51
|
Drown MK, Oleksiak MF, Crawford DL. Trans-Acting Genotypes Associted with mRNA Expression Affect Metabolic and Thermal Tolerance Traits. Genome Biol Evol 2023:evad123. [PMID: 37392472 PMCID: PMC10370451 DOI: 10.1093/gbe/evad123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/23/2023] [Accepted: 06/28/2023] [Indexed: 07/03/2023] Open
Abstract
Evolutionary processes driving physiological trait variation depend on the underlying genomic mechanisms. Evolution of these mechanisms depends on the genetic complexity (involving many genes) and how gene expression impacting the traits is converted to phenotype. Yet, genomic mechanisms that impact physiological traits are diverse and context dependent (e.g., vary by environment, tissues), making them difficult to discern. We examine the relationships between genotype, mRNA expression, and physiological traits to discern the genetic complexity and whether the gene expression affecting the physiological traits is primarily cis or trans-acting. We use low-coverage whole genome sequencing and heart- or brain-specific mRNA expression to identify polymorphisms directly associated with physiological traits and expressed quantitative trait loci (eQTL) indirectly associated with variation in six temperature specific physiological traits (standard metabolic rate, thermal tolerance, and four substrate specific cardiac metabolic rates). Focusing on a select set of mRNAs belonging to co-expression modules that explain up to 82% of temperature specific traits, we identified hundreds of significant eQTL for mRNA whose expression affects physiological traits. Surprisingly, most eQTL (97.4% for heart and 96.7% for brain) were trans-acting. This could be due to higher effect size of trans versus cis-acting eQTL for mRNAs that are central to co-expression modules. That is, we may have enhanced the identification of trans-acting factors by looking for SNPs associated with mRNAs in co-expression modules that broadly influence gene expression patterns. Overall, these data indicate that the genomic mechanism driving physiological variation across environments is driven by trans-acting heart- or brain-specific mRNA expression.
Collapse
Affiliation(s)
- Melissa K Drown
- Department of Marine Biology and Ecology, The Rosenstiel School, University of Miami, Miami, FL, USA
| | - Marjorie F Oleksiak
- Department of Marine Biology and Ecology, The Rosenstiel School, University of Miami, Miami, FL, USA
| | - Douglas L Crawford
- Department of Marine Biology and Ecology, The Rosenstiel School, University of Miami, Miami, FL, USA
| |
Collapse
|
52
|
Wang YH, Luo PP, Geng AY, Li X, Liu TH, He YJ, Huang L, Tang YQ. Identification of highly reliable risk genes for Alzheimer's disease through joint-tissue integrative analysis. Front Aging Neurosci 2023; 15:1183119. [PMID: 37416324 PMCID: PMC10320295 DOI: 10.3389/fnagi.2023.1183119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 05/30/2023] [Indexed: 07/08/2023] Open
Abstract
Numerous genetic variants associated with Alzheimer's disease (AD) have been identified through genome-wide association studies (GWAS), but their interpretation is hindered by the strong linkage disequilibrium (LD) among the variants, making it difficult to identify the causal variants directly. To address this issue, the transcriptome-wide association study (TWAS) was employed to infer the association between gene expression and a trait at the genetic level using expression quantitative trait locus (eQTL) cohorts. In this study, we applied the TWAS theory and utilized the improved Joint-Tissue Imputation (JTI) approach and Mendelian Randomization (MR) framework (MR-JTI) to identify potential AD-associated genes. By integrating LD score, GTEx eQTL data, and GWAS summary statistic data from a large cohort using MR-JTI, a total of 415 AD-associated genes were identified. Then, 2873 differentially expressed genes from 11 AD-related datasets were used for the Fisher test of these AD-associated genes. We finally obtained 36 highly reliable AD-associated genes, including APOC1, CR1, ERBB2, and RIN3. Moreover, the GO and KEGG enrichment analysis revealed that these genes are primarily involved in antigen processing and presentation, amyloid-beta formation, tau protein binding, and response to oxidative stress. The identification of these potential AD-associated genes not only provides insights into the pathogenesis of AD but also offers biomarkers for early diagnosis of the disease.
Collapse
Affiliation(s)
- Yong Heng Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
- Joint International Research Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China
| | - Pan Pan Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Ao Yi Geng
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Xinwei Li
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
| | - Tai-Hang Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
- Joint International Research Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China
| | - Yi Jie He
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Lin Huang
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Ya Qin Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| |
Collapse
|
53
|
Gedik H, Nguyen TH, Peterson RE, Chatzinakos C, Vladimirov VI, Riley BP, Bacanu SA. Identifying potential risk genes and pathways for neuropsychiatric and substance use disorders using intermediate molecular mediator information. Front Genet 2023; 14:1191264. [PMID: 37415601 PMCID: PMC10320396 DOI: 10.3389/fgene.2023.1191264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/23/2023] [Indexed: 07/08/2023] Open
Abstract
Neuropsychiatric and substance use disorders (NPSUDs) have a complex etiology that includes environmental and polygenic risk factors with significant cross-trait genetic correlations. Genome-wide association studies (GWAS) of NPSUDs yield numerous association signals. However, for most of these regions, we do not yet have a firm understanding of either the specific risk variants or the effects of these variants. Post-GWAS methods allow researchers to use GWAS summary statistics and molecular mediators (transcript, protein, and methylation abundances) infer the effect of these mediators on risk for disorders. One group of post-GWAS approaches is commonly referred to as transcriptome/proteome/methylome-wide association studies, which are abbreviated as T/P/MWAS (or collectively as XWAS). Since these approaches use biological mediators, the multiple testing burden is reduced to the number of genes (∼20,000) instead of millions of GWAS SNPs, which leads to increased signal detection. In this work, our aim is to uncover likely risk genes for NPSUDs by performing XWAS analyses in two tissues-blood and brain. First, to identify putative causal risk genes, we performed an XWAS using the Summary-data-based Mendelian randomization, which uses GWAS summary statistics, reference xQTL data, and a reference LD panel. Second, given the large comorbidities among NPSUDs and the shared cis-xQTLs between blood and the brain, we improved XWAS signal detection for underpowered analyses by performing joint concordance analyses between XWAS results i) across the two tissues and ii) across NPSUDs. All XWAS signals i) were adjusted for heterogeneity in dependent instruments (HEIDI) (non-causality) p-values and ii) used to test for pathway enrichment. The results suggest that there were widely shared gene/protein signals within the major histocompatibility complex region on chromosome 6 (BTN3A2 and C4A) and elsewhere in the genome (FURIN, NEK4, RERE, and ZDHHC5). The identification of putative molecular genes and pathways underlying risk may offer new targets for therapeutic development. Our study revealed an enrichment of XWAS signals in vitamin D and omega-3 gene sets. So, including vitamin D and omega-3 in treatment plans may have a modest but beneficial effect on patients with bipolar disorder.
Collapse
Affiliation(s)
- Huseyin Gedik
- Integrative Life Sciences, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Tan Hoang Nguyen
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Roseann E. Peterson
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, United States
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, United States
| | - Christos Chatzinakos
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Belmont, MA, United States
| | - Vladimir I. Vladimirov
- Department of Psychiatry, College of Medicine, University of Arizona Phoenix, Phoenix, AZ, United States
| | - Brien P. Riley
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Silviu-Alin Bacanu
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| |
Collapse
|
54
|
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. Res Sq 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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
55
|
Gjorgjieva T, Chaloemtoem A, Shahin T, Bayaraa O, Dieng MM, Alshaikh M, Abdalbaqi M, Del Monte J, Begum G, Leonor C, Manikandan V, Drou N, Arshad M, Arnoux M, Kumar N, Jabari A, Abdulle A, ElGhazali G, Ali R, Shaheen SY, Abdalla J, Piano F, Gunsalus KC, Daggag H, Al Nahdi H, Abuzeid H, Idaghdour Y. Systems genetics identifies miRNA-mediated regulation of host response in COVID-19. Hum Genomics 2023; 17:49. [PMID: 37303042 DOI: 10.1186/s40246-023-00494-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 05/10/2023] [Indexed: 06/13/2023] Open
Abstract
BACKGROUND Individuals infected with SARS-CoV-2 vary greatly in their disease severity, ranging from asymptomatic infection to severe disease. The regulation of gene expression is an important mechanism in the host immune response and can modulate the outcome of the disease. miRNAs play important roles in post-transcriptional regulation with consequences on downstream molecular and cellular host immune response processes. The nature and magnitude of miRNA perturbations associated with blood phenotypes and intensive care unit (ICU) admission in COVID-19 are poorly understood. RESULTS We combined multi-omics profiling-genotyping, miRNA and RNA expression, measured at the time of hospital admission soon after the onset of COVID-19 symptoms-with phenotypes from electronic health records to understand how miRNA expression contributes to variation in disease severity in a diverse cohort of 259 unvaccinated patients in Abu Dhabi, United Arab Emirates. We analyzed 62 clinical variables and expression levels of 632 miRNAs measured at admission and identified 97 miRNAs associated with 8 blood phenotypes significantly associated with later ICU admission. Integrative miRNA-mRNA cross-correlation analysis identified multiple miRNA-mRNA-blood endophenotype associations and revealed the effect of miR-143-3p on neutrophil count mediated by the expression of its target gene BCL2. We report 168 significant cis-miRNA expression quantitative trait loci, 57 of which implicate miRNAs associated with either ICU admission or a blood endophenotype. CONCLUSIONS This systems genetics study has given rise to a genomic picture of the architecture of whole blood miRNAs in unvaccinated COVID-19 patients and pinpoints post-transcriptional regulation as a potential mechanism that impacts blood traits underlying COVID-19 severity. The results also highlight the impact of host genetic regulatory control of miRNA expression in early stages of COVID-19 disease.
Collapse
Affiliation(s)
- T Gjorgjieva
- Biology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
| | - A Chaloemtoem
- Biology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - T Shahin
- Biology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - O Bayaraa
- Biology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - M M Dieng
- Biology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - M Alshaikh
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - M Abdalbaqi
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - J Del Monte
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - G Begum
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - C Leonor
- Biology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - V Manikandan
- Biology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - N Drou
- Center for Genomics and Systems Biology, NYU Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - M Arshad
- Center for Genomics and Systems Biology, NYU Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - M Arnoux
- Center for Genomics and Systems Biology, NYU Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - N Kumar
- Seha (Abu Dhabi Health Services Company), Abu Dhabi, United Arab Emirates
| | - A Jabari
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - A Abdulle
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - G ElGhazali
- Sheikh Khalifa Medical City-Union 71 PureHealth, Abu Dhabi, United Arab Emirates
- Department of Medical Microbiology and Immunology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - R Ali
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - S Y Shaheen
- Department of Medical Microbiology and Immunology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - J Abdalla
- Department of Medical Microbiology and Immunology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - F Piano
- Biology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Center for Genomics and Systems Biology, NYU Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - K C Gunsalus
- Biology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Center for Genomics and Systems Biology, NYU Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - H Daggag
- Seha (Abu Dhabi Health Services Company), Abu Dhabi, United Arab Emirates
| | - H Al Nahdi
- Seha (Abu Dhabi Health Services Company), Abu Dhabi, United Arab Emirates
| | - H Abuzeid
- Seha (Abu Dhabi Health Services Company), Abu Dhabi, United Arab Emirates
| | - Y Idaghdour
- Biology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
- Center for Genomics and Systems Biology, NYU Abu Dhabi, Abu Dhabi, United Arab Emirates.
| |
Collapse
|
56
|
Pudjihartono N, Ho D, Golovina E, Fadason T, Kempa-Liehr AW, O'Sullivan JM. Juvenile idiopathic arthritis-associated genetic loci exhibit spatially constrained gene regulatory effects across multiple tissues and immune cell types. J Autoimmun 2023; 138:103046. [PMID: 37229810 DOI: 10.1016/j.jaut.2023.103046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/04/2023] [Accepted: 04/15/2023] [Indexed: 05/27/2023]
Abstract
Juvenile idiopathic arthritis (JIA) is an autoimmune, inflammatory joint disease with complex genetic etiology. Previous GWAS have found many genetic loci associated with JIA. However, the biological mechanism behind JIA remains unknown mainly because most risk loci are located in non-coding genetic regions. Interestingly, increasing evidence has found that regulatory elements in the non-coding regions can regulate the expression of distant target genes through spatial (physical) interactions. Here, we used information on the 3D genome organization (Hi-C data) to identify target genes that physically interact with SNPs within JIA risk loci. Subsequent analysis of these SNP-gene pairs using data from tissue and immune cell type-specific expression quantitative trait loci (eQTL) databases allowed the identification of risk loci that regulate the expression of their target genes. In total, we identified 59 JIA-risk loci that regulate the expression of 210 target genes across diverse tissues and immune cell types. Functional annotation of spatial eQTLs within JIA risk loci identified significant overlap with gene regulatory elements (i.e., enhancers and transcription factor binding sites). We found target genes involved in immune-related pathways such as antigen processing and presentation (e.g., ERAP2, HLA class I and II), the release of pro-inflammatory cytokines (e.g., LTBR, TYK2), proliferation and differentiation of specific immune cell types (e.g., AURKA in Th17 cells), and genes involved in physiological mechanisms related to pathological joint inflammation (e.g., LRG1 in arteries). Notably, many of the tissues where JIA-risk loci act as spatial eQTLs are not classically considered central to JIA pathology. Overall, our findings highlight the potential tissue and immune cell type-specific regulatory changes contributing to JIA pathogenesis. Future integration of our data with clinical studies can contribute to the development of improved JIA therapy.
Collapse
Affiliation(s)
- N Pudjihartono
- The Liggins Institute, The University of Auckland, Auckland, New Zealand.
| | - D Ho
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - E Golovina
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - T Fadason
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - A W Kempa-Liehr
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
| | - J M O'Sullivan
- The Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand; MRC Lifecourse Epidemiology Unit, University of Southampton, United Kingdom; Australian Parkinsons Mission, Garvan Institute of Medical Research, Sydney, New South Wales, 384 Victoria Street, Darlinghurst, NSW, 2010, Australia; A*STAR Singapore Institute for Clinical Sciences, Singapore, Singapore.
| |
Collapse
|
57
|
Le NN, Tran TQB, du Toit C, Gill D, Padmanabhan S. Establishing plausibility of cardiovascular adverse effects of immunotherapies using Mendelian randomisation. Front Cardiovasc Med 2023; 10:1116799. [PMID: 37273876 PMCID: PMC10235787 DOI: 10.3389/fcvm.2023.1116799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/17/2023] [Indexed: 06/06/2023] Open
Abstract
Immune checkpoint inhibitors (ICIs) and Janus kinase inhibitors (JAKis) have raised concerns over serious unexpected cardiovascular adverse events. The widespread pleiotropy in genome-wide association studies offers an opportunity to identify cardiovascular risks from in-development drugs to help inform appropriate trial design and pharmacovigilance strategies. This study uses the Mendelian randomization (MR) approach to study the causal effects of 9 cardiovascular risk factors on ischemic stroke risk both independently and by mediation, followed by an interrogation of the implicated expression quantitative trait loci (eQTLs) to determine if the enriched pathways can explain the adverse stroke events observed with ICI or JAKi treatment. Genetic predisposition to higher systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), waist-to-hip ratio (WHR), low-density lipoprotein cholesterol (LDL), triglycerides (TG), type 2 diabetes (T2DM), and smoking index were associated with higher ischemic stroke risk. The associations of genetically predicted BMI, WHR, and TG on the outcome were attenuated after adjusting for genetically predicted T2DM [BMI: 53.15% mediated, 95% CI 17.21%-89.10%; WHR: 42.92% (4.17%-81.67%); TG: 72.05% (10.63%-133.46%)]. JAKis, programmed cell death protein 1 and programmed death ligand 1 inhibitors were implicated in the pathways enriched by the genes related to the instruments for each of SBP, DBP, WHR, T2DM, and LDL. Overall, MR mediation analyses support the role of T2DM in mediating the effects of BMI, WHR, and TG on ischemic stroke risk and follow-up pathway enrichment analysis highlights the utility of this approach in the early identification of potential harm from drugs.
