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Kim BH, Lee H, Ham H, Kim HJ, Jang H, Kim JP, Park YH, Kim M, Seo SW. Clinical effects of novel susceptibility genes for beta-amyloid: a gene-based association study in the Korean population. Front Aging Neurosci 2023; 15:1278998. [PMID: 37901794 PMCID: PMC10602697 DOI: 10.3389/fnagi.2023.1278998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
Amyloid-beta (Aβ) is a pathological hallmark of Alzheimer's disease (AD). We aimed to identify genes related to Aβ uptake in the Korean population and investigate the effects of these novel genes on clinical outcomes, including neurodegeneration and cognitive impairments. We recruited a total of 759 Korean participants who underwent neuropsychological tests, brain magnetic resonance imaging, 18F-flutemetamol positron emission tomography, and microarray genotyping data. We performed gene-based association analysis, and also performed expression quantitative trait loci and network analysis. In genome-wide association studies, no single nucleotide polymorphism (SNP) passed the genome-wide significance threshold. In gene-based association analysis, six genes (LCMT1, SCRN2, LRRC46, MRPL10, SP6, and OSBPL7) were significantly associated with Aβ standardised uptake value ratio in the brain. The three most significant SNPs (rs4787307, rs9903904, and rs11079797) on these genes are associated with the regulation of the LCMT1, OSBPL7, and SCRN2 genes, respectively. These SNPs are involved in decreasing hippocampal volume and cognitive scores by mediating Aβ uptake. The 19 enriched gene sets identified by pathway analysis included axon and chemokine activity. Our findings suggest novel susceptibility genes associated with the uptake of Aβ, which in turn leads to worse clinical outcomes. Our findings might lead to the discovery of new AD treatment targets.
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Affiliation(s)
- Bo-Hyun Kim
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - HyunWoo Lee
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hongki Ham
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jin Kim
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyemin Jang
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yu Hyun Park
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Mansu Kim
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Sang Won Seo
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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2
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The association of genetic alterations with response rate in newly diagnosed chronic myeloid leukemia patients. Leuk Res 2022; 114:106791. [DOI: 10.1016/j.leukres.2022.106791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 01/09/2022] [Accepted: 01/17/2022] [Indexed: 11/23/2022]
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3
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Reddy JS, Allen M, Ho CCG, Oatman SR, İş Ö, Quicksall ZS, Wang X, Jin J, Patel TA, Carnwath TP, Nguyen TT, Malphrus KG, Lincoln SJ, Carrasquillo MM, Crook JE, Kanekiyo T, Murray ME, Bu G, Dickson DW, Ertekin-Taner N. Genome-wide analysis identifies a novel LINC-PINT splice variant associated with vascular amyloid pathology in Alzheimer's disease. Acta Neuropathol Commun 2021; 9:93. [PMID: 34020725 PMCID: PMC8147512 DOI: 10.1186/s40478-021-01199-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 05/12/2021] [Indexed: 01/09/2023] Open
Abstract
Cerebral amyloid angiopathy (CAA) contributes to accelerated cognitive decline in Alzheimer’s disease (AD) dementia and is a common finding at autopsy. The APOEε4 allele and male sex have previously been reported to associate with increased CAA in AD. To inform biomarker and therapeutic target discovery, we aimed to identify additional genetic risk factors and biological pathways involved in this vascular component of AD etiology. We present a genome-wide association study of CAA pathology in AD cases and report sex- and APOE-stratified assessment of this phenotype. Genome-wide genotypes were collected from 853 neuropathology-confirmed AD cases scored for CAA across five brain regions, and imputed to the Haplotype Reference Consortium panel. Key variables and genome-wide genotypes were tested for association with CAA in all individuals and in sex and APOEε4 stratified subsets. Pathway enrichment was run for each of the genetic analyses. Implicated loci were further investigated for functional consequences using brain transcriptome data from 1,186 samples representing seven brain regions profiled as part of the AMP-AD consortium. We confirmed association of male sex, AD neuropathology and APOEε4 with increased CAA, and identified a novel locus, LINC-PINT, associated with lower CAA amongst APOEε4-negative individuals (rs10234094-C, beta = −3.70 [95% CI −0.49—−0.24]; p = 1.63E-08). Transcriptome profiling revealed higher LINC-PINT expression levels in AD cases, and association of rs10234094-C with altered LINC-PINT splicing. Pathway analysis indicates variation in genes involved in neuronal health and function are linked to CAA in AD patients. Further studies in additional and diverse cohorts are needed to assess broader translation of our findings.
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4
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Kim BH, Nho K, Lee JM. Genome-wide association study identifies susceptibility loci of brain atrophy to NFIA and ST18 in Alzheimer's disease. Neurobiol Aging 2021; 102:200.e1-200.e11. [PMID: 33640202 DOI: 10.1016/j.neurobiolaging.2021.01.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 01/08/2021] [Accepted: 01/25/2021] [Indexed: 02/04/2023]
Abstract
To identify genetic variants influencing cortical atrophy in Alzheimer's disease (AD), we performed genome-wide association studies (GWAS) of mean cortical thicknesses in 17 AD-related brain. In this study, we used neuroimaging and genetic data of 919 participants from the Alzheimer's Disease Neuroimaging Initiative cohort, which include 268 cognitively normal controls, 488 mild cognitive impairment, 163 AD individuals. We performed GWAS with 3,041,429 single nucleotide polymorphisms (SNPs) for cortical thickness. The results of GWAS indicated that rs10109716 in ST18 (ST18 C2H2C-type zinc finger transcription factor) and rs661526 in NFIA (nuclear factor I A) genes are significantly associated with mean cortical thicknesses of the left inferior frontal gyrus and left parahippocampal gyrus, respectively. The rs661526 regulates the expression levels of NFIA in the substantia nigra and frontal cortex and rs10109716 regulates the expression levels of ST18 in the thalamus. These results suggest a crucial role of identified genes for cortical atrophy and could provide further insights into the genetic basis of AD.
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Affiliation(s)
- Bo-Hyun Kim
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea.
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5
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Yao X, Cong S, Yan J, Risacher SL, Saykin AJ, Moore JH, Shen L. Regional imaging genetic enrichment analysis. Bioinformatics 2020; 36:2554-2560. [PMID: 31860065 DOI: 10.1093/bioinformatics/btz948] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 11/19/2019] [Accepted: 12/18/2019] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Brain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful interpretation and modest dimensionality compared with voxelwise approaches. Typical ROI-level measures used in these studies are summary statistics from voxelwise measures in the region, without making full use of individual voxel signals. RESULTS In this article, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxelwise enrichment analysis, which embraces the collective effect of weak voxel-level signals and integrates brain anatomical annotation information. Our proposed method achieves three goals at the same time: (i) increase the statistical power by substantially reducing the burden of multiple comparison correction; (ii) employ brain annotation information to enable biologically meaningful interpretation and (iii) make full use of fine-grained voxelwise signals. We demonstrate our method on an imaging genetic analysis using data from the Alzheimer's Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxelwise FDG-positron emission tomography measures between 116 ROIs and 565 373 single-nucleotide polymorphisms. Compared with traditional ROI-wise and voxelwise approaches, our method identified 2946 novel imaging genetic associations in addition to 33 ones overlapping with the two benchmark methods. In particular, two newly reported variants were further supported by transcriptome evidences from region-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain. AVAILABILITY AND IMPLEMENTATION The R code and sample data are freely available at https://github.com/lshen/RIGEA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shan Cong
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jingwen Yan
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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6
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He T, Li C. Harness the power of genomic selection and the potential of germplasm in crop breeding for global food security in the era with rapid climate change. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.cj.2020.04.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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7
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Huminiecki Ł. Models of the Gene Must Inform Data-Mining Strategies in Genomics. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E942. [PMID: 33286713 PMCID: PMC7597212 DOI: 10.3390/e22090942] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 08/21/2020] [Accepted: 08/22/2020] [Indexed: 12/22/2022]
Abstract
The gene is a fundamental concept of genetics, which emerged with the Mendelian paradigm of heredity at the beginning of the 20th century. However, the concept has since diversified. Somewhat different narratives and models of the gene developed in several sub-disciplines of genetics, that is in classical genetics, population genetics, molecular genetics, genomics, and, recently, also, in systems genetics. Here, I ask how the diversity of the concept impacts data-integration and data-mining strategies for bioinformatics, genomics, statistical genetics, and data science. I also consider theoretical background of the concept of the gene in the ideas of empiricism and experimentalism, as well as reductionist and anti-reductionist narratives on the concept. Finally, a few strategies of analysis from published examples of data-mining projects are discussed. Moreover, the examples are re-interpreted in the light of the theoretical material. I argue that the choice of an optimal level of abstraction for the gene is vital for a successful genome analysis.
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Affiliation(s)
- Łukasz Huminiecki
- Department of Molecular Biology, Institute of Genetics and Animal Biotechnology, Polish Academy of Sciences, 00-901 Warsaw, Poland
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8
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Rotroff DM. A Bioinformatics Crash Course for Interpreting Genomics Data. Chest 2020; 158:S113-S123. [PMID: 32658646 PMCID: PMC8176646 DOI: 10.1016/j.chest.2020.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 11/11/2019] [Accepted: 03/09/2020] [Indexed: 10/23/2022] Open
Abstract
Reductions in genotyping costs and improvements in computational power have made conducting genome-wide association studies (GWAS) standard practice for many complex diseases. GWAS is the assessment of genetic variants across the genome of many individuals to determine which, if any, genetic variants are associated with a specific trait. As with any analysis, there are evolving best practices that should be followed to ensure scientific rigor and reliability in the conclusions. This article presents a brief summary for many of the key bioinformatics considerations when either planning or evaluating GWAS. This review is meant to serve as a guide to those without deep expertise in bioinformatics and GWAS and give them tools to critically evaluate this popular approach to investigating complex diseases. In addition, a checklist is provided that can be used by investigators to evaluate whether a GWAS has appropriately accounted for the many potential sources of bias and generally followed current best practices.
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Affiliation(s)
- Daniel M Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH.
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9
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Genome-wide association study identifies zonisamide responsive gene in Parkinson's disease patients. J Hum Genet 2020; 65:693-704. [PMID: 32355309 PMCID: PMC8075945 DOI: 10.1038/s10038-020-0760-8] [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: 02/03/2020] [Revised: 03/31/2020] [Accepted: 03/31/2020] [Indexed: 12/21/2022]
Abstract
Long-term treatment of Parkinson's disease (PD) by levodopa leads to motor complication "wearing-off". Zonisamide is a nondopaminergic antiparkinsonian drug that can improve "wearing-off" although response to the treatment varies between individuals. To clarify the genetic basis of zonisamide responsiveness, we conducted a genome-wide association study (GWAS) on 200 PD patients from a placebo-controlled clinical trial, including 67 responders whose "off" time decreased ≥1.5 h after 12 weeks of zonisamide treatment and 133 poor responders. We genotyped and evaluated the association between 611,492 single nucleotide polymorphisms (SNPs) and "off" time reduction. We also performed whole-genome imputation, gene- and pathway-based analyses of GWAS data. For promising SNPs, we examined single-tissue expression quantitative trait loci (eQTL) data in the GTEx database. SNP rs16854023 (Mouse double minute 4, MDM4) showed genome-wide significant association with reduced "off" time (PAdjusted = 4.85 × 10-9). Carriers of responsive genotype showed >7-fold decrease in mean "off" time compared to noncarriers (1.42 h vs 0.19 h; P = 2.71 × 10-7). In silico eQTL data indicated that zonisamide sensitivity is associated with higher MDM4 expression. Among the 37 pathways significantly influencing "off" time, calcium and glutamate signaling have also been associated with anti-epileptic effect of zonisamide. MDM4 encodes a negative regulator of p53. The association between improved motor fluctuation and MDM4 upregulation implies that p53 inhibition may prevent dopaminergic neuron loss and consequent motor symptoms. This is the first genome-wide pharmacogenetics study on antiparkinsonian drug. The findings provide a basis for improved management of "wearing-off" in PD by genotype-guided zonisamide treatment.
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10
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Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges. ENTROPY 2020; 22:e22040427. [PMID: 33286201 PMCID: PMC7516904 DOI: 10.3390/e22040427] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 03/18/2020] [Accepted: 04/03/2020] [Indexed: 12/22/2022]
Abstract
Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.
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11
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Ko G, Kim PG, Cho Y, Jeong S, Kim JY, Kim KH, Lee HY, Han J, Yu N, Ham S, Jang I, Kang B, Shin S, Kim L, Lee SW, Nam D, Kim JF, Kim N, Kim SY, Lee S, Roh TY, Lee B. Bioinformatics services for analyzing massive genomic datasets. Genomics Inform 2020; 18:e8. [PMID: 32224841 PMCID: PMC7120352 DOI: 10.5808/gi.2020.18.1.e8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 03/11/2020] [Indexed: 11/25/2022] Open
Abstract
The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.
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Affiliation(s)
- Gunhwan Ko
- Korea Bioinformation Center (KOBIC), KRIBB, Daejeon 34141, Korea
| | - Pan-Gyu Kim
- Korea Bioinformation Center (KOBIC), KRIBB, Daejeon 34141, Korea
| | - Youngbum Cho
- Genome Editing Research Center, KRIBB, Daejeon 34141, Korea
| | - Seongmun Jeong
- Genome Editing Research Center, KRIBB, Daejeon 34141, Korea
| | - Jae-Yoon Kim
- Genome Editing Research Center, KRIBB, Daejeon 34141, Korea
| | | | - Ho-Yeon Lee
- Genome Editing Research Center, KRIBB, Daejeon 34141, Korea
| | - Jiyeon Han
- Department of BioInformation Science, Ewha Womans University, Seoul 03760, Korea
| | - Namhee Yu
- Department of BioInformation Science, Ewha Womans University, Seoul 03760, Korea
| | - Seokjin Ham
- Department of Life Sciences and Division of Integrative Biosciences & Biotechnology, Pohang University of Science & Technology (POSTECH), Pohang 37673, Korea
| | - Insoon Jang
- Department of Life Sciences and Division of Integrative Biosciences & Biotechnology, Pohang University of Science & Technology (POSTECH), Pohang 37673, Korea
| | - Byunghee Kang
- Department of Life Sciences and Division of Integrative Biosciences & Biotechnology, Pohang University of Science & Technology (POSTECH), Pohang 37673, Korea
| | - Sunguk Shin
- Department of Systems, Biology Division of Life Sciences, and Institute for Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Lian Kim
- Bioposh Inc., Daejeon 34016, Korea
| | | | - Dougu Nam
- School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
| | - Jihyun F Kim
- Department of Systems, Biology Division of Life Sciences, and Institute for Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea.,Strategic Initiative for Microbiomes in Agriculture and Food, Yonsei University, Seoul 03722, Korea
| | - Namshin Kim
- Genome Editing Research Center, KRIBB, Daejeon 34141, Korea
| | - Seon-Young Kim
- Genome Structure Research Center, KRIBB, Daejeon 34141, Korea
| | - Sanghyuk Lee
- Department of BioInformation Science, Ewha Womans University, Seoul 03760, Korea
| | - Tae-Young Roh
- Department of Life Sciences and Division of Integrative Biosciences & Biotechnology, Pohang University of Science & Technology (POSTECH), Pohang 37673, Korea.,SysGenLab Inc., Pohang 37613, Korea
| | - Byungwook Lee
- Korea Bioinformation Center (KOBIC), KRIBB, Daejeon 34141, Korea
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12
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Ma Y, Li J, Xu Y, Wang Y, Yao Y, Liu Q, Wang M, Zhao X, Fan R, Chen J, Zhang B, Cai Z, Han H, Yang Z, Yuan W, Zhong Y, Chen X, Ma JZ, Payne TJ, Xu Y, Ning Y, Cui W, Li MD. Identification of 34 genes conferring genetic and pharmacological risk for the comorbidity of schizophrenia and smoking behaviors. Aging (Albany NY) 2020; 12:2169-2225. [PMID: 32012119 PMCID: PMC7041787 DOI: 10.18632/aging.102735] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 01/02/2020] [Indexed: 12/13/2022]
Abstract
The prevalence of smoking is significantly higher in persons with schizophrenia (SCZ) than in the general population. However, the biological mechanisms of the comorbidity of smoking and SCZ are largely unknown. This study aimed to reveal shared biological pathways for the two diseases by analyzing data from two genome-wide association studies with a total sample size of 153,898. With pathway-based analysis, we first discovered 18 significantly enriched pathways shared by SCZ and smoking, which were classified into five groups: postsynaptic density, cadherin binding, dendritic spine, long-term depression, and axon guidance. Then, by using an integrative analysis of genetic, epigenetic, and expression data, we found not only 34 critical genes (e.g., PRKCZ, ARHGEF3, and CDKN1A) but also various risk-associated SNPs in these genes, which convey susceptibility to the comorbidity of the two disorders. Finally, using both in vivo and in vitro data, we demonstrated that the expression profiles of the 34 genes were significantly altered by multiple psychotropic drugs. Together, this multi-omics study not only reveals target genes for new drugs to treat SCZ but also reveals new insights into the shared genetic vulnerabilities of SCZ and smoking behaviors.
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Affiliation(s)
- Yunlong Ma
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yi Xu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yan Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yinghao Yao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qiang Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Maiqiu Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xinyi Zhao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Rongli Fan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Chen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bin Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhen Cai
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haijun Han
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhongli Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenji Yuan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yigang Zhong
- Department of Cardiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiangning Chen
- Institute of Personalized Medicine, University of Nevada at Las Vegas, Las Vegas, NV 89154, USA
| | - Jennie Z Ma
- , Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22904, USA
| | - Thomas J Payne
- Department of Otolaryngology and Communicative Sciences, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Yizhou Xu
- Department of Cardiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wenyan Cui
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ming D Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Research Center for Air Pollution and Health, Zhejiang University, Hangzhou, China
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13
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Shen L, Thompson PM. Brain Imaging Genomics: Integrated Analysis and Machine Learning. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:125-162. [PMID: 31902950 PMCID: PMC6941751 DOI: 10.1109/jproc.2019.2947272] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.
