1
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Ma Y, Deng C, Zhou Y, Zhang Y, Qiu F, Jiang D, Zheng G, Li J, Shuai J, Zhang Y, Yang J, Su J. Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data. CELL GENOMICS 2023; 3:100383. [PMID: 37719150 PMCID: PMC10504677 DOI: 10.1016/j.xgen.2023.100383] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/26/2023] [Accepted: 07/25/2023] [Indexed: 09/19/2023]
Abstract
Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ T cells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.
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Affiliation(s)
- Yunlong Ma
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Chunyu Deng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China
| | - Yijun Zhou
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Yaru Zhang
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Fei Qiu
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Dingping Jiang
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Gongwei Zheng
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Jingjing Li
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Jianwei Shuai
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Yan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310012, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Jianzhong Su
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
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2
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Carlin DE, Larsen SJ, Sirupurapu V, Cho MH, Silverman EK, Baumbach J, Ideker T. Hierarchical association of COPD to principal genetic components of biological systems. PLoS One 2023; 18:e0286064. [PMID: 37228113 DOI: 10.1371/journal.pone.0286064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 05/08/2023] [Indexed: 05/27/2023] Open
Abstract
Many disease-causing genetic variants converge on common biological functions and pathways. Precisely how to incorporate pathway knowledge in genetic association studies is not yet clear, however. Previous approaches employ a two-step approach, in which a regular association test is first performed to identify variants associated with the disease phenotype, followed by a test for functional enrichment within the genes implicated by those variants. Here we introduce a concise one-step approach, Hierarchical Genetic Analysis (Higana), which directly computes phenotype associations against each function in the large hierarchy of biological functions documented by the Gene Ontology. Using this approach, we identify risk genes and functions for Chronic Obstructive Pulmonary Disease (COPD), highlighting microtubule transport, muscle adaptation, and nicotine receptor signaling pathways. Microtubule transport has not been previously linked to COPD, as it integrates genetic variants spread over numerous genes. All associations validate strongly in a second COPD cohort.
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Affiliation(s)
- Daniel E Carlin
- Department of Medicine, Division of Genetics, University of California San Diego, La Jolla, CA, United States of America
| | | | - Vikram Sirupurapu
- Department of Medicine, Division of Genetics, University of California San Diego, La Jolla, CA, United States of America
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Jan Baumbach
- Department of Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Trey Ideker
- Department of Medicine, Division of Genetics, University of California San Diego, La Jolla, CA, United States of America
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3
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Johnson TO, Akinsanmi AO, Ejembi SA, Adeyemi OE, Oche JR, Johnson GI, Adegboyega AE. Modern drug discovery for inflammatory bowel disease: The role of computational methods. World J Gastroenterol 2023; 29:310-331. [PMID: 36687123 PMCID: PMC9846937 DOI: 10.3748/wjg.v29.i2.310] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/02/2022] [Accepted: 12/21/2022] [Indexed: 01/06/2023] Open
Abstract
Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided (in silico) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.
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Affiliation(s)
| | | | | | | | - Jane-Rose Oche
- Department of Biochemistry, University of Jos, Jos 930222, Plateau, Nigeria
| | - Grace Inioluwa Johnson
- Faculty of Clinical Sciences, College of Health Sciences, University of Jos, Jos 930222, Plateau, Nigeria
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4
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Kim SH, Lim KH, Yang S, Joo JY. Boosting of tau protein aggregation by CD40 and CD48 gene expression in Alzheimer's disease. FASEB J 2023; 37:e22702. [PMID: 36520044 DOI: 10.1096/fj.202201197r] [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: 07/28/2022] [Revised: 11/09/2022] [Accepted: 11/28/2022] [Indexed: 12/16/2022]
Abstract
Neurodegenerative diseases result from the interplay of abnormal gene expression and various pathological factors. Therefore, a disease-specific integrative genetic approach is required to understand the complexities and causes of target diseases. Recent studies have identified the correlation between genes encoding several transmembrane proteins, such as the cluster of differentiation (CD) and Alzheimer's disease (AD) pathogenesis. In this study, CD48 and CD40 gene expression in AD, a neurodegenerative disease, was analyzed to infer this link. Total RNA sequencing was performed using an Alzheimer's disease mouse model brain and blood, and gene expression was determined using a genome-wide association study (GWAS). We observed a marked elevation of CD48 and CD40 genes in Alzheimer's disease. Indeed, the upregulation of both CD48 and CD40 genes was significantly increased in the severe Alzheimer's disease group. With the elevation of CD48 and CD40 genes in Alzheimer's disease, associations of protein levels were also markedly increased in tissues. In addition, overexpression of CD48 and CD40 genes triggered tau aggregation, and co-expression of these genes accelerated aggregation. The nuclear factor kappa B (NF-ĸB) signaling pathway was enriched by CD48 and CD40 gene expression: it was also associated with tau pathology. Our data suggested that the CD48 and CD40 genes are novel AD-related genes, and this approach may be useful as a diagnostic or therapeutic target for the disease.
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Affiliation(s)
- Sung-Hyun Kim
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Republic of Korea
| | - Key-Hwan Lim
- Neurodegenerative Disease Research Group, Korea Brain Research Institute, Daegu, Republic of Korea.,Department of Pharmacy, College of Pharmacy, Chungbuk National University, Cheongju-si, Republic of Korea
| | - Sumin Yang
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Republic of Korea
| | - Jae-Yeol Joo
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Republic of Korea
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5
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [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: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- *Correspondence: Arnaud Droit,
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6
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Crowder SL, Hoogland AI, Welniak TL, LaFranchise EA, Carpenter KM, Li D, Rotroff DM, Mariam A, Pierce CM, Extermann M, Kim RD, Tometich DB, Figueiredo JC, Muzaffar J, Bari S, Turner K, Weinstock GM, Jim HS. Metagenomics and chemotherapy-induced nausea: A roadmap for future research. Cancer 2022; 128:461-470. [PMID: 34643945 PMCID: PMC8776572 DOI: 10.1002/cncr.33892] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 07/06/2021] [Accepted: 08/13/2021] [Indexed: 02/03/2023]
Abstract
Uncontrolled chemotherapy-induced nausea and vomiting can reduce patients' quality of life and may result in premature discontinuation of chemotherapy. Although nausea and vomiting are commonly grouped together, research has shown that antiemetics are clinically effective against chemotherapy-induced vomiting (CIV) but less so against chemotherapy-induced nausea (CIN). Nausea remains a problem for up to 68% of patients who are prescribed guideline-consistent antiemetics. Despite the high prevalence of CIN, relatively little is known regarding its etiology independent of CIV. This review summarizes a metagenomics approach to the study and treatment of CIN with the goal of encouraging future research. Metagenomics focuses on genetic risk factors and encompasses both human (ie, host) and gut microbial genetic variation. Little work to date has focused on metagenomics as a putative biological mechanism of CIN. Metagenomics has the potential to be a powerful tool in advancing scientific understanding of CIN by identifying new biological pathways and intervention targets. The investigation of metagenomics in the context of well-established demographic, clinical, and patient-reported risk factors may help to identify patients at risk and facilitate the prevention and management of CIN.
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Affiliation(s)
| | | | | | | | | | - Daneng Li
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center
| | - Daniel M. Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Arshiya Mariam
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | | | - Richard D. Kim
- Department of Hematology Oncology, Moffitt Cancer Center
| | | | | | - Jameel Muzaffar
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center
| | - Shahla Bari
- Department of Hematology Oncology, Moffitt Cancer Center
| | - Kea Turner
- Department of Health Outcomes and Behavior, Moffitt Cancer Center
| | | | - Heather S.L. Jim
- Department of Health Outcomes and Behavior, Moffitt Cancer Center
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7
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Chen SY, Wang J, Jia F, Shen ZD, Zhang WB, Wang YX, Ren KF, Fu GS, Ji J. Bioinspired NO release coating enhances endothelial cells and inhibits smooth muscle cells. J Mater Chem B 2021; 10:2454-2462. [PMID: 34698745 DOI: 10.1039/d1tb01828k] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Thrombus and restenosis after stent implantation are the major complications because traditional drugs such as rapamycin delay the process of endothelialization. Nitric oxide (NO) is mainly produced by endothelial nitric oxide synthase (eNOS) on the membrane of endothelial cells (ECs) in the cardiovascular system and plays an important role in vasomotor function. It strongly inhibits the proliferation of smooth muscle cells (SMCs) and ameliorates endothelial function when ECs get hurt. Inspired by this, introducing NO to traditional stent coating may alleviate endothelial insufficiency caused by rapamycin. Here, we introduced SNAP as the NO donor, mimicking how NO affects in vivo, into rapamycin coating to alleviate endothelial damage while inhibiting SMC proliferation. Through wicking effects, SNAP was absorbed into a hierarchical coating that had an upper porous layer and a dense polymer layer with rapamycin at the bottom. Cells were cultured on the coatings, and it was observed that the injured ECs were restored while the growth of SMCs further diminished. Genome analysis was conducted to further clarify possible signaling pathways: the effect of cell growth attenuated by NO may cause by affecting cell cycle and enhancing inflammation. These findings supported the idea that introducing NO to traditional drug-eluting stents alleviates incomplete endothelialization and further inhibits the stenosis caused by the proliferation of SMCs.
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Affiliation(s)
- Sheng-Yu Chen
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China.
| | - Jing Wang
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Fan Jia
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Zhi-da Shen
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China.
| | - Wen-Bin Zhang
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China.
| | - You-Xiang Wang
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Ke-Feng Ren
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China. .,MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Guo-Sheng Fu
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China.
| | - Jian Ji
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China. .,MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
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8
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Ho CH, Huang YJ, Lai YJ, Mukherjee R, Hsiao CK. The misuse of distributional assumptions in functional class scoring gene-set and pathway analysis. G3-GENES GENOMES GENETICS 2021; 12:6409857. [PMID: 34791175 PMCID: PMC8728032 DOI: 10.1093/g3journal/jkab365] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 10/14/2021] [Indexed: 12/14/2022]
Abstract
Gene-set analysis (GSA) is a standard procedure for exploring potential biological functions of a group of genes. The development of its methodology has been an active research topic in recent decades. Many GSA methods, when newly proposed, rely on simulation studies to evaluate their performance with an implicit assumption that the multivariate expression values are normally distributed. This assumption is commonly adopted in GSAs, particularly those in the group of functional class scoring (FCS) methods. The validity of the normality assumption, however, has been disputed in several studies, yet no systematic analysis has been carried out to assess the effect of this distributional assumption. Our goal in this study is not to propose a new GSA method but to first examine if the multi-dimensional gene expression data in gene sets follow a multivariate normal (MVN) distribution. Six statistical methods in three categories of MVN tests were considered and applied to a total of 24 RNA data sets. These RNA values were collected from cancer patients as well as normal subjects, and the values were derived from microarray experiments, RNA sequencing, and single-cell RNA sequencing. Our first finding suggests that the MVN assumption is not always satisfied. This assumption does not hold true in many applications tested here. In the second part of this research, we evaluated the influence of non-normality on the statistical power of current FCS methods, both parametric and nonparametric ones. Specifically, the scenario of mixture distributions representing more than one population for the RNA values was considered. This second investigation demonstrates that the non-normality distribution of the RNA values causes a loss in the statistical power of these GSA tests, especially when subtypes exist. Among the FCS GSA tools examined here and among the scenarios studied in this research, the N-statistics outperform the others. Based on the results from these two investigations, we conclude that the assumption of MVN should be used with caution when evaluating new GSA tools, since this assumption cannot be guaranteed and violation may lead to spurious results, loss of power, and incorrect comparison between methods. If a newly proposed GSA tool is to be evaluated, we recommend the incorporation of a wide range of multivariate non-normal distributions or sampling from large databases if available.