Collapse
Affiliation(s)
- Nhu Ngoc Le
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
| | - Tran Quoc Bao Tran
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
| | - Clea du Toit
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Sandosh Padmanabhan
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
| |
Collapse
|
58
|
Gibbs KD, Wang L, Yang Z, Anderson CE, Bourgeois JS, Cao Y, Gaggioli MR, Biel M, Puertollano R, Chen CC, Ko DC. Human variation impacting MCOLN2 restricts Salmonella Typhi replication by magnesium deprivation. Cell Genom 2023; 3:100290. [PMID: 37228749 PMCID: PMC10203047 DOI: 10.1016/j.xgen.2023.100290] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/24/2023] [Accepted: 02/27/2023] [Indexed: 05/27/2023]
Abstract
Human genetic diversity can reveal critical factors in host-pathogen interactions. This is especially useful for human-restricted pathogens like Salmonella enterica serovar Typhi (S. Typhi), the cause of typhoid fever. One key defense during bacterial infection is nutritional immunity: host cells attempt to restrict bacterial replication by denying bacteria access to key nutrients or supplying toxic metabolites. Here, a cellular genome-wide association study of intracellular replication by S. Typhi in nearly a thousand cell lines from around the world-and extensive follow-up using intracellular S. Typhi transcriptomics and manipulation of magnesium availability-demonstrates that the divalent cation channel mucolipin-2 (MCOLN2 or TRPML2) restricts S. Typhi intracellular replication through magnesium deprivation. Mg2+ currents, conducted through MCOLN2 and out of endolysosomes, were measured directly using patch-clamping of the endolysosomal membrane. Our results reveal Mg2+ limitation as a key component of nutritional immunity against S. Typhi and as a source of variable host resistance.
Collapse
Affiliation(s)
- Kyle D. Gibbs
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Liuyang Wang
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Zhuo Yang
- Department of Pharmacy, Center for Drug Research, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Caroline E. Anderson
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Jeffrey S. Bourgeois
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
- University Program in Genetics and Genomics, Duke University, Durham, NC 27710, USA
| | - Yanlu Cao
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Margaret R. Gaggioli
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Martin Biel
- Department of Pharmacy, Center for Drug Research, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Rosa Puertollano
- Cell and Developmental Biology Center, National Heart, Lung, & Blood Institute, NIH, Bethesda, MD 20892, USA
| | - Cheng-Chang Chen
- Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei 100, Taiwan
- Department of Laboratory Medicine, National Taiwan University Hospital, Taipei 100, Taiwan
| | - Dennis C. Ko
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
- University Program in Genetics and Genomics, Duke University, Durham, NC 27710, USA
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Duke University, Durham, NC 27710, USA
| |
Collapse
|
59
|
Lee YL, Bosse M, Takeda H, Moreira GCM, Karim L, Druet T, Oget-Ebrad C, Coppieters W, Veerkamp RF, Groenen MAM, Georges M, Bouwman AC, Charlier C. High-resolution structural variants catalogue in a large-scale whole genome sequenced bovine family cohort data. BMC Genomics 2023; 24:225. [PMID: 37127590 PMCID: PMC10152703 DOI: 10.1186/s12864-023-09259-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 03/20/2023] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND Structural variants (SVs) are chromosomal segments that differ between genomes, such as deletions, duplications, insertions, inversions and translocations. The genomics revolution enabled the discovery of sub-microscopic SVs via array and whole-genome sequencing (WGS) data, paving the way to unravel the functional impact of SVs. Recent human expression QTL mapping studies demonstrated that SVs play a disproportionally large role in altering gene expression, underlining the importance of including SVs in genetic analyses. Therefore, this study aimed to generate and explore a high-quality bovine SV catalogue exploiting a unique cattle family cohort data (total 266 samples, forming 127 trios). RESULTS We curated 13,731 SVs segregating in the population, consisting of 12,201 deletions, 1,509 duplications, and 21 multi-allelic CNVs (> 50-bp). Of these, we validated a subset of copy number variants (CNVs) utilising a direct genotyping approach in an independent cohort, indicating that at least 62% of the CNVs are true variants, segregating in the population. Among gene-disrupting SVs, we prioritised two likely high impact duplications, encompassing ORM1 and POPDC3 genes, respectively. Liver expression QTL mapping results revealed that these duplications are likely causing altered gene expression, confirming the functional importance of SVs. Although most of the accurately genotyped CNVs are tagged by single nucleotide polymorphisms (SNPs) ascertained in WGS data, most CNVs were not captured by individual SNPs obtained from a 50K genotyping array. CONCLUSION We generated a high-quality SV catalogue exploiting unique whole genome sequenced bovine family cohort data. Two high impact duplications upregulating the ORM1 and POPDC3 are putative candidates for postpartum feed intake and hoof health traits, thus warranting further investigation. Generally, CNVs were in low LD with SNPs on the 50K array. Hence, it remains crucial to incorporate CNVs via means other than tagging SNPs, such as investigation of tagging haplotypes, direct imputation of CNVs, or direct genotyping as done in the current study. The SV catalogue and the custom genotyping array generated in the current study will serve as valuable resources accelerating utilisation of full spectrum of genetic variants in bovine genomes.
Collapse
Affiliation(s)
- Young-Lim Lee
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, the Netherlands.
- Unit of Animal Genomics, Faculty of Veterinary Medicine, GIGA-R &, University of Liège, Liège, Belgium.
| | - Mirte Bosse
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, the Netherlands
| | - Haruko Takeda
- Unit of Animal Genomics, Faculty of Veterinary Medicine, GIGA-R &, University of Liège, Liège, Belgium
| | | | - Latifa Karim
- GIGA Institute, GIGA Genomics Platform, University of Liège, Liège, Belgium
| | - Tom Druet
- Unit of Animal Genomics, Faculty of Veterinary Medicine, GIGA-R &, University of Liège, Liège, Belgium
| | - Claire Oget-Ebrad
- Unit of Animal Genomics, Faculty of Veterinary Medicine, GIGA-R &, University of Liège, Liège, Belgium
| | - Wouter Coppieters
- Unit of Animal Genomics, Faculty of Veterinary Medicine, GIGA-R &, University of Liège, Liège, Belgium
- GIGA Institute, GIGA Genomics Platform, University of Liège, Liège, Belgium
| | - Roel F Veerkamp
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, the Netherlands
| | - Martien A M Groenen
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, the Netherlands
| | - Michel Georges
- Unit of Animal Genomics, Faculty of Veterinary Medicine, GIGA-R &, University of Liège, Liège, Belgium
| | - Aniek C Bouwman
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, the Netherlands
| | - Carole Charlier
- Unit of Animal Genomics, Faculty of Veterinary Medicine, GIGA-R &, University of Liège, Liège, Belgium
| |
Collapse
|
60
|
Wagner M, Sobczyński M, Jasek M, Pawełczyk K, Porębska I, Kuśnierczyk P, Wiśniewski A. Down-regulation of ERAP1 mRNA expression in non-small cell lung cancer. BMC Cancer 2023; 23:383. [PMID: 37101107 PMCID: PMC10134604 DOI: 10.1186/s12885-023-10785-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 03/28/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND ERAP1 is a major aminopeptidase that serves as an editor of the peptide repertoire by trimming N-terminal residues of antigenic peptides, creating a pool of peptides with the optimal length for MHC-I binding. As an important component of the antigen processing and presenting machinery - APM, ERAP1 is frequently down-regulated in many cancers. Since ERAP1 expression has not yet been thoroughly investigated in non-small cell lung cancer (NSCLC), we decided to analyze ERAP1 mRNA levels in tissues collected from NSCLC patients. METHODS Using real-time qPCR, we evaluated ERAP1 mRNA expression in samples of tumor and adjacent non-tumor tissue (serving as control tissue) from 61 NSCLC patients. RESULTS We observed a significantly lower level of ERAP1 mRNA expression in tumor tissue (MedTumor = 0.75) in comparison to non-tumor tissue (MedNon-tumor = 1.1), p = 0.008. One of the five tested polymorphisms, namely rs26653, turned out to be significantly associated with ERAP1 expression in non-tumor tissue (difference [d] = 0.59 CI95% (0.14;1.05), p = 0.0086), but not in tumor tissue. The levels of ERAP1 mRNA expression did not affect the overall survival of NSCLC patients, either in the case of the tumor (p = 0.788) or in non-tumor (p = 0.298) tissue. We did not detect any association between mRNA ERAP1 expression level in normal tissue and: (i) age at diagnosis (p = 0.8386), (ii) patient's sex (p = 0.3616), (iii) histological type of cancer (p = 0.7580) and (iv) clinical stage of NSCLC (p = 0.7549). Furthermore, in the case of tumor tissue none of the abovementioned clinical parameters were associated with ERAP1 expression (p = 0.76). CONCLUSION Down-regulation of ERAP1 mRNA observed in NSCLC tissue may be related to tumor immune evasion strategy. The rs26653 polymorphism can be considered an expression quantitative trait locus (eQTL) associated with ERAP1 expression in normal lung tissue.
Collapse
Affiliation(s)
- Marta Wagner
- Laboratory of Genetics and Epigenetics of Human Diseases, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland
| | - Maciej Sobczyński
- Laboratory of Molecular Neurobiology, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
| | - Monika Jasek
- Laboratory of Genetics and Epigenetics of Human Diseases, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland
| | - Konrad Pawełczyk
- Department of Thoracic Surgery, Lower Silesian Centre of Oncology, Pulmonology and Haematology, Wrocław, Poland
| | - Irena Porębska
- Department of Pulmonology and Lung Oncology, Wrocław Medical University, Wrocław, Poland
| | - Piotr Kuśnierczyk
- Laboratory of Immunogenetics and Tissue Immunology, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland
| | - Andrzej Wiśniewski
- Laboratory of Immunogenetics and Tissue Immunology, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland.
| |
Collapse
|
61
|
Zhang C, Li X, Zhao L, Guo W, Deng W, Wang Q, Hu X, Du X, Sham PC, Luo X, Li T. Brain transcriptome-wide association study implicates novel risk genes underlying schizophrenia risk. Psychol Med 2023:1-11. [PMID: 37092861 DOI: 10.1017/s0033291723000417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
BACKGROUND To identify risk genes whose expression are regulated by the reported risk variants and to explore the potential regulatory mechanism in schizophrenia (SCZ). METHODS We systematically integrated three independent brain expression quantitative traits (eQTLs) (CommonMind, GTEx, and BrainSeq Phase 2, a total of 1039 individuals) and GWAS data (56 418 cases and 78 818 controls), with the use of transcriptome-wide association study (TWAS). Diffusion magnetic resonance imaging was utilized to quantify the integrity of white matter bundles and determine whether polygenic risk of novel genes linked to brain structure was present in patients with first-episode antipsychotic SCZ. RESULTS TWAS showed that eight risk genes (CORO7, DDAH2, DDHD2, ELAC2, GLT8D1, PCDHA8, THOC7, and TYW5) reached transcriptome-wide significance (TWS) level. These findings were confirmed by an independent integrative approach (i.e. Sherlock). We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Gene expression analysis showed that several TWS genes (including CORO7, DDAH2, DDHD2, ELAC2, GLT8D1, THOC7 and TYW5) were dysregulated in the dorsolateral prefrontal cortex of SCZ cases compared with controls. TWS genes were mainly expressed on the surface of glutamatergic neurons, GABAergic neurons, and microglia. Finally, SCZ cases had a substantially greater TWS genes-based polygenic risk (PRS) compared to controls, and we showed that fractional anisotropy of the cingulum-hippocampus mediates the influence of TWS genes PRS on SCZ. CONCLUSIONS Our findings identified novel SCZ risk genes and highlighted the importance of the TWS genes in frontal-limbic dysfunctions in SCZ, indicating possible therapeutic targets.
Collapse
Affiliation(s)
- Chengcheng Zhang
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiaojing Li
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wanjun Guo
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wei Deng
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xun Hu
- The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xiangdong Du
- Suzhou Psychiatric Hospital, Soochow University's Affiliated Guangji Hospital, Suzhou, Jiangsu, China
| | - Pak Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Xiongjian Luo
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Tao Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China
| |
Collapse
|
62
|
Li S, Schmid KT, de Vries DH, Korshevniuk M, Losert C, Oelen R, van Blokland IV, Groot HE, Swertz MA, van der Harst P, Westra HJ, van der Wijst MGP, Heinig M, Franke L. Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data. Genome Biol 2023; 24:80. [PMID: 37072791 PMCID: PMC10111756 DOI: 10.1186/s13059-023-02897-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 03/16/2023] [Indexed: 04/20/2023] Open
Abstract
BACKGROUND Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. RESULTS We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. CONCLUSION Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.
Collapse
Affiliation(s)
- Shuang Li
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Katharina T Schmid
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Computer Science, School of Computation, Information and Technology, Technical University Munich, Munich, Germany
| | - Dylan H de Vries
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
| | - Maryna Korshevniuk
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
| | - Corinna Losert
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Computer Science, School of Computation, Information and Technology, Technical University Munich, Munich, Germany
| | - Roy Oelen
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
| | - Irene V van Blokland
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Hilde E Groot
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Morris A Swertz
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Harm-Jan Westra
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Matthias Heinig
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Computer Science, School of Computation, Information and Technology, Technical University Munich, Munich, Germany.