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Affiliation(s)
- Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90232, USA
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14
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He T, Hill CB, Angessa TT, Zhang XQ, Chen K, Moody D, Telfer P, Westcott S, Li C. Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:5603-5616. [PMID: 31504706 PMCID: PMC6812734 DOI: 10.1093/jxb/erz332] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 08/13/2019] [Indexed: 05/10/2023]
Abstract
Single-marker genome-wide association studies (GWAS) have successfully detected associations between single nucleotide polymorphisms (SNPs) and agronomic traits such as flowering time and grain yield in barley. However, the analysis of individual SNPs can only account for a small proportion of genetic variation, and can only provide limited knowledge on gene network interactions. Gene-based GWAS approaches provide enormous opportunity both to combine genetic information and to examine interactions among genetic variants. Here, we revisited a previously published phenotypic and genotypic data set of 895 barley varieties grown in two years at four different field locations in Australia. We employed statistical models to examine gene-phenotype associations, as well as two-way epistasis analyses to increase the capability to find novel genes that have significant roles in controlling flowering time in barley. Genetic associations were tested between flowering time and corresponding genotypes of 174 putative flowering time-related genes. Gene-phenotype association analysis detected 113 genes associated with flowering time in barley, demonstrating the unprecedented power of gene-based analysis. Subsequent two-way epistasis analysis revealed 19 pairs of gene×gene interactions involved in controlling flowering time. Our study demonstrates that gene-based association approaches can provide higher capacity for future crop improvement to increase crop performance and adaptation to different environments.
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Affiliation(s)
- Tianhua He
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Camilla Beate Hill
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Tefera Tolera Angessa
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Xiao-Qi Zhang
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Kefei Chen
- SAGI-WEST, Faculty of Science and Engineering, Curtin University, Bentley, WA, Australia
| | | | - Paul Telfer
- Australian Grain Technologies Pty Ltd (AGT), SA, Australia
| | - Sharon Westcott
- Agriculture and Food, Department of Primary Industries and Regional Development, South Perth, WA, Australia
| | - Chengdao Li
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
- Agriculture and Food, Department of Primary Industries and Regional Development, South Perth, WA, Australia
- Hubei Collaborative Innovation Centre for Grain Industry, Yangtze University, Hubei Jingzhou, China
- Correspondence:
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15
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Mora A. Gene set analysis methods for the functional interpretation of non-mRNA data—Genomic range and ncRNA data. Brief Bioinform 2019; 21:1495-1508. [DOI: 10.1093/bib/bbz090] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 05/30/2019] [Accepted: 06/28/2019] [Indexed: 12/31/2022] Open
Abstract
Abstract
Gene set analysis (GSA) is one of the methods of choice for analyzing the results of current omics studies; however, it has been mainly developed to analyze mRNA (microarray, RNA-Seq) data. The following review includes an update regarding general methods and resources for GSA and then emphasizes GSA methods and tools for non-mRNA omics datasets, specifically genomic range data (ChIP-Seq, SNP and methylation) and ncRNA data (miRNAs, lncRNAs and others). In the end, the state of the GSA field for non-mRNA datasets is discussed, and some current challenges and trends are highlighted, especially the use of network approaches to face complexity issues.
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Affiliation(s)
- Antonio Mora
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health - Chinese Academy of Sciences
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16
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Horgusluoglu-Moloch E, Risacher SL, Crane PK, Hibar D, Thompson PM, Saykin AJ, Nho K. Genome-wide association analysis of hippocampal volume identifies enrichment of neurogenesis-related pathways. Sci Rep 2019; 9:14498. [PMID: 31601890 PMCID: PMC6787090 DOI: 10.1038/s41598-019-50507-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 09/09/2019] [Indexed: 01/04/2023] Open
Abstract
Adult neurogenesis occurs in the dentate gyrus of the hippocampus during adulthood and contributes to sustaining the hippocampal formation. To investigate whether neurogenesis-related pathways are associated with hippocampal volume, we performed gene-set enrichment analysis using summary statistics from a large-scale genome-wide association study (N = 13,163) of hippocampal volume from the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium and two year hippocampal volume changes from baseline in cognitively normal individuals from Alzheimer's Disease Neuroimaging Initiative Cohort (ADNI). Gene-set enrichment analysis of hippocampal volume identified 44 significantly enriched biological pathways (FDR corrected p-value < 0.05), of which 38 pathways were related to neurogenesis-related processes including neurogenesis, generation of new neurons, neuronal development, and neuronal migration and differentiation. For genes highly represented in the significantly enriched neurogenesis-related pathways, gene-based association analysis identified TESC, ACVR1, MSRB3, and DPP4 as significantly associated with hippocampal volume. Furthermore, co-expression network-based functional analysis of gene expression data in the hippocampal subfields, CA1 and CA3, from 32 normal controls showed that distinct co-expression modules were mostly enriched in neurogenesis related pathways. Our results suggest that neurogenesis-related pathways may be enriched for hippocampal volume and that hippocampal volume may serve as a potential phenotype for the investigation of human adult neurogenesis.
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Affiliation(s)
- Emrin Horgusluoglu-Moloch
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.,Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shannon L Risacher
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Paul K Crane
- Department of Medicine, University of Washington, School of Medicine, Seattle, WA, USA
| | - Derrek Hibar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Neuroscience Biomarkers, Janssen Research and Development, LLC, San Diego, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andrew J Saykin
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA. .,Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA. .,Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA. .,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Kwangsik Nho
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA. .,Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA. .,Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA. .,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
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17
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Yao X, Cong S, Yan J, Risacher SL, Saykin AJ, Moore JH, Shen L. Mining Regional Imaging Genetic Associations via Voxel-wise Enrichment Analysis. ... IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS. IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS 2019; 2019. [PMID: 31742256 DOI: 10.1109/bhi.2019.8834450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Brain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful annotation and modest dimensionality compared with voxel-wise approaches. Typical ROI-level measures used in these studies are summary statistics from voxel-wise measures in the region, without making full use of individual voxel signals. In this paper, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxel-wise enrichment analysis, which embraces the collective effect of weak voxel-level signals within an ROI. We demonstrate our method on an imaging genetic analysis using data from the Alzheimers Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxel-wise FDGPET measures between 116 ROIs and 19 AD candidate SNPs. Compared with traditional ROI-wise and voxel-wise approaches, our method identified 102 additional significant associations, some of which were further supported by evidences in brain tissue-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain.
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Affiliation(s)
- Xiaohui Yao
- Biostatistics, Epidemiology and Informatics University of Pennsylvania, Philadelphia, PA
| | - Shan Cong
- Electrical and Computer Engineering Purdue University, West Lafayette, IN
| | - Jingwen Yan
- Informatics and Computing Indiana University, Indianapolis, IN
| | - Shannon L Risacher
- Radiology and Imaging Sciences Indiana University School of Medicine, Indianapolis, IN
| | - Andrew J Saykin
- Radiology and Imaging Sciences Indiana University School of Medicine, Indianapolis, IN
| | - Jason H Moore
- Biostatistics, Epidemiology and Informatics University of Pennsylvania, Philadelphia, PA
| | - Li Shen
- Biostatistics, Epidemiology and Informatics University of Pennsylvania, Philadelphia, PA
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18
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Yoon S, Nguyen HCT, Yoo YJ, Kim J, Baik B, Kim S, Kim J, Kim S, Nam D. Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2. Nucleic Acids Res 2019; 46:e60. [PMID: 29562348 PMCID: PMC6007455 DOI: 10.1093/nar/gky175] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 03/13/2018] [Indexed: 01/19/2023] Open
Abstract
Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2.
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Affiliation(s)
- Sora Yoon
- School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
| | - Hai C T Nguyen
- School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
| | - Yun J Yoo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, South Korea.,Department of Mathematics Education, Seoul National University, Seoul 08826, Republic of Korea
| | - Jinhwan Kim
- School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
| | - Bukyung Baik
- School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
| | - Sounkou Kim
- School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
| | - Jin Kim
- SK Telecom, Seoul 04539, Republic of Korea
| | - Sangsoo Kim
- School of Systems Biomedical Science, Soongsil University, Seoul 06978, Republic of Korea
| | - Dougu Nam
- School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.,Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
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19
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Tiensuu H, Haapalainen AM, Karjalainen MK, Pasanen A, Huusko JM, Marttila R, Ojaniemi M, Muglia LJ, Hallman M, Rämet M. Risk of spontaneous preterm birth and fetal growth associates with fetal SLIT2. PLoS Genet 2019; 15:e1008107. [PMID: 31194736 PMCID: PMC6563950 DOI: 10.1371/journal.pgen.1008107] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/18/2019] [Indexed: 12/13/2022] Open
Abstract
Spontaneous preterm birth (SPTB) is the leading cause of neonatal death and morbidity worldwide. Both maternal and fetal genetic factors likely contribute to SPTB. We performed a genome-wide association study (GWAS) on a population of Finnish origin that included 247 infants with SPTB (gestational age [GA] < 36 weeks) and 419 term controls (GA 38-41 weeks). The strongest signal came within the gene encoding slit guidance ligand 2 (SLIT2; rs116461311, minor allele frequency 0.05, p = 1.6×10-6). Pathway analysis revealed the top-ranking pathway was axon guidance, which includes SLIT2. In 172 very preterm-born infants (GA <32 weeks), rs116461311 was clearly overrepresented (odds ratio 4.06, p = 1.55×10-7). SLIT2 variants were associated with SPTB in another European population that comprised 260 very preterm infants and 9,630 controls. To gain functional insight, we used immunohistochemistry to visualize SLIT2 and its receptor ROBO1 in placentas from spontaneous preterm and term births. Both SLIT2 and ROBO1 were located in villous and decidual trophoblasts of embryonic origin. Based on qRT-PCR, the mRNA levels of SLIT2 and ROBO1 were higher in the basal plate of SPTB placentas compared to those from term or elective preterm deliveries. In addition, in spontaneous term and preterm births, placental SLIT2 expression was correlated with variations in fetal growth. Knockdown of ROBO1 in trophoblast-derived HTR8/SVneo cells by siRNA indicated that it regulate expression of several pregnancy-specific beta-1-glycoprotein (PSG) genes and genes involved in inflammation. Our results show that the fetal SLIT2 variant and both SLIT2 and ROBO1 expression in placenta and trophoblast cells may be correlated with susceptibility to SPTB. SLIT2-ROBO1 signaling was linked with regulation of genes involved in inflammation, PSG genes, decidualization and fetal growth. We propose that this receptor-ligand couple is a component of the signaling network that promotes SPTB.
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Affiliation(s)
- Heli Tiensuu
- PEDEGO Research Unit, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Antti M. Haapalainen
- PEDEGO Research Unit, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Minna K. Karjalainen
- PEDEGO Research Unit, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Anu Pasanen
- PEDEGO Research Unit, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Johanna M. Huusko
- PEDEGO Research Unit, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
- Division of Human Genetics, Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, March of Dimes Prematurity Research Center Ohio Collaborative, Cincinnati, Ohio, United States of America
| | - Riitta Marttila
- PEDEGO Research Unit, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Marja Ojaniemi
- PEDEGO Research Unit, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Louis J. Muglia
- Division of Human Genetics, Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, March of Dimes Prematurity Research Center Ohio Collaborative, Cincinnati, Ohio, United States of America
| | - Mikko Hallman
- PEDEGO Research Unit, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Mika Rämet
- PEDEGO Research Unit, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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20
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Jiao H, Zang Y, Zhang M, Zhang Y, Wang Y, Wang K, Price RA, Li WD. Genome-Wide Interaction and Pathway Association Studies for Body Mass Index. Front Genet 2019; 10:404. [PMID: 31118946 PMCID: PMC6504780 DOI: 10.3389/fgene.2019.00404] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Accepted: 04/12/2019] [Indexed: 11/24/2022] Open
Abstract
Objective: We investigated gene interactions (epistasis) for body mass index (BMI) in a European-American adult female cohort via genome-wide interaction analyses (GWIA) and pathway association analyses. Methods: Genome-wide pairwise interaction analyses were carried out for BMI in 493 extremely obese cases (BMI > 35 kg/m2) and 537 never-overweight controls (BMI < 25 kg/m2). To further validate the results, specific SNPs were selected based on the GWIA results for haplotype-based association studies. Pathway-based association analyses were performed using a modified Gene Set Enrichment Algorithm (GSEA) (GenGen program) to further explore BMI-related pathways using our genome wide association study (GWAS) data set, GIANT, ENGAGE, and DIAGRAM Consortia. Results: The EXOC4-1q23.1 interaction was associated with BMI, with the most significant epistasis between rs7800006 and rs10797020 (P = 2.63 × 10-11). In the pathway-based association analysis, Tob1 pathway showed the most significant association with BMI (empirical P < 0.001, FDR = 0.044, FWER = 0.040). These findings were further validated in different populations. Conclusion: Genome-wide pairwise SNP-SNP interaction and pathway analyses suggest that EXOC4 and TOB1-related pathways may contribute to the development of obesity.
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Affiliation(s)
- Hongxiao Jiao
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Yong Zang
- Department of Genetics, College of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Miaomiao Zhang
- Department of Genetics, College of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Yuan Zhang
- Department of Genetics, College of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Yaogang Wang
- College of Public Health, Tianjin Medical University, Tianjin, China
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Laboratory Medicine, Department of Pathology, University of Pennsylvania, Philadelphia, PA, United States
| | - R. Arlen Price
- Department of Psychiatry, Center for Neurobiology and Behavior, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Wei-Dong Li
- Department of Genetics, College of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
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21
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White MJ, Yaspan BL, Veatch OJ, Goddard P, Risse-Adams OS, Contreras MG. Strategies for Pathway Analysis Using GWAS and WGS Data. ACTA ACUST UNITED AC 2018; 100:e79. [PMID: 30387919 DOI: 10.1002/cphg.79] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Single-allele study designs, commonly used in genome-wide association studies (GWAS) as well as the more recently developed whole genome sequencing (WGS) studies, are a standard approach for investigating the relationship of common variation within the human genome to a given phenotype of interest. However, single-allele association results published for many GWAS studies represent only the tip of the iceberg for the information that can be extracted from these datasets. The primary analysis strategy for GWAS entails association analysis in which only the single nucleotide polymorphisms (SNPs) with the strongest p-values are declared statistically significant due to issues arising from multiple testing and type I errors. Factors such as locus heterogeneity, epistasis, and multiple genes conferring small effects contribute to the complexity of the genetic models underlying phenotype expression. Thus, many biologically meaningful associations having lower effect sizes at individual genes are overlooked, making it difficult to separate true associations from a sea of false-positive associations. Organizing these individual SNPs into biologically meaningful groups to look at the overall effects of minor perturbations to genes and pathways is desirable. This pathway-based approach provides researchers with insight into the functional foundations of the phenotype being studied and allows testing of various genetic scenarios. © 2018 by John Wiley & Sons, Inc.
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Affiliation(s)
- Marquitta J White
- Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, California
| | | | - Olivia J Veatch
- Center for Sleep and Circadian Neurobiology, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Pagé Goddard
- Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, California
| | - Oona S Risse-Adams
- Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, California
| | - Maria G Contreras
- Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, California.,MARC, San Francisco State University, San Francisco, California
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22
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Pasanen A, Karjalainen MK, Kummola L, Waage J, Bønnelykke K, Ruotsalainen M, Piippo-Savolainen E, Goksör E, Nuolivirta K, Chawes B, Vissing N, Bisgaard H, Jartti T, Wennergren G, Junttila I, Hallman M, Korppi M, Rämet M. NKG2D gene variation and susceptibility to viral bronchiolitis in childhood. Pediatr Res 2018; 84:451-457. [PMID: 29967528 DOI: 10.1038/s41390-018-0086-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 05/22/2018] [Accepted: 05/30/2018] [Indexed: 01/28/2023]
Abstract
BACKGROUND Genetic factors associated with bronchiolitis are inadequately characterized. We therefore inspected a selected subpopulation of our previous genome-wide association study (GWAS) of bronchiolitis for overlap with known quantitative trait loci (QTLs) to identify susceptibility loci that potentially affect mRNA and protein levels. METHODS GWAS included a Finnish-Swedish case-control population (n = 187), matched for age and site. We integrated GWAS variants (p < 10-4) with QTL data. We subsequently verified allele-specific expression of identified QTLs by flow cytometry. Association of the resulting candidate loci with bronchiolitis was tested in three additional cohorts from Finland and Denmark (n = 1201). RESULTS Bronchiolitis-susceptibility variant rs10772271 resided within QTLs previously associated with NKG2D (NK group 2, member D) mRNA and protein levels. Flow cytometric analysis confirmed the association with protein level in NK cells. The GWAS susceptibility allele (A) of rs10772271 (odds ratio [OR] = 2.34) corresponded with decreased NKG2D expression. The allele was nominally associated with bronchiolitis in one Finnish replicate (OR = 1.50), and the other showed directional consistency (OR = 1.43). No association was detected in Danish population CONCLUSIONS: The bronchiolitis GWAS susceptibility allele was linked to decreased NKG2D expression in the QTL data and in our expression analysis. We propose that reduced NKG2D expression predisposes infants to severe bronchiolitis.
