1
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Jeong R, Bulyk ML. Chromatin accessibility variation provides insights into missing regulation underlying immune-mediated diseases. bioRxiv 2024:2024.04.12.589213. [PMID: 38659802 PMCID: PMC11042205 DOI: 10.1101/2024.04.12.589213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Most genetic loci associated with complex traits and diseases through genome-wide association studies (GWAS) are noncoding, suggesting that the causal variants likely have gene regulatory effects. However, only a small number of loci have been linked to expression quantitative trait loci (eQTLs) detected currently. To better understand the potential reasons for many trait-associated loci lacking eQTL colocalization, we investigated whether chromatin accessibility QTLs (caQTLs) in lymphoblastoid cell lines (LCLs) explain immune-mediated disease associations that eQTLs in LCLs did not. The power to detect caQTLs was greater than that of eQTLs and was less affected by the distance from the transcription start site of the associated gene. Meta-analyzing LCL eQTL data to increase the sample size to over a thousand led to additional loci with eQTL colocalization, demonstrating that insufficient statistical power is still likely to be a factor. Moreover, further eQTL colocalization loci were uncovered by surveying eQTLs of other immune cell types. Altogether, insufficient power and context-specificity of eQTLs both contribute to the 'missing regulation.'
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Affiliation(s)
- Raehoon Jeong
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
| | - Martha L. Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
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2
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Mackay TFC, Anholt RRH. Pleiotropy, epistasis and the genetic architecture of quantitative traits. Nat Rev Genet 2024:10.1038/s41576-024-00711-3. [PMID: 38565962 DOI: 10.1038/s41576-024-00711-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2024] [Indexed: 04/04/2024]
Abstract
Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.
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Affiliation(s)
- Trudy F C Mackay
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
| | - Robert R H Anholt
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
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3
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Sakaue S, Weinand K, Isaac S, Dey KK, Jagadeesh K, Kanai M, Watts GFM, Zhu Z, Brenner MB, McDavid A, Donlin LT, Wei K, Price AL, Raychaudhuri S. Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles. Nat Genet 2024; 56:615-626. [PMID: 38594305 DOI: 10.1038/s41588-024-01682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/07/2024] [Indexed: 04/11/2024]
Abstract
Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.
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Affiliation(s)
- Saori Sakaue
- 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
| | - Kathryn Weinand
- 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
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shakson Isaac
- 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
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kushal K Dey
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Karthik Jagadeesh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Gerald F M Watts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhu Zhu
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew McDavid
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Soumya Raychaudhuri
- 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.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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4
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Natri HM, Del Azodi CB, Peter L, Taylor CJ, Chugh S, Kendle R, Chung MI, Flaherty DK, Matlock BK, Calvi CL, Blackwell TS, Ware LB, Bacchetta M, Walia R, Shaver CM, Kropski JA, McCarthy DJ, Banovich NE. Cell-type-specific and disease-associated expression quantitative trait loci in the human lung. Nat Genet 2024; 56:595-604. [PMID: 38548990 PMCID: PMC11018522 DOI: 10.1038/s41588-024-01702-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 02/28/2024] [Indexed: 04/04/2024]
Abstract
Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis. Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA sequencing of lung tissue from 66 individuals with pulmonary fibrosis and 48 unaffected donors. Using a pseudobulk approach, we mapped expression quantitative trait loci (eQTLs) across 38 cell types, observing both shared and cell-type-specific regulatory effects. Furthermore, we identified disease interaction eQTLs and demonstrated that this class of associations is more likely to be cell-type-specific and linked to cellular dysregulation in pulmonary fibrosis. Finally, we connected lung disease risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression and implicates context-specific eQTLs as key regulators of lung homeostasis and disease.
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Affiliation(s)
- Heini M Natri
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Christina B Del Azodi
- St. Vincent's Institute of Medical Research, Melbourne, Victoria, Australia
- Melbourne Integrative Genomics, University of Melbourne, Melbourne, Victoria, Australia
| | - Lance Peter
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Chase J Taylor
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sagrika Chugh
- St. Vincent's Institute of Medical Research, Melbourne, Victoria, Australia
- Melbourne Integrative Genomics, University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, Victoria, Australia
| | - Robert Kendle
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Mei-I Chung
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - David K Flaherty
- Flow Cytometry Shared Resource, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Brittany K Matlock
- Flow Cytometry Shared Resource, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Carla L Calvi
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Timothy S Blackwell
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Department of Veterans Affairs Medical Center, Nashville, TN, USA
| | - Lorraine B Ware
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Matthew Bacchetta
- Department of Cardiac Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rajat Walia
- Department of Thoracic Disease and Transplantation, Norton Thoracic Institute, Phoenix, AZ, USA
| | - Ciara M Shaver
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan A Kropski
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Department of Veterans Affairs Medical Center, Nashville, TN, USA
| | - Davis J McCarthy
- St. Vincent's Institute of Medical Research, Melbourne, Victoria, Australia
- Melbourne Integrative Genomics, University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, Victoria, Australia
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5
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Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. bioRxiv 2024:2024.03.18.585576. [PMID: 38562822 PMCID: PMC10983939 DOI: 10.1101/2024.03.18.585576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
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Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA, 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Department of Opthalmology, Perlman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona, 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Inc., Chicago, IL, 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA, 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Michael Margolis
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Manman Shi
- Tempus Labs, Inc., Chicago, IL, 60654, USA
| | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, 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, 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA, 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT, 06520, USA
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6
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Dressman D, Tasaki S, Yu L, Schneider J, Bennett DA, Elyaman W, Vardarajan B. Polygenic risk associated with Alzheimer's disease and other traits influences genes involved in T cell signaling and activation. Front Immunol 2024; 15:1337831. [PMID: 38590520 PMCID: PMC10999606 DOI: 10.3389/fimmu.2024.1337831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/22/2024] [Indexed: 04/10/2024] Open
Abstract
Introduction T cells, known for their ability to respond to an enormous variety of pathogens and other insults, are increasingly recognized as important mediators of pathology in neurodegeneration and other diseases. T cell gene expression phenotypes can be regulated by disease-associated genetic variants. Many complex diseases are better represented by polygenic risk than by individual variants. Methods We first compute a polygenic risk score (PRS) for Alzheimer's disease (AD) using genomic sequencing data from a cohort of Alzheimer's disease (AD) patients and age-matched controls, and validate the AD PRS against clinical metrics in our cohort. We then calculate the PRS for several autoimmune disease, neurological disorder, and immune function traits, and correlate these PRSs with T cell gene expression data from our cohort. We compare PRS-associated genes across traits and four T cell subtypes. Results Several genes and biological pathways associated with the PRS for these traits relate to key T cell functions. The PRS-associated gene signature generally correlates positively for traits within a particular category (autoimmune disease, neurological disease, immune function) with the exception of stroke. The trait-associated gene expression signature for autoimmune disease traits was polarized towards CD4+ T cell subtypes. Discussion Our findings show that polygenic risk for complex disease and immune function traits can have varying effects on T cell gene expression trends. Several PRS-associated genes are potential candidates for therapeutic modulation in T cells, and could be tested in in vitro applications using cells from patients bearing high or low polygenic risk for AD or other conditions.
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Affiliation(s)
- Dallin Dressman
- Department of Neurology, Columbia University, New York, NY, United States
- The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, United States
| | - Shinya Tasaki
- Rush University Medical Center, Rush Alzheimer’s Disease Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Lei Yu
- Rush University Medical Center, Rush Alzheimer’s Disease Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Julie Schneider
- Rush University Medical Center, Rush Alzheimer’s Disease Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
- Department of Pathology, Rush University Medical Center, Chicago, IL, United States
| | - David A. Bennett
- Rush University Medical Center, Rush Alzheimer’s Disease Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Wassim Elyaman
- Department of Neurology, Columbia University, New York, NY, United States
- The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, United States
| | - Badri Vardarajan
- Department of Neurology, Columbia University, New York, NY, United States
- The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, United States
- College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, The Gertrude H. Sergievsky Center, New York, NY, United States
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7
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Lin W, Wall JD, Li G, Newman D, Yang Y, Abney M, VandeBerg JL, Olivier M, Gilad Y, Cox LA. Genetic regulatory effects in response to a high-cholesterol, high-fat diet in baboons. Cell Genom 2024; 4:100509. [PMID: 38430910 PMCID: PMC10943580 DOI: 10.1016/j.xgen.2024.100509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/20/2023] [Accepted: 02/05/2024] [Indexed: 03/05/2024]
Abstract
Steady-state expression quantitative trait loci (eQTLs) explain only a fraction of disease-associated loci identified through genome-wide association studies (GWASs), while eQTLs involved in gene-by-environment (GxE) interactions have rarely been characterized in humans due to experimental challenges. Using a baboon model, we found hundreds of eQTLs that emerge in adipose, liver, and muscle after prolonged exposure to high dietary fat and cholesterol. Diet-responsive eQTLs exhibit genomic localization and genic features that are distinct from steady-state eQTLs. Furthermore, the human orthologs associated with diet-responsive eQTLs are enriched for GWAS genes associated with human metabolic traits, suggesting that context-responsive eQTLs with more complex regulatory effects are likely to explain GWAS hits that do not seem to overlap with standard eQTLs. Our results highlight the complexity of genetic regulatory effects and the potential of eQTLs with disease-relevant GxE interactions in enhancing the understanding of GWAS signals for human complex disease using non-human primate models.
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Affiliation(s)
- Wenhe Lin
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA.
| | - Jeffrey D Wall
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Ge Li
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Deborah Newman
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX 78229, USA
| | - Yunqi Yang
- Committee on Genetics, Genomics and System Biology, The University of Chicago, Chicago, IL 60637, USA
| | - Mark Abney
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
| | - John L VandeBerg
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA
| | - Michael Olivier
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Yoav Gilad
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL 60637, USA.
| | - Laura A Cox
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA; Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX 78229, USA.
