery disease (CAD), while its GE was positively linked to CAD. Conclusion While total GE and protein levels are only weakly correlated, we found high correlations between their genetically regulated components across multiple tissues. Of note, strong negative causal effects of tissue-specific GE on five protein levels were detected. Causal network analyses revealed that GE effects on CAD risks was in general mediated by protein levels.
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Affiliation(s)
- Janne Pott
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
- * E-mail: (JP); (MS)
| | - Tarcyane Garcia
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Stefanie M. Hauck
- Research Unit Protein Science and Metabolomics and Proteomics Core Facility, Helmholtz Zentrum Munich - German Research Center for Environmental Health, Neuherberg, Germany
| | - Agnese Petrera
- Research Unit Protein Science and Metabolomics and Proteomics Core Facility, Helmholtz Zentrum Munich - German Research Center for Environmental Health, Neuherberg, Germany
| | - Kerstin Wirkner
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Annette Peters
- Research Unit Protein Science and Metabolomics and Proteomics Core Facility, Helmholtz Zentrum Munich - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
- * E-mail: (JP); (MS)
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Salek Ardestani S, Zandi MB, Vahedi SM, Mahboudi H, Mahboudi F, Meskoob A. Detection of common copy number of variation underlying selection pressure in Middle Eastern horse breeds using whole-genome sequence data. J Hered 2022; 113:421-430. [PMID: 35605262 DOI: 10.1093/jhered/esac027] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 05/21/2022] [Indexed: 11/14/2022] Open
Abstract
Dareshouri, Arabian, and Akhal-Teke are three Middle Eastern horse breeds that have been selected for endurance and adaptation to harsh climates. Deciphering the genetic characteristics of these horses by tracing selection footprints and copy number of variations will be helpful in improving our understanding of equine breeds' development and adaptation. For this purpose, we sequenced the whole-genome of four Dareshouri horses using Illumina Hiseq panels and compared them with publicly available whole-genome sequences of Arabian (n=3) and Akhal-Teke (n=3) horses . Three tests of FLK, hapFLK, and pooled heterozygosity were applied using a sliding window (window size=100kb, step size=50kb) approach to detect putative selection signals. Copy number variation analysis was applied to investigate copy number of variants (CNVs), and the results were used to suggest selection signatures involving CNVs. Whole-genome sequencing demonstrated 8,837,950 single nucleotide polymorphisms (SNPs) in autosomal chromosomes. We suggested 58 genes and three quantitative trait loci (QTLs), including some related to horse gait, insect bite hypersensitivity, and withers height, based on selective signals detected by adjusted p-value of Mahalanobis distance based on the rank-based P-values (Md-rank-P) method. We proposed 12 genomic regions under selection pressure involving CNVs which were previously reported to be associated with metabolism energy (SLC5A8), champagne dilution in horses (SLC36A1), and synthesis of polyunsaturated fatty acids (FAT2). Only 10 Middle Eastern horses were tested in this study; therefore, the conclusions are speculative. Our findings are useful to better understanding the evolution and adaptation of Middle Eastern horse breeds.
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Affiliation(s)
- Siavash Salek Ardestani
- Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | - Seyed Milad Vahedi
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, Canada
| | - Hossein Mahboudi
- Department of Biotechnology, School of Pharmacy, Alborz University of Medical Sciences, Karaj, Iran
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Zhang J, Dutta D, Köttgen A, Tin A, Schlosser P, Grams ME, Harvey B, Yu B, Boerwinkle E, Coresh J, Chatterjee N. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies. Nat Genet 2022; 54:593-602. [PMID: 35501419 PMCID: PMC9236177 DOI: 10.1038/s41588-022-01051-w] [Citation(s) in RCA: 107] [Impact Index Per Article: 53.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 03/10/2022] [Indexed: 01/02/2023]
Abstract
Improved understanding of genetic regulation of the proteome can facilitate identification of the causal mechanisms for complex traits. We analyzed data on 4,657 plasma proteins from 7,213 European American (EA) and 1,871 African American (AA) individuals from the Atherosclerosis Risk in Communities study, and further replicated findings on 467 AA individuals from the African American Study of Kidney Disease and Hypertension study. Here, we identified 2,004 proteins in EA and 1,618 in AA, with most overlapping, which showed associations with common variants in cis-regions. Availability of AA samples led to smaller credible sets and notable number of population-specific cis-protein quantitative trait loci. Elastic Net produced powerful models for protein prediction in both populations. An application of proteome-wide association studies to serum urate and gout implicated several proteins, including IL1RN, revealing the promise of the drug anakinra to treat acute gout flares. Our study demonstrates the value of large and diverse ancestry study to investigate the genetic mechanisms of molecular phenotypes and their relationship with complex traits.