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Affiliation(s)
- Anu Pasanen
- PEDEGO Research Unit, Medical Research Center Oulu, and Department of Children and Adolescents, University of Oulu, Oulu University Hospital, Oulu, Finland.
| | - Minna K Karjalainen
- PEDEGO Research Unit, Medical Research Center Oulu, and Department of Children and Adolescents, University of Oulu, Oulu University Hospital, Oulu, Finland
| | - Laura Kummola
- Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | - Johannes Waage
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Klaus Bønnelykke
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Marja Ruotsalainen
- Department of Pediatrics, University of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Eija Piippo-Savolainen
- Department of Pediatrics, University of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Emma Goksör
- Department of Pediatrics, University of Gothenburg, Queen Silvia Children's Hospital, Gothenburg, Sweden
| | - Kirsi Nuolivirta
- Department of Pediatrics, Seinäjoki Central Hospital, Seinäjoki, Finland
| | - Bo Chawes
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Nadja Vissing
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Hans Bisgaard
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Tuomas Jartti
- Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland
| | - Göran Wennergren
- Department of Pediatrics, University of Gothenburg, Queen Silvia Children's Hospital, Gothenburg, Sweden
| | - Ilkka Junttila
- Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.,Fimlab Laboratories, Tampere, Finland
| | - Mikko Hallman
- PEDEGO Research Unit, Medical Research Center Oulu, and Department of Children and Adolescents, University of Oulu, Oulu University Hospital, Oulu, Finland
| | - Matti Korppi
- Center for Child Health Research, Tampere University and Tampere University Hospital, Tampere, Finland
| | - Mika Rämet
- PEDEGO Research Unit, Medical Research Center Oulu, and Department of Children and Adolescents, University of Oulu, Oulu University Hospital, Oulu, Finland.,BioMediTech Institute and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33014, Finland
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Ji X, Bossé Y, Landi MT, Gui J, Xiao X, Qian D, Joubert P, Lamontagne M, Li Y, Gorlov I, de Biasi M, Han Y, Gorlova O, Hung RJ, Wu X, McKay J, Zong X, Carreras-Torres R, Christiani DC, Caporaso N, Johansson M, Liu G, Bojesen SE, Le Marchand L, Albanes D, Bickeböller H, Aldrich MC, Bush WS, Tardon A, Rennert G, Chen C, Teare MD, Field JK, Kiemeney LA, Lazarus P, Haugen A, Lam S, Schabath MB, Andrew AS, Shen H, Hong YC, Yuan JM, Bertazzi PA, Pesatori AC, Ye Y, Diao N, Su L, Zhang R, Brhane Y, Leighl N, Johansen JS, Mellemgaard A, Saliba W, Haiman C, Wilkens L, Fernandez-Somoano A, Fernandez-Tardon G, van der Heijden EHFM, Kim JH, Dai J, Hu Z, Davies MPA, Marcus MW, Brunnström H, Manjer J, Melander O, Muller DC, Overvad K, Trichopoulou A, Tumino R, Doherty J, Goodman GE, Cox A, Taylor F, Woll P, Brüske I, Manz J, Muley T, Risch A, Rosenberger A, Grankvist K, Johansson M, Shepherd F, Tsao MS, Arnold SM, Haura EB, Bolca C, Holcatova I, Janout V, Kontic M, Lissowska J, Mukeria A, Ognjanovic S, Orlowski TM, Scelo G, Swiatkowska B, Zaridze D, Bakke P, Skaug V, Zienolddiny S, Duell EJ, Butler LM, Koh WP, Gao YT, Houlston R, McLaughlin J, Stevens V, Nickle DC, Obeidat M, Timens W, Zhu B, Song L, Artigas MS, Tobin MD, Wain LV, Gu F, Byun J, Kamal A, Zhu D, Tyndale RF, Wei WQ, Chanock S, Brennan P, Amos CI. Identification of susceptibility pathways for the role of chromosome 15q25.1 in modifying lung cancer risk. Nat Commun 2018; 9:3221. [PMID: 30104567 PMCID: PMC6089967 DOI: 10.1038/s41467-018-05074-y] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 05/01/2018] [Indexed: 12/20/2022] Open
Abstract
Genome-wide association studies (GWAS) identified the chromosome 15q25.1 locus as a leading susceptibility region for lung cancer. However, the pathogenic pathways, through which susceptibility SNPs within chromosome 15q25.1 affects lung cancer risk, have not been explored. We analyzed three cohorts with GWAS data consisting 42,901 individuals and lung expression quantitative trait loci (eQTL) data on 409 individuals to identify and validate the underlying pathways and to investigate the combined effect of genes from the identified susceptibility pathways. The KEGG neuroactive ligand receptor interaction pathway, two Reactome pathways, and 22 Gene Ontology terms were identified and replicated to be significantly associated with lung cancer risk, with P values less than 0.05 and FDR less than 0.1. Functional annotation of eQTL analysis results showed that the neuroactive ligand receptor interaction pathway and gated channel activity were involved in lung cancer risk. These pathways provide important insights for the etiology of lung cancer.
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Grants
- P30 CA023108 NCI NIH HHS
- P30 CA076292 NCI NIH HHS
- U01 CA063464 NCI NIH HHS
- P50 CA070907 NCI NIH HHS
- R01 CA111703 NCI NIH HHS
- UM1 CA182876 NCI NIH HHS
- UL1 TR000117 NCATS NIH HHS
- P20 CA090578 NCI NIH HHS
- U19 CA148127 NCI NIH HHS
- P20 GM103534 NIGMS NIH HHS
- UL1 TR000445 NCATS NIH HHS
- R01 LM012012 NLM NIH HHS
- R01 CA092824 NCI NIH HHS
- R35 CA197449 NCI NIH HHS
- UM1 CA164973 NCI NIH HHS
- U01 CA167462 NCI NIH HHS
- U19 CA203654 NCI NIH HHS
- R01 CA144034 NCI NIH HHS
- P20 RR018787 NCRR NIH HHS
- S10 RR025141 NCRR NIH HHS
- R01 CA074386 NCI NIH HHS
- R01 CA176568 NCI NIH HHS
- K07 CA172294 NCI NIH HHS
- P50 CA119997 NCI NIH HHS
- G0902313 Medical Research Council
- R01 CA063464 NCI NIH HHS
- P01 CA033619 NCI NIH HHS
- R01 HL133786 NHLBI NIH HHS
- P30 CA177558 NCI NIH HHS
- P50 CA090578 NCI NIH HHS
- U01 HG004798 NHGRI NIH HHS
- R01 CA151989 NCI NIH HHS
- 001 World Health Organization
- 202849/Z/16/Z Wellcome Trust
- UM1 CA167462 NCI NIH HHS
- U01 CA164973 NCI NIH HHS
- This work was supported by National Institutes of Health (NIH) for the research of lung cancer (grant P30CA023108, P20GM103534 and R01LM012012); Trandisciplinary Research in Cancer of the Lung (TRICL) (grant U19CA148127); UICC American Cancer Society Beginning Investigators Fellowship funded by the Union for International Cancer Control (UICC) (to X.Ji). CAPUA study. This work was supported by FIS-FEDER/Spain grant numbers FIS-01/310, FIS-PI03-0365, and FIS-07-BI060604, FICYT/Asturias grant numbers FICYT PB02-67 and FICYT IB09-133, and the University Institute of Oncology (IUOPA), of the University of Oviedo and the Ciber de Epidemiologia y Salud Pública. CIBERESP, SPAIN. The work performed in the CARET study was supported by the The National Institute of Health / National Cancer Institute: UM1 CA167462 (PI: Goodman), National Institute of Health UO1-CA6367307 (PIs Omen, Goodman); National Institute of Health R01 CA111703 (PI Chen), National Institute of Health 5R01 CA151989-01A1(PI Doherty). The Liverpool Lung project is supported by the Roy Castle Lung Cancer Foundation. The Harvard Lung Cancer Study was supported by the NIH (National Cancer Institute) grants CA092824, CA090578, CA074386 The Multiethnic Cohort Study was partially supported by NIH Grants CA164973, CA033619, CA63464 and CA148127 The work performed in MSH-PMH study was supported by The Canadian Cancer Society Research Institute (020214), Ontario Institute of Cancer and Cancer Care Ontario Chair Award to R.J.H. and G.L. and the Alan Brown Chair and Lusi Wong Programs at the Princess Margaret Hospital Foundation. NJLCS was funded by the State Key Program of National Natural Science of China (81230067), the National Key Basic Research Program Grant (2011CB503805), the Major Program of the National Natural Science Foundation of China (81390543). Norway study was supported by Norwegian Cancer Society, Norwegian Research Council The Shanghai Cohort Study (SCS) was supported by National Institutes of Health R01 CA144034 (PI: Yuan) and UM1 CA182876 (PI: Yuan). The Singapore Chinese Health Study (SCHS) was supported by National Institutes of Health R01 CA144034 (PI: Yuan) and UM1 CA182876 (PI: Yuan). The work in TLC study has been supported in part the James & Esther King Biomedical Research Program (09KN-15), National Institutes of Health Specialized Programs of Research Excellence (SPORE) Grant (P50 CA119997), and by a Cancer Center Support Grant (CCSG) at the H. Lee Moffitt Cancer Center and Research Institute, an NCI designated Comprehensive Cancer Center (grant number P30-CA76292) The Vanderbilt Lung Cancer Study – BioVU dataset used for the analyses described was obtained from Vanderbilt University Medical Center’s BioVU, which is supported by institutional funding, the 1S10RR025141-01 instrumentation award, and by the Vanderbilt CTSA grant UL1TR000445 from NCATS/NIH. Dr. Aldrich was supported by NIH/National Cancer Institute K07CA172294 (PI: Aldrich) and Dr. Bush was supported by NHGRI/NIH U01HG004798 (PI: Crawford). The Copenhagen General Population Study (CGPS) was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council and Herlev Hospital. The NELCS study: Grant Number P20RR018787 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). The MDACC study was supported in part by grants from the NIH (P50 CA070907, R01 CA176568) (to X. Wu), Cancer Prevention & Research Institute of Texas (RP130502) (to X. Wu), and The University of Texas MD Anderson Cancer Center institutional support for the Center for Translational and Public Health Genomics. The study in Lodz center was partially funded by Nofer Institute of Occupational Medicine, under task NIOM 10.13: Predictors of mortality from non-small cell lung cancer - field study. Kentucky Lung Cancer Research Initiative was supported by the Department of Defense [Congressionally Directed Medical Research Program, U.S. Army Medical Research and Materiel Command Program] under award number: 10153006 (W81XWH-11-1-0781). Views and opinions of, and endorsements by the author(s) do not reflect those of the US Army or the Department of Defense. This research was also supported by unrestricted infrastructure funds from the UK Center for Clinical and Translational Science, NIH grant UL1TR000117 and Markey Cancer Center NCI Cancer Center Support Grant (P30 CA177558) Shared Resource Facilities: Cancer Research Informatics, Biospecimen and Tissue Procurement, and Biostatistics and Bioinformatics. The Resource for the Study of Lung Cancer Epidemiology in North Trent (ReSoLuCENT) study was funded by the Sheffield Hospitals Charity, Sheffield Experimental Cancer Medicine Centre and Weston Park Hospital Cancer Charity. FT was supported by a clinical PhD fellowship funded by the Yorkshire Cancer Research/Cancer Research UK Sheffield Cancer Centre. The authors would like to thank the staff at the Respiratory Health Network Tissue Bank of the FRQS for their valuable assistance with the lung eQTL dataset at Laval University. The lung eQTL study at Laval University was supported by the Fondation de l’Institut universitaire de cardiologie et de pneumologie de Québec, the Respiratory Health Network of the FRQS, the Canadian Institutes of Health Research (MOP - 123369). Y.B. holds a Canada Research Chair in Genomics of Heart and Lung Diseases. The research undertaken by M.D.T., L.V.W. and M.S.A. was partly funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. M.D.T. holds a Medical Research Council Senior Clinical Fellowship (G0902313).
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Affiliation(s)
- Xuemei Ji
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, 03750, NH, USA
| | - Yohan Bossé
- Department of Molecular Medicine, Laval University, Québec, G1V 4G5, Canada
- Institut universitaire de cardiologie et de pneumologie de Québec, Québec, G1V 4G5, Canada
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Jiang Gui
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, 03750, NH, USA
| | - Xiangjun Xiao
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, 03750, NH, USA
| | - David Qian
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, 03750, NH, USA
| | - Philippe Joubert
- Institut universitaire de cardiologie et de pneumologie de Québec, Québec, G1V 4G5, Canada
| | - Maxime Lamontagne
- Institut universitaire de cardiologie et de pneumologie de Québec, Québec, G1V 4G5, Canada
| | - Yafang Li
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, 03750, NH, USA
| | - Ivan Gorlov
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, 03750, NH, USA
| | - Mariella de Biasi
- Annenberg School of Communication, University of Pennsylvania, Philadelphia, 19104, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Younghun Han
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, 03750, NH, USA
| | - Olga Gorlova
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, 03750, NH, USA
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System and University of Toronto, Toronto, M5T 3L9, Canada
| | - Xifeng Wu
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA
| | - James McKay
- International Agency for Research on Cancer, World Health Organization, Lyon, 69372 CEDEX 08, France
| | - Xuchen Zong
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System and University of Toronto, Toronto, M5T 3L9, Canada
| | - Robert Carreras-Torres
- International Agency for Research on Cancer, World Health Organization, Lyon, 69372 CEDEX 08, France
| | - David C Christiani
- Department of Environmental Health, Harvard School of Public Health, Boston, 02115, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, 02115, MA, USA
| | - Neil Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Mattias Johansson
- International Agency for Research on Cancer, World Health Organization, Lyon, 69372 CEDEX 08, France
| | - Geoffrey Liu
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System and University of Toronto, Toronto, M5T 3L9, Canada
| | - Stig E Bojesen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Herlev 2730, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200 København N, Denmark
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Ringvej 75, Copenhagen, Herlev 2730, Denmark
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, 96813, HI, USA
| | - Demetrios Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, 37073, Germany
| | - Melinda C Aldrich
- Department of Thoracic Surgery, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, 37203, TN, USA
| | - William S Bush
- Department of Thoracic Surgery, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, 37203, TN, USA
- Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Adonina Tardon
- Faculty of Medicine, University of Oviedo, Oviedo, 33006, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, Campus del Cristo s/n, Oviedo, 33006, Spain
| | - Gad Rennert
- Clalit National Cancer Control Center, Carmel Medical Center, Haifa, 34361, Israel
- Faculty of Medicine, Technion, Haifa, 34361, Israel
| | - Chu Chen
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, 98109, WA, USA
| | - M Dawn Teare
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - John K Field
- Roy Castle Lung Cancer Research Programme, Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3BX, UK
| | - Lambertus A Kiemeney
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, 6525 EZ, The Netherlands
| | - Philip Lazarus
- Department of Pharmaceutical Sciences, College of Pharmacy, Washington State University, Spokane, 99210-1495, WA, USA
| | - Aage Haugen
- National Institute of Occupational Health, 0033, Gydas vei 8, 0033, Oslo, Norway
| | - Stephen Lam
- British Columbia Cancer Agency, 675 West 10th Avenue, Vancouver, V5Z1L3, Canada
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, 33612, FL, USA
| | - Angeline S Andrew
- Department of Epidemiology, Geisel School of Medicine, 1 Medical Center Drive, Hanover, 03755, NH, USA
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, 101 Longmian Ave, Nanjing, 211166, PR China
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, 1 Gwanak-ro, Gwanak-gu, Seoul, 151 742, Republic of Korea
| | - Jian-Min Yuan
- University of Pittsburgh Cancer Institute, Pittsburgh, 15232, PA, USA
| | - Pier A Bertazzi
- Department of Preventive Medicine, IRCCS Foundation Ca'Granda Ospedale Maggiore Policlinico, Milan, 20133, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, 20133, Italy
| | - Angela C Pesatori
- Department of Preventive Medicine, IRCCS Foundation Ca'Granda Ospedale Maggiore Policlinico, Milan, 20133, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, 20133, Italy
| | - Yuanqing Ye
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA
| | - Nancy Diao
- Department of Environmental Health, Harvard School of Public Health, Boston, 02115, MA, USA
| | - Li Su
- Department of Environmental Health, Harvard School of Public Health, Boston, 02115, MA, USA
| | - Ruyang Zhang
- Department of Environmental Health, Harvard School of Public Health, Boston, 02115, MA, USA
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, 101 Longmian Ave, Nanjing, 211166, PR China
| | - Yonathan Brhane
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System and University of Toronto, Toronto, M5T 3L9, Canada
| | - Natasha Leighl
- University Health Network-The Princess Margaret Cancer Centre, 600 University Avenue, Toronto, M5G 2C4, Canada
| | - Jakob S Johansen
- Department of Oncology, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, 2730, Denmark
| | - Anders Mellemgaard
- Department of Oncology, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, 2730, Denmark
| | - Walid Saliba
- Clalit National Cancer Control Center, Carmel Medical Center, Haifa, 34361, Israel
- Faculty of Medicine, Technion, Haifa, 34361, Israel
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, 90033, CA, USA
| | - Lynne Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, 96813, HI, USA
| | - Ana Fernandez-Somoano
- Faculty of Medicine, University of Oviedo, Oviedo, 33006, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, Campus del Cristo s/n, Oviedo, 33006, Spain
| | - Guillermo Fernandez-Tardon
- Faculty of Medicine, University of Oviedo, Oviedo, 33006, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, Campus del Cristo s/n, Oviedo, 33006, Spain
| | - Erik H F M van der Heijden
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, 6525 EZ, The Netherlands
| | - Jin Hee Kim
- Department of Integrative Bioscience & Biotechnology, Sejong University, Gwangjin-gu, Seoul, 05029, Republic of Korea
| | - Juncheng Dai
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, 101 Longmian Ave, Nanjing, 211166, PR China
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, 101 Longmian Ave, Nanjing, 211166, PR China
| | - Michael P A Davies
- Roy Castle Lung Cancer Research Programme, Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3BX, UK
| | - Michael W Marcus
- Roy Castle Lung Cancer Research Programme, Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3BX, UK
| | - Hans Brunnström
- Department of Pathology, Lund University, Lund, 222 41, Sweden
| | - Jonas Manjer
- Faculty of Medicine, Lund University, Lund, 22100, Sweden
| | - Olle Melander
- Faculty of Medicine, Lund University, Lund, 22100, Sweden
| | - David C Muller
- School of Public Health, St Mary's Campus, Imperial College London, London, W2 1PG, UK
| | - Kim Overvad
- Faculty of Medicine, Lund University, Lund, 22100, Sweden
| | | | - Rosario Tumino
- Cancer Registry and Histopathology Department, "Civic-M.P. Arezzo" Hospital, ASP, Ragusa, 97100, Italy
| | - Jennifer Doherty
- Department of Epidemiology, Geisel School of Medicine, 1 Medical Center Drive, Hanover, 03755, NH, USA
- Fred Hutchinson Cancer Research Center, Seattle, 98109-1024, WA, USA
| | - Gary E Goodman
- Fred Hutchinson Cancer Research Center, Seattle, 98109-1024, WA, USA
- Swedish Medical Group, Arnold Pavilion, Suite 200, Seattle, 98104, WA, USA
| | - Angela Cox
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, S10 2RX, UK
| | - Fiona Taylor
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, S10 2RX, UK
| | - Penella Woll
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, S10 2RX, UK
| | - Irene Brüske
- Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, D-85764, Germany
| | - Judith Manz
- Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, D-85764, Germany
| | - Thomas Muley
- Thoraxklinik at University Hospital Heidelberg, Heidelberg, 69126, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, 69120, Germany
| | - Angela Risch
- Cancer Cluster Salzburg, University of Salzburg, Salzburg, 5020, Austria
| | - Albert Rosenberger
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, 37073, Germany
| | - Kjell Grankvist
- Department of Medical Biosciences, Umeå University, Umeå, 901 85, Sweden
| | - Mikael Johansson
- Department of Radiation Sciences, Umeå University, Umeå, 901 85, Sweden
| | | | | | - Susanne M Arnold
- Markey Cancer Center, University of Kentucky, First Floor, 800 Rose Street, Lexington, 40508, KY, USA
| | - Eric B Haura
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, 33612, KY, USA
| | - Ciprian Bolca
- Institute of Pneumology "Marius Nasta", Bucharest, RO-050159, Romania
| | - Ivana Holcatova
- 1st Faculty of Medicine, Charles University, Kateřinská 32, Prague, 121 08 Praha 2, Czech Republic
| | - Vladimir Janout
- 1st Faculty of Medicine, Charles University, Kateřinská 32, Prague, 121 08 Praha 2, Czech Republic
| | - Milica Kontic
- Clinical Center of Serbia, Clinic for Pulmonology, School of Medicine, University of Belgrade, Belgrade, 11000, Serbia
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Institute-Oncology Center, Warsaw, 02-781, Poland
| | - Anush Mukeria
- Department of Epidemiology and Prevention, Russian N.N. Blokhin Cancer Research Centre, Moscow, 115478, Russian Federation
| | - Simona Ognjanovic
- International Organization for Cancer Prevention and Research, Belgrade, 11070, Serbia
| | - Tadeusz M Orlowski
- Department of Surgery, National Tuberculosis and Lung Diseases Research Institute, Warsaw, PL-01-138, Poland
| | - Ghislaine Scelo
- International Agency for Research on Cancer, World Health Organization, Lyon, 69372 CEDEX 08, France
| | - Beata Swiatkowska
- Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine, Lodz, 91-348, Poland
| | - David Zaridze
- Department of Epidemiology and Prevention, Russian N.N. Blokhin Cancer Research Centre, Moscow, 115478, Russian Federation
| | - Per Bakke
- Department of Clinical Science, University of Bergen, Bergen, 5021, Norway
| | - Vidar Skaug
- National Institute of Occupational Health, 0033, Gydas vei 8, 0033, Oslo, Norway
| | - Shanbeh Zienolddiny
- National Institute of Occupational Health, 0033, Gydas vei 8, 0033, Oslo, Norway
| | - Eric J Duell
- Unit of Nutrition and Cancer, Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, 08908, Spain
| | - Lesley M Butler
- University of Pittsburgh Cancer Institute, Pittsburgh, 15232, PA, USA
| | - Woon-Puay Koh
- Duke-NUS Medical School, Singapore, 119077, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117549, Singapore
| | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, 2200, China
| | | | | | | | - David C Nickle
- Department of Genetics and Pharmacogenomics, Merck Research Laboratories, Boston, 02115-5727, MA, USA
| | - Ma'en Obeidat
- Centre for Heart Lung Innovation, St Paul's Hospital, The University of British Columbia, Vancouver, V6Z 1Y6, BC, Canada
| | - Wim Timens
- Department of Pathology and Medical Biology, GRIAC, University of Groningen, University Medical Center Groningen, Groningen, NL - 9713 GZ, The Netherlands
| | - Bin Zhu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - María Soler Artigas
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
- Leicester Respiratory Biomedical Research Unit, National Institute for Health Research (NIHR), Glenfield Hospital, Leicester, LE3 9QP, UK
| | - Martin D Tobin
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
- Leicester Respiratory Biomedical Research Unit, National Institute for Health Research (NIHR), Glenfield Hospital, Leicester, LE3 9QP, UK
| | - Louise V Wain
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
- Leicester Respiratory Biomedical Research Unit, National Institute for Health Research (NIHR), Glenfield Hospital, Leicester, LE3 9QP, UK
| | - Fangyi Gu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Jinyoung Byun
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, 03750, NH, USA
| | - Ahsan Kamal
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, 03750, NH, USA
| | - Dakai Zhu
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, 03750, NH, USA
| | - Rachel F Tyndale
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, M5S 1A8, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, M5T 1R8, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, M6J 1H4, ON, Canada
| | - Wei-Qi Wei
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, 37235, USA
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Paul Brennan
- International Agency for Research on Cancer, World Health Organization, Lyon, 69372 CEDEX 08, France
| | - Christopher I Amos
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, 03750, NH, USA.