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Affiliation(s)
- Chi-Hsuan Ho
- Division of Biostatistics and Data Science, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10055, Taiwan
| | - Yu-Jyun Huang
- Division of Biostatistics and Data Science, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10055, Taiwan
| | - Ying-Ju Lai
- Division of Biostatistics and Data Science, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10055, Taiwan
| | | | - Chuhsing Kate Hsiao
- Division of Biostatistics and Data Science, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10055, Taiwan.,Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei 10055, Taiwan
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9
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Xie S, Zeng Q, Ouyang S, Liang Y, Xiao C. Bioinformatics analysis of epigenetic and SNP-related molecular markers in systemic lupus erythematosus. Am J Transl Res 2021; 13:6312-6329. [PMID: 34306371 PMCID: PMC8290799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 02/23/2021] [Indexed: 06/13/2023]
Abstract
We analyzed gene expression in peripheral blood mononuclear cells (PBMCs) from patients with systemic lupus erythematosus (SLE) using public databases. The goal was to identify lupus biomarkers by determining whether differentially expressed genes are mediated by methylation, miRNA, or SNP. Two cDNA microarrays were subjected to integration analysis, and we calculated the mutually differentially expressed genes (|log2fold change (FC)| > 1, P < 0.05). These genes were analyzed using gene otology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. The differences in methylation sites for two methylation chips were calculated and the differentially methylated sites were annotated. These genes were compared to the differentially expressed genes. We obtained 135 differentially expressed microRNAs from the microRNA-chip results using PBMCs from SLE and healthy individuals. Predictive microRNA target genes were identified using GO, KEGG pathways, and PPI networks. The target genes identified were compared to the differentially expressed genes. We downloaded Chinese SLE genome-wide association study data from SLE-related literature, analyzed the loci with a P value < 0.05, and used annotated SLE-associated SNPs. We selected the genes corresponding to an SNP located on an exon and determined the intersection with the differentially expressed genes. We found 18 differentially expressed genes in both cDNA microarrays. The methylation chips had 50 corresponding methylation sites. On the basis of these results, we identified two genes, IFI44 and IFI44L. We further identified 135 differentially expressed microRNAs predicted to affect 5766 target genes. Two identified genes were in common with the differentially expressed genes. Finally, SNP annotated genes and cDNA chip genes overlap with identified MX1. Therefore, we used existing data to analyze the causes of differential gene expression in SLE, introducing new methods for determining biomarkers and therapeutic targets.
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Affiliation(s)
- Shuoshan Xie
- Nephrology Department and Laboratory of Kidney Disease, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal UniversityChangsha, PR China
- Changsha Clinical Research Center for Kidney DiseaseChangsha, PR China
- Hunan Clinical Research Center for Chronic Kidney DiseaseChangsha, PR China
| | - Qinghua Zeng
- Nephrology Department and Laboratory of Kidney Disease, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal UniversityChangsha, PR China
- Changsha Clinical Research Center for Kidney DiseaseChangsha, PR China
- Hunan Clinical Research Center for Chronic Kidney DiseaseChangsha, PR China
| | - Shaxi Ouyang
- Nephrology Department and Laboratory of Kidney Disease, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal UniversityChangsha, PR China
- Changsha Clinical Research Center for Kidney DiseaseChangsha, PR China
- Hunan Clinical Research Center for Chronic Kidney DiseaseChangsha, PR China
| | - Yumei Liang
- Nephrology Department and Laboratory of Kidney Disease, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal UniversityChangsha, PR China
- Changsha Clinical Research Center for Kidney DiseaseChangsha, PR China
- Hunan Clinical Research Center for Chronic Kidney DiseaseChangsha, PR China
| | - Changjuan Xiao
- Nephrology Department and Laboratory of Kidney Disease, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal UniversityChangsha, PR China
- Changsha Clinical Research Center for Kidney DiseaseChangsha, PR China
- Hunan Clinical Research Center for Chronic Kidney DiseaseChangsha, PR China
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10
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Anene CA, Khan F, Bewicke-Copley F, Maniati E, Wang J. ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles. PATTERNS (NEW YORK, N.Y.) 2021; 2:100270. [PMID: 34179848 PMCID: PMC8212143 DOI: 10.1016/j.patter.2021.100270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/10/2021] [Accepted: 04/28/2021] [Indexed: 11/01/2022]
Abstract
Determining the tissue- and disease-specific circuit of biological pathways remains a fundamental goal of molecular biology. Many components of these biological pathways still remain unknown, hindering the full and accurate characterization of biological processes of interest. Here we describe ACSNI, an algorithm that combines prior knowledge of biological processes with a deep neural network to effectively decompose gene expression profiles (GEPs) into multi-variable pathway activities and identify unknown pathway components. Experiments on public GEP data show that ACSNI predicts cogent components of mTOR, ATF2, and HOTAIRM1 signaling that recapitulate regulatory information from genetic perturbation and transcription factor binding datasets. Our framework provides a fast and easy-to-use method to identify components of signaling pathways as a tool for molecular mechanism discovery and to prioritize genes for designing future targeted experiments (https://github.com/caanene1/ACSNI).
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Affiliation(s)
- Chinedu Anthony Anene
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Faraz Khan
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Findlay Bewicke-Copley
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Eleni Maniati
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Jun Wang
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK
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11
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Queirós P, Delogu F, Hickl O, May P, Wilmes P. Mantis: flexible and consensus-driven genome annotation. Gigascience 2021; 10:6291114. [PMID: 34076241 PMCID: PMC8170692 DOI: 10.1093/gigascience/giab042] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 03/22/2021] [Accepted: 05/14/2021] [Indexed: 12/22/2022] Open
Abstract
Background The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation can be described as the identification of regions of interest (i.e., domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, challenges remain in terms of speed, flexibility, and reproducibility. In the big data era, it is also increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, and thus overcoming some limitations in overly relying on computationally generated data from single sources. Results We implemented a protein annotation tool, Mantis, which uses database identifiers intersection and text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for the customization of reference data and execution parameters, and is reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which significantly improved annotation performance compared to sequence-wide annotation. The parallelized implementation of Mantis results in short runtimes while also outputting high coverage and high-quality protein function annotations. Conclusions Mantis is a protein function annotation tool that produces high-quality consensus-driven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license at https://github.com/PedroMTQ/mantis.
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Affiliation(s)
- Pedro Queirós
- Systems Ecology, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
| | - Francesco Delogu
- Systems Ecology, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
| | - Oskar Hickl
- Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
| | - Patrick May
- Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
| | - Paul Wilmes
- Systems Ecology, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
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12
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Althouse AD, Below JE, Claggett BL, Cox NJ, de Lemos JA, Deo RC, Duval S, Hachamovitch R, Kaul S, Keith SW, Secemsky E, Teixeira-Pinto A, Roger VL. Recommendations for Statistical Reporting in Cardiovascular Medicine: A Special Report From the American Heart Association. Circulation 2021; 144:e70-e91. [PMID: 34032474 DOI: 10.1161/circulationaha.121.055393] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Statistical analyses are a crucial component of the biomedical research process and are necessary to draw inferences from biomedical research data. The application of sound statistical methodology is a prerequisite for publication in the American Heart Association (AHA) journal portfolio. The objective of this document is to summarize key aspects of statistical reporting that might be most relevant to the authors, reviewers, and readership of AHA journals. The AHA Scientific Publication Committee convened a task force to inventory existing statistical standards for publication in biomedical journals and to identify approaches suitable for the AHA journal portfolio. The experts on the task force were selected by the AHA Scientific Publication Committee, who identified 12 key topics that serve as the section headers for this document. For each topic, the members of the writing group identified relevant references and evaluated them as a resource to make the standards summarized herein. Each section was independently reviewed by an expert reviewer who was not part of the task force. Expert reviewers were also permitted to comment on other sections if they chose. Differences of opinion were adjudicated by consensus. The standards presented in this report are intended to serve as a guide for high-quality reporting of statistical analyses methods and results.
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Affiliation(s)
- Andrew D Althouse
- Center for Research on Health Care Data Center, Division of General Internal Medicine, University of Pittsburgh, PA (A.D.A.)
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN (J.E.B., N.J.C.)
| | - Brian L Claggett
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA (B.L.C., R.C.D.)
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN (J.E.B., N.J.C.)
| | - James A de Lemos
- Division of Cardiology, University of Texas Southwestern Medical Center, Dallas (J.A.d.L.)
| | - Rahul C Deo
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA (B.L.C., R.C.D.)
| | - Sue Duval
- Cardiovascular Division, University of Minnesota Medical School, Minneapolis (S.D.)
| | - Rory Hachamovitch
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic Foundation, OH (R.H.)
| | - Sanjay Kaul
- Department of Cardiology, Cedars-Sinai Medical Center, and the David Geffen School of Medicine, University of California, Los Angeles (S.K.)
| | - Scott W Keith
- Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College of Thomas Jefferson University, Philadelphia, PA (S.W.K.)
| | - Eric Secemsky
- Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA (E.S.)
| | - Armando Teixeira-Pinto
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Australia (A.T.-P.)
| | - Veronique L Roger
- Department of Cardiovascular Diseases Medicine, Mayo Clinic College of Medicine, Rochester, MN (V.L.R.).,now with Epidemiology and Community Health Branch National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD (V.L.R.)
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13
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Parra-Galindo MA, Soto-Sedano JC, Mosquera-Vásquez T, Roda F. Pathway-based analysis of anthocyanin diversity in diploid potato. PLoS One 2021; 16:e0250861. [PMID: 33914830 PMCID: PMC8084248 DOI: 10.1371/journal.pone.0250861] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 04/14/2021] [Indexed: 12/21/2022] Open
Abstract
Anthocyanin biosynthesis is one of the most studied pathways in plants due to the important ecological role played by these compounds and the potential health benefits of anthocyanin consumption. Given the interest in identifying new genetic factors underlying anthocyanin content we studied a diverse collection of diploid potatoes by combining a genome-wide association study and pathway-based analyses. By using an expanded SNP dataset, we identified candidate genes that had not been associated with anthocyanin variation in potatoes, namely a Myb transcription factor, a Leucoanthocyanidin dioxygenase gene and a vacuolar membrane protein. Importantly, a genomic region in chromosome 10 harbored the SNPs with strongest associations with anthocyanin content in GWAS. Some of these SNPs were associated with multiple anthocyanin compounds and therefore could underline the existence of pleiotropic genes or anthocyanin biosynthetic clusters. We identified multiple anthocyanin homologs in this genomic region, including four transcription factors and five enzymes that could be governing anthocyanin variation. For instance, a SNP linked to the phenylalanine ammonia-lyase gene, encoding the first enzyme in the phenylpropanoid biosynthetic pathway, was associated with all of the five anthocyanins measured. Finally, we combined a pathway analysis and GWAS of other agronomic traits to identify pathways related to anthocyanin biosynthesis in potatoes. We found that methionine metabolism and the production of sugars and hydroxycinnamic acids are genetically correlated to anthocyanin biosynthesis. The results contribute to the understanding of anthocyanins regulation in potatoes and can be used in future breeding programs focused on nutraceutical food.
