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Saferali A, Kim W, Chase RP, Vollmers C, Silverman EK, Cho MH, Castaldi PJ, Hersh CP. Overlap between COPD genetic association results and transcriptional quantitative trait loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.08.24310079. [PMID: 39040180 PMCID: PMC11261918 DOI: 10.1101/2024.07.08.24310079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Rationale Genome-wide association studies (GWAS) have identified multiple genetic loci associated with chronic obstructive pulmonary disease (COPD). When integrated with GWAS results, expression quantitative trait locus (eQTL) studies can provide insight into biological mechanisms involved in disease by identifying single nucleotide polymorphisms (SNPs) that contribute to whole gene expression. However, there are multiple genetically driven regulatory and isoform-specific effects which cannot be detected in traditional eQTL analyses. Here, we identify SNPs that are associated with alternative splicing (sQTL) in addition to eQTLs to identify novel functions for COPD associated genetic variants. Methods We performed RNA sequencing on whole blood from 3743 subjects in the COPDGene Study. RNA sequencing data from lung tissue of 1241 subjects from the Lung Tissue Research Consortium (LTRC), and whole genome sequencing data on all subjects. Associations between all SNPs within 1000 kb of a gene (cis-) and splice and gene expression quantifications were tested using tensorQTL. In COPDGene a total of 11,869,333 SNPs were tested for association with 58,318 splice clusters, and 8,792,206 SNPs were tested for association with 70,094 splice clusters in LTRC. We assessed colocalization with COPD-associated SNPs from a published GWAS[1]. Results After adjustment for multiple statistical testing, we identified 28,110 splice-sites corresponding to 3,889 unique genes that were significantly associated with genotype in COPDGene whole blood, and 58,258 splice-sites corresponding to 10,307 unique genes associated with genotype in LTRC lung tissue. We found 7,576 sQTL splice-sites corresponding to 2,110 sQTL genes were shared between whole blood and lung, while 20,534 sQTL splice-sites in 3,518 genes were unique to blood and 50,682 splice-sites in 9,677 genes were unique to lung. To determine what proportion of COPD-associated SNPs were associated with transcriptional splicing, we performed colocalization analysis between COPD GWAS and sQTL data, and found that 38 genomic windows, corresponding to 38 COPD GWAS loci had evidence of colocalization between QTLs and COPD. The top five colocalizations between COPD and lung sQTLs include NPNT , FBXO38 , HHIP , NTN4 and BTC . Conclusions A total of 38 COPD GWAS loci contain evidence of sQTLs, suggesting that analysis of sQTLs in whole blood and lung tissue can provide novel insights into disease mechanisms.
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Zeng L, Fujita M, Gao Z, White CC, Green GS, Habib N, Menon V, Bennett DA, Boyle P, Klein HU, De Jager PL. A Single-Nucleus Transcriptome-Wide Association Study Implicates Novel Genes in Depression Pathogenesis. Biol Psychiatry 2024; 96:34-43. [PMID: 38141910 PMCID: PMC11168890 DOI: 10.1016/j.biopsych.2023.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 12/01/2023] [Accepted: 12/17/2023] [Indexed: 12/25/2023]
Abstract
BACKGROUND Depression, a common psychiatric illness and global public health problem, remains poorly understood across different life stages, which hampers the development of novel treatments. METHODS To identify new candidate genes for therapeutic development, we performed differential gene expression analysis of single-nucleus RNA sequencing data from the dorsolateral prefrontal cortex of older adults (n = 424) in relation to antemortem depressive symptoms. Additionally, we integrated genome-wide association study results for depression (n = 500,199) along with genetic tools for inferring the expression of 14,048 unique genes in 7 cell types and 52 cell subtypes to perform a transcriptome-wide association study of depression followed by Mendelian randomization. RESULTS Our single-nucleus transcriptome-wide association study analysis identified 68 candidate genes for depression and showed the greatest number being in excitatory and inhibitory neurons. Of the 68 genes, 53 were novel compared to previous studies. Notably, gene expression in different neuronal subtypes had varying effects on depression risk. Traits with high genetic correlations with depression, such as neuroticism, shared more transcriptome-wide association study genes than traits that were not highly correlated with depression. Complementing these analyses, differential gene expression analysis across 52 neocortical cell subtypes showed that genes such as KCNN2, SCAI, WASF3, and SOCS6 were associated with late-life depressive symptoms in specific cell subtypes. CONCLUSIONS These 2 sets of analyses illustrate the utility of large single-nucleus RNA sequencing data both to uncover genes whose expression is altered in specific cell subtypes in the context of depressive symptoms and to enhance the interpretation of well-powered genome-wide association studies so that we can prioritize specific susceptibility genes for further analysis and therapeutic development.
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Affiliation(s)
- Lu Zeng
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, New York
| | - Masashi Fujita
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, New York
| | - Zongmei Gao
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, New York
| | - Charles C White
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, New York
| | - Gilad S Green
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Naomi Habib
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Vilas Menon
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, New York
| | - David A Bennett
- Rush Alzheimer Disease Center, Rush University Medical Center, Chicago, Illinois; Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois
| | - Patricia Boyle
- Rush Alzheimer Disease Center, Rush University Medical Center, Chicago, Illinois; Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, Illinois
| | - Hans-Ulrich Klein
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, New York
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, New York.
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Li Z, Zhang W, Li S, Tao X, Xu H, Wu Y, Chen Q, Ning A, Tian T, Zhang L, Cui J, Wang W, Chu M. Integration of apaQTL and eQTL analysis reveals novel SNPs associated with occupational pulmonary fibrosis risk. Arch Toxicol 2024; 98:2117-2129. [PMID: 38538875 DOI: 10.1007/s00204-024-03734-1] [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: 01/10/2024] [Accepted: 03/07/2024] [Indexed: 06/13/2024]
Abstract
To explore the association between apaQTL/eQTL-SNPs and the susceptibility to silicosis. A silicosis-related GWAS was initially conducted to screen for single nucleotide polymorphisms (SNPs) associated with the risk of silicosis. Candidate SNPs with apaQTL and eQTL functions were then obtained from the 3'aQTL-atlas and GTEx databases. Subsequently, additional case-control studies were performed to validate the relationship between the candidate apaQTL/eQTL-SNPs and the risk of silicosis. Finally, experiments were conducted to illustrate APA events occurring at different alleles of the identified apaQTL/eQTL-SNPs. The combined results of the GWAS and iMLDR validations indicate that the variant T allele of the rs2974341 located on SMIM19 (additive model: OR = 0.66, the 95% CI = 0.53-0.84, P = 0.001) and the variant T allele of the rs2390488 located on TMTC4 (additive model: OR = 0.72, 95% CI = 0.57-0.90, P = 0.005) were significantly associated with decreased risk of developing silicosis susceptibility. Furthermore, 3'RACE experiments verified the presence of two poly (A) sites (proximal and distal) in SMIM19, rs2974341 may remotely regulate the binding between miRNA-3646 and SMIM19 with its high LD locus rs2974353 to affect the expression level of SMIM19. The rs2974341 variant T allele may contribute to the generation of the shorter 3'UTR transcript of SMIM19 and affect the binding of miRNA-3646 to the target gene SMIM19. The apaQTL/eQTL-SNPs may provide new perspectives for evaluating the regulatory function of SNPs in the development of silicosis.
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Affiliation(s)
- Zhenyu Li
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Wendi Zhang
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Siqi Li
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Xiaobo Tao
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Huiwen Xu
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Yutong Wu
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Qiong Chen
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Anhui Ning
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Tian Tian
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Lei Zhang
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Jiahua Cui
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Wei Wang
- Department of Occupational Health, Center for Disease Control and Prevention of Wuxi, Wuxi, China
| | - Minjie Chu
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China.
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Saferali A, Kim W, Xu Z, Chase RP, Cho MH, Laederach A, Castaldi PJ, Hersh CP. Colocalization analysis of 3' UTR alternative polyadenylation quantitative trait loci reveals novel mechanisms underlying associations with lung function. Hum Mol Genet 2024; 33:1164-1175. [PMID: 38569558 DOI: 10.1093/hmg/ddae055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 02/02/2024] [Indexed: 04/05/2024] Open
Abstract
While many disease-associated single nucleotide polymorphisms (SNPs) are expression quantitative trait loci (eQTLs), a large proportion of genome-wide association study (GWAS) variants are of unknown function. Alternative polyadenylation (APA) plays an important role in posttranscriptional regulation by allowing genes to shorten or extend 3' untranslated regions (UTRs). We hypothesized that genetic variants that affect APA in lung tissue may lend insight into the function of respiratory associated GWAS loci. We generated alternative polyadenylation (apa) QTLs using RNA sequencing and whole genome sequencing on 1241 subjects from the Lung Tissue Research Consortium (LTRC) as part of the NHLBI TOPMed project. We identified 56 179 APA sites corresponding to 13 582 unique genes after filtering out APA sites with low usage. We found that a total of 8831 APA sites were associated with at least one SNP with q-value < 0.05. The genomic distribution of lead APA SNPs indicated that the majority are intronic variants (33%), followed by downstream gene variants (26%), 3' UTR variants (17%), and upstream gene variants (within 1 kb region upstream of transcriptional start site, 10%). APA sites in 193 genes colocalized with GWAS data for at least one phenotype. Genes containing the top APA sites associated with GWAS variants include membrane associated ring-CH-type finger 2 (MARCHF2), nectin cell adhesion molecule 2 (NECTIN2), and butyrophilin subfamily 3 member A2 (BTN3A2). Overall, these findings suggest that APA may be an important mechanism for genetic variants in lung function and chronic obstructive pulmonary disease (COPD).
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Affiliation(s)
- Aabida Saferali
- Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, United States
| | - Wonji Kim
- Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, United States
| | - Zhonghui Xu
- Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, United States
| | - Robert P Chase
- Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, United States
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, United States
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, United States
| | - Alain Laederach
- Department of Biology, University of North Carolina at Chapel Hill, 120 South Road, Chapel Hill, NC 27599, United States
| | - Peter J Castaldi
- Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, United States
- Division of General Medicine and Primary Care, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, United States
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, United States
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, United States
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Hao RH, Zhang TP, Jiang F, Liu JH, Dong SS, Li M, Guo Y, Yang TL. Revealing brain cell-stratified causality through dissecting causal variants according to their cell-type-specific effects on gene expression. Nat Commun 2024; 15:4890. [PMID: 38849352 PMCID: PMC11161590 DOI: 10.1038/s41467-024-49263-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 05/29/2024] [Indexed: 06/09/2024] Open
Abstract
The human brain has been implicated in the pathogenesis of several complex diseases. Taking advantage of single-cell techniques, genome-wide association studies (GWAS) have taken it a step further and revealed brain cell-type-specific functions for disease loci. However, genetic causal associations inferred by Mendelian randomization (MR) studies usually include all instrumental variables from GWAS, which hampers the understanding of cell-specific causality. Here, we developed an analytical framework, Cell-Stratified MR (csMR), to investigate cell-stratified causality through colocalizing GWAS signals with single-cell eQTL from different brain cells. By applying to obesity-related traits, our results demonstrate the cell-type-specific effects of GWAS variants on gene expression, and indicate the benefits of csMR to identify cell-type-specific causal effect that is often hidden from bulk analyses. We also found csMR valuable to reveal distinct causal pathways between different obesity indicators. These findings suggest the value of our approach to prioritize target cells for extending genetic causation studies.
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Affiliation(s)
- Ruo-Han Hao
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Tian-Pei Zhang
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Feng Jiang
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Jun-Hui Liu
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Shan-Shan Dong
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Meng Li
- Department of Orthopedics, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, P. R. China
| | - Yan Guo
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China.
| | - Tie-Lin Yang
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China.
- Department of Orthopedics, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, P. R. China.
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Carnes MU, Quach BC, Zhou L, Han S, Tao R, Mandal M, Deep-Soboslay A, Marks JA, Page GP, Maher BS, Jaffe AE, Won H, Bierut LJ, Hyde TM, Kleinman JE, Johnson EO, Hancock DB. Smoking-informed methylation and expression QTLs in human brain and colocalization with smoking-associated genetic loci. Neuropsychopharmacology 2024:10.1038/s41386-024-01885-4. [PMID: 38830989 DOI: 10.1038/s41386-024-01885-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/19/2024] [Accepted: 05/06/2024] [Indexed: 06/05/2024]
Abstract
Smoking is a leading cause of preventable morbidity and mortality. Smoking is heritable, and genome-wide association studies (GWASs) of smoking behaviors have identified hundreds of significant loci. Most GWAS-identified variants are noncoding with unknown neurobiological effects. We used genome-wide genotype, DNA methylation, and RNA sequencing data in postmortem human nucleus accumbens (NAc) to identify cis-methylation/expression quantitative trait loci (meQTLs/eQTLs), investigate variant-by-cigarette smoking interactions across the genome, and overlay QTL evidence at smoking GWAS-identified loci to evaluate their regulatory potential. Active smokers (N = 52) and nonsmokers (N = 171) were defined based on cotinine biomarker levels and next-of-kin reporting. We simultaneously tested variant and variant-by-smoking interaction effects on methylation and expression, separately, adjusting for biological and technical covariates and correcting for multiple testing using a two-stage procedure. We found >2 million significant meQTL variants (padj < 0.05) corresponding to 41,695 unique CpGs. Results were largely driven by main effects, and five meQTLs, mapping to NUDT12, FAM53B, RNF39, and ADRA1B, showed a significant interaction with smoking. We found 57,683 significant eQTL variants for 958 unique eGenes (padj < 0.05) and no smoking interactions. Colocalization analyses identified loci with smoking-associated GWAS variants that overlapped meQTLs/eQTLs, suggesting that these heritable factors may influence smoking behaviors through functional effects on methylation/expression. One locus containing MUSTN1 and ITIH4 colocalized across all data types (GWAS, meQTL, and eQTL). In this first genome-wide meQTL map in the human NAc, the enriched overlap with smoking GWAS-identified genetic loci provides evidence that gene regulation in the brain helps explain the neurobiology of smoking behaviors.
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Affiliation(s)
- Megan Ulmer Carnes
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Bryan C Quach
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Linran Zhou
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Shizhong Han
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | - Ran Tao
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | - Meisha Mandal
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | | | - Jesse A Marks
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Grier P Page
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
- Fellow Program, RTI International, Research Triangle Park, NC, USA
| | - Brion S Maher
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | - Eric O Johnson
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
- Fellow Program, RTI International, Research Triangle Park, NC, USA
| | - Dana B Hancock
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA.
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Shen S, Sobczyk MK, Paternoster L, Brown SJ. From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics. J Invest Dermatol 2024; 144:1189-1199.e8. [PMID: 38782533 DOI: 10.1016/j.jid.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/13/2024] [Accepted: 03/06/2024] [Indexed: 05/25/2024]
Abstract
Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.
