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Schaffner SL, Casazza W, Artaud F, Konwar C, Merrill SM, Domenighetti C, Schulze-Hentrich JM, Lesage S, Brice A, Corvol JC, Mostafavi S, Dennis JK, Elbaz A, Kobor MS. Genetic variation and pesticide exposure influence blood DNA methylation signatures in females with early-stage Parkinson's disease. NPJ Parkinsons Dis 2024; 10:98. [PMID: 38714693 PMCID: PMC11076573 DOI: 10.1038/s41531-024-00704-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 04/05/2024] [Indexed: 05/10/2024] Open
Abstract
Although sex, genetics, and exposures can individually influence risk for sporadic Parkinson's disease (PD), the joint contributions of these factors to the epigenetic etiology of PD have not been comprehensively assessed. Here, we profiled sex-stratified genome-wide blood DNAm patterns, SNP genotype, and pesticide exposure in agricultural workers (71 early-stage PD cases, 147 controls) and explored replication in three independent samples of varying demographics (n = 218, 222, and 872). Using a region-based approach, we found more associations of blood DNAm with PD in females (69 regions) than in males (2 regions, Δβadj| ≥0.03, padj ≤ 0.05). For 48 regions in females, models including genotype or genotype and pesticide exposure substantially improved in explaining interindividual variation in DNAm (padj ≤ 0.05), and accounting for these variables decreased the estimated effect of PD on DNAm. The results suggested that genotype, and to a lesser degree, genotype-exposure interactions contributed to variation in PD-associated DNAm. Our findings should be further explored in larger study populations and in experimental systems, preferably with precise measures of exposure.
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Affiliation(s)
- S L Schaffner
- Edwin S. H. Leong Centre for Healthy Aging, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - W Casazza
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital, Vancouver, BC, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
| | - F Artaud
- Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, 94805, Villejuif, France
| | - C Konwar
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital, Vancouver, BC, Canada
| | - S M Merrill
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital, Vancouver, BC, Canada
| | - C Domenighetti
- Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, 94805, Villejuif, France
| | - J M Schulze-Hentrich
- Department of Genetics/Epigenetics, Faculty NT, Saarland University, 66041, Saarbrücken, Germany
| | - S Lesage
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, INSERM, CNRS, Assistance Publique Hôpitaux de Paris, Paris, France
| | - A Brice
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, INSERM, CNRS, Assistance Publique Hôpitaux de Paris, Paris, France
| | - J C Corvol
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, INSERM, CNRS, Assistance Publique Hôpitaux de Paris, Paris, France
- Sorbonne University, Assistance Publique Hôpitaux de Paris, Paris Brain Insitute - ICM, Inserm, CNRS, Department of Neurology and CIC Neurosciences, Pitié-Salpêtrière Hospital, Paris, France
| | - S Mostafavi
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
- Paul Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - J K Dennis
- Edwin S. H. Leong Centre for Healthy Aging, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
| | - A Elbaz
- Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, 94805, Villejuif, France
| | - M S Kobor
- Edwin S. H. Leong Centre for Healthy Aging, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital, Vancouver, BC, Canada.
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.
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2
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O'Brien CL, Summers KM, Martin NM, Carter-Cusack D, Yang Y, Barua R, Dixit OVA, Hume DA, Pavli P. The relationship between extreme inter-individual variation in macrophage gene expression and genetic susceptibility to inflammatory bowel disease. Hum Genet 2024; 143:233-261. [PMID: 38421405 PMCID: PMC11043138 DOI: 10.1007/s00439-024-02642-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 01/14/2024] [Indexed: 03/02/2024]
Abstract
The differentiation of resident intestinal macrophages from blood monocytes depends upon signals from the macrophage colony-stimulating factor receptor (CSF1R). Analysis of genome-wide association studies (GWAS) indicates that dysregulation of macrophage differentiation and response to microorganisms contributes to susceptibility to chronic inflammatory bowel disease (IBD). Here, we analyzed transcriptomic variation in monocyte-derived macrophages (MDM) from affected and unaffected sib pairs/trios from 22 IBD families and 6 healthy controls. Transcriptional network analysis of the data revealed no overall or inter-sib distinction between affected and unaffected individuals in basal gene expression or the temporal response to lipopolysaccharide (LPS). However, the basal or LPS-inducible expression of individual genes varied independently by as much as 100-fold between subjects. Extreme independent variation in the expression of pairs of HLA-associated transcripts (HLA-B/C, HLA-A/F and HLA-DRB1/DRB5) in macrophages was associated with HLA genotype. Correlation analysis indicated the downstream impacts of variation in the immediate early response to LPS. For example, variation in early expression of IL1B was significantly associated with local SNV genotype and with subsequent peak expression of target genes including IL23A, CXCL1, CXCL3, CXCL8 and NLRP3. Similarly, variation in early IFNB1 expression was correlated with subsequent expression of IFN target genes. Our results support the view that gene-specific dysregulation in macrophage adaptation to the intestinal milieu is associated with genetic susceptibility to IBD.
