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Mahmud M, Bekele M, Behera N. A computational investigation of cis-gene regulation in evolution. Theory Biosci 2023; 142:151-165. [PMID: 37041403 DOI: 10.1007/s12064-023-00391-3] [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: 01/13/2022] [Accepted: 03/27/2023] [Indexed: 04/13/2023]
Abstract
In biological processes involving gene networks, genes regulate other genes that determine the phenotypic traits. Gene regulation plays an important role in evolutionary dynamics. In a genetic algorithm, a trans-gene regulatory mechanism was shown to speed up adaptation and evolution. Here, we examine the effect of cis-gene regulation on an adaptive system. The model is haploid. A chromosome is partitioned into regulatory loci and structural loci. The regulatory genes regulate the expression and functioning of structural genes via the cis-elements in a probabilistic manner. In the simulation, the change in the allele frequency, the mean population fitness and the efficiency of phenotypic selection are monitored. Cis-gene regulation increases adaption and accelerates the evolutionary process in comparison with the case involving absence of gene regulation. Some special features of the simulation results are as follows. A low ratio of regulatory loci and structural loci gives higher adaptation for fixed total number of loci. Plasticity is advantageous beyond a threshold value. Adaptation is better for large number of total loci when the ratio of regulatory loci to structural loci is one. However, it reaches a saturation beyond which the increase in the total loci is not advantageous. Efficiency of the phenotypic selection is higher for larger value of the initial plasticity.
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Affiliation(s)
- Mohammed Mahmud
- Department of Physics, Addis Ababa University, P.O.Box 1176, Addis Ababa, Ethiopia
| | - Mulugeta Bekele
- Department of Physics, Addis Ababa University, P.O.Box 1176, Addis Ababa, Ethiopia
| | - Narayan Behera
- Department of Applied Physics, Adama Science and Technology University, P. O. Box 1888, Adama, Ethiopia.
- Division of Physical Science, SVYASA University, Eknath Bhavan, Kempegowda Nagar, Bengaluru, 560019, India.
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2
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Findley AS, Zhang X, Boye C, Lin YL, Kalita CA, Barreiro L, Lohmueller KE, Pique-Regi R, Luca F. A signature of Neanderthal introgression on molecular mechanisms of environmental responses. PLoS Genet 2021; 17:e1009493. [PMID: 34570765 PMCID: PMC8509894 DOI: 10.1371/journal.pgen.1009493] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 10/12/2021] [Accepted: 08/18/2021] [Indexed: 12/17/2022] Open
Abstract
Ancient human migrations led to the settlement of population groups in varied environmental contexts worldwide. The extent to which adaptation to local environments has shaped human genetic diversity is a longstanding question in human evolution. Recent studies have suggested that introgression of archaic alleles in the genome of modern humans may have contributed to adaptation to environmental pressures such as pathogen exposure. Functional genomic studies have demonstrated that variation in gene expression across individuals and in response to environmental perturbations is a main mechanism underlying complex trait variation. We considered gene expression response to in vitro treatments as a molecular phenotype to identify genes and regulatory variants that may have played an important role in adaptations to local environments. We investigated if Neanderthal introgression in the human genome may contribute to the transcriptional response to environmental perturbations. To this end we used eQTLs for genes differentially expressed in a panel of 52 cellular environments, resulting from 5 cell types and 26 treatments, including hormones, vitamins, drugs, and environmental contaminants. We found that SNPs with introgressed Neanderthal alleles (N-SNPs) disrupt binding of transcription factors important for environmental responses, including ionizing radiation and hypoxia, and for glucose metabolism. We identified an enrichment for N-SNPs among eQTLs for genes differentially expressed in response to 8 treatments, including glucocorticoids, caffeine, and vitamin D. Using Massively Parallel Reporter Assays (MPRA) data, we validated the regulatory function of 21 introgressed Neanderthal variants in the human genome, corresponding to 8 eQTLs regulating 15 genes that respond to environmental perturbations. These findings expand the set of environments where archaic introgression may have contributed to adaptations to local environments in modern humans and provide experimental validation for the regulatory function of introgressed variants.