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Affiliation(s)
- Silvia Shen
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Institute for Evolution and Ecology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
| | - Maria K Sobczyk
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Sara J Brown
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Department of Dermatology, NHS Lothian, Edinburgh, United Kingdom
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Tung KF, Pan CY, Lin WC. Housekeeping protein-coding genes interrogated with tissue and individual variations. Sci Rep 2024; 14:12454. [PMID: 38816574 PMCID: PMC11139953 DOI: 10.1038/s41598-024-63269-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 05/27/2024] [Indexed: 06/01/2024] Open
Abstract
Housekeeping protein-coding genes are stably expressed genes in cells and tissues that are thought to be engaged in fundamental cellular biological functions. They are often utilized as normalization references in molecular biology research and are especially important in integrated bioinformatic investigations. Prior studies have examined human housekeeping protein-coding genes by analyzing various gene expression datasets. The inclusion of different tissue types significantly impacted the discovery of housekeeping genes. In this report, we investigated particularly individual human subject expression differences in protein-coding genes across different tissue types. We used GTEx V8 gene expression datasets obtained from more than 16,000 human normal tissue samples. Furthermore, the Gini index is utilized to investigate the expression variations of protein-coding genes between tissue and individual donor subjects. Housekeeping protein-coding genes found using Gini index profiles may vary depending on the tissue subtypes investigated, particularly given the diverse sample size collections across the GTEx tissue subtypes. We subsequently selected major tissues and identified subsets of housekeeping genes with stable expression levels among human donors within those tissues. In this work, we provide alternative sets of housekeeping protein-coding genes that show more consistent expression patterns in human subjects across major solid organs. Weblink: https://hpsv.ibms.sinica.edu.tw .
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Affiliation(s)
- Kuo-Feng Tung
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan, R.O.C
| | - Chao-Yu Pan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan, R.O.C
| | - Wen-Chang Lin
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan, R.O.C..
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9
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Dorans E, Jagadeesh K, Dey K, Price AL. Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.24.24307813. [PMID: 38826240 PMCID: PMC11142273 DOI: 10.1101/2024.05.24.24307813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Methods that analyze single-cell paired RNA-seq and ATAC-seq multiome data have shown great promise in linking regulatory elements to genes. However, existing methods differ in their modeling assumptions and approaches to account for biological and technical noise-leading to low concordance in their linking scores-and do not capture the effects of genomic distance. We propose pgBoost, an integrative modeling framework that trains a non-linear combination of existing linking strategies (including genomic distance) on fine-mapped eQTL data to assign a probabilistic score to each candidate SNP-gene link. We applied pgBoost to single-cell multiome data from 85k cells representing 6 major immune/blood cell types. pgBoost attained higher enrichment for fine-mapped eSNP-eGene pairs (e.g. 21x at distance >10kb) than existing methods (1.2-10x; p-value for difference = 5e-13 vs. distance-based method and < 4e-35 for each other method), with larger improvements at larger distances (e.g. 35x vs. 0.89-6.6x at distance >100kb; p-value for difference < 0.002 vs. each other method). pgBoost also outperformed existing methods in enrichment for CRISPR-validated links (e.g. 4.8x vs. 1.6-4.1x at distance >10kb; p-value for difference = 0.25 vs. distance-based method and < 2e-5 for each other method), with larger improvements at larger distances (e.g. 15x vs. 1.6-2.5x at distance >100kb; p-value for difference < 0.009 for each other method). Similar improvements in enrichment were observed for links derived from Activity-By-Contact (ABC) scores and GWAS data. We further determined that restricting pgBoost to features from a focal cell type improved the identification of SNP-gene links relevant to that cell type. We highlight several examples where pgBoost linked fine-mapped GWAS variants to experimentally validated or biologically plausible target genes that were not implicated by other methods. In conclusion, a non-linear combination of linking strategies, including genomic distance, improves power to identify target genes underlying GWAS associations.
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Kim A, Zhang Z, Legros C, Lu Z, de Smith A, Moore JE, Mancuso N, Gazal S. Inferring causal cell types of human diseases and risk variants from candidate regulatory elements. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.17.24307556. [PMID: 38798383 PMCID: PMC11118635 DOI: 10.1101/2024.05.17.24307556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The heritability of human diseases is extremely enriched in candidate regulatory elements (cRE) from disease-relevant cell types. Critical next steps are to infer which and how many cell types are truly causal for a disease (after accounting for co-regulation across cell types), and to understand how individual variants impact disease risk through single or multiple causal cell types. Here, we propose CT-FM and CT-FM-SNP, two methods that leverage cell-type-specific cREs to fine-map causal cell types for a trait and for its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using nearly one thousand cRE annotations, primarily coming from ENCODE4. CT-FM inferred 81 causal cell types with corresponding SNP-annotations explaining a high fraction of trait SNP-heritability (~2/3 of the SNP-heritability explained by existing cREs), identified 16 traits with multiple causal cell types, highlighted cell-disease relationships consistent with known biology, and uncovered previously unexplored cellular mechanisms in psychiatric and immune-related diseases. Finally, we applied CT-FM-SNP to 39 UK Biobank traits and predicted high confidence causal cell types for 2,798 candidate causal non-coding SNPs. Our results suggest that most SNPs impact a phenotype through a single cell type, and that pleiotropic SNPs target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to impact disease risk.
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Affiliation(s)
- Artem Kim
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zixuan Zhang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Come Legros
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zeyun Lu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Adam de Smith
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jill E Moore
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
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11
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Breeyear JH, Mautz BS, Keaton JM, Hellwege JN, Torstenson ES, Liang J, Bray MJ, Giri A, Warren HR, Munroe PB, Velez Edwards DR, Zhu X, Li C, Edwards TL. A new test for trait mean and variance detects unreported loci for blood-pressure variation. Am J Hum Genet 2024; 111:954-965. [PMID: 38614075 PMCID: PMC11080606 DOI: 10.1016/j.ajhg.2024.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 04/15/2024] Open
Abstract
Variability in quantitative traits has clinical, ecological, and evolutionary significance. Most genetic variants identified for complex quantitative traits have only a detectable effect on the mean of trait. We have developed the mean-variance test (MVtest) to simultaneously model the mean and log-variance of a quantitative trait as functions of genotypes and covariates by using estimating equations. The advantages of MVtest include the facts that it can detect effect modification, that multiple testing can follow conventional thresholds, that it is robust to non-normal outcomes, and that association statistics can be meta-analyzed. In simulations, we show control of type I error of MVtest over several alternatives. We identified 51 and 37 previously unreported associations for effects on blood-pressure variance and mean, respectively, in the UK Biobank. Transcriptome-wide association studies revealed 633 significant unique gene associations with blood-pressure mean variance. MVtest is broadly applicable to studies of complex quantitative traits and provides an important opportunity to detect novel loci.
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Affiliation(s)
- Joseph H Breeyear
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Brian S Mautz
- Population Analytics and Insights, Data Sciences, Janssen Research and Development, Spring House, PA, USA
| | - Jacob M Keaton
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jacklyn N Hellwege
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric S Torstenson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jingjing Liang
- Department of Pharmacy Practice and Science, University of Arizona, Tucson, AZ, USA
| | - Michael J Bray
- Department of Maternal and Fetal Medicine, Orlando Health, Orlando, FL, USA; Genetic Counseling Program, Bay Path University, Longmeadow, MA, USA
| | - Ayush Giri
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA; Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Helen R Warren
- Center of Clinical Pharmacology and Precision Medicine, Queen Mary University, London, England
| | - Patricia B Munroe
- Center of Clinical Pharmacology and Precision Medicine, Queen Mary University, London, England
| | - Digna R Velez Edwards
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiaofeng Zhu
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Chun Li
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
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12
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Bertonnier‐Brouty L, Andersson J, Kaprio T, Hagström J, Bsharat S, Asplund O, Hatem G, Haglund C, Seppänen H, Prasad RB, Artner I. E2F transcription factors promote tumorigenicity in pancreatic ductal adenocarcinoma. Cancer Med 2024; 13:e7187. [PMID: 38686617 PMCID: PMC11058697 DOI: 10.1002/cam4.7187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 03/14/2024] [Accepted: 04/02/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with limited treatment options, illustrating an urgent need to identify new drugable targets in PDACs. OBJECTIVE Using the similarities between tumor development and normal embryonic development, which is accompanied by rapid cell expansion, we aimed to identify and characterize embryonic signaling pathways that were reinitiated during tumor formation and expansion. METHODS AND RESULTS Here, we report that the transcription factors E2F1 and E2F8 are potential key regulators in PDAC. E2F1 and E2F8 RNA expression is mainly localized in proliferating cells in the developing pancreas and in malignant ductal cells in PDAC. Silencing of E2F1 and E2F8 in PANC-1 pancreatic tumor cells inhibited cell proliferation and impaired cell spreading and migration. Moreover, loss of E2F1 also affected cell viability and apoptosis with E2F expression in PDAC tissues correlating with expression of apoptosis and mitosis pathway genes, suggesting that E2F factors promote cell cycle regulation and tumorigenesis in PDAC cells. CONCLUSION Our findings illustrate that E2F1 and E2F8 transcription factors are expressed in pancreatic progenitor and PDAC cells, where they contribute to tumor cell expansion by regulation of cell proliferation, viability, and cell migration making these genes attractive therapeutic targets and potential prognostic markers for pancreatic cancer.
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Affiliation(s)
- Ludivine Bertonnier‐Brouty
- Lund Stem Cell CenterLund UniversityLundSweden
- Lund University Diabetes Center, Lund UniversityMalmöSweden
| | | | - Tuomas Kaprio
- Department of SurgeryHelsinki University HospitalHelsinkiFinland
- Translational Cancer Medicine Research Program, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
- iCAN, Digital Cancer Precision MedicineUniversity of Helsinki and HUS Helsinki University HospitalHelsinkiFinland
| | - Jaana Hagström
- Department of SurgeryHelsinki University HospitalHelsinkiFinland
- Translational Cancer Medicine Research Program, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
- iCAN, Digital Cancer Precision MedicineUniversity of Helsinki and HUS Helsinki University HospitalHelsinkiFinland
- Department of Oral Pathology and RadiologyUniversity of TurkuTurkuFinland
| | - Sara Bsharat
- Lund Stem Cell CenterLund UniversityLundSweden
- Lund University Diabetes Center, Lund UniversityMalmöSweden
| | - Olof Asplund
- Lund University Diabetes Center, Lund UniversityMalmöSweden
| | - Gad Hatem
- Lund University Diabetes Center, Lund UniversityMalmöSweden
| | - Caj Haglund
- Department of SurgeryHelsinki University HospitalHelsinkiFinland
- Translational Cancer Medicine Research Program, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
- iCAN, Digital Cancer Precision MedicineUniversity of Helsinki and HUS Helsinki University HospitalHelsinkiFinland
| | - Hanna Seppänen
- Department of SurgeryHelsinki University HospitalHelsinkiFinland
- Translational Cancer Medicine Research Program, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
- iCAN, Digital Cancer Precision MedicineUniversity of Helsinki and HUS Helsinki University HospitalHelsinkiFinland
| | | | - Isabella Artner
- Lund Stem Cell CenterLund UniversityLundSweden
- Lund University Diabetes Center, Lund UniversityMalmöSweden
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13
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Fujita M, Gao Z, Zeng L, McCabe C, White CC, Ng B, Green GS, Rozenblatt-Rosen O, Phillips D, Amir-Zilberstein L, Lee H, Pearse RV, Khan A, Vardarajan BN, Kiryluk K, Ye CJ, Klein HU, Wang G, Regev A, Habib N, Schneider JA, Wang Y, Young-Pearse T, Mostafavi S, Bennett DA, Menon V, De Jager PL. Cell subtype-specific effects of genetic variation in the Alzheimer's disease brain. Nat Genet 2024; 56:605-614. [PMID: 38514782 DOI: 10.1038/s41588-024-01685-y] [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: 12/16/2022] [Accepted: 02/08/2024] [Indexed: 03/23/2024]
Abstract
The relationship between genetic variation and gene expression in brain cell types and subtypes remains understudied. Here, we generated single-nucleus RNA sequencing data from the neocortex of 424 individuals of advanced age; we assessed the effect of genetic variants on RNA expression in cis (cis-expression quantitative trait loci) for seven cell types and 64 cell subtypes using 1.5 million transcriptomes. This effort identified 10,004 eGenes at the cell type level and 8,099 eGenes at the cell subtype level. Many eGenes are only detected within cell subtypes. A new variant influences APOE expression only in microglia and is associated with greater cerebral amyloid angiopathy but not Alzheimer's disease pathology, after adjusting for APOEε4, providing mechanistic insights into both pathologies. Furthermore, only a TMEM106B variant affects the proportion of cell subtypes. Integration of these results with genome-wide association studies highlighted the targeted cell type and probable causal gene within Alzheimer's disease, schizophrenia, educational attainment and Parkinson's disease loci.
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Affiliation(s)
- Masashi Fujita
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Zongmei Gao
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Lu Zeng
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Cristin McCabe
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Charles C White
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Gilad Sahar Green
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Devan Phillips
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | | | - Hyo Lee
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Richard V Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Atlas Khan
- Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Badri N Vardarajan
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, New York, NY, USA
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Krzysztof Kiryluk
- Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Chun Jimmie Ye
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Hans-Ulrich Klein
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Gao Wang
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, New York, NY, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Naomi Habib
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Tracy Young-Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sara Mostafavi
- Department of Statistics, Centre for Molecular Medicine and Therapeutics, British Columbia Children's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Vilas Menon
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA.
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14
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Lu Y, Oliva M, Pierce BL, Liu J, Chen LS. Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits. Nat Commun 2024; 15:2383. [PMID: 38493154 PMCID: PMC10944527 DOI: 10.1038/s41467-024-46675-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
Genetic effects on functionally related 'omic' traits often co-occur in relevant cellular contexts, such as tissues. Motivated by the multi-tissue methylation quantitative trait loci (mQTLs) and expression QTLs (eQTLs) analysis, we propose X-ING (Cross-INtegrative Genomics) for cross-omics and cross-context integrative analysis. X-ING takes as input multiple matrices of association statistics, each obtained from different omics data types across multiple cellular contexts. It models the latent binary association status of each statistic, captures the major association patterns among omics data types and contexts, and outputs the posterior mean and probability for each input statistic. X-ING enables the integration of effects from different omics data with varying effect distributions. In the multi-tissue cis-association analysis, X-ING shows improved detection and replication of mQTLs by integrating eQTL maps. In the trans-association analysis, X-ING reveals an enrichment of trans-associations in many disease/trait-relevant tissues.
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Affiliation(s)
- Yihao Lu
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Meritxell Oliva
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
- Genomics Research Center, AbbVie, North Chicago, IL, USA
| | - Brandon L Pierce
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Jin Liu
- School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, China.
| | - Lin S Chen
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA.
