1
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Kanchibhotla SC, Mather KA, Armstrong NJ, Ciobanu LG, Baune BT, Catts VS, Schofield PR, Trollor JN, Ames D, Sachdev PS, Thalamuthu A. Heritability of Gene Expression Measured from Peripheral Blood in Older Adults. Genes (Basel) 2024; 15:495. [PMID: 38674429 PMCID: PMC11049887 DOI: 10.3390/genes15040495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
The contributions of genetic variation and the environment to gene expression may change across the lifespan. However, few studies have investigated the heritability of blood gene expression in older adults. The current study therefore aimed to investigate this question in a community sample of older adults. A total of 246 adults (71 MZ and 52 DZ twins, 69.91% females; mean age-75.79 ± 5.44) were studied. Peripheral blood gene expression was assessed using Illumina microarrays. A heritability analysis was performed using structural equation modelling. There were 5269 probes (19.9%) from 4603 unique genes (23.9%) (total 26,537 probes from 19,256 genes) that were significantly heritable (mean h2 = 0.40). A pathway analysis of the top 10% of significant genes showed enrichment for the immune response and ageing-associated genes. In a comparison with two other gene expression twin heritability studies using adults from across the lifespan, there were 38 out of 9479 overlapping genes that were significantly heritable. In conclusion, our study found ~24% of the available genes for analysis were heritable in older adults, with only a small number common across studies that used samples from across adulthood, indicating the importance of examining gene expression in older age groups.
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Affiliation(s)
- Sri C. Kanchibhotla
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
| | - Karen A. Mather
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Neuroscience Research Australia, Sydney, NSW 2031, Australia
| | - Nicola J. Armstrong
- Department of Mathematics and Statistics, Curtin University, Perth, WA 6845, Australia
| | - Liliana G. Ciobanu
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Bernhard T. Baune
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA 5005, Australia
- Department of Psychiatry, University of Münster, 48149 Münster, Germany
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3052, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Vibeke S. Catts
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
| | - Peter R. Schofield
- Neuroscience Research Australia, Sydney, NSW 2031, Australia
- School of Medical Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Julian N. Trollor
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Department of Developmental Disability Neuropsychiatry, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
| | - David Ames
- Academic Unit for Psychiatry of Old Age, University of Melbourne, St George’s Hospital, Kew, Melbourne, VIC 3010, Australia
- National Ageing Research Institute, Parkville, VIC 3052, Australia
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Neuropsychiatric Institute, Euroa Centre, Prince of Wales Hospital, Sydney, NSW 2031, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
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2
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Tsouris A, Brach G, Friedrich A, Hou J, Schacherer J. Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast. Mol Syst Biol 2024; 20:362-373. [PMID: 38355920 PMCID: PMC10987670 DOI: 10.1038/s44320-024-00021-0] [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: 09/15/2023] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.
- Institut Universitaire de France (IUF), Paris, France.
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3
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Wittich H, Ardlie K, Taylor KD, Durda P, Liu Y, Mikhaylova A, Gignoux CR, Cho MH, Rich SS, Rotter JI, Manichaikul A, Im HK, Wheeler HE. Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits. Am J Hum Genet 2024; 111:445-455. [PMID: 38320554 PMCID: PMC10940016 DOI: 10.1016/j.ajhg.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/12/2024] [Accepted: 01/12/2024] [Indexed: 02/08/2024] Open
Abstract
Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR < 0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R = 0.17) than observed expression (R = 0.10, p = 7.50 × 10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics + environment) and is useful in uncovering the function of SNPs associated with complex traits.
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Affiliation(s)
- Henry Wittich
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
| | - Kristin Ardlie
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - Anna Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Chris R Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Heather E Wheeler
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA; Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA.
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4
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LaPierre N, Pimentel H. Accounting for isoform expression increases power to identify genetic regulation of gene expression. PLoS Comput Biol 2024; 20:e1011857. [PMID: 38346082 PMCID: PMC10890775 DOI: 10.1371/journal.pcbi.1011857] [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: 09/29/2023] [Revised: 02/23/2024] [Accepted: 01/23/2024] [Indexed: 02/25/2024] Open
Abstract
A core problem in genetics is molecular quantitative trait locus (QTL) mapping, in which genetic variants associated with changes in the molecular phenotypes are identified. One of the most-studied molecular QTL mapping problems is expression QTL (eQTL) mapping, in which the molecular phenotype is gene expression. It is common in eQTL mapping to compute gene expression by aggregating the expression levels of individual isoforms from the same gene and then performing linear regression between SNPs and this aggregated gene expression level. However, SNPs may regulate isoforms from the same gene in different directions due to alternative splicing, or only regulate the expression level of one isoform, causing this approach to lose power. Here, we examine a broader question: which genes have at least one isoform whose expression level is regulated by genetic variants? In this study, we propose and evaluate several approaches to answering this question, demonstrating that "isoform-aware" methods-those that account for the expression levels of individual isoforms-have substantially greater power to answer this question than standard "gene-level" eQTL mapping methods. We identify settings in which different approaches yield an inflated number of false discoveries or lose power. In particular, we show that calling an eGene if there is a significant association between a SNP and any isoform fails to control False Discovery Rate, even when applying standard False Discovery Rate correction. We show that similar trends are observed in real data from the GEUVADIS and GTEx studies, suggesting the possibility that similar effects are present in these consortia.