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Affiliation(s)
- Anthony S. Findley
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
| | - Xinjun Zhang
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, California, United States of America
| | - Carly Boye
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
| | - Yen Lung Lin
- Genetics Section, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Cynthia A. Kalita
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
| | - Luis Barreiro
- Genetics Section, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Kirk E. Lohmueller
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, California, United States of America
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California, United States of America
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, United States of America
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, United States of America
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3
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Davenport EE, Amariuta T, Gutierrez-Arcelus M, Slowikowski K, Westra HJ, Luo Y, Shen C, Rao DA, Zhang Y, Pearson S, von Schack D, Beebe JS, Bing N, John S, Vincent MS, Zhang B, Raychaudhuri S. Discovering in vivo cytokine-eQTL interactions from a lupus clinical trial. Genome Biol 2018; 19:168. [PMID: 30340504 PMCID: PMC6195724 DOI: 10.1186/s13059-018-1560-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 10/05/2018] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Cytokines are critical to human disease and are attractive therapeutic targets given their widespread influence on gene regulation and transcription. Defining the downstream regulatory mechanisms influenced by cytokines is central to defining drug and disease mechanisms. One promising strategy is to use interactions between expression quantitative trait loci (eQTLs) and cytokine levels to define target genes and mechanisms. RESULTS In a clinical trial for anti-IL-6 in patients with systemic lupus erythematosus, we measure interferon (IFN) status, anti-IL-6 drug exposure, and whole blood genome-wide gene expression at three time points. We show that repeat transcriptomic measurements increases the number of cis eQTLs identified compared to using a single time point. We observe a statistically significant enrichment of in vivo eQTL interactions with IFN status and anti-IL-6 drug exposure and find many novel interactions that have not been previously described. Finally, we find transcription factor binding motifs interrupted by eQTL interaction SNPs, which point to key regulatory mediators of these environmental stimuli and therefore potential therapeutic targets for autoimmune diseases. In particular, genes with IFN interactions are enriched for ISRE binding site motifs, while those with anti-IL-6 interactions are enriched for IRF4 motifs. CONCLUSIONS This study highlights the potential to exploit clinical trial data to discover in vivo eQTL interactions with therapeutically relevant environmental variables.
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Affiliation(s)
- Emma E Davenport
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Tiffany Amariuta
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Maria Gutierrez-Arcelus
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Kamil Slowikowski
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Harm-Jan Westra
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Ciyue Shen
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Deepak A Rao
- Division of Rheumatology, Allergy, Immunology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | | | - Stephen Pearson
- Pfizer New Haven Clinical Research Unit, New Haven, CT, 06511, USA
| | | | | | - Nan Bing
- Pfizer Inc., Cambridge, MA, 02139, USA
| | | | | | | | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
- Faculty of Medical and Human Sciences, University of Manchester, M13 9PL, Manchester, UK.
- Harvard New Research Building, 77 Avenue Louis Pasteur, Suite 250D, Boston, MA, 02446, USA.
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4
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Josephs EB. Determining the evolutionary forces shaping G × E. THE NEW PHYTOLOGIST 2018; 219:31-36. [PMID: 29574919 DOI: 10.1111/nph.15103] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 02/09/2018] [Indexed: 06/08/2023]
Abstract
Contents Summary 31 I. Introduction 31 II. The maintenance of genetic variation for plasticity 32 III. Why is there environmental variation for genetic effects? 33 IV. Conclusions 35 Acknowledgements 35 References 35 SUMMARY: Phenotypic plasticity is common in nature, yet we lack a comprehensive understanding of the evolutionary forces that shape genetic variation for plasticity. This endeavor is especially important because variation for plasticity will result in genotype-by-environment interactions (G × E), a crucial component of variation in quantitative traits. Here, I review our understanding of the evolutionary forces shaping G × E, focusing specifically on: what evolutionary forces maintain variation for plasticity; and what forces maintain different genetic architectures across environments. My specific goal is to show that genomic data can be leveraged to explain the maintenance of G × E by contrasting patterns of genetic variation for plasticity with neutral expectations.