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8
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Zhou Z, Du J, Wang J, Liu L, Gordon MG, Ye CJ, Powell JE, Li MJ, Rao S. SingleQ: a comprehensive database of single-cell expression quantitative trait loci (sc-eQTLs) cross human tissues. Database (Oxford) 2024; 2024:baae010. [PMID: 38459946 PMCID: PMC10924434 DOI: 10.1093/database/baae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 01/09/2024] [Accepted: 02/11/2024] [Indexed: 03/11/2024]
Abstract
Mapping of expression quantitative trait loci (eQTLs) and other molecular QTLs can help characterize the modes of action of disease-associated genetic variants. However, current eQTL databases present data from bulk RNA-seq approaches, which cannot shed light on the cell type- and environment-specific regulation of disease-associated genetic variants. Here, we introduce our Single-cell eQTL Interactive Database which collects single-cell eQTL (sc-eQTL) datasets and provides online visualization of sc-eQTLs across different cell types in a user-friendly manner. Although sc-eQTL mapping is still in its early stage, our database curates the most comprehensive summary statistics of sc-eQTLs published to date. sc-eQTL studies have revolutionized our understanding of gene regulation in specific cellular contexts, and we anticipate that our database will further accelerate the research of functional genomics. Database URL: http://www.sqraolab.com/scqtl.
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Affiliation(s)
- Zhiwei Zhou
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin 300020, China
- Tianjin Institutes of Health Science, 28 Tuanbo Avenue, Tianjin 301600, China
| | - Jingyi Du
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin 300020, China
- Tianjin Institutes of Health Science, 28 Tuanbo Avenue, Tianjin 301600, China
| | - Jianhua Wang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, 22 Qixiangtai Road, Tianjin 300070, China
| | - Liangyi Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin 300020, China
| | - M Gracie Gordon
- Biological and Medical Informatics Graduate Program, University of California, 500 Parnassus Avenue, San Francisco, CA 94143, USA
- Division of Rheumatology, Department of Medicine, University of California, 500 Parnassus Avenue, San Francisco, CA 94143, USA
- Institute for Human Genetics, University of California, 500 Parnassus Avenue, San Francisco, CA 94143, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, 500 Parnassus Avenue, San Francisco, CA 94143, USA
| | - Chun Jimmie Ye
- Division of Rheumatology, Department of Medicine, University of California, 500 Parnassus Avenue, San Francisco, CA 94143, USA
- Institute for Human Genetics, University of California, 500 Parnassus Avenue, San Francisco, CA 94143, USA
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, University of California, 500 Parnassus Avenue, San Francisco, CA 94143, USA
- Department of Epidemiology and Biostatistics, University of California, 500 Parnassus Avenue, San Francisco, CA 94143, USA
- Parker Institute for Cancer Immunotherapy, University of California, 500 Parnassus Avenue, San Francisco, CA 94143, USA
- Chan Zuckerberg Biohub, 499 Illinois Street, San Francisco, CA 94158, USA
- Bakar Computational Health Sciences Institute, University of California, 500 Parnassus Avenue, San Francisco, CA 94143, USA
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, 384 Victoria Street, Sydney, NSW 2010, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, UNSW Sydney, Sydney, NSW 2052, Australia
| | - Mulin Jun Li
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, 22 Qixiangtai Road, Tianjin 300070, China
| | - Shuquan Rao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin 300020, China
- Tianjin Institutes of Health Science, 28 Tuanbo Avenue, Tianjin 301600, China
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9
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Kashima Y, Reteng P, Haga Y, Yamagishi J, Suzuki Y. Single-cell analytical technologies: uncovering the mechanisms behind variations in immune responses. FEBS J 2024; 291:819-831. [PMID: 36082537 DOI: 10.1111/febs.16622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/25/2022] [Accepted: 09/08/2022] [Indexed: 11/30/2022]
Abstract
The immune landscape varies among individuals. It determines the immune response and results in surprisingly diverse symptoms, even in response to similar external stimuli. However, the detailed mechanisms underlying such diverse immune responses have remained mostly elusive. The utilization of recently developed single-cell multimodal analysis platforms has started to answer this question. Emerging studies have elucidated several molecular networks that may explain diversity with respect to age or other factors. An elaborate interplay between inherent physical conditions and environmental conditions has been demonstrated. Furthermore, the importance of modifications by the epigenome resulting in transcriptome variation among individuals is gradually being revealed. Accordingly, epigenomes and transcriptomes are direct indicators of the medical history and dynamic interactions with environmental factors. Coronavirus disease 2019 (COVID-19) has recently become one of the most remarkable examples of the necessity of in-depth analyses of diverse responses with respect to various factors to improve treatment in severe cases and to prevent viral transmission from asymptomatic carriers. In fact, determining why some patients develop serious symptoms is still a pressing issue. Here, we review the current "state of the art" in single-cell analytical technologies and their broad applications to healthy individuals and representative diseases, including COVID-19.
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Affiliation(s)
- Yukie Kashima
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Patrick Reteng
- Division of Collaboration and Education, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Yasuhiko Haga
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Junya Yamagishi
- Division of Collaboration and Education, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
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10
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Yasumizu Y, Takeuchi D, Morimoto R, Takeshima Y, Okuno T, Kinoshita M, Morita T, Kato Y, Wang M, Motooka D, Okuzaki D, Nakamura Y, Mikami N, Arai M, Zhang X, Kumanogoh A, Mochizuki H, Ohkura N, Sakaguchi S. Single-cell transcriptome landscape of circulating CD4 + T cell populations in autoimmune diseases. Cell Genom 2024; 4:100473. [PMID: 38359792 PMCID: PMC10879034 DOI: 10.1016/j.xgen.2023.100473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 09/07/2023] [Accepted: 12/05/2023] [Indexed: 02/17/2024]
Abstract
CD4+ T cells are key mediators of various autoimmune diseases; however, their role in disease progression remains unclear due to cellular heterogeneity. Here, we evaluated CD4+ T cell subpopulations using decomposition-based transcriptome characterization and canonical clustering strategies. This approach identified 12 independent gene programs governing whole CD4+ T cell heterogeneity, which can explain the ambiguity of canonical clustering. In addition, we performed a meta-analysis using public single-cell datasets of over 1.8 million peripheral CD4+ T cells from 953 individuals by projecting cells onto the reference and cataloging cell frequency and qualitative alterations of the populations in 20 diseases. The analyses revealed that the 12 transcriptional programs were useful in characterizing each autoimmune disease and predicting its clinical status. Moreover, genetic variants associated with autoimmune diseases showed disease-specific enrichment within the 12 gene programs. The results collectively provide a landscape of single-cell transcriptomes of CD4+ T cell subpopulations involved in autoimmune disease.
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Affiliation(s)
- Yoshiaki Yasumizu
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan; Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan
| | - Daiki Takeuchi
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan; Faculty of Medicine, Osaka University, Osaka, Japan
| | - Reo Morimoto
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Yusuke Takeshima
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Tatsusada Okuno
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Makoto Kinoshita
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Takayoshi Morita
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yasuhiro Kato
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan; Department of Immunopathology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Min Wang
- Clinical Immunology Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Department of Rheumatology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Daisuke Motooka
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan; Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Daisuke Okuzaki
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan; Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Yamami Nakamura
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Norihisa Mikami
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Masaya Arai
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Xuan Zhang
- Department of Rheumatology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Atsushi Kumanogoh
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan; Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan; Department of Immunopathology, Immunology Frontier Research Center, Osaka University, Osaka, Japan; Center for Infectious Diseases for Education and Research, Osaka University, Osaka, Japan
| | - Hideki Mochizuki
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan
| | - Naganari Ohkura
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan; Department of Frontier Research in Tumor Immunology, Graduate School of Medicine, Osaka University, Osaka, Japan.
| | - Shimon Sakaguchi
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan; Department of Experimental Immunology, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan.
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11
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Lagattuta KA, Park HL, Rumker L, Ishigaki K, Nathan A, Raychaudhuri S. The genetic basis of autoimmunity seen through the lens of T cell functional traits. Nat Commun 2024; 15:1204. [PMID: 38331990 PMCID: PMC10853555 DOI: 10.1038/s41467-024-45170-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 01/15/2024] [Indexed: 02/10/2024] Open
Abstract
Autoimmune disease heritability is enriched in T cell-specific regulatory regions of the genome. Modern-day T cell datasets now enable association studies between single nucleotide polymorphisms (SNPs) and a myriad of molecular phenotypes, including chromatin accessibility, gene expression, transcriptional programs, T cell antigen receptor (TCR) amino acid usage, and cell state abundances. Such studies have identified hundreds of quantitative trait loci (QTLs) in T cells that colocalize with genetic risk for autoimmune disease. The key challenge facing immunologists today lies in synthesizing these results toward a unified understanding of the autoimmune T cell: which genes, cell states, and antigens drive tissue destruction?
