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Gasperi C, Chun S, Sunyaev SR, Cotsapas C. Shared associations identify causal relationships between gene expression and immune cell phenotypes. Commun Biol 2021; 4:279. [PMID: 33664438 PMCID: PMC7933159 DOI: 10.1038/s42003-021-01823-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 02/04/2021] [Indexed: 12/22/2022] Open
Abstract
Genetic mapping studies have identified thousands of associations between common variants and hundreds of human traits. Translating these associations into mechanisms is complicated by two factors: they fall into gene regulatory regions; and they are rarely mapped to one causal variant. One way around these limitations is to find groups of traits that share associations, using this genetic link to infer a biological connection. Here, we assess how many trait associations in the same locus are due to the same genetic variant, and thus shared; and if these shared associations are due to causal relationships between traits. We find that only a subset of traits share associations, with many due to causal relationships rather than pleiotropy. We therefore suggest that simply observing overlapping associations at a genetic locus is insufficient to infer causality; direct evidence of shared associations is required to support mechanistic hypotheses in genetic studies of complex traits.
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Affiliation(s)
- Christiane Gasperi
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, Germany
| | - Sung Chun
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Shamil R Sunyaev
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Chris Cotsapas
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
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2
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Lin D, Chen J, Duan K, Perrone-Bizzozero N, Sui J, Calhoun V, Liu J. Network modules linking expression and methylation in prefrontal cortex of schizophrenia. Epigenetics 2020; 16:876-893. [PMID: 33079616 PMCID: PMC8331039 DOI: 10.1080/15592294.2020.1827718] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Tremendous work has demonstrated the critical roles of genetics, epigenetics as well as their interplay in brain transcriptional regulations in the pathology of schizophrenia (SZ). There is great success currently in the dissection of the genetic components underlying risk-conferring transcriptomic networks. However, the study of regulating effect of epigenetics in the etiopathogenesis of SZ still faces many challenges. In this work, we investigated DNA methylation and gene expression from the dorsolateral prefrontal cortex (DLPFC) region of schizophrenia patients and healthy controls using weighted correlation network approach. We identified and replicated two expression and two methylation modules significantly associated with SZ. Among them, one pair of expression and methylation modules were significantly overlapped in the module genes which were significantly enriched in astrocyte-associated functional pathways, and specifically expressed in astrocytes. Another two linked expression-methylation module pairs were involved ageing process with module genes mostly related to oligodendrocyte development and myelination, and specifically expressed in oligodendrocytes. Further examination of underlying quantitative trait loci (QTLs) showed significant enrichment in genetic risk of most psychiatric disorders for expression QTLs but not for methylation QTLs. These results support the coherence between methylation and gene expression at the network level, and suggest a combinatorial effect of genetics and epigenetics in regulating gene expression networks specific to glia cells in relation to SZ and ageing process.
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Affiliation(s)
- Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, USA
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, USA
| | - Kuaikuai Duan
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
| | - Nora Perrone-Bizzozero
- Department of Neurosciences, University of New Mexico, Albuquerque, NM, USA.,Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, USA
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, USA.,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA.,Department of Psychology, Georgia State University, Atlanta, USA.,Department of Computer Science, Georgia State University, Atlanta, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, USA.,Department of Computer Science, Georgia State University, Atlanta, USA
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3
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Salnikova LE, Khadzhieva MB, Kolobkov DS, Gracheva AS, Kuzovlev AN, Abilev SK. Cytokines mapping for tissue-specific expression, eQTLs and GWAS traits. Sci Rep 2020; 10:14740. [PMID: 32895400 PMCID: PMC7477549 DOI: 10.1038/s41598-020-71018-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 07/28/2020] [Indexed: 12/02/2022] Open
Abstract
Dysregulation in cytokine production has been linked to the pathogenesis of various immune-mediated traits, in which genetic variability contributes to the etiopathogenesis. GWA studies have identified many genetic variants in or near cytokine genes, nonetheless, the translation of these findings into knowledge of functional determinants of complex traits remains a fundamental challenge. In this study we aimed at collection, analysis and interpretation of data on cytokines focused on their tissue-specific expression, eQTLs and GWAS traits. Using GO annotations, we generated a list of 314 cytokines and analyzed them with the GTEx resource. Cytokines were highly tissue-specific, 82.3% of cytokines had Tau expression metrics ≥ 0.8. In total, 3077 associations for 1760 unique SNPs in or near 244 cytokines were mapped in the NHGRI-EBI GWAS Catalog. According to the Experimental Factor Ontology resource, the largest numbers of disease associations were related to 'Inflammatory disease', 'Immune system disease' and 'Asthma'. The GTEx-based analysis revealed that among GWAS SNPs, 1142 SNPs had eQTL effects and influenced expression levels of 999 eGenes, among them 178 cytokines. Several types of enrichment analysis showed that it was cytokines expression variability that fundamentally contributed to the molecular origins of considered immune-mediated conditions.