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15
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Everman ER, Macdonald SJ. Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster. G3 (BETHESDA, MD.) 2024; 14:jkae015. [PMID: 38262701 PMCID: PMC11021028 DOI: 10.1093/g3journal/jkae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/25/2024]
Abstract
Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper results in cell and tissue damage ranging in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes respond to nonessential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the heavy metal stress response. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource using a combination of differential expression analysis and expression quantitative trait locus mapping. Differential expression analysis revealed clear patterns of tissue-specific expression. Tissue and treatment specific responses to copper stress were also detected using expression quantitative trait locus mapping. Expression quantitative trait locus associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited both genotype-by-tissue and genotype-by-treatment effects on gene expression under copper stress, illuminating tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight candidate genes for future functional characterization.
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Affiliation(s)
- Elizabeth R Everman
- School of Biological Sciences, The University of Oklahoma, 730 Van Vleet Oval, Norman, OK 73019, USA
| | - Stuart J Macdonald
- Molecular Biosciences, University of Kansas, 1200 Sunnyside Ave, Lawrence, KS 66045, USA
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16
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Advani J, Mehta PA, Hamel AR, Mehrotra S, Kiel C, Strunz T, Corso-Díaz X, Kwicklis M, van Asten F, Ratnapriya R, Chew EY, Hernandez DG, Montezuma SR, Ferrington DA, Weber BHF, Segrè AV, Swaroop A. QTL mapping of human retina DNA methylation identifies 87 gene-epigenome interactions in age-related macular degeneration. Nat Commun 2024; 15:1972. [PMID: 38438351 PMCID: PMC10912779 DOI: 10.1038/s41467-024-46063-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 02/12/2024] [Indexed: 03/06/2024] Open
Abstract
DNA methylation provides a crucial epigenetic mark linking genetic variations to environmental influence. We have analyzed array-based DNA methylation profiles of 160 human retinas with co-measured RNA-seq and >8 million genetic variants, uncovering sites of genetic regulation in cis (37,453 methylation quantitative trait loci and 12,505 expression quantitative trait loci) and 13,747 DNA methylation loci affecting gene expression, with over one-third specific to the retina. Methylation and expression quantitative trait loci show non-random distribution and enrichment of biological processes related to synapse, mitochondria, and catabolism. Summary data-based Mendelian randomization and colocalization analyses identify 87 target genes where methylation and gene-expression changes likely mediate the genotype effect on age-related macular degeneration. Integrated pathway analysis reveals epigenetic regulation of immune response and metabolism including the glutathione pathway and glycolysis. Our study thus defines key roles of genetic variations driving methylation changes, prioritizes epigenetic control of gene expression, and suggests frameworks for regulation of macular degeneration pathology by genotype-environment interaction in retina.
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Affiliation(s)
- Jayshree Advani
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Puja A Mehta
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrew R Hamel
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sudeep Mehrotra
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christina Kiel
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
| | - Tobias Strunz
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
| | - Ximena Corso-Díaz
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Madeline Kwicklis
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Freekje van Asten
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rinki Ratnapriya
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, Clinical Trials Branch, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dena G Hernandez
- Laboratory of Neurogenetics, National Institute of Aging, National Institutes of Health, Bethesda, MD, USA
| | - Sandra R Montezuma
- Department of Ophthalmology and Visual Neurosciences, University of Minnesota, Minneapolis, MN, USA
| | - Deborah A Ferrington
- Department of Ophthalmology and Visual Neurosciences, University of Minnesota, Minneapolis, MN, USA
- Doheny Eye Institute, Pasadena, CA, USA
| | - Bernhard H F Weber
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
- Institute of Clinical Human Genetics, University Hospital Regensburg, Regensburg, Germany
| | - Ayellet V Segrè
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Anand Swaroop
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
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17
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Stone K, Platig J, Quackenbush J, Fagny M. Complex Traits Heritability is Highly Clustered in the eQTL Bipartite Network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.582063. [PMID: 38464142 PMCID: PMC10925220 DOI: 10.1101/2024.02.27.582063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Single Nucleotide Polymorphisms (SNPs) associated with traits typically explain a small part of the trait genetic heritability-with the remainder thought to be distributed throughout the genome. Such SNPs are likely to alter expression levels of biologically relevant genes. Expression Quantitative Trait Locus (eQTL) networks analysis has helped to functionally characterize such variants. We systematically analyze the distribution of SNP heritability for ten traits across 29 tissue-specific eQTL networks. We find that heritability is clustered in a small number or tissue-specific, functionally relevant SNP-gene modules and that the greatest occurs in local "hubs" that are both the cornerstone of the network's modules and tissue-specific regulatory elements. The network structure could thus both amplify the genotype-phenotype connection and buffer the deleterious effect of the genetic variations on other traits. Together, these results define a conceptual framework for understanding complex trait architecture and identifying key mutations carrying most of the heritability.
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Affiliation(s)
- Katherine Stone
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
- Department of Data Science and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - John Platig
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
- Department of Data Science and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - Maud Fagny
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
- Department of Data Science and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Genetique Quantitative et Evolution - Le Moulon, Gif-sur-Yvette 91190 France
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18
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Chen H, Wang Z, Gong L, Wang Q, Chen W, Wang J, Ma X, Ding R, Li X, Zou X, Plass M, Lian C, Ni T, Wei GH, Li W, Deng L, Li L. A distinct class of pan-cancer susceptibility genes revealed by an alternative polyadenylation transcriptome-wide association study. Nat Commun 2024; 15:1729. [PMID: 38409266 PMCID: PMC10897204 DOI: 10.1038/s41467-024-46064-7] [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: 08/09/2023] [Accepted: 02/12/2024] [Indexed: 02/28/2024] Open
Abstract
Alternative polyadenylation plays an important role in cancer initiation and progression; however, current transcriptome-wide association studies mostly ignore alternative polyadenylation when identifying putative cancer susceptibility genes. Here, we perform a pan-cancer 3' untranslated region alternative polyadenylation transcriptome-wide association analysis by integrating 55 well-powered (n > 50,000) genome-wide association studies datasets across 22 major cancer types with alternative polyadenylation quantification from 23,955 RNA sequencing samples across 7,574 individuals. We find that genetic variants associated with alternative polyadenylation are co-localized with 28.57% of cancer loci and contribute a significant portion of cancer heritability. We further identify 642 significant cancer susceptibility genes predicted to modulate cancer risk via alternative polyadenylation, 62.46% of which have been overlooked by traditional expression- and splicing- studies. As proof of principle validation, we show that alternative alleles facilitate 3' untranslated region lengthening of CRLS1 gene leading to increased protein abundance and promoted proliferation of breast cancer cells. Together, our study highlights the significant role of alternative polyadenylation in discovering new cancer susceptibility genes and provides a strong foundational framework for enhancing our understanding of the etiology underlying human cancers.
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Affiliation(s)
- Hui Chen
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Zeyang 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
| | - Qixuan Wang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Wenyan Chen
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Jia Wang
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Xuelian Ma
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Ruofan Ding
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Xing Li
- 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
| | - Mireya Plass
- Gene Regulation of Cell Identity Group, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet de Llobregat, Barcelona, 08908, Spain
- Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P-CMR[C], L'Hospitalet de Llobregat, Barcelona, 08908, Spain
- Center for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, 28029, Spain
| | - Cheng Lian
- Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032, China
| | - Ting Ni
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, School of Life Sciences and Huashan Hospital, Fudan University, Shanghai, 200438, China
| | - Gong-Hong Wei
- Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032, China
- Disease Networks Research Unit, Faculty of Biochemistry and Molecular Medicine & Biocenter Oulu, University of Oulu, Oulu, 90410, Finland
| | - Wei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, The University of California, Irvine, CA, 92697, USA.
| | - Lin Deng
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.
| | - Lei Li
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.
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19
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Casazza W, Inkster AM, Del Gobbo GF, Yuan V, Delahaye F, Marsit C, Park YP, Robinson WP, Mostafavi S, Dennis JK. Sex-dependent placental methylation quantitative trait loci provide insight into the prenatal origins of childhood onset traits and conditions. iScience 2024; 27:109047. [PMID: 38357671 PMCID: PMC10865402 DOI: 10.1016/j.isci.2024.109047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 06/19/2023] [Accepted: 01/23/2024] [Indexed: 02/16/2024] Open
Abstract
Molecular quantitative trait loci (QTLs) allow us to understand the biology captured in genome-wide association studies (GWASs). The placenta regulates fetal development and shows sex differences in DNA methylation. We therefore hypothesized that placental methylation QTL (mQTL) explain variation in genetic risk for childhood onset traits, and that effects differ by sex. We analyzed 411 term placentas from two studies and found 49,252 methylation (CpG) sites with mQTL and 2,489 CpG sites with sex-dependent mQTL. All mQTL were enriched in regions that typically affect gene expression in prenatal tissues. All mQTL were also enriched in GWAS results for growth- and immune-related traits, but male- and female-specific mQTL were more enriched than cross-sex mQTL. mQTL colocalized with trait loci at 777 CpG sites, with 216 (28%) specific to males or females. Overall, mQTL specific to male and female placenta capture otherwise overlooked variation in childhood traits.
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Affiliation(s)
- William Casazza
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital, Vancouver, BC, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
| | - Amy M. Inkster
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Giulia F. Del Gobbo
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Victor Yuan
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | | | - Carmen Marsit
- Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yongjin P. Park
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Wendy P. Robinson
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Sara Mostafavi
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital, Vancouver, BC, Canada
- Paul Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Jessica K. Dennis
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital, Vancouver, BC, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
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20
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Jiang F, Hu SY, Tian W, Wang NN, Yang N, Dong SS, Song HM, Zhang DJ, Gao HW, Wang C, Wu H, He CY, Zhu DL, Chen XF, Guo Y, Yang Z, Yang TL. A landscape of gene expression regulation for synovium in arthritis. Nat Commun 2024; 15:1409. [PMID: 38360850 PMCID: PMC10869817 DOI: 10.1038/s41467-024-45652-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 01/29/2024] [Indexed: 02/17/2024] Open
Abstract
The synovium is an important component of any synovial joint and is the major target tissue of inflammatory arthritis. However, the multi-omics landscape of synovium required for functional inference is absent from large-scale resources. Here we integrate genomics with transcriptomics and chromatin accessibility features of human synovium in up to 245 arthritic patients, to characterize the landscape of genetic regulation on gene expression and the regulatory mechanisms mediating arthritic diseases predisposition. We identify 4765 independent primary and 616 secondary cis-expression quantitative trait loci (cis-eQTLs) in the synovium and find that the eQTLs with multiple independent signals have stronger effects and heritability than single independent eQTLs. Integration of genome-wide association studies (GWASs) and eQTLs identifies 84 arthritis related genes, revealing 38 novel genes which have not been reported by previous studies using eQTL data from the GTEx project or immune cells. We further develop a method called eQTac to identify variants that could affect gene expression by affecting chromatin accessibility and identify 1517 regions with potential regulatory function of chromatin accessibility. Altogether, our study provides a comprehensive synovium multi-omics resource for arthritic diseases and gains new insights into the regulation of gene expression.
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Affiliation(s)
- Feng Jiang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Shou-Ye Hu
- Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, 710054, P.R. China
| | - Wen Tian
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Nai-Ning Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Ning Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Hui-Miao Song
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Da-Jin Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Hui-Wu Gao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Chen Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Hao Wu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Chang-Yi He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Dong-Li Zhu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Xiao-Feng Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Zhi Yang
- Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, 710054, P.R. China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China.
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21
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Wu K, Bu F, Wu Y, Zhang G, Wang X, He S, Liu MF, Chen R, Yuan H. Exploring noncoding variants in genetic diseases: from detection to functional insights. J Genet Genomics 2024; 51:111-132. [PMID: 38181897 DOI: 10.1016/j.jgg.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/26/2023] [Accepted: 01/01/2024] [Indexed: 01/07/2024]
Abstract
Previous studies on genetic diseases predominantly focused on protein-coding variations, overlooking the vast noncoding regions in the human genome. The development of high-throughput sequencing technologies and functional genomics tools has enabled the systematic identification of functional noncoding variants. These variants can impact gene expression, regulation, and chromatin conformation, thereby contributing to disease pathogenesis. Understanding the mechanisms that underlie the impact of noncoding variants on genetic diseases is indispensable for the development of precisely targeted therapies and the implementation of personalized medicine strategies. The intricacies of noncoding regions introduce a multitude of challenges and research opportunities. In this review, we introduce a spectrum of noncoding variants involved in genetic diseases, along with research strategies and advanced technologies for their precise identification and in-depth understanding of the complexity of the noncoding genome. We will delve into the research challenges and propose potential solutions for unraveling the genetic basis of rare and complex diseases.
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Affiliation(s)
- Ke Wu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Fengxiao Bu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Yang Wu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Gen Zhang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Xin Wang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, Zhejiang 310024, China
| | - Shunmin He
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mo-Fang Liu
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, Zhejiang 310024, China; State Key Laboratory of Molecular Biology, State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Runsheng Chen
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
| | - Huijun Yuan
- Institute of Rare Diseases, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
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22
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Mei H, Simino J, Li L, Jiang F, Bis JC, Davies G, Hill WD, Xia C, Gudnason V, Yang Q, Lahti J, Smith JA, Kirin M, De Jager P, Armstrong NJ, Ghanbari M, Kolcic I, Moran C, Teumer A, Sargurupremraj M, Mahmud S, Fornage M, Zhao W, Satizabal CL, Polasek O, Räikkönen K, Liewald DC, Homuth G, Callisaya M, Mather KA, Windham BG, Zemunik T, Palotie A, Pattie A, van der Auwera S, Thalamuthu A, Knopman DS, Rudan I, Starr JM, Wittfeld K, Kochan NA, Griswold ME, Vitart V, Brodaty H, Gottesman R, Cox SR, Psaty BM, Boerwinkle E, Chasman DI, Grodstein F, Sachdev PS, Srikanth V, Hayward C, Wilson JF, Eriksson JG, Kardia SLR, Grabe HJ, Bennett DA, Ikram MA, Deary IJ, van Duijn CM, Launer L, Fitzpatrick AL, Seshadri S, Bressler J, Debette S, Mosley TH. Multi-omics and pathway analyses of genome-wide associations implicate regulation and immunity in verbal declarative memory performance. Alzheimers Res Ther 2024; 16:14. [PMID: 38245754 PMCID: PMC10799499 DOI: 10.1186/s13195-023-01376-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 12/26/2023] [Indexed: 01/22/2024]
Abstract
BACKGROUND Uncovering the functional relevance underlying verbal declarative memory (VDM) genome-wide association study (GWAS) results may facilitate the development of interventions to reduce age-related memory decline and dementia. METHODS We performed multi-omics and pathway enrichment analyses of paragraph (PAR-dr) and word list (WL-dr) delayed recall GWAS from 29,076 older non-demented individuals of European descent. We assessed the relationship between single-variant associations and expression quantitative trait loci (eQTLs) in 44 tissues and methylation quantitative trait loci (meQTLs) in the hippocampus. We determined the relationship between gene associations and transcript levels in 53 tissues, annotation as immune genes, and regulation by transcription factors (TFs) and microRNAs. To identify significant pathways, gene set enrichment was tested in each cohort and meta-analyzed across cohorts. Analyses of differential expression in brain tissues were conducted for pathway component genes. RESULTS The single-variant associations of VDM showed significant linkage disequilibrium (LD) with eQTLs across all tissues and meQTLs within the hippocampus. Stronger WL-dr gene associations correlated with reduced expression in four brain tissues, including the hippocampus. More robust PAR-dr and/or WL-dr gene associations were intricately linked with immunity and were influenced by 31 TFs and 2 microRNAs. Six pathways, including type I diabetes, exhibited significant associations with both PAR-dr and WL-dr. These pathways included fifteen MHC genes intricately linked to VDM performance, showing diverse expression patterns based on cognitive status in brain tissues. CONCLUSIONS VDM genetic associations influence expression regulation via eQTLs and meQTLs. The involvement of TFs, microRNAs, MHC genes, and immune-related pathways contributes to VDM performance in older individuals.
