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1
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Wen C, Margolis M, Dai R, Zhang P, Przytycki PF, Vo DD, Bhattacharya A, Matoba N, Tang M, Jiao C, Kim M, Tsai E, Hoh C, Aygün N, Walker RL, Chatzinakos C, Clarke D, Pratt H, Peters MA, Gerstein M, Daskalakis NP, Weng Z, Jaffe AE, Kleinman JE, Hyde TM, Weinberger DR, Bray NJ, Sestan N, Geschwind DH, Roeder K, Gusev A, Pasaniuc B, Stein JL, Love MI, Pollard KS, Liu C, Gandal MJ. Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain. Science 2024; 384:eadh0829. [PMID: 38781368 DOI: 10.1126/science.adh0829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/07/2024] [Indexed: 05/25/2024]
Abstract
Neuropsychiatric genome-wide association studies (GWASs), including those for autism spectrum disorder and schizophrenia, show strong enrichment for regulatory elements in the developing brain. However, prioritizing risk genes and mechanisms is challenging without a unified regulatory atlas. Across 672 diverse developing human brains, we identified 15,752 genes harboring gene, isoform, and/or splicing quantitative trait loci, mapping 3739 to cellular contexts. Gene expression heritability drops during development, likely reflecting both increasing cellular heterogeneity and the intrinsic properties of neuronal maturation. Isoform-level regulation, particularly in the second trimester, mediated the largest proportion of GWAS heritability. Through colocalization, we prioritized mechanisms for about 60% of GWAS loci across five disorders, exceeding adult brain findings. Finally, we contextualized results within gene and isoform coexpression networks, revealing the comprehensive landscape of transcriptome regulation in development and disease.
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Affiliation(s)
- Cindy Wen
- 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
| | - Michael Margolis
- 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
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Pan Zhang
- 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
| | - Pawel F Przytycki
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA
| | - Daniel D Vo
- 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
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nana Matoba
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Miao Tang
- 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
| | - Chuan Jiao
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Team Krebs, 75014 Paris, France
| | - Minsoo Kim
- 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
| | - Ellen Tsai
- 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
| | - Celine Hoh
- 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
| | - Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rebecca L Walker
- 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
| | - Christos Chatzinakos
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- McLean Hospital, Belmont, MA 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Declan Clarke
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Henry Pratt
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Mette A Peters
- CNS Data Coordination Group, Sage Bionetworks, Seattle, WA 98109, USA
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Nikolaos P Daskalakis
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- McLean Hospital, Belmont, MA 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Neumora Therapeutics, Watertown, MA 02472, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Nicholas J Bray
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University School of Medicine, Cardiff CF24 4HQ, UK
| | - Nenad Sestan
- Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Daniel H Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kathryn Roeder
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Alexander Gusev
- Department of Medical Oncology, Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Harvard Medical School, Boston, MA 02215, USA
- Division of Genetics, Brigham and Women's Hospital, Boston, MA 02215, USA
| | - Bogdan Pasaniuc
- Interdepartmental Program in Bioinformatics, 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 Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Michael I Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Katherine S Pollard
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA
- Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - 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
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Wang H, Makowski C, Zhang Y, Qi A, Kaufmann T, Smeland OB, Fiecas M, Yang J, Visscher PM, Chen CH. Chromosomal inversion polymorphisms shape human brain morphology. Cell Rep 2023; 42:112896. [PMID: 37505983 PMCID: PMC10508191 DOI: 10.1016/j.celrep.2023.112896] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 06/27/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
The impact of chromosomal inversions on human brain morphology remains underexplored. We studied 35 common inversions classified from genotypes of 33,018 adults with European ancestry. The inversions at 2p22.3, 16p11.2, and 17q21.31 reach genome-wide significance, followed by 8p23.1 and 6p21.33, in their association with cortical and subcortical morphology. The 17q21.31, 8p23.1, and 16p11.2 regions comprise the LRRC37, OR7E, and NPIP duplicated gene families. We find the 17q21.31 MAPT inversion region, known for harboring neurological risk, to be the most salient locus among common variants for shaping and patterning the cortex. Overall, we observe the inverted orientations decreasing brain size, with the exception that the 2p22.3 inversion is associated with increased subcortical volume and the 8p23.1 inversion is associated with increased motor cortex. These significant inversions are in the genomic hotspots of neuropsychiatric loci. Our findings are generalizable to 3,472 children and demonstrate inversions as essential genetic variation to understand human brain phenotypes.