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Affiliation(s)
- Claire L O'Brien
- Centre for Research in Therapeutics Solutions, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia
| | - Kim M Summers
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Natalia M Martin
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia
| | - Dylan Carter-Cusack
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Yuanhao Yang
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Rasel Barua
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia
| | - Ojas V A Dixit
- Centre for Research in Therapeutics Solutions, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
| | - David A Hume
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia.
| | - Paul Pavli
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia.
- School of Medicine and Psychology, College of Health and Medicine, Australian National University, Canberra, ACT, Australia.
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3
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Tan M, Andersen LJ, Bruun NE, Lindholm MG, Tan Q, Snoer M. Transcription Factor Regulation of Gene Expression Network by ZNF385D and HAND2 in Carotid Atherosclerosis. Genes (Basel) 2024; 15:213. [PMID: 38397203 PMCID: PMC10888454 DOI: 10.3390/genes15020213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/01/2024] [Accepted: 02/03/2024] [Indexed: 02/25/2024] Open
Abstract
Carotid intima-media thickness (CIMT) is a surrogate indicator for atherosclerosis and has been shown to predict cardiovascular risk in multiple large studies. Identification of molecular markers for carotid atheroma plaque formation can be critical for early intervention and prevention of atherosclerosis. This study performed transcription factor (TF) network analysis of global gene expression data focusing on two TF genes, ZNF385D and HAND2, whose polymorphisms have been recently reported to show association with CIMT. Genome-wide gene expression data were measured from pieces of carotid endarterectomy collected from 34 hypertensive patients (atheroma plaque of stages IV and above according to the Stary classification) each paired with one sample of distant macroscopically intact tissue (stages I and II). Transcriptional regulation networks or the regulons were reconstructed for ZNF385D (5644 target genes) and HAND2 (781 target genes) using network inference. Their association with the progression of carotid atheroma was examined using gene-set enrichment analysis with extremely high statistical significance for regulons of both ZNF385D and HAND2 (p < 6.95 × 10-7) suggesting the involvement of expression quantitative loci (eQTL). Functional annotation of the regulon genes found heavy involvement in the immune system's response to inflammation and infection in the development of atherosclerosis. Detailed examination of the regulation and correlation patterns suggests that activities of the two TF genes could have high clinical and interventional impacts on impairing carotid atheroma plaque formation and preventing carotid atherosclerosis.
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Affiliation(s)
- Ming Tan
- Department of Cardiology, Zealand University Hospital, 4000 Roskilde, Denmark; (M.T.); (L.J.A.); (N.E.B.); (M.G.L.); (M.S.)
| | - Lars Juel Andersen
- Department of Cardiology, Zealand University Hospital, 4000 Roskilde, Denmark; (M.T.); (L.J.A.); (N.E.B.); (M.G.L.); (M.S.)
| | - Niels Eske Bruun
- Department of Cardiology, Zealand University Hospital, 4000 Roskilde, Denmark; (M.T.); (L.J.A.); (N.E.B.); (M.G.L.); (M.S.)
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
- Department of Clinical Medicine, University of Aalborg, 9260 Aalborg, Denmark
| | - Matias Greve Lindholm
- Department of Cardiology, Zealand University Hospital, 4000 Roskilde, Denmark; (M.T.); (L.J.A.); (N.E.B.); (M.G.L.); (M.S.)
| | - Qihua Tan
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, Faculty of Health Science, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
- Unit of Human Genetics, Department of Clinical Research, Faculty of Health Science, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
| | - Martin Snoer
- Department of Cardiology, Zealand University Hospital, 4000 Roskilde, Denmark; (M.T.); (L.J.A.); (N.E.B.); (M.G.L.); (M.S.)