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Affiliation(s)
- Nathan LaPierre
- Department of Computer Science, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, University of Chicago, Illinois, United States of America
| | - Harold Pimentel
- Department of Human Genetics, University of California, Los Angeles, California, United States of America
- Howard Hughes Medical Institute, Chevy Chase, Maryland, United States of America
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
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5
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Tsouris A, Brach G, Schacherer J, Hou J. Non-additive genetic components contribute significantly to population-wide gene expression variation. CELL GENOMICS 2024; 4:100459. [PMID: 38190102 PMCID: PMC10794783 DOI: 10.1016/j.xgen.2023.100459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/19/2023] [Accepted: 11/09/2023] [Indexed: 01/09/2024]
Abstract
Gene expression variation, an essential step between genotype and phenotype, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence gene expression variation remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originating from genetically divergent Saccharomyces cerevisiae isolates. Our analysis across 5,087 transcript abundance traits showed that non-additive components account for 36% of the gene expression variance on average. By comparing allele-specific read counts in parent-hybrid trios, we found that trans-regulatory changes underlie the majority of gene expression variation in the population. Remarkably, most cis-regulatory variations are also exaggerated or attenuated by additional trans effects. Overall, we showed that the transcriptome is globally buffered at the genetic level mainly due to trans-regulatory variation in the population.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France; Institut Universitaire de France (IUF), Paris, France.
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France.
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6
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Lim KS, Cheng J, Tuggle C, Dyck M, Canada P, Fortin F, Harding J, Plastow G, Dekkers J. Genetic analysis of the blood transcriptome of young healthy pigs to improve disease resilience. Genet Sel Evol 2023; 55:90. [PMID: 38087235 PMCID: PMC10714454 DOI: 10.1186/s12711-023-00860-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Disease resilience is the ability of an animal to maintain productive performance under disease conditions and is an important selection target. In pig breeding programs, disease resilience must be evaluated on selection candidates without exposing them to disease. To identify potential genetic indicators for disease resilience that can be measured on selection candidates, we focused on the blood transcriptome of 1594 young healthy pigs with subsequent records on disease resilience. Transcriptome data were obtained by 3'mRNA sequencing and genotype data were from a 650 K genotyping array. RESULTS Heritabilities of the expression of 16,545 genes were estimated, of which 5665 genes showed significant estimates of heritability (p < 0.05), ranging from 0.05 to 0.90, with or without accounting for white blood cell composition. Genes with heritable expression levels were spread across chromosomes, but were enriched in the swine leukocyte antigen region (average estimate > 0.2). The correlation of heritability estimates with the corresponding estimates obtained for genes expressed in human blood was weak but a sizable number of genes with heritable expression levels overlapped. Genes with heritable expression levels were significantly enriched for biological processes such as cell activation, immune system process, stress response, and leukocyte activation, and were involved in various disease annotations such as RNA virus infection, including SARS-Cov2, as well as liver disease, and inflammation. To estimate genetic correlations with disease resilience, 3205 genotyped pigs, including the 1594 pigs with transcriptome data, were evaluated for disease resilience following their exposure to a natural polymicrobial disease challenge. Significant genetic correlations (p < 0.05) were observed with all resilience phenotypes, although few exceeded expected false discovery rates. Enrichment analysis of genes ranked by estimates of genetic correlations with resilience phenotypes revealed significance for biological processes such as regulation of cytokines, including interleukins and interferons, and chaperone mediated protein folding. CONCLUSIONS These results suggest that expression levels in the blood of young healthy pigs for genes in biological pathways related to immunity and endoplasmic reticulum stress have potential to be used as genetic indicator traits to select for disease resilience.
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Affiliation(s)
- Kyu-Sang Lim
- Department of Animal Science, Iowa State University, Ames, IA, USA
- Department of Animal Resource Science, Kongju National University, Yesan, Chungnam, Republic of Korea
| | - Jian Cheng
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | | | - Michael Dyck
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - PigGen Canada
- PigGen Canada Research Consortium, Guelph, ON, Canada
| | - Frederic Fortin
- Centre de Développement du Porc du Québec Inc. (CDPQ), Québec City, QC, Canada
| | - John Harding
- Department of Large Animal Clinical Sciences, University of Saskatchewan, Saskatoon, SK, Canada
| | - Graham Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Jack Dekkers
- Department of Animal Science, Iowa State University, Ames, IA, USA.