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Affiliation(s)
- Hao Wang
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Carolina Makowski
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Yanxiao Zhang
- Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Anna Qi
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72076 Tübingen, Germany; Norwegian Centre for Mental Disorders Research, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway
| | - Olav B Smeland
- Norwegian Centre for Mental Disorders Research, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway
| | - Mark Fiecas
- Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN 55455, USA
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Chi-Hua Chen
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA.
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3
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Wen C, Margolis M, Dai R, Zhang P, Przytycki PF, Vo DD, Bhattacharya A, Matoba N, Jiao C, Kim M, Tsai E, Hoh C, Aygün N, Walker RL, Chatzinakos C, Clarke D, Pratt H, Consortium P, Peters MA, Gerstein M, Daskalakis NP, Weng Z, Jaffe AE, Kleinman JE, Hyde TM, Weinberger DR, Bray NJ, Sestan N, Geschwind DH, Roeder K, Gusev A, Pasaniuc B, Stein JL, Love MI, Pollard KS, Liu C, Gandal MJ. Cross-ancestry, cell-type-informed atlas of gene, isoform, and splicing regulation in the developing human brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.03.23286706. [PMID: 36945630 PMCID: PMC10029021 DOI: 10.1101/2023.03.03.23286706] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Genomic regulatory elements active in the developing human brain are notably enriched in genetic risk for neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia, and bipolar disorder. However, prioritizing the specific risk genes and candidate molecular mechanisms underlying these genetic enrichments has been hindered by the lack of a single unified large-scale gene regulatory atlas of human brain development. Here, we uniformly process and systematically characterize gene, isoform, and splicing quantitative trait loci (xQTLs) in 672 fetal brain samples from unique subjects across multiple ancestral populations. We identify 15,752 genes harboring a significant xQTL and map 3,739 eQTLs to a specific cellular context. We observe a striking drop in gene expression and splicing heritability as the human brain develops. Isoform-level regulation, particularly in the second trimester, mediates the greatest proportion of heritability across multiple psychiatric GWAS, compared with eQTLs. Via colocalization and TWAS, we prioritize biological mechanisms for ~60% of GWAS loci across five neuropsychiatric disorders, nearly two-fold that observed in the adult brain. Finally, we build a comprehensive set of developmentally regulated gene and isoform co-expression networks capturing unique genetic enrichments across disorders. Together, this work provides a comprehensive view of genetic regulation across human brain development as well as the stage-and cell type-informed mechanistic underpinnings of neuropsychiatric disorders.
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Affiliation(s)
- Cindy Wen
- 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
| | - Michael Margolis
- 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
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University; Syracuse, NY, 13210, USA
| | - Pan Zhang
- 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
| | - Pawel F Przytycki
- Gladstone Institute of Data Science and Biotechnology; San Francisco, CA, 94158, USA
| | - Daniel D Vo
- 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
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA
- Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA
| | - Nana Matoba
- Department of Genetics, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA
| | - Chuan Jiao
- Department of Psychiatry, SUNY Upstate Medical University; Syracuse, NY, 13210, USA
| | - Minsoo Kim
- 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
| | - Ellen Tsai
- 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
| | - Celine Hoh
- 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
| | - Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA
| | - Rebecca L Walker
- 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
| | - Christos Chatzinakos
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02215, USA
- McLean Hospital; Belmont, MA, 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Declan Clarke
- Department of Molecular Biophysics and Biochemistry, Yale University; New Haven, CT, 06520, USA
| | - Henry Pratt
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School; Worcester, MA, 01605, USA
| | - PsychENCODE Consortium
- 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, SUNY Upstate Medical University; Syracuse, NY, 13210, USA
- Gladstone Institute of Data Science and Biotechnology; San Francisco, CA, 94158, 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
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA
- Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA
- Department of Genetics, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02215, USA
- McLean Hospital; Belmont, MA, 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Molecular Biophysics and Biochemistry, Yale University; New Haven, CT, 06520, USA
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School; Worcester, MA, 01605, USA
- CNS Data Coordination Group, Sage Bionetworks; Seattle, WA, 98109, USA
- Program in Computational Biology and Bioinformatics, Yale University; New Haven, CT, 06520, USA
- Department of Computer Science, Yale University; New Haven, CT, 06520, USA
- Department of Statistics and Data Science, Yale University; New Haven, CT, 06520, USA
- Lieber Institute for Brain Development; Baltimore, MD, 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health; Baltimore, MD, 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health; Baltimore, MD, 21205, USA