- Munich Heart Alliance, DZHK (German Center for Cardiovascular Research), Munich, Germany.
| | - Lude Franke
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands.
| |
Collapse
|
63
|
Zeng L, White CC, Bennett DA, Klein HU, De Jager PL. Genetic insights into the association between inflammatory bowel disease and Alzheimer's disease. medRxiv 2023:2023.04.17.23286845. [PMID: 37131588 PMCID: PMC10153331 DOI: 10.1101/2023.04.17.23286845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Background Myeloid cells, including monocytes, macrophages, microglia, dendritic cells and neutrophils are a part of innate immunity, playing a major role in orchestrating innate and adaptive immune responses. Microglia are the resident myeloid cells of the central nervous system, and many Alzheimer's disease (AD) risk loci are found in or near genes that are highly or sometimes uniquely expressed in myeloid cells. Similarly, inflammatory bowel disease (IBD) loci are also enriched for genes expressed by myeloid cells. However, the extent to which there is overlap between the effects of AD and IBD susceptibility loci in myeloid cells remains poorly described, and the substantial IBD genetic maps may help to accelerate AD research. Methods Here, we leveraged summary statistics from large-scale genome-wide association studies (GWAS) to investigate the causal effect of IBD (including ulcerative colitis and Crohn's disease) variants on AD and AD endophenotypes. Microglia and monocyte expression Quantitative Trait Locus (eQTLs) were used to examine the functional consequences of IBD and AD risk variants enrichment in two different myeloid cell subtypes. Results Our results showed that, while PTK2B is implicated in both diseases and both sets of risk loci are enriched for myeloid genes, AD and IBD susceptibility loci largely implicate distinct sets of genes and pathways. AD loci are significantly more enriched for microglial eQTLs than IBD. We also found that genetically determined IBD is associated with a lower risk of AD, which may driven by a negative effect on the accumulation of neurofibrillary tangles (beta=-1.04, p=0.013). In addition, IBD displayed a significant positive genetic correlation with psychiatric disorders and multiple sclerosis, while AD showed a significant positive genetic correlation with amyotrophic lateral sclerosis. Conclusion To our knowledge, this is the first study to systematically contrast the genetic association between IBD and AD, our findings highlight a possible genetically protective effect of IBD on AD even if the majority of effects on myeloid cell gene expression by the two sets of disease variants are distinct. Thus, IBD myeloid studies may not help to accelerate AD functional studies, but our observation reinforces the role of myeloid cells in the accumulation of tau proteinopathy and provides a new avenue for discovering a protective factor.
Collapse
Affiliation(s)
- Lu Zeng
- Center for Translational and Computational Neuroimmunology, Department of Neurology and the Taub Institute for Research on Alzheimer's disease and the Aging brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles C White
- Center for Translational and Computational Neuroimmunology, Department of Neurology and the Taub Institute for Research on Alzheimer's disease and the Aging brain, Columbia University Irving Medical Center, New York, NY, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Hans-Ulrich Klein
- Center for Translational and Computational Neuroimmunology, Department of Neurology and the Taub Institute for Research on Alzheimer's disease and the Aging brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology and the Taub Institute for Research on Alzheimer's disease and the Aging brain, Columbia University Irving Medical Center, New York, NY, USA
| |
Collapse
|
64
|
Aydin S, Pham DT, Zhang T, Keele GR, Skelly DA, Paulo JA, Pankratz M, Choi T, Gygi SP, Reinholdt LG, Baker CL, Churchill GA, Munger SC. Genetic dissection of the pluripotent proteome through multi-omics data integration. Cell Genom 2023; 3:100283. [PMID: 37082146 PMCID: PMC10112288 DOI: 10.1016/j.xgen.2023.100283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 09/12/2022] [Accepted: 02/27/2023] [Indexed: 04/22/2023]
Abstract
Genetic background drives phenotypic variability in pluripotent stem cells (PSCs). Most studies to date have used transcript abundance as the primary molecular readout of cell state in PSCs. We performed a comprehensive proteogenomics analysis of 190 genetically diverse mouse embryonic stem cell (mESC) lines. The quantitative proteome is highly variable across lines, and we identified pluripotency-associated pathways that were differentially activated in the proteomics data that were not evident in transcriptome data from the same lines. Integration of protein abundance to transcript levels and chromatin accessibility revealed broad co-variation across molecular layers as well as shared and unique drivers of quantitative variation in pluripotency-associated pathways. Quantitative trait locus (QTL) mapping localized the drivers of these multi-omic signatures to genomic hotspots. This study reveals post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome and provides a regulatory map for mESCs that can provide a basis for future mechanistic studies.
Collapse
Affiliation(s)
- Selcan Aydin
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Duy T. Pham
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Tian Zhang
- Harvard Medical School, Boston, MA 02115, USA
| | | | | | | | | | - Ted Choi
- Predictive Biology, Inc., Carlsbad, CA 92010, USA
| | | | - Laura G. Reinholdt
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Christopher L. Baker
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Gary A. Churchill
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Steven C. Munger
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| |
Collapse
|
65
|
Li Z, Zhang B, Liu Q, Tao Z, Ding L, Guo B, Zhang E, Zhang H, Meng Z, Guo S, Chen Y, Peng J, Li J, Wang C, Huang Y, Xu H, Wu Y. Genetic association of lipids and lipid-lowering drug target genes with non-alcoholic fatty liver disease. EBioMedicine 2023; 90:104543. [PMID: 37002989 PMCID: PMC10070091 DOI: 10.1016/j.ebiom.2023.104543] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 03/09/2023] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Some observational studies found that dyslipidaemia is a risk factor for non-alcoholic fatty liver disease (NAFLD), and lipid-lowering drugs may lower NAFLD risk. However, it remains unclear whether dyslipidaemia is causative for NAFLD. This Mendelian randomisation (MR) study aimed to explore the causal role of lipid traits in NAFLD and evaluate the potential effect of lipid-lowering drug targets on NAFLD. METHODS Genetic variants associated with lipid traits and variants of genes encoding lipid-lowering drug targets were extracted from the Global Lipids Genetics Consortium genome-wide association study (GWAS). Summary statistics for NAFLD were obtained from two independent GWAS datasets. Lipid-lowering drug targets that reached significance were further tested using expression quantitative trait loci data in relevant tissues. Colocalisation and mediation analyses were performed to validate the robustness of the results and explore potential mediators. FINDINGS No significant effect of lipid traits and eight lipid-lowering drug targets on NAFLD risk was found. Genetic mimicry of lipoprotein lipase (LPL) enhancement was associated with lower NAFLD risks in two independent datasets (OR1 = 0.60 [95% CI 0.50-0.72], p1 = 2.07 × 10-8; OR2 = 0.57 [95% CI 0.39-0.82], p2 = 3.00 × 10-3). A significant MR association (OR = 0.71 [95% CI, 0.58-0.87], p = 1.20 × 10-3) and strong colocalisation association (PP.H4 = 0.85) with NAFLD were observed for LPL expression in subcutaneous adipose tissue. Fasting insulin and type 2 diabetes mediated 7.40% and 9.15%, respectively, of the total effect of LPL on NAFLD risk. INTERPRETATION Our findings do not support dyslipidaemia as a causal factor for NAFLD. Among nine lipid-lowering drug targets, LPL is a promising candidate drug target in NAFLD. The mechanism of action of LPL in NAFLD may be independent of its lipid-lowering effects. FUNDING Capital's Funds for Health Improvement and Research (2022-4-4037). CAMS Innovation Fund for Medical Sciences (CIFMS, grant number: 2021-I2M-C&T-A-010).
Collapse
Affiliation(s)
- Ziang Li
- State Key Laboratory of Cardiovascular Disease, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Bin Zhang
- State Key Laboratory of Cardiovascular Disease, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Qingrong Liu
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Zhihang Tao
- State Key Laboratory for Oncogenes and Related Genes, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lu Ding
- Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Bo Guo
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Erli Zhang
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Haitong Zhang
- Department of Cardiology, the Third-Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zhen Meng
- Department of Cardiology, China-Japan Friendship Hospital, Beijing, China
| | - Shuai Guo
- State Key Laboratory of Cardiovascular Disease, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Yang Chen
- State Key Laboratory of Cardiovascular Disease, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Jia Peng
- Department of Cardiology, the First-Affiliated Hospital, Xiangya Hospital Central South University, Changsha, China
| | - Jinyue Li
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Can Wang
- State Key Laboratory of Cardiovascular Disease, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Yingbo Huang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haiyan Xu
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
| | - Yongjian Wu
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
| |
Collapse
|
66
|
Huang M, Coral D, Ardalani H, Spegel P, Saadat A, Claussnitzer M, Mulder H, Franks PW, Kalamajski S. Identification of a weight loss-associated causal eQTL in MTIF3 and the effects of MTIF3 deficiency on human adipocyte function. eLife 2023; 12:84168. [PMID: 36876906 PMCID: PMC10023155 DOI: 10.7554/elife.84168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 03/05/2023] [Indexed: 03/07/2023] Open
Abstract
Genetic variation at the MTIF3 (Mitochondrial Translational Initiation Factor 3) locus has been robustly associated with obesity in humans, but the functional basis behind this association is not known. Here, we applied luciferase reporter assay to map potential functional variants in the haplotype block tagged by rs1885988 and used CRISPR-Cas9 to edit the potential functional variants to confirm the regulatory effects on MTIF3 expression. We further conducted functional studies on MTIF3-deficient differentiated human white adipocyte cell line (hWAs-iCas9), generated through inducible expression of CRISPR-Cas9 combined with delivery of synthetic MTIF3-targeting guide RNA. We demonstrate that rs67785913-centered DNA fragment (in LD with rs1885988, r2 > 0.8) enhances transcription in a luciferase reporter assay, and CRISPR-Cas9-edited rs67785913 CTCT cells show significantly higher MTIF3 expression than rs67785913 CT cells. Perturbed MTIF3 expression led to reduced mitochondrial respiration and endogenous fatty acid oxidation, as well as altered expression of mitochondrial DNA-encoded genes and proteins, and disturbed mitochondrial OXPHOS complex assembly. Furthermore, after glucose restriction, the MTIF3 knockout cells retained more triglycerides than control cells. This study demonstrates an adipocyte function-specific role of MTIF3, which originates in the maintenance of mitochondrial function, providing potential explanations for why MTIF3 genetic variation at rs67785913 is associated with body corpulence and response to weight loss interventions.
Collapse
Affiliation(s)
- Mi Huang
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund UniversityMalmöSweden
| | - Daniel Coral
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund UniversityMalmöSweden
| | - Hamidreza Ardalani
- Department of Chemistry, Centre for Analysis and Synthesis, Lund UniversityLundSweden
| | - Peter Spegel
- Department of Chemistry, Centre for Analysis and Synthesis, Lund UniversityLundSweden
| | - Alham Saadat
- Metabolism Program, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Melina Claussnitzer
- Metabolism Program, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Hindrik Mulder
- Unit of Molecular Metabolism, Department of Clinical Sciences, Clinical Research Centre, Lund UniversityMalmöSweden
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund UniversityMalmöSweden
- Department of Nutrition, Harvard T.H. Chan School of Public HealthBostonUnited States
| | - Sebastian Kalamajski
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund UniversityMalmöSweden
| |
Collapse
|
67
|
van Wijk MH, Riksen JAG, Elvin M, Poulin GB, Maulana MI, Kammenga JE, Snoek BL, Sterken MG. Cryptic genetic variation of eQTL architecture revealed by genetic perturbation in C. elegans. G3 (Bethesda) 2023; 13:7067301. [PMID: 36861370 PMCID: PMC10151397 DOI: 10.1093/g3journal/jkad050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 12/27/2022] [Accepted: 02/07/2023] [Indexed: 03/03/2023]
Abstract
Genetic perturbation in different genetic backgrounds can cause a range of phenotypes within a species. These phenotypic differences can be the result of the interaction between the genetic background and the perturbation. Previously we reported that perturbation of gld-1, an important player in developmental control of C. elegans, released cryptic genetic variation affecting fitness in different genetic backgrounds. Here we investigated the change in transcriptional architecture. We found 414 genes with a cis-eQTL and 991 genes with a trans-eQTL that were specifically found in the gld-1 RNAi treatment. In total, we detected 16 eQTL-hotspots, of which 7 were only found in the gld-1 RNAi treatment. Enrichment analysis of those 7 hotspots showed that the regulated genes were associated with neurons and the pharynx. Furthermore, we found evidence of accelerated transcriptional aging in the gld-1 RNAi treated nematodes. Overall, our results illustrate that studying CGV leads to the discovery of hidden polymorphic regulators.
Collapse
Affiliation(s)
- Marijke H van Wijk
- Laboratory of Nematology, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Joost A G Riksen
- Laboratory of Nematology, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Mark Elvin
- Peak Proteins, Alderley Park, Macclesfield, Cheshire, SK10 4TG, United Kingdom
| | - Gino B Poulin
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, M13 9PL, United Kingdom
| | - Muhammad I Maulana
- Laboratory of Nematology, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Jan E Kammenga
- Laboratory of Nematology, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Basten L Snoek
- Theoretical Biology and Bioinformatics, Utrecht University, 3584 CH Utrecht, The Netherlands
| | - Mark G Sterken
- Laboratory of Nematology, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| |
Collapse
|
68
|
Yermakovich D, Pankratov V, Võsa U, Yunusbayev B, Dannemann M. Long-range regulatory effects of Neandertal DNA in modern humans. Genetics 2023; 223:6957427. [PMID: 36560850 PMCID: PMC9991505 DOI: 10.1093/genetics/iyac188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 10/13/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
The admixture between modern humans and Neandertals has resulted in ∼2% of the genomes of present-day non-Africans being composed of Neandertal DNA. Introgressed Neandertal DNA has been demonstrated to significantly affect the transcriptomic landscape in people today and via this molecular mechanism influence phenotype variation as well. However, little is known about how much of that regulatory impact is mediated through long-range regulatory effects that have been shown to explain ∼20% of expression variation. Here we identified 60 transcription factors (TFs) with their top cis-eQTL SNP in GTEx being of Neandertal ancestry and predicted long-range Neandertal DNA-induced regulatory effects by screening for the predicted target genes of those TFs. We show that the TFs form a significantly connected protein-protein interaction network. Among them are JUN and PRDM5, two brain-expressed TFs that have their predicted target genes enriched in regions devoid of Neandertal DNA. Archaic cis-eQTLs for the 60 TFs include multiple candidates for local adaptation, some of which show significant allele frequency increases over the last ∼10,000 years. A large proportion of the cis-eQTL-associated archaic SNPs have additional associations with various immune traits, schizophrenia, blood cell type composition and anthropometric measures. Finally, we demonstrate that our results are consistent with those of Neandertal DNA-associated empirical trans-eQTLs. Our results suggest that Neandertal DNA significantly influences regulatory networks, that its regulatory reach goes beyond the 40% of genomic sequence it still covers in present-day non-Africans and that via the investigated mechanism Neandertal DNA influences the phenotypic variation in people today.