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Affiliation(s)
- Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Diptavo Dutta
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- MIND Center and Division of Nephrology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Pascal Schlosser
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Benjamin Harvey
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Bing Yu
- Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Eric Boerwinkle
- Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Josef Coresh
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Flynn E, Lappalainen T. Functional Characterization of Genetic Variant Effects on Expression. Annu Rev Biomed Data Sci 2022; 5:119-139. [PMID: 35483347 DOI: 10.1146/annurev-biodatasci-122120-010010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Elise Flynn
- New York Genome Center, New York, NY, USA; , .,Department of Systems Biology, Columbia University, New York, NY, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; , .,Department of Systems Biology, Columbia University, New York, NY, USA.,Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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Phitaktansakul R, Kim KW, Aung KM, Maung TZ, Min MH, Somsri A, Lee W, Lee SB, Nam J, Kim SH, Lee J, Kwon SW, Nawade B, Chu SH, Park SW, Kang KK, Cho YH, Lee YS, Chung IM, Park YJ. Multi-omics analysis reveals the genetic basis of rice fragrance mediated by betaine aldehyde dehydrogenase 2. J Adv Res 2021; 42:303-314. [PMID: 36513420 PMCID: PMC9788947 DOI: 10.1016/j.jare.2021.12.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 11/15/2021] [Accepted: 12/11/2021] [Indexed: 02/08/2023] Open
Abstract
INTRODUCTION Fragrance is an important economic and quality trait in rice. The trait is controlled by the recessive gene betaine aldehyde dehydrogenase 2 (BADH2) via the production of 2-acetyl-1-pyrroline (2AP). OBJECTIVES Variation in BADH2 was evaluated at the population, genetic, transcriptional, and metabolic levels to obtain insights into fragrance regulation in rice. METHODS Whole-genome resequencing of the Korean World Rice Collection of 475 rice accessions, including 421 breeding lines and 54 wild accessions, was performed. Transcriptome analyses of a subset of 279 accessions, proteome analyses of 64 accessions, and volatile profiling of 421 breeding lines were also performed. RESULTS We identified over 3.1 million high-quality single nucleotide polymorphisms (SNPs) in Korean rice collection. Most SNPs were present in intergenic regions (79%), and 190,148 SNPs (6%) were located in the coding sequence, of which 53% were nonsynonymous. In total, 38 haplotypes were identified in the BADH2 coding region, including four novel haplotypes (one in cultivated and three in wild accessions). Tajima's D values suggested that BADH2 was under balancing selection in japonica rice. Furthermore, we identified 316 expression quantitative trait loci (eQTL), including 185 cis-eQTLs and 131 trans-eQTLs, involved in BADH2 regulation. A protein quantitative trait loci (pQTL) analysis revealed the presence of trans-pQTLs; 13 pQTLs were mapped 1 Mbp from the BADH2 region. Based on variable importance in projection (VIP) scores, 15 volatile compounds, including 2AP, discriminated haplotypes and were potential biomarkers for rice fragrance. CONCLUSION We generated a catalog of haplotypes based on a resequencing analysis of a large number of rice accessions. eQTLs and pQTLs associated with BADH2 gene expression and protein accumulation are likely involved in the regulation of 2AP variation in fragrant rice. These data improve our understanding of fragrance and provide valuable information for rice breeding.