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Affiliation(s)
- Kaitlyn A Lagattuta
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, 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
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Hannah L Park
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, 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
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Laurie Rumker
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, 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
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kazuyoshi Ishigaki
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, 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
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, 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.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, 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.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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12
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Alda-Catalinas C, Ibarra-Soria X, Flouri C, Gordillo JE, Cousminer D, Hutchinson A, Sun B, Pembroke W, Ullrich S, Krejci A, Cortes A, Acevedo A, Malla S, Fishwick C, Drewes G, Rapiteanu R. Mapping the functional impact of non-coding regulatory elements in primary T cells through single-cell CRISPR screens. Genome Biol 2024; 25:42. [PMID: 38308274 PMCID: PMC10835965 DOI: 10.1186/s13059-024-03176-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 01/18/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Drug targets with genetic evidence are expected to increase clinical success by at least twofold. Yet, translating disease-associated genetic variants into functional knowledge remains a fundamental challenge of drug discovery. A key issue is that the vast majority of complex disease associations cannot be cleanly mapped to a gene. Immune disease-associated variants are enriched within regulatory elements found in T-cell-specific open chromatin regions. RESULTS To identify genes and molecular programs modulated by these regulatory elements, we develop a CRISPRi-based single-cell functional screening approach in primary human T cells. Our pipeline enables the interrogation of transcriptomic changes induced by the perturbation of regulatory elements at scale. We first optimize an efficient CRISPRi protocol in primary CD4+ T cells via CROPseq vectors. Subsequently, we perform a screen targeting 45 non-coding regulatory elements and 35 transcription start sites and profile approximately 250,000 T -cell single-cell transcriptomes. We develop a bespoke analytical pipeline for element-to-gene (E2G) mapping and demonstrate that our method can identify both previously annotated and novel E2G links. Lastly, we integrate genetic association data for immune-related traits and demonstrate how our platform can aid in the identification of effector genes for GWAS loci. CONCLUSIONS We describe "primary T cell crisprQTL" - a scalable, single-cell functional genomics approach for mapping regulatory elements to genes in primary human T cells. We show how this framework can facilitate the interrogation of immune disease GWAS hits and propose that the combination of experimental and QTL-based techniques is likely to address the variant-to-function problem.
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Affiliation(s)
| | | | | | | | | | | | - Bin Sun
- Genomic Sciences, GSK, Stevenage, UK
| | | | | | | | | | | | | | | | - Gerard Drewes
- Genomic Sciences, GSK, Stevenage, UK
- Genomic Sciences, GSK, Collegeville, PA, USA
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13
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Strober BJ, Tayeb K, Popp J, Qi G, Gordon MG, Perez R, Ye CJ, Battle A. SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models. Genome Biol 2024; 25:28. [PMID: 38254214 PMCID: PMC10801966 DOI: 10.1186/s13059-023-03152-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.
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Affiliation(s)
- Benjamin J Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Karl Tayeb
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Joshua Popp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Guanghao Qi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - M Grace Gordon
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Richard Perez
- Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Chun Jimmie Ye
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
- Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
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14
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Tegtmeyer M, Arora J, Asgari S, Cimini BA, Nadig A, Peirent E, Liyanage D, Way GP, Weisbart E, Nathan A, Amariuta T, Eggan K, Haghighi M, McCarroll SA, O'Connor L, Carpenter AE, Singh S, Nehme R, Raychaudhuri S. High-dimensional phenotyping to define the genetic basis of cellular morphology. Nat Commun 2024; 15:347. [PMID: 38184653 PMCID: PMC10771466 DOI: 10.1038/s41467-023-44045-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 11/28/2023] [Indexed: 01/08/2024] Open
Abstract
The morphology of cells is dynamic and mediated by genetic and environmental factors. Characterizing how genetic variation impacts cell morphology can provide an important link between disease association and cellular function. Here, we combine genomic sequencing and high-content imaging approaches on iPSCs from 297 unique donors to investigate the relationship between genetic variants and cellular morphology to map what we term cell morphological quantitative trait loci (cmQTLs). We identify novel associations between rare protein altering variants in WASF2, TSPAN15, and PRLR with several morphological traits related to cell shape, nucleic granularity, and mitochondrial distribution. Knockdown of these genes by CRISPRi confirms their role in cell morphology. Analysis of common variants yields one significant association and nominate over 300 variants with suggestive evidence (P < 10-6) of association with one or more morphology traits. We then use these data to make predictions about sample size requirements for increasing discovery in cellular genetic studies. We conclude that, similar to molecular phenotypes, morphological profiling can yield insight about the function of genes and variants.
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Affiliation(s)
- Matthew Tegtmeyer
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Centre for Gene Therapy and Regenerative Medicine, King's College, London, UK
| | - Jatin Arora
- Center for Data Sciences, 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
- 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
| | - Samira Asgari
- Center for Data Sciences, 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
- 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
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ajay Nadig
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Emily Peirent
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dhara Liyanage
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gregory P Way
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aparna Nathan
- Center for Data Sciences, 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
- 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
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tiffany Amariuta
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Halıcıoğlu Data Science Institute, University of California, La Jolla, CA, USA
- Department of Medicine, University of California, La Jolla, CA, USA
| | - Kevin Eggan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Marzieh Haghighi
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven A McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Luke O'Connor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shantanu Singh
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Ralda Nehme
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
| | - Soumya Raychaudhuri
- Center for Data Sciences, 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.
- 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.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Centre for Genetics and Genomics Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
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15
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Ding R, Wang Q, Gong L, Zhang T, Zou X, Xiong K, Liao Q, Plass M, Li L. scQTLbase: an integrated human single-cell eQTL database. Nucleic Acids Res 2024; 52:D1010-D1017. [PMID: 37791879 PMCID: PMC10767909 DOI: 10.1093/nar/gkad781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/24/2023] [Accepted: 09/15/2023] [Indexed: 10/05/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified numerous genetic variants associated with diseases and traits. However, the functional interpretation of these variants remains challenging. Expression quantitative trait loci (eQTLs) have been widely used to identify mutations linked to disease, yet they explain only 20-50% of disease-related variants. Single-cell eQTLs (sc-eQTLs) studies provide an immense opportunity to identify new disease risk genes with expanded eQTL scales and transcriptional regulation at a much finer resolution. However, there is no comprehensive database dedicated to single-cell eQTLs that users can use to search, analyse and visualize them. Therefore, we developed the scQTLbase (http://bioinfo.szbl.ac.cn/scQTLbase), the first integrated human sc-eQTLs portal, featuring 304 datasets spanning 57 cell types and 95 cell states. It contains ∼16 million SNPs significantly associated with cell-type/state gene expression and ∼0.69 million disease-associated sc-eQTLs from 3 333 traits/diseases. In addition, scQTLbase offers sc-eQTL search, gene expression visualization in UMAP plots, a genome browser, and colocalization visualization based on the GWAS dataset of interest. scQTLbase provides a one-stop portal for sc-eQTLs that will significantly advance the discovery of disease susceptibility genes.
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Affiliation(s)
- Ruofan Ding
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Qixuan Wang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Lihai Gong
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Ting Zhang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Xudong Zou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Kewei Xiong
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Qi Liao
- School of Public Health, Health Science Center, Ningbo University, Ningbo 315211, China
| | - Mireya Plass
- Gene Regulation of Cell Identity Group, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), and Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P-CMR[C], L′Hospitalet de Llobregat, Barcelona, Spain
- Center for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Lei Li
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
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16
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Teng J, Gao Y, Yin H, Bai Z, Liu S, Zeng H, Bai L, Cai Z, Zhao B, Li X, Xu Z, Lin Q, Pan Z, Yang W, Yu X, Guan D, Hou Y, Keel BN, Rohrer GA, Lindholm-Perry AK, Oliver WT, Ballester M, Crespo-Piazuelo D, Quintanilla R, Canela-Xandri O, Rawlik K, Xia C, Yao Y, Zhao Q, Yao W, Yang L, Li H, Zhang H, Liao W, Chen T, Karlskov-Mortensen P, Fredholm M, Amills M, Clop A, Giuffra E, Wu J, Cai X, Diao S, Pan X, Wei C, Li J, Cheng H, Wang S, Su G, Sahana G, Lund MS, Dekkers JCM, Kramer L, Tuggle CK, Corbett R, Groenen MAM, Madsen O, Gòdia M, Rocha D, Charles M, Li CJ, Pausch H, Hu X, Frantz L, Luo Y, Lin L, Zhou Z, Zhang Z, Chen Z, Cui L, Xiang R, Shen X, Li P, Huang R, Tang G, Li M, Zhao Y, Yi G, Tang Z, Jiang J, Zhao F, Yuan X, Liu X, Chen Y, Xu X, Zhao S, Zhao P, Haley C, Zhou H, Wang Q, Pan Y, Ding X, Ma L, Li J, Navarro P, Zhang Q, Li B, Tenesa A, Li K, Liu GE, Zhang Z, Fang L. A compendium of genetic regulatory effects across pig tissues. Nat Genet 2024; 56:112-123. [PMID: 38177344 PMCID: PMC10786720 DOI: 10.1038/s41588-023-01585-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 10/13/2023] [Indexed: 01/06/2024]
Abstract
The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.