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Affiliation(s)
- Hao Mei
- Department of Data Science, John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA.
- Gertrude C. Ford Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA.
| | - Jeannette Simino
- Department of Data Science, John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA.
- Gertrude C. Ford Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA.
| | - Lianna Li
- Department of Biology, Tougaloo College, Jackson, MS, USA
| | - Fan Jiang
- Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Joshua C Bis
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Gail Davies
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - W David Hill
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Charley Xia
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Jari Lahti
- Turku Institute for Advanced Research, University of Turku, Turku, Finland
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Mirna Kirin
- Work completed while at The University of Edinburgh, Edinburgh, UK
| | - Philip De Jager
- Taub Institute for Research On Alzheimer's Disease and the Aging Brain, Columbia Irving University Medical Center, New York, NY, USA
- Center for Translational and Computational Neuro-Immunology, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | | | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus Medical Center University Medical Center, Rotterdam, The Netherlands
| | - Ivana Kolcic
- School of Medicine, University of Split, Split, Croatia
| | - Christopher Moran
- Department of Geriatric Medicine, Frankston Hospital, Peninsula Health, Melbourne, Australia
- Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, Australia
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Murali Sargurupremraj
- Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, University of Bordeaux, Bordeaux, France
| | - Shamsed Mahmud
- Department of Data Science, John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA
| | - Myriam Fornage
- The Brown Foundation Institute of Molecular Medicine for the Prevention of Human Diseases, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Claudia L Satizabal
- The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Ozren Polasek
- School of Medicine, University of Split, Split, Croatia
- Algebra University College, Ilica 242, Zagreb, Croatia
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - David C Liewald
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Michele Callisaya
- Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
- Neuroscience Research Australia, Sydney, Australia
| | - B Gwen Windham
- Gertrude C. Ford Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Medicine, Division of Geriatrics, School of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Aarno Palotie
- Department of Medicine, Department of Neurology and Department of Psychiatry, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alison Pattie
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Sandra van der Auwera
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
- Neuroscience Research Australia, Sydney, Australia
| | | | - Igor Rudan
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - John M Starr
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/ Greifswald, Rostock, Germany
| | - Nicole A Kochan
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Michael E Griswold
- Gertrude C. Ford Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Medicine, School of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
- Dementia Centre for Research Collaboration, University of New South Wales, Sydney, NSW, Australia
| | - Rebecca Gottesman
- Stroke, Cognition, and Neuroepidemiology (SCAN) Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Simon R Cox
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Bruce M Psaty
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Daniel I Chasman
- Harvard Medical School, Boston, MA, USA
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Francine Grodstein
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, Australia
| | - Velandai Srikanth
- Department of Geriatric Medicine, Frankston Hospital, Peninsula Health, Melbourne, Australia
- Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Public Health Solutions, Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
- Folkhälsan Research Centre, Helsinki, Finland
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/ Greifswald, Rostock, Germany
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center University Medical Center, Rotterdam, The Netherlands
| | - Ian J Deary
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Cornelia M van Duijn
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Lenore Launer
- Laboratory of Epidemiology and Population Sciences, National Institute On Aging, Bethesda, MD, USA
| | - Annette L Fitzpatrick
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Family Medicine, University of Washington, Seattle, WA, USA
| | - Sudha Seshadri
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, TX, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Jan Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Stephanie Debette
- Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, University of Bordeaux, Bordeaux, France
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Department of Neurology, CHU de Bordeaux, Bordeaux, France
| | - Thomas H Mosley
- Gertrude C. Ford Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Medicine, School of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Neurology, University of Mississippi Medical Center, Jackson, MS, USA
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23
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Sarsani V, Brotman SM, Xianyong Y, Fernandes Silva L, Laakso M, Spracklen CN. A cross-ancestry genome-wide meta-analysis, fine-mapping, and gene prioritization approach to characterize the genetic architecture of adiponectin. HGG ADVANCES 2024; 5:100252. [PMID: 37859345 PMCID: PMC10652123 DOI: 10.1016/j.xhgg.2023.100252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/16/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023] Open
Abstract
Previous genome-wide association studies (GWASs) for adiponectin, a complex trait linked to type 2 diabetes and obesity, identified >20 associated loci. However, most loci were identified in populations of European ancestry, and many of the target genes underlying the associations remain unknown. We conducted a cross-ancestry adiponectin GWAS meta-analysis in ≤46,434 individuals from the Metabolic Syndrome in Men (METSIM) cohort and the ADIPOGen and AGEN consortiums. We combined study-specific association summary statistics using a fixed-effects, inverse variance-weighted approach. We identified 22 loci associated with adiponectin (p < 5×10-8), including 15 known and seven previously unreported loci. Among individuals of European ancestry, Genome-wide Complex Traits Analysis joint conditional analysis (GCTA-COJO) identified 14 additional distinct signals at the ADIPOQ, CDH13, HCAR1, and ZNF664 loci. Leveraging the cross-ancestry data, FINEMAP + SuSiE identified 45 causal variants (PP > 0.9), which also exhibited potential pleiotropy for cardiometabolic traits. To prioritize target genes at associated loci, we propose a combinatorial likelihood scoring formalism (Gene Priority Score [GPScore]) based on measures derived from 11 gene prioritization strategies and the physical distance to the transcription start site. With GPScore, we prioritize the 30 most probable target genes underlying the adiponectin-associated variants in the cross-ancestry analysis, including well-known causal genes (e.g., ADIPOQ, CDH13) and additional genes (e.g., CSF1, RGS17). Functional association networks revealed complex interactions of prioritized genes, their functionally connected genes, and their underlying pathways centered around insulin and adiponectin signaling, indicating an essential role in regulating energy balance in the body, inflammation, coagulation, fibrinolysis, insulin resistance, and diabetes. Overall, our analyses identify and characterize adiponectin association signals and inform experimental interrogation of target genes for adiponectin.
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Affiliation(s)
- Vishal Sarsani
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, USA
| | - Sarah M Brotman
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yin Xianyong
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Lillian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Cassandra N Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA.
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24
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Hamel AR, Yan W, Rouhana JM, Monovarfeshani A, Jiang X, Mehta PA, Advani J, Luo Y, Liang Q, Rajasundaram S, Shrivastava A, Duchinski K, Mantena S, Wang J, van Zyl T, Pasquale LR, Swaroop A, Gharahkhani P, Khawaja AP, MacGregor S, Chen R, Vitart V, Sanes JR, Wiggs JL, Segrè AV. Integrating genetic regulation and single-cell expression with GWAS prioritizes causal genes and cell types for glaucoma. Nat Commun 2024; 15:396. [PMID: 38195602 PMCID: PMC10776627 DOI: 10.1038/s41467-023-44380-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 12/12/2023] [Indexed: 01/11/2024] Open
Abstract
Primary open-angle glaucoma (POAG), characterized by retinal ganglion cell death, is a leading cause of irreversible blindness worldwide. However, its molecular and cellular causes are not well understood. Elevated intraocular pressure (IOP) is a major risk factor, but many patients have normal IOP. Colocalization and Mendelian randomization analysis of >240 POAG and IOP genome-wide association study (GWAS) loci and overlapping expression and splicing quantitative trait loci (e/sQTLs) in 49 GTEx tissues and retina prioritizes causal genes for 60% of loci. These genes are enriched in pathways implicated in extracellular matrix organization, cell adhesion, and vascular development. Analysis of single-nucleus RNA-seq of glaucoma-relevant eye tissues reveals that the POAG and IOP colocalizing genes and genome-wide associations are enriched in specific cell types in the aqueous outflow pathways, retina, optic nerve head, peripapillary sclera, and choroid. This study nominates IOP-dependent and independent regulatory mechanisms, genes, and cell types that may contribute to POAG pathogenesis.
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Affiliation(s)
- Andrew R Hamel
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Wenjun Yan
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - John M Rouhana
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Aboozar Monovarfeshani
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Xinyi Jiang
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK
| | - Puja A Mehta
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jayshree Advani
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MA, USA
| | - Yuyang Luo
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Qingnan Liang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Skanda Rajasundaram
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Centre for Evidence-Based Medicine, University of Oxford, Oxford, UK
- Faculty of Medicine, Imperial College London, London, UK
| | - Arushi Shrivastava
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Katherine Duchinski
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Bioinformatics and Integrative Genomics (BIG) PhD Program, Harvard Medical School, Boston, MA, USA
| | - Sreekar Mantena
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Harvard/MIT MD-PhD Program, Harvard Medical School, Boston, MA, USA
| | - Jiali Wang
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Tavé van Zyl
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Ophthalmology and Visual Sciences, Yale School of Medicine, New Haven, CT, USA
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anand Swaroop
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MA, USA
| | - Puya Gharahkhani
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4029, Australia
| | - Anthony P Khawaja
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4029, Australia
| | - Rui Chen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Joshua R Sanes
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Janey L Wiggs
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ayellet V Segrè
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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25
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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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26
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Martorella M, Kasela S, Garcia-Flores R, Gokden A, Castel SE, Lappalainen T. Evaluation of noninvasive biospecimens for transcriptome studies. BMC Genomics 2023; 24:790. [PMID: 38114913 PMCID: PMC10729488 DOI: 10.1186/s12864-023-09875-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
Transcriptome studies disentangle functional mechanisms of gene expression regulation and may elucidate the underlying biology of disease processes. However, the types of tissues currently collected typically assay a single post-mortem timepoint or are limited to investigating cell types found in blood. Noninvasive tissues may improve disease-relevant discovery by enabling more complex longitudinal study designs, by capturing different and potentially more applicable cell types, and by increasing sample sizes due to reduced collection costs and possible higher enrollment from vulnerable populations. Here, we develop methods for sampling noninvasive biospecimens, investigate their performance across commercial and in-house library preparations, characterize their biology, and assess the feasibility of using noninvasive tissues in a multitude of transcriptomic applications. We collected buccal swabs, hair follicles, saliva, and urine cell pellets from 19 individuals over three to four timepoints, for a total of 300 unique biological samples, which we then prepared with replicates across three library preparations, for a final tally of 472 transcriptomes. Of the four tissues we studied, we found hair follicles and urine cell pellets to be most promising due to the consistency of sample quality, the cell types and expression profiles we observed, and their performance in disease-relevant applications. This is the first study to thoroughly delineate biological and technical features of noninvasive samples and demonstrate their use in a wide array of transcriptomic and clinical analyses. We anticipate future use of these biospecimens will facilitate discovery and development of clinical applications.
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Affiliation(s)
- Molly Martorella
- New York Genome Center, New York, NY, USA.
- Department of Systems Biology, Columbia University, New York, NY, USA.
| | - Silva Kasela
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Renee Garcia-Flores
- New York Genome Center, New York, NY, USA
- Department of Computer Science, Columbia University, New York, NY, USA
- Undergraduate Program On Genomic Sciences, National Autonomous University of Mexico, Cuernavaca, Morelos, Mexico
| | | | - Stephane E Castel
- New York Genome Center, New York, NY, USA.
- Department of Systems Biology, Columbia University, New York, NY, USA.
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA.
- Department of Systems Biology, Columbia University, New York, NY, USA.
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
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27
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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] [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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28
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Amariuta T. The power paradox of detecting disease-associated and gene-expression-associated variants. Nat Genet 2023; 55:1782-1783. [PMID: 37857936 DOI: 10.1038/s41588-023-01525-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Affiliation(s)
- Tiffany Amariuta
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA.
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
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29
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Bhati M, Mapel XM, Lloret-Villas A, Pausch H. Structural variants and short tandem repeats impact gene expression and splicing in bovine testis tissue. Genetics 2023; 225:iyad161. [PMID: 37655920 PMCID: PMC10627265 DOI: 10.1093/genetics/iyad161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/05/2023] [Accepted: 08/24/2023] [Indexed: 09/02/2023] Open
Abstract
Structural variants (SVs) and short tandem repeats (STRs) are significant sources of genetic variation. However, the impacts of these variants on gene regulation have not been investigated in cattle. Here, we genotyped and characterized 19,408 SVs and 374,821 STRs in 183 bovine genomes and investigated their impact on molecular phenotypes derived from testis transcriptomes. We found that 71% STRs were multiallelic. The vast majority (95%) of STRs and SVs were in intergenic and intronic regions. Only 37% SVs and 40% STRs were in high linkage disequilibrium (LD) (R2 > 0.8) with surrounding SNPs/insertions and deletions (Indels), indicating that SNP-based association testing and genomic prediction are blind to a nonnegligible portion of genetic variation. We showed that both SVs and STRs were more than 2-fold enriched among expression and splicing QTL (e/sQTL) relative to SNPs/Indels and were often associated with differential expression and splicing of multiple genes. Deletions and duplications had larger impacts on splicing and expression than any other type of SV. Exonic duplications predominantly increased gene expression either through alternative splicing or other mechanisms, whereas expression- and splicing-associated STRs primarily resided in intronic regions and exhibited bimodal effects on the molecular phenotypes investigated. Most e/sQTL resided within 100 kb of the affected genes or splicing junctions. We pinpoint candidate causal STRs and SVs associated with the expression of SLC13A4 and TTC7B and alternative splicing of a lncRNA and CAPP1. We provide a catalog of STRs and SVs for taurine cattle and show that these variants contribute substantially to gene expression and splicing variation.