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Wang QS, Edahiro R, Namkoong H, Hasegawa T, Shirai Y, Sonehara K, Kumanogoh A, Ishii M, Koike R, Kimura A, Imoto S, Miyano S, Ogawa S, Kanai T, Fukunaga K, Okada Y. Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision. NAR Genom Bioinform 2023; 5:lqad090. [PMID: 37915762 PMCID: PMC10616627 DOI: 10.1093/nargab/lqad090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/10/2023] [Accepted: 09/25/2023] [Indexed: 11/03/2023] Open
Abstract
Statistical fine-mapping prioritizes putative causal variants from a large number of candidate variants, and is widely used in expression quantitative loci (eQTLs) studies. In eQTL fine-mapping, the existence of causal variants for gene expression is not guaranteed, since the genetic heritability of gene expression explained by nearby (cis-) variants is limited. Here we introduce a refined fine-mapping algorithm, named Knockoff-Finemap combination (KFc). KFc estimates the probability that the causal variant(s) exist in the cis-window of a gene through construction of knockoff genotypes (i.e. a set of synthetic genotypes that resembles the original genotypes), and uses it to adjust the posterior inclusion probabilities (PIPs). Utilizing simulated gene expression data, we show that KFc results in calibrated PIP distribution with improved precision. When applied to gene expression data of 465 genotyped samples from the Japan COVID-19 Task Force (JCTF), KFc resulted in significant enrichment of a functional score as well as reporter assay hits in the top PIP bins. When combined with functional priors derived from an external fine-mapping study (GTEx), KFc resulted in a significantly higher proportion of hematopoietic trait putative causal variants in the top PIP bins. Our work presents improvements in the precision of a major fine-mapping algorithm.
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Affiliation(s)
- Qingbo S Wang
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, 113-0033, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, 160-8582, Japan
| | - Takanori Hasegawa
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, 113-0033, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, 565-0871, Japan
| | | | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, 565-0871, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, 565-0871, Japan
| | - Makoto Ishii
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, 65 tsurumai, Showa-ku, Nagoya, 466-8550, Japan
| | - Ryuji Koike
- Health Science Research and Development Center (HeRD), Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, 108-8639, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, 606-8315, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, 606-8303, Japan
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, 171 77, Sweden
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Medicine, Keio University School of Medicine, Tokyo, 160-8582, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, 160-8582, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, 113-0033, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, 565-0871, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, 565-0871, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
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8
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Fuller H, Zhu Y, Nicholas J, Chatelaine HA, Drzymalla EM, Sarvestani AK, Julián-Serrano S, Tahir UA, Sinnott-Armstrong N, Raffield LM, Rahnavard A, Hua X, Shutta KH, Darst BF. Metabolomic epidemiology offers insights into disease aetiology. Nat Metab 2023; 5:1656-1672. [PMID: 37872285 PMCID: PMC11164316 DOI: 10.1038/s42255-023-00903-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/06/2023] [Indexed: 10/25/2023]
Abstract
Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.
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Affiliation(s)
- Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jayna Nicholas
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Haley A Chatelaine
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Emily M Drzymalla
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Afrand K Sarvestani
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | | | - Usman A Tahir
- Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Xinwei Hua
- Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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9
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Tsouris A, Brach G, Friedrich A, Hou J, Schacherer J. Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.550015. [PMID: 37503053 PMCID: PMC10370210 DOI: 10.1101/2023.07.21.550015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
- Institut Universitaire de France (IUF), Paris, France
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10
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Tsouris A, Brach G, Schacherer J, Hou J. Non-additive genetic components contribute significantly to population-wide gene expression variation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.550013. [PMID: 37546809 PMCID: PMC10401925 DOI: 10.1101/2023.07.21.550013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Gene expression variation, an essential step between genomic variation and phenotypic landscape, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence the heritability of expression traits remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originated from genetically divergent yeast isolates. We estimated the broad- and narrow-sense heritability across 5,087 transcript abundance traits and showed that non-additive components account for 36% of the phenotypic variance on average. By comparing allelic expression ratios in the hybrid and the corresponding parental pair, we identified regulatory changes in 25% of all cases, with a majority acting in trans. We further showed that trans-regulation could underlie coordinated expression variation across highly connected genes, resulting in significantly higher non-additive variance and most likely in some of the missing heritability of gene expression traits.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
- Institut Universitaire de France (IUF), Paris, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
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11
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Hagenbeek FA, Hirzinger JS, Breunig S, Bruins S, Kuznetsov DV, Schut K, Odintsova VV, Boomsma DI. Maximizing the value of twin studies in health and behaviour. Nat Hum Behav 2023:10.1038/s41562-023-01609-6. [PMID: 37188734 DOI: 10.1038/s41562-023-01609-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/19/2023] [Indexed: 05/17/2023]
Abstract
In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, and gene-environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and we conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.
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Affiliation(s)
- Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
| | - Jana S Hirzinger
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Sophie Breunig
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Psychology & Neuroscience, University of Colorado Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Susanne Bruins
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Dmitry V Kuznetsov
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Faculty of Sociology, Bielefeld University, Bielefeld, Germany
| | - Kirsten Schut
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Nightingale Health Plc, Helsinki, Finland
| | - Veronika V Odintsova
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Department of Psychiatry, University Medical Center of Groningen, University of Groningen, Groningen, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands.