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Affiliation(s)
- Lyubov E Salnikova
- Laboratory of Ecological Genetics, N.I. Vavilov Institute of General Genetics, Russian Academy of Sciences, 3 Gubkin Street, Moscow, Russia, 117971.
- Laboratory of Clinical Pathophysiology of Critical Conditions, Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Petrovka str, 25, b.2, Moscow, Russia, 107031.
| | - Maryam B Khadzhieva
- Laboratory of Ecological Genetics, N.I. Vavilov Institute of General Genetics, Russian Academy of Sciences, 3 Gubkin Street, Moscow, Russia, 117971
- Laboratory of Clinical Pathophysiology of Critical Conditions, Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Petrovka str, 25, b.2, Moscow, Russia, 107031
| | - Dmitry S Kolobkov
- Laboratory of Ecological Genetics, N.I. Vavilov Institute of General Genetics, Russian Academy of Sciences, 3 Gubkin Street, Moscow, Russia, 117971
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 234 Herzl St., PO Box 26, 7610001, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, 234 Herzl St., PO Box 26, 7610001, Rehovot, Israel
| | - Alesya S Gracheva
- Laboratory of Ecological Genetics, N.I. Vavilov Institute of General Genetics, Russian Academy of Sciences, 3 Gubkin Street, Moscow, Russia, 117971
- Laboratory of Clinical Pathophysiology of Critical Conditions, Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Petrovka str, 25, b.2, Moscow, Russia, 107031
| | - Artem N Kuzovlev
- Laboratory of Clinical Pathophysiology of Critical Conditions, Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Petrovka str, 25, b.2, Moscow, Russia, 107031
| | - Serikbay K Abilev
- Laboratory of Ecological Genetics, N.I. Vavilov Institute of General Genetics, Russian Academy of Sciences, 3 Gubkin Street, Moscow, Russia, 117971
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4
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Mitchelmore J, Grinberg NF, Wallace C, Spivakov M. Functional effects of variation in transcription factor binding highlight long-range gene regulation by epromoters. Nucleic Acids Res 2020; 48:2866-2879. [PMID: 32112106 PMCID: PMC7102942 DOI: 10.1093/nar/gkaa123] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 02/14/2020] [Accepted: 02/17/2020] [Indexed: 02/06/2023] Open
Abstract
Identifying DNA cis-regulatory modules (CRMs) that control the expression of specific genes is crucial for deciphering the logic of transcriptional control. Natural genetic variation can point to the possible gene regulatory function of specific sequences through their allelic associations with gene expression. However, comprehensive identification of causal regulatory sequences in brute-force association testing without incorporating prior knowledge is challenging due to limited statistical power and effects of linkage disequilibrium. Sequence variants affecting transcription factor (TF) binding at CRMs have a strong potential to influence gene regulatory function, which provides a motivation for prioritizing such variants in association testing. Here, we generate an atlas of CRMs showing predicted allelic variation in TF binding affinity in human lymphoblastoid cell lines and test their association with the expression of their putative target genes inferred from Promoter Capture Hi-C and immediate linear proximity. We reveal >1300 CRM TF-binding variants associated with target gene expression, the majority of them undetected with standard association testing. A large proportion of CRMs showing associations with the expression of genes they contact in 3D localize to the promoter regions of other genes, supporting the notion of 'epromoters': dual-action CRMs with promoter and distal enhancer activity.
