1
|
Sahelijo N, Rajagopalan P, Qian L, Rahman R, Priyadarshi D, Goldstein D, Thomopoulos SI, Bennett DA, Farrer LA, Stein TD, Shen L, Huang H, Nho K, Andrew SJ, Davatzikos C, Thompson PM, Tcw J, Jun GR. Brain Cell-based Genetic Subtyping and Drug Repositioning for Alzheimer Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.21.24309255. [PMID: 38947056 PMCID: PMC11213108 DOI: 10.1101/2024.06.21.24309255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Alzheimer's Disease (AD) is characterized by its complex and heterogeneous etiology and gradual progression, leading to high drug failure rates in late-stage clinical trials. In order to better stratify individuals at risk for AD and discern potential therapeutic targets we employed a novel procedure utilizing cell-based co-regulated gene networks and polygenic risk scores (cbPRSs). After defining genetic subtypes using extremes of cbPRS distributions, we evaluated correlations of the genetic subtypes with previously defined AD subtypes defined on the basis of domain-specific cognitive functioning and neuroimaging biomarkers. Employing a PageRank algorithm, we identified priority gene targets for the genetic subtypes. Pathway analysis of priority genes demonstrated associations with neurodegeneration and suggested candidate drugs currently utilized in diabetes, hypertension, and epilepsy for repositioning in AD. Experimental validation utilizing human induced pluripotent stem cell (hiPSC)-derived astrocytes demonstrated the modifying effects of estradiol, levetiracetam, and pioglitazone on expression of APOE and complement C4 genes, suggesting potential repositioning for AD.
Collapse
|
2
|
Feng X, Liu S, Li K, Bu F, Yuan H. NCAD v1.0: a database for non-coding variant annotation and interpretation. J Genet Genomics 2024; 51:230-242. [PMID: 38142743 DOI: 10.1016/j.jgg.2023.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/15/2023] [Accepted: 12/18/2023] [Indexed: 12/26/2023]
Abstract
The application of whole genome sequencing is expanding in clinical diagnostics across various genetic disorders, and the significance of non-coding variants in penetrant diseases is increasingly being demonstrated. Therefore, it is urgent to improve the diagnostic yield by exploring the pathogenic mechanisms of variants in non-coding regions. However, the interpretation of non-coding variants remains a significant challenge, due to the complex functional regulatory mechanisms of non-coding regions and the current limitations of available databases and tools. Hence, we develop the non-coding variant annotation database (NCAD, http://www.ncawdb.net/), encompassing comprehensive insights into 665,679,194 variants, regulatory elements, and element interaction details. Integrating data from 96 sources, spanning both GRCh37 and GRCh38 versions, NCAD v1.0 provides vital information to support the genetic diagnosis of non-coding variants, including allele frequencies of 12 diverse populations, with a particular focus on the population frequency information for 230,235,698 variants in 20,964 Chinese individuals. Moreover, it offers prediction scores for variant functionality, five categories of regulatory elements, and four types of non-coding RNAs. With its rich data and comprehensive coverage, NCAD serves as a valuable platform, empowering researchers and clinicians with profound insights into non-coding regulatory mechanisms while facilitating the interpretation of non-coding variants.
Collapse
Affiliation(s)
- Xiaoshu Feng
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China
| | - Sihan Liu
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China
| | - Ke Li
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China
| | - Fengxiao Bu
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China.
| | - Huijun Yuan
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China.
| |
Collapse
|
3
|
Zhang J, Zhao H. eQTL studies: from bulk tissues to single cells. J Genet Genomics 2023; 50:925-933. [PMID: 37207929 PMCID: PMC10656365 DOI: 10.1016/j.jgg.2023.05.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/02/2023] [Accepted: 05/04/2023] [Indexed: 05/21/2023]
Abstract
An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to a better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detection of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.
Collapse
Affiliation(s)
- Jingfei Zhang
- Information Systems and Operations Management, Emory University, Atlanta, GA 30322, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 208034, USA.
| |
Collapse
|
4
|
Khatiwada A, Yilmaz AS, Wolf BJ, Pietrzak M, Chung D. multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results. PLoS Comput Biol 2023; 19:e1011686. [PMID: 38060592 PMCID: PMC10729974 DOI: 10.1371/journal.pcbi.1011686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 12/19/2023] [Accepted: 11/13/2023] [Indexed: 12/20/2023] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to 'pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.