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Affiliation(s)
- Jinyan Teng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Yahui Gao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA
| | - Hongwei Yin
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhonghao Bai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Shuli Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
- School of Life Sciences, Westlake University, Hangzhou, China
| | - Haonan Zeng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Lijing Bai
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zexi Cai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Bingru Zhao
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xiujin Li
- Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Zhiting Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Qing Lin
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Zhangyuan Pan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wenjing Yang
- College of Animal Science and Technology, China Agricultural University, Beijing, China
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Xiaoshan Yu
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Dailu Guan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Yali Hou
- Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Brittney N Keel
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Gary A Rohrer
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | | | - William T Oliver
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Maria Ballester
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Daniel Crespo-Piazuelo
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Raquel Quintanilla
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Konrad Rawlik
- Baillie Gifford Pandemic Science Hub, University of Edinburgh, Edinburgh, UK
| | - Charley Xia
- Lothian Birth Cohort studies, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Yuelin Yao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- School of Informatics, The University of Edinburgh, Edinburgh, UK
| | - Qianyi Zhao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenye Yao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Liu Yang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Houcheng Li
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Huicong Zhang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Wang Liao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Tianshuo Chen
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Peter Karlskov-Mortensen
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Merete Fredholm
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marcel Amills
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Alex Clop
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
- Consejo Superior de Investigaciones Científicas, Barcelona, Spain
| | - Elisabetta Giuffra
- Paris-Saclay University, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Jun Wu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiaodian Cai
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Shuqi Diao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiangchun Pan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Chen Wei
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Jinghui Li
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Hao Cheng
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Jack C M Dekkers
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Luke Kramer
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | | | - Ryan Corbett
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Martien A M Groenen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Ole Madsen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Marta Gòdia
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Dominique Rocha
- Paris-Saclay University, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Mathieu Charles
- Paris-Saclay University, INRAE, AgroParisTech, GABI, SIGENAE, Jouy-en-Josas, France
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, Zurich, Switzerland
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Laurent Frantz
- Palaeogenomics Group, Department of Veterinary Sciences, Ludwig Maximilian University, Munich, Germany
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Yonglun Luo
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Research, Qingdao, China
| | - Lin Lin
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Zhongyin Zhou
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Zhe Zhang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Zitao Chen
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Leilei Cui
- School of Life Sciences, Nanchang University, Nanchang, China
- Human Aging Research Institute and School of Life Science, Nanchang University, and Jiangxi Key Laboratory of Human Aging, Jiangxi, China
- UCL Genetics Institute, University College London, London, UK
| | - Ruidong Xiang
- Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, Victoria, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria, Australia
| | - Xia Shen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine, Fudan University, Guangzhou, China
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Pinghua Li
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Ruihua Huang
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Guoqing Tang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Mingzhou Li
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Yunxiang Zhao
- College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Guoqiang Yi
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhonglin Tang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Jicai Jiang
- Department of Animal Science, North Carolina State University, Raleigh, NC, USA
| | - Fuping Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaolong Yuan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiaohong Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yaosheng Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Xuewen Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education and College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education and College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Pengju Zhao
- Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya, China
| | - Chris Haley
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Qishan Wang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Yuchun Pan
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Xiangdong Ding
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA
| | - Jiaqi Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Pau Navarro
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Qin Zhang
- College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China
| | - Bingjie Li
- Scotland's Rural College (SRUC), Roslin Institute Building, Midlothian, UK
| | - Albert Tenesa
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK.
| | - Kui Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA.
| | - Zhe Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China.
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
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17
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Boocock J, Alexander N, Tapia LA, Walter-McNeill L, Munugala C, Bloom JS, Kruglyak L. Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction. bioRxiv 2023:2023.12.07.570640. [PMID: 38106186 PMCID: PMC10723400 DOI: 10.1101/2023.12.07.570640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.
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Affiliation(s)
- James Boocock
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Noah Alexander
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Leslie Alamo Tapia
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Laura Walter-McNeill
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Chetan Munugala
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Leonid Kruglyak
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
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18
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Abe H, Lin P, Zhou D, Ruderfer DM, Gamazon ER. Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease. medRxiv 2023:2023.10.24.23297476. [PMID: 37961453 PMCID: PMC10635195 DOI: 10.1101/2023.10.24.23297476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human biology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference resource from population scale studies, data sparsity in single-cell RNA sequencing, and the complex cell-state pattern of expression within individual cell types. Here we develop genetic models of cell type specific and cell state adjusted gene expression in mid-brain neurons in the process of specializing from induced pluripotent stem cells. The resulting framework quantifies the dynamics of the genetic regulation of gene expression and estimates its cell type specificity. As an application, we show that the approach detects known and new genes associated with schizophrenia and enables insights into context-dependent disease mechanisms. We provide a genomic resource from a phenome-wide application of our models to more than 1500 phenotypes from the UK Biobank. Using longitudinal genetically determined expression, we implement a predictive causality framework, evaluating the prediction of future values of a target gene expression using prior values of a putative regulatory gene. Collectively, this work demonstrates the insights that can be gained into the molecular underpinnings of diseases by quantifying the genetic control of gene expression at single-cell resolution.
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Affiliation(s)
- Hanna Abe
- Vanderbilt University, Nashville, TN
| | - Phillip Lin
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Dan Zhou
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Douglas M Ruderfer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Department of Biomedical Informatics and Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Clare Hall, University of Cambridge, Cambridge, England
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19
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Zhang J, Zhao H. eQTL studies: from bulk tissues to single cells. J Genet Genomics 2023; 50:925-933. [PMID: 37207929 PMCID: PMC10656365 DOI: 10.1016/j.jgg.2023.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/02/2023] [Accepted: 05/04/2023] [Indexed: 05/21/2023]
Abstract
An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to a better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detection of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.
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Affiliation(s)
- Jingfei Zhang
- Information Systems and Operations Management, Emory University, Atlanta, GA 30322, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 208034, USA.
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20
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Kang JB, Shen AZ, Gurajala S, Nathan A, Rumker L, Aguiar VRC, Valencia C, Lagattuta KA, Zhang F, Jonsson AH, Yazar S, Alquicira-Hernandez J, Khalili H, Ananthakrishnan AN, Jagadeesh K, Dey K, Daly MJ, Xavier RJ, Donlin LT, Anolik JH, Powell JE, Rao DA, Brenner MB, Gutierrez-Arcelus M, Luo Y, Sakaue S, Raychaudhuri S. Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution. Nat Genet 2023; 55:2255-2268. [PMID: 38036787 PMCID: PMC10787945 DOI: 10.1038/s41588-023-01586-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 10/19/2023] [Indexed: 12/02/2023]
Abstract
The human leukocyte antigen (HLA) locus plays a critical role in complex traits spanning autoimmune and infectious diseases, transplantation and cancer. While coding variation in HLA genes has been extensively documented, regulatory genetic variation modulating HLA expression levels has not been comprehensively investigated. Here we mapped expression quantitative trait loci (eQTLs) for classical HLA genes across 1,073 individuals and 1,131,414 single cells from three tissues. To mitigate technical confounding, we developed scHLApers, a pipeline to accurately quantify single-cell HLA expression using personalized reference genomes. We identified cell-type-specific cis-eQTLs for every classical HLA gene. Modeling eQTLs at single-cell resolution revealed that many eQTL effects are dynamic across cell states even within a cell type. HLA-DQ genes exhibit particularly cell-state-dependent effects within myeloid, B and T cells. For example, a T cell HLA-DQA1 eQTL ( rs3104371 ) is strongest in cytotoxic cells. Dynamic HLA regulation may underlie important interindividual variability in immune responses.
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Affiliation(s)
- 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Amber Z Shen
- 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Saisriram Gurajala
- 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Vitor R C Aguiar
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology and the Center for Health Artificial Intelligence, University of Colorado School of Medicine, Aurora, CO, USA
| | - Anna Helena Jonsson
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Seyhan Yazar
- Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | | | - Hamed Khalili
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ashwin N Ananthakrishnan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Kushal Dey
- Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Physiology, Biophysics and Systems Biology Program, Weill Cornell Medicine, New York, NY, USA
| | - Mark J Daly
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Ramnik J Xavier
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Jennifer H Anolik
- Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Joseph E Powell
- Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Deepak A Rao
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Maria Gutierrez-Arcelus
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yang Luo
- 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, 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.
- 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.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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21
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Wang J, Cheng X, Liang Q, Owen LA, Lu J, Zheng Y, Wang M, Chen S, DeAngelis MM, Li Y, Chen R. Single-cell multiomics of the human retina reveals hierarchical transcription factor collaboration in mediating cell type-specific effects of genetic variants on gene regulation. Genome Biol 2023; 24:269. [PMID: 38012720 PMCID: PMC10680294 DOI: 10.1186/s13059-023-03111-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 11/15/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Systematic characterization of how genetic variation modulates gene regulation in a cell type-specific context is essential for understanding complex traits. To address this question, we profile gene expression and chromatin accessibility in cells from healthy retinae of 20 human donors through single-cell multiomics and genomic sequencing. RESULTS We map eQTL, caQTL, allelic-specific expression, and allelic-specific chromatin accessibility in major retinal cell types. By integrating these results, we identify and characterize regulatory elements and genetic variants effective on gene regulation in individual cell types. The majority of identified sc-eQTLs and sc-caQTLs display cell type-specific effects, while the cis-elements containing genetic variants with cell type-specific effects are often accessible in multiple cell types. Furthermore, the transcription factors whose binding sites are perturbed by genetic variants tend to have higher expression levels in the cell types where the variants exert their effects, compared to the cell types where the variants have no impact. We further validate our findings with high-throughput reporter assays. Lastly, we identify the enriched cell types, candidate causal variants and genes, and cell type-specific regulatory mechanism underlying GWAS loci. CONCLUSIONS Overall, genetic effects on gene regulation are highly context dependent. Our results suggest that cell type-dependent genetic effect is driven by precise modulation of both trans-factor expression and chromatin accessibility of cis-elements. Our findings indicate hierarchical collaboration among transcription factors plays a crucial role in mediating cell type-specific effects of genetic variants on gene regulation.
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Affiliation(s)
- Jun Wang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Xuesen Cheng
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Qingnan Liang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Leah A Owen
- Department of Ophthalmology and Visual Sciences, John A. Moran Eye Center, University of Utah, Salt Lake City, UT, USA
| | - Jiaxiong Lu
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Yiqiao Zheng
- Department of Ophthalmology and Visual Sciences, Washington University in St Louis, Saint Louis, MO, USA
| | - Meng Wang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Shiming Chen
- Department of Ophthalmology and Visual Sciences, Washington University in St Louis, Saint Louis, MO, USA
- Department of Developmental Biology, Washington University in St Louis, Saint Louis, MO, USA
| | - Margaret M DeAngelis
- Department of Ophthalmology, University at Buffalo the State University of New York, Buffalo, NY, USA
| | - Yumei Li
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Rui Chen
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
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22
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Minow MAA, Marand AP, Schmitz RJ. Leveraging Single-Cell Populations to Uncover the Genetic Basis of Complex Traits. Annu Rev Genet 2023; 57:297-319. [PMID: 37562412 PMCID: PMC10775913 DOI: 10.1146/annurev-genet-022123-110824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics.