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Affiliation(s)
| | - Johana Carolina Soto-Sedano
- Departamento de Biología, Facultad de Ciencias, Universidad Nacional de Colombia, Sede Bogotá, Bogotá, Colombia
| | - Teresa Mosquera-Vásquez
- Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Bogotá, Bogotá, Colombia
| | - Federico Roda
- Max Planck Tandem Group, Facultad de Ciencias, Universidad Nacional de Colombia, Sede Bogotá, Bogotá, Colombia
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14
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Dutta D, VandeHaar P, Fritsche LG, Zöllner S, Boehnke M, Scott LJ, Lee S. A powerful subset-based method identifies gene set associations and improves interpretation in UK Biobank. Am J Hum Genet 2021; 108:669-681. [PMID: 33730541 DOI: 10.1016/j.ajhg.2021.02.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Accepted: 02/19/2021] [Indexed: 02/06/2023] Open
Abstract
Tests of association between a phenotype and a set of genes in a biological pathway can provide insights into the genetic architecture of complex phenotypes beyond those obtained from single-variant or single-gene association analysis. However, most existing gene set tests have limited power to detect gene set-phenotype association when a small fraction of the genes are associated with the phenotype and cannot identify the potentially "active" genes that might drive a gene set-based association. To address these issues, we have developed Gene set analysis Association Using Sparse Signals (GAUSS), a method for gene set association analysis that requires only GWAS summary statistics. For each significantly associated gene set, GAUSS identifies the subset of genes that have the maximal evidence of association and can best account for the gene set association. Using pre-computed correlation structure among test statistics from a reference panel, our p value calculation is substantially faster than other permutation- or simulation-based approaches. In simulations with varying proportions of causal genes, we find that GAUSS effectively controls type 1 error rate and has greater power than several existing methods, particularly when a small proportion of genes account for the gene set signal. Using GAUSS, we analyzed UK Biobank GWAS summary statistics for 10,679 gene sets and 1,403 binary phenotypes. We found that GAUSS is scalable and identified 13,466 phenotype and gene set association pairs. Within these gene sets, we identify an average of 17.2 (max = 405) genes that underlie these gene set associations.
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Affiliation(s)
- Diptavo Dutta
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Peter VandeHaar
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lars G Fritsche
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sebastian Zöllner
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Michael Boehnke
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Laura J Scott
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Seunggeun Lee
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Graduate School of Data Science, Seoul National University, Seoul 08826, Republic of Korea.
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15
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Thistlethwaite LR, Petrosyan V, Li X, Miller MJ, Elsea SH, Milosavljevic A. CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models. PLoS Comput Biol 2021; 17:e1008550. [PMID: 33513132 PMCID: PMC7875364 DOI: 10.1371/journal.pcbi.1008550] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 02/10/2021] [Accepted: 11/16/2020] [Indexed: 01/17/2023] Open
Abstract
We consider the following general family of algorithmic problems that arises in transcriptomics, metabolomics and other fields: given a weighted graph G and a subset of its nodes S, find subsets of S that show significant connectedness within G. A specific solution to this problem may be defined by devising a scoring function, the Maximum Clique problem being a classic example, where S includes all nodes in G and where the score is defined by the size of the largest subset of S fully connected within G. Major practical obstacles for the plethora of algorithms addressing this type of problem include computational efficiency and, particularly for more complex scores which take edge weights into account, the computational cost of permutation testing, a statistical procedure required to obtain a bound on the p-value for a connectedness score. To address these problems, we developed CTD, "Connect the Dots", a fast algorithm based on data compression that detects highly connected subsets within S. CTD provides information-theoretic upper bounds on p-values when S contains a small fraction of nodes in G without requiring computationally costly permutation testing. We apply the CTD algorithm to interpret multi-metabolite perturbations due to inborn errors of metabolism and multi-transcript perturbations associated with breast cancer in the context of disease-specific Gaussian Markov Random Field networks learned directly from respective molecular profiling data.
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Affiliation(s)
- Lillian R. Thistlethwaite
- Quantitative and Computational Biosciences Program, Baylor College of Medicine, Houston, Texas, United States of America
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Varduhi Petrosyan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Xiqi Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Marcus J. Miller
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Sarah H. Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Aleksandar Milosavljevic
- Quantitative and Computational Biosciences Program, Baylor College of Medicine, Houston, Texas, United States of America
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
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16
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Chen YX, Rong Y, Jiang F, Chen JB, Duan YY, Dong SS, Zhu DL, Chen H, Yang TL, Dai Z, Guo Y. An integrative multi-omics network-based approach identifies key regulators for breast cancer. Comput Struct Biotechnol J 2020; 18:2826-2835. [PMID: 33133424 PMCID: PMC7585874 DOI: 10.1016/j.csbj.2020.10.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 09/13/2020] [Accepted: 10/01/2020] [Indexed: 02/06/2023] Open
Abstract
Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.
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Affiliation(s)
- Yi-Xiao Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Yu Rong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Feng Jiang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Jia-Bin Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Yuan-Yuan Duan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Dong-Li Zhu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China.,Research Institute of Xi'an Jiaotong University, Zhejiang Province 311215, PR China
| | - Hao Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China.,Research Institute of Xi'an Jiaotong University, Zhejiang Province 311215, PR China
| | - Zhijun Dai
- Department of Breast Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province 310003, PR China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
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17
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Manjang K, Tripathi S, Yli-Harja O, Dehmer M, Emmert-Streib F. Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance. Sci Rep 2020; 10:16672. [PMID: 33028846 PMCID: PMC7542435 DOI: 10.1038/s41598-020-73326-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 08/17/2020] [Indexed: 12/12/2022] Open
Abstract
Gene ontology (GO) is an eminent knowledge base frequently used for providing biological interpretations for the analysis of genes or gene sets from biological, medical and clinical problems. Unfortunately, the interpretation of such results is challenging due to the large number of GO terms, their hierarchical and connected organization as directed acyclic graphs (DAGs) and the lack of tools allowing to exploit this structural information explicitly. For this reason, we developed the R package GOxploreR. The main features of GOxploreR are (I) easy and direct access to structural features of GO, (II) structure-based ranking of GO-terms, (III) mapping to reduced GO-DAGs including visualization capabilities and (IV) prioritizing of GO-terms. The underlying idea of GOxploreR is to exploit a graph-theoretical perspective of GO as manifested by its DAG-structure and the containing hierarchy levels for cumulating semantic information. That means all these features enhance the utilization of structural information of GO and complement existing analysis tools. Overall, GOxploreR provides exploratory as well as confirmatory tools for complementing any kind of analysis resulting in a list of GO-terms, e.g., from differentially expressed genes or gene sets, GWAS or biomarkers. Our R package GOxploreR is freely available from CRAN.
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Affiliation(s)
- Kalifa Manjang
- Predictive Society and Data Analytics Lab, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland
| | - Shailesh Tripathi
- Predictive Society and Data Analytics Lab, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland
| | - Olli Yli-Harja
- Computational Systems Biology, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland.,Institute for Systems Biology, Seattle, WA, USA.,Institute of Biosciences and Medical Technology, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland
| | - Matthias Dehmer
- Department of Biomedical Computer Science and Mechatronics, UMIT-The Health and Life Science University, 6060, Hall in Tyrol, Austria.,College of Artificial Intelligence, Nankai University, Tianjin, 300350, China
| | - Frank Emmert-Streib
- Predictive Society and Data Analytics Lab, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland. .,Institute of Biosciences and Medical Technology, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland.
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18
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Espíndola-Hernández P, Mueller JC, Carrete M, Boerno S, Kempenaers B. Genomic Evidence for Sensorial Adaptations to a Nocturnal Predatory Lifestyle in Owls. Genome Biol Evol 2020; 12:1895-1908. [PMID: 32770228 PMCID: PMC7566403 DOI: 10.1093/gbe/evaa166] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/05/2020] [Indexed: 12/17/2022] Open
Abstract
Owls (Strigiformes) evolved specific adaptations to their nocturnal predatory lifestyle, such as asymmetrical ears, a facial disk, and a feather structure allowing silent flight. Owls also share some traits with diurnal raptors and other nocturnal birds, such as cryptic plumage patterns, reversed sexual size dimorphism, and acute vision and hearing. The genetic basis of some of these adaptations to a nocturnal predatory lifestyle has been studied by candidate gene approaches but rarely with genome-wide scans. Here, we used a genome-wide comparative analysis to test for selection in the early history of the owls. We estimated the substitution rates in the coding regions of 20 bird genomes, including 11 owls of which five were newly sequenced. Then, we tested for functional overrepresentation across the genes that showed signals of selection. In the ancestral branch of the owls, we found traces of positive selection in the evolution of genes functionally related to visual perception, especially to phototransduction, and to chromosome packaging. Several genes that have been previously linked to acoustic perception, circadian rhythm, and feather structure also showed signals of an accelerated evolution in the origin of the owls. We discuss the functions of the genes under positive selection and their putative association with the adaptation to the nocturnal predatory lifestyle of the owls.
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Affiliation(s)
- Pamela Espíndola-Hernández
- Department of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Jakob C Mueller
- Department of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Martina Carrete
- Department of Physical, Chemical and Natural Systems, Universidad Pablo de Olavide, Sevilla, Spain
| | - Stefan Boerno
- Sequencing Core Facility, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Bart Kempenaers
- Department of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, Seewiesen, Germany
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19
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Integrative Analysis of MAPK14 as a Potential Biomarker for Cardioembolic Stroke. BIOMED RESEARCH INTERNATIONAL 2020; 2020:9502820. [PMID: 32879891 PMCID: PMC7448239 DOI: 10.1155/2020/9502820] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 07/09/2020] [Accepted: 07/15/2020] [Indexed: 01/22/2023]
Abstract
The aim of this study was to obtain the candidate genes and biomarkers that are significantly related to cardioembolic stroke (CS) by applying bioinformatics analysis. In accordance with the results of the weighted gene coexpression network analysis (WGCNA) in the GSE58294 dataset, 11 CS-related coexpression network modules were identified in this study. Correlation analysis showed that the black and pink modules are significantly associated with CS. A total of 18 core genes in the black module and one core gene in the pink module were determined. We then identified differentially expressed genes (DEGs) of CS at 3 h, 5 h, and 24 h postonset. After performing intersection, it was found that 311 genes were coexpressed at these three time points. These genes were majorly enriched in positive regulation of transferase activity and regulation of peptidase activity. The abovementioned coexpressed DEGs were subjected to protein-protein interaction analysis and subnetwork module analysis. Subsequently, we used cytoHubba to obtain 11 key genes from DEGs. The intersection of the core genes screened from WGCNA and the key genes selected from DEGs yielded the MAPK14 gene. The expression level of MAPK14 on the receiver operating characteristic (ROC) curves of CS at 3 h, 5 h, and 24 h showed that the area under the ROC curve (AUC) was 0.923, 0.934, and 0.941, respectively. In a nutshell, MAPK14 screened out by using WGCNA showed differential expression in CS. We conclude that MAPK14 can be used as a potential biological marker of CS and exhibits potential to predict the physiopathological condition of CS patients.
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20
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Pralle RS, Schultz NE, White HM, Weigel KA. Hyperketonemia GWAS and parity-dependent SNP associations in Holstein dairy cows intensively sampled for blood β-hydroxybutyrate concentration. Physiol Genomics 2020; 52:347-357. [PMID: 32628084 DOI: 10.1152/physiolgenomics.00016.2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Hyperketonemia (HYK) is a metabolic disorder that affects early postpartum dairy cows; however, there has been limited success in identifying genomic variants contributing to HYK susceptibility. We conducted a genome-wide association study (GWAS) using HYK phenotypes based on an intensive screening protocol, interrogated genotype interactions with parity group (GWIS), and evaluated the enrichment of annotated metabolic pathways. Holstein cows were enrolled into the experiment after parturition, and blood samples were collected at four timepoints between 5 and 18 days postpartum. Concentration of blood β-hydroxybutyrate (BHB) was quantified cow-side via a handheld BHB meter. Cows were labeled as a HYK case when at least one blood sample had BHB ≥ 1.2 mmol/L, and all other cows were considered non-HYK controls. After quality control procedures, 1,710 cows and 58,699 genotypes were available for further analysis. The GWAS and GWIS were performed using the forward feature select linear mixed model method. There was evidence for an association between ARS-BFGL-NGS-91238 and HYK susceptibility, as well as parity-dependent associations to HYK for BovineHD0600024247 and BovineHD1400023753. Candidate genes annotated to these single nuclear polymorphism associations have been previously associated with obesity, diabetes, insulin resistance, and fatty liver in humans and rodent models. Enrichment analysis revealed focal adhesion and axon guidance as metabolic pathways contributing to HYK etiology, while genetic variation in pathways related to insulin secretion and sensitivity may affect HYK susceptibility in a parity-dependent matter. In conclusion, the present work proposes several novel marker associations and metabolic pathways contributing to genetic risk for HYK susceptibility.