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Affiliation(s)
- Meenu Bhati
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
| | - Xena Marie Mapel
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
| | | | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
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30
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Kim DJ, Lim JE, Jung HU, Chung JY, Baek EJ, Jung H, Kwon SY, Kim HK, Kang JO, Park K, Won S, Kim TB, Oh B. Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients. BMC Med Genomics 2023; 16:259. [PMID: 37875944 PMCID: PMC10599017 DOI: 10.1186/s12920-023-01677-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/29/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND More than 200 asthma-associated genetic variants have been identified in genome-wide association studies (GWASs). Expression quantitative trait loci (eQTL) data resources can help identify causal genes of the GWAS signals, but it can be difficult to find an eQTL that reflects the disease state because most eQTL data are obtained from normal healthy subjects. METHODS We performed a blood eQTL analysis using transcriptomic and genotypic data from 433 Korean asthma patients. To identify asthma-related genes, we carried out colocalization, Summary-based Mendelian Randomization (SMR) analysis, and Transcriptome-Wide Association Study (TWAS) using the results of asthma GWASs and eQTL data. In addition, we compared the results of disease eQTL data and asthma-related genes with two normal blood eQTL data from Genotype-Tissue Expression (GTEx) project and a Japanese study. RESULTS We identified 340,274 cis-eQTL and 2,875 eGenes from asthmatic eQTL analysis. We compared the disease eQTL results with GTEx and a Japanese study and found that 64.1% of the 2,875 eGenes overlapped with the GTEx eGenes and 39.0% with the Japanese eGenes. Following the integrated analysis of the asthmatic eQTL data with asthma GWASs, using colocalization and SMR methods, we identified 15 asthma-related genes specific to the Korean asthmatic eQTL data. CONCLUSIONS We provided Korean asthmatic cis-eQTL data and identified asthma-related genes by integrating them with GWAS data. In addition, we suggested these asthma-related genes as therapeutic targets for asthma. We envisage that our findings will contribute to understanding the etiological mechanisms of asthma and provide novel therapeutic targets.
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Affiliation(s)
- Dong Jun Kim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Ju Yeon Chung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | | | - Hyein Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Shin Young Kwon
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Han Kyul Kim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Kyungtaek Park
- Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Sungho Won
- Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Department of Public Health Sciences, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Tae-Bum Kim
- Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Bermseok Oh
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea.
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea.
- Mendel Inc, Seoul, Republic of Korea.
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Li C, Wei Q, Hou Y, Lin J, Ou R, Zhang L, Jiang Q, Xiao Y, Liu K, Chen X, Yang T, Song W, Zhao B, Wu Y, Shang H. Genome-wide analyses identify NEAT1 as genetic modifier of age at onset of amyotrophic lateral sclerosis. Mol Neurodegener 2023; 18:77. [PMID: 37872557 PMCID: PMC10594666 DOI: 10.1186/s13024-023-00669-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 10/16/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND Patients with amyotrophic lateral sclerosis (ALS) demonstrate great heterogeneity in the age at onset (AAO), which is closely related to the course of disease. However, most genetic studies focused on the risk of ALS, while the genetic background underlying AAO of ALS is still unknown. METHODS To identify genetic determinants influencing AAO of ALS, we performed genome-wide association analysis using a Cox proportional hazards model in 2,841 patients with ALS (Ndiscovery = 2,272, Nreplication = 569) in the Chinese population. We further conducted colocalization analysis using public cis-eQTL dataset, and Mendelian randomization analysis to identify risk factors for AAO of ALS. Finally, functional experiments including dual-luciferase reporter assay and RT-qPCR were performed to explore the regulatory effect of the target variant. RESULTS The total heritability of AAO of ALS was ~ 0.24. One novel locus rs10128627 (FRMD8) was significantly associated with earlier AAO by ~ 3.15 years (P = 1.54E-08, beta = 0.31, SE = 0.05). This locus was cis-eQTL of NEAT1 in multiple brain tissues and blood. Colocalization analysis detected association signals at this locus between AAO of ALS and expression of NEAT1. Furthermore, functional exploration supported the variant rs10128627 was associated with upregulated expression of NEAT1 in cell models and patients with ALS. Causal inference suggested higher total cholesterol, low-density lipoprotein, and eosinophil were nominally associated with earlier AAO of ALS, while monocyte might delay the AAO. CONCLUSIONS Collective evidence from genetic, bioinformatic, and functional results suggested NEAT1 as a key player in the disease progression of ALS. These findings improve the current understanding of the genetic role in AAO of ALS, and provide a novel target for further research on the pathogenesis and therapeutic options to delay the disease onset.
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Affiliation(s)
- Chunyu Li
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Qianqian Wei
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Yanbing Hou
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Junyu Lin
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Ruwei Ou
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Lingyu Zhang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Qirui Jiang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Yi Xiao
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Kuncheng Liu
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Xueping Chen
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - TianMi Yang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Wei Song
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Bi Zhao
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Ying Wu
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Huifang Shang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China.
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Hartmann S, Yasmeen S, Jacobs BM, Denaxas S, Pirmohamed M, Gamazon ER, Caulfield MJ, Hemingway H, Pietzner M, Langenberg C. ADRA2A and IRX1 are putative risk genes for Raynaud's phenomenon. Nat Commun 2023; 14:6156. [PMID: 37828025 PMCID: PMC10570309 DOI: 10.1038/s41467-023-41876-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 09/21/2023] [Indexed: 10/14/2023] Open
Abstract
Raynaud's phenomenon (RP) is a common vasospastic disorder that causes severe pain and ulcers, but despite its high reported heritability, no causal genes have been robustly identified. We conducted a genome-wide association study including 5,147 RP cases and 439,294 controls, based on diagnoses from electronic health records, and identified three unreported genomic regions associated with the risk of RP (p < 5 × 10-8). We prioritized ADRA2A (rs7090046, odds ratio (OR) per allele: 1.26; 95%-CI: 1.20-1.31; p < 9.6 × 10-27) and IRX1 (rs12653958, OR: 1.17; 95%-CI: 1.12-1.22, p < 4.8 × 10-13) as candidate causal genes through integration of gene expression in disease relevant tissues. We further identified a likely causal detrimental effect of low fasting glucose levels on RP risk (rG = -0.21; p-value = 2.3 × 10-3), and systematically highlighted drug repurposing opportunities, like the antidepressant mirtazapine. Our results provide the first robust evidence for a strong genetic contribution to RP and highlight a so far underrated role of α2A-adrenoreceptor signalling, encoded at ADRA2A, as a possible mechanism for hypersensitivity to catecholamine-induced vasospasms.
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Affiliation(s)
- Sylvia Hartmann
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Summaira Yasmeen
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Benjamin M Jacobs
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- British Heart Foundation Data Science Centre, London, UK
- National Institute of Health Research University College London Hospitals Biomedical Research Centre, London, UK
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, The Wolfson Centre for Personalised Medicine, University Liverpool, Liverpool, UK
| | - Eric R Gamazon
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Mark J Caulfield
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- National Institute of Health Research University College London Hospitals Biomedical Research Centre, London, UK
| | - Maik Pietzner
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
| | - Claudia Langenberg
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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Xiao Q, Mears J, Nathan A, Ishigaki K, Baglaenko Y, Lim N, Cooney LA, Harris KM, Anderson MS, Fox DA, Smilek DE, Krueger JG, Raychaudhuri S. Immunosuppression causes dynamic changes in expression QTLs in psoriatic skin. Nat Commun 2023; 14:6268. [PMID: 37805522 PMCID: PMC10560299 DOI: 10.1038/s41467-023-41984-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 09/25/2023] [Indexed: 10/09/2023] Open
Abstract
Psoriasis is a chronic, systemic inflammatory condition primarily affecting skin. While the role of the immune compartment (e.g., T cells) is well established, the changes in the skin compartment are more poorly understood. Using longitudinal skin biopsies (n = 375) from the "Psoriasis Treatment with Abatacept and Ustekinumab: A Study of Efficacy"(PAUSE) clinical trial (n = 101), we report 953 expression quantitative trait loci (eQTLs). Of those, 116 eQTLs have effect sizes that were modulated by local skin inflammation (eQTL interactions). By examining these eQTL genes (eGenes), we find that most are expressed in the skin tissue compartment, and a subset overlap with the NRF2 pathway. Indeed, the strongest eQTL interaction signal - rs1491377616-LCE3C - links a psoriasis risk locus with a gene specifically expressed in the epidermis. This eQTL study highlights the potential to use biospecimens from clinical trials to discover in vivo eQTL interactions with therapeutically relevant environmental variables.
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Affiliation(s)
- Qian Xiao
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, 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
| | - Joseph Mears
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, 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
| | - 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, 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, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, 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 City, Kanagawa, Japan
| | - Yuriy Baglaenko
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, 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
| | - Noha Lim
- Immune Tolerance Network, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
| | - Laura A Cooney
- Immune Tolerance Network, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
- Division of Rheumatology, Department of Internal Medicine and Clinical Autoimmunity Center of Excellence, University of Michigan, Ann Arbor, MI, USA
| | - Kristina M Harris
- Immune Tolerance Network, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
| | - Mark S Anderson
- Immune Tolerance Network, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
| | - David A Fox
- Division of Rheumatology, Department of Internal Medicine and Clinical Autoimmunity Center of Excellence, University of Michigan, Ann Arbor, MI, USA
| | - Dawn E Smilek
- Immune Tolerance Network, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
| | - James G Krueger
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, 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, 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.
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK.
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Antontseva EV, Degtyareva AO, Korbolina EE, Damarov IS, Merkulova TI. Human-genome single nucleotide polymorphisms affecting transcription factor binding and their role in pathogenesis. Vavilovskii Zhurnal Genet Selektsii 2023; 27:662-675. [PMID: 37965371 PMCID: PMC10641029 DOI: 10.18699/vjgb-23-77] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/24/2023] [Accepted: 03/30/2023] [Indexed: 11/16/2023] Open
Abstract
Single nucleotide polymorphisms (SNPs) are the most common type of variation in the human genome. The vast majority of SNPs identified in the human genome do not have any effect on the phenotype; however, some can lead to changes in the function of a gene or the level of its expression. Most SNPs associated with certain traits or pathologies are mapped to regulatory regions of the genome and affect gene expression by changing transcription factor binding sites. In recent decades, substantial effort has been invested in searching for such regulatory SNPs (rSNPs) and understanding the mechanisms by which they lead to phenotypic differences, primarily to individual differences in susceptibility to diseases and in sensitivity to drugs. The development of the NGS (next-generation sequencing) technology has contributed not only to the identification of a huge number of SNPs and to the search for their association (genome-wide association studies, GWASs) with certain diseases or phenotypic manifestations, but also to the development of more productive approaches to their functional annotation. It should be noted that the presence of an association does not allow one to identify a functional, truly disease-associated DNA sequence variant among multiple marker SNPs that are detected due to linkage disequilibrium. Moreover, determination of associations of genetic variants with a disease does not provide information about the functionality of these variants, which is necessary to elucidate the molecular mechanisms of the development of pathology and to design effective methods for its treatment and prevention. In this regard, the functional analysis of SNPs annotated in the GWAS catalog, both at the genome-wide level and at the level of individual SNPs, became especially relevant in recent years. A genome-wide search for potential rSNPs is possible without any prior knowledge of their association with a trait. Thus, mapping expression quantitative trait loci (eQTLs) makes it possible to identify an SNP for which - among transcriptomes of homozygotes and heterozygotes for its various alleles - there are differences in the expression level of certain genes, which can be located at various distances from the SNP. To predict rSNPs, approaches based on searches for allele-specific events in RNA-seq, ChIP-seq, DNase-seq, ATAC-seq, MPRA, and other data are also used. Nonetheless, for a more complete functional annotation of such rSNPs, it is necessary to establish their association with a trait, in particular, with a predisposition to a certain pathology or sensitivity to drugs. Thus, approaches to finding SNPs important for the development of a trait can be categorized into two groups: (1) starting from data on an association of SNPs with a certain trait, (2) starting from the determination of allele-specific changes at the molecular level (in a transcriptome or regulome). Only comprehensive use of strategically different approaches can considerably enrich our knowledge about the role of genetic determinants in the molecular mechanisms of trait formation, including predisposition to multifactorial diseases.
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Affiliation(s)
- E V Antontseva
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - A O Degtyareva
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - E E Korbolina
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - I S Damarov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - T I Merkulova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
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Xiong X, James BT, Boix CA, Park YP, Galani K, Victor MB, Sun N, Hou L, Ho LL, Mantero J, Scannail AN, Dileep V, Dong W, Mathys H, Bennett DA, Tsai LH, Kellis M. Epigenomic dissection of Alzheimer's disease pinpoints causal variants and reveals epigenome erosion. Cell 2023; 186:4422-4437.e21. [PMID: 37774680 PMCID: PMC10782612 DOI: 10.1016/j.cell.2023.08.040] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 04/04/2023] [Accepted: 08/29/2023] [Indexed: 10/01/2023]
Abstract
Recent work has identified dozens of non-coding loci for Alzheimer's disease (AD) risk, but their mechanisms and AD transcriptional regulatory circuitry are poorly understood. Here, we profile epigenomic and transcriptomic landscapes of 850,000 nuclei from prefrontal cortexes of 92 individuals with and without AD to build a map of the brain regulome, including epigenomic profiles, transcriptional regulators, co-accessibility modules, and peak-to-gene links in a cell-type-specific manner. We develop methods for multimodal integration and detecting regulatory modules using peak-to-gene linking. We show AD risk loci are enriched in microglial enhancers and for specific TFs including SPI1, ELF2, and RUNX1. We detect 9,628 cell-type-specific ATAC-QTL loci, which we integrate alongside peak-to-gene links to prioritize AD variant regulatory circuits. We report differential accessibility of regulatory modules in late AD in glia and in early AD in neurons. Strikingly, late-stage AD brains show global epigenome dysregulation indicative of epigenome erosion and cell identity loss.