Collapse
Affiliation(s)
- Danat Yermakovich
- Centre for Genomics, Evolution and Medicine, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Vasili Pankratov
- Centre for Genomics, Evolution and Medicine, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Bayazit Yunusbayev
- Centre for Genomics, Evolution and Medicine, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | | | - Michael Dannemann
- Centre for Genomics, Evolution and Medicine, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| |
Collapse
|
69
|
Kelly DE, Ramdas S, Ma R, Rawlings-Goss RA, Grant GR, Ranciaro A, Hirbo JB, Beggs W, Yeager M, Chanock S, Nyambo TB, Omar SA, Woldemeskel D, Belay G, Li H, Brown CD, Tishkoff SA. The genetic and evolutionary basis of gene expression variation in East Africans. Genome Biol 2023; 24:35. [PMID: 36829244 PMCID: PMC9951478 DOI: 10.1186/s13059-023-02874-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 02/13/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Mapping of quantitative trait loci (QTL) associated with molecular phenotypes is a powerful approach for identifying the genes and molecular mechanisms underlying human traits and diseases, though most studies have focused on individuals of European descent. While important progress has been made to study a greater diversity of human populations, many groups remain unstudied, particularly among indigenous populations within Africa. To better understand the genetics of gene regulation in East Africans, we perform expression and splicing QTL mapping in whole blood from a cohort of 162 diverse Africans from Ethiopia and Tanzania. We assess replication of these QTLs in cohorts of predominantly European ancestry and identify candidate genes under selection in human populations. RESULTS We find the gene regulatory architecture of African and non-African populations is broadly shared, though there is a considerable amount of variation at individual loci across populations. Comparing our analyses to an equivalently sized cohort of European Americans, we find that QTL mapping in Africans improves the detection of expression QTLs and fine-mapping of causal variation. Integrating our QTL scans with signatures of natural selection, we find several genes related to immunity and metabolism that are highly differentiated between Africans and non-Africans, as well as a gene associated with pigmentation. CONCLUSION Extending QTL mapping studies beyond European ancestry, particularly to diverse indigenous populations, is vital for a complete understanding of the genetic architecture of human traits and can reveal novel functional variation underlying human traits and disease.
Collapse
Affiliation(s)
- Derek E Kelly
- Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Shweta Ramdas
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Rong Ma
- Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | - Jibril B Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - William Beggs
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Meredith Yeager
- Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Institutes of Health, Rockville, MD, USA
| | - Thomas B Nyambo
- Department of Biochemistry, Kampala International University in Tanzania, Dar Es Salaam, Tanzania
| | - Sabah A Omar
- Center for Biotechnology Research and Development, Kenya Medical Research Institute, Nairobi, Kenya
| | - Dawit Woldemeskel
- Microbial Cellular and Molecular Biology Department, Addis Ababa University, Addis Ababa, Ethiopia
| | - Gurja Belay
- Microbial Cellular and Molecular Biology Department, Addis Ababa University, Addis Ababa, Ethiopia
| | - Hongzhe Li
- Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher D Brown
- Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah A Tishkoff
- Genetics, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Biology, University of Pennsylvania, Philadelphia, USA.
| |
Collapse
|
70
|
Zhong L, Zheng M, Huang Y, Jiang T, Yang B, Huang L, Ma J. An atlas of expression quantitative trait loci of microRNA in longissimus muscle of eight-way crossbred pigs. J Genet Genomics 2023:S1673-8527(23)00046-2. [PMID: 36822265 DOI: 10.1016/j.jgg.2023.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/24/2023]
Abstract
MicroRNAs (miRNAs) are key regulators of myocyte development and traits, yet insight into the genetic basis of variation in miRNA expression is still limited. Here, we present a systematic analysis of expression quantitative trait loci (eQTL) for miRNA profiling in longissimus muscle of pigs from an eight-breed crossed heterogeneous population. By integrating whole-genome sequencing and miRNAomics data, we map 54 cis- and 292 trans-eQTLs at high resolution that are associated with the expression of 54 and 92 miRNAs, respectively. Twenty-three trans-acting loci are identified to affect the expression of nine myomiRs (known muscle-specific miRNAs). MiRNAs in mammalian conserved miRNA clusters are found to be subjected to regulation by shared cis-eQTLs, while the expression of mature miRNA-5p/-3p counterparts is more likely to be regulated by different cis-eQTLs. Fine mapping and bioinformatics analyses pinpoint the peak cis-eSNP of miR-4331-5p, rs344650810, which is located in its seed region, as a causal variant for the changes in expression and function of this miRNA. Additionally, rs344650810 is significantly (P < 0.01) correlated with the density and percentage of type I muscle fibers. Altogether, this study provides a comprehensive atlas of miRNA-eQTLs in porcine skeletal muscle and new insights into regulatory mechanisms of miRNA expression.
Collapse
Affiliation(s)
- Liepeng Zhong
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Min Zheng
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Yizhong Huang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Tao Jiang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Bin Yang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Lusheng Huang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China.
| | - Junwu Ma
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China.
| |
Collapse
|
71
|
Behera S, LeFaive J, Orchard P, Mahmoud M, Paulin LF, Farek J, Soto DC, Parker SCJ, Smith AV, Dennis MY, Zook JM, Sedlazeck FJ. FixItFelix: improving genomic analysis by fixing reference errors. Genome Biol 2023; 24:31. [PMID: 36810122 PMCID: PMC9942314 DOI: 10.1186/s13059-023-02863-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 01/20/2023] [Indexed: 02/23/2023] Open
Abstract
The current version of the human reference genome, GRCh38, contains a number of errors including 1.2 Mbp of falsely duplicated and 8.04 Mbp of collapsed regions. These errors impact the variant calling of 33 protein-coding genes, including 12 with medical relevance. Here, we present FixItFelix, an efficient remapping approach, together with a modified version of the GRCh38 reference genome that improves the subsequent analysis across these genes within minutes for an existing alignment file while maintaining the same coordinates. We showcase these improvements over multi-ethnic control samples, demonstrating improvements for population variant calling as well as eQTL studies.
Collapse
Affiliation(s)
- Sairam Behera
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jonathon LeFaive
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Peter Orchard
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Medhat Mahmoud
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Luis F Paulin
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jesse Farek
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Daniela C Soto
- Genome Center, MIND Institute, Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, CA, USA
| | - Stephen C J Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Albert V Smith
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Megan Y Dennis
- Genome Center, MIND Institute, Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, CA, USA
| | - Justin M Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Computer Science, Rice University, Houston, TX, USA.
| |
Collapse
|
72
|
Wu Y, Zhang CY, Wang L, Li Y, Xiao X. Genetic Insights of Schizophrenia via Single Cell RNA-Sequencing Analyses. Schizophr Bull 2023:7048714. [PMID: 36805283 DOI: 10.1093/schbul/sbad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
BACKGROUND Schizophrenia is a complex and heterogeneous disorder involving multiple regions and types of cells in the brain. Despite rapid progress made by genome-wide association studies (GWAS) of schizophrenia, the mechanisms of the illness underlying the GWAS significant loci remain less clear. STUDY DESIGN We investigated schizophrenia risk genes using summary-data-based Mendelian randomization based on single-cell sequencing data, and explored the types of brain cells involved in schizophrenia through the expression weighted cell-type enrichment analysis. RESULTS We identified 54 schizophrenia risk genes (two-thirds of these genes were not identified using sequencing data of bulk tissues) using single-cell RNA-sequencing data. Further cell type enrichment analysis showed that schizophrenia risk genes were highly expressed in excitatory neurons and caudal ganglionic eminence interneurons, suggesting putative roles of these cells in the pathogenesis of schizophrenia. We also found that these risk genes identified using single-cell sequencing results could form a large protein-protein interaction network with genes affected by disease-causing rare variants. CONCLUSIONS Through integrative analyses using expression data at single-cell levels, we identified 54 risk genes associated with schizophrenia. Notably, many of these genes were only identified using single-cell RNA-sequencing data, and their altered expression levels in particular types of cells, rather than in the bulk tissues, were related to the increased risk of schizophrenia. Our results provide novel insight into the biological mechanisms of schizophrenia, and future single-cell studies are necessary to further facilitate the understanding of the disorder.
Collapse
Affiliation(s)
- Yong Wu
- Research Center for Mental Health and Neuroscience, Wuhan Mental Health Center, Wuhan, Hubei, China.,Affiliated Wuhan Mental Health Center, Jianghan University, Wuhan, Hubei, China
| | - Chu-Yi Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Lu Wang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Yi Li
- Research Center for Mental Health and Neuroscience, Wuhan Mental Health Center, Wuhan, Hubei, China.,Affiliated Wuhan Mental Health Center, Jianghan University, Wuhan, Hubei, China.,Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, Hubei, China
| | - Xiao Xiao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| |
Collapse
|
73
|
Taylor HJ, Hung YH, Narisu N, Erdos MR, Kanke M, Yan T, Grenko CM, Swift AJ, Bonnycastle LL, Sethupathy P, Collins FS, Taylor DL. Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis. Proc Natl Acad Sci U S A 2023; 120:e2206797120. [PMID: 36757889 PMCID: PMC9963967 DOI: 10.1073/pnas.2206797120] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 01/11/2023] [Indexed: 02/10/2023] Open
Abstract
Genetic studies have identified ≥240 loci associated with the risk of type 2 diabetes (T2D), yet most of these loci lie in non-coding regions, masking the underlying molecular mechanisms. Recent studies investigating mRNA expression in human pancreatic islets have yielded important insights into the molecular drivers of normal islet function and T2D pathophysiology. However, similar studies investigating microRNA (miRNA) expression remain limited. Here, we present data from 63 individuals, the largest sequencing-based analysis of miRNA expression in human islets to date. We characterized the genetic regulation of miRNA expression by decomposing the expression of highly heritable miRNAs into cis- and trans-acting genetic components and mapping cis-acting loci associated with miRNA expression [miRNA-expression quantitative trait loci (eQTLs)]. We found i) 84 heritable miRNAs, primarily regulated by trans-acting genetic effects, and ii) 5 miRNA-eQTLs. We also used several different strategies to identify T2D-associated miRNAs. First, we colocalized miRNA-eQTLs with genetic loci associated with T2D and multiple glycemic traits, identifying one miRNA, miR-1908, that shares genetic signals for blood glucose and glycated hemoglobin (HbA1c). Next, we intersected miRNA seed regions and predicted target sites with credible set SNPs associated with T2D and glycemic traits and found 32 miRNAs that may have altered binding and function due to disrupted seed regions. Finally, we performed differential expression analysis and identified 14 miRNAs associated with T2D status-including miR-187-3p, miR-21-5p, miR-668, and miR-199b-5p-and 4 miRNAs associated with a polygenic score for HbA1c levels-miR-216a, miR-25, miR-30a-3p, and miR-30a-5p.
Collapse
Affiliation(s)
- Henry J. Taylor
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, CambridgeCB2 0BB, UK
- Heart and Lung Research Institute, University of Cambridge, CambridgeCB2 0BB, UK
| | - Yu-Han Hung
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY14853
| | - Narisu Narisu
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Michael R. Erdos
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Matthew Kanke
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY14853
| | - Tingfen Yan
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Caleb M. Grenko
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Amy J. Swift
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Lori L. Bonnycastle
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Praveen Sethupathy
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY14853
| | - Francis S. Collins
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - D. Leland Taylor
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| |
Collapse
|
74
|
Broadaway KA, Yin X, Williamson A, Parsons VA, Wilson EP, Moxley AH, Vadlamudi S, Varshney A, Jackson AU, Ahuja V, Bornstein SR, Corbin LJ, Delgado GE, Dwivedi OP, Fernandes Silva L, Frayling TM, Grallert H, Gustafsson S, Hakaste L, Hammar U, Herder C, Herrmann S, Højlund K, Hughes DA, Kleber ME, Lindgren CM, Liu CT, Luan J, Malmberg A, Moissl AP, Morris AP, Perakakis N, Peters A, Petrie JR, Roden M, Schwarz PEH, Sharma S, Silveira A, Strawbridge RJ, Tuomi T, Wood AR, Wu P, Zethelius B, Baldassarre D, Eriksson JG, Fall T, Florez JC, Fritsche A, Gigante B, Hamsten A, Kajantie E, Laakso M, Lahti J, Lawlor DA, Lind L, März W, Meigs JB, Sundström J, Timpson NJ, Wagner R, Walker M, Wareham NJ, Watkins H, Barroso I, O'Rahilly S, Grarup N, Parker SC, Boehnke M, Langenberg C, Wheeler E, Mohlke KL. Loci for insulin processing and secretion provide insight into type 2 diabetes risk. Am J Hum Genet 2023; 110:284-299. [PMID: 36693378 PMCID: PMC9943750 DOI: 10.1016/j.ajhg.2023.01.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 01/03/2023] [Indexed: 01/25/2023] Open
Abstract
Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.
Collapse
Affiliation(s)
- K Alaine Broadaway
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Xianyong Yin
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Alice Williamson
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK; University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK
| | - Victoria A Parsons
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Emma P Wilson
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Anne H Moxley
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | | | - Arushi Varshney
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Anne U Jackson
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Vasudha Ahuja
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Stefan R Bornstein
- Department of Internal Medicine, Metabolic and Vascular Medicine, MedicCal Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Laura J Corbin
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Om P Dwivedi
- University of Helsinki, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland
| | | | | | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Stefan Gustafsson
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Liisa Hakaste
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Ulf Hammar
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Christian Herder
- German Center for Diabetes Research, Neuherberg, Germany; Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sandra Herrmann
- Department of Internal Medicine, Prevention and Care of Diabetes, Medical Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany
| | | | - David A Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Marcus E Kleber
- Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany; SYNLAB MVZ Humangenetik Mannheim, Mannheim, BW, Germany
| | - Cecilia M Lindgren
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Nuffield Department of Population Health, University of Oxford, Oxford, UK; Wellcome Trust Centre Human Genetics, University of Oxford, Oxford, UK; Broad Institute, Cambridge, MA, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Anni Malmberg
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Angela P Moissl
- Institute of Nutritional Sciences, Friedrich-Schiller-University, Jena, Germany; Competence Cluster for Nutrition and Cardiovascular Health, Halle-Jena-Leipzig, Germany; Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Nikolaos Perakakis
- Department of Internal Medicine, Metabolic and Vascular Medicine, MedicCal Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - John R Petrie
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Peter E H Schwarz
- Department of Internal Medicine, Prevention and Care of Diabetes, Medical Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Sapna Sharma
- German Center for Diabetes Research, Neuherberg, Germany; Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Chair of Food Chemistry and Molecular Sensory Science, Technische Universität München, Freising, Germany
| | - Angela Silveira
- Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden; Oxford Biomedical Research Centre, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Rona J Strawbridge
- Institute of Health and Wellbeing, Mental Health and Wellbeing, University of Glasgow, Glasgow, UK; Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland; Abdominal Center, Endocrinology, Helsinki University Hospital, Helsinki, Finland
| | - Andrew R Wood
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Björn Zethelius
- Department of Geriatrics, Uppsala University, Uppsala, Sweden
| | - Damiano Baldassarre
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy; Cardiovascular Prevention Area, Centro Cardiologico Monzino I.R.C.C.S., Milan, Italy
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Folkhälsan Research Centre, Helsinki, Finland; Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore
| | - Tove Fall
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Andreas Fritsche
- Department of Internal Medicine, Diabetology, Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Bruna Gigante
- Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Anders Hamsten
- Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Eero Kajantie
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland; PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway; Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lars Lind
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Winfried März
- Synlab Academy, SYNLAB Holding Deutschland GmbH, Mannheim, BW, Germany; Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany
| | - James B Meigs
- Department of Medicine, Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Johan Sundström
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Robert Wagner
- Department of Internal Medicine, Diabetology, Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Mark Walker
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK; Health Data Research UK, Gibbs Building, London, UK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research, Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Stephen O'Rahilly
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Stephen Cj Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK; Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
| |
Collapse
|
75
|
Xu P, Wang M, Sharma NK, Comeau ME, Wabitsch M, Langefeld CD, Civelek M, Zhang B, Das SK. Multi-omic integration reveals cell-type-specific regulatory networks of insulin resistance in distinct ancestry populations. Cell Syst 2023; 14:41-57.e8. [PMID: 36630956 PMCID: PMC9852073 DOI: 10.1016/j.cels.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 09/26/2022] [Accepted: 12/13/2022] [Indexed: 01/12/2023]
Abstract
Our knowledge of the cell-type-specific mechanisms of insulin resistance remains limited. To dissect the cell-type-specific molecular signatures of insulin resistance, we performed a multiscale gene network analysis of adipose and muscle tissues in African and European ancestry populations. In adipose tissues, a comparative analysis revealed ethnically conserved cell-type signatures and two adipocyte subtype-enriched modules with opposite insulin sensitivity responses. The modules enriched for adipose stem and progenitor cells as well as immune cells showed negative correlations with insulin sensitivity. In muscle tissues, the modules enriched for stem cells and fibro-adipogenic progenitors responded to insulin sensitivity oppositely. The adipocyte and muscle fiber-enriched modules shared cellular-respiration-related genes but had tissue-specific rearrangements of gene regulations in response to insulin sensitivity. Integration of the gene co-expression and causal networks further pinpointed key drivers of insulin resistance. Together, this study revealed the cell-type-specific transcriptomic networks and signaling maps underlying insulin resistance in major glucose-responsive tissues. A record of this paper's transparent peer review process is included in the supplemental information.