- The Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, 77030, TX, USA.
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Kim IW, Kim JH, Han N, Kim S, Kim YS, Oh JM. Gene expression profiles for predicting antibody‑mediated kidney allograft rejection: Analysis of GEO datasets. Int J Mol Med 2018; 42:2303-2311. [PMID: 30066908 DOI: 10.3892/ijmm.2018.3798] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 07/24/2018] [Indexed: 11/06/2022] Open
Abstract
Antibody‑mediated rejections (AMRs) are one of the most challenging complications that result in the deterioration of renal allograft function and graft loss in a large majority of cases. The purpose of the present study was to characterize a meta‑signature of differentially expressed RNAs associated with AMR in cases of kidney transplantation. Gene Expression Omnibus (GEO) dataset searches up to September 11, 2017, using Medical Subject Heading terms and keywords associated with kidney transplantation, AMR and mRNA arrays were downloaded from the GEO dataset. Using a computational analysis, a meta‑signature was determined that characterized the significant intersection of differentially expressed genes (DEGs). Gene‑set and network analyses were also performed to identify gene sets and sub‑networks associated with the AMR‑related traits. A statistically significant mRNA meta‑signature of upregulated and downregulated gene expression levels that were significantly associated with AMR was identified. C‑X‑C motif chemokine ligand 10 (CXCL10), CXCL9 and guanylate binding protein 1 were the most significantly associated with AMR. DEGs were efficiently identified and were found to be able to predict the occurrence of AMR according to a meta‑analysis approach from publicly available datasets. These methods and results can be applied for a more accurate diagnosis of AMR in transplant cases.
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Affiliation(s)
- In-Wha Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Jae Hyun Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Nayoung Han
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Sangsoo Kim
- Department of Bioinformatics and Life Science, Soongsil University, Seoul 06978, Republic of Korea
| | - Yon Su Kim
- Kidney Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Jung Mi Oh
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Republic of Korea
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25
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Yao X, Yan J, Liu K, Kim S, Nho K, Risacher SL, Greene CS, Moore JH, Saykin AJ, Shen L. Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules. Bioinformatics 2018; 33:3250-3257. [PMID: 28575147 DOI: 10.1093/bioinformatics/btx344] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 05/26/2017] [Indexed: 11/13/2022] Open
Abstract
Motivation Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. Results We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype. Availability and implementation The R code and sample data are freely available at http://www.iu.edu/shenlab/tools/gwasmodule/. Contact shenli@iu.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaohui Yao
- Department of BioHealth Informatics, Indiana University School of Informatics & Computing, Indianapolis, IN 46202, USA.,Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Jingwen Yan
- Department of BioHealth Informatics, Indiana University School of Informatics & Computing, Indianapolis, IN 46202, USA.,Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Kefei Liu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sungeun Kim
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.,Department of Electrical and Computer Engineering, SUNY Oswego, NY 13126, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Li Shen
- Department of BioHealth Informatics, Indiana University School of Informatics & Computing, Indianapolis, IN 46202, USA.,Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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26
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Mühleisen TW, Reinbold CS, Forstner AJ, Abramova LI, Alda M, Babadjanova G, Bauer M, Brennan P, Chuchalin A, Cruceanu C, Czerski PM, Degenhardt F, Fischer SB, Fullerton JM, Gordon SD, Grigoroiu-Serbanescu M, Grof P, Hauser J, Hautzinger M, Herms S, Hoffmann P, Kammerer-Ciernioch J, Khusnutdinova E, Kogevinas M, Krasnov V, Lacour A, Laprise C, Leber M, Lissowska J, Lucae S, Maaser A, Maier W, Martin NG, Mattheisen M, Mayoral F, McKay JD, Medland SE, Mitchell PB, Moebus S, Montgomery GW, Müller-Myhsok B, Oruc L, Pantelejeva G, Pfennig A, Pojskic L, Polonikov A, Reif A, Rivas F, Rouleau GA, Schenk LM, Schofield PR, Schwarz M, Streit F, Strohmaier J, Szeszenia-Dabrowska N, Tiganov AS, Treutlein J, Turecki G, Vedder H, Witt SH, Schulze TG, Rietschel M, Nöthen MM, Cichon S. Gene set enrichment analysis and expression pattern exploration implicate an involvement of neurodevelopmental processes in bipolar disorder. J Affect Disord 2018; 228:20-25. [PMID: 29197740 DOI: 10.1016/j.jad.2017.11.068] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 10/20/2017] [Accepted: 11/12/2017] [Indexed: 11/22/2022]
Abstract
BACKGROUND Bipolar disorder (BD) is a common and highly heritable disorder of mood. Genome-wide association studies (GWAS) have identified several independent susceptibility loci. In order to extract more biological information from GWAS data, multi-locus approaches represent powerful tools since they utilize knowledge about biological processes to integrate functional sets of genes at strongly to moderately associated loci. METHODS We conducted gene set enrichment analyses (GSEA) using 2.3 million single-nucleotide polymorphisms, 397 Reactome pathways and 24,025 patients with BD and controls. RNA expression of implicated individual genes and gene sets were examined in post-mortem brains across lifespan. RESULTS Two pathways showed a significant enrichment after correction for multiple comparisons in the GSEA: GRB2 events in ERBB2 signaling, for which 6 of 21 genes were BD associated (PFDR = 0.0377), and NCAM signaling for neurite out-growth, for which 11 out of 62 genes were BD associated (PFDR = 0.0451). Most pathway genes showed peaks of RNA co-expression during fetal development and infancy and mapped to neocortical areas and parts of the limbic system. LIMITATIONS Pathway associations were technically reproduced by two methods, although they were not formally replicated in independent samples. Gene expression was explored in controls but not in patients. CONCLUSIONS Pathway analysis in large GWAS data of BD and follow-up of gene expression patterns in healthy brains provide support for an involvement of neurodevelopmental processes in the etiology of this neuropsychiatric disease. Future studies are required to further evaluate the relevance of the implicated genes on pathway functioning and clinical aspects of BD.
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Affiliation(s)
- Thomas W Mühleisen
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Department of Biomedicine & Institute of Medical Genetics and Pathology, Human Genomics Research Group and Division of Medical Genetics, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland
| | - Céline S Reinbold
- Department of Biomedicine & Institute of Medical Genetics and Pathology, Human Genomics Research Group and Division of Medical Genetics, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland
| | - Andreas J Forstner
- Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | - Lilia I Abramova
- Russian Academy of Medical Sciences, Mental Health Research Center, Moscow, Russian Federation
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, Canada; National Institute of Mental Health, Klecany, Czech Republic
| | - Gulja Babadjanova
- Institute of Pulmonology, Russian State Medical University, Moscow, Russian Federation
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Paul Brennan
- Genetic Epidemiology Group, International Agency for Research on Cancer (IARC), Lyon, France
| | - Alexander Chuchalin
- Institute of Pulmonology, Russian State Medical University, Moscow, Russian Federation
| | - Cristiana Cruceanu
- Montreal Neurological Institute, McGill University, Montreal, Canada; Department of Human Genetics, McGill University, Montreal, Canada; McGill Group for Suicide Studies & Douglas Research Institute, Montreal, Canada
| | - Piotr M Czerski
- Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Franziska Degenhardt
- Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | - Sascha B Fischer
- Department of Biomedicine & Institute of Medical Genetics and Pathology, Human Genomics Research Group and Division of Medical Genetics, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland
| | - Janice M Fullerton
- Neuroscience Research Australia, Sydney, Australia; School of Medical Sciences Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Scott D Gordon
- Queensland Institute of Medical Research (QIMR), Brisbane, Australia
| | - Maria Grigoroiu-Serbanescu
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania
| | - Paul Grof
- The International Group for the Study of Lithium-Treated Patients (IGSLI), Berlin, Germany; Mood Disorders Center of Ottawa, Ottawa, Ontario, Canada K1G 4G3; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada M5T 1R8
| | - Joanna Hauser
- Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Martin Hautzinger
- Department of Psychology, Clinical Psychology and Psychotherapy, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Stefan Herms
- Department of Biomedicine & Institute of Medical Genetics and Pathology, Human Genomics Research Group and Division of Medical Genetics, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland; Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | - Per Hoffmann
- Department of Biomedicine & Institute of Medical Genetics and Pathology, Human Genomics Research Group and Division of Medical Genetics, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland; Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | | | - Elza Khusnutdinova
- Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Ufa, Russian Federation; Department of Genetics and Fundamental Medicine of Bashkir State University, Ufa, Russian Federation
| | - Manolis Kogevinas
- Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
| | - Valery Krasnov
- Moscow Research Institute of Psychiatry, Moscow, Russian Federation
| | - André Lacour
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Catherine Laprise
- Département des sciences fondamentales, Université du Québec à Chicoutimi, Saguenay, QC, Canada; Centre intégré universitaire de santé et services sociaux du Saguenay-Lac-Saint-Jean, Saguenay, Québec, Canada
| | - Markus Leber
- Department of Psychiatry & Psychotherapy, University of Cologne, Cologne, Germany
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, Maria Sklodowska-Curie Memorial Cancer Centre and Institute of Oncology Warsaw, Warsaw, Poland
| | | | - Anna Maaser
- Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | - Wolfgang Maier
- Department of Psychiatry, University of Bonn, Bonn, Germany
| | - Nicholas G Martin
- Queensland Institute of Medical Research (QIMR), Brisbane, Australia
| | - Manuel Mattheisen
- Department of Biomedicine and Centre for integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark; The Lundbeck Foundation Initiative for integrative Psychiatric Research, iPSYCH, Aarhus and Copenhagen, Denmark
| | - Fermin Mayoral
- Department of Psychiatry, Hospital Regional Universitario, Biomedical Institute of Malaga, Malaga, Spain
| | - James D McKay
- Genetic Cancer Susceptibility Group, International Agency for Research on Cancer (IARC), Lyon, France
| | - Sarah E Medland
- Queensland Institute of Medical Research (QIMR), Brisbane, Australia
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, Randwick, Australia; Black Dog Institute, Prince of Wales Hospital, Randwick, Australia
| | - Susanne Moebus
- Institute of Medical Informatics, Biometry and Epidemiology, University Duisburg-Essen, Essen, Germany
| | | | - Bertram Müller-Myhsok
- Max Planck Institute of Psychiatry, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; University of Liverpool, Institute of Translational Medicine, Liverpool, United Kingdom
| | - Lilijana Oruc
- Psychiatric Clinic, Clinical Center University of Sarajevo, Bolnička 25, 71000 Sarajevo, Bosnia and Herzegovina
| | - Galina Pantelejeva
- Russian Academy of Medical Sciences, Mental Health Research Center, Moscow, Russian Federation
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Lejla Pojskic
- Institute for Genetic Engineering and Biotechnology, University of Sarajevo, Zmaja od Bosne 8 - Campus, 71000 Sarajevo, Bosnia and Herzegovina
| | - Alexey Polonikov
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, Kursk, Russian Federation; Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, Kursk, Russian Federation
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt am Main, Frankfurt am Main, Germany
| | - Fabio Rivas
- Department of Psychiatry, Hospital Regional Universitario, Biomedical Institute of Malaga, Malaga, Spain
| | - Guy A Rouleau
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Lorena M Schenk
- Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, Australia; School of Medical Sciences Faculty of Medicine, University of New South Wales, Sydney, Australia
| | | | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany
| | - Jana Strohmaier
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany
| | | | - Alexander S Tiganov
- Russian Academy of Medical Sciences, Mental Health Research Center, Moscow, Russian Federation
| | - Jens Treutlein
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany
| | - Gustavo Turecki
- Department of Human Genetics, McGill University, Montreal, Canada; McGill Group for Suicide Studies & Douglas Research Institute, Montreal, Canada; Department of Psychiatry, McGill University, Montreal, Canada
| | | | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | - Sven Cichon
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Department of Biomedicine & Institute of Medical Genetics and Pathology, Human Genomics Research Group and Division of Medical Genetics, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland; Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany.
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27
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Baillie JK, Bretherick A, Haley CS, Clohisey S, Gray A, Neyton LPA, Barrett J, Stahl EA, Tenesa A, Andersson R, Brown JB, Faulkner GJ, Lizio M, Schaefer U, Daub C, Itoh M, Kondo N, Lassmann T, Kawai J, Mole D, Bajic VB, Heutink P, Rehli M, Kawaji H, Sandelin A, Suzuki H, Satsangi J, Wells CA, Hacohen N, Freeman TC, Hayashizaki Y, Carninci P, Forrest ARR, Hume DA. Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease. PLoS Comput Biol 2018; 14:e1005934. [PMID: 29494619 PMCID: PMC5849332 DOI: 10.1371/journal.pcbi.1005934] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 03/13/2018] [Accepted: 12/18/2017] [Indexed: 12/13/2022] Open
Abstract
Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn's disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.
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Affiliation(s)
- J. Kenneth Baillie
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
- Intensive Care Unit, Royal Infirmary Edinburgh, Edinburgh, United Kingdom
| | - Andrew Bretherick
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Christopher S. Haley
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
- Institute for Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Sara Clohisey
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Alan Gray
- Edinburgh Parallel Computing Centre, The University of Edinburgh, Edinburgh, United Kingdom
| | - Lucile P. A. Neyton
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Jeffrey Barrett
- Statistical Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Eli A. Stahl
- Center for Statistical Genetics, Icahn School of Medicine at Mount Sinai, New York, United States of America
| | - Albert Tenesa
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Robin Andersson
- The Bioinformatics Centre, Department of Biology & Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark
| | - J. Ben Brown
- Department of Statistics, University of California, Berkeley, United States of America
| | - Geoffrey J. Faulkner
- Mater Research Institute, University of Queensland, University of Queensland, Brisbane, Australia
| | - Marina Lizio
- RIKEN Omics Science Center, Yokohama, Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan
| | - Ulf Schaefer
- Department for Infectious Disease Informatics, Public Health England, Colindale, United Kingdom
| | - Carsten Daub
- RIKEN Omics Science Center, Yokohama, Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan
| | - Masayoshi Itoh
- RIKEN Omics Science Center, Yokohama, Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Japan
| | - Naoto Kondo
- RIKEN Omics Science Center, Yokohama, Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan
| | - Timo Lassmann
- RIKEN Omics Science Center, Yokohama, Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan
| | - Jun Kawai
- RIKEN Omics Science Center, Yokohama, Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan
| | - IIBDGC Consortium
- Harry Perkins Institute of Medical Research, and the Centre for Medical Research, University of Western Australia, QEII Medical Centre, Nedlands, Perth, Western Australia, Australia
| | - Damian Mole
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Vladimir B. Bajic
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center, Thuwal, Kingdom of Saudi Arabia
| | - Peter Heutink
- German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Michael Rehli
- Dept. Hematology, University Hospital Regensburg, Regensburg, Germany
| | - Hideya Kawaji
- RIKEN Omics Science Center, Yokohama, Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Japan
| | - Albin Sandelin
- The Bioinformatics Centre, Department of Biology & Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark
| | - Harukazu Suzuki
- RIKEN Omics Science Center, Yokohama, Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan
| | - Jack Satsangi
- Institute for Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Christine A. Wells
- Australian Institute for Bioengineering and Nanotechnology, University of Queensland, St Lucia, Brisbane Australia
| | - Nir Hacohen
- Broad Institute of Harvard and MIT, Cambridge, United States of America
| | - Thomas C. Freeman
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Yoshihide Hayashizaki
- RIKEN Omics Science Center, Yokohama, Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan
| | - Piero Carninci
- RIKEN Omics Science Center, Yokohama, Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan
| | - Alistair R. R. Forrest
- Harry Perkins Institute of Medical Research, and the Centre for Medical Research, University of Western Australia, QEII Medical Centre, Nedlands, Perth, Western Australia, Australia
| | - David A. Hume
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
- Mater Research Institute, University of Queensland, University of Queensland, Brisbane, Australia
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28
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Nature vs. nurture in human sociality: multi-level genomic analyses of social conformity. J Hum Genet 2018; 63:605-619. [DOI: 10.1038/s10038-018-0418-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Revised: 12/14/2017] [Accepted: 01/16/2018] [Indexed: 11/08/2022]
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29
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Srivastava PK, Bagnati M, Delahaye-Duriez A, Ko JH, Rotival M, Langley SR, Shkura K, Mazzuferi M, Danis B, van Eyll J, Foerch P, Behmoaras J, Kaminski RM, Petretto E, Johnson MR. Genome-wide analysis of differential RNA editing in epilepsy. Genome Res 2018; 27:440-450. [PMID: 28250018 PMCID: PMC5340971 DOI: 10.1101/gr.210740.116] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Accepted: 01/10/2017] [Indexed: 02/03/2023]
Abstract
The recoding of genetic information through RNA editing contributes to proteomic diversity, but the extent and significance of RNA editing in disease is poorly understood. In particular, few studies have investigated the relationship between RNA editing and disease at a genome-wide level. Here, we developed a framework for the genome-wide detection of RNA sites that are differentially edited in disease. Using RNA-sequencing data from 100 hippocampi from mice with epilepsy (pilocarpine–temporal lobe epilepsy model) and 100 healthy control hippocampi, we identified 256 RNA sites (overlapping with 87 genes) that were significantly differentially edited between epileptic cases and controls. The degree of differential RNA editing in epileptic mice correlated with frequency of seizures, and the set of genes differentially RNA-edited between case and control mice were enriched for functional terms highly relevant to epilepsy, including “neuron projection” and “seizures.” Genes with differential RNA editing were preferentially enriched for genes with a genetic association to epilepsy. Indeed, we found that they are significantly enriched for genes that harbor nonsynonymous de novo mutations in patients with epileptic encephalopathy and for common susceptibility variants associated with generalized epilepsy. These analyses reveal a functional convergence between genes that are differentially RNA-edited in acquired symptomatic epilepsy and those that contribute risk for genetic epilepsy. Taken together, our results suggest a potential role for RNA editing in the epileptic hippocampus in the occurrence and severity of epileptic seizures.