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Affiliation(s)
- Rungnapa Phitaktansakul
- Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan 32439, Republic of Korea
| | - Kyu-Won Kim
- Center of Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan 32439, Republic of Korea
| | - Kyaw Myo Aung
- Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan 32439, Republic of Korea
| | - Thant Zin Maung
- Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan 32439, Republic of Korea
| | - Myeong-Hyeon Min
- Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan 32439, Republic of Korea
| | - Aueangporn Somsri
- Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan 32439, Republic of Korea
| | - Wondo Lee
- Seedpia, 85 Maesil-ro, Kwonsun-ku, Suwon 16395, Republic of Korea
| | - Sang-Beom Lee
- Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan 32439, Republic of Korea
| | - Jungrye Nam
- Center of Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan 32439, Republic of Korea
| | - Seung-Hyun Kim
- Department of Applied Bioscience, Konkuk University, Seoul 05029, Republic of Korea
| | - Joohyun Lee
- Department of Applied Bioscience, Konkuk University, Seoul 05029, Republic of Korea
| | - Soon-Wook Kwon
- Department of Plant Bioscience, Pusan National University, Pusan 46241, Republic of Korea
| | - Bhagwat Nawade
- Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan 32439, Republic of Korea
| | - Sang-Ho Chu
- Center of Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan 32439, Republic of Korea
| | - Sang-Won Park
- Chemical Safety Division, National Institute of Agriculture Science (NIAS), Wanju 55365, Republic of Korea
| | - Kwon Kyoo Kang
- Department of Horticultural Life Science, Hankyong National University, Anseong 17579, Republic of Korea
| | - Yoo-Hyun Cho
- Seedpia, 85 Maesil-ro, Kwonsun-ku, Suwon 16395, Republic of Korea
| | - Young-Sang Lee
- Department of Medical Biotechnology, Soonchunhyang University, Asan 31538, Republic of Korea
| | - Ill-Min Chung
- Department of Applied Bioscience, Konkuk University, Seoul 05029, Republic of Korea,Corresponding authors at: Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan 32439, Korea (Yong-Jin Park); Department of Applied Bioscience, Konkuk University, Seoul 05029, Korea (Ill-Min Chung).
| | - Yong-Jin Park
- Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan 32439, Republic of Korea,Center of Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan 32439, Republic of Korea,Corresponding authors at: Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan 32439, Korea (Yong-Jin Park); Department of Applied Bioscience, Konkuk University, Seoul 05029, Korea (Ill-Min Chung).
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Pharmaco-proteogenomic profiling of pediatric diffuse midline glioma to inform future treatment strategies. Oncogene 2021; 41:461-475. [PMID: 34759345 PMCID: PMC8782719 DOI: 10.1038/s41388-021-02102-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 10/20/2021] [Accepted: 10/26/2021] [Indexed: 12/13/2022]
Abstract
Diffuse midline glioma (DMG) is a deadly pediatric and adolescent central nervous system (CNS) tumor localized along the midline structures of the brain atop the spinal cord. With a median overall survival (OS) of just 9–11-months, DMG is characterized by global hypomethylation of histone H3 at lysine 27 (H3K27me3), driven by recurring somatic mutations in H3 genes including, HIST1H3B/C (H3.1K27M) or H3F3A (H3.3K27M), or through overexpression of EZHIP in patients harboring wildtype H3. The recent World Health Organization’s 5th Classification of CNS Tumors now designates DMG as, ‘H3 K27-altered’, suggesting that global H3K27me3 hypomethylation is a ubiquitous feature of DMG and drives devastating transcriptional programs for which there are no treatments. H3-alterations co-segregate with various other somatic driver mutations, highlighting the high-level of intertumoral heterogeneity of DMG. Furthermore, DMG is also characterized by very high-level intratumoral diversity with tumors harboring multiple subclones within each primary tumor. Each subclone contains their own combinations of driver and passenger lesions that continually evolve, making precision-based medicine challenging to successful execute. Whilst the intertumoral heterogeneity of DMG has been extensively investigated, this is yet to translate to an increase in patient survival. Conversely, our understanding of the non-genomic factors that drive the rapid growth and fatal nature of DMG, including endogenous and exogenous microenvironmental influences, neurological cues, and the posttranscriptional and posttranslational architecture of DMG remains enigmatic or at best, immature. However, these factors are likely to play a significant role in the complex biological sequelae that drives the disease. Here we summarize the heterogeneity of DMG and emphasize how analysis of the posttranslational architecture may improve treatment paradigms. We describe factors that contribute to treatment response and disease progression, as well as highlight the potential for pharmaco-proteogenomics (i.e., the integration of genomics, proteomics and pharmacology) in the management of this uniformly fatal cancer.