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Affiliation(s)
- Ryan S Pralle
- Department of Dairy Science, University of Wisconsin-Madison, Madison, Wisconsin
| | - Nichol E Schultz
- Department of Dairy Science, University of Wisconsin-Madison, Madison, Wisconsin
| | - Heather M White
- Department of Dairy Science, University of Wisconsin-Madison, Madison, Wisconsin
| | - Kent A Weigel
- Department of Dairy Science, University of Wisconsin-Madison, Madison, Wisconsin
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21
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Mueller JC, Carrete M, Boerno S, Kuhl H, Tella JL, Kempenaers B. Genes acting in synapses and neuron projections are early targets of selection during urban colonization. Mol Ecol 2020; 29:3403-3412. [DOI: 10.1111/mec.15451] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 04/08/2020] [Indexed: 02/06/2023]
Affiliation(s)
- Jakob C. Mueller
- Department of Behavioural Ecology & Evolutionary Genetics Max Planck Institute for Ornithology Seewiesen Germany
| | - Martina Carrete
- Department of Conservation Biology Estación Biológica de Doñana – CSIC Sevilla Spain
- Department of Physical, Chemical and Natural Systems University Pablo de Olavide Sevilla Spain
| | - Stefan Boerno
- Sequencing Core Facility Max Planck Institute for Molecular Genetics Berlin Germany
| | - Heiner Kuhl
- Sequencing Core Facility Max Planck Institute for Molecular Genetics Berlin Germany
- Department of Ecophysiology and Aquaculture Leibniz‐Institute of Freshwater Ecology and Inland Fisheries Berlin Germany
| | - José L. Tella
- Department of Conservation Biology Estación Biológica de Doñana – CSIC Sevilla Spain
| | - Bart Kempenaers
- Department of Behavioural Ecology & Evolutionary Genetics Max Planck Institute for Ornithology Seewiesen Germany
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22
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Pfenninger M, Foucault Q. Genomic processes underlying rapid adaptation of a natural
Chironomus riparius
population to unintendedly applied experimental selection pressures. Mol Ecol 2020; 29:536-548. [DOI: 10.1111/mec.15347] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 12/13/2019] [Accepted: 12/24/2019] [Indexed: 12/15/2022]
Affiliation(s)
- Markus Pfenninger
- Department of Molecular Ecology Senckenberg Biodiversity and Climate Research Centre Frankfurt am Main Germany
- Institute for Molecular and Organismic Evolution Johannes Gutenberg University Mainz Germany
- LOEWE Centre for Translational Biodiversity Genomics Senckenberg Biodiversity and Climate Research Centre Frankfurt am Main Germany
| | - Quentin Foucault
- Department of Molecular Ecology Senckenberg Biodiversity and Climate Research Centre Frankfurt am Main Germany
- Institute for Molecular and Organismic Evolution Johannes Gutenberg University Mainz Germany
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23
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Verbeek JS, Hirose S, Nishimura H. The Complex Association of FcγRIIb With Autoimmune Susceptibility. Front Immunol 2019; 10:2061. [PMID: 31681256 PMCID: PMC6803437 DOI: 10.3389/fimmu.2019.02061] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Accepted: 08/15/2019] [Indexed: 12/20/2022] Open
Abstract
FcγRIIb is the only inhibitory Fc receptor and controls many aspects of immune and inflammatory responses. The observation 19 years ago that Fc γ RIIb -/- mice generated by gene targeting in 129 derived ES cells developed severe lupus like disease when backcrossed more than 7 generations into C57BL/6 background initiated extensive research on the functional understanding of this strong autoimmune phenotype. The genomic region in the distal part of Chr1 both in human and mice in which the Fc γ R gene cluster is located shows a high level of complexity in relation to the susceptibility to SLE. Specific haplotypes of closely linked genes including the Fc γ RIIb and Slamf genes are associated with increased susceptibility to SLE both in mice and human. Using forward and reverse genetic approaches including in human GWAS and in mice congenic strains, KO mice (germline and cell type specific, on different genetic background), knockin mice, overexpressing transgenic mice combined with immunological models such as adoptive transfer of B cells from Ig transgenic mice the involved genes and the causal mutations and their associated functional alterations were analyzed. In this review the results of this 19 years extensive research are discussed with a focus on (genetically modified) mouse models.
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Affiliation(s)
- J Sjef Verbeek
- Department of Biomedical Engineering, Toin University of Yokohama, Yokohama, Japan
| | - Sachiko Hirose
- Department of Biomedical Engineering, Toin University of Yokohama, Yokohama, Japan
| | - Hiroyuki Nishimura
- Department of Biomedical Engineering, Toin University of Yokohama, Yokohama, Japan
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24
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Gene set enrichment analysis to create polygenic scores: a developmental examination of aggression. Transl Psychiatry 2019; 9:212. [PMID: 31477688 PMCID: PMC6718657 DOI: 10.1038/s41398-019-0513-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 05/14/2019] [Accepted: 06/01/2019] [Indexed: 12/13/2022] Open
Abstract
Previous approaches for creating polygenic risk scores (PRSs) do not explicitly consider the biological or developmental relevance of the genetic variants selected for inclusion. We applied gene set enrichment analysis to meta-GWAS data to create developmentally targeted, functionally informed PRSs. Using two developmentally matched meta-GWAS discovery samples, separate PRSs were formed, then examined in time-varying effect models of aggression in a second, longitudinal sample of children (n = 515, 49% female) in early childhood (2-5 years old), and middle childhood (7.5-10.5 years old). Functional PRSs were associated with aggression in both the early and middle childhood models.
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25
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François L, Hoskens H, Velie BD, Stinckens A, Tinel S, Lamberigts C, Peeters L, Savelkoul HFJ, Tijhaar E, Lindgren G, Janssens S, Ducro BJ, Buys N, Schurink AA. Genomic Regions Associated with IgE Levels against Culicoides spp. Antigens in Three Horse Breeds. Genes (Basel) 2019; 10:genes10080597. [PMID: 31398914 PMCID: PMC6723964 DOI: 10.3390/genes10080597] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 07/25/2019] [Accepted: 08/06/2019] [Indexed: 11/16/2022] Open
Abstract
Insect bite hypersensitivity (IBH), which is a cutaneous allergic reaction to antigens from Culicoides spp., is the most prevalent skin disorder in horses. Misdiagnosis is possible, as IBH is usually diagnosed based on clinical signs. Our study is the first to employ IgE levels against several recombinant Culicoides spp. allergens as an objective, independent, and quantitative phenotype to improve the power to detect genetic variants that underlie IBH. Genotypes of 200 Shetland ponies, 127 Icelandic horses, and 223 Belgian Warmblood horses were analyzed while using a mixed model approach. No single-nucleotide polymorphism (SNP) passed the Bonferroni corrected significance threshold, but several regions were identified within and across breeds, which confirmed previously identified regions of interest and, in addition, identifying new regions of interest. Allergen-specific IgE levels are a continuous and objective phenotype that allow for more powerful analyses when compared to a case-control set-up, as more significant associations were obtained. However, the use of a higher density array seems necessary to fully employ the use of IgE levels as a phenotype. While these results still require validation in a large independent dataset, the use of allergen-specific IgE levels showed value as an objective and continuous phenotype that can deepen our understanding of the biology underlying IBH.
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Affiliation(s)
- Liesbeth François
- Livestock Genetics, Department of Biosystems, KU Leuven, B-3001 Leuven, Belgium
| | - Hanne Hoskens
- Department of Human Genetics, KU Leuven, B-3000 Leuven, Belgium
| | - Brandon D Velie
- School of Life & Environmental Sciences, B19-603 University of Sydney, Sydney, NSW 2006,Australia
| | - Anneleen Stinckens
- Livestock Genetics, Department of Biosystems, KU Leuven, B-3001 Leuven, Belgium
| | - Susanne Tinel
- Livestock Genetics, Department of Biosystems, KU Leuven, B-3001 Leuven, Belgium
| | - Chris Lamberigts
- Research Group Livestock Physiology, Department of Biosystems, KU Leuven, Leuven, B-3001 Leuven, Belgium
| | - Liesbet Peeters
- Biomedical Research Institute, Hasselt University, B-3590 Diepenbeek, Belgium
| | - Huub F J Savelkoul
- Cell Biology and Immunology Group, Wageningen University & Research, 6700 AH Wageningen, The Netherlands
| | - Edwin Tijhaar
- Cell Biology and Immunology Group, Wageningen University & Research, 6700 AH Wageningen, The Netherlands
| | - Gabriella Lindgren
- Livestock Genetics, Department of Biosystems, KU Leuven, B-3001 Leuven, Belgium
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden
| | - Steven Janssens
- Livestock Genetics, Department of Biosystems, KU Leuven, B-3001 Leuven, Belgium
| | - Bart J Ducro
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, The Netherlands
| | - Nadine Buys
- Livestock Genetics, Department of Biosystems, KU Leuven, B-3001 Leuven, Belgium
| | - And Anouk Schurink
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, The Netherlands.
- Centre for Genetic Resources, The Netherlands (CGN), Wageningen University & Research, 6700 AH Wageningen, The Netherlands.
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26
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Wu M, Lin Z, Ma S, Chen T, Jiang R, Wong WH. Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks. J Mol Cell Biol 2019; 9:436-452. [PMID: 29300920 DOI: 10.1093/jmcb/mjx059] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 12/20/2017] [Indexed: 02/07/2023] Open
Abstract
Although genome-wide association studies (GWAS) have successfully identified thousands of genomic loci associated with hundreds of complex traits in the past decade, the debate about such problems as missing heritability and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and anticipated genetic data. Towards this goal, gene-level integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advantages as straightforward interpretation, less multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tissue-specific gene networks and thus lack the ability to utilize informative tissue-specific characteristics. To overcome this limitation, we proposed a Bayesian approach called SIGNET (Simultaneously Inference of GeNEs and Tissues) to integrate GWAS data and multiple tissue-specific gene networks for the simultaneous inference of phenotype-associated genes and relevant tissues. Through extensive simulation studies, we showed the effectiveness of our method in finding both associated genes and relevant tissues for a phenotype. In applications to real GWAS data of 14 complex phenotypes, we demonstrated the power of our method in both deciphering genetic basis and discovering biological insights of a phenotype. With this understanding, we expect to see SIGNET as a valuable tool for integrative GWAS analysis, thereby boosting the prevention, diagnosis, and treatment of human inherited diseases and eventually facilitating precision medicine.