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Affiliation(s)
- Xushen Xiong
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
| | - Benjamin T James
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Carles A Boix
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Yongjin P Park
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Department of Pathology and Laboratory Medicine, Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Kyriaki Galani
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Matheus B Victor
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Na Sun
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Lei Hou
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Li-Lun Ho
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Julio Mantero
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Aine Ni Scannail
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Vishnu Dileep
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Weixiu Dong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Hansruedi Mathys
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Li-Huei Tsai
- The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
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Carnes MU, Quach BC, Zhou L, Han S, Tao R, Mandal M, Deep-Soboslay A, Marks JA, Page GP, Maher BS, Jaffe AE, Won H, Bierut LJ, Hyde TM, Kleinman JE, Johnson EO, Hancock DB. Smoking-informed methylation and expression QTLs in human brain and colocalization with smoking-associated genetic loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.18.23295431. [PMID: 37790540 PMCID: PMC10543041 DOI: 10.1101/2023.09.18.23295431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Smoking is a leading cause of preventable morbidity and mortality. Smoking is heritable, and genome-wide association studies (GWAS) of smoking behaviors have identified hundreds of significant loci. Most GWAS-identified variants are noncoding with unknown neurobiological effects. We used genome-wide genotype, DNA methylation, and RNA sequencing data in postmortem human nucleus accumbens (NAc) to identify cis-methylation/expression quantitative trait loci (meQTLs/eQTLs), investigate variant-by-cigarette smoking interactions across the genome, and overlay QTL evidence at smoking GWAS-identified loci to evaluate their regulatory potential. Active smokers (N=52) and nonsmokers (N=171) were defined based on cotinine biomarker levels and next-of-kin reporting. We simultaneously tested variant and variant-by-smoking interaction effects on methylation and expression, separately, adjusting for biological and technical covariates and using a two-stage multiple testing approach with eigenMT and Bonferroni corrections. We found >2 million significant meQTL variants (padj<0.05) corresponding to 41,695 unique CpGs. Results were largely driven by main effects; five meQTLs, mapping to NUDT12, FAM53B, RNF39, and ADRA1B, showed a significant interaction with smoking. We found 57,683 significant eQTLs for 958 unique eGenes (padj<0.05) and no smoking interactions. Colocalization analyses identified loci with smoking-associated GWAS variants that overlapped meQTLs/eQTLs, suggesting that these heritable factors may influence smoking behaviors through functional effects on methylation/expression. One locus containing MUSTIN1 and ITIH4 colocalized across all data types (GWAS + meQTL + eQTL). In this first genome-wide meQTL map in the human NAc, the enriched overlap with smoking GWAS-identified genetic loci provides evidence that gene regulation in the brain helps explain the neurobiology of smoking behaviors.
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Affiliation(s)
- Megan Ulmer Carnes
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
| | - Bryan C. Quach
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
| | - Linran Zhou
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
| | - Shizhong Han
- Lieber Institute for Brain Development (LIBD), Baltimore, Maryland
| | - Ran Tao
- Lieber Institute for Brain Development (LIBD), Baltimore, Maryland
| | - Meisha Mandal
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
| | | | - Jesse A. Marks
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
| | - Grier P. Page
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
- Fellow Program, RTI International, Research Triangle Park, North Carolina
| | - Brion S. Maher
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Andrew E. Jaffe
- Lieber Institute for Brain Development (LIBD), Baltimore, Maryland
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Laura J. Bierut
- Department of Psychiatry, Washington University in St. Louis, Missouri
| | - Thomas M. Hyde
- Lieber Institute for Brain Development (LIBD), Baltimore, Maryland
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland
| | - Joel E. Kleinman
- Lieber Institute for Brain Development (LIBD), Baltimore, Maryland
| | - Eric O. Johnson
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
- Fellow Program, RTI International, Research Triangle Park, North Carolina
| | - Dana B. Hancock
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
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Amariuta T, Siewert-Rocks K, Price AL. Modeling tissue co-regulation estimates tissue-specific contributions to disease. Nat Genet 2023; 55:1503-1511. [PMID: 37580597 PMCID: PMC10904330 DOI: 10.1038/s41588-023-01474-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 07/13/2023] [Indexed: 08/16/2023]
Abstract
Integrative analyses of genome-wide association studies and gene expression data have implicated many disease-critical tissues. However, co-regulation of genetic effects on gene expression across tissues impedes distinguishing biologically causal tissues from tagging tissues. In the present study, we introduce tissue co-regulation score regression (TCSC), which disentangles causal tissues from tagging tissues by regressing gene-disease association statistics (from transcriptome-wide association studies) on tissue co-regulation scores, reflecting correlations of predicted gene expression across genes and tissues. We applied TCSC to 78 diseases/traits (average n = 302,000) and gene expression prediction models for 48 GTEx tissues. TCSC identified 21 causal tissue-trait pairs at a 5% false discovery rate (FDR), including well-established findings, biologically plausible new findings (for example, aorta artery and glaucoma) and increased specificity of known tissue-trait associations (for example, subcutaneous adipose, but not visceral adipose, and high-density lipoprotein). TCSC also identified 17 causal tissue-trait covariance pairs at 5% FDR. In conclusion, TCSC is a precise method for distinguishing causal tissues from tagging tissues.
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Affiliation(s)
- Tiffany Amariuta
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
- Department of Medicine, University of California San Diego, La Jolla, CA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Katherine Siewert-Rocks
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Brown AA, Fernandez-Tajes JJ, Hong MG, Brorsson CA, Koivula RW, Davtian D, Dupuis T, Sartori A, Michalettou TD, Forgie IM, Adam J, Allin KH, Caiazzo R, Cederberg H, De Masi F, Elders PJM, Giordano GN, Haid M, Hansen T, Hansen TH, Hattersley AT, Heggie AJ, Howald C, Jones AG, Kokkola T, Laakso M, Mahajan A, Mari A, McDonald TJ, McEvoy D, Mourby M, Musholt PB, Nilsson B, Pattou F, Penet D, Raverdy V, Ridderstråle M, Romano L, Rutters F, Sharma S, Teare H, 't Hart L, Tsirigos KD, Vangipurapu J, Vestergaard H, Brunak S, Franks PW, Frost G, Grallert H, Jablonka B, McCarthy MI, Pavo I, Pedersen O, Ruetten H, Walker M, Adamski J, Schwenk JM, Pearson ER, Dermitzakis ET, Viñuela A. Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits. Nat Commun 2023; 14:5062. [PMID: 37604891 PMCID: PMC10442420 DOI: 10.1038/s41467-023-40569-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/02/2023] [Indexed: 08/23/2023] Open
Abstract
We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.
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Affiliation(s)
- Andrew A Brown
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Juan J Fernandez-Tajes
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Mun-Gwan Hong
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Solna, SE-171 21, Sweden
| | - Caroline A Brorsson
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Robert W Koivula
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, OX3 7LJ, United Kingdom
| | - David Davtian
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Théo Dupuis
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Ambra Sartori
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Theodora-Dafni Michalettou
- Biosciences Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, NE1 4EP, United Kingdom
| | - Ian M Forgie
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Jonathan Adam
- German Center for Diabetes Research (DZD), Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Kristine H Allin
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Robert Caiazzo
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | - Henna Cederberg
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Federico De Masi
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Petra J M Elders
- Department of General Practice, Amsterdam UMC- location Vumc, Amsterdam Public Health research institute, Amsterdam, The Netherlands
| | - Giuseppe N Giordano
- Department of Clinical Science, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Malmö, Sweden
| | - Mark Haid
- Metabolomics and Proteomics Core, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Torben Hansen
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Tue H Hansen
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, University of Exeter College of Medicine & Health, Exeter, EX25DW, United Kingdom
| | - Alison J Heggie
- Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Cédric Howald
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Angus G Jones
- Department of Clinical and Biomedical Sciences, University of Exeter College of Medicine & Health, Exeter, EX25DW, United Kingdom
| | - Tarja Kokkola
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Markku Laakso
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Andrea Mari
- Institute of Neuroscience, National Research Council, Padova, 35127, Italy
| | - Timothy J McDonald
- Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, EX2 5DW, United Kingdom
| | - Donna McEvoy
- Diabetes Research Network, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Miranda Mourby
- Nuffield Department of Population Health, Centre for Health, Law and Emerging Technologies (HeLEX), University of Oxford, Oxford, OX2 7DD, United Kingdom
| | - Petra B Musholt
- Global Development, Sanofi-Aventis Deutschland GmbH, Hoechst Industrial Park, Frankfurt am Main, 65926, Germany
| | - Birgitte Nilsson
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Francois Pattou
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | - Deborah Penet
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Violeta Raverdy
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | | | - Luciana Romano
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Femke Rutters
- Epidemiology and Data Science, VUMC, Amsterdam, The Netherlands
| | - Sapna Sharma
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- Food Chemistry and Molecular and Sensory Science, Technical University of Munich, München, Germany
| | - Harriet Teare
- Centre for Health Law and Emerging Technologies, Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7DQ, United Kingdom
| | - Leen 't Hart
- Epidemiology and Data Science, VUMC, Amsterdam, The Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Molecular Epidemiology section, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Jagadish Vangipurapu
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Henrik Vestergaard
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
- Steno Diabetes Center Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Paul W Franks
- Department of Clinical Science, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Malmö, Sweden
| | - Gary Frost
- Nutrition and Dietetics Research Group, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Harald Grallert
- German Center for Diabetes Research (DZD), Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Bernd Jablonka
- Sanofi Partnering, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, 65926, Germany
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
- GENENTECH, 1 DNA Way, San Francisco, CA, 94080, USA
| | - Imre Pavo
- Eli Lilly Regional Operations Ges.m.b.H, Vienna, 1030, Austria
| | - Oluf Pedersen
- Center for Clinical Metabolic Research, Herlev and Gentofte University Hospital, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Hartmut Ruetten
- Sanofi Partnering, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, 65926, Germany
| | - Mark Walker
- Translational and Clinical Research Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, United Kingdom
| | - Jerzy Adamski
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
- Institute of Experimental Genetics, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Solna, SE-171 21, Sweden
| | - Ewan R Pearson
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland.
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland.
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland.
| | - Ana Viñuela
- Biosciences Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, NE1 4EP, United Kingdom.
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Schweighofer N, Strasser M, Obermayer A, Trummer O, Sourij H, Sourij C, Obermayer-Pietsch B. Identification of Novel Intronic SNPs in Transporter Genes Associated with Metformin Side Effects. Genes (Basel) 2023; 14:1609. [PMID: 37628660 PMCID: PMC10454417 DOI: 10.3390/genes14081609] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Metformin is a widely used and effective medication in type 2 diabetes (T2DM) as well as in polycystic ovary syndrome (PCOS). Single nucleotide polymorphisms (SNPs) contribute to the occurrence of metformin side effects. The aim of the present study was to identify intronic genetic variants modifying the occurrence of metformin side effects and to replicate them in individuals with T2DM and in women with PCOS. We performed Next Generation Sequencing (Illumina Next Seq) of 115 SNPs in a discovery cohort of 120 metformin users and conducted a systematic literature review. Selected SNPs were analysed in two independent cohorts of individuals with either T2DM or PCOS, using 5'-3'exonucleaseassay. A total of 14 SNPs in the organic cation transporters (OCTs) showed associations with side effects in an unadjusted binary logistic regression model, with eight SNPs remaining significantly associated after appropriate adjustment in the discovery cohort. Five SNPs were confirmed in a combined analysis of both replication cohorts but showed different association patterns in subgroup analyses. In an unweighted polygenic risk score (PRS), the risk for metformin side effects increased with the number of risk alleles. Intronic SNPs in the OCT cluster contribute to the development of metformin side effects in individuals with T2DM and in women with PCOS and are therefore of interest for personalized therapy options.
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Affiliation(s)
- Natascha Schweighofer
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria; (N.S.); (M.S.); (A.O.); (H.S.); barbar (B.O.-P.)
- Center for Biomarker Research in Medicine, CBmed, 8010 Graz, Austria
| | - Moritz Strasser
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria; (N.S.); (M.S.); (A.O.); (H.S.); barbar (B.O.-P.)
- Department of Health Studies, Institute of Biomedical, FH Joanneum University of Applied Sciences, 8020 Graz, Austria
| | - Anna Obermayer
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria; (N.S.); (M.S.); (A.O.); (H.S.); barbar (B.O.-P.)
- Interdisciplinary Metabolic Medicine Trials Unit, Medical University of Graz, 8036 Graz, Austria
| | - Olivia Trummer
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria; (N.S.); (M.S.); (A.O.); (H.S.); barbar (B.O.-P.)
| | - Harald Sourij
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria; (N.S.); (M.S.); (A.O.); (H.S.); barbar (B.O.-P.)
- Interdisciplinary Metabolic Medicine Trials Unit, Medical University of Graz, 8036 Graz, Austria
| | - Caren Sourij
- Division of Cardiology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria;
| | - Barbara Obermayer-Pietsch
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria; (N.S.); (M.S.); (A.O.); (H.S.); barbar (B.O.-P.)
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40
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Simonovsky E, Sharon M, Ziv M, Mauer O, Hekselman I, Jubran J, Vinogradov E, Argov CM, Basha O, Kerber L, Yogev Y, Segrè AV, Im HK, Birk O, Rokach L, Yeger‐Lotem E. Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation. Mol Syst Biol 2023; 19:e11407. [PMID: 37232043 PMCID: PMC10407743 DOI: 10.15252/msb.202211407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 04/30/2023] [Accepted: 05/10/2023] [Indexed: 05/27/2023] Open
Abstract
How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.
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Affiliation(s)
- Eyal Simonovsky
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Moran Sharon
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Maya Ziv
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Omry Mauer
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Idan Hekselman
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Juman Jubran
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Ekaterina Vinogradov
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Chanan M Argov
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Omer Basha
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Lior Kerber
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Yuval Yogev
- Morris Kahn Laboratory of Human Genetics and the Genetics Institute at Soroka Medical Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
| | - Ayellet V Segrè
- Ocular Genomics Institute, Massachusetts Eye and EarHarvard Medical SchoolBostonMAUSA
- The Broad Institute of MIT and HarvardCambridgeMAUSA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of MedicineThe University of ChicagoChicagoILUSA
| | | | - Ohad Birk
- Morris Kahn Laboratory of Human Genetics and the Genetics Institute at Soroka Medical Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
- The National Institute for Biotechnology in the NegevBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Lior Rokach
- Department of Software & Information Systems EngineeringBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Esti Yeger‐Lotem
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
- The National Institute for Biotechnology in the NegevBen‐Gurion University of the NegevBeer ShevaIsrael
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41
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Rouskas K, Katsareli EA, Amerikanou C, Dimopoulos AC, Glentis S, Kalantzi A, Skoulakis A, Panousis N, Ongen H, Bielser D, Planchon A, Romano L, Harokopos V, Reczko M, Moulos P, Griniatsos I, Diamantis T, Dermitzakis ET, Ragoussis J, Dedoussis G, Dimas AS. Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population. BMC Genomics 2023; 24:442. [PMID: 37543566 PMCID: PMC10403965 DOI: 10.1186/s12864-023-09532-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 07/25/2023] [Indexed: 08/07/2023] Open
Abstract
BACKGROUND Expression quantitative trait loci (eQTL) studies provide insights into regulatory mechanisms underlying disease risk. Expanding studies of gene regulation to underexplored populations and to medically relevant tissues offers potential to reveal yet unknown regulatory variants and to better understand disease mechanisms. Here, we performed eQTL mapping in subcutaneous (S) and visceral (V) adipose tissue from 106 Greek individuals (Greek Metabolic study, GM) and compared our findings to those from the Genotype-Tissue Expression (GTEx) resource. RESULTS We identified 1,930 and 1,515 eGenes in S and V respectively, over 13% of which are not observed in GTEx adipose tissue, and that do not arise due to different ancestry. We report additional context-specific regulatory effects in genes of clinical interest (e.g. oncogene ST7) and in genes regulating responses to environmental stimuli (e.g. MIR21, SNX33). We suggest that a fraction of the reported differences across populations is due to environmental effects on gene expression, driving context-specific eQTLs, and suggest that environmental effects can determine the penetrance of disease variants thus shaping disease risk. We report that over half of GM eQTLs colocalize with GWAS SNPs and of these colocalizations 41% are not detected in GTEx. We also highlight the clinical relevance of S adipose tissue by revealing that inflammatory processes are upregulated in individuals with obesity, not only in V, but also in S tissue. CONCLUSIONS By focusing on an understudied population, our results provide further candidate genes for investigation regarding their role in adipose tissue biology and their contribution to disease risk and pathogenesis.