Collapse
Affiliation(s)
- Peng Xu
- Department of Genetics & Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Minghui Wang
- Department of Genetics & Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Neeraj K Sharma
- Department of Internal Medicine, Section of Endocrinology and Metabolism, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Mary E Comeau
- Department of Biostatistics and Data Science, Division of Public Health Sciences, and Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Martin Wabitsch
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics and Adolescent Medicine, University Medical Center Ulm, Eythstr. 24, D-89075 Ulm, Germany
| | - Carl D Langefeld
- Department of Biostatistics and Data Science, Division of Public Health Sciences, and Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Mete Civelek
- Center for Public Health Genomics, Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Bin Zhang
- Department of Genetics & Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Swapan K Das
- Department of Internal Medicine, Section of Endocrinology and Metabolism, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
| |
Collapse
|
76
|
Lafferty MJ, Aygün N, Patel NK, Krupa O, Liang D, Wolter JM, Geschwind DH, de la Torre-Ubieta L, Stein JL. MicroRNA- eQTLs in the developing human neocortex link miR-4707-3p expression to brain size. eLife 2023; 12:e79488. [PMID: 36629315 PMCID: PMC9859047 DOI: 10.7554/elife.79488] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 01/10/2023] [Indexed: 01/12/2023] Open
Abstract
Expression quantitative trait loci (eQTL) data have proven important for linking non-coding loci to protein-coding genes. But eQTL studies rarely measure microRNAs (miRNAs), small non-coding RNAs known to play a role in human brain development and neurogenesis. Here, we performed small-RNA sequencing across 212 mid-gestation human neocortical tissue samples, measured 907 expressed miRNAs, discovering 111 of which were novel, and identified 85 local-miRNA-eQTLs. Colocalization of miRNA-eQTLs with GWAS summary statistics yielded one robust colocalization of miR-4707-3p expression with educational attainment and brain size phenotypes, where the miRNA expression increasing allele was associated with decreased brain size. Exogenous expression of miR-4707-3p in primary human neural progenitor cells decreased expression of predicted targets and increased cell proliferation, indicating miR-4707-3p modulates progenitor gene regulation and cell fate decisions. Integrating miRNA-eQTLs with existing GWAS yielded evidence of a miRNA that may influence human brain size and function via modulation of neocortical brain development.
Collapse
Affiliation(s)
- Michael J Lafferty
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Nil Aygün
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Niyanta K Patel
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Dan Liang
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Justin M Wolter
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel HillChapel HillUnited States
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel HillChapel HillUnited States
| | - Daniel H Geschwind
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Luis de la Torre-Ubieta
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel HillChapel HillUnited States
| |
Collapse
|
77
|
Okamoto J, Wang L, Yin X, Luca F, Pique-Regi R, Helms A, Im HK, Morrison J, Wen X. Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits. Am J Hum Genet 2023; 110:44-57. [PMID: 36608684 PMCID: PMC9892769 DOI: 10.1016/j.ajhg.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 12/06/2022] [Indexed: 01/07/2023] Open
Abstract
Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.
Collapse
Affiliation(s)
- Jeffrey Okamoto
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Lijia Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xianyong Yin
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Adam Helms
- University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Jean Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
| |
Collapse
|
78
|
Zhong Y, De T, Mishra M, Avitia J, Alarcon C, Perera MA. Leveraging drug perturbation to reveal genetic regulators of hepatic gene expression in African Americans. Am J Hum Genet 2023; 110:58-70. [PMID: 36608685 PMCID: PMC9892765 DOI: 10.1016/j.ajhg.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 12/07/2022] [Indexed: 01/07/2023] Open
Abstract
Expression quantitative locus (eQTL) studies have paved the way in identifying genetic variation impacting gene expression levels. African Americans (AAs) are disproportionately underrepresented in eQTL studies, resulting in a lack of power to identify population-specific regulatory variants especially related to drug response. Specific drugs are known to affect the biosynthesis of drug metabolism enzymes as well as other genes. We used drug perturbation in cultured primary hepatocytes derived from AAs to determine the effect of drug treatment on eQTL mapping and to identify the drug response eQTLs (reQTLs) that show altered effect size following drug treatment. Whole-genome genotyping (Illumina MEGA array) and RNA sequencing were performed on 60 primary hepatocyte cultures after treatment with six drugs (Rifampin, Phenytoin, Carbamazepine, Dexamethasone, Phenobarbital, and Omeprazole) and at baseline (no treatment). eQTLs were mapped by treatment and jointly with Meta-Tissue. We found varying transcriptional changes across different drug treatments and identified Nrf2 as a potential general transcriptional regulator. We jointly mapped eQTLs with gene expression data across all drug treatments and baseline, which increased our power to detect eQTLs by 2.7-fold. We also identified 2,988 reQTLs (eQTLs with altered effect size after drug treatment). reQTLs were more likely to overlap transcription factor binding sites, and we uncovered reQTLs for drug metabolizing genes such as CYP3A5. Our results provide insights into the genetic regulation of gene expression in hepatocytes through drug perturbation and provide insight into SNPs that effect the liver's ability to respond to transcription upregulation.
Collapse
Affiliation(s)
- Yizhen Zhong
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Tanima De
- Integrative Translational Genetic, Regeneron Genetic Center, Tarrytown, NY 10591, USA
| | - Mrinal Mishra
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Juan Avitia
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Cristina Alarcon
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Minoli A Perera
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
| |
Collapse
|
79
|
Hoellinger T, Mestre C, Aschard H, Le Goff W, Foissac S, Faraut T, Djebali S. Enhancer/gene relationships: Need for more reliable genome-wide reference sets. Front Bioinform 2023; 3:1092853. [PMID: 36909938 PMCID: PMC9999192 DOI: 10.3389/fbinf.2023.1092853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/07/2023] [Indexed: 02/26/2023] Open
Abstract
Differences in cells' functions arise from differential activity of regulatory elements, including enhancers. Enhancers are cis-regulatory elements that cooperate with promoters through transcription factors to activate the expression of one or several genes by getting physically close to them in the 3D space of the nucleus. There is increasing evidence that genetic variants associated with common diseases are enriched in enhancers active in cell types relevant to these diseases. Identifying the enhancers associated with genes and conversely, the sets of genes activated by each enhancer (the so-called enhancer/gene or E/G relationships) across cell types, can help understanding the genetic mechanisms underlying human diseases. There are three broad approaches for the genome-wide identification of E/G relationships in a cell type: 1) genetic link methods or eQTL, 2) functional link methods based on 1D functional data such as open chromatin, histone mark or gene expression and 3) spatial link methods based on 3D data such as HiC. Since 1) and 3) are costly, the current strategy is to develop functional link methods and to use data from 1) and 3) as reference to evaluate them. However, there is still no consensus on the best functional link method to date, and method comparison remain seldom. Here, we compared the relative performances of three recent methods for the identification of enhancer-gene links, TargetFinder, Average-Rank, and the ABC model, using the three latest benchmarks from the field: a reference that combines 3D and eQTL data, called BENGI, and two genetic screening references, called CRiFF and CRiSPRi. Overall, none of the three methods performed best on the three references. CRiFF and CRISPRi reference sets are likely more reliable, but CRiFF is not genome-wide and CRiFF and CRISPRi are mostly available on the K562 cancer cell line. The BENGI reference set is genome-wide but likely contains many false positives. This study therefore calls for new reliable and genome-wide E/G reference data rather than new functional link E/G identification methods.
Collapse
Affiliation(s)
- Tristan Hoellinger
- IRSD, Université de Toulouse, INSERM, INRAE, ENVT, Univ Toulouse III - Paul Sabatier (UPS), Toulouse, France.,INSA Toulouse, INP-ENSEEIHT, Toulouse, France
| | - Camille Mestre
- GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, Toulouse, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, France.,Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Wilfried Le Goff
- Sorbonne Université, INSERM, Institute of Cardiometabolism and Nutrition (ICAN), UMR_S1166, Paris, France
| | - Sylvain Foissac
- GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, Toulouse, France
| | - Thomas Faraut
- GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, Toulouse, France
| | - Sarah Djebali
- IRSD, Université de Toulouse, INSERM, INRAE, ENVT, Univ Toulouse III - Paul Sabatier (UPS), Toulouse, France.,GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, Toulouse, France
| |
Collapse
|
80
|
Ratnapriya R. The Role of Gene Expression Regulation on Genetic Risk of Age-Related Macular Degeneration. Adv Exp Med Biol 2023; 1415:61-66. [PMID: 37440015 DOI: 10.1007/978-3-031-27681-1_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
Age-related macular degeneration (AMD) is a major cause of irreversible vision loss in the elderly. It is a complex multifactorial disease that is caused by the cumulative impact of genetic predisposition, environmental stress, and advanced aging. Knowledge of genetic risk factors underlying AMD susceptibility has advanced rapidly in the past decade that can be largely credited to genome-wide association studies (GWAS) and next-generation sequencing (NGS) efforts. GWAS have identified 34 genetic risk loci for AMD; the majority of which are in the noncoding genome. Several lines of evidence suggest that a complex trait-associated variant is likely to regulate the gene expression (acting as expression quantitative trait loci (eQTLs)), and there is a significant enrichment of GWAS-associated variants within eQTLs. In the last two years, eQTL studies in AMD-relevant tissues have provided functional interpretation of several AMD-GWAS loci. This review highlights the knowledge gained to date and discusses future directions to bridge the gap between genetic predisposition and biological mechanisms to reap the full benefits of GWAS findings.
Collapse
Affiliation(s)
- Rinki Ratnapriya
- Department of Ophthalmology, Baylor College of Medicine, Houston, TX, USA.
| |
Collapse
|
81
|
Falker-Gieske C, Bennewitz J, Tetens J. Structural variation and eQTL analysis in two experimental populations of chickens divergently selected for feather-pecking behavior. Neurogenetics 2023; 24:29-41. [PMID: 36449109 PMCID: PMC9823035 DOI: 10.1007/s10048-022-00705-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 11/19/2022] [Indexed: 12/02/2022]
Abstract
Feather pecking (FP) is a damaging nonaggressive behavior in laying hens with a heritable component. Its occurrence has been linked to the immune system, the circadian clock, and foraging behavior. Furthermore, dysregulation of miRNA biogenesis, disturbance of the gamma-aminobutyric acid (GABAergic) system, as well as neurodevelopmental deficiencies are currently under debate as factors influencing the propensity for FP behavior. Past studies, which focused on the dissection of the genetic factors involved in FP, relied on single nucleotide polymorphisms (SNPs) and short insertions and deletions < 50 bp (InDels). These variant classes only represent a certain fraction of the genetic variation of an organism. Hence, we reanalyzed whole-genome sequencing data from two experimental populations, which have been divergently selected for FP behavior for over more than 15 generations, performed variant calling for structural variants (SVs) as well as tandem repeats (TRs), and jointly analyzed the data with SNPs and InDels. Genotype imputation and subsequent genome-wide association studies, in combination with expression quantitative trait loci analysis, led to the discovery of multiple variants influencing the GABAergic system. These include a significantly associated TR downstream of the GABA receptor subunit beta-3 (GABRB3) gene, two microRNAs targeting several GABA receptor genes, and dystrophin (DMD), a direct regulator of GABA receptor clustering. Furthermore, we found the transcription factor ETV1 to be associated with the differential expression of 23 genes, which points toward a role of ETV1, together with SMAD4 and KLF14, in the disturbed neurodevelopment of high-feather pecking chickens.