Collapse
Affiliation(s)
- Aastha Khatiwada
- Department of Biostatistics and Bioinformatics, National Jewish Health, Denver, Colorado, United States of America
| | - Ayse Selen Yilmaz
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
| | - Bethany J. Wolf
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - Maciej Pietrzak
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, United States of America
| |
Collapse
|
5
|
Moore A, Marks JA, Quach BC, Guo Y, Bierut LJ, Gaddis NC, Hancock DB, Page GP, Johnson EO. Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value. Commun Biol 2023; 6:1199. [PMID: 38001305 PMCID: PMC10673847 DOI: 10.1038/s42003-023-05413-w] [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: 04/05/2022] [Accepted: 10/03/2023] [Indexed: 11/26/2023] Open
Abstract
Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 17 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies.
Collapse
Affiliation(s)
- Amy Moore
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA.
| | - Jesse A Marks
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
| | - Bryan C Quach
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
| | - Yuelong Guo
- GeneCentric Therapeutics, Inc., Cary, NC, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Nathan C Gaddis
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
| | - Dana B Hancock
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
| | - Grier P Page
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
- Fellow Program, RTI International, Research Triangle Park, NC, 27709, USA
| | - Eric O Johnson
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA.
- Fellow Program, RTI International, Research Triangle Park, NC, 27709, USA.
| |
Collapse
|
6
|
Hao X, Shao Z, Zhang N, Jiang M, Cao X, Li S, Guan Y, Wang C. Integrative genome-wide analyses identify novel loci associated with kidney stones and provide insights into its genetic architecture. Nat Commun 2023; 14:7498. [PMID: 37980427 PMCID: PMC10657403 DOI: 10.1038/s41467-023-43400-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 11/08/2023] [Indexed: 11/20/2023] Open
Abstract
Kidney stone disease (KSD) is a complex disorder with high heritability and prevalence. We performed a large genome-wide association study (GWAS) meta-analysis for KSD to date, including 720,199 individuals with 17,969 cases in European population. We identified 44 susceptibility loci, including 28 novel loci. Cell type-specific analysis pinpointed the proximal tubule as the most relevant cells where susceptibility variants might act through a tissue-specific fashion. By integrating kidney-specific omics data, we prioritized 223 genes which strengthened the importance of ion homeostasis, including calcium and magnesium in stone formation, and suggested potential target drugs for the treatment. The genitourinary and digestive diseases showed stronger genetic correlations with KSD. In this study, we generate an atlas of candidate genes, tissue and cell types involved in the formation of KSD. In addition, we provide potential drug targets for KSD treatment and insights into shared regulation with other diseases.
Collapse
Affiliation(s)
- Xingjie Hao
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
| | - Zhonghe Shao
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Ning Zhang
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China
| | - Minghui Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Xi Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Si Li
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Yunlong Guan
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
| |
Collapse
|
7
|
Chung J, Sahelijo N, Maruyama T, Hu J, Panitch R, Xia W, Mez J, Stein TD, Saykin AJ, Takeyama H, Farrer LA, Crane PK, Nho K, Jun GR. Alzheimer's disease heterogeneity explained by polygenic risk scores derived from brain transcriptomic profiles. Alzheimers Dement 2023; 19:5173-5184. [PMID: 37166019 PMCID: PMC10638468 DOI: 10.1002/alz.13069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 03/03/2023] [Accepted: 03/08/2023] [Indexed: 05/12/2023]
Abstract
INTRODUCTION Alzheimer's disease (AD) is heterogeneous, both clinically and neuropathologically. We investigated whether polygenic risk scores (PRSs) integrated with transcriptome profiles from AD brains can explain AD clinical heterogeneity. METHODS We conducted co-expression network analysis and identified gene sets (modules) that were preserved in three AD transcriptome datasets and associated with AD-related neuropathological traits including neuritic plaques (NPs) and neurofibrillary tangles (NFTs). We computed the module-based PRSs (mbPRSs) for each module and tested associations with mbPRSs for cognitive test scores, cognitively defined AD subgroups, and brain imaging data. RESULTS Of the modules significantly associated with NPs and/or NFTs, the mbPRSs from two modules (M6 and M9) showed distinct associations with language and visuospatial functioning, respectively. They matched clinical subtypes and brain atrophy at specific regions. DISCUSSION Our findings demonstrate that polygenic profiling based on co-expressed gene sets can explain heterogeneity in AD patients, enabling genetically informed patient stratification and precision medicine in AD. HIGHLIGHTS Co-expression gene-network analysis in Alzheimer's disease (AD) brains identified gene sets (modules) associated with AD heterogeneity. AD-associated modules were selected when genes in each module were enriched for neuritic plaques and neurofibrillary tangles. Polygenic risk scores from two selected modules were linked to the matching cognitively defined AD subgroups (language and visuospatial subgroups). Polygenic risk scores from the two modules were associated with cognitive performance in language and visuospatial domains and the associations were confirmed in regional-specific brain atrophy data.