- Neumora Therapeutics; Watertown, MA, 02472, USA
- Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University School of Medicine; Cardiff, CF24 4HQ, UK
- Department of Comparative Medicine, Yale University School of Medicine; New Haven, CT, 06520, USA
- Department of Neuroscience, Yale University School of Medicine; New Haven, CT, 06520, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA
- Institute for Precision Health, University of California, Los Angeles; Los Angeles, CA, 90095, USA
- Department of Statistics & Data Science, Carnegie Mellon University; Pittsburgh, PA, 15213, USA
- Computational Biology Department, Carnegie Mellon University; Pittsburgh, PA, 15213, USA
- Department of Medical Oncology, Division of Population Sciences, Dana-Farber Cancer Institute; Boston, MA, 02215, USA
- Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Harvard Medical School; Boston, MA, 02215, USA
- Division of Genetics, Brigham and Women's Hospital; Boston, MA, 02215, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA
- Department of Epidemiology & Biostatistics, University of California, San Francisco; San Francisco, CA, 94158, USA
- Chan Zuckerberg Biohub; San Francisco, CA, 94158, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University; Changsha, Hunan, 410008, China
| | - Mette A Peters
- CNS Data Coordination Group, Sage Bionetworks; Seattle, WA, 98109, USA
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University; New Haven, CT, 06520, USA
- Program in Computational Biology and Bioinformatics, Yale University; New Haven, CT, 06520, USA
- Department of Computer Science, Yale University; New Haven, CT, 06520, USA
- Department of Statistics and Data Science, Yale University; New Haven, CT, 06520, USA
| | - Nikolaos P Daskalakis
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02215, USA
- McLean Hospital; Belmont, MA, 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School; Worcester, MA, 01605, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development; Baltimore, MD, 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health; Baltimore, MD, 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health; Baltimore, MD, 21205, USA
- Neumora Therapeutics; Watertown, MA, 02472, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development; Baltimore, MD, 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development; Baltimore, MD, 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development; Baltimore, MD, 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Nicholas J Bray
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University School of Medicine; Cardiff, CF24 4HQ, UK
| | - Nenad Sestan
- Department of Comparative Medicine, Yale University School of Medicine; New Haven, CT, 06520, USA
- Department of Neuroscience, Yale University School of Medicine; New Haven, CT, 06520, USA
| | - Daniel H Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA
- Institute for Precision Health, University of California, Los Angeles; Los Angeles, CA, 90095, USA
| | - Kathryn Roeder
- Department of Statistics & Data Science, Carnegie Mellon University; Pittsburgh, PA, 15213, USA
- Computational Biology Department, Carnegie Mellon University; Pittsburgh, PA, 15213, USA
| | - Alexander Gusev
- Department of Medical Oncology, Division of Population Sciences, Dana-Farber Cancer Institute; Boston, MA, 02215, USA
- Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Harvard Medical School; Boston, MA, 02215, USA
- Division of Genetics, Brigham and Women's Hospital; Boston, MA, 02215, USA
| | - Bogdan Pasaniuc
- Interdepartmental Program in Bioinformatics, 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 Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA
- Institute for Precision Health, University of California, Los Angeles; Los Angeles, CA, 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA
| | - Michael I Love
- Department of Genetics, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA
| | - Katherine S Pollard
- Gladstone Institute of Data Science and Biotechnology; San Francisco, CA, 94158, USA
- Department of Epidemiology & Biostatistics, University of California, San Francisco; San Francisco, CA, 94158, USA
- Chan Zuckerberg Biohub; San Francisco, CA, 94158, USA
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University; Syracuse, NY, 13210, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University; Changsha, Hunan, 410008, China
| | - 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
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4
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Hanlon VCT, Lansdorp PM, Guryev V. A survey of current methods to detect and genotype inversions. Hum Mutat 2022; 43:1576-1589. [PMID: 36047337 DOI: 10.1002/humu.24458] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/11/2022]
Abstract
Polymorphic inversions are ubiquitous in humans, and they have been linked to both adaptation and disease. Following their discovery in Drosophila more than a century ago, inversions have proved to be more elusive than other structural variants. A wide variety of methods for the detection and genotyping of inversions have recently been developed: multiple techniques based on selective amplification by PCR, short- and long-read sequencing approaches, principal component analysis of small variant haplotypes, template strand sequencing, optical mapping, and various genome assembly methods. Many methods apply complex wet lab protocols or increasingly refined bioinformatic analyses. This review is an attempt to provide a practical summary and comparison of the methods that are in current use, with a focus on metrics such as the maximum size of segmental duplications at inversion breakpoints that each method can tolerate, the size range of inversions that they recover, their throughput, and whether the locations of putative inversions must be known beforehand. This article is protected by copyright. All rights reserved.