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12
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Mezuk B, Kelly K, Bennion E, Concha JB. Leveraging a genetically-informative study design to explore depression as a risk factor for type 2 diabetes: Rationale and participant characteristics of the Mood and Immune Regulation in Twins Study. FRONTIERS IN CLINICAL DIABETES AND HEALTHCARE 2023; 4:1026402. [PMID: 37008275 PMCID: PMC10064086 DOI: 10.3389/fcdhc.2023.1026402] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 03/01/2023] [Indexed: 03/19/2023]
Abstract
BackgroundComorbidity between depression and type 2 diabetes is thought to arise from the joint effects of psychological, behavioral, and biological processes. Studies of monozygotic twins may provide a unique opportunity for clarifying how these processes inter-relate. This paper describes the rationale, characteristics, and initial findings of a longitudinal co-twin study aimed at examining the biopsychosocial mechanisms linking depression and risk of diabetes in mid-life.MethodsParticipants in the Mood and Immune Regulation in Twins (MIRT) Study were recruited from the Mid-Atlantic Twin Registry. MIRT consisted of 94 individuals who do not have diabetes at baseline, representing 43 twin pairs (41 monozygotic and 2 dizygotic), one set of monozygotic triplets, and 5 individuals whose co-twin did not participate. A broad set of variables were assessed including psychological factors (e.g., lifetime history major depression (MD)); social factors (e.g., stress perceptions and experiences); and biological factors, including indicators of metabolic risk (e.g., BMI, blood pressure (BP), HbA1c) and immune functioning (e.g., pro- and anti-inflammatory cytokines), as well as collection of RNA. Participants were re-assessed 6-month later. Intra-class correlation coefficients (ICC) and descriptive comparisons were used to explore variation in these psychological, social, and biological factors across time and within pairs.ResultsMean age was 53 years, 68% were female, and 77% identified as white. One-third had a history of MD, and 18 sibling sets were discordant for MD. MD was associated with higher systolic (139.1 vs 132.2 mmHg, p=0.05) and diastolic BP (87.2 vs. 80.8 mmHg, p=0.002) and IL-6 (1.47 vs. 0.93 pg/mL, p=0.001). MD was not associated with BMI, HbA1c, or other immune markers. While the biological characteristics of the co-twins were significantly correlated, all within-person ICCs were higher than the within-pair correlations (e.g., HbA1c within-person ICC=0.88 vs. within-pair ICC=0.49; IL-6 within-person ICC=0.64 vs. within-pair=0.54). Among the pairs discordant for MD, depression was not substantially associated with metabolic or immune markers, but was positively associated with stress.ConclusionsTwin studies have the potential to clarify the biopsychosocial processes linking depression and diabetes, and recently completed processing of RNA samples from MIRT permits future exploration of gene expression as a potential mechanism.
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Affiliation(s)
- Briana Mezuk
- Center for Social Epidemiology and Population Health, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States
- Research Center for Group Dynamics, Institute for Social Research, University of Michigan, Ann Arbor, MI, United States
- *Correspondence: Briana Mezuk,
| | - Kristen Kelly
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States
| | - Erica Bennion
- Office of Maternal and Child Health, Utah Department of Health and Human Services, Salt Lake, UT, United States
| | - Jeannie B. Concha
- College of Health Sciences, University of Texas at El Paso, El Paso, TX, United States
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13
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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] [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.
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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
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14
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García-Sancha N, Corchado-Cobos R, Gómez-Vecino A, Jiménez-Navas A, Pérez-Baena MJ, Blanco-Gómez A, Holgado-Madruga M, Mao JH, Cañueto J, Castillo-Lluva S, Mendiburu-Eliçabe M, Pérez-Losada J. Evolutionary Origins of Metabolic Reprogramming in Cancer. Int J Mol Sci 2022; 23:ijms232012063. [PMID: 36292921 PMCID: PMC9603151 DOI: 10.3390/ijms232012063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/29/2022] [Accepted: 10/06/2022] [Indexed: 11/23/2022] Open
Abstract
Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. These changes are not specific to tumors but also take place during the physiological growth of tissues. Indeed, the cellular and tissue mechanisms present in the tumor have their physiological counterpart in the repair of tissue lesions and wound healing. These molecular mechanisms have been acquired during metazoan evolution, first to eliminate the infection of the tissue injury, then to enter an effective regenerative phase. Cancer itself could be considered a phenomenon of antagonistic pleiotropy of the genes involved in effective tissue repair. Cancer and tissue repair are complex traits that share many intermediate phenotypes at the molecular, cellular, and tissue levels, and all of these are integrated within a Systems Biology structure. Complex traits are influenced by a multitude of common genes, each with a weak effect. This polygenic component of complex traits is mainly unknown and so makes up part of the missing heritability. Here, we try to integrate these different perspectives from the point of view of the metabolic changes observed in cancer.