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Affiliation(s)
- Mengmeng Wu
- Department of Computer Science, Tsinghua University, Beijing 100084, China.,Ministry of Education Key Laboratory of Bioinformatics and Bioinformatics Division, Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China.,Department of Statistics, Stanford University, CA 94305, USA
| | - Zhixiang Lin
- Department of Statistics, Stanford University, CA 94305, USA
| | - Shining Ma
- Department of Statistics, Stanford University, CA 94305, USA
| | - Ting Chen
- Department of Computer Science, Tsinghua University, Beijing 100084, China.,Ministry of Education Key Laboratory of Bioinformatics and Bioinformatics Division, Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics and Bioinformatics Division, Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China.,Department of Automation, Tsinghua University, Beijing 100084, China
| | - Wing Hung Wong
- Department of Statistics, Stanford University, CA 94305, USA
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27
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Dozmorov MG. Disease classification: from phenotypic similarity to integrative genomics and beyond. Brief Bioinform 2019; 20:1769-1780. [DOI: 10.1093/bib/bby049] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 05/01/2018] [Indexed: 02/06/2023] Open
Abstract
Abstract
A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).
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Affiliation(s)
- Mikhail G Dozmorov
- Department of Biostatistics, Virginia Commonwealth University, 830 East Main Street, Richmond, VA, USA
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28
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Malatras A, Duguez S, Duddy W. Muscle Gene Sets: a versatile methodological aid to functional genomics in the neuromuscular field. Skelet Muscle 2019; 9:10. [PMID: 31053169 PMCID: PMC6498474 DOI: 10.1186/s13395-019-0196-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 04/09/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The approach of building large collections of gene sets and then systematically testing hypotheses across these collections is a powerful tool in functional genomics, both in the pathway analysis of omics data and to uncover the polygenic effects associated with complex diseases in genome-wide association study. The Molecular Signatures Database includes collections of oncogenic and immunologic signatures enabling researchers to compare transcriptional datasets across hundreds of previous studies and leading to important insights in these fields, but such a resource does not currently exist for neuromuscular research. In previous work, we have shown the utility of gene set approaches to understand muscle cell physiology and pathology. METHODS Following a systematic survey of public muscle data, we passed gene expression profiles from 4305 samples through a robust pre-processing and standardized data analysis pipeline. Two hundred eighty-two samples were discarded based on a battery of rigorous global quality controls. From among the remaining studies, 578 comparisons of interest were identified by a combination of text mining and manual curation of the study meta-data. For each comparison, significantly dysregulated genes (FDR adjusted p < 0.05) were identified. RESULTS Lists of dysregulated genes were divided between upregulated and downregulated to give 1156 Muscle Gene Sets (MGS). This resource is available for download ( www.sys-myo.com/muscle_gene_sets ) and is accessible through three commonly used functional genomics platforms (GSEA, EnrichR, and WebGestalt). Basic guidance and recommendations are provided for the use of MGS through these platforms. In addition, consensus muscle gene sets were created to capture the overlap between the results of similar studies, and analysis of these highlighted the potential for novel disease-relevant findings. CONCLUSIONS The MGS resource can be used to investigate the behaviour of any list of genes across previous comparisons of muscle conditions, to compare previous studies to one another, and to explore the functional relationship of muscle dysregulation to the Gene Ontology. Its major intended use is in enrichment testing for functional genomics analysis.
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Affiliation(s)
- Apostolos Malatras
- Myologie Centre de Recherche, Université Sorbonne, UMRS 974 UPMC, INSERM, FRE 3617 CNRS, AIM, Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Ulster University, Altnagelvin Hospital Campus, Glenshane Road, Derry/Londonderry, BT47 6SB UK
- Department of Biological Sciences, Molecular Medicine Research Center, University of Cyprus, 1 University Avenue, 2109 Nicosia, Cyprus
| | - Stephanie Duguez
- Myologie Centre de Recherche, Université Sorbonne, UMRS 974 UPMC, INSERM, FRE 3617 CNRS, AIM, Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Ulster University, Altnagelvin Hospital Campus, Glenshane Road, Derry/Londonderry, BT47 6SB UK
| | - William Duddy
- Myologie Centre de Recherche, Université Sorbonne, UMRS 974 UPMC, INSERM, FRE 3617 CNRS, AIM, Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Ulster University, Altnagelvin Hospital Campus, Glenshane Road, Derry/Londonderry, BT47 6SB UK
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29
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Jhamb D, Magid-Slav M, Hurle MR, Agarwal P. Pathway analysis of GWAS loci identifies novel drug targets and repurposing opportunities. Drug Discov Today 2019; 24:1232-1236. [PMID: 30935985 DOI: 10.1016/j.drudis.2019.03.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/09/2019] [Accepted: 03/26/2019] [Indexed: 12/29/2022]
Abstract
Genome-wide association studies (GWAS) have made considerable progress and there is emerging evidence that genetics-based targets can lead to 28% more launched drugs. We analyzed 1589 GWAS across 1456 pathways to translate these often imprecise genetic loci into therapeutic hypotheses for 182 diseases. These pathway-based genetic targets were validated by testing whether current drug targets were enriched in the pathway space for the same indication. Remarkably, 30% of diseases had significantly more targets in these pathways than expected by chance; the comparable number for GWAS alone (without pathway analysis) was zero. This study shows that a systematic global pathway analysis can translate genetic findings into therapeutic hypotheses for both new drug discovery and repositioning opportunities for current drugs.
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Affiliation(s)
- Deepali Jhamb
- Computational Biology, GSK R&D, Collegeville, PA, USA
| | | | - Mark R Hurle
- Computational Biology, GSK R&D, Collegeville, PA, USA
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30
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Weissenböck M, Latham R, Nishita M, Wolff LI, Ho HYH, Minami Y, Hartmann C. Genetic interactions between Ror2 and Wnt9a, Ror1 and Wnt9a and Ror2 and Ror1: Phenotypic analysis of the limb skeleton and palate in compound mutants. Genes Cells 2019; 24:307-317. [PMID: 30801848 DOI: 10.1111/gtc.12676] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 02/18/2019] [Accepted: 02/19/2019] [Indexed: 01/20/2023]
Abstract
Mutations in the human receptor tyrosine kinase ROR2 are associated with Robinow syndrome (RRS) and brachydactyly type B1. Amongst others, the shortened limb phenotype associated with RRS is recapitulated in Ror2-/- mutant mice. In contrast, Ror1-/- mutant mice are viable and show no limb phenotype. Ror1-/- ;Ror2-/- double mutants are embryonic lethal, whereas double mutants containing a hypomorphic Ror1 allele (Ror1hyp ) survive up to birth and display a more severe shortened limb phenotype. Both orphan receptors have been shown to act as possible Wnt coreceptors and to mediate the Wnt5a signal. Here, we analyzed genetic interactions between the Wnt ligand, Wnt9a, and Ror2 or Ror1, as Wnt9a has also been implicated in skeletal development. Wnt9a-/- single mutants display a mild shortening of the long bones, whereas these are severely shortened in Ror2-/- mutants. Ror2-/- ;Wnt9a-/- double mutants displayed even more severely shortened long bones, and intermediate phenotypes were observed in compound Ror2;Wnt9a mutants. Long bones were also shorter in Ror1hyp/hyp ;Wnt9a-/- double mutants. In addition, Ror1hyp/hyp ;Wnt9a-/- double mutants displayed a secondary palate cleft phenotype, which was not present in the respective single mutants. Interestingly, 50% of compound mutant pups heterozygous for Ror2 and homozygous mutant for Ror1 also developed a secondary palate cleft phenotype.
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Affiliation(s)
| | - Richard Latham
- Research Institute of Molecular Pathology, Vienna, Austria
| | - Michiru Nishita
- Division of Cell Physiology, Department of Physiology and Cell Biology, Graduate School of Medicine, Kobe University, Kobe, Japan
| | - Lena Ingeborg Wolff
- Department of Bone and Skeletal Research, Medical Faculty, Institute of Musculoskeletal Medicine, University of Münster, Münster, Germany
| | - Hsin-Yi Henry Ho
- Department of Cell Biology and Human Anatomy, University of California Davis School of Medicine, Davis, California
| | - Yasuhiro Minami
- Division of Cell Physiology, Department of Physiology and Cell Biology, Graduate School of Medicine, Kobe University, Kobe, Japan
| | - Christine Hartmann
- Department of Bone and Skeletal Research, Medical Faculty, Institute of Musculoskeletal Medicine, University of Münster, Münster, Germany
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31
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Sun R, Hui S, Bader GD, Lin X, Kraft P. Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic. PLoS Genet 2019; 15:e1007530. [PMID: 30875371 PMCID: PMC6436759 DOI: 10.1371/journal.pgen.1007530] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 03/27/2019] [Accepted: 02/28/2019] [Indexed: 11/19/2022] Open
Abstract
A common complementary strategy in Genome-Wide Association Studies (GWAS) is to perform Gene Set Analysis (GSA), which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms (SNPs) residing in selected genes. While there exist many tools for performing GSA, popular methods often include a number of ad-hoc steps that are difficult to justify statistically, provide complicated interpretations based on permutation inference, and demonstrate poor operating characteristics. Additionally, the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype. We introduce the Generalized Berk-Jones (GBJ) statistic for GSA, a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings. To adjust for confounding introduced by different gene set lists, we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects. We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS, and we show how GBJ can increase power by incorporating information from multiple signals in the same gene. In addition, we illustrate how breast cancer pathway analysis can be confounded by the frequency of FGFR2 in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.
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Affiliation(s)
- Ryan Sun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Shirley Hui
- The Donnelly Center, University of Toronto, Toronto, Ontario, Canada
| | - Gary D. Bader
- The Donnelly Center, University of Toronto, Toronto, Ontario, Canada
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Peter Kraft
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
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32
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Margres MJ, Ruiz-Aravena M, Hamede R, Jones ME, Lawrance MF, Hendricks SA, Patton A, Davis BW, Ostrander EA, McCallum H, Hohenlohe PA, Storfer A. The Genomic Basis of Tumor Regression in Tasmanian Devils (Sarcophilus harrisii). Genome Biol Evol 2018; 10:3012-3025. [PMID: 30321343 PMCID: PMC6251476 DOI: 10.1093/gbe/evy229] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/10/2018] [Indexed: 02/06/2023] Open
Abstract
Understanding the genetic basis of disease-related phenotypes, such as cancer susceptibility, is crucial for the advancement of personalized medicine. Although most cancers are somatic in origin, a small number of transmissible cancers have been documented. Two such cancers have emerged in the Tasmanian devil (Sarcophilus harrisii) and now threaten the species with extinction. Recently, cases of natural tumor regression in Tasmanian devils infected with the clonally contagious cancer have been detected. We used whole-genome sequencing and FST-based approaches to identify the genetic basis of tumor regression by comparing the genomes of seven individuals that underwent tumor regression with those of three infected individuals that did not. We found three highly differentiated candidate genomic regions containing several genes related to immune response and/or cancer risk, indicating that the genomic basis of tumor regression was polygenic. Within these genomic regions, we identified putative regulatory variation in candidate genes but no nonsynonymous variation, suggesting that natural tumor regression may be driven, at least in part, by differential host expression of key loci. Comparative oncology can provide insight into the genetic basis of cancer risk, tumor development, and the pathogenicity of cancer, particularly due to our limited ability to monitor natural, untreated tumor progression in human patients. Our results support the hypothesis that host immune response is necessary for triggering tumor regression, providing candidate genes that may translate to novel treatments in human and nonhuman cancers.