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Affiliation(s)
| | - Peter M Lansdorp
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, V5Z 1L3, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, The Netherlands
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5
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Campoy E, Puig M, Yakymenko I, Lerga-Jaso J, Cáceres M. Genomic architecture and functional effects of potential human inversion supergenes. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210209. [PMID: 35694745 PMCID: PMC9189494 DOI: 10.1098/rstb.2021.0209] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Supergenes are involved in adaptation in multiple organisms, but they are little known in humans. Genomic inversions are the most common mechanism of supergene generation and maintenance. Here, we review the information about two large inversions that are the best examples of potential human supergenes. In addition, we do an integrative analysis of the newest data to understand better their functional effects and underlying genetic changes. We have found that the highly divergent haplotypes of the 17q21.31 inversion of approximately 1.5 Mb have multiple phenotypic associations, with consistent effects in brain-related traits, red and white blood cells, lung function, male and female characteristics and disease risk. By combining gene expression and nucleotide variation data, we also analysed the molecular differences between haplotypes, including gene duplications, amino acid substitutions and regulatory changes, and identify CRHR1, KANLS1 and MAPT as good candidates to be responsible for these phenotypes. The situation is more complex for the 8p23.1 inversion, where there is no clear genetic differentiation. However, the inversion is associated with several related phenotypes and gene expression differences that could be linked to haplotypes specific of one orientation. Our work, therefore, contributes to the characterization of both exceptional variants and illustrates the important role of inversions. This article is part of the theme issue 'Genomic architecture of supergenes: causes and evolutionary consequences'.
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Affiliation(s)
- Elena Campoy
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra (Barcelona), Spain
| | - Marta Puig
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra (Barcelona), Spain.,Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Bellaterra (Barcelona), Spain
| | - Illya Yakymenko
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra (Barcelona), Spain
| | - Jon Lerga-Jaso
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra (Barcelona), Spain
| | - Mario Cáceres
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra (Barcelona), Spain.,ICREA, Barcelona, Spain
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6
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Mayor A, da Silva C, Rovira-Vallbona E, Roca-Feltrer A, Bonnington C, Wharton-Smith A, Greenhouse B, Bever C, Chidimatembue A, Guinovart C, Proctor JL, Rodrigues M, Canana N, Arnaldo P, Boene S, Aide P, Enosse S, Saute F, Candrinho B. Prospective surveillance study to detect antimalarial drug resistance, gene deletions of diagnostic relevance and genetic diversity of Plasmodium falciparum in Mozambique: protocol. BMJ Open 2022; 12:e063456. [PMID: 35820756 PMCID: PMC9274532 DOI: 10.1136/bmjopen-2022-063456] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
INTRODUCTION Genomic data constitute a valuable adjunct to routine surveillance that can guide programmatic decisions to reduce the burden of infectious diseases. However, genomic capacities remain low in Africa. This study aims to operationalise a functional malaria molecular surveillance system in Mozambique for guiding malaria control and elimination. METHODS AND ANALYSES This prospective surveillance study seeks to generate Plasmodium falciparum genetic data to (1) monitor molecular markers of drug resistance and deletions in rapid diagnostic test targets; (2) characterise transmission sources in low transmission settings and (3) quantify transmission levels and the effectiveness of antimalarial interventions. The study will take place across 19 districts in nine provinces (Maputo city, Maputo, Gaza, Inhambane, Niassa, Manica, Nampula, Zambézia and Sofala) which span a range of transmission strata, geographies and malaria intervention types. Dried blood spot samples and rapid diagnostic tests will be collected across the study districts in 2022 and 2023 through a combination of dense (all malaria clinical cases) and targeted (a selection of malaria clinical cases) sampling. Pregnant women attending their first antenatal care visit will also be included to assess their value for molecular surveillance. We will use a multiplex amplicon-based next-generation sequencing approach targeting informative single nucleotide polymorphisms, gene deletions and microhaplotypes. Genetic data will be incorporated into epidemiological and transmission models to identify the most informative relationship between genetic features, sources of malaria transmission and programmatic effectiveness of new malaria interventions. Strategic genomic information will be ultimately integrated into the national malaria information and surveillance system to improve the use of the genetic information for programmatic decision-making. ETHICS AND DISSEMINATION The protocol was reviewed and approved by the institutional (CISM) and national ethics committees of Mozambique (Comité Nacional de Bioética para Saúde) and Spain (Hospital Clinic of Barcelona). Project results will be presented to all stakeholders and published in open-access journals. TRIAL REGISTRATION NUMBER NCT05306067.