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Affiliation(s)
- Joanna Mitchelmore
- Nuclear Dynamics Programme, Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Nastasiya F Grinberg
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0AW, UK
| | - Chris Wallace
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0AW, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK
| | - Mikhail Spivakov
- Nuclear Dynamics Programme, Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
- MRC London Institute of Medical Sciences, Du Cane Road, London W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College, Du Cane Road, London W12 0NN, UK
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5
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Khramtsova EA, Davis LK, Stranger BE. The role of sex in the genomics of human complex traits. Nat Rev Genet 2019; 20:173-190. [PMID: 30581192 DOI: 10.1038/s41576-018-0083-1] [Citation(s) in RCA: 166] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Nearly all human complex traits and disease phenotypes exhibit some degree of sex differences, including differences in prevalence, age of onset, severity or disease progression. Until recently, the underlying genetic mechanisms of such sex differences have been largely unexplored. Advances in genomic technologies and analytical approaches are now enabling a deeper investigation into the effect of sex on human health traits. In this Review, we discuss recent insights into the genetic models and mechanisms that lead to sex differences in complex traits. This knowledge is critical for developing deeper insight into the fundamental biology of sex differences and disease processes, thus facilitating precision medicine.
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Affiliation(s)
- Ekaterina A Khramtsova
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.,Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Lea K Davis
- Division of Medical Genetics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. .,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Barbara E Stranger
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA. .,Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA. .,Center for Data Intensive Science, University of Chicago, Chicago, IL, USA.
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6
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Savova V, Vinogradova S, Pruss D, Gimelbrant AA, Weiss LA. Risk alleles of genes with monoallelic expression are enriched in gain-of-function variants and depleted in loss-of-function variants for neurodevelopmental disorders. Mol Psychiatry 2017; 22:1785-1794. [PMID: 28265118 PMCID: PMC5589474 DOI: 10.1038/mp.2017.13] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Revised: 12/01/2016] [Accepted: 01/09/2017] [Indexed: 02/06/2023]
Abstract
Over 3000 human genes can be expressed from a single allele in one cell, and from the other allele-or both-in neighboring cells. Little is known about the consequences of this epigenetic phenomenon, monoallelic expression (MAE). We hypothesized that MAE increases expression variability, with a potential impact on human disease. Here, we use a chromatin signature to infer MAE for genes in lymphoblastoid cell lines and human fetal brain tissue. We confirm that across clones MAE status correlates with expression level, and that in human tissue data sets, MAE genes show increased expression variability. We then compare mono- and biallelic genes at three distinct scales. In the human population, we observe that genes with polymorphisms influencing expression variance are more likely to be MAE (P<1.1 × 10-6). At the trans-species level, we find gene expression differences and directional selection between humans and chimpanzees more common among MAE genes (P<0.05). Extending to human disease, we show that MAE genes are under-represented in neurodevelopmental copy number variants (CNVs) (P<2.2 × 10-10), suggesting that pathogenic variants acting via expression level are less likely to involve MAE genes. Using neuropsychiatric single-nucleotide polymorphism (SNP) and single-nucleotide variant (SNV) data, we see that genes with pathogenic expression-altering or loss-of-function variants are less likely MAE (P<7.5 × 10-11) and genes with only missense or gain-of-function variants are more likely MAE (P<1.4 × 10-6). Together, our results suggest that MAE genes tolerate a greater range of expression level than biallelic expression (BAE) genes, and this information may be useful in prediction of pathogenicity.
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Affiliation(s)
- V Savova
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - S Vinogradova
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - D Pruss
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - A A Gimelbrant
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - L A Weiss
- Department of Psychiatry and Institute for Human Genetics, University of California San Francisco, Langley Porter Psychiatric Institute, Nina Ireland Lab, San Francisco, CA, USA
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7
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Youssef I, Clarke R, Shih IM, Wang Y, Yu G. Biologically inspired survival analysis based on integrating gene expression as mediator with genomic variants. Comput Biol Med 2016; 77:231-9. [PMID: 27619193 DOI: 10.1016/j.compbiomed.2016.08.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Revised: 08/29/2016] [Accepted: 08/30/2016] [Indexed: 12/20/2022]
Abstract
Accurately linking cancer molecular profiling with survival can lead to improvements in the clinical management of cancer. However, existing survival analysis relies on statistical evidence from a single level of data, without paying much attention to the integration of interacting multi-level data and the underlying biology. Advances in genomic techniques provide unprecedented power of characterizing the cancer tissue in a more complete manner than before, offering the opportunity to design biologically informed and integrative approaches for survival data analysis. Human cancer is characterized by somatic copy number alternation and unique gene expression profiles. However, it remains largely unclear how to integrate the gene expression and genetic variant data to achieve a better prediction of patient survival and an improved understanding of disease progression. Consistent with the biological hierarchy from DNA to RNA, we prioritize each survival-relevant feature with two separate scores, predictive and mechanistic. For mRNA expression levels, predictive features are those mRNAs whose variation in expression levels is associated with survival outcome, and mechanistic features are those mRNAs whose variation in expression levels is associated with genomic variants. Further, we simultaneously integrate information from both the predictive model and the mechanistic model through our new approach, GEMPS (Gene Expression as a Mediator for Predicting Survival). Applied on two cancer types (ovarian and glioblastoma multiforme), our method achieved better prediction power (p-value: 6.18E-03-5.15E-11) than peer methods (GE.CNAs and GE.CNAs. Lasso). Gene set enrichment analysis confirms that the genes utilized for the final survival analysis are biologically important and relevant.