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Affiliation(s)
- Emily B Josephs
- Department of Evolution and Ecology, University of California, Davis, CA, 95616, USA
- Center for Population Biology, University of California, Davis, CA, 95616, USA
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5
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Taylor DL, Knowles DA, Scott LJ, Ramirez AH, Casale FP, Wolford BN, Guan L, Varshney A, Albanus RD, Parker SCJ, Narisu N, Chines PS, Erdos MR, Welch RP, Kinnunen L, Saramies J, Sundvall J, Lakka TA, Laakso M, Tuomilehto J, Koistinen HA, Stegle O, Boehnke M, Birney E, Collins FS. Interactions between genetic variation and cellular environment in skeletal muscle gene expression. PLoS One 2018; 13:e0195788. [PMID: 29659628 PMCID: PMC5901994 DOI: 10.1371/journal.pone.0195788] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Accepted: 03/29/2018] [Indexed: 12/18/2022] Open
Abstract
From whole organisms to individual cells, responses to environmental conditions are influenced by genetic makeup, where the effect of genetic variation on a trait depends on the environmental context. RNA-sequencing quantifies gene expression as a molecular trait, and is capable of capturing both genetic and environmental effects. In this study, we explore opportunities of using allele-specific expression (ASE) to discover cis-acting genotype-environment interactions (GxE)—genetic effects on gene expression that depend on an environmental condition. Treating 17 common, clinical traits as approximations of the cellular environment of 267 skeletal muscle biopsies, we identify 10 candidate environmental response expression quantitative trait loci (reQTLs) across 6 traits (12 unique gene-environment trait pairs; 10% FDR per trait) including sex, systolic blood pressure, and low-density lipoprotein cholesterol. Although using ASE is in principle a promising approach to detect GxE effects, replication of such signals can be challenging as validation requires harmonization of environmental traits across cohorts and a sufficient sampling of heterozygotes for a transcribed SNP. Comprehensive discovery and replication will require large human transcriptome datasets, or the integration of multiple transcribed SNPs, coupled with standardized clinical phenotyping.
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Affiliation(s)
- D. Leland Taylor
- National Human Genome Research Institute, National Institutes of Health, Bethesda, United States of America
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - David A. Knowles
- Department of Computer Science, Stanford University, Stanford, California, United States of America
| | - Laura J. Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Andrea H. Ramirez
- Department of Medicine, Vanderbilt University Medical Center, Tennessee, United States of America
| | - Francesco Paolo Casale
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Brooke N. Wolford
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Li Guan
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Arushi Varshney
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Ricardo D’Oliveira Albanus
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Stephen C. J. Parker
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Narisu Narisu
- National Human Genome Research Institute, National Institutes of Health, Bethesda, United States of America
| | - Peter S. Chines
- National Human Genome Research Institute, National Institutes of Health, Bethesda, United States of America
| | - Michael R. Erdos
- National Human Genome Research Institute, National Institutes of Health, Bethesda, United States of America
| | - Ryan P. Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Leena Kinnunen
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - Jouko Saramies
- South Karelia Social and Health Care District, Lappeenranta, Finland
| | - Jouko Sundvall
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - Timo A. Lakka
- Institute of Biomedicine/Physiology, University of Eastern Finland, Kuopio, Finland
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Jaakko Tuomilehto
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- Department of Neurosciences and Preventive Medicine, Danube University Krems, Krems, Austria
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
- Dasman Diabetes Institute, Dasman, Kuwait
| | - Heikki A. Koistinen
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- Department of Medicine and Abdominal Center: Endocrinology, University of Helsinki and Helsinki University Central Hospital, Haartmaninkatu 4, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Biomedicum 2U, Tukholmankatu 8, Helsinki, Finland
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
- * E-mail: (EB); (FSC)
| | - Francis S. Collins
- National Human Genome Research Institute, National Institutes of Health, Bethesda, United States of America
- * E-mail: (EB); (FSC)
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Maranville JC, Di Rienzo A. Combining genetic and nongenetic biomarkers to realize the promise of pharmacogenomics for inflammatory diseases. Pharmacogenomics 2015; 15:1931-40. [PMID: 25495413 DOI: 10.2217/pgs.14.129] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Many drugs used to treat inflammatory diseases are ineffective in a substantial proportion of patients. Identifying patients that are likely to respond to specific therapies would facilitate personalized treatment strategies that could improve outcomes while reducing costs and risks of adverse events. Despite these clear benefits, there are limited examples of predictive biomarkers of drug efficacy currently implemented into clinical practice for inflammatory diseases. We review efforts to identify genetic and nongenetic biomarkers of drug response in these diseases and consider potential benefits from combining multiple sources of biological data into multifeature predictive models.
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Affiliation(s)
- Joseph C Maranville
- Committee on Clinical Pharmacology & Pharmacogenomics, The University of Chicago, Chicago, IL, USA
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7
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Borinskaya SA, Yankovsky NK. Combination of genetic and humanitarian (cross-cultural) methods for the identification of human genes involved in the process of adaptation to evolutionary new environmental factors. RUSS J GENET+ 2015. [DOI: 10.1134/s1022795415040031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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8
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Siddle KJ, Quintana-Murci L. The Red Queen's long race: human adaptation to pathogen pressure. Curr Opin Genet Dev 2014; 29:31-8. [PMID: 25170983 DOI: 10.1016/j.gde.2014.07.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Revised: 07/09/2014] [Accepted: 07/25/2014] [Indexed: 12/19/2022]
Abstract
Pathogens, and the infectious diseases they cause, have been paramount among the threats encountered by humans in their expansions throughout the globe. Numerous studies have identified immunity and host defence genes as being among the functions most strongly targeted by selection, most likely pathogen-driven. The dissection of the form and intensity of such selective pressures have increased our knowledge of the biological relevance of the underlying immunological mechanisms in host defence. Although the identities of the specific infectious agents imposing these selective pressures remain, in most cases, elusive, the impact of several pathogens, notably malaria and cholera, has been described. However, past selection against infectious diseases may have some fitness costs upon environmental changes, potentially leading to maladaptation and immunopathology.