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Affiliation(s)
- Alfredo Mayor
- Centro de Investigação em Saúde de Manhiça, Manhiça, Maputo, Mozambique
- Barcelona Institute for Global Health, Hospital Clínic-Universitat de Barcelona, Barcelona, Spain
- Spanish Consortium for Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Physiologic Sciences, Faculty of Medicine, Universidade Eduardo Mondlane, Maputo, Mozambique
| | - Clemente da Silva
- Centro de Investigação em Saúde de Manhiça, Manhiça, Maputo, Mozambique
| | - Eduard Rovira-Vallbona
- Barcelona Institute for Global Health, Hospital Clínic-Universitat de Barcelona, Barcelona, Spain
| | | | | | | | - Bryan Greenhouse
- Department of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Caitlin Bever
- Bill & Melinda Gates Foundation, Seattle, Washington, USA
| | | | - Caterina Guinovart
- Barcelona Institute for Global Health, Hospital Clínic-Universitat de Barcelona, Barcelona, Spain
| | | | | | | | | | - Simone Boene
- Centro de Investigação em Saúde de Manhiça, Manhiça, Maputo, Mozambique
| | - Pedro Aide
- Centro de Investigação em Saúde de Manhiça, Manhiça, Maputo, Mozambique
- Instituto Nacional de Saúde, Maputo, Mozambique
| | | | - Francisco Saute
- Centro de Investigação em Saúde de Manhiça, Manhiça, Maputo, Mozambique
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7
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Carreras-Gallo N, Cáceres A, Balagué-Dobón L, Ruiz-Arenas C, Andrusaityte S, Carracedo Á, Casas M, Chatzi L, Grazuleviciene R, Gutzkow KB, Lepeule J, Maitre L, Nieuwenhuijsen M, Slama R, Stratakis N, Thomsen C, Urquiza J, Wright J, Yang T, Escaramís G, Bustamante M, Vrijheid M, Pérez-Jurado LA, González JR. The early-life exposome modulates the effect of polymorphic inversions on DNA methylation. Commun Biol 2022; 5:455. [PMID: 35550596 PMCID: PMC9098634 DOI: 10.1038/s42003-022-03380-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 04/19/2022] [Indexed: 11/14/2022] Open
Abstract
Polymorphic genomic inversions are chromosomal variants with intrinsic variability that play important roles in evolution, environmental adaptation, and complex traits. We investigated the DNA methylation patterns of three common human inversions, at 8p23.1, 16p11.2, and 17q21.31 in 1,009 blood samples from children from the Human Early Life Exposome (HELIX) project and in 39 prenatal heart tissue samples. We found inversion-state specific methylation patterns within and nearby flanking each inversion region in both datasets. Additionally, numerous inversion-exposure interactions on methylation levels were identified from early-life exposome data comprising 64 exposures. For instance, children homozygous at inv-8p23.1 and higher meat intake were more susceptible to TDH hypermethylation (P = 3.8 × 10−22); being the inversion, exposure, and gene known risk factors for adult obesity. Inv-8p23.1 associated hypermethylation of GATA4 was also detected across numerous exposures. Our data suggests that the pleiotropic influence of inversions during development and lifetime could be substantially mediated by allele-specific methylation patterns which can be modulated by the exposome. Analysis of the relationship between presence of common DNA sequence inversions and DNA methylation patterns suggests a role for environmental exposures (such as food intake) in mediating inversion state-specific methylation patterns.