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Vochteloo M, Deelen P, Vink B, Tsai EA, Runz H, Andreu-Sánchez S, Fu J, Zhernakova A, Westra HJ, Franke L. PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs. Genome Biol 2024; 25:29. [PMID: 38254182 PMCID: PMC10802033 DOI: 10.1186/s13059-023-03151-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets. These contexts are biologically informative and reproducible, outperforming cell counts or expression-based principal components. Furthermore, we show that RNA quality and cell type proportions interact with thousands of eQTLs. Knowledge of hidden eQTL contexts may aid in the inference of functional mechanisms underlying disease variants.
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Affiliation(s)
- Martijn Vochteloo
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Patrick Deelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Britt Vink
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Institute for Life Science & Technology, Hanze University of Applied Sciences, Groningen, The Netherlands
| | - Ellen A Tsai
- Translational Sciences, Research and Development, Biogen, Cambridge, MA, USA
| | - Heiko Runz
- Translational Sciences, Research and Development, Biogen, Cambridge, MA, USA
| | - Sergio Andreu-Sánchez
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jingyuan Fu
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Harm-Jan Westra
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
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5
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Zhu Z, Chen X, Zhang S, Yu R, Qi C, Cheng L, Zhang X. Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective. Hum Genet 2023; 142:1543-1560. [PMID: 37755483 DOI: 10.1007/s00439-023-02602-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.
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Affiliation(s)
- Zijun Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Xinyu Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Sainan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Rui Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China.
| | - Xue Zhang
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China
- McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
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6
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Wu EY, Singh NP, Choi K, Zakeri M, Vincent M, Churchill GA, Ackert-Bicknell CL, Patro R, Love MI. SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty. Genome Biol 2023; 24:165. [PMID: 37438847 PMCID: PMC10337143 DOI: 10.1186/s13059-023-03003-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 06/29/2023] [Indexed: 07/14/2023] Open
Abstract
Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time.
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Affiliation(s)
- Euphy Y Wu
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Noor P Singh
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | | | - Mohsen Zakeri
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | | | | | - Cheryl L Ackert-Bicknell
- Department of Orthopedics, School of Medicine, University of Colorado, Anschutz Campus, Aurora, CO, USA
| | - Rob Patro
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Michael I Love
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
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7
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Fabo T, Khavari P. Functional characterization of human genomic variation linked to polygenic diseases. Trends Genet 2023; 39:462-490. [PMID: 36997428 PMCID: PMC11025698 DOI: 10.1016/j.tig.2023.02.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/30/2023]
Abstract
The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.
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Affiliation(s)
- Tania Fabo
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Graduate Program in Genetics, Stanford University, Stanford, CA, USA; Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Paul Khavari
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Graduate Program in Genetics, Stanford University, Stanford, CA, USA; Stanford University School of Medicine, Stanford University, Stanford, CA, USA; Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA.