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Affiliation(s)
- Mark A A Minow
- Department of Genetics, University of Georgia, Athens, Georgia, USA;
| | | | - Robert J Schmitz
- Department of Genetics, University of Georgia, Athens, Georgia, USA;
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23
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Chen Y, Paramo MI, Zhang Y, Yao L, Shah SR, Jin Y, Zhang J, Pan X, Yu H. Finding Needles in the Haystack: Strategies for Uncovering Noncoding Regulatory Variants. Annu Rev Genet 2023; 57:201-222. [PMID: 37562413 DOI: 10.1146/annurev-genet-030723-120717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Despite accumulating evidence implicating noncoding variants in human diseases, unraveling their functionality remains a significant challenge. Systematic annotations of the regulatory landscape and the growth of sequence variant data sets have fueled the development of tools and methods to identify causal noncoding variants and evaluate their regulatory effects. Here, we review the latest advances in the field and discuss potential future research avenues to gain a more in-depth understanding of noncoding regulatory variants.
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Affiliation(s)
- You Chen
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Mauricio I Paramo
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Yingying Zhang
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Li Yao
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
| | - Sagar R Shah
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Yiyang Jin
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Junke Zhang
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
| | - Xiuqi Pan
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Haiyuan Yu
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
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24
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Lin W, Wall JD, Li G, Newman D, Yang Y, Abney M, VandeBerg JL, Olivier M, Gilad Y, Cox LA. Genetic regulatory effects in response to a high cholesterol, high fat diet in baboons. bioRxiv 2023:2023.08.01.551489. [PMID: 37577666 PMCID: PMC10418186 DOI: 10.1101/2023.08.01.551489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Steady-state expression quantitative trait loci (eQTLs) explain only a fraction of disease-associated loci identified through genome-wide association studies (GWAS), while eQTLs involved in gene-by-environment (GxE) interactions have rarely been characterized in humans due to experimental challenges. Using a baboon model, we found hundreds of eQTLs that emerge in adipose, liver, and muscle after prolonged exposure to high dietary fat and cholesterol. Diet-responsive eQTLs exhibit genomic localization and genic features that are distinct from steady-state eQTLs. Furthermore, the human orthologs associated with diet-responsive eQTLs are enriched for GWAS genes associated with human metabolic traits, suggesting that context-responsive eQTLs with more complex regulatory effects are likely to explain GWAS hits that do not seem to overlap with standard eQTLs. Our results highlight the complexity of genetic regulatory effects and the potential of eQTLs with disease-relevant GxE interactions in enhancing the understanding of GWAS signals for human complex disease using nonhuman primate models.
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Affiliation(s)
- Wenhe Lin
- Department of Human Genetics, The University of Chicago, Chicago, USA
| | - Jeffrey D. Wall
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Present address: Galatea Bio, Hialeah, FL, USA
| | - Ge Li
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Deborah Newman
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Yunqi Yang
- Committee on Genetics, Genomics and System Biology, The University of Chicago, Chicago, USA
| | - Mark Abney
- Department of Human Genetics, The University of Chicago, Chicago, USA
| | - John L. VandeBerg
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grand Valley, Brownsville, TX, USA
| | - Michael Olivier
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Yoav Gilad
- Department of Human Genetics, The University of Chicago, Chicago, USA
- Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
- Lead contact
| | - Laura A. Cox
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, USA
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25
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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 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] [What about the content of this article? (0)] [Affiliation(s)] [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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26
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Tyebally R, Xue A, Powell JE. The potential clinical impact of cell type-specific genetic regulation: Crohn's disease. Clin Transl Med 2023; 13:e1474. [PMID: 37983917 PMCID: PMC10659767 DOI: 10.1002/ctm2.1474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 10/30/2023] [Indexed: 11/22/2023] Open
Abstract
Complex diseases are heterogenous due to variation in their genetic and environmental underpinnings, leading to varied treatment responses. Genome-wide association studies (GWAS) integrated with single-cell expression quantitative trait loci analyses (eQTL) can pinpoint cell-type specific candidate disease-relevant genes and pathways. This knowledge can be applied to patient stratification and novel therapeutic target identification. Here, we describe the translational potential of cell-type specific genetic regulation, using Crohn's disease as an example.
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Affiliation(s)
- Rika Tyebally
- Translational GenomicsGarvan Institute of Medical Research, DarlinghurstSydneyNew South WalesAustralia
- UNSW Cellular Genomics Futures InstituteUniversity of New South Wales, KingstonSydneyNew South WalesAustralia
| | - Angli Xue
- Translational GenomicsGarvan Institute of Medical Research, DarlinghurstSydneyNew South WalesAustralia
- School of Biomedical SciencesUniversity of New South WalesSydneyNew South WalesAustralia
| | - Joseph E. Powell
- Translational GenomicsGarvan Institute of Medical Research, DarlinghurstSydneyNew South WalesAustralia
- UNSW Cellular Genomics Futures InstituteUniversity of New South Wales, KingstonSydneyNew South WalesAustralia
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27
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Zhu Z, Chen X, Zhang S, Yu R, Qi C, Cheng L, Zhang X. Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective. Hum Genet 2023; 142:1543-1560. [PMID: 37755483 DOI: 10.1007/s00439-023-02602-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.
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Affiliation(s)
- Zijun Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Xinyu Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Sainan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Rui Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China.
| | - Xue Zhang
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China
- McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
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28
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Weinstock JS, Arce MM, Freimer JW, Ota M, Marson A, Battle A, Pritchard JK. Gene regulatory network inference from CRISPR perturbations in primary CD4+ T cells elucidates the genomic basis of immune disease. bioRxiv 2023:2023.09.17.557749. [PMID: 37745614 PMCID: PMC10516010 DOI: 10.1101/2023.09.17.557749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The effects of genetic variation on complex traits act mainly through changes in gene regulation. Although many genetic variants have been linked to target genes in cis, the trans-regulatory cascade mediating their effects remains largely uncharacterized. Mapping trans-regulators based on natural genetic variation, including eQTL mapping, has been challenging due to small effects. Experimental perturbation approaches offer a complementary and powerful approach to mapping trans-regulators. We used CRISPR knockouts of 84 genes in primary CD4+ T cells to perturb an immune cell gene network, targeting both inborn error of immunity (IEI) disease transcription factors (TFs) and background TFs matched in constraint and expression level, but without a known immune disease association. We developed a novel Bayesian structure learning method called Linear Latent Causal Bayes (LLCB) to estimate the gene regulatory network from perturbation data and observed 211 directed edges among the genes which could not be detected in existing CD4+ trans-eQTL data. We used LLCB to characterize the differences between the IEI and background TFs, finding that the gene groups were highly interconnected, but that IEI TFs were much more likely to regulate immune cell specific pathways and immune GWAS genes. We further characterized nine coherent gene programs based on downstream effects of the TFs and linked these modules to regulation of GWAS genes, finding that canonical JAK-STAT family members are regulated by KMT2A, a global epigenetic regulator. These analyses reveal the trans-regulatory cascade from upstream epigenetic regulator to intermediate TFs to downstream effector cytokines and elucidate the logic linking immune GWAS genes to key signaling pathways.
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Affiliation(s)
- Joshua S. Weinstock
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
- Department of Genetics, Stanford University, Stanford, CA
| | - Maya M. Arce
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA
- Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Jacob W. Freimer
- Department of Genetics, Stanford University, Stanford, CA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA
| | - Mineto Ota
- Department of Genetics, Stanford University, Stanford, CA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA
| | - Alexander Marson
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA
- Department of Medicine, University of California, San Francisco, San Francisco, CA
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, 94720
- Institute for Human Genetics (IHG), University of California, San Francisco, San Francisco, CA, 94143
- Diabetes Center, University of California, San Francisco, San Francisco, CA, 94143
- Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA, 94129
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, 94143
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, 94158
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD
- Department of Computer Science, Johns Hopkins University, Baltimore, MD
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD
| | - Jonathan K. Pritchard
- Department of Genetics, Stanford University, Stanford, CA
- Department of Biology, Stanford University, Stanford, CA
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29
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Chitra U, Arnold BJ, Sarkar H, Ma C, Lopez-Darwin S, Sanno K, Raphael BJ. Mapping the topography of spatial gene expression with interpretable deep learning. bioRxiv 2023:2023.10.10.561757. [PMID: 37873258 PMCID: PMC10592770 DOI: 10.1101/2023.10.10.561757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a novel quantity called the isodepth. Contours of constant isodepth enclose spatial domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in gene expression. We develop GASTON, an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gene expression gradients, and piecewise linear functions of the isodepth that model both continuous gradients and discontinuous spatial variation in the expression of individual genes. We validate GASTON by showing that it accurately identifies spatial domains and marker genes across several biological systems. In SRT data from the brain, GASTON reveals gradients of neuronal differentiation and firing, and in SRT data from a tumor sample, GASTON infers gradients of metabolic activity and epithelial-mesenchymal transition (EMT)-related gene expression in the tumor microenvironment.
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Affiliation(s)
- Uthsav Chitra
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Brian J. Arnold
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
| | - Hirak Sarkar
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Ludwig Cancer Institute, Princeton Branch, Princeton University, Princeton, NJ, USA
| | - Cong Ma
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | | | - Kohei Sanno
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
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30
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Michoel T, Zhang JD. Causal inference in drug discovery and development. Drug Discov Today 2023; 28:103737. [PMID: 37591410 DOI: 10.1016/j.drudis.2023.103737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 07/31/2023] [Accepted: 08/10/2023] [Indexed: 08/19/2023]
Abstract
To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision-making in drug discovery. Although it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a nontechnical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.
