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Farbehi N, Neavin DR, Cuomo ASE, Studer L, MacArthur DG, Powell JE. Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases. Nat Genet 2024; 56:758-766. [PMID: 38741017 DOI: 10.1038/s41588-024-01731-9] [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: 01/24/2023] [Accepted: 03/20/2024] [Indexed: 05/16/2024]
Abstract
Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.
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Affiliation(s)
- Nona Farbehi
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, New South Wales, Australia
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA
| | - Drew R Neavin
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Anna S E Cuomo
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research, University of New South Wales, Sydney, New South Wales, Australia
| | - Lorenz Studer
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA
- The Center for Stem Cell Biology and Developmental Biology Program, Sloan-Kettering Institute for Cancer Research, New York, NY, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, University of New South Wales, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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Lin S, Wu S, Zhao W, Fang Z, Kang H, Liu X, Pan S, Yu F, Bao Y, Jia P. TargetGene: a comprehensive database of cell-type-specific target genes for genetic variants. Nucleic Acids Res 2024; 52:D1072-D1081. [PMID: 37870478 PMCID: PMC10767789 DOI: 10.1093/nar/gkad901] [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: 08/15/2023] [Revised: 09/27/2023] [Accepted: 10/09/2023] [Indexed: 10/24/2023] Open
Abstract
Annotating genetic variants to their target genes is of great importance in unraveling the causal variants and genetic mechanisms that underlie complex diseases. However, disease-associated genetic variants are often located in non-coding regions and manifest context-specific effects, making it challenging to accurately identify the target genes and regulatory mechanisms. Here, we present TargetGene (https://ngdc.cncb.ac.cn/targetgene/), a comprehensive database reporting target genes for human genetic variants from various aspects. Specifically, we collected a comprehensive catalog of multi-omics data at the single-cell and bulk levels and from various human tissues, cell types and developmental stages. To facilitate the identification of Single Nucleotide Polymorphism (SNP)-to-gene connections, we have implemented multiple analytical tools based on chromatin co-accessibility, 3D interaction, enhancer activities and quantitative trait loci, among others. We applied the pipeline to evaluate variants from nearly 1300 Genome-wide association studies (GWAS) and assembled a comprehensive atlas of multiscale regulation of genetic variants. TargetGene is equipped with user-friendly web interfaces that enable intuitive searching, navigation and browsing through the results. Overall, TargetGene provides a unique resource to empower researchers to study the regulatory mechanisms of genetic variants in complex human traits.
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Affiliation(s)
- Shiqi Lin
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Song Wu
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Wei Zhao
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zhanjie Fang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongen Kang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinxuan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Siyu Pan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fudong Yu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Yiming Bao
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Varshney A, Manickam N, Orchard P, Tovar A, Zhang Z, Feng F, Erdos MR, Narisu N, Ventresca C, Nishino K, Rai V, Stringham HM, Jackson AU, Tamsen T, Gao C, Yang M, Koues OI, Welch JD, Burant CF, Williams LK, Jenkinson C, DeFronzo RA, Norton L, Saramies J, Lakka TA, Laakso M, Tuomilehto J, Mohlke KL, Kitzman JO, Koistinen HA, Liu J, Boehnke M, Collins FS, Scott LJ, Parker SCJ. Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.15.571696. [PMID: 38168419 PMCID: PMC10760134 DOI: 10.1101/2023.12.15.571696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Skeletal muscle, the largest human organ by weight, is relevant to several polygenic metabolic traits and diseases including type 2 diabetes (T2D). Identifying genetic mechanisms underlying these traits requires pinpointing the relevant cell types, regulatory elements, target genes, and causal variants. Here, we used genetic multiplexing to generate population-scale single nucleus (sn) chromatin accessibility (snATAC-seq) and transcriptome (snRNA-seq) maps across 287 frozen human skeletal muscle biopsies representing 456,880 nuclei. We identified 13 cell types that collectively represented 983,155 ATAC summits. We integrated genetic variation to discover 6,866 expression quantitative trait loci (eQTL) and 100,928 chromatin accessibility QTL (caQTL) (5% FDR) across the five most abundant cell types, cataloging caQTL peaks that atlas-level snATAC maps often miss. We identified 1,973 eGenes colocalized with caQTL and used mediation analyses to construct causal directional maps for chromatin accessibility and gene expression. 3,378 genome-wide association study (GWAS) signals across 43 relevant traits colocalized with sn-e/caQTL, 52% in a cell-specific manner. 77% of GWAS signals colocalized with caQTL and not eQTL, highlighting the critical importance of population-scale chromatin profiling for GWAS functional studies. GWAS-caQTL colocalization showed distinct cell-specific regulatory paradigms. For example, a C2CD4A/B T2D GWAS signal colocalized with caQTL in muscle fibers and multiple chromatin loop models nominated VPS13C, a glucose uptake gene. Sequence of the caQTL peak overlapping caSNP rs7163757 showed allelic regulatory activity differences in a human myocyte cell line massively parallel reporter assay. These results illuminate the genetic regulatory architecture of human skeletal muscle at high-resolution epigenomic, transcriptomic, and cell state scales and serve as a template for population-scale multi-omic mapping in complex tissues and traits.
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Affiliation(s)
- Arushi Varshney
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nandini Manickam
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Peter Orchard
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Adelaide Tovar
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Zhenhao Zhang
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fan Feng
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Michael R Erdos
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Narisu Narisu
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Christa Ventresca
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Dept. of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Kirsten Nishino
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Vivek Rai
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Heather M Stringham
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Anne U Jackson
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Tricia Tamsen
- Biomedical Research Core Facilities Advanced Genomics Core, University of Michigan, Ann Arbor, MI, USA
| | - Chao Gao
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Mao Yang
- Department of Internal Medicine, Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, USA
| | - Olivia I Koues
- Biomedical Research Core Facilities Advanced Genomics Core, University of Michigan, Ann Arbor, MI, USA
| | - Joshua D Welch
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - L Keoki Williams
- Department of Internal Medicine, Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, USA
| | - Chris Jenkinson
- South Texas Diabetes and Obesity Research Institute, School of Medicine, University of Texas, Rio Grande Valley, TX, USA
| | - Ralph A DeFronzo
- Department of Medicine/Diabetes Division, University of Texas Health, San Antonio, TX, USA
| | - Luke Norton
- Department of Medicine/Diabetes Division, University of Texas Health, San Antonio, TX, USA
| | - Jouko Saramies
- Savitaipale Health Center, South Karelia Central Hospital, Lappeenranta, Finland
| | - Timo A Lakka
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Jaakko Tuomilehto
- Dept. of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Dept. of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Karen L Mohlke
- Dept. of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Jacob O Kitzman
- Dept. of Human Genetics, University of Michigan, Ann Arbor, MI, USA
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Heikki A Koistinen
- Dept. of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Jie Liu
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Michael Boehnke
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Francis S Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Laura J Scott
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Stephen C J Parker
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Dept. of Human Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
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