Collapse
Affiliation(s)
- Jaeyoon Chung
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Nathan Sahelijo
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Toru Maruyama
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
| | - Junming Hu
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Rebecca Panitch
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Weiming Xia
- Department of Pharmacology & Experimental Therapeutics, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Veterans Affairs Medical Center, Bedford, MA 01730, USA
| | - Jesse Mez
- Department of Neurology, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Thor D. Stein
- Department of Veterans Affairs Medical Center, Bedford, MA 01730, USA
- Department of Pathology & Laboratory Medicine, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Boston VA Healthcare Center, Boston, MA 02130, USA
| | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Haruko Takeyama
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
- Computational Bio Big-Data Open Innovation Laboratory, AIST-Waseda University, Japan, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Research Organization for Nano and Life Innovations, Waseda University, 513, Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
- Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Lindsay A. Farrer
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Ophthalmology, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA
| | - Paul K. Crane
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences and Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Gyungah R. Jun
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Ophthalmology, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA
| |
Collapse
|
8
|
Andersen MS, Leikfoss IS, Brorson IS, Cappelletti C, Bettencourt C, Toft M, Pihlstrøm L. Epigenome-wide association study of peripheral immune cell populations in Parkinson's disease. NPJ Parkinsons Dis 2023; 9:149. [PMID: 37903812 PMCID: PMC10616224 DOI: 10.1038/s41531-023-00594-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 10/19/2023] [Indexed: 11/01/2023] Open
Abstract
Understanding the contribution of immune mechanisms to Parkinson's disease pathogenesis is an important challenge, potentially of major therapeutic implications. To further elucidate the involvement of peripheral immune cells, we studied epigenome-wide DNA methylation in isolated populations of CD14+ monocytes, CD19+ B cells, CD4+ T cells, and CD8+ T cells from Parkinson's disease patients and healthy control participants. We included 25 patients with a maximum five years of disease duration and 25 controls, and isolated four immune cell populations from each fresh blood sample. Epigenome-wide DNA methylation profiles were generated from 186 samples using the Illumina MethylationEpic array and association with disease status was tested using linear regression models. We identified six differentially methylated CpGs in CD14+ monocytes and one in CD8 + T cells. Four differentially methylated regions were identified in monocytes, including a region upstream of RAB32, a gene that has been linked to LRRK2. Methylation upstream of RAB32 correlated negatively with mRNA expression, and RAB32 expression was upregulated in Parkinson's disease both in our samples and in summary statistics from a previous study. Our epigenome-wide association study of early Parkinson's disease provides evidence for methylation changes across different peripheral immune cell types, highlighting monocytes and the RAB32 locus. The findings were predominantly cell-type-specific, demonstrating the value of isolating purified cell populations for genomic studies.
Collapse
Affiliation(s)
- Maren Stolp Andersen
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | | | | | - Conceicao Bettencourt
- Department of Neurodegenerative Disease and Queen Square Brain Bank for Neurological Disorders, Queen Square Institute of Neurology, University College London, London, UK
| | - Mathias Toft
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, Oslo, Norway.
| |
Collapse
|
9
|
Cai M, Wang Z, Xiao J, Hu X, Chen G, Yang C. XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias. Nat Commun 2023; 14:6870. [PMID: 37898663 PMCID: PMC10613261 DOI: 10.1038/s41467-023-42614-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 10/17/2023] [Indexed: 10/30/2023] Open
Abstract
Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution.