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Affiliation(s)
- Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Postboks 7803, 5020 Bergen, Norway
| | - Jitao David Zhang
- Pharma Early Research and Development, Roche Innovation Centre Basel, F. Hoffmann-La Roche, Grenzacherstrasse 124, 4070 Basel, Switzerland; Department of Mathematics and Computer Science, University of Basel, Spiegelgasse 1, 4051 Basel, Switzerland.
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31
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Abstract
RNA is integral to the regulatory circuits that control cell identity and behavior. Cis-regulatory elements in mRNAs interact with RNA-binding proteins (RBPs) that can alter RNA sequence, stability, and translation into protein. Similarly, long noncoding RNAs (lncRNAs) scaffold ribonucleoprotein complexes that mediate transcriptional and post-transcriptional regulation of gene expression. Indeed, cell programming is fundamental to multicellular life and, in this era of cellular therapies, it is of particular interest in T cells. Here, we review key concepts and recent advances in our understanding of the RNA circuits and RBPs that govern mammalian T cell differentiation and immune function.
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Affiliation(s)
- Wandi S Zhu
- Department of Microbiology & Immunology, Sandler Asthma Basic Research Center, University of California San Francisco, San Francisco, CA 94143, USA
| | - Benjamin D Wheeler
- Department of Microbiology & Immunology, Sandler Asthma Basic Research Center, University of California San Francisco, San Francisco, CA 94143, USA
| | - K Mark Ansel
- Department of Microbiology & Immunology, Sandler Asthma Basic Research Center, University of California San Francisco, San Francisco, CA 94143, USA.
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32
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Shi Q, Chen X, Zhang Z. Decoding Human Biology and Disease Using Single-cell Omics Technologies. Genomics Proteomics Bioinformatics 2023; 21:926-949. [PMID: 37739168 PMCID: PMC10928380 DOI: 10.1016/j.gpb.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 09/24/2023]
Abstract
Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.
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Affiliation(s)
- Qiang Shi
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Chen
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing 102206, China.
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33
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Riley-Gillis B, Tsaih SW, King E, Wollenhaupt S, Reeb J, Peck AR, Wackman K, Lemke A, Rui H, Dezso Z, Flister MJ. Machine learning reveals genetic modifiers of the immune microenvironment of cancer. iScience 2023; 26:107576. [PMID: 37664640 PMCID: PMC10470213 DOI: 10.1016/j.isci.2023.107576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 05/01/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Heritability in the immune tumor microenvironment (iTME) has been widely observed yet remains largely uncharacterized. Here, we developed a machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWASs) for breast cancer (BrCa) incidence. A random forest model was trained on a positive set of immune-oncology (I-O) targets, and then used to assign I-O target probability scores to 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Cluster analysis of the most probable candidates revealed two subfamilies of genes related to effector functions and adaptive immune responses, suggesting that iTME modifiers impact multiple aspects of anticancer immunity. Two of the top ranking BrCa candidates, LSP1 and TLR1, were orthogonally validated as iTME modifiers using BrCa patient biopsies and comparative mapping studies, respectively. Collectively, these data demonstrate a robust and flexible framework for functionally fine-mapping GWAS risk loci to identify translatable therapeutic targets.
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Affiliation(s)
- Bridget Riley-Gillis
- Genomics Research Center, AbbVie Inc, 1 North Waukegan Road, North Chicago, IL 60064, USA
| | - Shirng-Wern Tsaih
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Emily King
- Genomics Research Center, AbbVie Inc, 1 North Waukegan Road, North Chicago, IL 60064, USA
| | - Sabrina Wollenhaupt
- Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany
| | - Jonas Reeb
- Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany
| | - Amy R. Peck
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Kelsey Wackman
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Angela Lemke
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Hallgeir Rui
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Zoltan Dezso
- Genomics Research Center, AbbVie Bay Area, 1000 Gateway Boulevard, South San Francisco, CA 94080, USA
| | - Michael J. Flister
- Genomics Research Center, AbbVie Inc, 1 North Waukegan Road, North Chicago, IL 60064, USA
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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34
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Abstract
Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.
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Affiliation(s)
- Joyce B Kang
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | | | - Aparna Nathan
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | - Nicole Soranzo
- Human Technopole, Milan, Italy; ,
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
- British Heart Foundation Centre of Research Excellence and Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Soumya Raychaudhuri
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, United Kingdom
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Su C, Xu Z, Shan X, Cai B, Zhao H, Zhang J. Cell-type-specific co-expression inference from single cell RNA-sequencing data. Nat Commun 2023; 14:4846. [PMID: 37563115 PMCID: PMC10415381 DOI: 10.1038/s41467-023-40503-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 07/28/2023] [Indexed: 08/12/2023] Open
Abstract
The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. For this task, the high sequencing depth variations and measurement errors in scRNA-seq data present two significant challenges, and they have not been adequately addressed by existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, that explicitly models sequencing depth variations and measurement errors in scRNA-seq data. Systematic evaluations show that most existing methods suffered from inflated false positives as well as biased co-expression estimates and clustering analysis, whereas CS-CORE gave accurate estimates in these experiments. When applied to scRNA-seq data from postmortem brain samples from Alzheimer's disease patients/controls and blood samples from COVID-19 patients/controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from existing methods.
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Affiliation(s)
- Chang Su
- Department of Biostatistics, Yale University, New Haven, CT, USA
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Zichun Xu
- Department of Biostatistics, Yale University, New Haven, CT, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Xinning Shan
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Biao Cai
- Department of Biostatistics, Yale University, New Haven, CT, USA
- Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, USA.
| | - Jingfei Zhang
- Information Systems and Operations Management, Emory University, Atlanta, GA, USA.
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Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE. Single-cell genomics meets human genetics. Nat Rev Genet 2023; 24:535-549. [PMID: 37085594 PMCID: PMC10784789 DOI: 10.1038/s41576-023-00599-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 04/23/2023]
Abstract
Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.
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Affiliation(s)
- Anna S E Cuomo
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, 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
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, 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
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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Abstract
Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.
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Affiliation(s)
- Kyle J Gaulton
- Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego School of Medicine, La Jolla, CA, USA.
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Bing Ren
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
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Lagattuta KA, Nathan A, Rumker L, Birnbaum ME, Raychaudhuri S. The T cell receptor sequence influences the likelihood of T cell memory formation. bioRxiv 2023:2023.07.20.549939. [PMID: 37502994 PMCID: PMC10370203 DOI: 10.1101/2023.07.20.549939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
T cell differentiation depends on activation through the T cell receptor (TCR), whose amino acid sequence varies cell to cell. Particular TCR amino acid sequences nearly guarantee Mucosal-Associated Invariant T (MAIT) and Natural Killer T (NKT) cell fates. To comprehensively define how TCR amino acids affects all T cell fates, we analyze the paired αβTCR sequence and transcriptome of 819,772 single cells. We find that hydrophobic CDR3 residues promote regulatory T cell transcriptional states in both the CD8 and CD4 lineages. Most strikingly, we find a set of TCR sequence features, concentrated in CDR2α, that promotes positive selection in the thymus as well as transition from naïve to memory in the periphery. Even among T cells that recognize the same antigen, these TCR sequence features help to explain which T cells form immunological memory, which is essential for effective pathogen response.
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Affiliation(s)
- 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael E. Birnbaum
- Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
- Department of Biomedical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Ragon Institute of MGH, 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
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Song L, Sun X, Qi T, Yang J. Mixed model-based deconvolution of cell-state abundances (MeDuSA) along a one-dimensional trajectory. Nat Comput Sci 2023; 3:630-643. [PMID: 38177744 PMCID: PMC10766563 DOI: 10.1038/s43588-023-00487-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 06/13/2023] [Indexed: 01/06/2024]
Abstract
Deconvoluting cell-state abundances from bulk RNA-sequencing data can add considerable value to existing data, but achieving fine-resolution and high-accuracy deconvolution remains a challenge. Here we introduce MeDuSA, a mixed model-based method that leverages single-cell RNA-sequencing data as a reference to estimate cell-state abundances along a one-dimensional trajectory in bulk RNA-sequencing data. The advantage of MeDuSA lies primarily in estimating cell abundance in each state while fitting the remaining cells of the same type individually as random effects. Extensive simulations and real-data benchmark analyses demonstrate that MeDuSA greatly improves the estimation accuracy over existing methods for one-dimensional trajectories. Applying MeDuSA to cohort-level RNA-sequencing datasets reveals associations of cell-state abundances with disease or treatment conditions and cell-state-dependent genetic control of transcription. Our study provides a high-accuracy and fine-resolution method for cell-state deconvolution along a one-dimensional trajectory and demonstrates its utility in characterizing the dynamics of cell states in various biological processes.
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Affiliation(s)
- Liyang Song
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Xiwei Sun
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Ting Qi
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
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40
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Natri HM, Del Azodi CB, Peter L, Taylor CJ, Chugh S, Kendle R, Chung MI, Flaherty DK, Matlock BK, Calvi CL, Blackwell TS, Ware LB, Bacchetta M, Walia R, Shaver CM, Kropski JA, McCarthy DJ, Banovich NE. Cell type-specific and disease-associated eQTL in the human lung. bioRxiv 2023:2023.03.17.533161. [PMID: 36993211 PMCID: PMC10055257 DOI: 10.1101/2023.03.17.533161] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis (PF). Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA-sequencing of lung tissue from 67 PF and 49 unaffected donors. Employing a pseudo-bulk approach, we mapped expression quantitative trait loci (eQTL) across 38 cell types, observing both shared and cell type-specific regulatory effects. Further, we identified disease-interaction eQTL and demonstrated that this class of associations is more likely to be cell-type specific and linked to cellular dysregulation in PF. Finally, we connected PF risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression, and implicates context-specific eQTL as key regulators of lung homeostasis and disease.