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Perrot N, Pelletier W, Bourgault J, Couture C, Li Z, Mitchell PL, Ghodsian N, Bossé Y, Thériault S, Mathieu P, Arsenault BJ. A trans-omic Mendelian randomization study of parental lifespan uncovers novel aging biology and therapeutic candidates for chronic diseases. Aging Cell 2021; 20:e13497. [PMID: 34704651 PMCID: PMC8590095 DOI: 10.1111/acel.13497] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 05/20/2021] [Accepted: 09/29/2021] [Indexed: 12/13/2022] Open
Abstract
The study of parental lifespan has emerged as an innovative tool to advance aging biology and our understanding of the genetic architecture of human longevity and aging-associated diseases. Here, we leveraged summary statistics of a genome-wide association study including over one million parental lifespans to identify genetically regulated genes from the Genotype-Tissue Expression project. Through a combination of multi-tissue transcriptome-wide association analyses and genetic colocalization, we identified novel genes that may be associated with parental lifespan. Mendelian randomization (MR) analyses also identified circulating proteins and metabolites causally associated with parental lifespan and chronic diseases offering new drug repositioning opportunities such as those targeting apolipoprotein-B-containing lipoproteins. Liver expression of HP, the gene encoding haptoglobin, and plasma haptoglobin levels were causally linked with parental lifespan. Phenome-wide MR analyses were used to map genetically regulated genes, proteins and metabolites with other human traits as well as the disease-related phenome in the FinnGen cohorts (n = 135,638). Altogether, this study identified new candidate genes, circulating proteins and metabolites that may influence human aging as well as potential therapeutic targets for chronic diseases that warrant further investigation.
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Affiliation(s)
- Nicolas Perrot
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
- Department of MedicineFaculty of MedicineUniversité LavalQuébecQCCanada
| | - William Pelletier
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
- Department of MedicineFaculty of MedicineUniversité LavalQuébecQCCanada
| | - Jérôme Bourgault
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
| | - Christian Couture
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
| | - Zhonglin Li
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
| | - Patricia L. Mitchell
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
| | - Nooshin Ghodsian
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
| | - Yohan Bossé
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
- Department of Molecular MedicineFaculty of MedicineUniversité LavalQuébecQCCanada
| | - Sébastien Thériault
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
- Department of Molecular Biology, Medical Biochemistry and PathologyFaculty of MedicineUniversité LavalQuébecQCCanada
| | - Patrick Mathieu
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
- Department of SurgeryFaculty of MedicineUniversité LavalQuébecQCCanada
| | - Benoit J. Arsenault
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
- Department of MedicineFaculty of MedicineUniversité LavalQuébecQCCanada
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Keele GR, Zhang T, Pham DT, Vincent M, Bell TA, Hock P, Shaw GD, Paulo JA, Munger SC, Pardo-Manuel de Villena F, Ferris MT, Gygi SP, Churchill GA. Regulation of protein abundance in genetically diverse mouse populations. CELL GENOMICS 2021; 1:100003. [PMID: 36212994 PMCID: PMC9536773 DOI: 10.1016/j.xgen.2021.100003] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 04/01/2021] [Accepted: 05/10/2021] [Indexed: 12/12/2022]
Abstract
Genetically diverse mouse populations are powerful tools for characterizing the regulation of the proteome and its relationship to whole-organism phenotypes. We used mass spectrometry to profile and quantify the abundance of 6,798 proteins in liver tissue from mice of both sexes across 58 Collaborative Cross (CC) inbred strains. We previously collected liver proteomics data from the related Diversity Outbred (DO) mice and their founder strains. We show concordance across the proteomics datasets despite being generated from separate experiments, allowing comparative analysis. We map protein abundance quantitative trait loci (pQTLs), identifying 1,087 local and 285 distal in the CC mice and 1,706 local and 414 distal in the DO mice. We find that regulatory effects on individual proteins are conserved across the mouse populations, in particular for local genetic variation and sex differences. In comparison, proteins that form complexes are often co-regulated, displaying varying genetic architectures, and overall show lower heritability and map fewer pQTLs. We have made this resource publicly available to enable quantitative analyses of the regulation of the proteome.