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Affiliation(s)
- Natalia García-Sancha
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain
| | - Roberto Corchado-Cobos
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain
| | - Aurora Gómez-Vecino
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain
| | - Alejandro Jiménez-Navas
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain
| | - Manuel Jesús Pérez-Baena
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain
| | - Adrián Blanco-Gómez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain
| | - Marina Holgado-Madruga
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain
- Departamento de Fisiología y Farmacología, Universidad de Salamanca, 37007 Salamanca, Spain
- Instituto de Neurociencias de Castilla y León (INCyL), 37007 Salamanca, Spain
| | - Jian-Hua Mao
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA 94720, USA
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Javier Cañueto
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain
- Departamento de Dermatología, Hospital Universitario de Salamanca, Paseo de San Vicente 58-182, 37007 Salamanca, Spain
| | - Sonia Castillo-Lluva
- Departamento de Bioquímica y Biología Molecular, Facultad de Ciencias Químicas, Universidad Complutense, 28040 Madrid, Spain
- Instituto de Investigaciones Sanitarias San Carlos (IdISSC), 28040 Madrid, Spain
| | - Marina Mendiburu-Eliçabe
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain
- Correspondence: (M.M.-E.); (J.P.-L.)
| | - Jesús Pérez-Losada
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain
- Correspondence: (M.M.-E.); (J.P.-L.)
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15
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Vlaanderen J, Vermeulen R, Whitaker M, Chadeau-Hyam M, Hottenga JJ, de Geus E, Willemsen G, Penninx BWJH, Jansen R, Boomsma DI. Impact of long-term exposure to PM 2.5 on peripheral blood gene expression pathways involved in cell signaling and immune response. ENVIRONMENT INTERNATIONAL 2022; 168:107491. [PMID: 36081220 DOI: 10.1016/j.envint.2022.107491] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/02/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Exposure to ambient air pollution, even at low levels, is a major environmental health risk. The peripheral blood transcriptome provides a potential avenue for the elucidation of ambient air pollution related biological perturbations. We assessed the association between long-term estimates for seven priority air pollutants and perturbations in peripheral blood transcriptomics data collected in the Dutch National Twin Register (NTR) and Netherlands Study of Depression and Anxiety (NESDA) cohorts. METHODS In both the discovery (n = 2438) and replication (n = 1567) cohort, outdoor concentration of 7 air pollutants (NO2, NOx, particulate matter (PM2.5, PM2.5abs, PM10, PMcoarse), and ultrafine particles) was predicted with land use regression models. Gene expression was assessed by Affymetrix U219 arrays. Multi-variable univariate mixed-effect models were applied to test for an association between the air pollutants and the transcriptome. Functional analysis was conducted in DAVID. RESULTS In the discovery cohort, we observed for 335 genes (374 probes with FDR < 5 %) a perturbation in peripheral blood gene expression that was associated with long-term average levels of PM2.5. For 69 genes pooled effect estimates from the NTR and NESDA cohorts were significant. Identified genes play a role in biological pathways related to cell signaling and immune response. Sixty-two out of 69 genes had a similar direction of effect in an analysis in which we regressed the probes on differential PM2.5 exposure within monozygotic twin pairs, indicating that the observed differences in gene expression were likely driven by differences in air pollution, rather than by confounding by genetic factors. CONCLUSION Our results indicate that PM2.5 can elicit a response in cell signaling and the immune system, both hallmarks of environmental diseases. The differential effect that we observed between air pollutants may aid in the understanding of differential health effects that have been observed with these exposures.
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Affiliation(s)
- Jelle Vlaanderen
- Institute for Risk Assessment Sciences, Utrecht University, the Netherlands.
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | | | | | - Jouke-Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Eco de Geus
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Rick Jansen
- Department of Psychiatry, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
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16
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Castaneda AB, Petty LE, Scholz M, Jansen R, Weiss S, Zhang X, Schramm K, Beutner F, Kirsten H, Schminke U, Hwang SJ, Marzi C, Dhana K, Seldenrijk A, Krohn K, Homuth G, Wolf P, Peters MJ, Dörr M, Peters A, van Meurs JBJ, Uitterlinden AG, Kavousi M, Levy D, Herder C, van Grootheest G, Waldenberger M, Meisinger C, Rathmann W, Thiery J, Polak J, Koenig W, Seissler J, Bis JC, Franceshini N, Giambartolomei C, Hofman A, Franco OH, Penninx BWJH, Prokisch H, Völzke H, Loeffler M, O'Donnell CJ, Below JE, Dehghan A, de Vries PS. Associations of carotid intima media thickness with gene expression in whole blood and genetically predicted gene expression across 48 tissues. Hum Mol Genet 2022; 31:1171-1182. [PMID: 34788810 PMCID: PMC8976428 DOI: 10.1093/hmg/ddab236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/11/2021] [Accepted: 08/11/2021] [Indexed: 11/13/2022] Open
Abstract
Carotid intima media thickness (cIMT) is a biomarker of subclinical atherosclerosis and a predictor of future cardiovascular events. Identifying associations between gene expression levels and cIMT may provide insight to atherosclerosis etiology. Here, we use two approaches to identify associations between mRNA levels and cIMT: differential gene expression analysis in whole blood and S-PrediXcan. We used microarrays to measure genome-wide whole blood mRNA levels of 5647 European individuals from four studies. We examined the association of mRNA levels with cIMT adjusted for various potential confounders. Significant associations were tested for replication in three studies totaling 3943 participants. Next, we applied S-PrediXcan to summary statistics from a cIMT genome-wide association study (GWAS) of 71 128 individuals to estimate the association between genetically determined mRNA levels and cIMT and replicated these analyses using S-PrediXcan on an independent GWAS on cIMT that included 22 179 individuals from the UK Biobank. mRNA levels of TNFAIP3, CEBPD and METRNL were inversely associated with cIMT, but these associations were not significant in the replication analysis. S-PrediXcan identified associations between cIMT and genetically determined mRNA levels for 36 genes, of which six were significant in the replication analysis, including TLN2, which had not been previously reported for cIMT. There was weak correlation between our results using differential gene expression analysis and S-PrediXcan. Differential expression analysis and S-PrediXcan represent complementary approaches for the discovery of associations between phenotypes and gene expression. Using these approaches, we prioritize TNFAIP3, CEBPD, METRNL and TLN2 as new candidate genes whose differential expression might modulate cIMT.