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41
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Suliman S, Nieto-Caballero VE, Asgari S, Lopez K, Iwany SK, Luo Y, Nathan A, Fernandez-Salinas D, Chiñas M, Huang CC, Zhang Z, León SR, Calderon RI, Lecca L, Murray M, Van Rhijn I, Raychaudhuri S, Moody DB, Gutierrez-Arcelus M. History of tuberculosis disease is associated with genetic regulatory variation in Peruvians. medRxiv 2023:2023.06.20.23291558. [PMID: 37425785 PMCID: PMC10327177 DOI: 10.1101/2023.06.20.23291558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
A quarter of humanity is estimated to be latently infected with Mycobacterium tuberculosis (Mtb) with a 5-10% risk of developing tuberculosis (TB) disease. Variability in responses to Mtb infection could be due to host or pathogen heterogeneity. Here, we focused on host genetic variation in a Peruvian population and its associations with gene regulation in monocyte-derived macrophages and dendritic cells (DCs). We recruited former household contacts of TB patients who previously progressed to TB (cases, n=63) or did not progress to TB (controls, n=63). Transcriptomic profiling of monocyte-derived dendritic cells (DCs) and macrophages measured the impact of genetic variants on gene expression by identifying expression quantitative trait loci (eQTL). We identified 330 and 257 eQTL genes in DCs and macrophages (False Discovery Rate (FDR) < 0.05), respectively. Five genes in DCs showed interaction between eQTL variants and TB progression status. The top eQTL interaction for a protein-coding gene was with FAH, the gene encoding fumarylacetoacetate hydrolase, which mediates the last step in mammalian tyrosine catabolism. FAH expression was associated with genetic regulatory variation in cases but not controls. Using public transcriptomic and epigenomic data of Mtb-infected monocyte-derived dendritic cells, we found that Mtb infection results in FAH downregulation and DNA methylation changes in the locus. Overall, this study demonstrates effects of genetic variation on gene expression levels that are dependent on history of infectious disease and highlights a candidate pathogenic mechanism through pathogen-response genes. Furthermore, our results point to tyrosine metabolism and related candidate TB progression pathways for further investigation.
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Affiliation(s)
- Sara Suliman
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Zuckerberg San Francisco General Hospital, Division of Experimental Medicine, University of California San Francisco, San Francisco, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, University of California San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Initiative Biohub, San Francisco, CA, USA
| | - Victor E. Nieto-Caballero
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Undergraduate Program in Genomic Sciences, Center for Genomic Sciences, Universidad Nacional Autónoma de México (UNAM), Morelos 62210, Mexico
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samira Asgari
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kattya Lopez
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Socios En Salud Sucursal Peru, Lima, Peru
| | - Sarah K. Iwany
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Yang Luo
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Aparna Nathan
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniela Fernandez-Salinas
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Marcos Chiñas
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chuan-Chin Huang
- Department of Global Health and Social Medicine, and Division of Global Health Equity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Zibiao Zhang
- Department of Global Health and Social Medicine, and Division of Global Health Equity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Segundo R León
- Socios En Salud Sucursal Peru, Lima, Peru
- Medical Technology School and Global Health Research Institute, San Juan Bautista Private University, Lima, Perú
| | | | | | - Megan Murray
- Department of Global Health and Social Medicine, and Division of Global Health Equity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ildiko Van Rhijn
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Soumya Raychaudhuri
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - D. Branch Moody
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Maria Gutierrez-Arcelus
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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42
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Van de Sande B, Lee JS, Mutasa-Gottgens E, Naughton B, Bacon W, Manning J, Wang Y, Pollard J, Mendez M, Hill J, Kumar N, Cao X, Chen X, Khaladkar M, Wen J, Leach A, Ferran E. Applications of single-cell RNA sequencing in drug discovery and development. Nat Rev Drug Discov 2023; 22:496-520. [PMID: 37117846 PMCID: PMC10141847 DOI: 10.1038/s41573-023-00688-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/10/2023] [Indexed: 04/30/2023]
Abstract
Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.
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Affiliation(s)
| | | | | | - Bart Naughton
- Computational Neurobiology, Eisai, Cambridge, MA, USA
| | - Wendi Bacon
- EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
- The Open University, Milton Keynes, UK
| | | | - Yong Wang
- Precision Bioinformatics, Prometheus Biosciences, San Diego, CA, USA
| | | | - Melissa Mendez
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | - Jon Hill
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | - Namit Kumar
- Informatics & Predictive Sciences, Bristol Myers Squibb, San Diego, CA, USA
| | - Xiaohong Cao
- Genomic Research Center, AbbVie Inc., Cambridge, MA, USA
| | - Xiao Chen
- Magnet Biomedicine, Cambridge, MA, USA
| | - Mugdha Khaladkar
- Human Genetics and Computational Biology, GlaxoSmithKline, Collegeville, PA, USA
| | - Ji Wen
- Oncology Research and Development Unit, Pfizer, La Jolla, CA, USA
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43
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Ziyani C, Delaneau O, Ribeiro DM. Multimodal single cell analysis infers widespread enhancer co-activity in a lymphoblastoid cell line. Commun Biol 2023; 6:563. [PMID: 37237005 DOI: 10.1038/s42003-023-04954-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/18/2023] [Indexed: 05/28/2023] Open
Abstract
Non-coding regulatory elements such as enhancers are key in controlling the cell-type specificity and spatio-temporal expression of genes. To drive stable and precise gene transcription robust to genetic variation and environmental stress, genes are often targeted by multiple enhancers with redundant action. However, it is unknown whether enhancers targeting the same gene display simultaneous activity or whether some enhancer combinations are more often co-active than others. Here, we take advantage of recent developments in single cell technology that permit assessing chromatin status (scATAC-seq) and gene expression (scRNA-seq) in the same single cells to correlate gene expression to the activity of multiple enhancers. Measuring activity patterns across 24,844 human lymphoblastoid single cells, we find that the majority of enhancers associated with the same gene display significant correlation in their chromatin profiles. For 6944 expressed genes associated with enhancers, we predict 89,885 significant enhancer-enhancer associations between nearby enhancers. We find that associated enhancers share similar transcription factor binding profiles and that gene essentiality is linked with higher enhancer co-activity. We provide a set of predicted enhancer-enhancer associations based on correlation derived from a single cell line, which can be further investigated for functional relevance.
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Affiliation(s)
- Chaymae Ziyani
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Olivier Delaneau
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Diogo M Ribeiro
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
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44
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Luo J, Wu X, Cheng Y, Chen G, Wang J, Song X. Expression quantitative trait locus studies in the era of single-cell omics. Front Genet 2023; 14:1182579. [PMID: 37284065 PMCID: PMC10239882 DOI: 10.3389/fgene.2023.1182579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 04/26/2023] [Indexed: 06/08/2023] Open
Abstract
Genome-wide association studies have revealed that the regulation of gene expression bridges genetic variants and complex phenotypes. Profiling of the bulk transcriptome coupled with linkage analysis (expression quantitative trait locus (eQTL) mapping) has advanced our understanding of the relationship between genetic variants and gene regulation in the context of complex phenotypes. However, bulk transcriptomics has inherited limitations as the regulation of gene expression tends to be cell-type-specific. The advent of single-cell RNA-seq technology now enables the identification of the cell-type-specific regulation of gene expression through a single-cell eQTL (sc-eQTL). In this review, we first provide an overview of sc-eQTL studies, including data processing and the mapping procedure of the sc-eQTL. We then discuss the benefits and limitations of sc-eQTL analyses. Finally, we present an overview of the current and future applications of sc-eQTL discoveries.
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Affiliation(s)
- Jie Luo
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xinyi Wu
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Yuan Cheng
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Guang Chen
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Jian Wang
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xijiao Song
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
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45
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Harroud A, Hafler DA. Common genetic factors among autoimmune diseases. Science 2023; 380:485-490. [PMID: 37141355 DOI: 10.1126/science.adg2992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Autoimmune diseases display a high degree of comorbidity within individuals and families, suggesting shared risk factors. Over the past 15 years, genome-wide association studies have established the polygenic basis of these common conditions and revealed widespread sharing of genetic effects, indicative of a shared immunopathology. Despite ongoing challenges in determining the precise genes and molecular consequences of these risk variants, functional experiments and integration with multimodal genomic data are providing valuable insights into key immune cells and pathways driving these diseases, with potential therapeutic implications. Moreover, genetic studies of ancient populations are shedding light on the contribution of pathogen-driven selection pressures to the increased prevalence of autoimmune disease. This Review summarizes the current understanding of autoimmune disease genetics, including shared effects, mechanisms, and evolutionary origins.
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Affiliation(s)
- Adil Harroud
- Department of Neurology and Neurosurgery, McGill University, Montréal, Quebec, Canada
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada
- The Neuro (Montreal Neurological Institute and Hospital), McGill University, Montréal, Quebec, Canada
| | - David A Hafler
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
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Gupta RM, Schnitzler GR, Fang S, Lee-Kim VS, Barry A. Multiomic Analysis and CRISPR Perturbation Screens Identify Endothelial Cell Programs and Novel Therapeutic Targets for Coronary Artery Disease. Arterioscler Thromb Vasc Biol 2023; 43:600-608. [PMID: 36994731 PMCID: PMC10170398 DOI: 10.1161/atvbaha.123.318328] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/01/2023] [Indexed: 03/31/2023]
Abstract
Endothelial cells (EC) are an important mediator of atherosclerosis and vascular disease. Their exposure to atherogenic risk factors such as hypertension and serum cholesterol leads to endothelial dysfunction and many disease-associated processes. Identifying which of these multiple EC functions is causally related to disease risk has been challenging. There is evidence from in vivo models and human sequencing studies that dysregulation of nitric oxide production directly affects risk of coronary artery disease. Human genetics can help prioritize the other EC functions with causal relationships because germline mutations are acquired at birth and serve as a randomized test of which pathways affect disease risk. Though several coronary artery disease risk variants have been linked to EC function, this process has been slow and laborious. Unbiased analyses of EC dysfunction using multiomic approaches promise to identify the causal genetic mechanisms responsible for vascular disease. Here, we review the data from genomic, epigenomic, and transcriptomic studies that prioritize EC-specific causal pathways. New methods that CRISPR (clustered regularly interspaced short palindromic repeats) perturbation technology with genomic, epigenomic, and transcriptomic analysis promise to speed up the characterization of disease-associated genetic variation. We summarize several recent studies in ECs which use high-throughput genetic perturbation to identify disease-relevant pathways and novel mechanisms of disease. These genetically validated pathways can accelerate the identification of drug targets for the prevention and treatment of atherosclerosis.