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Affiliation(s)
- Ibrahim Youssef
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA; Biomedical Engineering Department, Cairo University, Giza 12613, Egypt
| | - Robert Clarke
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington DC 20057, USA
| | - Ie-Ming Shih
- Departments of Gynecology and Obstetrics, Pathology, and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Yue Wang
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Guoqiang Yu
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
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8
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Naz M, Kodamullil AT, Hofmann-Apitius M. Reasoning over genetic variance information in cause-and-effect models of neurodegenerative diseases. Brief Bioinform 2016; 17:505-16. [PMID: 26249223 PMCID: PMC4870396 DOI: 10.1093/bib/bbv063] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 07/09/2015] [Indexed: 12/18/2022] Open
Abstract
The work we present here is based on the recent extension of the syntax of the Biological Expression Language (BEL), which now allows for the representation of genetic variation information in cause-and-effect models. In our article, we describe, how genetic variation information can be used to identify candidate disease mechanisms in diseases with complex aetiology such as Alzheimer's disease and Parkinson's disease. In those diseases, we have to assume that many genetic variants contribute moderately to the overall dysregulation that in the case of neurodegenerative diseases has such a long incubation time until the first clinical symptoms are detectable. Owing to the multilevel nature of dysregulation events, systems biomedicine modelling approaches need to combine mechanistic information from various levels, including gene expression, microRNA (miRNA) expression, protein-protein interaction, genetic variation and pathway. OpenBEL, the open source version of BEL, has recently been extended to match this requirement, and we demonstrate in our article, how candidate mechanisms for early dysregulation events in Alzheimer's disease can be identified based on an integrative mining approach that identifies 'chains of causation' that include single nucleotide polymorphism information in BEL models.
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9
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Delarami HS, Ebrahimi A. Theoretical investigation of the backbone···π and π···π stacking interactions in substituted-benzene||3-methyl-2′-deoxyadenosine: a perspective to the DNA repair. Mol Phys 2015. [DOI: 10.1080/00268976.2015.1118569] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Hojat Samareh Delarami
- Computational Quantum Chemistry Laboratory, Department of Chemistry, University of Sistan and Baluchestan, Zahedan, Iran
| | - Ali Ebrahimi
- Computational Quantum Chemistry Laboratory, Department of Chemistry, University of Sistan and Baluchestan, Zahedan, Iran
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10
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Thompson D, Regev A, Roy S. Comparative analysis of gene regulatory networks: from network reconstruction to evolution. Annu Rev Cell Dev Biol 2015; 31:399-428. [PMID: 26355593 DOI: 10.1146/annurev-cellbio-100913-012908] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Regulation of gene expression is central to many biological processes. Although reconstruction of regulatory circuits from genomic data alone is therefore desirable, this remains a major computational challenge. Comparative approaches that examine the conservation and divergence of circuits and their components across strains and species can help reconstruct circuits as well as provide insights into the evolution of gene regulatory processes and their adaptive contribution. In recent years, advances in genomic and computational tools have led to a wealth of methods for such analysis at the sequence, expression, pathway, module, and entire network level. Here, we review computational methods developed to study transcriptional regulatory networks using comparative genomics, from sequence to functional data. We highlight how these methods use evolutionary conservation and divergence to reliably detect regulatory components as well as estimate the extent and rate of divergence. Finally, we discuss the promise and open challenges in linking regulatory divergence to phenotypic divergence and adaptation.