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Affiliation(s)
| | - Marta Bagnati
- Centre for Complement and Inflammation Research (CCIR), Imperial College London, London W12 0NN, United Kingdom
| | - Andree Delahaye-Duriez
- Division of Brain Sciences, Imperial College Faculty of Medicine, London W12 0NN, United Kingdom
| | - Jeong-Hun Ko
- Centre for Complement and Inflammation Research (CCIR), Imperial College London, London W12 0NN, United Kingdom
| | - Maxime Rotival
- Institut Pasteur, Unit of Human Evolutionary Genetics, Paris 75015, France
| | - Sarah R Langley
- Duke-NUS Medical School, Singapore 169857, Republic of Singapore
| | - Kirill Shkura
- Division of Brain Sciences, Imperial College Faculty of Medicine, London W12 0NN, United Kingdom
| | | | | | | | - Patrik Foerch
- Neuroscience TA, UCB Pharma, 1420 Braine-l'Alleud, Belgium
| | - Jacques Behmoaras
- Centre for Complement and Inflammation Research (CCIR), Imperial College London, London W12 0NN, United Kingdom
| | | | - Enrico Petretto
- Duke-NUS Medical School, Singapore 169857, Republic of Singapore
| | - Michael R Johnson
- Division of Brain Sciences, Imperial College Faculty of Medicine, London W12 0NN, United Kingdom
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30
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Primes G, Fieder M. Real-life helping behaviours in North America: A genome-wide association approach. PLoS One 2018; 13:e0190950. [PMID: 29324852 PMCID: PMC5764334 DOI: 10.1371/journal.pone.0190950] [Citation(s) in RCA: 4] [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: 05/25/2017] [Accepted: 12/24/2017] [Indexed: 11/21/2022] Open
Abstract
In humans, prosocial behaviour is essential for social functioning. Twin studies suggest this distinct human trait to be partly hardwired. In the last decade research on the genetics of prosocial behaviour focused on neurotransmitters and neuropeptides, such as oxytocin, dopamine, and their respective pathways. Recent trends towards large scale medical studies targeting the genetic basis of complex diseases such as Alzheimer’s disease and schizophrenia pave the way for new directions also in behavioural genetics. Based on data from 10,713 participants of the American Health and Retirement Study we estimated heritability of helping behaviour–its total variance explained by 1.2 million single nucleotide polymorphisms–to be 11%. Both, fixed models and mixed linear models identified rs11697300, an intergene variant on chromosome 20, as a candidate variant moderating this particular helping behaviour. We assume that this so far undescribed area is worth further investigation in association with human prosocial behaviour.
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Affiliation(s)
- Georg Primes
- Department of Anthropology, University of Vienna, Vienna, Austria
- * E-mail:
| | - Martin Fieder
- Department of Anthropology, University of Vienna, Vienna, Austria
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31
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Szefer E, Lu D, Nathoo F, Beg MF, Graham J. Multivariate association between single-nucleotide polymorphisms in Alzgene linkage regions and structural changes in the brain: discovery, refinement and validation. Stat Appl Genet Mol Biol 2017; 16:349-365. [PMID: 29091582 PMCID: PMC9008768 DOI: 10.1515/sagmb-2016-0077] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
AbstractUsing publicly-available data from the Alzheimer’s Disease Neuroimaging Initiative, we investigate the joint association between single-nucleotide polymorphisms (SNPs) in previously established linkage regions for Alzheimer’s disease (AD) and rates of decline in brain structure. In an initial, discovery stage of analysis, we applied a weighted
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Affiliation(s)
- Elena Szefer
- Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Dr, Burnaby, BC V5A 1S6, Canada
| | - Donghuan Lu
- School of Engineering Science, Simon Fraser University, 8888 University Dr, Burnaby, BC V5A 1S6, Canada
| | - Farouk Nathoo
- Department of Mathematics and Statistics, University of Victoria, PO Box 1700 STN CSC Victoria, BC V8W 2Y2, Canada
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, 8888 University Dr, Burnaby, BC V5A 1S6, Canada
| | - Jinko Graham
- Corresponding author: Jinko Graham, Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Dr, Burnaby, BC V5A 1S6, Canada,
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32
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Deters KD, Nho K, Risacher SL, Kim S, Ramanan VK, Crane PK, Apostolova LG, Saykin AJ. Genome-wide association study of language performance in Alzheimer's disease. BRAIN AND LANGUAGE 2017; 172:22-29. [PMID: 28577822 PMCID: PMC5583024 DOI: 10.1016/j.bandl.2017.04.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 04/25/2017] [Accepted: 04/27/2017] [Indexed: 05/04/2023]
Abstract
Language impairment is common in prodromal stages of Alzheimer's disease (AD) and progresses over time. However, the genetic architecture underlying language performance is poorly understood. To identify novel genetic variants associated with language performance, we analyzed brain MRI and performed a genome-wide association study (GWAS) using a composite measure of language performance from the Alzheimer's Disease Neuroimaging Initiative (ADNI; n=1560). The language composite score was associated with brain atrophy on MRI in language and semantic areas. GWAS identified GLI3 (GLI family zinc finger 3) as significantly associated with language performance (p<5×10-8). Enrichment of GWAS association was identified in pathways related to nervous system development and glutamate receptor function and trafficking. Our results, which warrant further investigation in independent and larger cohorts, implicate GLI3, a developmental transcription factor involved in patterning brain structures, as a putative gene associated with language dysfunction in AD.
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Affiliation(s)
- Kacie D Deters
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA; Program in Medical Neuroscience, Paul and Carole Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kwangsik Nho
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shannon L Risacher
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sungeun Kim
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Vijay K Ramanan
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Paul K Crane
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Liana G Apostolova
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medicine, University of Washington, Seattle, WA, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medicine, University of Washington, Seattle, WA, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
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33
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Jo K, Jung I, Moon JH, Kim S. Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways. Bioinformatics 2017; 32:i128-i136. [PMID: 27307609 PMCID: PMC4908359 DOI: 10.1093/bioinformatics/btw275] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Motivation: To understand the dynamic nature of the biological process, it is crucial to identify perturbed pathways in an altered environment and also to infer regulators that trigger the response. Current time-series analysis methods, however, are not powerful enough to identify perturbed pathways and regulators simultaneously. Widely used methods include methods to determine gene sets such as differentially expressed genes or gene clusters and these genes sets need to be further interpreted in terms of biological pathways using other tools. Most pathway analysis methods are not designed for time series data and they do not consider gene-gene influence on the time dimension. Results: In this article, we propose a novel time-series analysis method TimeTP for determining transcription factors (TFs) regulating pathway perturbation, which narrows the focus to perturbed sub-pathways and utilizes the gene regulatory network and protein–protein interaction network to locate TFs triggering the perturbation. TimeTP first identifies perturbed sub-pathways that propagate the expression changes along the time. Starting points of the perturbed sub-pathways are mapped into the network and the most influential TFs are determined by influence maximization technique. The analysis result is visually summarized in TF-Pathway map in time clock. TimeTP was applied to PIK3CA knock-in dataset and found significant sub-pathways and their regulators relevant to the PIP3 signaling pathway. Availability and Implementation: TimeTP is implemented in Python and available at http://biohealth.snu.ac.kr/software/TimeTP/. Supplementary information:Supplementary data are available at Bioinformatics online. Contact:sunkim.bioinfo@snu.ac.kr
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Affiliation(s)
- Kyuri Jo
- Department of Computer Science and Engineering
| | - Inuk Jung
- Interdisciplinary Program in Bioinformatics
| | | | - Sun Kim
- Department of Computer Science and Engineering Interdisciplinary Program in Bioinformatics Bioinformatics Institute, Seoul National University, Seoul 08826, Korea
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34
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Witt SH, Streit F, Jungkunz M, Frank J, Awasthi S, Reinbold CS, Treutlein J, Degenhardt F, Forstner AJ, Heilmann-Heimbach S, Dietl L, Schwarze CE, Schendel D, Strohmaier J, Abdellaoui A, Adolfsson R, Air TM, Akil H, Alda M, Alliey-Rodriguez N, Andreassen OA, Babadjanova G, Bass NJ, Bauer M, Baune BT, Bellivier F, Bergen S, Bethell A, Biernacka JM, Blackwood DHR, Boks MP, Boomsma DI, Børglum AD, Borrmann-Hassenbach M, Brennan P, Budde M, Buttenschøn HN, Byrne EM, Cervantes P, Clarke TK, Craddock N, Cruceanu C, Curtis D, Czerski PM, Dannlowski U, Davis T, de Geus EJC, Di Florio A, Djurovic S, Domenici E, Edenberg HJ, Etain B, Fischer SB, Forty L, Fraser C, Frye MA, Fullerton JM, Gade K, Gershon ES, Giegling I, Gordon SD, Gordon-Smith K, Grabe HJ, Green EK, Greenwood TA, Grigoroiu-Serbanescu M, Guzman-Parra J, Hall LS, Hamshere M, Hauser J, Hautzinger M, Heilbronner U, Herms S, Hitturlingappa S, Hoffmann P, Holmans P, Hottenga JJ, Jamain S, Jones I, Jones LA, Juréus A, Kahn RS, Kammerer-Ciernioch J, Kirov G, Kittel-Schneider S, Kloiber S, Knott SV, Kogevinas M, Landén M, Leber M, Leboyer M, Li QS, Lissowska J, Lucae S, Martin NG, Mayoral-Cleries F, McElroy SL, McIntosh AM, McKay JD, McQuillin A, Medland SE, Middeldorp CM, Milaneschi Y, Mitchell PB, Montgomery GW, Morken G, Mors O, Mühleisen TW, Müller-Myhsok B, Myers RM, Nievergelt CM, Nurnberger JI, O'Donovan MC, Loohuis LMO, Ophoff R, Oruc L, Owen MJ, Paciga SA, Penninx BWJH, Perry A, Pfennig A, Potash JB, Preisig M, Reif A, Rivas F, Rouleau GA, Schofield PR, Schulze TG, Schwarz M, Scott L, Sinnamon GCB, Stahl EA, Strauss J, Turecki G, Van der Auwera S, Vedder H, Vincent JB, Willemsen G, Witt CC, Wray NR, Xi HS, Tadic A, Dahmen N, Schott BH, Cichon S, Nöthen MM, Ripke S, Mobascher A, Rujescu D, Lieb K, Roepke S, Schmahl C, Bohus M, Rietschel M. Genome-wide association study of borderline personality disorder reveals genetic overlap with bipolar disorder, major depression and schizophrenia. Transl Psychiatry 2017; 7:e1155. [PMID: 28632202 PMCID: PMC5537640 DOI: 10.1038/tp.2017.115] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 04/10/2017] [Indexed: 01/02/2023] Open
Abstract
Borderline personality disorder (BOR) is determined by environmental and genetic factors, and characterized by affective instability and impulsivity, diagnostic symptoms also observed in manic phases of bipolar disorder (BIP). Up to 20% of BIP patients show comorbidity with BOR. This report describes the first case-control genome-wide association study (GWAS) of BOR, performed in one of the largest BOR patient samples worldwide. The focus of our analysis was (i) to detect genes and gene sets involved in BOR and (ii) to investigate the genetic overlap with BIP. As there is considerable genetic overlap between BIP, major depression (MDD) and schizophrenia (SCZ) and a high comorbidity of BOR and MDD, we also analyzed the genetic overlap of BOR with SCZ and MDD. GWAS, gene-based tests and gene-set analyses were performed in 998 BOR patients and 1545 controls. Linkage disequilibrium score regression was used to detect the genetic overlap between BOR and these disorders. Single marker analysis revealed no significant association after correction for multiple testing. Gene-based analysis yielded two significant genes: DPYD (P=4.42 × 10-7) and PKP4 (P=8.67 × 10-7); and gene-set analysis yielded a significant finding for exocytosis (GO:0006887, PFDR=0.019; FDR, false discovery rate). Prior studies have implicated DPYD, PKP4 and exocytosis in BIP and SCZ. The most notable finding of the present study was the genetic overlap of BOR with BIP (rg=0.28 [P=2.99 × 10-3]), SCZ (rg=0.34 [P=4.37 × 10-5]) and MDD (rg=0.57 [P=1.04 × 10-3]). We believe our study is the first to demonstrate that BOR overlaps with BIP, MDD and SCZ on the genetic level. Whether this is confined to transdiagnostic clinical symptoms should be examined in future studies.