Collapse
Affiliation(s)
- Clemens Falker-Gieske
- Department of Animal Sciences, Georg-August-University, Burckhardtweg 2, 37077, Göttingen, Germany.
| | - Jörn Bennewitz
- grid.9464.f0000 0001 2290 1502Institute of Animal Science, University of Hohenheim, Garbenstr. 17, 70599 Stuttgart, Germany
| | - Jens Tetens
- grid.7450.60000 0001 2364 4210Department of Animal Sciences, Georg-August-University, Burckhardtweg 2, 37077 Göttingen, Germany ,grid.7450.60000 0001 2364 4210Center for Integrated Breeding Research, Georg-August-University, Albrecht-Thaer-Weg 3, 37075 Göttingen, Germany
| |
Collapse
|
82
|
Cardamone G, Paraboschi EM, Soldà G, Liberatore G, Rimoldi V, Cibella J, Airi F, Tisato V, Cantoni C, Gallia F, Gemmati D, Piccio L, Duga S, Nobile-Orazio E, Asselta R. The circular RNA landscape in multiple sclerosis: Disease-specific associated variants and exon methylation shape circular RNA expression profile. Mult Scler Relat Disord 2023; 69:104426. [PMID: 36446168 DOI: 10.1016/j.msard.2022.104426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/16/2022] [Accepted: 11/22/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Circular RNAs (circRNAs) are a class of non-coding RNAs increasingly emerging as crucial actors in the pathogenesis of human diseases, including autoimmune and neurological disorders as multiple sclerosis (MS). Despite several efforts, the mechanisms regulating circRNAs expression are still largely unknown and the circRNA profile and regulation in MS-relevant cell models has not been completely investigated. In this work, we aimed at exploring the global landscape of circRNA expression in MS patients, also evaluating a possible correlation with their genetic and epigenetic background. METHODS We performed RNA-seq experiments on circRNA-enriched samples, derived from peripheral blood mononuclear cells (PBMCs) of 10 MS patients and 10 matched controls and performed differential circRNA expression. The genetic background was evaluated using array genotyping, and an expression quantitative trait loci (eQTL) analysis was carried out. RESULTS Expression analysis revealed 166 differentially expressed circRNAs in MS patients, 125 of which are downregulated. One of the top dysregulated circRNAs, hsa_circ_0007990, derives from the PGAP3 gene, encoding a protein relevant for the control of autoimmune responses. The downregulation of this circRNA was confirmed in two independent replication cohorts, suggesting its implementation as a possible RNA-based biomarker. The eQTL analysis evidenced a significant association between 89 MS-associated loci and the expression of at least one circRNA, suggesting that MS-associated variants could impact on disease pathogenesis by altering circRNA profiles. Finally, we found a significant correlation between exon methylation and circRNA expression levels, supporting the hypothesis that epigenetic features may play an important role in the definition of the cell circRNA pool. CONCLUSION We described the circRNA expression profile of PBMCs in MS patients, suggesting that MS-associated variants may tune the expression levels of circRNAs acting as "circ-QTLs", and proposing a role for exon-based DNA methylation in regulating circRNA expression.
Collapse
Affiliation(s)
- Giulia Cardamone
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy
| | - Elvezia Maria Paraboschi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy.
| | - Giulia Soldà
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Giuseppe Liberatore
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Valeria Rimoldi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Javier Cibella
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Federica Airi
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Veronica Tisato
- Department of Translational Medicine, University of Ferrara, Italy
| | - Claudia Cantoni
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Francesca Gallia
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Donato Gemmati
- Department of Translational Medicine, University of Ferrara, Italy; Center Haemostasis & Thrombosis, University of Ferrara, Italy
| | - Laura Piccio
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA; Brain and Mind Centre, University of Sydney, Sydney, NSW 2050, Australia
| | - Stefano Duga
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Eduardo Nobile-Orazio
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy; Department of Medical Biotechnology and Translational Medicine, Milan University, Milan, Italy
| | - Rosanna Asselta
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| |
Collapse
|
83
|
Crespo-Piazuelo D, Acloque H, González-Rodríguez O, Mongellaz M, Mercat MJ, Bink MCAM, Huisman AE, Ramayo-Caldas Y, Sánchez JP, Ballester M. Identification of transcriptional regulatory variants in pig duodenum, liver, and muscle tissues. Gigascience 2022; 12:giad042. [PMID: 37354463 PMCID: PMC10290502 DOI: 10.1093/gigascience/giad042] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/13/2023] [Accepted: 05/25/2023] [Indexed: 06/26/2023] Open
Abstract
BACKGROUND In humans and livestock species, genome-wide association studies (GWAS) have been applied to study the association between variants distributed across the genome and a phenotype of interest. To discover genetic polymorphisms affecting the duodenum, liver, and muscle transcriptomes of 300 pigs from 3 different breeds (Duroc, Landrace, and Large White), we performed expression GWAS between 25,315,878 polymorphisms and the expression of 13,891 genes in duodenum, 12,748 genes in liver, and 11,617 genes in muscle. RESULTS More than 9.68 × 1011 association tests were performed, yielding 14,096,080 significantly associated variants, which were grouped in 26,414 expression quantitative trait locus (eQTL) regions. Over 56% of the variants were within 1 Mb of their associated gene. In addition to the 100-kb region upstream of the transcription start site, we identified the importance of the 100-kb region downstream of the 3'UTR for gene regulation, as most of the cis-regulatory variants were located within these 2 regions. We also observed 39,874 hotspot regulatory polymorphisms associated with the expression of 10 or more genes that could modify the protein structure or the expression of a regulator gene. In addition, 2 motifs (5'-GATCCNGYGTTGCYG-3' and a poly(A) sequence) were enriched across the 3 tissues within the neighboring sequences of the most significant single-nucleotide polymorphisms in each cis-eQTL region. CONCLUSIONS The 14 million significant associations obtained in this study are publicly available and have enabled the identification of expression-associated cis-, trans-, and hotspot regulatory variants within and across tissues, thus shedding light on the molecular mechanisms of regulatory variations that shape end-trait phenotypes.
Collapse
Affiliation(s)
- Daniel Crespo-Piazuelo
- Animal Breeding and Genetics Program, IRTA, Torre Marimon, Caldes de Montbui (08140), Spain
| | - Hervé Acloque
- GABI, Université Paris-Saclay, INRAE, AgroParisTech, Jouy-en-Josas (78350), France
| | | | - Mayrone Mongellaz
- GABI, Université Paris-Saclay, INRAE, AgroParisTech, Jouy-en-Josas (78350), France
| | | | - Marco C A M Bink
- Hendrix Genetics Research Technology & Services B.V., Boxmeer (5830 AC), The Netherlands
| | | | - Yuliaxis Ramayo-Caldas
- Animal Breeding and Genetics Program, IRTA, Torre Marimon, Caldes de Montbui (08140), Spain
| | - Juan Pablo Sánchez
- Animal Breeding and Genetics Program, IRTA, Torre Marimon, Caldes de Montbui (08140), Spain
| | - Maria Ballester
- Animal Breeding and Genetics Program, IRTA, Torre Marimon, Caldes de Montbui (08140), Spain
| |
Collapse
|
84
|
Zhang X, Geng T, Li N, Wu L, Wang Y, Zheng D, Guo B, Wang B. Associations of Lipids and Lipid-Lowering Drugs with Risk of Vascular Dementia: A Mendelian Randomization Study. Nutrients 2022; 15:nu15010069. [PMID: 36615727 PMCID: PMC9824558 DOI: 10.3390/nu15010069] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Accumulating observational studies suggested that hypercholesterolemia is associated with vascular dementia (VaD); however, the causality between them remains unclear. Hence, the aim of this study is to infer causal associations of circulating lipid-related traits [including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), apolipoprotein A-I (apoA-I), and apolipoprotein B (apoB)] with VaD jointly using univariable MR (uvMR), multivariable MR (mvMR) and bidirectional two-sample MR methods. Then, the summary-data-based MR (SMR) and two-sample MR analysis were conducted to investigate the association of lipid-lowering drugs target genes expression (including HMGCR, PCSK9, NPC1L1, and APOB) and LDL-C level mediated by these target genes with VaD. The results of forward MR analyses found that genetically predicted HDL-C, LDL-C, TG, apoA-I, and apoB concentrations were not significantly associated with the risk of VaD (all p > 0.05). Notably, there was suggestive evidence for a causal effect of genetically predicted VaD on HDL-C via reverse MR analysis [odds ratio (OR), 0.997; 95% confidence interval (CI), 0.994−0.999; p = 0.022]. On the contrary, the MR results showed no significant relationship between VaD with LDL-C, TG, apoA-I, and apoB. The results for the SMR method found that there was no evidence of association for expression of HMGCR, PCSK9, NPC1L1, and APOB gene with risk of VaD. Furthermore, the result of MR analysis provided evidence for the decreased LDL-C level mediated by gene HMGCR reduced the risk of VaD (OR, 18.381; 95% CI, 2.092−161.474; p = 0.009). Oppositely, none of the IVW methods indicated any causal effects for the other three genes. Using genetic data, this study provides evidence that the VaD risk may cause a reduction of HDL-C level. Additionally, the finding supports the hypothesis that lowering LDL-C levels using statins may be an effective prevention strategy for VaD risk, which requires clinical trials to confirm this result in the future.
Collapse
Affiliation(s)
- Xiaoyu Zhang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Tao Geng
- Geriatric Department, Emergency General Hospital, Beijing 100028, China
| | - Ning Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Lijuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Youxin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Deqiang Zheng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Bo Guo
- Department of Hematology, The Second Medical Centre & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China
- Correspondence: (B.G.); (B.W.); Tel.: +86-1066876227 (B.G.); +86-1062856765 (B.W.)
| | - Baoguo Wang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
- Correspondence: (B.G.); (B.W.); Tel.: +86-1066876227 (B.G.); +86-1062856765 (B.W.)
| |
Collapse
|
85
|
Connally NJ, Nazeen S, Lee D, Shi H, Stamatoyannopoulos J, Chun S, Cotsapas C, Cassa CA, Sunyaev SR. The missing link between genetic association and regulatory function. eLife 2022; 11:74970. [PMID: 36515579 PMCID: PMC9842386 DOI: 10.7554/elife.74970] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/02/2022] [Indexed: 12/15/2022] Open
Abstract
The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis-linked genes. However, despite the availability of gene expression and epigenomic datasets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic expression QTLs, suggesting that better models are needed. The field must confront this deficit and pursue this 'missing regulation.'
Collapse
Affiliation(s)
- Noah J Connally
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Sumaiya Nazeen
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Department of Neurology, Harvard Medical SchoolBostonUnited States
| | - Daniel Lee
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Huwenbo Shi
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
- Department of Epidemiology, Harvard T.H. Chan School of Public HealthBostonUnited States
| | | | - Sung Chun
- Division of Pulmonary Medicine, Boston Children’s HospitalBostonUnited States
| | - Chris Cotsapas
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
- Department of Neurology, Yale Medical SchoolNew HavenUnited States
- Department of Genetics, Yale Medical SchoolNew HavenUnited States
| | - Christopher A Cassa
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Shamil R Sunyaev
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| |
Collapse
|
86
|
Li J, Mukiibi R, Jiminez J, Wang Z, Akanno EC, Timsit E, Plastow GS. Applying multi-omics data to study the genetic background of bovine respiratory disease infection in feedlot crossbred cattle. Front Genet 2022; 13:1046192. [PMID: 36579334 PMCID: PMC9790935 DOI: 10.3389/fgene.2022.1046192] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022] Open
Abstract
Bovine respiratory disease (BRD) is the most common and costly infectious disease affecting the wellbeing and productivity of beef cattle in North America. BRD is a complex disease whose development is dependent on environmental factors and host genetics. Due to the polymicrobial nature of BRD, our understanding of the genetic and molecular mechanisms underlying the disease is still limited. This knowledge would augment the development of better genetic/genomic selection strategies and more accurate diagnostic tools to reduce BRD prevalence. Therefore, this study aimed to utilize multi-omics data (genomics, transcriptomics, and metabolomics) analyses to study the genetic and molecular mechanisms of BRD infection. Blood samples of 143 cattle (80 BRD; 63 non-BRD animals) were collected for genotyping, RNA sequencing, and metabolite profiling. Firstly, a genome-wide association study (GWAS) was performed for BRD susceptibility using 207,038 SNPs. Two SNPs (Chr5:25858264 and BovineHD1800016801) were identified as associated (p-value <1 × 10-5) with BRD susceptibility. Secondly, differential gene expression between BRD and non-BRD animals was studied. At the significance threshold used (log2FC>2, logCPM>2, and FDR<0.01), 101 differentially expressed (DE) genes were identified. These DE genes significantly (p-value <0.05) enriched several immune responses related functions such as inflammatory response. Additionally, we performed expression quantitative trait loci (eQTL) analysis and identified 420 cis-eQTLs and 144 trans-eQTLs significantly (FDR <0.05) associated with the expression of DE genes. Interestingly, eQTL results indicated the most significant SNP (Chr5:25858264) identified via GWAS was a cis-eQTL for DE gene GPR84. This analysis also demonstrated that an important SNP (rs209419196) located in the promoter region of the DE gene BPI significantly influenced the expression of this gene. Finally, the abundance of 31 metabolites was significantly (FDR <0.05) different between BRD and non-BRD animals, and 17 of them showed correlations with multiple DE genes, which shed light on the interactions between immune response and metabolism. This study identified associations between genome, transcriptome, metabolome, and BRD phenotype of feedlot crossbred cattle. The findings may be useful for the development of genomic selection strategies for BRD susceptibility, and for the development of new diagnostic and therapeutic tools.
Collapse
Affiliation(s)
- Jiyuan Li
- Livestock Gentec, Department of Agriculture, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Robert Mukiibi
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Janelle Jiminez
- Livestock Gentec, Department of Agriculture, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Zhiquan Wang
- Livestock Gentec, Department of Agriculture, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Everestus C. Akanno
- Livestock Gentec, Department of Agriculture, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Edouard Timsit
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Graham S. Plastow
- Livestock Gentec, Department of Agriculture, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada,*Correspondence: Graham S. Plastow,
| |
Collapse
|
87
|
Gerussi A, Soskic B, Asselta R, Invernizzi P, Gershwin ME. GWAS and autoimmunity: What have we learned and what next. J Autoimmun 2022; 133:102922. [PMID: 36209690 DOI: 10.1016/j.jaut.2022.102922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/22/2022] [Accepted: 09/23/2022] [Indexed: 12/07/2022]
Abstract
Autoimmune diseases are common conditions characterized by loss of tolerance, female predominance and a remarkable heterogeneity among different populations. Most often they are polygenic and several genetic loci have been linked with the risk of developing autoimmune diseases. However, causal inference is difficult. When the genomic revolution began there were high hopes of translating fast genetic analyses to the bedside but this has proven to be challenging. Nonetheless, over the last decade, fine-mapping strategies have greatly improved; one of the most significant research lines focuses on the in vivo and ex vivo definition of the effect of genetic variants within the target tissues and within specific subpopulations of immune cells that are involved in the disease pathogenesis. This strategy also includes the longitudinal tracking of a large number of immunophenotypes in many individuals to build a large reference atlas for variant characterization. In this review, we discuss the results obtained by GWAS in autoimmune diseases and review recent advances in fine mapping strategies. More importantly, we discuss gaps and future directions.