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8
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Zhong C, Wu C, Lin Y, Lin D. Refined expression quantitative trait locus analysis on adenocarcinoma at the gastroesophageal junction reveals susceptibility and prognostic markers. Front Genet 2023; 14:1180500. [PMID: 37265963 PMCID: PMC10230079 DOI: 10.3389/fgene.2023.1180500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/03/2023] [Indexed: 06/03/2023] Open
Abstract
Objectives: This study aimed to explore cell type level expression quantitative trait loci (eQTL) in adenocarcinoma at the gastroesophageal junction (ACGEJ) and identify susceptibility and prognosis markers. Methods: Whole-genome sequencing (WGS) was performed on 120 paired samples from Chinese ACGEJ patients. Germline mutations were detected by GATK tools. RNA sequencing (RNA-seq) data on ACGEJ samples were taken from our previous studies. Public single-cell RNA sequencing (scRNA-seq) data were used to produce the proportion of epithelial cells. Matrix eQTL and a linear mixed model were used to identify condition-specific cis-eQTLs. The R package coloc was used to perform co-localization analysis with the public data of genome-wide association studies (GWASs). Log-rank and Cox regression tests were used to identify survival-associated eQTL and genes. Functions of candidate risk loci were explored by experimental validation. Results: Refined eQTL analyses of paired ACGEJ samples were performed and 2,036 potential ACGEJ-specific eQTLs with East Asian specificity were identified in total. ACGEJ-gain eQTLs were enriched at promoter regions more than ACGEJ-loss eQTLs. rs658524 was identified as the top eQTL close to the transcription start site of its paired gene (CTSW). rs2240191-RASAL1, rs4236599-FOXP2, rs4947311-PSORS1C1, rs13134812-LOC391674, and rs17508585-CDK13-DT were identified as ACGEJ-specific susceptibility eQTLs. rs309483-LINC01355 was associated with the overall survival of ACGEJ patients. We explored functions of candidate eQTLs such as rs658524, rs309483, rs2240191, and rs4947311 by experimental validation. Conclusion: This study provides new risk loci for ACGEJ susceptibility and effective disease prognosis biomarkers.
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Affiliation(s)
- Ce Zhong
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chen Wu
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuan Lin
- Beijing Advanced Innovation Center for Genomics (ICG), Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
| | - Dongxin Lin
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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9
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Boccuto L, Tack J, Ianiro G, Abenavoli L, Scarpellini E. Human Genes Involved in the Interaction between Host and Gut Microbiome: Regulation and Pathogenic Mechanisms. Genes (Basel) 2023; 14:genes14040857. [PMID: 37107615 PMCID: PMC10137629 DOI: 10.3390/genes14040857] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/24/2023] [Accepted: 03/29/2023] [Indexed: 04/05/2023] Open
Abstract
Introduction: The umbrella term “human gut microbiota” describes the complex ecosystem harboring our gut. It includes bacteria, viruses, protozoa, archaea, fungi, and yeasts. This taxonomic classification does not describe its functions, which encompass nutrients digestion and absorption, immune system regulation, and host metabolism. “Gut microbiome” indicates instead the genome belonging to these “microbes” actively involved in these functions. However, the interaction between the host genome and the microbial ones determines the fine functioning of our organism. Methods: We reviewed the data available in the scientific literature on the definition of gut microbiota, gut microbiome, and the data on human genes involved in the interaction with the latter. We consulted the main medical databases using the following keywords, acronyms, and their associations: gut microbiota, gut microbiome, human genes, immune function, and metabolism. Results: Candidate human genes encoding enzymes, inflammatory cytokines, and proteins show similarity with those included in the gut microbiome. These findings have become available through newer artificial intelligence (AI) algorithms allowing big data analysis. From an evolutionary point of view, these pieces of evidence explain the strict and sophisticated interaction at the basis of human metabolism and immunity regulation in humans. They unravel more and more physiopathologic pathways included in human health and disease. Discussion: Several lines of evidence also obtained through big data analysis support the bi-directional role of gut microbiome and human genome in host metabolism and immune system regulation.