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Affiliation(s)
- Mark J Margres
- School of Biological Sciences, Washington State University
| | - Manuel Ruiz-Aravena
- School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
| | - Rodrigo Hamede
- School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia.,Centre for Integrative Ecology, Deakin University, Waurn Ponds, Victoria, Australia
| | - Menna E Jones
- School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
| | | | - Sarah A Hendricks
- Department of Biological Sciences, Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow
| | - Austin Patton
- School of Biological Sciences, Washington State University
| | - Brian W Davis
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station.,Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Elaine A Ostrander
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Hamish McCallum
- School of Environment, Griffith University, Nathan, Queensland, Australia
| | - Paul A Hohenlohe
- Department of Biological Sciences, Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow
| | - Andrew Storfer
- School of Biological Sciences, Washington State University
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33
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Bime C, Pouladi N, Sammani S, Batai K, Casanova N, Zhou T, Kempf CL, Sun X, Camp SM, Wang T, Kittles RA, Lussier YA, Jones TK, Reilly JP, Meyer NJ, Christie JD, Karnes JH, Gonzalez-Garay M, Christiani DC, Yates CR, Wurfel MM, Meduri GU, Garcia JGN. Genome-Wide Association Study in African Americans with Acute Respiratory Distress Syndrome Identifies the Selectin P Ligand Gene as a Risk Factor. Am J Respir Crit Care Med 2018; 197:1421-1432. [PMID: 29425463 PMCID: PMC6005557 DOI: 10.1164/rccm.201705-0961oc] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Accepted: 02/08/2018] [Indexed: 12/29/2022] Open
Abstract
RATIONALE Genetic factors are involved in acute respiratory distress syndrome (ARDS) susceptibility. Identification of novel candidate genes associated with increased risk and severity will improve our understanding of ARDS pathophysiology and enhance efforts to develop novel preventive and therapeutic approaches. OBJECTIVES To identify genetic susceptibility targets for ARDS. METHODS A genome-wide association study was performed on 232 African American patients with ARDS and 162 at-risk control subjects. The Identify Candidate Causal SNPs and Pathways platform was used to infer the association of known gene sets with the top prioritized intragenic SNPs. Preclinical validation of SELPLG (selectin P ligand gene) was performed using mouse models of LPS- and ventilator-induced lung injury. Exonic variation within SELPLG distinguishing patients with ARDS from sepsis control subjects was confirmed in an independent cohort. MEASUREMENTS AND MAIN RESULTS Pathway prioritization analysis identified a nonsynonymous coding SNP (rs2228315) within SELPLG, encoding P-selectin glycoprotein ligand 1, to be associated with increased susceptibility. In an independent cohort, two exonic SELPLG SNPs were significantly associated with ARDS susceptibility. Additional support for SELPLG as an ARDS candidate gene was derived from preclinical ARDS models where SELPLG gene expression in lung tissues was significantly increased in both ventilator-induced (twofold increase) and LPS-induced (5.7-fold increase) murine lung injury models compared with controls. Furthermore, Selplg-/- mice exhibited significantly reduced LPS-induced inflammatory lung injury compared with wild-type C57/B6 mice. Finally, an antibody that neutralizes P-selectin glycoprotein ligand 1 significantly attenuated LPS-induced lung inflammation. CONCLUSIONS These findings identify SELPLG as a novel ARDS susceptibility gene among individuals of European and African descent.
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Affiliation(s)
| | - Nima Pouladi
- Department of Medicine
- Center for Biomedical Informatics and Biostatistics
| | | | | | | | | | | | | | | | | | | | - Yves A. Lussier
- Department of Medicine
- Center for Biomedical Informatics and Biostatistics
| | - Tiffanie K. Jones
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - John P. Reilly
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Nuala J. Meyer
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Jason D. Christie
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Jason H. Karnes
- Department of Pharmacy Practice and Science, University of Arizona, Tucson, Arizona
| | | | - David C. Christiani
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
- Pulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | | | - Mark M. Wurfel
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Washington, Seattle, Washington
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Cuperfain AB, Zhang ZL, Kennedy JL, Gonçalves VF. The Complex Interaction of Mitochondrial Genetics and Mitochondrial Pathways in Psychiatric Disease. MOLECULAR NEUROPSYCHIATRY 2018; 4:52-69. [PMID: 29998118 DOI: 10.1159/000488031] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 02/27/2018] [Indexed: 12/18/2022]
Abstract
While accounting for only 2% of the body's weight, the brain utilizes up to 20% of the body's total energy. Not surprisingly, metabolic dysfunction and energy supply-and-demand mismatch have been implicated in a variety of neurological and psychiatric disorders. Mitochondria are responsible for providing the brain with most of its energetic demands, and the brain uses glucose as its exclusive energy source. Exploring the role of mitochondrial dysfunction in the etiology of psychiatric disease is a promising avenue to investigate further. Genetic analysis of mitochondrial activity is a cornerstone in understanding disease pathogenesis related to metabolic dysfunction. In concert with neuroimaging and pathological study, genetics provides an important bridge between biochemical findings and clinical correlates in psychiatric disease. Mitochondrial genetics has several unique aspects to its analysis, and corresponding special considerations. Here, we review the components of mitochondrial genetic analysis - nuclear DNA, mitochon-drial DNA, mitochondrial pathways, pseudogenes, nuclear-mitochondrial mismatch, and microRNAs - that could contribute to an observable clinical phenotype. Throughout, we highlight psychiatric diseases that can arise due to dysfunction in these processes, with a focus on schizophrenia and bipolar disorder.
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Affiliation(s)
- Ari B Cuperfain
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Neuroscience Section, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Zhi Lun Zhang
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Neuroscience Section, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - James L Kennedy
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Neuroscience Section, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Vanessa F Gonçalves
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Neuroscience Section, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
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Rohde PD, Østergaard S, Kristensen TN, Sørensen P, Loeschcke V, Mackay TFC, Sarup P. Functional Validation of Candidate Genes Detected by Genomic Feature Models. G3 (BETHESDA, MD.) 2018; 8:1659-1668. [PMID: 29519937 PMCID: PMC5940157 DOI: 10.1534/g3.118.200082] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 03/07/2018] [Indexed: 12/11/2022]
Abstract
Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) categories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach.
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Affiliation(s)
- Palle Duun Rohde
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8000 Aarhus, Denmark
- Center for Integrative Sequencing, Aarhus University, 8000 Aarhus, Denmark
| | - Solveig Østergaard
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Torsten Nygaard Kristensen
- Section for Genetics, Ecology and Evolution, Department of Bioscience, Aarhus University, 8000 Aarhus, Denmark
- Section for Biology and Environmental Science, Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg, Denmark
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Volker Loeschcke
- Section for Genetics, Ecology and Evolution, Department of Bioscience, Aarhus University, 8000 Aarhus, Denmark
| | - Trudy F C Mackay
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
| | - Pernille Sarup
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
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Mocellin S, Tropea S, Benna C, Rossi CR. Circadian pathway genetic variation and cancer risk: evidence from genome-wide association studies. BMC Med 2018; 16:20. [PMID: 29455641 PMCID: PMC5817863 DOI: 10.1186/s12916-018-1010-1] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 01/18/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Dysfunction of the circadian clock and single polymorphisms of some circadian genes have been linked to cancer susceptibility, although data are scarce and findings inconsistent. We aimed to investigate the association between circadian pathway genetic variation and risk of developing common cancers based on the findings of genome-wide association studies (GWASs). METHODS Single nucleotide polymorphisms (SNPs) of 17 circadian genes reported by three GWAS meta-analyses dedicated to breast (Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Consortium; cases, n = 15,748; controls, n = 18,084), prostate (Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE) Consortium; cases, n = 14,160; controls, n = 12,724) and lung carcinoma (Transdisciplinary Research In Cancer of the Lung (TRICL) Consortium; cases, n = 12,160; controls, n = 16,838) in patients of European ancestry were utilized to perform pathway analysis by means of the adaptive rank truncated product (ARTP) method. Data were also available for the following subgroups: estrogen receptor negative breast cancer, aggressive prostate cancer, squamous lung carcinoma and lung adenocarcinoma. RESULTS We found a highly significant statistical association between circadian pathway genetic variation and the risk of breast (pathway P value = 1.9 × 10-6; top gene RORA, gene P value = 0.0003), prostate (pathway P value = 4.1 × 10-6; top gene ARNTL, gene P value = 0.0002) and lung cancer (pathway P value = 6.9 × 10-7; top gene RORA, gene P value = 2.0 × 10-6), as well as all their subgroups. Out of 17 genes investigated, 15 were found to be significantly associated with the risk of cancer: four genes were shared by all three malignancies (ARNTL, CLOCK, RORA and RORB), two by breast and lung cancer (CRY1 and CRY2) and three by prostate and lung cancer (NPAS2, NR1D1 and PER3), whereas four genes were specific for lung cancer (ARNTL2, CSNK1E, NR1D2 and PER2) and two for breast cancer (PER1, RORC). CONCLUSIONS Our findings, based on the largest series ever utilized for ARTP-based gene and pathway analysis, support the hypothesis that circadian pathway genetic variation is involved in cancer predisposition.
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Affiliation(s)
- Simone Mocellin
- Department of Surgery Oncology and Gastroenterology, University of Padova, Via Giustiniani 2, 35128, Padova, Italy. .,Istituto Oncologico Veneto, IOV-IRCCS, Padova, Italy.
| | | | - Clara Benna
- Department of Surgery Oncology and Gastroenterology, University of Padova, Via Giustiniani 2, 35128, Padova, Italy
| | - Carlo Riccardo Rossi
- Department of Surgery Oncology and Gastroenterology, University of Padova, Via Giustiniani 2, 35128, Padova, Italy.,Istituto Oncologico Veneto, IOV-IRCCS, Padova, Italy
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Goldstein JA, Bastarache LA, Denny JC, Roden DM, Pulley JM, Aronoff DM. Calcium channel blockers as drug repurposing candidates for gestational diabetes: Mining large scale genomic and electronic health records data to repurpose medications. Pharmacol Res 2018; 130:44-51. [PMID: 29448118 DOI: 10.1016/j.phrs.2018.02.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 12/28/2017] [Accepted: 02/09/2018] [Indexed: 02/07/2023]
Abstract
New therapeutic approaches are needed for gestational diabetes mellitus (GDM), but must show safety and efficacy in a historically understudied population. We studied associations between electronic medical record (EMR) phenotypes and genetic variants to uncover drugs currently considered safe in pregnancy that could treat or prevent GDM. We identified 129 systemically active drugs considered safe in pregnancy targeting the proteins produced from 196 genes. We tested for associations between GDM and/or type 2 diabetes (DM2) and 306 SNPs in 130 genes represented on the Illumina Infinium Human Exome Bead Chip (DM2 was included due to shared pathophysiological features with GDM). In parallel, we tested the association between drugs and glucose tolerance during pregnancy as measured by the glucose recorded during a routine 50-g glucose tolerance test (GTT). We found an association between GDM/DM2 and the genes targeted by 11 drug classes. In the EMR analysis, 6 drug classes were associated with changes in GTT. Two classes were identified in both analyses. L-type calcium channel blocking antihypertensives (CCBs), were associated with a 3.18 mg/dL (95% CI -6.18 to -0.18) decrease in glucose during GTT, and serotonin receptor type 3 (5HT-3) antagonist antinausea medications were associated with a 3.54 mg/dL (95% CI 1.86-5.23) increase in glucose during GTT. CCBs were identified as a class of drugs considered safe in pregnancy could have efficacy in treating or preventing GDM. 5HT-3 antagonists may be associated with worse glucose tolerance.
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Affiliation(s)
- Jeffery A Goldstein
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, United States
| | - Lisa A Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, United States
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, United States; Department of Medicine, Vanderbilt University Medical Center, United States
| | - Dan M Roden
- Department of Biomedical Informatics, Vanderbilt University Medical Center, United States; Department of Medicine, Vanderbilt University Medical Center, United States; Department of Pharmacology, Vanderbilt University School of Medicine, United States
| | - Jill M Pulley
- Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, United States
| | - David M Aronoff
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, United States; Department of Medicine, Vanderbilt University Medical Center, United States.