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Affiliation(s)
- S H Witt
- Central Institute of Mental Health, Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - F Streit
- Central Institute of Mental Health, Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - M Jungkunz
- Central Institute of Mental Health, Clinic of Psychosomatic and Psychotherapeutic Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Central Institute of Mental Health, Institute for Psychiatric and Psychosomatic Psychotherapy (IPPP)/Psychosomatic Medicine and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - J Frank
- Central Institute of Mental Health, Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - S Awasthi
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - C S Reinbold
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - J Treutlein
- Central Institute of Mental Health, Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - F Degenhardt
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Life and Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
| | - A J Forstner
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Life and Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | | | - L Dietl
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - C E Schwarze
- Department of Clinical Psychology and Psychotherapy, University of Heidelberg, Heidelberg, Germany
| | - D Schendel
- Central Institute of Mental Health, Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - J Strohmaier
- Central Institute of Mental Health, Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - A Abdellaoui
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - R Adolfsson
- Department of Clinical Sciences, Psychiatry, Umeå University, Umeå, Sweden
| | - T M Air
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
| | - H Akil
- Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
| | - M Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - N Alliey-Rodriguez
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - O A Andreassen
- Division Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- NORMENT, University of Oslo, Oslo, Norway
| | - G Babadjanova
- Institute of Pulmonology, Russian State Medical University, Moscow, Russian Federation
| | - N J Bass
- Division of Psychiatry, University College London, London, UK
| | - M Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Dresden, Germany
| | - B T Baune
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
| | - F Bellivier
- Inserm, U1144, AP-HP, GH Saint-Louis, Département de Psychiatrie et de Médecine Addictologique, Paris, France
| | - S Bergen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - A Bethell
- National Center for Mental Health, Cardiff University, Cardiff, UK
| | - J M Biernacka
- Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - D H R Blackwood
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - M P Boks
- Urain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - D I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - A D Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
| | | | - P Brennan
- Genetic Epidemiology Group, International Agency for Research on Cancer, Lyon, France
| | - M Budde
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Goettingen, Germany
- Medical Center of the University of Munich, Campus Innenstadt, Institute of Psychiatric Phenomics and Genomics (IPPG), Munich, Germany
| | - H N Buttenschøn
- Translational Neuropsychiatry Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - E M Byrne
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - P Cervantes
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - T-K Clarke
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - N Craddock
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - C Cruceanu
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - D Curtis
- Centre for Psychiatry, Queen Mary University of London, London, UK
- UCL Genetics Institute, University College London, London, UK
| | - P M Czerski
- Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - U Dannlowski
- Department of Psychiatry, University of Marburg, Marburg, Germany
- Department of Psychiatry, University of Münste, Münster, Germany
| | - T Davis
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
| | - E J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - A Di Florio
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - S Djurovic
- Department of Medical Genetics, Oslo University Hospital Ullevål, Oslo, Norway
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - E Domenici
- Centre for Integrative Biology, Università degli Studi di Trento, Trento, Italy
| | - H J Edenberg
- Indiana University School of Medicine, Department of Biochemistry and Molecular Biology, Indianapolis, IN, USA
- Indiana University School of Medicine, Department of Medical and Molecular Genetics, Indianapolis, IN, USA
| | - B Etain
- Faculté de Médecine, Université Paris Est, Créteil, France
| | - S B Fischer
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - L Forty
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - C Fraser
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - M A Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - J M Fullerton
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
| | - K Gade
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Goettingen, Germany
- Medical Center of the University of Munich, Campus Innenstadt, Institute of Psychiatric Phenomics and Genomics (IPPG), Munich, Germany
| | - E S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - I Giegling
- Department of Psychiatry, University of Halle, Halle, Germany
| | - S D Gordon
- Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - K Gordon-Smith
- Department of Psychological Medicine, University of Worcester, Worcester, UK
| | - H J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - E K Green
- School of Biomedical and Healthcare Sciences, Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth, UK
| | - T A Greenwood
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - M Grigoroiu-Serbanescu
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania
| | - J Guzman-Parra
- Mental Health Department, Biomedicine Institute, University Regional Hospital, Málaga, Spain
| | - L S Hall
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK
| | - M Hamshere
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - J Hauser
- Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - M Hautzinger
- Department of Psychology, Eberhard Karls Universität Tübingen, Tubingen, Germany
| | - U Heilbronner
- Medical Center of the University of Munich, Campus Innenstadt, Institute of Psychiatric Phenomics and Genomics (IPPG), Munich, Germany
| | - S Herms
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Life and Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
| | - S Hitturlingappa
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
| | - P Hoffmann
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Life and Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
| | - P Holmans
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - J-J Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - S Jamain
- Faculté de Médecine, Université Paris Est, Créteil, France
- Inserm U955, Psychiatrie Translationnelle, Créteil, France
| | - I Jones
- National Center for Mental Health, Cardiff University, Cardiff, UK
| | - L A Jones
- Department of Psychological Medicine, University of Worcester, Worcester, UK
| | - A Juréus
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - R S Kahn
- University Medical Center Utrecht, Division of Neuroscience, Department of Psychiatry, Utrecht, The Netherlands
| | | | - G Kirov
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - S Kittel-Schneider
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt am Main, Frankfurt am Main, Germany
| | - S Kloiber
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Max Planck Institute of Psychiatry, Munich, Germany
| | - S V Knott
- Department of Psychological Medicine, University of Worcester, Worcester, UK
| | - M Kogevinas
- Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
| | - M Landén
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - M Leber
- Clinic for Psychiatry and Psychotherapy, University Hospital Cologne, Cologne, Germany
| | - M Leboyer
- Inserm U955, Translational Psychiatry Laboratory, AP-HP, DHU PePSY, Department of Psychiatry, Université Paris Est, Créteil, France
| | - Q S Li
- Janssen Research and Development, LLC, Neuroscience Therapeutic Area, Titusville, NJ, USA
| | - J Lissowska
- M. Sklodowska-Curie Cancer Center and Institute of Oncology, Cancer Epidemiology and Prevention, Warsaw, Poland
| | - S Lucae
- Max Planck Institute of Psychiatry, Munich, Germany
| | - N G Martin
- Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
| | - F Mayoral-Cleries
- Mental Health Department, Biomedicine Institute, University Regional Hospital, Málaga, Spain
| | - S L McElroy
- Lindner Center of HOPE, Research Institute, Mason, OH, USA
| | - A M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - J D McKay
- Genetic Cancer Susceptibility Group, International Agency for Research on Cancer, Lyon, France
| | - A McQuillin
- Division of Psychiatry, University College London, London, UK
| | - S E Medland
- Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - C M Middeldorp
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Y Milaneschi
- VU University Medical Center and GGZ inGeest, Department of Psychiatry, Amsterdam, The Netherlands
| | - P B Mitchell
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Black Dog Institute, Sydney, NSW, Australia
| | - G W Montgomery
- Institute for Molecular Biology, University of Queensland, Brisbane, QLD, Australia
| | - G Morken
- Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Psychiatry, St Olavs University Hospital, Trondheim, Norway
| | - O Mors
- Risskov, Psychosis Research Unit, Aarhus University Hospital, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - T W Mühleisen
- Research Center Juelich, Institute of Neuroscience and Medicine (INM-1), Juelich, Germany
- Division of Medical Genetics, University of Basel, Basel, Switzerland
| | - B Müller-Myhsok
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- University of Liverpool, Liverpool, UK
| | - R M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - C M Nievergelt
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - J I Nurnberger
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - M C O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - L M O Loohuis
- Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA
| | - R Ophoff
- University Medical Center Utrecht, Division of Brain Research, Utrecht, The Netherlands
| | - L Oruc
- Psychiatry Clinic, Clinical Center University of Sarajevo, Sarajevo, Bosnia-Herzegovina
| | - M J Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - S A Paciga
- Pfizer Global Research and Development, Human Genetics and Computational Biomedicine, Groton, CT, USA
| | - B W J H Penninx
- VU University Medical Center and GGZ inGeest, Department of Psychiatry, Amsterdam, The Netherlands
| | - A Perry
- Department of Psychological Medicine, University of Worcester, Worcester, UK
| | - A Pfennig
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Dresden, Germany
| | - J B Potash
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - M Preisig
- Department of Psychiatry, Psychiatric University Hospital of Lausanne, Lausanne, Switzerland
| | - A Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt am Main, Frankfurt am Main, Germany
| | - F Rivas
- Mental Health Department, Biomedicine Institute, University Regional Hospital, Málaga, Spain
| | - G A Rouleau
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - P R Schofield
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
| | - T G Schulze
- Central Institute of Mental Health, Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Goettingen, Germany
- Medical Center of the University of Munich, Campus Innenstadt, Institute of Psychiatric Phenomics and Genomics (IPPG), Munich, Germany
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
- NIMH Division of Intramural Research Programs, Human Genetics Branch, Bethesda, MD, USA
| | - M Schwarz
- Psychiatric Center Nordbaden, Wiesloch, Germany
| | - L Scott
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - G C B Sinnamon
- School of Medicine and Dentistry, James Cook University, Townsville, QLD, Australia
| | - E A Stahl
- Broad Institute of MIT and Harvard, Medical and Population Genetics, Cambridge, MA, USA
- Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - J Strauss
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - G Turecki
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - S Van der Auwera
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - H Vedder
- Psychiatric Center Nordbaden, Wiesloch, Germany
| | - J B Vincent
- Centre for Addiction and Mental Health, Molecular Neuropsychiatry and Development Laboratory, Toronto, ON, Canada
| | - G Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - C C Witt
- Department of Anaesthesiology and Operative Intensive Care, University Hospital Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - N R Wray
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - H S Xi
- Pfizer Global Research and Development, Computational Sciences Center of Emphasis, Cambridge, MA, USA
| | - Bipolar Disorders Working Group of the Psychiatric Genomics Consortium
- Central Institute of Mental Health, Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Central Institute of Mental Health, Clinic of Psychosomatic and Psychotherapeutic Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Central Institute of Mental Health, Institute for Psychiatric and Psychosomatic Psychotherapy (IPPP)/Psychosomatic Medicine and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Life and Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
- Department of Clinical Psychology and Psychotherapy, University of Heidelberg, Heidelberg, Germany
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Sciences, Psychiatry, Umeå University, Umeå, Sweden
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
- Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
- Division Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- NORMENT, University of Oslo, Oslo, Norway
- Institute of Pulmonology, Russian State Medical University, Moscow, Russian Federation
- Division of Psychiatry, University College London, London, UK
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Dresden, Germany
- Inserm, U1144, AP-HP, GH Saint-Louis, Département de Psychiatrie et de Médecine Addictologique, Paris, France
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- National Center for Mental Health, Cardiff University, Cardiff, UK
- Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Urain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Medical and Quality Assurance, Clinics of Upper Bavaria, Munich, Germany
- Genetic Epidemiology Group, International Agency for Research on Cancer, Lyon, France
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Goettingen, Germany
- Medical Center of the University of Munich, Campus Innenstadt, Institute of Psychiatric Phenomics and Genomics (IPPG), Munich, Germany
- Translational Neuropsychiatry Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Centre for Psychiatry, Queen Mary University of London, London, UK
- UCL Genetics Institute, University College London, London, UK
- Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
- Department of Psychiatry, University of Marburg, Marburg, Germany
- Department of Psychiatry, University of Münste, Münster, Germany
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Department of Medical Genetics, Oslo University Hospital Ullevål, Oslo, Norway
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Centre for Integrative Biology, Università degli Studi di Trento, Trento, Italy
- Indiana University School of Medicine, Department of Biochemistry and Molecular Biology, Indianapolis, IN, USA
- Indiana University School of Medicine, Department of Medical and Molecular Genetics, Indianapolis, IN, USA
- Faculté de Médecine, Université Paris Est, Créteil, France
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
- Department of Psychiatry, University of Halle, Halle, Germany
- Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Department of Psychological Medicine, University of Worcester, Worcester, UK
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- School of Biomedical and Healthcare Sciences, Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth, UK
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania
- Mental Health Department, Biomedicine Institute, University Regional Hospital, Málaga, Spain
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK
- Department of Psychology, Eberhard Karls Universität Tübingen, Tubingen, Germany
- Inserm U955, Psychiatrie Translationnelle, Créteil, France
- University Medical Center Utrecht, Division of Neuroscience, Department of Psychiatry, Utrecht, The Netherlands
- Center of Psychiatry Weinsberg, Weinsberg, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt am Main, Frankfurt am Main, Germany
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Max Planck Institute of Psychiatry, Munich, Germany
- Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
- Clinic for Psychiatry and Psychotherapy, University Hospital Cologne, Cologne, Germany
- Inserm U955, Translational Psychiatry Laboratory, AP-HP, DHU PePSY, Department of Psychiatry, Université Paris Est, Créteil, France
- Janssen Research and Development, LLC, Neuroscience Therapeutic Area, Titusville, NJ, USA
- M. Sklodowska-Curie Cancer Center and Institute of Oncology, Cancer Epidemiology and Prevention, Warsaw, Poland
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
- Lindner Center of HOPE, Research Institute, Mason, OH, USA
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Genetic Cancer Susceptibility Group, International Agency for Research on Cancer, Lyon, France
- Division of Psychiatry, University College London, London, UK
- VU University Medical Center and GGZ inGeest, Department of Psychiatry, Amsterdam, The Netherlands
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Black Dog Institute, Sydney, NSW, Australia
- Institute for Molecular Biology, University of Queensland, Brisbane, QLD, Australia
- Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Psychiatry, St Olavs University Hospital, Trondheim, Norway
- Risskov, Psychosis Research Unit, Aarhus University Hospital, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Research Center Juelich, Institute of Neuroscience and Medicine (INM-1), Juelich, Germany
- Division of Medical Genetics, University of Basel, Basel, Switzerland
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- University of Liverpool, Liverpool, UK
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA
- University Medical Center Utrecht, Division of Brain Research, Utrecht, The Netherlands
- Psychiatry Clinic, Clinical Center University of Sarajevo, Sarajevo, Bosnia-Herzegovina
- Pfizer Global Research and Development, Human Genetics and Computational Biomedicine, Groton, CT, USA
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
- Department of Psychiatry, Psychiatric University Hospital of Lausanne, Lausanne, Switzerland
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
- NIMH Division of Intramural Research Programs, Human Genetics Branch, Bethesda, MD, USA
- Psychiatric Center Nordbaden, Wiesloch, Germany
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- School of Medicine and Dentistry, James Cook University, Townsville, QLD, Australia
- Broad Institute of MIT and Harvard, Medical and Population Genetics, Cambridge, MA, USA
- Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Centre for Addiction and Mental Health, Molecular Neuropsychiatry and Development Laboratory, Toronto, ON, Canada
- Department of Anaesthesiology and Operative Intensive Care, University Hospital Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Pfizer Global Research and Development, Computational Sciences Center of Emphasis, Cambridge, MA, USA
- AGAPLESION Elisabethenstift gGmbh, Department of Psychiatry, Psychosomatics and Psychotherapy, Darmstadt, Germany
- University Medical Center, Department of Psychiatry and Psychotherapy, Mainz, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Biomedicine, University of Basel, Basel, Switzerland
- Stanley Center for Psychiatric Research and Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium
- Central Institute of Mental Health, Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Central Institute of Mental Health, Clinic of Psychosomatic and Psychotherapeutic Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Central Institute of Mental Health, Institute for Psychiatric and Psychosomatic Psychotherapy (IPPP)/Psychosomatic Medicine and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Life and Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
- Department of Clinical Psychology and Psychotherapy, University of Heidelberg, Heidelberg, Germany
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Sciences, Psychiatry, Umeå University, Umeå, Sweden
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
- Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
- Division Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- NORMENT, University of Oslo, Oslo, Norway
- Institute of Pulmonology, Russian State Medical University, Moscow, Russian Federation
- Division of Psychiatry, University College London, London, UK
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Dresden, Germany
- Inserm, U1144, AP-HP, GH Saint-Louis, Département de Psychiatrie et de Médecine Addictologique, Paris, France
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- National Center for Mental Health, Cardiff University, Cardiff, UK
- Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Urain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Medical and Quality Assurance, Clinics of Upper Bavaria, Munich, Germany
- Genetic Epidemiology Group, International Agency for Research on Cancer, Lyon, France
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Goettingen, Germany
- Medical Center of the University of Munich, Campus Innenstadt, Institute of Psychiatric Phenomics and Genomics (IPPG), Munich, Germany
- Translational Neuropsychiatry Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Centre for Psychiatry, Queen Mary University of London, London, UK
- UCL Genetics Institute, University College London, London, UK
- Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
- Department of Psychiatry, University of Marburg, Marburg, Germany
- Department of Psychiatry, University of Münste, Münster, Germany
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Department of Medical Genetics, Oslo University Hospital Ullevål, Oslo, Norway
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Centre for Integrative Biology, Università degli Studi di Trento, Trento, Italy
- Indiana University School of Medicine, Department of Biochemistry and Molecular Biology, Indianapolis, IN, USA
- Indiana University School of Medicine, Department of Medical and Molecular Genetics, Indianapolis, IN, USA
- Faculté de Médecine, Université Paris Est, Créteil, France
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
- Department of Psychiatry, University of Halle, Halle, Germany
- Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Department of Psychological Medicine, University of Worcester, Worcester, UK
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- School of Biomedical and Healthcare Sciences, Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth, UK
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania
- Mental Health Department, Biomedicine Institute, University Regional Hospital, Málaga, Spain
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK
- Department of Psychology, Eberhard Karls Universität Tübingen, Tubingen, Germany
- Inserm U955, Psychiatrie Translationnelle, Créteil, France
- University Medical Center Utrecht, Division of Neuroscience, Department of Psychiatry, Utrecht, The Netherlands
- Center of Psychiatry Weinsberg, Weinsberg, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt am Main, Frankfurt am Main, Germany
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Max Planck Institute of Psychiatry, Munich, Germany
- Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
- Clinic for Psychiatry and Psychotherapy, University Hospital Cologne, Cologne, Germany
- Inserm U955, Translational Psychiatry Laboratory, AP-HP, DHU PePSY, Department of Psychiatry, Université Paris Est, Créteil, France
- Janssen Research and Development, LLC, Neuroscience Therapeutic Area, Titusville, NJ, USA
- M. Sklodowska-Curie Cancer Center and Institute of Oncology, Cancer Epidemiology and Prevention, Warsaw, Poland
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
- Lindner Center of HOPE, Research Institute, Mason, OH, USA
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Genetic Cancer Susceptibility Group, International Agency for Research on Cancer, Lyon, France
- Division of Psychiatry, University College London, London, UK
- VU University Medical Center and GGZ inGeest, Department of Psychiatry, Amsterdam, The Netherlands
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Black Dog Institute, Sydney, NSW, Australia
- Institute for Molecular Biology, University of Queensland, Brisbane, QLD, Australia
- Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Psychiatry, St Olavs University Hospital, Trondheim, Norway
- Risskov, Psychosis Research Unit, Aarhus University Hospital, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Research Center Juelich, Institute of Neuroscience and Medicine (INM-1), Juelich, Germany
- Division of Medical Genetics, University of Basel, Basel, Switzerland
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- University of Liverpool, Liverpool, UK
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA
- University Medical Center Utrecht, Division of Brain Research, Utrecht, The Netherlands
- Psychiatry Clinic, Clinical Center University of Sarajevo, Sarajevo, Bosnia-Herzegovina
- Pfizer Global Research and Development, Human Genetics and Computational Biomedicine, Groton, CT, USA
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
- Department of Psychiatry, Psychiatric University Hospital of Lausanne, Lausanne, Switzerland
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
- NIMH Division of Intramural Research Programs, Human Genetics Branch, Bethesda, MD, USA
- Psychiatric Center Nordbaden, Wiesloch, Germany
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- School of Medicine and Dentistry, James Cook University, Townsville, QLD, Australia
- Broad Institute of MIT and Harvard, Medical and Population Genetics, Cambridge, MA, USA
- Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Centre for Addiction and Mental Health, Molecular Neuropsychiatry and Development Laboratory, Toronto, ON, Canada
- Department of Anaesthesiology and Operative Intensive Care, University Hospital Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Pfizer Global Research and Development, Computational Sciences Center of Emphasis, Cambridge, MA, USA
- AGAPLESION Elisabethenstift gGmbh, Department of Psychiatry, Psychosomatics and Psychotherapy, Darmstadt, Germany
- University Medical Center, Department of Psychiatry and Psychotherapy, Mainz, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Biomedicine, University of Basel, Basel, Switzerland
- Stanley Center for Psychiatric Research and Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Schizophrenia Working Group of the Psychiatric Genomics Consortium
- Central Institute of Mental Health, Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Central Institute of Mental Health, Clinic of Psychosomatic and Psychotherapeutic Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Central Institute of Mental Health, Institute for Psychiatric and Psychosomatic Psychotherapy (IPPP)/Psychosomatic Medicine and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Life and Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
- Department of Clinical Psychology and Psychotherapy, University of Heidelberg, Heidelberg, Germany
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Sciences, Psychiatry, Umeå University, Umeå, Sweden
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
- Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
- Division Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- NORMENT, University of Oslo, Oslo, Norway
- Institute of Pulmonology, Russian State Medical University, Moscow, Russian Federation
- Division of Psychiatry, University College London, London, UK
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Dresden, Germany
- Inserm, U1144, AP-HP, GH Saint-Louis, Département de Psychiatrie et de Médecine Addictologique, Paris, France
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- National Center for Mental Health, Cardiff University, Cardiff, UK
- Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Urain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Medical and Quality Assurance, Clinics of Upper Bavaria, Munich, Germany
- Genetic Epidemiology Group, International Agency for Research on Cancer, Lyon, France
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Goettingen, Germany
- Medical Center of the University of Munich, Campus Innenstadt, Institute of Psychiatric Phenomics and Genomics (IPPG), Munich, Germany
- Translational Neuropsychiatry Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Centre for Psychiatry, Queen Mary University of London, London, UK
- UCL Genetics Institute, University College London, London, UK
- Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
- Department of Psychiatry, University of Marburg, Marburg, Germany
- Department of Psychiatry, University of Münste, Münster, Germany
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Department of Medical Genetics, Oslo University Hospital Ullevål, Oslo, Norway
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Centre for Integrative Biology, Università degli Studi di Trento, Trento, Italy
- Indiana University School of Medicine, Department of Biochemistry and Molecular Biology, Indianapolis, IN, USA
- Indiana University School of Medicine, Department of Medical and Molecular Genetics, Indianapolis, IN, USA
- Faculté de Médecine, Université Paris Est, Créteil, France
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
- Department of Psychiatry, University of Halle, Halle, Germany
- Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Department of Psychological Medicine, University of Worcester, Worcester, UK
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- School of Biomedical and Healthcare Sciences, Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth, UK
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania
- Mental Health Department, Biomedicine Institute, University Regional Hospital, Málaga, Spain
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK
- Department of Psychology, Eberhard Karls Universität Tübingen, Tubingen, Germany
- Inserm U955, Psychiatrie Translationnelle, Créteil, France
- University Medical Center Utrecht, Division of Neuroscience, Department of Psychiatry, Utrecht, The Netherlands
- Center of Psychiatry Weinsberg, Weinsberg, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt am Main, Frankfurt am Main, Germany
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Max Planck Institute of Psychiatry, Munich, Germany
- Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
- Clinic for Psychiatry and Psychotherapy, University Hospital Cologne, Cologne, Germany
- Inserm U955, Translational Psychiatry Laboratory, AP-HP, DHU PePSY, Department of Psychiatry, Université Paris Est, Créteil, France
- Janssen Research and Development, LLC, Neuroscience Therapeutic Area, Titusville, NJ, USA
- M. Sklodowska-Curie Cancer Center and Institute of Oncology, Cancer Epidemiology and Prevention, Warsaw, Poland
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
- Lindner Center of HOPE, Research Institute, Mason, OH, USA
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Genetic Cancer Susceptibility Group, International Agency for Research on Cancer, Lyon, France
- Division of Psychiatry, University College London, London, UK
- VU University Medical Center and GGZ inGeest, Department of Psychiatry, Amsterdam, The Netherlands
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Black Dog Institute, Sydney, NSW, Australia
- Institute for Molecular Biology, University of Queensland, Brisbane, QLD, Australia
- Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Psychiatry, St Olavs University Hospital, Trondheim, Norway
- Risskov, Psychosis Research Unit, Aarhus University Hospital, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Research Center Juelich, Institute of Neuroscience and Medicine (INM-1), Juelich, Germany
- Division of Medical Genetics, University of Basel, Basel, Switzerland
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- University of Liverpool, Liverpool, UK
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA
- University Medical Center Utrecht, Division of Brain Research, Utrecht, The Netherlands
- Psychiatry Clinic, Clinical Center University of Sarajevo, Sarajevo, Bosnia-Herzegovina
- Pfizer Global Research and Development, Human Genetics and Computational Biomedicine, Groton, CT, USA
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
- Department of Psychiatry, Psychiatric University Hospital of Lausanne, Lausanne, Switzerland
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
- NIMH Division of Intramural Research Programs, Human Genetics Branch, Bethesda, MD, USA
- Psychiatric Center Nordbaden, Wiesloch, Germany
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- School of Medicine and Dentistry, James Cook University, Townsville, QLD, Australia
- Broad Institute of MIT and Harvard, Medical and Population Genetics, Cambridge, MA, USA
- Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Centre for Addiction and Mental Health, Molecular Neuropsychiatry and Development Laboratory, Toronto, ON, Canada
- Department of Anaesthesiology and Operative Intensive Care, University Hospital Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Pfizer Global Research and Development, Computational Sciences Center of Emphasis, Cambridge, MA, USA
- AGAPLESION Elisabethenstift gGmbh, Department of Psychiatry, Psychosomatics and Psychotherapy, Darmstadt, Germany
- University Medical Center, Department of Psychiatry and Psychotherapy, Mainz, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Biomedicine, University of Basel, Basel, Switzerland
- Stanley Center for Psychiatric Research and Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - A Tadic
- AGAPLESION Elisabethenstift gGmbh, Department of Psychiatry, Psychosomatics and Psychotherapy, Darmstadt, Germany
- University Medical Center, Department of Psychiatry and Psychotherapy, Mainz, Germany
| | - N Dahmen
- University Medical Center, Department of Psychiatry and Psychotherapy, Mainz, Germany
| | - B H Schott
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - S Cichon
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Research Center Juelich, Institute of Neuroscience and Medicine (INM-1), Juelich, Germany
- Division of Medical Genetics, University of Basel, Basel, Switzerland
- Department of Biomedicine, University of Basel, Basel, Switzerland
| | - M M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Life and Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
| | - S Ripke
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
- Stanley Center for Psychiatric Research and Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - A Mobascher
- University Medical Center, Department of Psychiatry and Psychotherapy, Mainz, Germany
| | - D Rujescu
- Department of Psychiatry, University of Halle, Halle, Germany
| | - K Lieb
- University Medical Center, Department of Psychiatry and Psychotherapy, Mainz, Germany
| | - S Roepke
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - C Schmahl
- Central Institute of Mental Health, Clinic of Psychosomatic and Psychotherapeutic Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - M Bohus
- Central Institute of Mental Health, Institute for Psychiatric and Psychosomatic Psychotherapy (IPPP)/Psychosomatic Medicine and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - M Rietschel
- Central Institute of Mental Health, Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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Yao X, Yan J, Risacher S, Moore J, Saykin A, Shen L. NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES. PROCEEDINGS OF THE ... IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING. ICASSP (CONFERENCE) 2017; 2017:6170-6174. [PMID: 28989328 DOI: 10.1109/icassp.2017.7953342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Identification of functional modules from biological network is a promising approach to enhance the statistical power of genome-wide association study (GWAS) and improve biological interpretation for complex diseases. The precise functions of genes are highly relevant to tissue context, while a majority of module identification studies are based on tissue-free biological networks that lacks phenotypic specificity. In this study, we propose a module identification method that maps the GWAS results of an imaging phenotype onto the corresponding tissue-specific functional interaction network by applying a machine learning framework. Ridge regression and support vector machine (SVM) models are constructed to re-prioritize GWAS results, followed by exploring hippocampus-relevant modules based on top predictions using GWAS top findings. We also propose a GWAS top-neighbor-based module identification approach and compare it with Ridge and SVM based approaches. Modules conserving both tissue specificity and GWAS discoveries are identified, showing the promise of the proposal method for providing insight into the mechanism of complex diseases.