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Affiliation(s)
- Katherine J Siddle
- Unit of Human Evolutionary Genetics, Department of Genomes and Genetics, Institut Pasteur, Paris 75015, France; CNRS URA3012, Paris 75015, France
| | - Lluis Quintana-Murci
- Unit of Human Evolutionary Genetics, Department of Genomes and Genetics, Institut Pasteur, Paris 75015, France; CNRS URA3012, Paris 75015, France.
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9
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Wolock S, Yates A, Petrill SA, Bohland JW, Blair C, Li N, Machiraju R, Huang K, Bartlett CW. Gene × smoking interactions on human brain gene expression: finding common mechanisms in adolescents and adults. J Child Psychol Psychiatry 2013; 54:1109-19. [PMID: 23909413 PMCID: PMC3809890 DOI: 10.1111/jcpp.12119] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/04/2013] [Indexed: 12/25/2022]
Abstract
BACKGROUND Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets to assess if DNA variation is associated with post-mortem brain gene expression changes based on smoking behavior, a biobehavioral construct that is part of a complex system of genetic and environmental influences. METHODS We conducted an expression quantitative trait locus (eQTL) study on two independent human brain gene expression datasets assessing G × E for selected psychiatric genes and smoking status. We employed linear regression to model the significance of the Gene × Smoking interaction term, followed by meta-analysis across datasets. RESULTS Overall, we observed that the effect of DNA variation on gene expression is moderated by smoking status. Expression of 16 genes was significantly associated with single nucleotide polymorphisms that demonstrated G × E effects. The strongest finding (p = 1.9 × 10⁻¹¹) was neurexin 3-alpha (NRXN3), a synaptic cell-cell adhesion molecule involved in maintenance of neural connections (such as the maintenance of smoking behavior). Other significant G × E associations include four glutamate genes. CONCLUSIONS This is one of the first studies to demonstrate G × E effects within the human brain. In particular, this study implicated NRXN3 in the maintenance of smoking. The effect of smoking on NRXN3 expression and downstream behavior is different based upon SNP genotype, indicating that DNA profiles based on SNPs could be useful in understanding the effects of smoking behaviors. These results suggest that better measurement of psychiatric conditions, and the environment in post-mortem brain studies may yield an important avenue for understanding the biological mechanisms of G × E interactions in psychiatry.
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Affiliation(s)
- Samuel Wolock
- Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH, USA
| | - Andrew Yates
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
| | | | - Jason W. Bohland
- Department of Health Sciences, Boston University, Boston, MA, USA
| | - Clancy Blair
- Department of Applied Psychology, New York University, New York, NY, USA
| | - Ning Li
- Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH, USA
| | - Raghu Machiraju
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
,Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
| | - Kun Huang
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
,Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
,The CCC Biomedical Informatics Shared Resource, The Ohio State University Columbus, OH, USA
| | - Christopher W. Bartlett
- Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH, USA
,Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
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10
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Idaghdour Y, Awadalla P. Exploiting gene expression variation to capture gene-environment interactions for disease. Front Genet 2013; 3:228. [PMID: 23755064 PMCID: PMC3668192 DOI: 10.3389/fgene.2012.00228] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2012] [Accepted: 10/10/2012] [Indexed: 12/18/2022] Open
Abstract
Gene-environment interactions have long been recognized as a fundamental concept in evolutionary, quantitative, and medical genetics. In the genomics era, study of how environment and genome interact to shape gene expression variation is relevant to understanding the genetic architecture of complex phenotypes. While genetic analysis of gene expression variation focused on main effects, little is known about the extent of interaction effects implicating regulatory variants and their consequences on transcriptional variation. Here we survey the current state of the concept of transcriptional gene-environment interactions and discuss its utility for mapping disease phenotypes in light of the insights gained from genome-wide association studies of gene expression.
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Affiliation(s)
- Youssef Idaghdour
- Sainte-Justine Research Center, University of Montreal Montreal, QC, Canada
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