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Gumpinger AC, Roqueiro D, Grimm DG, Borgwardt KM. Methods and Tools in Genome-wide Association Studies. Methods Mol Biol 2018; 1819:93-136. [PMID: 30421401 DOI: 10.1007/978-1-4939-8618-7_5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Many traits, such as height, the response to a given drug, or the susceptibility to certain diseases are presumably co-determined by genetics. Especially in the field of medicine, it is of major interest to identify genetic aberrations that alter an individual's risk to develop a certain phenotypic trait. Addressing this question requires the availability of comprehensive, high-quality genetic datasets. The technological advancements and the decreasing cost of genotyping in the last decade led to an increase in such datasets. Parallel to and in line with this technological progress, an analysis framework under the name of genome-wide association studies was developed to properly collect and analyze these data. Genome-wide association studies aim at finding statistical dependencies-or associations-between a trait of interest and point-mutations in the DNA. The statistical models used to detect such associations are diverse, spanning the whole range from the frequentist to the Bayesian setting.Since genetic datasets are inherently high-dimensional, the search for associations poses not only a statistical but also a computational challenge. As a result, a variety of toolboxes and software packages have been developed, each implementing different statistical methods while using various optimizations and mathematical techniques to enhance the computations.This chapter is devoted to the discussion of widely used methods and tools in genome-wide association studies. We present the different statistical models and the assumptions on which they are based, explain peculiarities of the data that have to be accounted for and, most importantly, introduce commonly used tools and software packages for the different tasks in a genome-wide association study, complemented with examples for their application.
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Affiliation(s)
- Anja C Gumpinger
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland. .,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Damian Roqueiro
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Dominik G Grimm
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Karsten M Borgwardt
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland. .,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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Nguyen HH, Tilton SC, Kemp CJ, Song M. Nonmonotonic Pathway Gene Expression Analysis Reveals Oncogenic Role of p27/Kip1 at Intermediate Dose. Cancer Inform 2017; 16:1176935117740132. [PMID: 29162974 PMCID: PMC5692148 DOI: 10.1177/1176935117740132] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2017] [Accepted: 09/16/2017] [Indexed: 11/15/2022] Open
Abstract
The mechanistic basis by which the level of p27Kip1 expression influences tumor aggressiveness and patient mortality remains unclear. To elucidate the competing tumor-suppressing and oncogenic effects of p27Kip1 on gene expression in tumors, we analyzed the transcriptomes of squamous cell papilloma derived from Cdkn1b nullizygous, heterozygous, and wild-type mice. We developed a novel functional pathway analysis method capable of testing directional and nonmonotonic dose response. This analysis can reveal potential causal relationships that might have been missed by other nondirectional pathway analysis methods. Applying this method to capture dose-response curves in papilloma gene expression data, we show that several known cancer pathways are dominated by low-high-low gene expression responses to increasing p27 gene doses. The oncogene cyclin D1, whose expression is elevated at an intermediate p27 dose, is the most responsive gene shared by these cancer pathways. Therefore, intermediate levels of p27 may promote cellular processes favoring tumorigenesis-strikingly consistent with the dominance of heterozygous mutations in CDKN1B seen in human cancers. Our findings shed new light on regulatory mechanisms for both pro- and anti-tumorigenic roles of p27Kip1. Functional pathway dose-response analysis provides a unique opportunity to uncover nonmonotonic patterns in biological systems.
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Affiliation(s)
- Hien H Nguyen
- Department of Computer Science, New Mexico State University, Las Cruces, NM, USA
| | - Susan C Tilton
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR, USA
| | - Christopher J Kemp
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Mingzhou Song
- Department of Computer Science, New Mexico State University, Las Cruces, NM, USA
- Mingzhou Song, Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA.
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HPA Axis Genes, and Their Interaction with Childhood Maltreatment, are Related to Cortisol Levels and Stress-Related Phenotypes. Neuropsychopharmacology 2017; 42:2446-2455. [PMID: 28589964 PMCID: PMC5645736 DOI: 10.1038/npp.2017.118] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Revised: 05/12/2017] [Accepted: 05/30/2017] [Indexed: 02/06/2023]
Abstract
Stress responses are controlled by the hypothalamus pituitary adrenal (HPA)-axis and maladaptive stress responses are associated with the onset and maintenance of stress-related disorders such as major depressive disorder (MDD). Genes that play a role in the HPA-axis regulation may likely contribute to the relation between relevant neurobiological substrates and stress-related disorders. Therefore, we performed gene-wide analyses for 30 a priori literature-based genes involved in HPA-axis regulation in 2014 subjects (34% male; mean age: 42.5) to study the relations with lifetime MDD diagnosis, cortisol awakening response, and dexamethasone suppression test (DST) levels (subsample N=1472) and hippocampal and amygdala volume (3T MR images; subsample N=225). Additionally, gene by childhood maltreatment (CM) interactions were investigated. Gene-wide significant results were found for dexamethasone suppression (CYP11A1, CYP17A1, POU1F1, AKR1D1), hippocampal volume (CYP17A1, CYP11A1, HSD3B2, PROP1, AVPRA1, SRD5A1), amygdala volume (POMC, CRH, HSD3B2), and lifetime MDD diagnosis (FKBP5 and CRH), all permutation p-values<0.05. Interactions with CM were found for several genes; the strongest interactions were found for NR3C2, where the minor allele of SNP rs17581262 was related to smaller hippocampal volume, smaller amygdala volume, higher DST levels, and higher odds of MDD diagnosis only in participants with CM. As hypothesized, several HPA-axis genes are associated with stress-related endophenotypes including cortisol response and reduced brain volumes. Furthermore, we found a pleiotropic interaction between CM and the mineralocorticoid receptor gene, suggesting that this gene plays an important moderating role in stress and stress-related disorders.
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Aleman F. The Necessity of Diploid Genome Sequencing to Unravel the Genetic Component of Complex Phenotypes. Front Genet 2017; 8:148. [PMID: 29075286 PMCID: PMC5641544 DOI: 10.3389/fgene.2017.00148] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 09/27/2017] [Indexed: 01/23/2023] Open
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Qian DC, Molfese DL, Jin JL, Titus AJ, He Y, Li Y, Vaissié M, Viswanath H, Baldwin PR, Krahe R, Salas R, Amos CI. Genome-wide imaging association study implicates functional activity and glial homeostasis of the caudate in smoking addiction. BMC Genomics 2017; 18:740. [PMID: 28927378 PMCID: PMC5605997 DOI: 10.1186/s12864-017-4124-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2017] [Accepted: 09/06/2017] [Indexed: 12/21/2022] Open
Abstract
Background Nearly 6 million deaths and over a half trillion dollars in healthcare costs worldwide are attributed to tobacco smoking each year. Extensive research efforts have been pursued to elucidate the molecular underpinnings of smoking addiction and facilitate cessation. In this study, we genotyped and obtained both resting state and task-based functional magnetic resonance imaging from 64 non-smokers and 42 smokers. Smokers were imaged after having smoked normally (“sated”) and after having not smoked for at least 12 h (“abstinent”). Results While abstinent smokers did not differ from non-smokers with respect to pairwise resting state functional connectivities (RSFCs) between 12 brain regions of interest, RSFCs involving the caudate and putamen of sated smokers significantly differed from those of non-smokers (P < 0.01). Further analyses of caudate and putamen activity during elicited experiences of reward and disappointment show that caudate activity during reward (CR) correlated with smoking status (P = 0.015). Moreover, abstinent smokers with lower CR experienced greater withdrawal symptoms (P = 0.024), which suggests CR may be related to smoking urges. Associations between genetic variants and CR, adjusted for smoking status, were identified by genome-wide association study (GWAS). Genes containing or exhibiting caudate-specific expression regulation by these variants were enriched within Gene Ontology terms that describe cytoskeleton functions, synaptic organization, and injury response (P < 0.001, FDR < 0.05). Conclusions By integrating genomic and imaging data, novel insights into potential mechanisms of caudate activation and homeostasis are revealed that may guide new directions of research toward improving our understanding of addiction pathology. Electronic supplementary material The online version of this article (10.1186/s12864-017-4124-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- David C Qian
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, NH, 03756, USA
| | - David L Molfese
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, 77030, USA.,Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, 77030, USA
| | - Jennifer L Jin
- Department of Mathematics, Dartmouth College, Hanover, NH, 03755, USA
| | - Alexander J Titus
- Department of Epidemiology, Dartmouth Geisel School of Medicine, Lebanon, NH, 03756, USA
| | - Yixuan He
- Department of Mathematics, Dartmouth College, Hanover, NH, 03755, USA.,Department of Biological Sciences, Dartmouth College, Hanover, NH, 03755, USA
| | - Yafang Li
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, NH, 03756, USA
| | - Maxime Vaissié
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, NH, 03756, USA
| | - Humsini Viswanath
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Philip R Baldwin
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, 77030, USA.,Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, 77030, USA
| | - Ralf Krahe
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Ramiro Salas
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, 77030, USA.,Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, 77030, USA
| | - Christopher I Amos
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, NH, 03756, USA.
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Shu L, Chan KHK, Zhang G, Huan T, Kurt Z, Zhao Y, Codoni V, Trégouët DA, Yang J, Wilson JG, Luo X, Levy D, Lusis AJ, Liu S, Yang X. Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States. PLoS Genet 2017; 13:e1007040. [PMID: 28957322 PMCID: PMC5634657 DOI: 10.1371/journal.pgen.1007040] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Revised: 10/10/2017] [Accepted: 09/21/2017] [Indexed: 12/18/2022] Open
Abstract
Cardiovascular diseases (CVD) and type 2 diabetes (T2D) are closely interrelated complex diseases likely sharing overlapping pathogenesis driven by aberrant activities in gene networks. However, the molecular circuitries underlying the pathogenic commonalities remain poorly understood. We sought to identify the shared gene networks and their key intervening drivers for both CVD and T2D by conducting a comprehensive integrative analysis driven by five multi-ethnic genome-wide association studies (GWAS) for CVD and T2D, expression quantitative trait loci (eQTLs), ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from CVD and T2D relevant tissues. We identified pathways regulating the metabolism of lipids, glucose, and branched-chain amino acids, along with those governing oxidation, extracellular matrix, immune response, and neuronal system as shared pathogenic processes for both diseases. Further, we uncovered 15 key drivers including HMGCR, CAV1, IGF1 and PCOLCE, whose network neighbors collectively account for approximately 35% of known GWAS hits for CVD and 22% for T2D. Finally, we cross-validated the regulatory role of the top key drivers using in vitro siRNA knockdown, in vivo gene knockout, and two Hybrid Mouse Diversity Panels each comprised of >100 strains. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide strong support that common sets of tissue-specific molecular networks drive the pathogenesis of both CVD and T2D across ethnicities and help prioritize new therapeutic avenues for both CVD and T2D.