Collapse
Affiliation(s)
- Alessio Gerussi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy.
| | - Blagoje Soskic
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157 Milan, Italy
| | - Rosanna Asselta
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Merrill E Gershwin
- Division of Rheumatology, Allergy and Clinical Immunology, University of California, Davis, Davis, CA, United States
| |
Collapse
|
88
|
Long E, Yin J, Funderburk KM, Xu M, Feng J, Kane A, Zhang T, Myers T, Golden A, Thakur R, Kong H, Jessop L, Kim EY, Jones K, Chari R, Machiela MJ, Yu K, Iles MM, Landi MT, Law MH, Chanock SJ, Brown KM, Choi J. Massively parallel reporter assays and variant scoring identified functional variants and target genes for melanoma loci and highlighted cell-type specificity. Am J Hum Genet 2022; 109:2210-2229. [PMID: 36423637 PMCID: PMC9748337 DOI: 10.1016/j.ajhg.2022.11.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/02/2022] [Indexed: 11/24/2022] Open
Abstract
The most recent genome-wide association study (GWAS) of cutaneous melanoma identified 54 risk-associated loci, but functional variants and their target genes for most have not been established. Here, we performed massively parallel reporter assays (MPRAs) by using malignant melanoma and normal melanocyte cells and further integrated multi-layer annotation to systematically prioritize functional variants and susceptibility genes from these GWAS loci. Of 1,992 risk-associated variants tested in MPRAs, we identified 285 from 42 loci (78% of the known loci) displaying significant allelic transcriptional activities in either cell type (FDR < 1%). We further characterized MPRA-significant variants by motif prediction, epigenomic annotation, and statistical/functional fine-mapping to create integrative variant scores, which prioritized one to six plausible candidate variants per locus for the 42 loci and nominated a single variant for 43% of these loci. Overlaying the MPRA-significant variants with genome-wide significant expression or methylation quantitative trait loci (eQTLs or meQTLs, respectively) from melanocytes or melanomas identified candidate susceptibility genes for 60% of variants (172 of 285 variants). CRISPRi of top-scoring variants validated their cis-regulatory effect on the eQTL target genes, MAFF (22q13.1) and GPRC5A (12p13.1). Finally, we identified 36 melanoma-specific and 45 melanocyte-specific MPRA-significant variants, a subset of which are linked to cell-type-specific target genes. Analyses of transcription factor availability in MPRA datasets and variant-transcription-factor interaction in eQTL datasets highlighted the roles of transcription factors in cell-type-specific variant functionality. In conclusion, MPRAs along with variant scoring effectively prioritized plausible candidates for most melanoma GWAS loci and highlighted cellular contexts where the susceptibility variants are functional.
Collapse
Affiliation(s)
- Erping Long
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jinhu Yin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Karen M. Funderburk
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Mai Xu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - James Feng
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Alexander Kane
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Timothy Myers
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Alyxandra Golden
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Rohit Thakur
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Hyunkyung Kong
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Lea Jessop
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Eun Young Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kristine Jones
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Raj Chari
- Genome Modification Core, Frederick National Lab for Cancer Research, National Cancer Institute, Frederick, MD, USA
| | - Mitchell J. Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | | | - Mark M. Iles
- Leeds Institute for Data Analytics, School of Medicine, University of Leeds, Leeds LS2 9NL, UK
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Matthew H. Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia,Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia,School of Biomedical Sciences, University of Queensland, Brisbane, QLD, Australia
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Kevin M. Brown
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA,Corresponding author
| |
Collapse
|
89
|
Rasooly D, Peloso GM, Giambartolomei C. Bayesian Genetic Colocalization Test of Two Traits Using coloc. Curr Protoc 2022; 2:e627. [PMID: 36515558 DOI: 10.1002/cpz1.627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Genetic colocalization is an approach for determining whether a genetic variant at a particular locus is shared across multiple phenotypes. Genome-wide association studies (GWAS) have successfully mapped genetic variants associated with thousands of complex traits and diseases. However, a large proportion of GWAS signals fall in non-coding regions of the genome, making functional interpretation a challenge. Colocalization relies on a Bayesian framework that can integrate summary statistics, for example those derived from GWAS and expression quantitative trait loci (eQTL) mapping, to assess whether two or more independent association signals at a region of interest are consistent with a shared causal variant. The results from a colocalization analysis may be used to evaluate putative causal relationships between omics-based molecular measurements and a complex disease, and can generate hypotheses that may be followed up by tailored experiments. In this article, we present an easy and straightforward protocol for conducting a Bayesian test for colocalization of two traits using the 'coloc' package in R with summary-level results derived from GWAS and eQTL studies. We also provide general guidelines that can assist in the interpretation of findings generated from colocalization analyses. © 2022 Wiley Periodicals LLC. Basic Protocol: Performing a genetic colocalization analysis using the 'coloc' package in R and summary-level data Support Protocol: Installing the 'coloc' R package.
Collapse
Affiliation(s)
- Danielle Rasooly
- Division of Aging, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Claudia Giambartolomei
- Non-Coding RNAs and RNA-Based Therapeutics, Istituto Italiano di Tecnologia, Via Morego, Genova, Italy
| |
Collapse
|
90
|
Abstract
Since the identification of sickle cell trait as a heritable form of resistance to malaria, candidate gene studies, linkage analysis paired with sequencing, and genome-wide association (GWA) studies have revealed many examples of genetic resistance and susceptibility to infectious diseases. GWA studies enabled the identification of many common variants associated with small shifts in susceptibility to infectious diseases. This is exemplified by multiple loci associated with leprosy, malaria, HIV, tuberculosis, and coronavirus disease 2019 (COVID-19), which illuminate genetic architecture and implicate pathways underlying pathophysiology. Despite these successes, most of the heritability of infectious diseases remains to be explained. As the field advances, current limitations may be overcome by applying methodological innovations such as cellular GWA studies and phenome-wide association (PheWA) studies as well as by improving methodological rigor with more precise case definitions, deeper phenotyping, increased cohort diversity, and functional validation of candidate loci in the laboratory or human challenge studies.
Collapse
Affiliation(s)
- Kyle D Gibbs
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, North Carolina, USA;
| | - Benjamin H Schott
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, North Carolina, USA; .,Duke University Program in Genetics and Genomics, Duke University, Durham, North Carolina, USA
| | - Dennis C Ko
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, North Carolina, USA; .,Duke University Program in Genetics and Genomics, Duke University, Durham, North Carolina, USA.,Division of Infectious Diseases, Department of Medicine, School of Medicine, Duke University, Durham, North Carolina, USA
| |
Collapse
|
91
|
Xiao X, Li R, Wu C, Yan Y, Yuan M, Cui B, Zhang Y, Zhang C, Zhang X, Zhang W, Hui R, Wang Y. A genome-wide association study identifies a novel association between SDC3 and apparent treatment-resistant hypertension. BMC Med 2022; 20:463. [PMID: 36447229 PMCID: PMC9710180 DOI: 10.1186/s12916-022-02665-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 11/14/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Compared with patients who require fewer antihypertensive agents, those with apparent treatment-resistant hypertension (aTRH) are at increased risk for cardiovascular and all-cause mortality, independent of blood pressure control. However, the etiopathogenesis of aTRH is still poorly elucidated. METHODS We performed a genome-wide association study (GWAS) in first cohort including 586 aTRHs and 871 healthy controls. Next, expression quantitative trait locus (eQTL) analysis was used to identify genes that are regulated by single nucleotide polymorphisms (SNPs) derived from the GWAS. Then, we verified the genes obtained from the eQTL analysis in the validation cohort including 65 aTRHs, 96 hypertensives, and 100 healthy controls through gene expression profiling analysis and real-time quantitative polymerase chain reaction (RT-qPCR) assay. RESULTS The GWAS in first cohort revealed four suggestive loci (1p35, 4q13.2-21.1, 5q22-23.2, and 15q11.1-q12) represented by 23 SNPs. The 23 significant SNPs were in or near LAPTM5, SDC3, UGT2A1, FTMT, and NIPA1. eQTL analysis uncovered 14 SNPs in 1p35 locus all had same regulation directions for SDC3 and LAPTM5. The disease susceptible alleles of SNPs in 1p35 locus were associated with lower gene expression for SDC3 and higher gene expression for LAPTM5. The disease susceptible alleles of SNPs in 4q13.2-21.1 were associated with higher gene expression for UGT2B4. GTEx database did not show any statistically significant eQTLs between the SNPs in 5q22-23.2 and 15q11.1-q12 loci and their influenced genes. Then, gene expression profiling analysis in the validation cohort confirmed lower expression of SDC3 in aTRH but no significant differences on LAPTM5 and UGT2B4, when compared with controls and hypertensives, respectively. RT-qPCR assay further verified the lower expression of SDC3 in aTRH. CONCLUSIONS Our study identified a novel association of SDC3 with aTRH, which contributes to the elucidation of its etiopathogenesis and provides a promising therapeutic target.
Collapse
Affiliation(s)
- Xiao Xiao
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Rui Li
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Cunjin Wu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Yupeng Yan
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Mengmeng Yuan
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Bing Cui
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Yu Zhang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Channa Zhang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Xiaoxia Zhang
- Department of Pharmacy, The First Affiliated Hospital, Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shaanxi, China
| | - Weili Zhang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Rutai Hui
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Yibo Wang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China.
| |
Collapse
|
92
|
Lohia R, Fox N, Gillis J. A global high-density chromatin interaction network reveals functional long-range and trans-chromosomal relationships. Genome Biol 2022; 23:238. [PMID: 36352464 DOI: 10.1186/s13059-022-02790-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 10/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Chromatin contacts are essential for gene-expression regulation; however, obtaining a high-resolution genome-wide chromatin contact map is still prohibitively expensive owing to large genome sizes and the quadratic scale of pairwise data. Chromosome conformation capture (3C)-based methods such as Hi-C have been extensively used to obtain chromatin contacts. However, since the sparsity of these maps increases with an increase in genomic distance between contacts, long-range or trans-chromatin contacts are especially challenging to sample. RESULTS Here, we create a high-density reference genome-wide chromatin contact map using a meta-analytic approach. We integrate 3600 human, 6700 mouse, and 500 fly Hi-C experiments to create species-specific meta-Hi-C chromatin contact maps with 304 billion, 193 billion, and 19 billion contacts in respective species. We validate that meta-Hi-C contact maps are uniquely powered to capture functional chromatin contacts in both cis and trans. We find that while individual dataset Hi-C networks are largely unable to predict any long-range coexpression (median 0.54 AUC), meta-Hi-C networks perform comparably in both cis and trans (0.65 AUC vs 0.64 AUC). Similarly, for long-range expression quantitative trait loci (eQTL), meta-Hi-C contacts outperform all individual Hi-C experiments, providing an improvement over the conventionally used linear genomic distance-based association. Assessing between species, we find patterns of chromatin contact conservation in both cis and trans and strong associations with coexpression even in species for which Hi-C data is lacking. CONCLUSIONS We have generated an integrated chromatin interaction network which complements a large number of methodological and analytic approaches focused on improved specificity or interpretation. This high-depth "super-experiment" is surprisingly powerful in capturing long-range functional relationships of chromatin interactions, which are now able to predict coexpression, eQTLs, and cross-species relationships. The meta-Hi-C networks are available at https://labshare.cshl.edu/shares/gillislab/resource/HiC/ .
Collapse
|
93
|
Tan Z, Peng Y, Xiong Y, Xiong F, Zhang Y, Guo N, Tu Z, Zong Z, Wu X, Ye J, Xia C, Zhu T, Liu Y, Lou H, Liu D, Lu S, Yao X, Liu K, Snowdon RJ, Golicz AA, Xie W, Guo L, Zhao H. Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus. Genome Biol 2022; 23:233. [PMID: 36345039 PMCID: PMC9639296 DOI: 10.1186/s13059-022-02801-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 10/22/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Regulation of gene expression plays an essential role in controlling the phenotypes of plants. Brassica napus (B. napus) is an important source for the vegetable oil in the world, and the seed oil content is an important trait of B. napus. RESULTS We perform a comprehensive analysis of the transcriptional variability in the seeds of B. napus at two developmental stages, 20 and 40 days after flowering (DAF). We detect 53,759 and 53,550 independent expression quantitative trait loci (eQTLs) for 79,605 and 76,713 expressed genes at 20 and 40 DAF, respectively. Among them, the local eQTLs are mapped to the adjacent genes more frequently. The adjacent gene pairs are regulated by local eQTLs with the same open chromatin state and show a stronger mode of expression piggybacking. Inter-subgenomic analysis indicates that there is a feedback regulation for the homoeologous gene pairs to maintain partial expression dosage. We also identify 141 eQTL hotspots and find that hotspot87-88 co-localizes with a QTL for the seed oil content. To further resolve the regulatory network of this eQTL hotspot, we construct the XGBoost model using 856 RNA-seq datasets and the Basenji model using 59 ATAC-seq datasets. Using these two models, we predict the mechanisms affecting the seed oil content regulated by hotspot87-88 and experimentally validate that the transcription factors, NAC13 and SCL31, positively regulate the seed oil content. CONCLUSIONS We comprehensively characterize the gene regulatory features in the seeds of B. napus and reveal the gene networks regulating the seed oil content of B. napus.
Collapse
Affiliation(s)
- Zengdong Tan
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Yan Peng
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Yao Xiong
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Feng Xiong
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Yuting Zhang
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Ning Guo
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Zhuo Tu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Zhanxiang Zong
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Xiaokun Wu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Jiang Ye
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Chunjiao Xia
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Tao Zhu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Yinmeng Liu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Hongxiang Lou
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Dongxu Liu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Shaoping Lu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Xuan Yao
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Kede Liu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Rod J. Snowdon
- grid.8664.c0000 0001 2165 8627Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Agnieszka A. Golicz
- grid.8664.c0000 0001 2165 8627Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Weibo Xie
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China ,grid.35155.370000 0004 1790 4137Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Liang Guo
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China ,grid.35155.370000 0004 1790 4137Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China ,grid.488316.00000 0004 4912 1102Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Hu Zhao
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| |
Collapse
|
94
|
Fan Z, Tieman DM, Knapp SJ, Zerbe P, Famula R, Barbey CR, Folta KM, Amadeu RR, Lee M, Oh Y, Lee S, Whitaker VM. A multi-omics framework reveals strawberry flavor genes and their regulatory elements. New Phytol 2022; 236:1089-1107. [PMID: 35916073 PMCID: PMC9805237 DOI: 10.1111/nph.18416] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
Flavor is essential to consumer preference of foods and is an increasing focus of plant breeding programs. In fruit crops, identifying genes underlying volatile organic compounds has great promise to accelerate flavor improvement, but polyploidy and heterozygosity in many species have slowed progress. Here we use octoploid cultivated strawberry to demonstrate how genomic heterozygosity, transcriptomic intricacy and fruit metabolomic diversity can be treated as strengths and leveraged to uncover fruit flavor genes and their regulatory elements. Multi-omics datasets were generated including an expression quantitative trait loci map with 196 diverse breeding lines, haplotype-phased genomes of a highly-flavored breeding selection, a genome-wide structural variant map using five haplotypes, and volatile genome-wide association study (GWAS) with > 300 individuals. Overlaying regulatory elements, structural variants and GWAS-linked allele-specific expression of numerous genes to variation in volatile compounds important to flavor. In one example, the functional role of anthranilate synthase alpha subunit 1 in methyl anthranilate biosynthesis was supported via fruit transient gene expression assays. These results demonstrate a framework for flavor gene discovery in fruit crops and a pathway to molecular breeding of cultivars with complex and desirable flavor.