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Affiliation(s)
| | - Alejandro Cáceres
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.,Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Department of Mathematics, Escola d'Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya, Barcelona, 08019, Spain
| | | | - Carlos Ruiz-Arenas
- Institut Hospital del Mar d'Investigacions Mediques (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain.,Department of Health and Experimental Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Sandra Andrusaityte
- Department of Environmental Science, Vytautas Magnus University, 44248, Kaunas, Lithuania
| | - Ángel Carracedo
- Medicine Genomics Group, Centro de Investigación Biomédica en Red Enfermedades Raras (CIBERER), University of Santiago de Compostela, CEGEN-PRB3, Santiago de Compostela, Spain.,Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Servicio Gallego de Salud (SERGAS), Santiago de Compostela, Galicia, Spain
| | - Maribel Casas
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.,Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Department of Health and Experimental Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Regina Grazuleviciene
- Department of Environmental Science, Vytautas Magnus University, 44248, Kaunas, Lithuania
| | - Kristine Bjerve Gutzkow
- Department of Environmental Health, Norwegian Institute of Public Health, 0456, Oslo, Norway
| | - Johanna Lepeule
- Institut national de la santé et de la recherche médicale (Inserm) and Université Grenoble-Alpes, Institute for Advanced Biosciences (IAB), Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Léa Maitre
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.,Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Department of Health and Experimental Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Mark Nieuwenhuijsen
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.,Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Department of Health and Experimental Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Remy Slama
- Institut national de la santé et de la recherche médicale (Inserm) and Université Grenoble-Alpes, Institute for Advanced Biosciences (IAB), Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Nikos Stratakis
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
| | - Cathrine Thomsen
- Department of Environmental Health, Norwegian Institute of Public Health, 0456, Oslo, Norway
| | - Jose Urquiza
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.,Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Department of Health and Experimental Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Tiffany Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Geòrgia Escaramís
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Department of Biomedical Science, Faculty of Medicine and Health Science, University of Barcelona, Barcelona, Spain.,Research Group on Statistics, Econometrics and Health (GRECS), UdG, Girona, Spain
| | - Mariona Bustamante
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.,Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Department of Health and Experimental Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain.,Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Martine Vrijheid
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.,Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Department of Health and Experimental Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Luis A Pérez-Jurado
- Institut Hospital del Mar d'Investigacions Mediques (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain.,Department of Health and Experimental Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain.,Genetics Service, Hospital del Mar, Barcelona, Spain
| | - Juan R González
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain. .,Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain. .,Department of Mathematics, Universitat Autònoma de Barcelona, Bellaterra, Spain.
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8
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Balagué-Dobón L, Cáceres A, González JR. Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure. Brief Bioinform 2022; 23:6535682. [PMID: 35211719 PMCID: PMC8921734 DOI: 10.1093/bib/bbac043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/25/2022] [Accepted: 01/28/2022] [Indexed: 12/12/2022] Open
Abstract
Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.
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9
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Makowski C, van der Meer D, Dong W, Wang H, Wu Y, Zou J, Liu C, Rosenthal SB, Hagler DJ, Fan CC, Kremen WS, Andreassen OA, Jernigan TL, Dale AM, Zhang K, Visscher PM, Yang J, Chen CH. Discovery of genomic loci of the human cerebral cortex using genetically informed brain atlases. Science 2022; 375:522-528. [PMID: 35113692 DOI: 10.1126/science.abe8457] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
To determine the impact of genetic variants on the brain, we used genetically informed brain atlases in genome-wide association studies of regional cortical surface area and thickness in 39,898 adults and 9136 children. We uncovered 440 genome-wide significant loci in the discovery cohort and 800 from a post hoc combined meta-analysis. Loci in adulthood were largely captured in childhood, showing signatures of negative selection, and were linked to early neurodevelopment and pathways associated with neuropsychiatric risk. Opposing gradations of decreased surface area and increased thickness were associated with common inversion polymorphisms. Inferior frontal regions, encompassing Broca's area, which is important for speech, were enriched for human-specific genomic elements. Thus, a mixed genetic landscape of conserved and human-specific features is concordant with brain hierarchy and morphogenetic gradients.
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Affiliation(s)
- Carolina Makowski
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Weixiu Dong
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Hao Wang
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Yan Wu
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Jingjing Zou
- Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, CA, USA
| | - Cin Liu
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Sara B Rosenthal
- Center for Computational Biology and Bioinformatics, University of California, San Diego, CA, USA
| | - Donald J Hagler
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Chun Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - William S Kremen
- Department of Psychiatry and Center for Behavior Genetics of Aging, University of California, San Diego, CA, USA
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Terry L Jernigan
- Center for Human Development, University of California, San Diego, CA, USA
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA.,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Chi-Hua Chen
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
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10
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Ferrari G, Atmore LM, Jentoft S, Jakobsen KS, Makowiecki D, Barrett JH, Star B. An accurate assignment test for extremely low-coverage whole-genome sequence data. Mol Ecol Resour 2021; 22:1330-1344. [PMID: 34779123 DOI: 10.1111/1755-0998.13551] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 10/28/2021] [Accepted: 11/02/2021] [Indexed: 11/28/2022]
Abstract
Genomic assignment tests can provide important diagnostic biological characteristics, such as population of origin or ecotype. Yet, assignment tests often rely on moderate- to high-coverage sequence data that can be difficult to obtain for fields such as molecular ecology and ancient DNA. We have developed a novel approach that efficiently assigns biologically relevant information (i.e., population identity or structural variants such as inversions) in extremely low-coverage sequence data. First, we generate databases from existing reference data using a subset of diagnostic single nucleotide polymorphisms (SNPs) associated with a biological characteristic. Low-coverage alignment files are subsequently compared to these databases to ascertain allelic state, yielding a joint probability for each association. To assess the efficacy of this approach, we assigned haplotypes and population identity in Heliconius butterflies, Atlantic herring, and Atlantic cod using chromosomal inversion sites and whole-genome data. We scored both modern and ancient specimens, including the first whole-genome sequence data recovered from ancient Atlantic herring bones. The method accurately assigns biological characteristics, including population membership, using extremely low-coverage data (as low as 0.0001x) based on genome-wide SNPs. This approach will therefore increase the number of samples in evolutionary, ecological and archaeological research for which relevant biological information can be obtained.