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Affiliation(s)
- Xiaohui Yao
- Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, 46202, USA.,Informatics and Computing, Indiana University, Indianapolis, IN, 46202, USA
| | - Jingwen Yan
- Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, 46202, USA.,Informatics and Computing, Indiana University, Indianapolis, IN, 46202, USA
| | - Shannon Risacher
- Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, 46202, USA
| | - Jason Moore
- Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Andrew Saykin
- Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, 46202, USA
| | - Li Shen
- Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, 46202, USA.,Informatics and Computing, Indiana University, Indianapolis, IN, 46202, USA
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36
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Improving the detection of pathways in genome-wide association studies by combined effects of SNPs from Linkage Disequilibrium blocks. Sci Rep 2017; 7:3512. [PMID: 28615668 PMCID: PMC5471232 DOI: 10.1038/s41598-017-03826-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 05/05/2017] [Indexed: 01/31/2023] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified single variants associated with diseases. To increase the power of GWAS, gene-based and pathway-based tests are commonly employed to detect more risk factors. However, the gene- and pathway-based association tests may be biased towards genes or pathways containing a large number of single-nucleotide polymorphisms (SNPs) with small P-values caused by high linkage disequilibrium (LD) correlations. To address such bias, numerous pathway-based methods have been developed. Here we propose a novel method, DGAT-path, to divide all SNPs assigned to genes in each pathway into LD blocks, and to sum the chi-square statistics of LD blocks for assessing the significance of the pathway by permutation tests. The method was proven robust with the type I error rate >1.6 times lower than other methods. Meanwhile, the method displays a higher power and is not biased by the pathway size. The applications to the GWAS summary statistics for schizophrenia and breast cancer indicate that the detected top pathways contain more genes close to associated SNPs than other methods. As a result, the method identified 17 and 12 significant pathways containing 20 and 21 novel associated genes, respectively for two diseases. The method is available online by http://sparks-lab.org/server/DGAT-path.
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37
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Sarzynski MA, Ghosh S, Bouchard C. Genomic and transcriptomic predictors of response levels to endurance exercise training. J Physiol 2017; 595:2931-2939. [PMID: 27234805 PMCID: PMC5407970 DOI: 10.1113/jp272559] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Accepted: 05/24/2016] [Indexed: 01/28/2023] Open
Abstract
Predicting the responsiveness to regular exercise is a topic of great relevance due to its potential role in personalized exercise medicine applications. The present review focuses on cardiorespiratory fitness (commonly measured by maximal oxygen uptake, V̇O2 max ), a trait with wide-ranging impact on health and performance indicators. Gains in V̇O2 max demonstrate large inter-individual variation even in response to standardized exercise training programmes. The estimated ΔVO2 max heritability of 47% suggests that genomic-based predictors alone are insufficient to account for the total trainability variance. Candidate gene and genome-wide linkage studies have not significantly contributed to our understanding of the molecular basis of trainability. A genome-wide association study suggested that V̇O2 max trainability is influenced by multiple genes of small effects, but these findings still await rigorous replication. Valuable evidence, however, has been obtained by combining skeletal muscle transcript abundance profiles with common DNA variants for the prediction of the V̇O2 max response to exercise training. Although the physiological determinants of V̇O2 max measured at a given time are largely enunciated, what is poorly understood are the details of tissue-specific molecular mechanisms that limit V̇O2 max and related signalling pathways in response to exercise training. Bioinformatics explorations based on thousands of variants have been used to interrogate pathways and systems instead of single variants and genes, and the main findings, along with those from exercise experimental studies, have been summarized here in a working model of V̇O2 max trainability.
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Affiliation(s)
- Mark A. Sarzynski
- Department of Exercise Science, Arnold School of Public HealthUniversity of South CarolinaColumbiaSCUSA
| | - Sujoy Ghosh
- Cardiovascular and Metabolic Disorders Program and Centre for Computational BiologyDuke‐NUS Medical SchoolSingapore
| | - Claude Bouchard
- Human Genomics LaboratoryPennington Biomedical Research CentreBaton RougeLAUSA
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38
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Moon JH, Lim S, Jo K, Lee S, Seo S, Kim S. PINTnet: construction of condition-specific pathway interaction network by computing shortest paths on weighted PPI. BMC SYSTEMS BIOLOGY 2017; 11:15. [PMID: 28361687 PMCID: PMC5374644 DOI: 10.1186/s12918-017-0387-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Background Identifying perturbed pathways in a given condition is crucial in understanding biological phenomena. In addition to identifying perturbed pathways individually, pathway analysis should consider interactions among pathways. Currently available pathway interaction prediction methods are based on the existence of overlapping genes between pathways, protein-protein interaction (PPI) or functional similarities. However, these approaches just consider the pathways as a set of genes, thus they do not take account of topological features. In addition, most of the existing approaches do not handle the explicit gene expression quantity information that is routinely measured by RNA-sequecing. Results To overcome these technical issues, we developed a new pathway interaction network construction method using PPI, closeness centrality and shortest paths. We tested our approach on three different high-throughput RNA-seq data sets: pregnant mice data to reveal the role of serotonin on beta cell mass, bone-metastatic breast cancer data and autoimmune thyroiditis data to study the role of IFN- α. Our approach successfully identified the pathways reported in the original papers. For the pathways that are not directly mentioned in the original papers, we were able to find evidences of pathway interactions by the literature search. Our method outperformed two existing approaches, overlapping gene-based approach (OGB) and protein-protein interaction-based approach (PB), in experiments with the three data sets. Conclusion Our results show that PINTnet successfully identified condition-specific perturbed pathways and the interactions between the pathways. We believe that our method will be very useful in characterizing biological mechanisms at the pathway level. PINTnet is available at http://biohealth.snu.ac.kr/software/PINTnet/.
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Affiliation(s)
- Ji Hwan Moon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Sangsoo Lim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Kyuri Jo
- Department of Computer Science & Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sangseon Lee
- Department of Computer Science & Engineering, Seoul National University, Seoul, Republic of Korea
| | - Seokjun Seo
- Department of Computer Science & Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea. .,Department of Computer Science & Engineering, Seoul National University, Seoul, Republic of Korea. .,Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea.
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39
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Delahaye-Duriez A, Srivastava P, Shkura K, Langley SR, Laaniste L, Moreno-Moral A, Danis B, Mazzuferi M, Foerch P, Gazina EV, Richards K, Petrou S, Kaminski RM, Petretto E, Johnson MR. Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery. Genome Biol 2016; 17:245. [PMID: 27955713 PMCID: PMC5154105 DOI: 10.1186/s13059-016-1097-7] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 11/02/2016] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND The relationship between monogenic and polygenic forms of epilepsy is poorly understood and the extent to which the genetic and acquired epilepsies share common pathways is unclear. Here, we use an integrated systems-level analysis of brain gene expression data to identify molecular networks disrupted in epilepsy. RESULTS We identified a co-expression network of 320 genes (M30), which is significantly enriched for non-synonymous de novo mutations ascertained from patients with monogenic epilepsy and for common variants associated with polygenic epilepsy. The genes in the M30 network are expressed widely in the human brain under tight developmental control and encode physically interacting proteins involved in synaptic processes. The most highly connected proteins within the M30 network were preferentially disrupted by deleterious de novo mutations for monogenic epilepsy, in line with the centrality-lethality hypothesis. Analysis of M30 expression revealed consistent downregulation in the epileptic brain in heterogeneous forms of epilepsy including human temporal lobe epilepsy, a mouse model of acquired temporal lobe epilepsy, and a mouse model of monogenic Dravet (SCN1A) disease. These results suggest functional disruption of M30 via gene mutation or altered expression as a convergent mechanism regulating susceptibility to epilepsy broadly. Using the large collection of drug-induced gene expression data from Connectivity Map, several drugs were predicted to preferentially restore the downregulation of M30 in epilepsy toward health, most notably valproic acid, whose effect on M30 expression was replicated in neurons. CONCLUSIONS Taken together, our results suggest targeting the expression of M30 as a potential new therapeutic strategy in epilepsy.
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Affiliation(s)
- Andree Delahaye-Duriez
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, UK.
- MRC Clinical Sciences Centre, Imperial College London, London, UK.
- Université Paris 13, Sorbonne Paris Cité, UFR de Santé, Médecine et Biologie Humaine, Paris, France.
- PROTECT, INSERM, Université Paris Diderot, Sorbonne Paris Cité, Paris, France.
| | - Prashant Srivastava
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, UK
| | - Kirill Shkura
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, UK
| | - Sarah R Langley
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, UK
- Duke-NUS Medical School, 8 College Road, 169857, Singapore, Republic of Singapore
| | - Liisi Laaniste
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, UK
| | - Aida Moreno-Moral
- MRC Clinical Sciences Centre, Imperial College London, London, UK
- Duke-NUS Medical School, 8 College Road, 169857, Singapore, Republic of Singapore
| | - Bénédicte Danis
- Neuroscience TA, UCB Pharma, S.A, Allée de la Recherche, 60, 1070, Brussels, Belgium
| | - Manuela Mazzuferi
- Neuroscience TA, UCB Pharma, S.A, Allée de la Recherche, 60, 1070, Brussels, Belgium
| | - Patrik Foerch
- Neuroscience TA, UCB Pharma, S.A, Allée de la Recherche, 60, 1070, Brussels, Belgium
| | - Elena V Gazina
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Kay Richards
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Steven Petrou
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, 3052, Australia
- The Centre for Neural Engineering, The Department of Electrical Engineering, The University of Melbourne, Parkville, Victoria, 3052, Australia
- The Australian Research Council Centre of Excellence for Integrative Brain Function, Parkville, Victoria, 3052, Australia
| | - Rafal M Kaminski
- Neuroscience TA, UCB Pharma, S.A, Allée de la Recherche, 60, 1070, Brussels, Belgium
| | - Enrico Petretto
- MRC Clinical Sciences Centre, Imperial College London, London, UK.
- Duke-NUS Medical School, 8 College Road, 169857, Singapore, Republic of Singapore.
| | - Michael R Johnson
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, UK.
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40
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Abstract
Genome-wide studies (GWS) of SNP associations and differential gene expressions have generated abundant results; next-generation sequencing technology has further boosted the number of variants and genes identified. Effective interpretation requires massive annotation and downstream analysis of these genome-wide results, a computationally challenging task. We developed the snpGeneSets package to simplify annotation and analysis of GWS results. Our package integrates local copies of knowledge bases for SNPs, genes, and gene sets, and implements wrapper functions in the R language to enable transparent access to low-level databases for efficient annotation of large genomic data. The package contains functions that execute three types of annotations: (1) genomic mapping annotation for SNPs and genes and functional annotation for gene sets; (2) bidirectional mapping between SNPs and genes, and genes and gene sets; and (3) calculation of gene effect measures from SNP associations and performance of gene set enrichment analyses to identify functional pathways. We applied snpGeneSets to type 2 diabetes (T2D) results from the NHGRI genome-wide association study (GWAS) catalog, a Finnish GWAS, and a genome-wide expression study (GWES). These studies demonstrate the usefulness of snpGeneSets for annotating and performing enrichment analysis of GWS results. The package is open-source, free, and can be downloaded at: https://www.umc.edu/biostats_software/.
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41
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Kao PYP, Leung KH, Chan LWC, Yip SP, Yap MKH. Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions. Biochim Biophys Acta Gen Subj 2016; 1861:335-353. [PMID: 27888147 DOI: 10.1016/j.bbagen.2016.11.030] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 10/17/2016] [Accepted: 11/19/2016] [Indexed: 12/20/2022]
Abstract
BACKGROUND Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data. SCOPE OF REVIEW 1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data. MAJOR CONCLUSIONS To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions. GENERAL SIGNIFICANCE Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.
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Affiliation(s)
- Patrick Y P Kao
- Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Kim Hung Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lawrence W C Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shea Ping Yip
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
| | - Maurice K H Yap
- Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Hong Kong SAR, China
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42
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Yoon S, Kim SY, Nam D. Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates. PLoS One 2016; 11:e0165919. [PMID: 27829002 PMCID: PMC5102490 DOI: 10.1371/journal.pone.0165919] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 10/19/2016] [Indexed: 11/18/2022] Open
Abstract
Deregulated pathways identified from transcriptome data of two sample groups have played a key role in many genomic studies. Gene-set enrichment analysis (GSEA) has been commonly used for pathway or functional analysis of microarray data, and it is also being applied to RNA-seq data. However, most RNA-seq data so far have only small replicates. This enforces to apply the gene-permuting GSEA method (or preranked GSEA) which results in a great number of false positives due to the inter-gene correlation in each gene-set. We demonstrate that incorporating the absolute gene statistic in one-tailed GSEA considerably improves the false-positive control and the overall discriminatory ability of the gene-permuting GSEA methods for RNA-seq data. To test the performance, a simulation method to generate correlated read counts within a gene-set was newly developed, and a dozen of currently available RNA-seq enrichment analysis methods were compared, where the proposed methods outperformed others that do not account for the inter-gene correlation. Analysis of real RNA-seq data also supported the proposed methods in terms of false positive control, ranks of true positives and biological relevance. An efficient R package (AbsFilterGSEA) coded with C++ (Rcpp) is available from CRAN.