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Affiliation(s)
- Andy B Castaneda
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Rick Jansen
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Xiaoling Zhang
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA.,Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,The Framingham Heart Study, Framingham, MA, USA
| | - Katharina Schramm
- Institute of Neurogenomics, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Human Genetics, Technical University Munich, Munich, Germany
| | | | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Ulf Schminke
- Department of Neurology, University Medicine Greifswald, Greifswald, Germany
| | - Shih-Jen Hwang
- The Framingham Heart Study, Framingham, MA, USA.,Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Carola Marzi
- Institute of Epidemiology, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Klodian Dhana
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Adrie Seldenrijk
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Knut Krohn
- Interdisciplinary Center of Clinical Research, University of Leipzig, Leipzig, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Petra Wolf
- Institute of Neurogenomics, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Marjolein J Peters
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Marcus Dörr
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.,Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Daniel Levy
- The Framingham Heart Study, Framingham, MA, USA.,Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Christian Herder
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,German Center for Diabetes Research (DZD e.V.), München-Neuherberg, Germany.,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - Melanie Waldenberger
- Institute of Epidemiology, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Christa Meisinger
- Institute of Epidemiology, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Augsburg, Germany
| | - Wolfgang Rathmann
- Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Joachim Thiery
- LIFE Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany.,Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
| | - Joseph Polak
- Tufts University School of Medicine, Boston, MA, USA
| | - Wolfgang Koenig
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.,Department of Internal Medicine II-Cardiology, University of Ulm Medical Center, Ulm, Germany
| | - Jochen Seissler
- Diabetes Center, Diabetes Research Group, Medizinische Klinik und Poliklinik IV, Ludwig-Maximilians-Universität, Munich, Germany
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Nora Franceshini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | | | | | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.,Institute of Social and Preventive Medicine, University of Bern, Switzerland
| | - Brenda W J H Penninx
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Holger Prokisch
- Institute of Neurogenomics, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Henry Völzke
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Christopher J O'Donnell
- The Framingham Heart Study, Framingham, MA, USA.,Cardiology Section, Department of Medicine, Boston Veteran's Administration Healthcare and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.,Department of Epidemiology and Biostatistics, Imperial College London, London, UK.,MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London, UK.,UK Dementia Research Institute at Imperial College London, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London W12 0NN UK
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.,Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
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17
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O'Neill MB, Quach H, Pothlichet J, Aquino Y, Bisiaux A, Zidane N, Deschamps M, Libri V, Hasan M, Zhang SY, Zhang Q, Matuozzo D, Cobat A, Abel L, Casanova JL, Naffakh N, Rotival M, Quintana-Murci L. Single-Cell and Bulk RNA-Sequencing Reveal Differences in Monocyte Susceptibility to Influenza A Virus Infection Between Africans and Europeans. Front Immunol 2021; 12:768189. [PMID: 34912340 PMCID: PMC8667309 DOI: 10.3389/fimmu.2021.768189] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/27/2021] [Indexed: 01/02/2023] Open
Abstract
There is considerable inter-individual and inter-population variability in response to viruses. The potential of monocytes to elicit type-I interferon responses has attracted attention to their role in viral infections. Here, we use single-cell RNA-sequencing to characterize the role of cellular heterogeneity in human variation of monocyte responses to influenza A virus (IAV) exposure. We show widespread inter-individual variability in the percentage of IAV-infected monocytes. Notably, individuals with high cellular susceptibility to IAV are characterized by a lower activation at basal state of an IRF/STAT-induced transcriptional network, which includes antiviral genes such as IFITM3, MX1 and OAS3. Upon IAV challenge, we find that cells escaping viral infection display increased mRNA expression of type-I interferon stimulated genes and decreased expression of ribosomal genes, relative to both infected cells and those never exposed to IAV. We also uncover a stronger resistance of CD16+ monocytes to IAV infection, together with CD16+ -specific mRNA expression of IL6 and TNF in response to IAV. Finally, using flow cytometry and bulk RNA-sequencing across 200 individuals of African and European ancestry, we observe a higher number of CD16 + monocytes and lower susceptibility to IAV infection among monocytes from individuals of African-descent. Based on these data, we hypothesize that higher basal monocyte activation, driven by environmental factors and/or weak-effect genetic variants, underlies the lower cellular susceptibility to IAV infection of individuals of African ancestry relative to those of European ancestry. Further studies are now required to investigate how such cellular differences in IAV susceptibility translate into population differences in clinical outcomes and susceptibility to severe influenza.