Collapse
Affiliation(s)
- Zhen Fan
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Denise M. Tieman
- Horticultural Sciences DepartmentUniversity of FloridaGainesvilleFL32611USA
| | - Steven J. Knapp
- Department of Plant SciencesUniversity of CaliforniaDavisDavisCA95616USA
| | - Philipp Zerbe
- Department of Plant BiologyUniversity of California DavisDavisCA95616USA
| | - Randi Famula
- Department of Plant SciencesUniversity of CaliforniaDavisDavisCA95616USA
| | - Christopher R. Barbey
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Kevin M. Folta
- Horticultural Sciences DepartmentUniversity of FloridaGainesvilleFL32611USA
| | - Rodrigo R. Amadeu
- Horticultural Sciences DepartmentUniversity of FloridaGainesvilleFL32611USA
| | - Manbo Lee
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Youngjae Oh
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Seonghee Lee
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Vance M. Whitaker
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| |
Collapse
|
95
|
Grosjean I, Roméo B, Domdom MA, Belaid A, D’Andréa G, Guillot N, Gherardi RK, Gal J, Milano G, Marquette CH, Hung RJ, Landi MT, Han Y, Brest P, Von Bergen M, Klionsky DJ, Amos CI, Hofman P, Mograbi B. Autophagopathies: from autophagy gene polymorphisms to precision medicine for human diseases. Autophagy 2022; 18:2519-2536. [PMID: 35383530 PMCID: PMC9629091 DOI: 10.1080/15548627.2022.2039994] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 01/20/2022] [Accepted: 02/06/2022] [Indexed: 12/15/2022] Open
Abstract
At a time when complex diseases affect globally 280 million people and claim 14 million lives every year, there is an urgent need to rapidly increase our knowledge into their underlying etiologies. Though critical in identifying the people at risk, the causal environmental factors (microbiome and/or pollutants) and the affected pathophysiological mechanisms are not well understood. Herein, we consider the variations of autophagy-related (ATG) genes at the heart of mechanisms of increased susceptibility to environmental stress. A comprehensive autophagy genomic resource is presented with 263 single nucleotide polymorphisms (SNPs) for 69 autophagy-related genes associated with 117 autoimmune, inflammatory, infectious, cardiovascular, neurological, respiratory, and endocrine diseases. We thus propose the term 'autophagopathies' to group together a class of complex human diseases the etiology of which lies in a genetic defect of the autophagy machinery, whether directly related or not to an abnormal flux in autophagy, LC3-associated phagocytosis, or any associated trafficking. The future of precision medicine for common diseases will lie in our ability to exploit these ATG SNP x environment relationships to develop new polygenetic risk scores, new management guidelines, and optimal therapies for afflicted patients.Abbreviations: ATG, autophagy-related; ALS-FTD, amyotrophic lateral sclerosis-frontotemporal dementia; ccRCC, clear cell renal cell carcinoma; CD, Crohn disease; COPD, chronic obstructive pulmonary disease; eQTL, expression quantitative trait loci; HCC, hepatocellular carcinoma; HNSCC, head and neck squamous cell carcinoma; GTEx, genotype-tissue expression; GWAS, genome-wide association studies; LAP, LC3-associated phagocytosis; LC3-II, phosphatidylethanolamine conjugated form of LC3; LD, linkage disequilibrium; LUAD, lung adenocarcinoma; MAF, minor allele frequency; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; NSCLC, non-small cell lung cancer; OS, overall survival; PtdIns3K CIII, class III phosphatidylinositol 3 kinase; PtdIns3P, phosphatidylinositol-3-phosphate; SLE, systemic lupus erythematosus; SNPs, single-nucleotide polymorphisms; mQTL, methylation quantitative trait loci; ULK, unc-51 like autophagy activating kinase; UTRs, untranslated regions; WHO, World Health Organization.
Collapse
Affiliation(s)
- Iris Grosjean
- University Côte d’Azur, CNRS, INSERM, IRCAN, FHU-OncoAge, Centre Antoine Lacassagne, France
| | - Barnabé Roméo
- University Côte d’Azur, CNRS, INSERM, IRCAN, FHU-OncoAge, Centre Antoine Lacassagne, France
| | - Marie-Angela Domdom
- University Côte d’Azur, CNRS, INSERM, IRCAN, FHU-OncoAge, Centre Antoine Lacassagne, France
| | - Amine Belaid
- Université Côte d’Azur (UCA), INSERM U1065, C3M, Team 5, F-06204, France
| | - Grégoire D’Andréa
- University Côte d’Azur, CNRS, INSERM, IRCAN, FHU-OncoAge, Centre Antoine Lacassagne, France
- ENT and Head and Neck surgery department, Institut Universitaire de la Face et du Cou, CHU de Nice, University Hospital, Côte d’Azur University, Nice, France
| | - Nicolas Guillot
- University Côte d’Azur, CNRS, INSERM, IRCAN, FHU-OncoAge, Centre Antoine Lacassagne, France
| | - Romain K Gherardi
- INSERM U955 Team Relais, Faculty of Health, Paris Est University, France
| | - Jocelyn Gal
- University Côte d’Azur, Centre Antoine Lacassagne, Epidemiology and Biostatistics Department, Nice, France
| | - Gérard Milano
- Université Côte d’Azur, Centre Antoine Lacassagne, UPR7497, Nice, France
| | - Charles Hugo Marquette
- University Côte d’Azur, CNRS, INSERM, IRCAN, FHU-OncoAge, Centre Antoine Lacassagne, France
- University Côte d’Azur, FHU-OncoAge, Department of Pulmonary Medicine and Oncology, CHU de Nice, Nice, France
| | - Rayjean J. Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada; Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Patrick Brest
- University Côte d’Azur, CNRS, INSERM, IRCAN, FHU-OncoAge, Centre Antoine Lacassagne, France
| | - Martin Von Bergen
- Helmholtz Centre for Environmental Research GmbH - UFZ, Dep. of Molecular Systems Biology; University of Leipzig, Faculty of Life Sciences, Institute of Biochemistry, Leipzig, Germany
| | - Daniel J. Klionsky
- University of Michigan, Life Sciences Institute, Ann Arbor, MI, 48109, USA
| | - Christopher I. Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Paul Hofman
- University Côte d’Azur, CNRS, INSERM, IRCAN, FHU-OncoAge, Centre Antoine Lacassagne, France
- University Côte d’Azur, FHU-OncoAge, CHU de Nice, Laboratory of Clinical and Experimental Pathology (LPCE) Biobank(BB-0033-00025), Nice, France
| | - Baharia Mograbi
- University Côte d’Azur, CNRS, INSERM, IRCAN, FHU-OncoAge, Centre Antoine Lacassagne, France
| |
Collapse
|
96
|
Yu Y, Bergland AO. Distinct signals of clinal and seasonal allele frequency change at eQTLs in Drosophila melanogaster. Evolution 2022; 76:2758-2768. [PMID: 36097359 PMCID: PMC9710195 DOI: 10.1111/evo.14617] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 07/31/2022] [Accepted: 08/17/2022] [Indexed: 01/22/2023]
Abstract
Populations of short-lived organisms can respond to spatial and temporal environmental heterogeneity through local adaptation. Local adaptation can be reflected on both phenotypic and genetic levels, and it has been documented in many organisms. Although complex fitness-related phenotypes have been shown to vary across latitudinal clines and seasons in similar ways in Drosophila melanogaster populations, the comparative signals of local adaptation across space and time remain poorly understood. Here, we examined patterns of allele frequency change across a latitudinal cline and between seasons at previously reported expression quantitative trait loci (eQTLs). We divided eQTLs into groups by using differential expression profiles of fly populations collected across latitudinal clines or exposed to different environmental conditions. In general, we find that eQTLs are enriched for clinally varying polymorphisms, and that these eQTLs change in frequency in concordant ways across the cline and in response to starvation and chill-coma. The enrichment of eQTLs among seasonally varying polymorphisms is more subtle, and the direction of allele frequency change at eQTLs appears to be somewhat idiosyncratic. Taken together, we suggest that clinal adaptation at eQTLs is at least partially distinct from seasonal adaptation.
Collapse
Affiliation(s)
- Yang Yu
- Department of BiologyUniversity of VirginiaCharlottesvilleVirginia22904
| | - Alan O. Bergland
- Department of BiologyUniversity of VirginiaCharlottesvilleVirginia22904
| |
Collapse
|
97
|
Krishnamoorthy S, Li GH, Cheung C. Transcriptome-wide summary data-based Mendelian randomization analysis reveals 38 novel genes associated with severe COVID-19. J Med Virol 2022; 95:e28162. [PMID: 36127160 PMCID: PMC9538104 DOI: 10.1002/jmv.28162] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 01/11/2023]
Abstract
Severe COVID-19 has a poor prognosis, while the genetic mechanism underlying severe COVID-19 remains largely unknown. We aimed to identify genes that are potentially causally associated with severe COVID-19. We conducted a summary data-based Mendelian randomization (SMR) analysis using expression quantitative trait loci (eQTL) data from 49 different tissues as the exposure and three COVID-19-phenotypes (very severe respiratory confirmed COVID-19 [severe COVID-19], hospitalized COVID-19, and SARS-CoV-2 infection) as the outcomes. SMR using multiple SNPs was used as a sensitivity analysis to reduce false positive rate. Multiple testing was corrected using the false discovery rate (FDR) q-value. We identified 309 significant gene-trait associations (FDR q value < 0.05) across 46 tissues for severe COVID-19, which mapped to 64 genes, of which 38 are novel. The top five most associated protein-coding genes were Interferon Alpha and Beta Receptor Subunit 2 (IFNAR2), 2'-5'-Oligoadenylate Synthetase 3 (OAS3), mucin 1 (MUC1), Interleukin 10 Receptor Subunit Beta (IL10RB), and Napsin A Aspartic Peptidase (NAPSA). The potential causal genes were enriched in biological processes related to type I interferons, interferon-gamma inducible protein 10 production, and chemokine (C-X-C motif) ligand 2 production. In addition, we further identified 23 genes and 5 biological processes which are unique to hospitalized COVID-19, as well as 13 genes that are unique to SARS-CoV-2 infection. We identified several genes that are potentially causally associated with severe COVID-19. These findings improve our limited understanding of the mechanism of COVID-19 and shed light on the development of therapeutic agents for treating severe COVID-19.
Collapse
Affiliation(s)
- Suhas Krishnamoorthy
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulamHong Kong
| | - Gloria H.‐Y. Li
- Department of Health Technology and Informatics, Faculty of Health and Social SciencesThe Hong Kong Polytechnic UniversityHung HomHong Kong
| | - Ching‐Lung Cheung
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulamHong Kong,Laboratory of Data Discovery for Health (D24H)Pak Shek KokHong Kong
| |
Collapse
|
98
|
Pudjihartono M, Perry JK, Print C, O'Sullivan JM, Schierding W. Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis. Clin Epigenetics 2022; 14:120. [PMID: 36171609 PMCID: PMC9520844 DOI: 10.1186/s13148-022-01342-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 09/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression. MAIN BODY We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data. CONCLUSION We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.
Collapse
Affiliation(s)
| | - Jo K Perry
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Cris Print
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Department of Molecular Medicine and Pathology, School of Medical Sciences, University of Auckland, Auckland, 1142, New Zealand
| | - Justin M O'Sullivan
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Australian Parkinson's Mission, Garvan Institute of Medical Research, Sydney, NSW, Australia
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - William Schierding
- Liggins Institute, The University of Auckland, Auckland, New Zealand.
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand.
| |
Collapse
|
99
|
Ma J, Qiu S. Genetic variant rs11136000 upregulates clusterin expression and reduces Alzheimer's disease risk. Front Neurosci 2022; 16:926830. [PMID: 36033622 PMCID: PMC9407972 DOI: 10.3389/fnins.2022.926830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 07/11/2022] [Indexed: 11/29/2022] Open
Abstract
Clusterin (CLU) is an extracellular chaperone involved in reducing amyloid beta (Aβ) toxicity and aggregation. Although previous genome-wide association studies (GWAS) have reported a potential protective effect of CLU on Alzheimer's disease (AD) patients, how intron-located rs11136000 (CLU) affects AD risk by regulating CLU expression remains unknown. In this study, we integrated multiple omics data to construct the regulated pathway of rs11136000-CLU-AD. In step 1, we investigated the effects of variant rs11136000 on AD risk with different genders and diagnostic methods using GWAS summary statistics for AD from International Genomics of Alzheimer's Project (IGAP) and UK Biobank. In step 2, we assessed the regulation of rs11136000 on CLU expression in AD brain samples from Mayo clinic and controls from Genotype-Tissue Expression (GTEx). In step 3, we investigated the differential gene/protein expression of CLU in AD and controls from four large cohorts. The results showed that rs11136000 T allele reduced AD risk in either clinically diagnosed or proxy AD patients. By using expression quantitative trait loci (eQTL) analysis, rs11136000 variant downregulated CLU expression in 13 normal brain tissues, but upregulated CLU expression in cerebellum and temporal cortex of AD samples. Importantly, CLU was significantly differentially expressed in temporal cortex, dorsolateral prefrontal cortex and anterior prefrontal cortex of AD patients compared with normal controls. Together, rs11136000 may reduce AD risk by regulating CLU expression, which may provide important information about the biological mechanism of rs9848497 in AD progress.
Collapse
Affiliation(s)
- Jin Ma
- Department of Emergency Medicine, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, China
| | - Shizheng Qiu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| |
Collapse
|
100
|
Cuomo ASE, Heinen T, Vagiaki D, Horta D, Marioni JC, Stegle O. CellRegMap: a statistical framework for mapping context-specific regulatory variants using scRNA-seq. Mol Syst Biol 2022; 18:e10663. [PMID: 35972065 PMCID: PMC9380406 DOI: 10.15252/msb.202110663] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 06/28/2022] [Accepted: 07/01/2022] [Indexed: 11/11/2022] Open
Abstract
Single‐cell RNA sequencing (scRNA‐seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population‐scale scRNA‐seq studies in hundreds of individuals, allowing to assay genetic effects with single‐cell resolution. However, existing strategies to analyze these data remain based on principles established for the genetic analysis of bulk RNA‐seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose the Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize genotype–context interactions of known eQTL variants using scRNA‐seq data. This model‐based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying heterogeneity of genetic effects across cellular contexts. Finally, we identify fine‐grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.
Collapse
Affiliation(s)
- Anna S E Cuomo
- European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.,Wellcome Sanger Institute, Cambridge, UK
| | - Tobias Heinen
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), Heidelberg, Germany.,European Molecular Biology Laboratory (EMBL), Genome Biology, Heidelberg, Germany.,Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
| | - Danai Vagiaki
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), Heidelberg, Germany.,European Molecular Biology Laboratory (EMBL), Genome Biology, Heidelberg, Germany.,Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Danilo Horta
- European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - John C Marioni
- European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.,Wellcome Sanger Institute, Cambridge, UK.,Cancer Research UK, Cambridge Institute, Cambridge, UK
| | - Oliver Stegle
- European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.,Wellcome Sanger Institute, Cambridge, UK.,Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), Heidelberg, Germany.,European Molecular Biology Laboratory (EMBL), Genome Biology, Heidelberg, Germany
| |
Collapse
|