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Affiliation(s)
- Luigi Boccuto
- School of Nursing, Healthcare Genetics Program, Clemson University, Clemson University School of Health Research, Clemson, SC 29631, USA
| | - Jan Tack
- Translational Research Center for Gastrointestinal Disorders (T.A.R.G.I.D.), Gasthuisberg University Hospital, KU Leuven, Herestraat 49, 3000 Lueven, Belgium
| | - Gianluca Ianiro
- Department of Medical and Surgical Sciences, Digestive Disease Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
- Department of Translational Medicine and Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Ludovico Abenavoli
- Department of Health Sciences, Magna Graecia University, 88100 Catanzaro, Italy
| | - Emidio Scarpellini
- Translational Research Center for Gastrointestinal Disorders (T.A.R.G.I.D.), Gasthuisberg University Hospital, KU Leuven, Herestraat 49, 3000 Lueven, Belgium
- Clinical Nutrition and Hepatology Unit, San Benedetto del Tronto General Hospital, 63074 San Benedetto del Tronto, Italy
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10
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Ghaffar A, Nyholt DR. Genome-wide imputed differential expression enrichment analysis identifies trait-relevant tissues. Front Genet 2023; 13:1008511. [PMID: 36699451 PMCID: PMC9870027 DOI: 10.3389/fgene.2022.1008511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 12/19/2022] [Indexed: 01/09/2023] Open
Abstract
The identification of pathogenically-relevant genes and tissues for complex traits can be a difficult task. We developed an approach named genome-wide imputed differential expression enrichment (GIDEE), to prioritise trait-relevant tissues by combining genome-wide association study (GWAS) summary statistic data with tissue-specific expression quantitative trait loci (eQTL) data from 49 GTEx tissues. Our GIDEE approach analyses robustly imputed gene expression and tests for enrichment of differentially expressed genes in each tissue. Two tests (mean squared z-score and empirical Brown's method) utilise the full distribution of differential expression p-values across all genes, while two binomial tests assess the proportion of genes with tissue-wide significant differential expression. GIDEE was applied to nine training datasets with known trait-relevant tissues and ranked 49 GTEx tissues using the individual and combined enrichment tests. The best-performing enrichment test produced an average rank of 1.55 out of 49 for the known trait-relevant tissue across the nine training datasets-ranking the correct tissue first five times, second three times, and third once. Subsequent application of the GIDEE approach to 20 test datasets-whose pathogenic tissues or cell types are uncertain or unknown-provided important prioritisation of tissues relevant to the trait's regulatory architecture. GIDEE prioritisation may thus help identify both pathogenic tissues and suitable proxy tissue/cell models (e.g., using enriched tissues/cells that are more easily accessible). The application of our GIDEE approach to GWAS datasets will facilitate follow-up in silico and in vitro research to determine the functional consequence(s) of their risk loci.
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11
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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] [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.
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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
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12
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Markowitz RHG, LaBella AL, Shi M, Rokas A, Capra JA, Ferguson JF, Mosley JD, Bordenstein SR. Microbiome-associated human genetic variants impact phenome-wide disease risk. Proc Natl Acad Sci U S A 2022; 119:e2200551119. [PMID: 35749358 PMCID: PMC9245617 DOI: 10.1073/pnas.2200551119] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 04/29/2022] [Indexed: 12/26/2022] Open
Abstract
Human genetic variation associates with the composition of the gut microbiome, yet its influence on clinical traits remains largely unknown. We analyzed the consequences of nearly a thousand gut microbiome-associated variants (MAVs) on phenotypes reported in electronic health records from tens of thousands of individuals. We discovered and replicated associations of MAVs with neurological, metabolic, digestive, and circulatory diseases. Five significant MAVs in these categories correlate with the relative abundance of microbes down to the strain level. We also demonstrate that these relationships are independently observed and concordant with microbe by disease associations reported in case-control studies. Moreover, a selective sweep and population differentiation impacted some disease-linked MAVs. Combined, these findings establish triad relationships among the human genome, microbiome, and disease. Consequently, human genetic influences may offer opportunities for precision diagnostics of microbiome-associated diseases but also highlight the relevance of genetic background for microbiome modulation and therapeutics.
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Affiliation(s)
- Robert H. George Markowitz
- Vanderbilt Microbiome Innovation Center, Vanderbilt University, Nashville, TN 37232
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37232
| | | | - Mingjian Shi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37232
| | - John A. Capra
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94143
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94143
| | - Jane F. Ferguson
- Vanderbilt Microbiome Innovation Center, Vanderbilt University, Nashville, TN 37232
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Jonathan D. Mosley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Seth R. Bordenstein
- Vanderbilt Microbiome Innovation Center, Vanderbilt University, Nashville, TN 37232
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37232
- Vanderbilt Institute for Infection, Immunology and Inflammation, Vanderbilt University Medical Center, Nashville, TN 37232
- Department of Pathology, Microbiology, and Immunology, School of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232
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13
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Flynn E, Lappalainen T. Functional Characterization of Genetic Variant Effects on Expression. Annu Rev Biomed Data Sci 2022; 5:119-139. [PMID: 35483347 DOI: 10.1146/annurev-biodatasci-122120-010010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Elise Flynn
- New York Genome Center, New York, NY, USA; , .,Department of Systems Biology, Columbia University, New York, NY, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; , .,Department of Systems Biology, Columbia University, New York, NY, USA.,Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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