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Affiliation(s)
- Mary B O'Neill
- Human Evolutionary Genetics Unit, Institut Pasteur, UMR 2000, Centre National de la Recherche Scientifique (CNRS), Paris, France
| | - Hélène Quach
- Muséum National d'Histoire Naturelle, UMR7206, Centre National de la Recherche Scientifique (CNRS), Université de Paris, Paris, France
| | | | - Yann Aquino
- Human Evolutionary Genetics Unit, Institut Pasteur, UMR 2000, Centre National de la Recherche Scientifique (CNRS), Paris, France.,Sorbonne Université, Collège doctoral, Paris, France
| | - Aurélie Bisiaux
- Human Evolutionary Genetics Unit, Institut Pasteur, UMR 2000, Centre National de la Recherche Scientifique (CNRS), Paris, France
| | - Nora Zidane
- Biodiversity and Epidemiology of Bacterial Pathogens Unit, Institut Pasteur, Paris, France
| | - Matthieu Deschamps
- Human Evolutionary Genetics Unit, Institut Pasteur, UMR 2000, Centre National de la Recherche Scientifique (CNRS), Paris, France
| | - Valentina Libri
- Cytometry and Biomarkers UTechS, Institut Pasteur, Paris, France
| | - Milena Hasan
- Cytometry and Biomarkers UTechS, Institut Pasteur, Paris, France
| | - Shen-Ying Zhang
- St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, United States.,Laboratory of Human Genetics of Infectious Diseases, Necker Hospital for Sick Children, INSERM UMR 1163, Necker Hospital for Sick Children, Paris, France.,Imagine Institute, Paris University, Paris, France
| | - Qian Zhang
- St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, United States.,Laboratory of Human Genetics of Infectious Diseases, Necker Hospital for Sick Children, INSERM UMR 1163, Necker Hospital for Sick Children, Paris, France.,Imagine Institute, Paris University, Paris, France
| | - Daniela Matuozzo
- Laboratory of Human Genetics of Infectious Diseases, Necker Hospital for Sick Children, INSERM UMR 1163, Necker Hospital for Sick Children, Paris, France.,Imagine Institute, Paris University, Paris, France
| | - Aurélie Cobat
- Laboratory of Human Genetics of Infectious Diseases, Necker Hospital for Sick Children, INSERM UMR 1163, Necker Hospital for Sick Children, Paris, France.,Imagine Institute, Paris University, Paris, France
| | - Laurent Abel
- St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, United States.,Laboratory of Human Genetics of Infectious Diseases, Necker Hospital for Sick Children, INSERM UMR 1163, Necker Hospital for Sick Children, Paris, France.,Imagine Institute, Paris University, Paris, France
| | - Jean-Laurent Casanova
- St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, United States.,Laboratory of Human Genetics of Infectious Diseases, Necker Hospital for Sick Children, INSERM UMR 1163, Necker Hospital for Sick Children, Paris, France.,Imagine Institute, Paris University, Paris, France.,Howard Hughes Medical Institute, New York, NY, United States
| | - Nadia Naffakh
- RNA Biology of Influenza Virus Unit, Institut Pasteur, Paris, France
| | - Maxime Rotival
- Human Evolutionary Genetics Unit, Institut Pasteur, UMR 2000, Centre National de la Recherche Scientifique (CNRS), Paris, France
| | - Lluis Quintana-Murci
- Human Evolutionary Genetics Unit, Institut Pasteur, UMR 2000, Centre National de la Recherche Scientifique (CNRS), Paris, France.,Chair of Human Genomics and Evolution, Collège de France, Paris, France
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18
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Zhu A, Matoba N, Wilson EP, Tapia AL, Li Y, Ibrahim JG, Stein JL, Love MI. MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity. PLoS Genet 2021; 17:e1009455. [PMID: 33872308 PMCID: PMC8084342 DOI: 10.1371/journal.pgen.1009455] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 04/29/2021] [Accepted: 02/26/2021] [Indexed: 11/18/2022] Open
Abstract
Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus's estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.
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Affiliation(s)
- Anqi Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Nana Matoba
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Emma P. Wilson
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Amanda L. Tapia
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Joseph G. Ibrahim
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Michael I. Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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19
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Sinnott-Armstrong N, Naqvi S, Rivas M, Pritchard JK. GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background. eLife 2021; 10:e58615. [PMID: 33587031 PMCID: PMC7884075 DOI: 10.7554/elife.58615] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 01/18/2021] [Indexed: 12/30/2022] Open
Abstract
Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for three molecular traits-urate, IGF-1, and testosterone-with better-understood biology than most other complex traits. We find that many of the most significant hits are readily interpretable. We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males. At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these three molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background.