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Affiliation(s)
- Rajat M Gupta
- Divisions of Genetics and Cardiology, Department of Medicine, Brigham and Women's Hospital, Boston MA (R.M.G., G.R.S., S.F., V.S.L.-K., A.B.)
- Broad Institute of MIT and Harvard, Cambridge, MA (R.M.G., G.R.S., S.F., V.S.L.-K., A.B.)
| | - Gavin R Schnitzler
- Divisions of Genetics and Cardiology, Department of Medicine, Brigham and Women's Hospital, Boston MA (R.M.G., G.R.S., S.F., V.S.L.-K., A.B.)
- Broad Institute of MIT and Harvard, Cambridge, MA (R.M.G., G.R.S., S.F., V.S.L.-K., A.B.)
| | - Shi Fang
- Divisions of Genetics and Cardiology, Department of Medicine, Brigham and Women's Hospital, Boston MA (R.M.G., G.R.S., S.F., V.S.L.-K., A.B.)
- Broad Institute of MIT and Harvard, Cambridge, MA (R.M.G., G.R.S., S.F., V.S.L.-K., A.B.)
| | - Vivian S Lee-Kim
- Divisions of Genetics and Cardiology, Department of Medicine, Brigham and Women's Hospital, Boston MA (R.M.G., G.R.S., S.F., V.S.L.-K., A.B.)
- Broad Institute of MIT and Harvard, Cambridge, MA (R.M.G., G.R.S., S.F., V.S.L.-K., A.B.)
| | - Aurelie Barry
- Divisions of Genetics and Cardiology, Department of Medicine, Brigham and Women's Hospital, Boston MA (R.M.G., G.R.S., S.F., V.S.L.-K., A.B.)
- Broad Institute of MIT and Harvard, Cambridge, MA (R.M.G., G.R.S., S.F., V.S.L.-K., A.B.)
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Weinand K, Sakaue S, Nathan A, Jonsson AH, Zhang F, Watts GFM, Zhu Z, Rao DA, Anolik JH, Brenner MB, Donlin LT, Wei K, Raychaudhuri S. The Chromatin Landscape of Pathogenic Transcriptional Cell States in Rheumatoid Arthritis. bioRxiv 2023:2023.04.07.536026. [PMID: 37066336 PMCID: PMC10104143 DOI: 10.1101/2023.04.07.536026] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Synovial tissue inflammation is the hallmark of rheumatoid arthritis (RA). Recent work has identified prominent pathogenic cell states in inflamed RA synovial tissue, such as T peripheral helper cells; however, the epigenetic regulation of these states has yet to be defined. We measured genome-wide open chromatin at single cell resolution from 30 synovial tissue samples, including 12 samples with transcriptional data in multimodal experiments. We identified 24 chromatin classes and predicted their associated transcription factors, including a CD8+ GZMK+ class associated with EOMES and a lining fibroblast class associated with AP-1. By integrating an RA tissue transcriptional atlas, we found that the chromatin classes represented 'superstates' corresponding to multiple transcriptional cell states. Finally, we demonstrated the utility of this RA tissue chromatin atlas through the associations between disease phenotypes and chromatin class abundance as well as the nomination of classes mediating the effects of putatively causal RA genetic variants.
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Affiliation(s)
- Kathryn Weinand
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, 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
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saori Sakaue
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, 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
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aparna Nathan
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, 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
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anna Helena Jonsson
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, 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
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology and the Center for Health Artificial Intelligence, University of Colorado School of Medicine, Aurora, CO, USA
| | - Gerald F. M. Watts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhu Zhu
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Deepak A. Rao
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Jennifer H. Anolik
- Division of Allergy, Immunology and Rheumatology; Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Michael B. Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Laura T. Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Soumya Raychaudhuri
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, 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
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
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Kang JB, Shen AZ, Sakaue S, Luo Y, Gurajala S, Nathan A, Rumker L, Aguiar VRC, Valencia C, Lagattuta K, Zhang F, Jonsson AH, Yazar S, Alquicira-Hernandez J, Khalili H, Ananthakrishnan AN, Jagadeesh K, Dey K, Daly MJ, Xavier RJ, Donlin LT, Anolik JH, Powell JE, Rao DA, Brenner MB, Gutierrez-Arcelus M, Raychaudhuri S. Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution. medRxiv 2023:2023.03.14.23287257. [PMID: 36993194 PMCID: PMC10055604 DOI: 10.1101/2023.03.14.23287257] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The human leukocyte antigen (HLA) locus plays a critical role in complex traits spanning autoimmune and infectious diseases, transplantation, and cancer. While coding variation in HLA genes has been extensively documented, regulatory genetic variation modulating HLA expression levels has not been comprehensively investigated. Here, we mapped expression quantitative trait loci (eQTLs) for classical HLA genes across 1,073 individuals and 1,131,414 single cells from three tissues, using personalized reference genomes to mitigate technical confounding. We identified cell-type-specific cis-eQTLs for every classical HLA gene. Modeling eQTLs at single-cell resolution revealed that many eQTL effects are dynamic across cell states even within a cell type. HLA-DQ genes exhibit particularly cell-state-dependent effects within myeloid, B, and T cells. Dynamic HLA regulation may underlie important interindividual variability in immune responses.
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Affiliation(s)
- 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Amber Z. Shen
- 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Yang Luo
- 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Saisriram Gurajala
- 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Vitor R. C. Aguiar
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Kaitlyn 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology and the Center for Health Artificial Intelligence, University of Colorado School of Medicine, Aurora, CO, USA
| | - Anna Helena Jonsson
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Seyhan Yazar
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | | | - Hamed Khalili
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ashwin N. Ananthakrishnan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Kushal Dey
- Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | | | - Mark J. Daly
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Ramnik J. Xavier
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Laura T. Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Jennifer H. Anolik
- Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Deepak A. Rao
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B. Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Maria Gutierrez-Arcelus
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, 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
- 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
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
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49
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Jung S, Lee JS. Single-Cell Genomics for Investigating Pathogenesis of Inflammatory Diseases. Mol Cells 2023; 46:120-129. [PMID: 36859476 PMCID: PMC9982059 DOI: 10.14348/molcells.2023.0002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 03/03/2023] Open
Abstract
Recent technical advances have enabled unbiased transcriptomic and epigenetic analysis of each cell, known as "single-cell analysis". Single-cell analysis has a variety of technical approaches to investigate the state of each cell, including mRNA levels (transcriptome), the immune repertoire (immune repertoire analysis), cell surface proteins (surface proteome analysis), chromatin accessibility (epigenome), and accordance with genome variants (eQTLs; expression quantitative trait loci). As an effective tool for investigating robust immune responses in coronavirus disease 2019 (COVID-19), many researchers performed single-cell analysis to capture the diverse, unbiased immune cell activation and differentiation. Despite challenges elucidating the complicated immune microenvironments of chronic inflammatory diseases using existing experimental methods, it is now possible to capture the simultaneous immune features of different cell types across inflamed tissues using various single-cell tools. In this review, we introduce patient-based and experimental mouse model research utilizing single-cell analyses in the field of chronic inflammatory diseases, as well as multi-organ atlas targeting immune cells.
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Affiliation(s)
- Seyoung Jung
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jeong Seok Lee
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
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50
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Xue A, Yazar S, Neavin D, Powell JE. Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses. Genome Biol 2023; 24:33. [PMID: 36823676 PMCID: PMC9948363 DOI: 10.1186/s13059-023-02873-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 02/13/2023] [Indexed: 02/25/2023] Open
Abstract
Using latent variables in gene expression data can help correct unobserved confounders and increase statistical power for expression quantitative trait Loci (eQTL) detection. The probabilistic estimation of expression residuals (PEER) and principal component analysis (PCA) are widely used methods that can remove unwanted variation and improve eQTL discovery power in bulk RNA-seq analysis. However, their performance has not been evaluated extensively in single-cell eQTL analysis, especially for different cell types. Potential challenges arise due to the structure of single-cell RNA-seq data, including sparsity, skewness, and mean-variance relationship. Here, we show by a series of analyses that PEER and PCA require additional quality control and data transformation steps on the pseudo-bulk matrix to obtain valid latent variables; otherwise, it can result in highly correlated factors (Pearson's correlation r = 0.63 ~ 0.99). Incorporating valid PFs/PCs in the eQTL association model would identify 1.7 ~ 13.3% more eGenes. Sensitivity analysis showed that the pattern of change between the number of eGenes detected and fitted PFs/PCs varied significantly in different cell types. In addition, using highly variable genes to generate latent variables could achieve similar eGenes discovery power as using all genes but save considerable computational resources (~ 6.2-fold faster).
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Affiliation(s)
- Angli Xue
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia. .,School of Biomedical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia.
| | - Seyhan Yazar
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Drew Neavin
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, 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.
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