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Affiliation(s)
- Le Shu
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Kei Hang K. Chan
- Departments of Epidemiology and Medicine and Center for Global Cardiometabolic Health, Brown University, Providence, RI, United States of America
- Hong Kong Institute of Diabetes and Obesity, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Guanglin Zhang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Tianxiao Huan
- The Framingham Heart Study, Framingham, MA, USA and the Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, United States of America
| | - Zeyneb Kurt
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Yuqi Zhao
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Veronica Codoni
- Sorbonne Universités, UPMC Univ. Paris 06, INSERM, UMR_S 1166, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris, France
| | - David-Alexandre Trégouët
- Sorbonne Universités, UPMC Univ. Paris 06, INSERM, UMR_S 1166, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris, France
| | | | - Jun Yang
- Department of Public Health, Hangzhou Normal University School of Medicine, Hangzhou, China
- Collaborative Innovation Center for the Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, United States of America
| | - Xi Luo
- Department of Biostatistics, Brown University, Providence, RI, United States of America
| | - Daniel Levy
- The Framingham Heart Study, Framingham, MA, USA and the Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, United States of America
| | - Aldons J. Lusis
- Departments of Medicine, Human Genetics, and Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Simin Liu
- Departments of Epidemiology and Medicine and Center for Global Cardiometabolic Health, Brown University, Providence, RI, United States of America
- Department of Endocrinology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States of America
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA, United States of America
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA, United States of America
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Rohde PD, Gaertner B, Ward K, Sørensen P, Mackay TFC. Genomic Analysis of Genotype-by-Social Environment Interaction for Drosophila melanogaster Aggressive Behavior. Genetics 2017; 206:1969-1984. [PMID: 28550016 PMCID: PMC5560801 DOI: 10.1534/genetics.117.200642] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 05/22/2017] [Indexed: 02/06/2023] Open
Abstract
Human psychiatric disorders such as schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder often include adverse behaviors including increased aggressiveness. Individuals with psychiatric disorders often exhibit social withdrawal, which can further increase the probability of conducting a violent act. Here, we used the inbred, sequenced lines of the Drosophila Genetic Reference Panel (DGRP) to investigate the genetic basis of variation in male aggressive behavior for flies reared in a socialized and socially isolated environment. We identified genetic variation for aggressive behavior, as well as significant genotype-by-social environmental interaction (GSEI); i.e., variation among DGRP genotypes in the degree to which social isolation affected aggression. We performed genome-wide association (GWA) analyses to identify genetic variants associated with aggression within each environment. We used genomic prediction to partition genetic variants into gene ontology (GO) terms and constituent genes, and identified GO terms and genes with high prediction accuracies in both social environments and for GSEI. The top predictive GO terms significantly increased the proportion of variance explained, compared to prediction models based on all segregating variants. We performed genomic prediction across environments, and identified genes in common between the social environments that turned out to be enriched for genome-wide associated variants. A large proportion of the associated genes have previously been associated with aggressive behavior in Drosophila and mice. Further, many of these genes have human orthologs that have been associated with neurological disorders, indicating partially shared genetic mechanisms underlying aggression in animal models and human psychiatric disorders.
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Affiliation(s)
- Palle Duun Rohde
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, 8000 Aarhus, Denmark
- ISEQ, Center for Integrative Sequencing, Aarhus University, 8000 Aarhus, Denmark
| | - Bryn Gaertner
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
| | - Kirsty Ward
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Trudy F C Mackay
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
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Abstract
Ascertaining the molecular and physiological basis of domestication and breeding is an active area of research. Due to the current wide distribution of its wild ancestor, the wild boar, the pig (Sus scrofa) is an excellent model to study these processes, which occurred independently in East Asia and Europe ca. 9000 yr ago. Analyzing genome variability patterns in terms of metabolic pathways is attractive since it considers the impact of interrelated functions of genes, in contrast to genome-wide scans that treat genes or genome windows in isolation. To that end, we studied 40 wild boars and 123 domestic pig genomes from Asia and Europe when metabolic pathway was the unit of analysis. We computed statistical significance for differentiation (Fst) and linkage disequilibrium (nSL) statistics at the pathway level. In terms of Fst, we found 21 and 12 pathways significantly differentiated at a q-value < 0.05 in Asia and Europe, respectively; five were shared across continents. In Asia, we found six significant pathways related to behavior, which involved essential neurotransmitters like dopamine and serotonin. Several significant pathways were interrelated and shared a variable percentage of genes. There were 12 genes present in >10 significant pathways (in terms of Fst), comprising genes involved in the transduction of a large number of signals, like phospholipase PCLB1, which is expressed in the brain, or ITPR3, which has an important role in taste transduction. In terms of nSL, significant pathways were mainly related to reproductive performance (ovarian steroidogenesis), a similarly important target trait during domestication and modern animal breeding. Different levels of recombination cannot explain these results, since we found no correlation between Fst and recombination rate. However, we did find an increased ratio of deleterious mutations in domestic vs. wild populations, suggesting a relaxed functional constraint associated with the domestication and breeding processes. Purifying selection was, nevertheless, stronger in significantly differentiated pathways than in random pathways, mainly in Europe. We conclude that pathway analysis facilitates the biological interpretation of genome-wide studies. Notably, in the case of pig, behavior played an important role, among other physiological and developmental processes.
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Hayden LP, Cho MH, McDonald MLN, Crapo JD, Beaty TH, Silverman EK, Hersh CP. Susceptibility to Childhood Pneumonia: A Genome-Wide Analysis. Am J Respir Cell Mol Biol 2017; 56:20-28. [PMID: 27508494 DOI: 10.1165/rcmb.2016-0101oc] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Previous studies have indicated that in adult smokers, a history of childhood pneumonia is associated with reduced lung function and chronic obstructive pulmonary disease. There have been few previous investigations using genome-wide association studies to investigate genetic predisposition to pneumonia. This study aims to identify the genetic variants associated with the development of pneumonia during childhood and over the course of the lifetime. Study subjects included current and former smokers with and without chronic obstructive pulmonary disease participating in the COPDGene Study. Pneumonia was defined by subject self-report, with childhood pneumonia categorized as having the first episode at <16 years. Genome-wide association studies for childhood pneumonia (843 cases, 9,091 control subjects) and lifetime pneumonia (3,766 cases, 5,659 control subjects) were performed separately in non-Hispanic whites and African Americans. Non-Hispanic white and African American populations were combined in the meta-analysis. Top genetic variants from childhood pneumonia were assessed in network analysis. No single-nucleotide polymorphisms reached genome-wide significance, although we identified potential regions of interest. In the childhood pneumonia analysis, this included variants in NGR1 (P = 6.3 × 10-8), PAK6 (P = 3.3 × 10-7), and near MATN1 (P = 2.8 × 10-7). In the lifetime pneumonia analysis, this included variants in LOC339862 (P = 8.7 × 10-7), RAPGEF2 (P = 8.4 × 10-7), PHACTR1 (P = 6.1 × 10-7), near PRR27 (P = 4.3 × 10-7), and near MCPH1 (P = 2.7 × 10-7). Network analysis of the genes associated with childhood pneumonia included top networks related to development, blood vessel morphogenesis, muscle contraction, WNT signaling, DNA damage, apoptosis, inflammation, and immune response (P ≤ 0.05). We have identified genes potentially associated with the risk of pneumonia. Further research will be required to confirm these associations and to determine biological mechanisms. CLINICAL TRIAL REGISTRATION NCT00608764.
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Affiliation(s)
- Lystra P Hayden
- 1 Division of Respiratory Diseases, Boston Children's Hospital, Boston, Massachusetts.,2 Channing Division of Network Medicine and
| | - Michael H Cho
- 2 Channing Division of Network Medicine and.,3 Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | | | | | - Terri H Beaty
- 5 Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland
| | - Edwin K Silverman
- 2 Channing Division of Network Medicine and.,3 Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Craig P Hersh
- 2 Channing Division of Network Medicine and.,3 Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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Gorlova OY, Demidenko EI, Amos CI, Gorlov IP. Downstream targets of GWAS-detected genes for breast, lung, and prostate and colon cancer converge to G1/S transition pathway. Hum Mol Genet 2017; 26:1465-1471. [PMID: 28334950 DOI: 10.1093/hmg/ddx050] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Accepted: 02/06/2017] [Indexed: 12/28/2022] Open
Abstract
Genome-wide association studies (GWASs) identified over 500 single nucleotide polymorphisms (SNPs) influencing cancer risk. It is logical to expect the cancer-associated genes to cluster in pathways directly involved in carcinogenesis, e.g. cell cycle. Nevertheless, analyses of the GWAS-detected cancer risk genes usually show no or weak enrichment by known cancer genes.We hypothesized that GWAS-detected cancer risk-associated genes function as upstream regulators of the genes directly involved in carcinogenesis. We have analyzed four common cancers: breast, colon, lung, and prostate. To identify downstream targets of GWAS-detected cancer risk genes we used MedScan, which is a text mining tool offered by PathwayStudio. We also used data on protein/protein interactions reported by BioGRID database. Among all identified targets we have selected common downstream targets. A gene was considered a common downstream target if it was a downstream target for at least three GWAS-detected genes for a given cancer type. Common downstream targets were identified separately for each cancer type. We found that common downstream targets for all four cancer types were enriched by cell cycle genes, more specifically, the genes involved in G1/S transition. Common downstream targets for bipolar disorder, Crohn's disease, and type 2 diabetes did not show G1/S transition enrichment.The results of this analysis suggest that many cancer risk genes function as upstream regulators of the genes directly involved in G1/S transition and exert their risk effects by reducing threshold for G1/S transition, elevating the background level of cell proliferation and cancer risk.
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Abstract
The rapid increase in loci discovered in genome-wide association studies has created a need to understand the biological implications of these results. Gene-set analysis provides a means of gaining such understanding, but the statistical properties of gene-set analysis are not well understood, which compromises our ability to interpret its results. In this Analysis article, we provide an extensive statistical evaluation of the core structure that is inherent to all gene- set analyses and we examine current implementations in available tools. We show which factors affect valid and successful detection of gene sets and which provide a solid foundation for performing and interpreting gene-set analysis.
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Greene CS, Himmelstein DS. Genetic Association-Guided Analysis of Gene Networks for the Study of Complex Traits. ACTA ACUST UNITED AC 2017; 9:179-84. [PMID: 27094199 DOI: 10.1161/circgenetics.115.001181] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 03/08/2016] [Indexed: 12/29/2022]
Affiliation(s)
- Casey S Greene
- From the Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia (C.S.G.); and Biological and Medical Informatics, University of California, San Francisco (D.S.H.).
| | - Daniel S Himmelstein
- From the Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia (C.S.G.); and Biological and Medical Informatics, University of California, San Francisco (D.S.H.)
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Moreno-Moral A, Pesce F, Behmoaras J, Petretto E. Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease. Methods Mol Biol 2017; 1488:337-362. [PMID: 27933533 DOI: 10.1007/978-1-4939-6427-7_16] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Systems genetics stems from systems biology and similarly employs integrative modeling approaches to describe the perturbations and phenotypic effects observed in a complex system. However, in the case of systems genetics the main source of perturbation is naturally occurring genetic variation, which can be analyzed at the systems-level to explain the observed variation in phenotypic traits. In contrast with conventional single-variant association approaches, the success of systems genetics has been in the identification of gene networks and molecular pathways that underlie complex disease. In addition, systems genetics has proven useful in the discovery of master trans-acting genetic regulators of functional networks and pathways, which in many cases revealed unexpected gene targets for disease. Here we detail the central components of a fully integrated systems genetics approach to complex disease, starting from assessment of genetic and gene expression variation, linking DNA sequence variation to mRNA (expression QTL mapping), gene regulatory network analysis and mapping the genetic control of regulatory networks. By summarizing a few illustrative (and successful) examples, we highlight how different data-modeling strategies can be effectively integrated in a systems genetics study.
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Affiliation(s)
- Aida Moreno-Moral
- Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
| | - Francesco Pesce
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, Hammersmith Campus, Imperial Centre for Translational and Experimental Medicine, London, UK
| | - Jacques Behmoaras
- Centre for Complement and Inflammation Research, Imperial College London, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK
| | - Enrico Petretto
- Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore.
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