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Affiliation(s)
| | - Sahin Naqvi
- Department of Genetics, Stanford UniversityStanfordUnited States
- Department of Chemical and Systems Biology, Stanford UniversityStanfordUnited States
| | - Manuel Rivas
- Department of Biomedical Data Sciences, Stanford UniversityStanfordUnited States
| | - Jonathan K Pritchard
- Department of Genetics, Stanford UniversityStanfordUnited States
- Department of Biology, Stanford UniversityStanfordUnited States
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20
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Magri C, Giacopuzzi E, Sacco C, Bocchio-Chiavetto L, Minelli A, Gennarelli M. Alterations observed in the interferon α and β signaling pathway in MDD patients are marginally influenced by cis-acting alleles. Sci Rep 2021; 11:727. [PMID: 33436853 PMCID: PMC7804189 DOI: 10.1038/s41598-020-80374-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 12/17/2020] [Indexed: 11/09/2022] Open
Abstract
Major depressive disorder (MDD) is a common psychiatric disorder with a multifactorial aetiology determined by the interaction between genetic and environmental risk factors. Pieces of evidence indicate that inflammation and immune activation may contribute to the onset of MDD playing a role in the pathogenetic mechanism. To date, it is not known to which extent the association between MDD and inflammation is shaped by the genetic background or by the presence of environmental factors. To clarify this issue, we analyzed genotype and blood RNA profiles of 463 MDD cases and 459 controls (NIMH-Study 88/Site621) estimating the Genetic and Environmental Regulated eXpression component of gene expression (GReX and EReX respectively). Both components were tested for association with MDD. Many genes belonging to the α/β interferon signaling pathway showed an association between MDD and EReX, only two between MDD and GReX. Also other MDD differentially expressed genes were more influenced by the EReX than by GReX. These results suggest that impact of the genetic background on MDD blood gene expression alterations is much lower than the contribution of environmental factors and almost absent for the genes of the interferon pathway.
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Affiliation(s)
- Chiara Magri
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.
| | - Edoardo Giacopuzzi
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Chiara Sacco
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Luisella Bocchio-Chiavetto
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Faculty of Psychology, eCampus University, Novedrate, Como, Italy
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.,Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.,Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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21
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Awany D, Chimusa ER. Heritability jointly explained by host genotype and microbiome: will improve traits prediction? Brief Bioinform 2020; 22:5893981. [PMID: 32810866 DOI: 10.1093/bib/bbaa175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/09/2020] [Accepted: 07/10/2020] [Indexed: 11/14/2022] Open
Abstract
As we observe the $70$th anniversary of the publication by Robertson that formalized the notion of 'heritability', geneticists remain puzzled by the problem of missing/hidden heritability, where heritability estimates from genome-wide association studies (GWASs) fall short of that from twin-based studies. Many possible explanations have been offered for this discrepancy, including existence of genetic variants poorly captured by existing arrays, dominance, epistasis and unaccounted-for environmental factors; albeit these remain controversial. We believe a substantial part of this problem could be solved or better understood by incorporating the host's microbiota information in the GWAS model for heritability estimation and may also increase human traits prediction for clinical utility. This is because, despite empirical observations such as (i) the intimate role of the microbiome in many complex human phenotypes, (ii) the overlap between genetic variants associated with both microbiome attributes and complex diseases and (iii) the existence of heritable bacterial taxa, current GWAS models for heritability estimate do not take into account the contributory role of the microbiome. Furthermore, heritability estimate from twin-based studies does not discern microbiome component of the observed total phenotypic variance. Here, we summarize the concept of heritability in GWAS and microbiome-wide association studies, focusing on its estimation, from a statistical genetics perspective. We then discuss a possible statistical method to incorporate the microbiome in the estimation of heritability in host GWAS.
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Affiliation(s)
- Denis Awany
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Emile R Chimusa
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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22
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Genetics and Not Shared Environment Explains Familial Resemblance in Adult Metabolomics Data. Twin Res Hum Genet 2020; 23:145-155. [PMID: 32635965 DOI: 10.1017/thg.2020.53] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Metabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term 'metabolomics' refers to the comprehensive study of these molecules. The concentrations of metabolites in biological tissues are under genetic control, but this is limited by environmental factors such as diet. In adult mono- and dizygotic twin pairs, we estimated the contribution of genetic and shared environmental influences on metabolite levels by structural equation modeling and tested whether the familial resemblance for metabolite levels is mainly explained by genetic or by environmental factors that are shared by family members. Metabolites were measured across three platforms: two based on proton nuclear magnetic resonance techniques and one employing mass spectrometry. These three platforms comprised 237 single metabolic traits of several chemical classes. For the three platforms, metabolites were assessed in 1407, 1037 and 1116 twin pairs, respectively. We carried out power calculations to establish what percentage of shared environmental variance could be detected given these sample sizes. Our study did not find evidence for a systematic contribution of shared environment, defined as the influence of growing up together in the same household, on metabolites assessed in adulthood. Significant heritability was observed for nearly all 237 metabolites; significant contribution of the shared environment was limited to 6 metabolites. The top quartile of the heritability distribution was populated by 5 of the 11 investigated chemical classes. In this quartile, metabolites of the class lipoprotein were significantly overrepresented, whereas metabolites of classes glycerophospholipids and glycerolipids were significantly underrepresented.
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