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Affiliation(s)
- Konstantinos Rouskas
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thessaloniki, Greece
| | - Efthymia A Katsareli
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Charalampia Amerikanou
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Alexandros C Dimopoulos
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Hellenic Naval Academy, Hatzikyriakou Avenue, Pireaus, Greece
| | - Stavros Glentis
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Pediatric Hematology/Oncology Unit (POHemU), First Department of Pediatrics, University of Athens, Aghia Sophia Children's Hospital, Athens, Greece
| | - Alexandra Kalantzi
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Anargyros Skoulakis
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | | | - Halit Ongen
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Swiss Institute of Bioinformatics, University of Geneva, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Deborah Bielser
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Alexandra Planchon
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Luciana Romano
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Vaggelis Harokopos
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Martin Reczko
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Panagiotis Moulos
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Center of New Biotechnologies & Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Ioannis Griniatsos
- First Department of Surgery, National and Kapodistrian University of Athens, Medical School, Laiko Hospital, Athens, Greece
| | - Theodoros Diamantis
- First Department of Surgery, National and Kapodistrian University of Athens, Medical School, Laiko Hospital, Athens, Greece
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University Genome Centre, McGill University, Montréal, QC, Canada
- Department of Bioengineering, McGill University, Montréal, QC, Canada
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Antigone S Dimas
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece.
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42
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Zhu C, Baumgarten N, Wu M, Wang Y, Das AP, Kaur J, Ardakani FB, Duong TT, Pham MD, Duda M, Dimmeler S, Yuan T, Schulz MH, Krishnan J. CVD-associated SNPs with regulatory potential reveal novel non-coding disease genes. Hum Genomics 2023; 17:69. [PMID: 37491351 PMCID: PMC10369730 DOI: 10.1186/s40246-023-00513-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 07/12/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Cardiovascular diseases (CVDs) are the leading cause of death worldwide. Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) appearing in non-coding genomic regions in CVDs. The SNPs may alter gene expression by modifying transcription factor (TF) binding sites and lead to functional consequences in cardiovascular traits or diseases. To understand the underlying molecular mechanisms, it is crucial to identify which variations are involved and how they affect TF binding. METHODS The SNEEP (SNP exploration and analysis using epigenomics data) pipeline was used to identify regulatory SNPs, which alter the binding behavior of TFs and link GWAS SNPs to their potential target genes for six CVDs. The human-induced pluripotent stem cells derived cardiomyocytes (hiPSC-CMs), monoculture cardiac organoids (MCOs) and self-organized cardiac organoids (SCOs) were used in the study. Gene expression, cardiomyocyte size and cardiac contractility were assessed. RESULTS By using our integrative computational pipeline, we identified 1905 regulatory SNPs in CVD GWAS data. These were associated with hundreds of genes, half of them non-coding RNAs (ncRNAs), suggesting novel CVD genes. We experimentally tested 40 CVD-associated non-coding RNAs, among them RP11-98F14.11, RPL23AP92, IGBP1P1, and CTD-2383I20.1, which were upregulated in hiPSC-CMs, MCOs and SCOs under hypoxic conditions. Further experiments showed that IGBP1P1 depletion rescued expression of hypertrophic marker genes, reduced hypoxia-induced cardiomyocyte size and improved hypoxia-reduced cardiac contractility in hiPSC-CMs and MCOs. CONCLUSIONS IGBP1P1 is a novel ncRNA with key regulatory functions in modulating cardiomyocyte size and cardiac function in our disease models. Our data suggest ncRNA IGBP1P1 as a potential therapeutic target to improve cardiac function in CVDs.
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Affiliation(s)
- Chaonan Zhu
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany
| | - Nina Baumgarten
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590, Frankfurt Am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany
| | - Meiqian Wu
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
| | - Yue Wang
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
| | - Arka Provo Das
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany
| | - Jaskiran Kaur
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
| | - Fatemeh Behjati Ardakani
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590, Frankfurt Am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany
| | - Thanh Thuy Duong
- Genome Biologics, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
| | - Minh Duc Pham
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany
- Department of Medicine III, Cardiology/Angiology/ Nephrology, Goethe University Hospital, Frankfurt, Germany
- Genome Biologics, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
| | - Maria Duda
- Genome Biologics, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
| | - Stefanie Dimmeler
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590, Frankfurt Am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany
| | - Ting Yuan
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany.
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany.
- Department of Medicine III, Cardiology/Angiology/ Nephrology, Goethe University Hospital, Frankfurt, Germany.
| | - Marcel H Schulz
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany.
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590, Frankfurt Am Main, Germany.
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany.
| | - Jaya Krishnan
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany.
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590, Frankfurt Am Main, Germany.
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany.
- Department of Medicine III, Cardiology/Angiology/ Nephrology, Goethe University Hospital, Frankfurt, Germany.
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43
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Everman ER, Macdonald SJ. Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.12.548746. [PMID: 37503205 PMCID: PMC10370140 DOI: 10.1101/2023.07.12.548746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper quickly results in cell and tissue damage that can range in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes also respond to other non-essential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the response to heavy metal stress. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource (DSPR) using a combination of differential expression analysis and expression quantitative trait locus (eQTL) mapping. Differential expression analysis revealed clear patterns of tissue-specific expression, primarily driven by a more pronounced gene expression response in gut tissue. eQTL mapping of gene expression under control and copper conditions as well as for the change in gene expression following copper exposure (copper response eQTL) revealed hundreds of genes with tissue-specific local cis-eQTL and many distant trans-eQTL. eQTL associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited genotype by environment effects on gene expression under copper stress, illuminating several tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight many candidate genes for future functional characterization.
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Affiliation(s)
- Elizabeth R Everman
- 1200 Sunnyside Ave, University of Kansas, Molecular Biosciences, Lawrence, KS 66045, USA
- 730 Van Vleet Oval, University of Oklahoma, Biology, Norman, OK 73019, USA
| | - Stuart J Macdonald
- 1200 Sunnyside Ave, University of Kansas, Molecular Biosciences, Lawrence, KS 66045, USA
- 1200 Sunnyside Ave, University of Kansas, Center for Computational Biology, Lawrence, KS 66045, USA
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44
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Coons JC, Empey PE. Pharmacogenomics in the Management of Pulmonary Arterial Hypertension: Current Perspectives. Pharmgenomics Pers Med 2023; 16:729-737. [PMID: 37457231 PMCID: PMC10349598 DOI: 10.2147/pgpm.s361222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/19/2023] [Indexed: 07/18/2023] Open
Abstract
Pulmonary arterial hypertension (PAH) is a rare disease with heterogeneous causes that can lead to right ventricular (RV) failure and death if left untreated. There are currently 10 medications representative of five unique pharmacologic classes that are approved for treatment. These have led to significant improvements in overall clinical outcome. However, substantial variability in dosing requirements and treatment response is evident, leading to suboptimal outcome for many patients. Furthermore, dosing is empiric and iterative and can lead to delays in meeting treatment goals and burdensome adverse effects. Pharmacogenomic (PGx) associations have been reported with certain PAH medications, such as treprostinil and bosentan, and can explain some of the variability in response. Relevant genes associated with treprostinil include CYP2C8, CYP2C9, CAMK2D, and PFAS. CYP2C8 and CYP2C9 are the genes encoding the major metabolizing liver enzymes for treprostinil, and reduced function variants (*2, *3) with CYP2C9 were associated with lower treatment persistence. Additionally, a higher CYP2C9 activity score was associated with a significantly less risk of treatment discontinuation. Other genes of interest that have been explored with treprostinil include CAMK2D, which is associated with right ventricular dysfunction and significantly higher dose requirements. Similarly, PFAS is associated with lower concentrations of cyclic adenosine monophosphate and significantly higher dose requirements. Genes of interest with the endothelin receptor antagonist (ERA) class include GNG2 and CYP2C9. A genetic variant in GNG2 (rs11157866) was linked to a significantly increased rate of clinical improvement with ERAs. The *2 variant with CYP2C9 (encoding for the major metabolizing enzyme for bosentan) was significantly associated with a higher risk for elevations in hepatic aminotransferases and liver injury. In summary, this article reviews the relevant pharmacogenes that have been associated to date with dosing and outcome among patients who received PAH medications.
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Affiliation(s)
- James C Coons
- Department of Pharmacy and Therapeutics, University of Pittsburgh School of Pharmacy, Pittsburgh, PA, USA
- Department of Pharmacy, UPMC Presbyterian-Shadyside Hospital, Pittsburgh, PA, USA
| | - Philip E Empey
- Department of Pharmacy and Therapeutics, University of Pittsburgh School of Pharmacy, Pittsburgh, PA, USA
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45
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Viñas R, Joshi CK, Georgiev D, Lin P, Dumitrascu B, Gamazon ER, Liò P. Hypergraph factorization for multi-tissue gene expression imputation. NAT MACH INTELL 2023; 5:739-753. [PMID: 37771758 PMCID: PMC10538467 DOI: 10.1038/s42256-023-00684-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 06/02/2023] [Indexed: 09/30/2023]
Abstract
Integrating gene expression across tissues and cell types is crucial for understanding the coordinated biological mechanisms that drive disease and characterise homeostasis. However, traditional multitissue integration methods cannot handle uncollected tissues or rely on genotype information, which is often unavailable and subject to privacy concerns. Here we present HYFA (Hypergraph Factorisation), a parameter-efficient graph representation learning approach for joint imputation of multi-tissue and cell-type gene expression. HYFA is genotype-agnostic, supports a variable number of collected tissues per individual, and imposes strong inductive biases to leverage the shared regulatory architecture of tissues and genes. In performance comparison on Genotype-Tissue Expression project data, HYFA achieves superior performance over existing methods, especially when multiple reference tissues are available. The HYFA-imputed dataset can be used to identify replicable regulatory genetic variations (eQTLs), with substantial gains over the original incomplete dataset. HYFA can accelerate the effective and scalable integration of tissue and cell-type transcriptome biorepositories.
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Affiliation(s)
- Ramon Viñas
- Department of Computer Science and Technology, University of Cambridge
| | | | - Dobrik Georgiev
- Department of Computer Science and Technology, University of Cambridge
| | - Phillip Lin
- Division of Genetic Medicine, Vanderbilt University Medical Center
| | - Bianca Dumitrascu
- Department of Statistics and Irving Institute for Cancer Dynamics, Columbia University
| | - Eric R. Gamazon
- Vanderbilt Genetics Institute and Data Science Institute, MRC Epidemiology Unit, University of Cambridge
| | - Pietro Liò
- Department of Computer Science and Technology, University of Cambridge
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46
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Advani J, Corso-Diaz X, Kwicklis M, van Asten F, Ratnapriya R, Mehta P, Hamel A, Mahrotra S, Segrè A, Kiel C, Strunz T, Weber B, Chew E, Hernandez D, Montezuma S, Ferrington D, Swaroop A. QTL mapping of human retina DNA methylation identifies 87 gene-epigenome interactions in age-related macular degeneration. RESEARCH SQUARE 2023:rs.3.rs-3011096. [PMID: 37398472 PMCID: PMC10312909 DOI: 10.21203/rs.3.rs-3011096/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
DNA methylation (DNAm) provides a crucial epigenetic mark linking genetic variations to environmental influence. We analyzed array-based DNAm profiles of 160 human retinas with co-measured RNA-seq and > 8 million genetic variants, uncovering sites of genetic regulation in cis (37,453 mQTLs and 12,505 eQTLs) and 13,747 eQTMs (DNAm loci affecting gene expression), with over one-third specific to the retina. mQTLs and eQTMs show non-random distribution and enrichment of biological processes related to synapse, mitochondria, and catabolism. Summary data-based Mendelian randomization and colocalization analyses identify 87 target genes where methylation and gene-expression changes likely mediate the genotype effect on age-related macular degeneration (AMD). Integrated pathway analysis reveals epigenetic regulation of immune response and metabolism including the glutathione pathway and glycolysis. Our study thus defines key roles of genetic variations driving methylation changes, prioritizes epigenetic control of gene expression, and suggests frameworks for regulation of AMD pathology by genotype-environment interaction in retina.
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Affiliation(s)
| | | | | | | | | | - Puja Mehta
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Andrew Hamel
- Department of Ophthalmology, Massachusetts Eye and Ear
| | | | | | | | | | | | - Emily Chew
- National Eye Institute/National Institutes of Health
| | | | | | | | - Anand Swaroop
- National Eye Institute, National Institutes of Health
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47
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Kenny TC, Khan A, Son Y, Yue L, Heissel S, Sharma A, Pasolli HA, Liu Y, Gamazon ER, Alwaseem H, Hite RK, Birsoy K. Integrative genetic analysis identifies FLVCR1 as a plasma-membrane choline transporter in mammals. Cell Metab 2023; 35:1057-1071.e12. [PMID: 37100056 PMCID: PMC10367582 DOI: 10.1016/j.cmet.2023.04.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 03/03/2023] [Accepted: 04/04/2023] [Indexed: 04/28/2023]
Abstract
Genome-wide association studies (GWASs) of serum metabolites have the potential to uncover genes that influence human metabolism. Here, we combined an integrative genetic analysis that associates serum metabolites to membrane transporters with a coessentiality map of metabolic genes. This analysis revealed a connection between feline leukemia virus subgroup C cellular receptor 1 (FLVCR1) and phosphocholine, a downstream metabolite of choline metabolism. Loss of FLVCR1 in human cells strongly impairs choline metabolism due to the inhibition of choline import. Consistently, CRISPR-based genetic screens identified phospholipid synthesis and salvage machinery as synthetic lethal with FLVCR1 loss. Cells and mice lacking FLVCR1 exhibit structural defects in mitochondria and upregulate integrated stress response (ISR) through heme-regulated inhibitor (HRI) kinase. Finally, Flvcr1 knockout mice are embryonic lethal, which is partially rescued by choline supplementation. Altogether, our findings propose FLVCR1 as a major choline transporter in mammals and provide a platform to discover substrates for unknown metabolite transporters.