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Affiliation(s)
- Giada Ferrari
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway
| | - Lane M Atmore
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway
| | - Sissel Jentoft
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway
| | - Kjetill S Jakobsen
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway
| | - Daniel Makowiecki
- Department of Environmental Archaeology and Human Paleoecology, Institute of Archaeology, Nicolaus Copernicus University, Torun, Poland
| | - James H Barrett
- McDonald Institute for Archaeological Research, Department of Archaeology, University of Cambridge, Cambridge, UK.,Department of Archaeology and Cultural History, NTNU University Museum, Trondheim, Norway
| | - Bastiaan Star
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway
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11
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Hollox EJ, Zuccherato LW, Tucci S. Genome structural variation in human evolution. Trends Genet 2021; 38:45-58. [PMID: 34284881 DOI: 10.1016/j.tig.2021.06.015] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/21/2021] [Accepted: 06/22/2021] [Indexed: 01/01/2023]
Abstract
Structural variation (SV) is a large difference (typically >100 bp) in the genomic structure of two genomes and includes both copy number variation and variation that does not change copy number of a genomic region, such as an inversion. Improved reference genomes, combined with widespread genome sequencing using short-read sequencing technology, and increasingly using long-read sequencing, have reignited interest in SV. Recent large-scale studies and functional focused analyses have highlighted the role of SV in human evolution. In this review, we highlight human-specific SVs involved in changes in the brain, population-specific SVs that affect response to the environment, including adaptation to diet and infectious diseases, and summarise the contribution of archaic hominin admixture to present-day human SV.
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Affiliation(s)
- Edward J Hollox
- Department of Genetics and Genome Biology, University of Leicester, UK.
| | - Luciana W Zuccherato
- Núcleo de Ensino e Pesquisa, Instituto Mário Penna, Belo Horizonte, Brazil; Departmento de Bioquímica e Imunologia, Universidade de Minas Gerais, Belo Horizonte, Brazil
| | - Serena Tucci
- Department of Anthropology, Yale University, New Haven, CT, USA
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12
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Nowling RJ, Manke KR, Emrich SJ. Detecting inversions with PCA in the presence of population structure. PLoS One 2020; 15:e0240429. [PMID: 33119626 PMCID: PMC7595445 DOI: 10.1371/journal.pone.0240429] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 09/28/2020] [Indexed: 12/26/2022] Open
Abstract
Chromosomal inversions can lead to reproductive isolation and adaptation in insects such as Drosophila melanogaster and the non-model malaria vector Anopheles gambiae. Inversions can be detected and characterized using principal component analysis (PCA) of single nucleotide polymorphisms (SNPs). To aid in developing such methods, we formed a new benchmark derived from three publicly-available insect data. We then used this benchmark to perform an extended validation of our software for inversion analysis (Asaph). Through that process, we identified and characterized several problematic test cases liable to misinterpretation that can help guide PCA-based inversion detection. Lastly, we re-analyzed the 2R chromosome arm of 150 An. gambiae and coluzzii samples and observed two inversions (2Rc and 2Rd) that were previously known but not annotated in these particular individuals. The resulting benchmark data set and methods will be useful for future inversion detection based solely on SNP data.