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Affiliation(s)
| | - Tian Zhang
- Harvard Medical School, Boston, MA 02115, USA
| | - Duy T. Pham
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | | | - Timothy A. Bell
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Pablo Hock
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ginger D. Shaw
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | | | | | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Martin T. Ferris
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
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Zhu A, Matoba N, Wilson EP, Tapia AL, Li Y, Ibrahim JG, Stein JL, Love MI. MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity. PLoS Genet 2021; 17:e1009455. [PMID: 33872308 PMCID: PMC8084342 DOI: 10.1371/journal.pgen.1009455] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 04/29/2021] [Accepted: 02/26/2021] [Indexed: 11/18/2022] Open
Abstract
Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus's estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.
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Affiliation(s)
- Anqi Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Nana Matoba
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Emma P. Wilson
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Amanda L. Tapia
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 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
| | - Joseph G. Ibrahim
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- UNC Neuroscience Center, 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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Yao S, Wu H, Liu TT, Wang JH, Ding JM, Guo J, Rong Y, Ke X, Hao RH, Dong SS, Yang TL, Guo Y. Epigenetic Element-Based Transcriptome-Wide Association Study Identifies Novel Genes for Bipolar Disorder. Schizophr Bull 2021; 47:1642-1652. [PMID: 33772305 PMCID: PMC8530404 DOI: 10.1093/schbul/sbab023] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Since the bipolar disorder (BD) signals identified by genome-wide association study (GWAS) often reside in the non-coding regions, understanding the biological relevance of these genetic loci has proven to be complicated. Transcriptome-wide association studies (TWAS) providing a powerful approach to identify novel disease risk genes and uncover possible causal genes at loci identified previously by GWAS. However, these methods did not consider the importance of epigenetic regulation in gene expression. Here, we developed a novel epigenetic element-based transcriptome-wide association study (ETWAS) that tested the effects of genetic variants on gene expression levels with the epigenetic features as prior and further mediated the association between predicted expression and BD. We conducted an ETWAS consisting of 20 352 cases and 31 358 controls and identified 44 transcriptome-wide significant hits. We found 14 conditionally independent genes, and 10 genes that did not previously implicate with BD were regarded as novel candidate genes, such as ASB16 in the cerebellar hemisphere (P = 9.29 × 10-8). We demonstrated that several genome-wide significant signals from the BD GWAS driven by genetically regulated expression, and NEK4 explained 90.1% of the GWAS signal. Additionally, ETWAS identified genes could explain heritability beyond that explained by GWAS-associated SNPs (P = 5.60 × 10-66). By querying the SNPs in the final models of identified genes in phenome databases, we identified several phenotypes previously associated with BD, such as schizophrenia and depression. In conclusion, ETWAS is a powerful method, and we identified several novel candidate genes associated with BD.