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Affiliation(s)
- Dawn Thompson
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142
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11
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Vockley CM, Guo C, Majoros WH, Nodzenski M, Scholtens DM, Hayes MG, Lowe WL, Reddy TE. Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort. Genome Res 2015; 25:1206-14. [PMID: 26084464 PMCID: PMC4510004 DOI: 10.1101/gr.190090.115] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Accepted: 06/15/2015] [Indexed: 12/30/2022]
Abstract
We report a novel high-throughput method to empirically quantify individual-specific regulatory element activity at the population scale. The approach combines targeted DNA capture with a high-throughput reporter gene expression assay. As demonstration, we measured the activity of more than 100 putative regulatory elements from 95 individuals in a single experiment. In agreement with previous reports, we found that most genetic variants have weak effects on distal regulatory element activity. Because haplotypes are typically maintained within but not between assayed regulatory elements, the approach can be used to identify causal regulatory haplotypes that likely contribute to human phenotypes. Finally, we demonstrate the utility of the method to functionally fine map causal regulatory variants in regions of high linkage disequilibrium identified by expression quantitative trait loci (eQTL) analyses.
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Affiliation(s)
- Christopher M Vockley
- Department of Cell Biology, Duke University Medical School, Durham, North Carolina 27710, USA; Center for Genomic and Computational Biology, Duke University Medical School, Durham, North Carolina 27710, USA
| | - Cong Guo
- Center for Genomic and Computational Biology, Duke University Medical School, Durham, North Carolina 27710, USA; University Program in Genetics and Genomics, Duke University, Durham, North Carolina 27710, USA
| | - William H Majoros
- Center for Genomic and Computational Biology, Duke University Medical School, Durham, North Carolina 27710, USA; Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina 27710, USA
| | - Michael Nodzenski
- Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - Denise M Scholtens
- Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - William L Lowe
- Division of Endocrinology, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - Timothy E Reddy
- Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina 27710, USA; Department of Biostatistics and Bioinformatics, Duke University Medical School, Durham, North Carolina 27710, USA
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12
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Parnell LD, Blokker BA, Dashti HS, Nesbeth PD, Cooper BE, Ma Y, Lee YC, Hou R, Lai CQ, Richardson K, Ordovás JM. CardioGxE, a catalog of gene-environment interactions for cardiometabolic traits. BioData Min 2014; 7:21. [PMID: 25368670 PMCID: PMC4217104 DOI: 10.1186/1756-0381-7-21] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2014] [Accepted: 10/18/2014] [Indexed: 12/29/2022] Open
Abstract
Background Genetic understanding of complex traits has developed immensely over the past decade but remains hampered by incomplete descriptions of contribution to phenotypic variance. Gene-environment (GxE) interactions are one of these contributors and in the guise of diet and physical activity are important modulators of cardiometabolic phenotypes and ensuing diseases. Results We mined the scientific literature to collect GxE interactions from 386 publications for blood lipids, glycemic traits, obesity anthropometrics, vascular measures, inflammation and metabolic syndrome, and introduce CardioGxE, a gene-environment interaction resource. We then analyzed the genes and SNPs supporting cardiometabolic GxEs in order to demonstrate utility of GxE SNPs and to discern characteristics of these important genetic variants. We were able to draw many observations from our extensive analysis of GxEs. 1) The CardioGxE SNPs showed little overlap with variants identified by main effect GWAS, indicating the importance of environmental interactions with genetic factors on cardiometabolic traits. 2) These GxE SNPs were enriched in adaptation to climatic and geographical features, with implications on energy homeostasis and response to physical activity. 3) Comparison to gene networks responding to plasma cholesterol-lowering or regression of atherosclerotic plaques showed that GxE genes have a greater role in those responses, particularly through high-energy diets and fat intake, than do GWAS-identified genes for the same traits. Other aspects of the CardioGxE dataset were explored. Conclusions Overall, we demonstrate that SNPs supporting cardiometabolic GxE interactions often exhibit transcriptional effects or are under positive selection. Still, not all such SNPs can be assigned potential functional or regulatory roles often because data are lacking in specific cell types or from treatments that approximate the environmental factor of the GxE. With research on metabolic related complex disease risk embarking on genome-wide GxE interaction tests, CardioGxE will be a useful resource.