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Affiliation(s)
- Sora Yoon
- School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Seon-Young Kim
- Medical Genomics Research Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon, Republic of Korea
- Department of Bioinformatics, University of Science and Technology, Daejeon, Republic of Korea
| | - Dougu Nam
- School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
- * E-mail:
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43
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Kim S, Nho K, Ramanan VK, Lai D, Foroud TM, Lane K, Murrell JR, Gao S, Hall KS, Unverzagt FW, Baiyewu O, Ogunniyi A, Gureje O, Kling MA, Doraiswamy PM, Kaddurah-Daouk R, Hendrie HC, Saykin AJ. Genetic Influences on Plasma Homocysteine Levels in African Americans and Yoruba Nigerians. J Alzheimers Dis 2016; 49:991-1003. [PMID: 26519441 DOI: 10.3233/jad-150651] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Plasma homocysteine, a metabolite involved in key cellular methylation processes seems to be implicated in cognitive functions and cardiovascular health with its high levels representing a potential modifiable risk factor for Alzheimer's disease (AD) and other dementias. A better understanding of the genetic factors regulating homocysteine levels, particularly in non-white populations, may help in risk stratification analyses of existing clinical trials and may point to novel targets for homocysteine-lowering therapy. To identify genetic influences on plasma homocysteine levels in individuals with African ancestry, we performed a targeted gene and pathway-based analysis using a priori biological information and then to identify new association performed a genome-wide association study. All analyses used combined data from the African American and Yoruba cohorts from the Indianapolis-Ibadan Dementia Project. Targeted analyses demonstrated significant associations of homocysteine and variants within the CBS (Cystathionine beta-Synthase) gene. We identified a novel genome-wide significant association of the AD risk gene CD2AP (CD2-associated protein) with plasma homocysteine levels in both cohorts. Minor allele (T) carriers of identified CD2AP variant (rs6940729) exhibited decreased homocysteine level. Pathway enrichment analysis identified several interesting pathways including the GABA receptor activation pathway. This is noteworthy given the known antagonistic effect of homocysteine on GABA receptors. These findings identify several new targets warranting further investigation in relation to the role of homocysteine in neurodegeneration.
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Affiliation(s)
- Sungeun Kim
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana University Network Science Institute, Bloomington, IN, USA
| | - Kwangsik Nho
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana University Network Science Institute, Bloomington, IN, USA
| | - Vijay K Ramanan
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.,Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.,Department of Internal Medicine, Preliminary Medicine Residency, St. Vincent Indianapolis, Indianapolis, IN, USA
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tatiana M Foroud
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana University Network Science Institute, Bloomington, IN, USA.,Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Katie Lane
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jill R Murrell
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.,Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sujuan Gao
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.,Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kathleen S Hall
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Frederick W Unverzagt
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Olusegun Baiyewu
- Department of Psychiatry, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Adesola Ogunniyi
- Department of Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oye Gureje
- Department of Psychiatry, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Mitchel A Kling
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Behavioral Health Service, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - P Murali Doraiswamy
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.,Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.,Duke Institute for Brain Sciences, Duke University, Durham, NC, USA.,Pharmacometabolomics Center, Duke University, Durham, NC, USA
| | - Hugh C Hendrie
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana University Center for Aging Research, Indianapolis, IN, USA.,Regenstrief Institute Inc., Indianapolis, IN, USA
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana University Network Science Institute, Bloomington, IN, USA.,Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
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Parsa A, Kanetsky PA, Xiao R, Gupta J, Mitra N, Limou S, Xie D, Xu H, Anderson AH, Ojo A, Kusek JW, Lora CM, Hamm LL, He J, Sandholm N, Jeff J, Raj DE, Böger CA, Bottinger E, Salimi S, Parekh RS, Adler SG, Langefeld CD, Bowden DW, Groop PH, Forsblom C, Freedman BI, Lipkowitz M, Fox CS, Winkler CA, Feldman HI. Genome-Wide Association of CKD Progression: The Chronic Renal Insufficiency Cohort Study. J Am Soc Nephrol 2016; 28:923-934. [PMID: 27729571 DOI: 10.1681/asn.2015101152] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 08/25/2016] [Indexed: 11/03/2022] Open
Abstract
The rate of decline of renal function varies significantly among individuals with CKD. To understand better the contribution of genetics to CKD progression, we performed a genome-wide association study among participants in the Chronic Renal Insufficiency Cohort Study. Our outcome of interest was CKD progression measured as change in eGFR over time among 1331 blacks and 1476 whites with CKD. We stratified all analyses by race and subsequently, diabetes status. Single-nucleotide polymorphisms (SNPs) that surpassed a significance threshold of P<1×10-6 for association with eGFR slope were selected as candidates for follow-up and secondarily tested for association with proteinuria and time to ESRD. We identified 12 such SNPs among black patients and six such SNPs among white patients. We were able to conduct follow-up analyses of three candidate SNPs in similar (replication) cohorts and eight candidate SNPs in phenotype-related (validation) cohorts. Among blacks without diabetes, rs653747 in LINC00923 replicated in the African American Study of Kidney Disease and Hypertension cohort (discovery P=5.42×10-7; replication P=0.039; combined P=7.42×10-9). This SNP also associated with ESRD (hazard ratio, 2.0 (95% confidence interval, 1.5 to 2.7); P=4.90×10-6). Similarly, rs931891 in LINC00923 associated with eGFR decline (P=1.44×10-4) in white patients without diabetes. In summary, SNPs in LINC00923, an RNA gene expressed in the kidney, significantly associated with CKD progression in individuals with nondiabetic CKD. However, the lack of equivalent cohorts hampered replication for most discovery loci. Further replication of our findings in comparable study populations is warranted.
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Affiliation(s)
- Afshin Parsa
- Division of Nephrology and .,Department of Medicine, Baltimore Veterans Affairs Medical Center, Baltimore, Maryland
| | - Peter A Kanetsky
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Rui Xiao
- Department of Biostatistics and Epidemiology and
| | - Jayanta Gupta
- Department of Health Sciences, College of Health Professions and Social Work, Florida Gulf Coast University, Fort Myers, FL
| | | | - Sophie Limou
- Molecular Genetic Epidemiology Section, Basic Research Laboratory, Center for Cancer Research, National Cancer Institute and Basic Science Program, Leidos Biomedical Research, Inc., Frederick National Laboratory, Frederick, Maryland
| | - Dawei Xie
- Department of Biostatistics and Epidemiology and
| | | | - Amanda Hyre Anderson
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Akinlolu Ojo
- Division of Nephrology, University of Michigan, Ann Arbor, Michigan
| | - John W Kusek
- Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Claudia M Lora
- Department of Medicine, Division of Nephrology, University of Illinois at Chicago, Chicago, Illinois
| | - L Lee Hamm
- Department of Medicine, Section of Nephrology, Tulane University, New Orleans, Louisiana
| | - Jiang He
- Department of Medicine, Section of Nephrology, Tulane University, New Orleans, Louisiana
| | - Niina Sandholm
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
| | - Janina Jeff
- Department of Medicine, The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine Mount Sinai, New York, New York
| | - Dominic E Raj
- Department of Medicine, The George Washington University School of Medicine, Washington, DC
| | - Carsten A Böger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Erwin Bottinger
- Department of Medicine, The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine Mount Sinai, New York, New York
| | - Shabnam Salimi
- Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
| | - Rulan S Parekh
- Division of Nephrology, Department of Pediatrics and Medicine, Hospital for Sick Children, University Health Network and the University of Toronto, Toronto, Ontario, Canada
| | - Sharon G Adler
- Department of Medicine, Division of Nephrology and Hypertension, Harbor-University of California, Los Angeles Medical Center, Los Angeles, California
| | | | | | | | - Per-Henrik Groop
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
| | - Carol Forsblom
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
| | - Barry I Freedman
- Department of Internal Medicine, Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Michael Lipkowitz
- Department of Medicine, Georgetown University Medical Center, Washington, DC; and
| | - Caroline S Fox
- Division of Intramural Research, National Heart, Lung and Blood Institute's Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, Massachusetts
| | | | - Harold I Feldman
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, Pennsylvania
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45
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Chen M, Rothman N, Ye Y, Gu J, Scheet PA, Huang M, Chang DW, Dinney CP, Silverman DT, Figueroa JD, Chanock SJ, Wu X. Pathway analysis of bladder cancer genome-wide association study identifies novel pathways involved in bladder cancer development. Genes Cancer 2016; 7:229-239. [PMID: 27738493 PMCID: PMC5059113 DOI: 10.18632/genesandcancer.113] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Accepted: 07/28/2016] [Indexed: 11/25/2022] Open
Abstract
Genome-wide association studies (GWAS) are designed to identify individual regions associated with cancer risk, but only explain a small fraction of the inherited variability. Alternative approach analyzing genetic variants within biological pathways has been proposed to discover networks of susceptibility genes with additional effects. The gene set enrichment analysis (GSEA) may complement and expand traditional GWAS analysis to identify novel genes and pathways associated with bladder cancer risk. We selected three GSEA methods: Gen-Gen, Aligator, and the SNP Ratio Test to evaluate cellular signaling pathways involved in bladder cancer susceptibility in a Texas GWAS population. The candidate genetic polymorphisms from the significant pathway selected by GSEA were validated in an independent NCI GWAS. We identified 18 novel pathways (P < 0.05) significantly associated with bladder cancer risk. Five of the most promising pathways (P ≤ 0.001 in any of the three GSEA methods) among the 18 pathways included two cell cycle pathways and neural cell adhesion molecule (NCAM), platelet-derived growth factor (PDGF), and unfolded protein response pathways. We validated the candidate polymorphisms in the NCI GWAS and found variants of RAPGEF1, SKP1, HERPUD1, CACNB2, CACNA1C, CACNA1S, COL4A2, SRC, and CACNA1C were associated with bladder cancer risk. Two CCNE1 variants, rs8102137 and rs997669, from cell cycle pathways showed the strongest associations; the CCNE1 signal at 19q12 has already been reported in previous GWAS. These findings offer additional etiologic insights highlighting the specific genes and pathways associated with bladder cancer development. GSEA may be a complementary tool to GWAS to identify additional loci of cancer susceptibility.
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Affiliation(s)
- Meng Chen
- Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Yuanqing Ye
- Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Jian Gu
- Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Paul A Scheet
- Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Maosheng Huang
- Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - David W Chang
- Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Colin P Dinney
- Department of Urology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Debra T Silverman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jonine D Figueroa
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Xifeng Wu
- Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
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46
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Abstract
Genetic factors contribute importantly to the risk of coronary artery disease (CAD), and in the past decade, there has been major progress in this area. The tools applied include genome-wide association studies encompassing >200,000 individuals complemented by bioinformatic approaches, including 1000 Genomes imputation, expression quantitative trait locus analyses, and interrogation of Encyclopedia of DNA Elements, Roadmap, and other data sets. close to 60 common SNPs (minor allele frequency>0.05) associated with CAD risk and reaching genome-wide significance (P<5 × 10(-8)) have been identified. Furthermore, a total of 202 independent signals in 109 loci have achieved a false discovery rate (q<0.05) and together explain 28% of the estimated heritability of CAD. These data have been used successfully to create genetic risk scores that can improve risk prediction beyond conventional risk factors and identify those individuals who will benefit most from statin therapy. Such information also has important applications in clinical medicine and drug discovery by using a Mendelian randomization approach to interrogate the causal nature of many factors found to associate with CAD risk in epidemiological studies. In contrast to genome-wide association studies, whole-exome sequencing has provided valuable information directly relevant to genes with known roles in plasma lipoprotein metabolism but has, thus far, failed to identify other rare coding variants linked to CAD. Overall, recent studies have led to a broader understanding of the genetic architecture of CAD and demonstrate that it largely derives from the cumulative effect of multiple common risk alleles individually of small effect size rather than rare variants with large effects on CAD risk. Despite this success, there has been limited progress in understanding the function of the novel loci; the majority of which are in noncoding regions of the genome.
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Affiliation(s)
- Ruth McPherson
- From the Department of Medicine, Atherogenomics Laboratory, Division of Cardiology, Ruddy Canadian Cardiovascular Genetics Center, University of Ottawa Heart Institute, Ottawa, Ontario, Canada (R.M.); and Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, and Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark (A.T.-H.).
| | - Anne Tybjaerg-Hansen
- From the Department of Medicine, Atherogenomics Laboratory, Division of Cardiology, Ruddy Canadian Cardiovascular Genetics Center, University of Ottawa Heart Institute, Ottawa, Ontario, Canada (R.M.); and Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, and Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark (A.T.-H.)
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47
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Chin YM, Tan LP, Abdul Aziz N, Mushiroda T, Kubo M, Mohd Kornain NK, Tan GW, Khoo ASB, Krishnan G, Pua KC, Yap YY, Teo SH, Lim PVH, Nakamura Y, Lum CL, Ng CC. Integrated pathway analysis of nasopharyngeal carcinoma implicates the axonemal dynein complex in the Malaysian cohort. Int J Cancer 2016; 139:1731-9. [PMID: 27236004 DOI: 10.1002/ijc.30207] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 04/20/2016] [Accepted: 05/06/2016] [Indexed: 01/22/2023]
Abstract
Nasopharyngeal carcinoma (NPC) is an epithelial squamous cell carcinoma on the mucosal lining of the nasopharynx. The etiology of NPC remains elusive despite many reported studies. Most studies employ a single platform approach, neglecting the cumulative influence of both the genome and transcriptome toward NPC development. We aim to employ an integrated pathway approach to identify dysregulated pathways linked to NPC. Our approach combines imputation NPC GWAS data from a Malaysian cohort as well as published expression data GSE12452 from both NPC and non-NPC nasopharynx tissues. Pathway association for GWAS data was performed using MAGENTA while for expression data, GSA-SNP was used with gene p values derived from differential expression values from GEO2R. Our study identified NPC association in the gene ontology (GO) axonemal dynein complex pathway (pGWAS-GSEA = 1.98 × 10(-2) ; pExpr-GSEA = 1.27 × 10(-24) ; pBonf-Combined = 4.15 × 10(-21) ). This association was replicated in a separate cohort using gene expression data from NPC and non-NPC nasopharynx tissues (pAmpliSeq-GSEA = 6.56 × 10(-4) ). Loss of function in the axonemal dynein complex causes impaired cilia function, leading to poor mucociliary clearance and subsequently upper or lower respiratory tract infection, the former of which includes the nasopharynx. Our approach illustrates the potential use of integrated pathway analysis in detecting gene sets involved in the development of NPC in the Malaysian cohort.
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Affiliation(s)
- Yoon-Ming Chin
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Lu Ping Tan
- Molecular Pathology Unit, Cancer Research Centre, Institute for Medical Research, Kuala Lumpur, Malaysia
| | - Norazlin Abdul Aziz
- Molecular Pathology Unit, Cancer Research Centre, Institute for Medical Research, Kuala Lumpur, Malaysia
| | - Taisei Mushiroda
- Laboratory for Pharmacogenetics, RIKEN Center for Integrative Medical Sciences, Tsurumi-Ku, Yokohama, Kanagawa, Japan
| | - Michiaki Kubo
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Tsurumi-Ku, Yokohama, Kanagawa, Japan
| | | | - Geok Wee Tan
- Molecular Pathology Unit, Cancer Research Centre, Institute for Medical Research, Kuala Lumpur, Malaysia
| | - Alan Soo-Beng Khoo
- Molecular Pathology Unit, Cancer Research Centre, Institute for Medical Research, Kuala Lumpur, Malaysia
| | - Gopala Krishnan
- Department of Otorhinolaryngology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Kin-Choo Pua
- Department of Otorhinolaryngology, Pulau Pinang General Hospital, Georgetown, Pulau Pinang, Malaysia
| | - Yoke-Yeow Yap
- Department of Surgery, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia.,Department of Otorhinolaryngology, Kuala Lumpur General Hospital, Kuala Lumpur, Malaysia
| | - Soo-Hwang Teo
- Cancer Research Malaysia, Sime Darby Medical Centre, Subang Jaya, Selangor, Malaysia
| | | | | | - Chee Lun Lum
- Department of Otorhinolaryngology, Queen Elizabeth Hospital, Kota Kinabalu, Sabah, Malaysia
| | | | - Ching-Ching Ng
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
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48
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Two-dimensional enrichment analysis for mining high-level imaging genetic associations. Brain Inform 2016; 4:27-37. [PMID: 27747820 PMCID: PMC5118198 DOI: 10.1007/s40708-016-0052-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 04/29/2016] [Indexed: 11/05/2022] Open
Abstract
Enrichment analysis has been widely applied in the genome-wide association studies, where gene sets corresponding to biological pathways are examined for significant associations with a phenotype to help increase statistical power and improve biological interpretation. In this work, we expand the scope of enrichment analysis into brain imaging genetics, an emerging field that studies how genetic variation influences brain structure and function measured by neuroimaging quantitative traits (QT). Given the high dimensionality of both imaging and genetic data, we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly considers meaningful gene sets (GS) and brain circuits (BC) and examines whether any given GS–BC pair is enriched in a list of gene–QT findings. Using gene expression data from Allen Human Brain Atlas and imaging genetics data from Alzheimer’s Disease Neuroimaging Initiative as test beds, we present an IGEA framework and conduct a proof-of-concept study. This empirical study identifies 25 significant high-level two-dimensional imaging genetics modules. Many of these modules are relevant to a variety of neurobiological pathways or neurodegenerative diseases, showing the promise of the proposal framework for providing insight into the mechanism of complex diseases.
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49
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Biological findings from the PheWAS catalog: focus on connective tissue-related disorders (pelvic floor dysfunction, abdominal hernia, varicose veins and hemorrhoids). Hum Genet 2016; 135:779-95. [DOI: 10.1007/s00439-016-1672-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Accepted: 04/17/2016] [Indexed: 01/31/2023]
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50
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Genome-wide association study of antidepressant response: involvement of the inorganic cation transmembrane transporter activity pathway. BMC Psychiatry 2016; 16:106. [PMID: 27091189 PMCID: PMC4836090 DOI: 10.1186/s12888-016-0813-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Accepted: 04/11/2016] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) represent the current frontier in pharmacogenomics. Thousands of subjects of Caucasian ancestry have been included in previous GWAS investigating antidepressant response. GWAS focused on this phenotype are lacking in Asian populations. METHODS A sample of 109 major depressive disorder (MDD) patients of Korean origin in antidepressant treatment was collected. Phenotypes were response and remission according to the Hamilton Rating Scale for Depression (HRSD). Genome-wide genotyping was performed using the Illumina Human Omni2.5-8 platform. The same phenotypes were used in the STAR*D level 1 (n = 1677) for independent replication. In order to corroborate findings and increase the comparability between the two datasets, three levels of analysis (SNPs, genes and pathways) were carried out. Bonferroni correction, permutations, and replication across samples were used to reduce the risk of false positives. RESULTS Among the genes replicated across the two samples (permutated p < 0.05 in both of them), CTNNA3 appeared promising. The inorganic cation transmembrane transporter activity pathway (GO:0022890) was associated with antidepressant response in both samples (p = 2.9e-5 and p = 0.001 in the Korean and STAR*D samples, respectively) and this pathway included CACNA1A, CACNA1C, and CACNB2 genes. CONCLUSIONS The present study supported the involvement of genes coding for subunits of L-type voltage-gated calcium channel in antidepressant efficacy across different ethnicities but replication of findings is required before any definitive statement.
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