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Affiliation(s)
- Shi Yao
- National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi 710004, P. R. China,Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China
| | - Hao Wu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China
| | - Tong-Tong Liu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China
| | - Jia-Hao Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China
| | - Jing-Miao Ding
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China
| | - Jing Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China
| | - Yu Rong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China
| | - Xin Ke
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China
| | - Ruo-Han Hao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China
| | - Tie-Lin Yang
- National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi 710004, P. R. China,Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China
| | - Yan Guo
- National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi 710004, P. R. China,Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China,To whom correspondence should be addressed; tel: +86-29-62818386, fax: +86-29-62818386, e-mail:
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Brion C, Lutz SM, Albert FW. Simultaneous quantification of mRNA and protein in single cells reveals post-transcriptional effects of genetic variation. eLife 2020; 9:60645. [PMID: 33191917 PMCID: PMC7707838 DOI: 10.7554/elife.60645] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 11/14/2020] [Indexed: 01/27/2023] Open
Abstract
Trans-acting DNA variants may specifically affect mRNA or protein levels of genes located throughout the genome. However, prior work compared trans-acting loci mapped in separate studies, many of which had limited statistical power. Here, we developed a CRISPR-based system for simultaneous quantification of mRNA and protein of a given gene via dual fluorescent reporters in single, live cells of the yeast Saccharomyces cerevisiae. In large populations of recombinant cells from a cross between two genetically divergent strains, we mapped 86 trans-acting loci affecting the expression of ten genes. Less than 20% of these loci had concordant effects on mRNA and protein of the same gene. Most loci influenced protein but not mRNA of a given gene. One locus harbored a premature stop variant in the YAK1 kinase gene that had specific effects on protein or mRNA of dozens of genes. These results demonstrate complex, post-transcriptional genetic effects on gene expression.
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Affiliation(s)
- Christian Brion
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
| | - Sheila M Lutz
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
| | - Frank Wolfgang Albert
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
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He B, Shi J, Wang X, Jiang H, Zhu HJ. Genome-wide pQTL analysis of protein expression regulatory networks in the human liver. BMC Biol 2020; 18:97. [PMID: 32778093 PMCID: PMC7418398 DOI: 10.1186/s12915-020-00830-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 07/16/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Previous expression quantitative trait loci (eQTL) studies have identified thousands of genetic variants to be associated with gene expression at the mRNA level in the human liver. However, protein expression often correlates poorly with mRNA levels. Thus, protein quantitative trait loci (pQTL) study is required to identify genetic variants that regulate protein expression in human livers. RESULTS We conducted a genome-wide pQTL study in 287 normal human liver samples and identified 900 local pQTL variants and 4026 distant pQTL variants. We further discovered 53 genome hotspots of pQTL variants. Transcriptional region mapping analysis showed that 1133 pQTL variants are in transcriptional regulatory regions. Genomic region enrichment analysis of the identified pQTL variants revealed 804 potential regulatory interactions among 595 predicted regulators (e.g., non-coding RNAs) and 394 proteins. Moreover, pQTL variants and trait-variant integration analysis implied several novel mechanisms underlying the relationships between protein expression and liver diseases, such as alcohol dependence. Notably, over 2000 of the identified pQTL variants have not been reported in previous eQTL studies, suggesting extensive involvement of genetic polymorphisms in post-transcriptional regulation of protein expression in human livers. CONCLUSIONS We have partially established protein expression regulation networks in human livers and generated a wealth of pQTL data that could serve as a valuable resource for the scientific community.
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Affiliation(s)
- Bing He
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, 428 Church Street, Room 4565 NUB, Ann Arbor, MI, 48109-1065, USA
| | - Jian Shi
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, 428 Church Street, Room 4565 NUB, Ann Arbor, MI, 48109-1065, USA
| | - Xinwen Wang
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, 428 Church Street, Room 4565 NUB, Ann Arbor, MI, 48109-1065, USA
| | - Hui Jiang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hao-Jie Zhu
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, 428 Church Street, Room 4565 NUB, Ann Arbor, MI, 48109-1065, USA.
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