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Affiliation(s)
- Laurence D Parnell
- JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA
| | - Britt A Blokker
- JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA
| | - Hassan S Dashti
- JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA
| | - Paula-Dene Nesbeth
- JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA
| | - Brittany Elle Cooper
- JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA
| | - Yiyi Ma
- JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA
| | - Yu-Chi Lee
- JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA
| | - Ruixue Hou
- JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA
| | - Chao-Qiang Lai
- JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA
| | - Kris Richardson
- JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA
| | - José M Ordovás
- JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA
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Pemov A, Sung H, Hyland PL, Sloan JL, Ruppert SL, Baldwin AM, Boland JF, Bass SE, Lee HJ, Jones KM, Zhang X, Mullikin JC, Widemann BC, Wilson AF, Stewart DR. Genetic modifiers of neurofibromatosis type 1-associated café-au-lait macule count identified using multi-platform analysis. PLoS Genet 2014; 10:e1004575. [PMID: 25329635 PMCID: PMC4199479 DOI: 10.1371/journal.pgen.1004575] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2013] [Accepted: 07/08/2014] [Indexed: 01/27/2023] Open
Abstract
Neurofibromatosis type 1 (NF1) is an autosomal dominant, monogenic disorder of dysregulated neurocutaneous tissue growth. Pleiotropy, variable expressivity and few NF1 genotype-phenotype correlates limit clinical prognostication in NF1. Phenotype complexity in NF1 is hypothesized to derive in part from genetic modifiers unlinked to the NF1 locus. In this study, we hypothesized that normal variation in germline gene expression confers risk for certain phenotypes in NF1. In a set of 79 individuals with NF1, we examined the association between gene expression in lymphoblastoid cell lines with NF1-associated phenotypes and sequenced select genes with significant phenotype/expression correlations. In a discovery cohort of 89 self-reported European-Americans with NF1 we examined the association between germline sequence variants of these genes with café-au-lait macule (CALM) count, a tractable, tumor-like phenotype in NF1. Two correlated, common SNPs (rs4660761 and rs7161) between DPH2 and ATP6V0B were significantly associated with the CALM count. Analysis with tiled regression also identified SNP rs4660761 as significantly associated with CALM count. SNP rs1800934 and 12 rare variants in the mismatch repair gene MSH6 were also associated with CALM count. Both SNPs rs7161 and rs4660761 (DPH2 and ATP6V0B) were highly significant in a mega-analysis in a combined cohort of 180 self-reported European-Americans; SNP rs1800934 (MSH6) was near-significant in a meta-analysis assuming dominant effect of the minor allele. SNP rs4660761 is predicted to regulate ATP6V0B, a gene associated with melanosome biology. Individuals with homozygous mutations in MSH6 can develop an NF1-like phenotype, including multiple CALMs. Through a multi-platform approach, we identified variants that influence NF1 CALM count. Neurofibromatosis type 1 (NF1) is a relatively common genetic disease that increases the chance to develop a variety of benign and malignant tumors. People with NF1 also typically feature a large number of birthmarks called café-au-lait macules. It is difficult to predict severity or specific problems in NF1. We sought to identify genes (other than NF1, the gene that causes the disease) that influence severity in NF1. We determined the number of café-au-lait macules in two groups of people with NF1. We measured the gene expression of about 10,000 genes in the cultured white blood cells from one group of people. We then sequenced a group of genes whose expression level was increased in people with higher numbers of café-au-lait macules. In the first group, we found common variants in genes MSH6 and near DPH2 and ATP6V0B that were significantly associated with the number of café-au-lait macules. Some of these variants were close to significant in the second group of people. The two variants near DPH2 and ATP6V0B were very significant when analysed in both groups combined. Our work is among the first to identify genetic variants that influence the severity of NF1.