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Affiliation(s)
- Ronald J. Nowling
- Electrical Engineering and Computer Science, Milwaukee School of Engineering, Milwaukee, WI
| | - Krystal R. Manke
- Physics and Chemistry, Milwaukee School of Engineering, Milwaukee, WI
| | - Scott J. Emrich
- Electrical Engineering and Computer Science, University of Tennessee–Knoxville, Knoxville, TN
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González JR, Ruiz-Arenas C, Cáceres A, Morán I, López-Sánchez M, Alonso L, Tolosana I, Guindo-Martínez M, Mercader JM, Esko T, Torrents D, González J, Pérez-Jurado LA. Polymorphic Inversions Underlie the Shared Genetic Susceptibility of Obesity-Related Diseases. Am J Hum Genet 2020; 106:846-858. [PMID: 32470372 DOI: 10.1016/j.ajhg.2020.04.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 04/28/2020] [Indexed: 11/25/2022] Open
Abstract
The burden of several common diseases including obesity, diabetes, hypertension, asthma, and depression is increasing in most world populations. However, the mechanisms underlying the numerous epidemiological and genetic correlations among these disorders remain largely unknown. We investigated whether common polymorphic inversions underlie the shared genetic influence of these disorders. We performed an inversion association analysis including 21 inversions and 25 obesity-related traits on a total of 408,898 Europeans and validated the results in 67,299 independent individuals. Seven inversions were associated with multiple diseases while inversions at 8p23.1, 16p11.2, and 11q13.2 were strongly associated with the co-occurrence of obesity with other common diseases. Transcriptome analysis across numerous tissues revealed strong candidate genes for obesity-related traits. Analyses in human pancreatic islets indicated the potential mechanism of inversions in the susceptibility of diabetes by disrupting the cis-regulatory effect of SNPs from their target genes. Our data underscore the role of inversions as major genetic contributors to the joint susceptibility to common complex diseases.
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Determining the impact of uncharacterized inversions in the human genome by droplet digital PCR. Genome Res 2020; 30:724-735. [PMID: 32424072 PMCID: PMC7263195 DOI: 10.1101/gr.255273.119] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 04/17/2020] [Indexed: 12/20/2022]
Abstract
Despite the interest in characterizing genomic variation, the presence of large repeats at the breakpoints hinders the analysis of many structural variants. This is especially problematic for inversions, since there is typically no gain or loss of DNA. Here, we tested novel linkage-based droplet digital PCR (ddPCR) assays to study 20 inversions ranging from 3.1 to 742 kb flanked by inverted repeats (IRs) up to 134 kb long. Of those, we validated 13 inversions predicted by different genome-wide techniques. In addition, we obtained new experimental human population information across 95 African, European, and East Asian individuals for 16 inversions, including four already validated variants without high-throughput genotyping methods. Through comparison with previous data, independent replicates and both inversion breakpoints, we demonstrate that the technique is highly accurate and reproducible. Most studied inversions are widespread across continents, and their frequency is negatively correlated with genetic length. Moreover, all except two show clear signs of being recurrent, and we could better define the factors affecting recurrence levels and estimate the inversion rate across the genome. Finally, the generated genotypes have allowed us to check inversion functional effects, validating gene expression differences reported before for two inversions and finding new candidate associations. Therefore, the developed methodology makes it possible to screen these and other complex genomic variants quickly in a large number of samples for the first time, highlighting the importance of direct genotyping to assess their potential consequences and clinical implications.
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Ruiz-Arenas C, Cáceres A, Moreno V, González JR. Common polymorphic inversions at 17q21.31 and 8p23.1 associate with cancer prognosis. Hum Genomics 2019; 13:57. [PMID: 31753042 PMCID: PMC6873427 DOI: 10.1186/s40246-019-0242-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Accepted: 10/09/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Chromosomal inversions are structural genetic variants where a chromosome segment changes its orientation. While sporadic de novo inversions are known genetic risk factors for cancer susceptibility, it is unknown if common polymorphic inversions are also associated with the prognosis of common tumors, as they have been linked to other complex diseases. We studied the association of two well-characterized human inversions at 17q21.31 and 8p23.1 with the prognosis of lung, liver, breast, colorectal, and stomach cancers. RESULTS Using data from The Cancer Genome Atlas (TCGA), we observed that inv8p23.1 was associated with overall survival in breast cancer and that inv17q21.31 was associated with overall survival in stomach cancer. In the meta-analysis of two independent studies, inv17q21.31 heterozygosity was significantly associated with colorectal disease-free survival. We found that the association was mediated by the de-methylation of cg08283464 and cg03999934, also linked to lower disease-free survival. CONCLUSIONS Our results suggest that chromosomal inversions are important genetic factors of tumor prognosis, likely affecting changes in methylation patterns.
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Affiliation(s)
- Carlos Ruiz-Arenas
- Barcelona Institute for Global Health, ISGlobal, Doctor Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Alejandro Cáceres
- Barcelona Institute for Global Health, ISGlobal, Doctor Aiguader 88, 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Victor Moreno
- Programa de Prevención y Control del Cáncer, Instituto Catalán de Oncología, L'Hospitalet, Barcelona, Spain
| | - Juan R González
- Barcelona Institute for Global Health, ISGlobal, Doctor Aiguader 88, 08003, Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.
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