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Affiliation(s)
- Timothy C Kenny
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Artem Khan
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Yeeun Son
- Structural Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lishu Yue
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Søren Heissel
- Proteomics Resource Center, The Rockefeller University, New York, NY, USA
| | - Anurag Sharma
- Electron Microscopy Resource Center, The Rockefeller University, New York, NY, USA
| | - H Amalia Pasolli
- Electron Microscopy Resource Center, The Rockefeller University, New York, NY, USA
| | - Yuyang Liu
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hanan Alwaseem
- Proteomics Resource Center, The Rockefeller University, New York, NY, USA
| | - Richard K Hite
- Structural Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kıvanç Birsoy
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA.
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48
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Zhu D, Zhao R, Yuan H, Xie Y, Jiang Y, Xu K, Zhang T, Chen X, Suo C. Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19. J Epidemiol Glob Health 2023; 13:279-291. [PMID: 37160831 PMCID: PMC10169198 DOI: 10.1007/s44197-023-00106-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 04/17/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was varied in disease symptoms. We aim to explore the effect of host genetic factors and comorbidities on severe COVID-19 risk. METHODS A total of 20,320 COVID-19 patients in the UK Biobank cohort were included. Genome-wide association analysis (GWAS) was used to identify host genetic factors in the progression of COVID-19 and a polygenic risk score (PRS) consisted of 86 SNPs was constructed to summarize genetic susceptibility. Colocalization analysis and Logistic regression model were used to assess the association of host genetic factors and comorbidities with COVID-19 severity. All cases were randomly split into training and validation set (1:1). Four algorithms were used to develop predictive models and predict COVID-19 severity. Demographic characteristics, comorbidities and PRS were included in the model to predict the risk of severe COVID-19. The area under the receiver operating characteristic curve (AUROC) was applied to assess the models' performance. RESULTS We detected an association with rs73064425 at locus 3p21.31 reached the genome-wide level in GWAS (odds ratio: 1.55, 95% confidence interval: 1.36-1.78). Colocalization analysis found that two genes (SLC6A20 and LZTFL1) may affect the progression of COVID-19. In the predictive model, logistic regression models were selected due to simplicity and high performance. Predictive model consisting of demographic characteristics, comorbidities and genetic factors could precisely predict the patient's progression (AUROC = 82.1%, 95% CI 80.6-83.7%). Nearly 20% of severe COVID-19 events could be attributed to genetic risk. CONCLUSION In this study, we identified two 3p21.31 genes as genetic susceptibility loci in patients with severe COVID-19. The predictive model includes demographic characteristics, comorbidities and genetic factors is useful to identify individuals who are predisposed to develop subsequent critical conditions among COVID-19 patients.
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Affiliation(s)
- Dongliang Zhu
- Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
| | - Renjia Zhao
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, Shanghai, China
| | - Huangbo Yuan
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, Shanghai, China
| | - Yijing Xie
- Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
| | - Yanfeng Jiang
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Yaocheng Road 799, Taizhou, Jiangsu, China
| | - Kelin Xu
- Fudan University Taizhou Institute of Health Sciences, Yaocheng Road 799, Taizhou, Jiangsu, China
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
| | - Tiejun Zhang
- Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
- Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Yaocheng Road 799, Taizhou, Jiangsu, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Yaocheng Road 799, Taizhou, Jiangsu, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Yiwu Research Institute of Fudan University, Yiwu, Zhejiang, China
| | - Chen Suo
- Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China.
- Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, China.
- Fudan University Taizhou Institute of Health Sciences, Yaocheng Road 799, Taizhou, Jiangsu, China.
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49
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Barry A, McNulty MT, Jia X, Gupta Y, Debiec H, Luo Y, Nagano C, Horinouchi T, Jung S, Colucci M, Ahram DF, Mitrotti A, Sinha A, Teeninga N, Jin G, Shril S, Caridi G, Bodria M, Lim TY, Westland R, Zanoni F, Marasa M, Turudic D, Giordano M, Gesualdo L, Magistroni R, Pisani I, Fiaccadori E, Reiterova J, Maringhini S, Morello W, Montini G, Weng PL, Scolari F, Saraga M, Tasic V, Santoro D, van Wijk JAE, Milošević D, Kawai Y, Kiryluk K, Pollak MR, Gharavi A, Lin F, Simœs E Silva AC, Loos RJF, Kenny EE, Schreuder MF, Zurowska A, Dossier C, Ariceta G, Drozynska-Duklas M, Hogan J, Jankauskiene A, Hildebrandt F, Prikhodina L, Song K, Bagga A, Cheong H, Ghiggeri GM, Vachvanichsanong P, Nozu K, Lee D, Vivarelli M, Raychaudhuri S, Tokunaga K, Sanna-Cherchi S, Ronco P, Iijima K, Sampson MG. Multi-population genome-wide association study implicates immune and non-immune factors in pediatric steroid-sensitive nephrotic syndrome. Nat Commun 2023; 14:2481. [PMID: 37120605 PMCID: PMC10148875 DOI: 10.1038/s41467-023-37985-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 04/10/2023] [Indexed: 05/01/2023] Open
Abstract
Pediatric steroid-sensitive nephrotic syndrome (pSSNS) is the most common childhood glomerular disease. Previous genome-wide association studies (GWAS) identified a risk locus in the HLA Class II region and three additional independent risk loci. But the genetic architecture of pSSNS, and its genetically driven pathobiology, is largely unknown. Here, we conduct a multi-population GWAS meta-analysis in 38,463 participants (2440 cases). We then conduct conditional analyses and population specific GWAS. We discover twelve significant associations-eight from the multi-population meta-analysis (four novel), two from the multi-population conditional analysis (one novel), and two additional novel loci from the European meta-analysis. Fine-mapping implicates specific amino acid haplotypes in HLA-DQA1 and HLA-DQB1 driving the HLA Class II risk locus. Non-HLA loci colocalize with eQTLs of monocytes and numerous T-cell subsets in independent datasets. Colocalization with kidney eQTLs is lacking but overlap with kidney cell open chromatin suggests an uncharacterized disease mechanism in kidney cells. A polygenic risk score (PRS) associates with earlier disease onset. Altogether, these discoveries expand our knowledge of pSSNS genetic architecture across populations and provide cell-specific insights into its molecular drivers. Evaluating these associations in additional cohorts will refine our understanding of population specificity, heterogeneity, and clinical and molecular associations.
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Affiliation(s)
- Alexandra Barry
- Division of Nephrology, Boston Children's Hospital, Boston, MA, USA
- Kidney Disease Initiative & Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michelle T McNulty
- Division of Nephrology, Boston Children's Hospital, Boston, MA, USA
- Kidney Disease Initiative & Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xiaoyuan Jia
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine (NCGM), Tokyo, Japan
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yask Gupta
- Division of Nephrology, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Hanna Debiec
- Sorbonne Université, UPMC Paris 06, Institut National de la Santé et de la Recherde Médicale, Unité Mixte de Rechereche, S 1155, Paris, France
| | - Yang Luo
- Kennedy Institute of Rheumatology, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7FY, United Kingdom
- 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
| | - China Nagano
- Division of Nephrology, Boston Children's Hospital, Boston, MA, USA
- Kidney Disease Initiative & Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pediatrics, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Tomoko Horinouchi
- Department of Pediatrics, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Seulgi Jung
- Department of Biochemistry and Molecular Biology, University of Ulsan College of Medicine, Songpa-gu, Seoul, Korea
| | - Manuela Colucci
- Renal Diseases Research Unit, Genetics and Rare Diseases Research Division, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Dina F Ahram
- Division of Nephrology, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Adele Mitrotti
- Division of Nephrology, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
- Nephrology, Dialysis and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, Bari, Italy
| | - Aditi Sinha
- Department of Pediatrics, AIIMS, New Delhi, India
| | - Nynke Teeninga
- Department of Pediatric Nephrology, Amalia Children's Hospital, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Gina Jin
- Division of Nephrology, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Shirlee Shril
- Department of Medicine, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Gianluca Caridi
- Laboratory on Molecular Nephrology, IRCCS Instituto Giannina Gaslini, Genoa, Italy
| | - Monica Bodria
- Department of Nephrology and Renal Transplantation, IRCCS Instituto Giannina Gaslini, Genoa, Italy
| | - Tze Y Lim
- Division of Nephrology, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Rik Westland
- Department of Pediatric Nephrology, VU University Medical Center, Amsterdam, The Netherlands
| | - Francesca Zanoni
- Division of Nephrology, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
- Division of Transplantation, Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Maddalena Marasa
- Division of Nephrology, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Daniel Turudic
- Department of Pediatric Nephrology, Dialysis and Transplantation, Clinical Hospital Hospital Center Zagreb, University of Zagreb Medical School, Zagreb, Croatia
| | - Mario Giordano
- Division of Nephrology and Pediatric Dialysis, Bari Polyclinic Giovanni XXIII Children's Hospital, Bari, Italy
| | - Loreto Gesualdo
- Nephrology, Dialysis and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, Bari, Italy
| | - Riccardo Magistroni
- Department of Nephrology, Dialysis and Transplant Unit, University Hospital of Modena, Modena, Italy
- Surgical, Medical and Dental Department of Morphological Sciences, Section of Nephrology, University of Modena and Reggio Emilia, Modena, Italy
| | - Isabella Pisani
- Unità Operativa Nefrologia, Azienda Ospedaliero-Universitaria di Parma, Dipartimento di Medicina e Chirurgia, Università di Parma, Parma, Italy
| | - Enrico Fiaccadori
- Unità Operativa Nefrologia, Azienda Ospedaliero-Universitaria di Parma, Dipartimento di Medicina e Chirurgia, Università di Parma, Parma, Italy
| | - Jana Reiterova
- Department of Nephrology, Medicine and General University Hospital, Charles University, Prague, Czech Republic
| | | | - William Morello
- Pediatric Nephrology, Dialysis and Transplant Unit, Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, Milano, Italy
| | - Giovanni Montini
- Pediatric Nephrology, Dialysis and Transplant Unit, Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, Milano, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Patricia L Weng
- Department of Pediatric Nephrology, UCLA Medical Center and UCLA Medical Center-Santa Monica, Los Angeles, CA, USA
| | - Francesco Scolari
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, Division of Nephrology and Dialysis, University of Brescia and ASST Spedali Civili of Brescia, Brescia, Italy
| | - Marijan Saraga
- Department of Pediatrics, University of Split, Split, Croatia
| | - Velibor Tasic
- Department of Pediatric Nephrology, University Children's Hospital, Skopje, Macedonia
| | - Domenica Santoro
- Division of Nephrology and Dialysis Unit, University of Messina, Sicily, Italy
| | - Joanna A E van Wijk
- Department of Pediatric Nephrology, VU University Medical Center, Amsterdam, The Netherlands
| | - Danko Milošević
- Department of Pediatric Nephrology, Dialysis and Transplantation, Clinical Hospital Hospital Center Zagreb, University of Zagreb Medical School, Zagreb, Croatia
- Croatian Academy of Medical Sciences, Praska 2/III p.p. 27, 10000, Zagreb, Croatia
| | - Yosuke Kawai
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine (NCGM), Tokyo, Japan
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Martin R Pollak
- Division of Nephrology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Pediatric, Division of Pediatric Nephrology, Columbia University Irving Medical Center New York-Presbyterian Morgan Stanley Children's Hospital in New York, New York, NY, USA
| | - Ali Gharavi
- Division of Nephrology, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Fangmin Lin
- Department of Pediatric, Division of Pediatric Nephrology, Columbia University Irving Medical Center New York-Presbyterian Morgan Stanley Children's Hospital in New York, New York, NY, USA
| | - Ana Cristina Simœs E Silva
- Department of Pediatrics, Interdisciplinary Laboratory of Medical Investigation, Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of General Internal Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michiel F Schreuder
- Department of Pediatric Nephrology, Amalia Children's Hospital, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Aleksandra Zurowska
- Department of Pediatrics, Nephrology and Hypertension, Medical University Gdansk, Gdansk, Poland
| | - Claire Dossier
- AP-HP, Pediatric Nephrology Department, Hôpital Robert-Debré, Paris, France
| | - Gema Ariceta
- Pediatric Nephrology, Hospital Universitari Vall d'Hebron, Universitat Autónoma de Barcelona, Barcelona, Spain
| | | | - Julien Hogan
- AP-HP, Pediatric Nephrology Department, Hôpital Robert-Debré, Paris, France
| | - Augustina Jankauskiene
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Friedhelm Hildebrandt
- Division of Nephrology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Larisa Prikhodina
- Research and Clinical Institute for Pediatrics, Pirogov Russian National Research Medical University, Taldomskava St, 2, Moscow, Russia
| | - Kyuyoung Song
- Department of Biochemistry and Molecular Biology, University of Ulsan College of Medicine, Songpa-gu, Seoul, Korea
| | - Arvind Bagga
- Department of Pediatrics, AIIMS, New Delhi, India
| | - Hae Cheong
- Department of Pediatrics, Hallym University Sacred Heart Hospital, 22, Gwanpyeong-ro 170 beon-gil, Dongan-gu, Anyang-si, Gyeonggi-do, 14068, Korea
| | - Gian Marco Ghiggeri
- Department of Nephrology and Renal Transplantation, IRCCS Instituto Giannina Gaslini, Genoa, Italy
| | - Prayong Vachvanichsanong
- Department of Pediatrics, Faculty of Medicine, Prince of Songkla University, Hat-Yai, Songkhla, 90110, Thailand
| | - Kandai Nozu
- Department of Pediatrics, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Dongwon Lee
- Division of Nephrology, Boston Children's Hospital, Boston, MA, USA
- Kidney Disease Initiative & Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Marina Vivarelli
- Division of Nephrology, and Dialysis, Department of Pediatric Subspecialities, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - 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
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, UK
| | - Katsushi Tokunaga
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine (NCGM), Tokyo, Japan
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Simone Sanna-Cherchi
- Division of Nephrology, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Pierre Ronco
- Sorbonne Université, UPMC Paris 06, Institut National de la Santé et de la Recherde Médicale, Unité Mixte de Rechereche, S 1155, Paris, France
- Department of Nephrology, Centre Hospitalier du Mans, Le Mans, France
| | - Kazumoto Iijima
- Hyogo Prefectural Kobe Children's Hospital, Kobe, Japan
- Department of Advanced Pediatric Medicine, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Matthew G Sampson
- Division of Nephrology, Boston Children's Hospital, Boston, MA, USA.
- Kidney Disease Initiative & Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Division of Renal Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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50
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Hicks EM, Seah C, Cote A, Marchese S, Brennand KJ, Nestler EJ, Girgenti MJ, Huckins LM. Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings. Transl Psychiatry 2023; 13:129. [PMID: 37076454 PMCID: PMC10115809 DOI: 10.1038/s41398-023-02412-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 03/17/2023] [Accepted: 03/24/2023] [Indexed: 04/21/2023] Open
Abstract
Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.
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Affiliation(s)
- Emily M Hicks
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Alanna Cote
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Shelby Marchese
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Kristen J Brennand
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Eric J Nestler
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
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