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Affiliation(s)
- Alexander Pemov
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | - Heejong Sung
- Genometrics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Paula L. Hyland
- Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | - Jennifer L. Sloan
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Sarah L. Ruppert
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Andrea M. Baldwin
- Pediatric Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Joseph F. Boland
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | - Sara E. Bass
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | - Hyo Jung Lee
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | - Kristine M. Jones
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | - Xijun Zhang
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | | | - James C. Mullikin
- NIH Intramural Sequencing Center, National Human Genome Research Institute, Rockville, Maryland, United States of America
| | - Brigitte C. Widemann
- Pediatric Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Alexander F. Wilson
- Genometrics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Douglas R. Stewart
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
- * E-mail:
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Wilson KA, Kellie JL, Wetmore SD. DNA-protein π-interactions in nature: abundance, structure, composition and strength of contacts between aromatic amino acids and DNA nucleobases or deoxyribose sugar. Nucleic Acids Res 2014; 42:6726-41. [PMID: 24744240 PMCID: PMC4041443 DOI: 10.1093/nar/gku269] [Citation(s) in RCA: 108] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Four hundred twenty-eight high-resolution DNA-protein complexes were chosen for a bioinformatics study. Although 164 crystal structures (38% of those searched) contained no interactions, 574 discrete π-contacts between the aromatic amino acids and the DNA nucleobases or deoxyribose were identified using strict criteria, including visual inspection. The abundance and structure of the interactions were determined by unequivocally classifying the contacts as either π-π stacking, π-π T-shaped or sugar-π contacts. Three hundred forty-four nucleobase-amino acid π-π contacts (60% of all interactions identified) were identified in 175 of the crystal structures searched. Unprecedented in the literature, 230 DNA-protein sugar-π contacts (40% of all interactions identified) were identified in 137 crystal structures, which involve C-H···π and/or lone-pair···π interactions, contain any amino acid and can be classified according to sugar atoms involved. Both π-π and sugar-π interactions display a range of relative monomer orientations and therefore interaction energies (up to -50 (-70) kJ mol(-1) for neutral (charged) interactions as determined using quantum chemical calculations). In general, DNA-protein π-interactions are more prevalent than perhaps currently accepted and the role of such interactions in many biological processes may yet to be uncovered.
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Affiliation(s)
- Katie A Wilson
- Department of Chemistry and Biochemistry, University of Lethbridge, 4401 University Drive West, Lethbridge, AB, T1K 3M4, Canada
| | - Jennifer L Kellie
- Department of Chemistry and Biochemistry, University of Lethbridge, 4401 University Drive West, Lethbridge, AB, T1K 3M4, Canada
| | - Stacey D Wetmore
- Department of Chemistry and Biochemistry, University of Lethbridge, 4401 University Drive West, Lethbridge, AB, T1K 3M4, Canada
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Parnell LD, Casas-Agustench P, Iyer LK, Ordovas JM. How Gene Networks Can Uncover Novel CVD Players. CURRENT CARDIOVASCULAR RISK REPORTS 2014; 8:372. [PMID: 24683432 DOI: 10.1007/s12170-013-0372-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Cardiovascular diseases (CVD) are complex, involving numerous biological entities from genes and small molecules to organ function. Placing these entities in networks where the functional relationships among the constituents are drawn can aid in our understanding of disease onset, progression and prevention. While networks, or interactomes, are often classified by a general term, say lipids or inflammation, it is a more encompassing class of network that is more informative in showing connections among the active entities and allowing better hypotheses of novel CVD players to be formulated. A range of networks will be presented whereby the potential to bring new objects into the CVD milieu will be exemplified.
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Affiliation(s)
- Laurence D Parnell
- Nutritional Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111
| | - Patricia Casas-Agustench
- Instituto Madrileño de Estudios Avanzados (IMDEA) Alimentación, CEI UAM+CSIC, C/Faraday, 7, 1 planta D1.11, Ciudad Universitaria de Cantoblanco, Ctra. de Colmenar Km.15, Madrid, 28049, Spain
| | - Lakshmanan K Iyer
- Tufts Center for Neuroscience Research, Tufts University School of Medicine, 136 Harrison Ave, Boston, MA 02111, Molecular Cardiology Research Institute, Tufts Medical Center, 15 Kneeland Street, Boston, MA 02111
| | - Jose M Ordovas
- Nutritional Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111 ; Instituto Madrileño de Estudios Avanzados (IMDEA) Alimentación, CEI UAM+CSIC, C/Faraday, 7, 1 planta D1.11, Ciudad Universitaria de Cantoblanco, Ctra. de Colmenar Km.15, Madrid, 28049, Spain
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Affiliation(s)
- Shamil R Sunyaev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.
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