Collapse
Affiliation(s)
- Mingxuan Cai
- Department of Biostatistics, City University of Hong Kong, Hong Kong SAR, China.
| | - Zhiwei Wang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou, 511458, China
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Jiashun Xiao
- Shenzhen Research Institute of Big Data, Shenzhen, 518172, China
| | - Xianghong Hu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou, 511458, China
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Gang Chen
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- WeGene, Shenzhen Zaozhidao Technology Co., Ltd, Shenzhen, 518040, China
- Graduate Affairs, Faculty of Medicine, Chulalongkorn University, 10330, Bangkok, Thailand
| | - Can Yang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou, 511458, China.
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
| |
Collapse
|
10
|
Gouilly D, Rafiq M, Nogueira L, Salabert AS, Payoux P, Péran P, Pariente J. Beyond the amyloid cascade: An update of Alzheimer's disease pathophysiology. Rev Neurol (Paris) 2023; 179:812-830. [PMID: 36906457 DOI: 10.1016/j.neurol.2022.12.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 10/02/2022] [Accepted: 12/02/2022] [Indexed: 03/13/2023]
Abstract
Alzheimer's disease (AD) is a multi-etiology disease. The biological system of AD is associated with multidomain genetic, molecular, cellular, and network brain dysfunctions, interacting with central and peripheral immunity. These dysfunctions have been primarily conceptualized according to the assumption that amyloid deposition in the brain, whether from a stochastic or a genetic accident, is the upstream pathological change. However, the arborescence of AD pathological changes suggests that a single amyloid pathway might be too restrictive or inconsistent with a cascading effect. In this review, we discuss the recent human studies of late-onset AD pathophysiology in an attempt to establish a general updated view focusing on the early stages. Several factors highlight heterogenous multi-cellular pathological changes in AD, which seem to work in a self-amplifying manner with amyloid and tau pathologies. Neuroinflammation has an increasing importance as a major pathological driver, and perhaps as a convergent biological basis of aging, genetic, lifestyle and environmental risk factors.
Collapse
Affiliation(s)
- D Gouilly
- Toulouse Neuroimaging Center, Toulouse, France.
| | - M Rafiq
- Toulouse Neuroimaging Center, Toulouse, France; Department of Cognitive Neurology, Epilepsy and Movement Disorders, CHU Toulouse Purpan, France
| | - L Nogueira
- Department of Cell Biology and Cytology, CHU Toulouse Purpan, France
| | - A-S Salabert
- Toulouse Neuroimaging Center, Toulouse, France; Department of Nuclear Medicine, CHU Toulouse Purpan, France
| | - P Payoux
- Toulouse Neuroimaging Center, Toulouse, France; Department of Nuclear Medicine, CHU Toulouse Purpan, France; Center of Clinical Investigation, CHU Toulouse Purpan (CIC1436), France
| | - P Péran
- Toulouse Neuroimaging Center, Toulouse, France
| | - J Pariente
- Toulouse Neuroimaging Center, Toulouse, France; Department of Cognitive Neurology, Epilepsy and Movement Disorders, CHU Toulouse Purpan, France; Center of Clinical Investigation, CHU Toulouse Purpan (CIC1436), France
| |
Collapse
|
11
|
He Q, Keding TJ, Zhang Q, Miao J, Russell JD, Herringa RJ, Lu Q, Travers BG, Li JJ. Neurogenetic mechanisms of risk for ADHD: Examining associations of polygenic scores and brain volumes in a population cohort. J Neurodev Disord 2023; 15:30. [PMID: 37653373 PMCID: PMC10469494 DOI: 10.1186/s11689-023-09498-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 08/21/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND ADHD polygenic scores (PGSs) have been previously shown to predict ADHD outcomes in several studies. However, ADHD PGSs are typically correlated with ADHD but not necessarily reflective of causal mechanisms. More research is needed to elucidate the neurobiological mechanisms underlying ADHD. We leveraged functional annotation information into an ADHD PGS to (1) improve the prediction performance over a non-annotated ADHD PGS and (2) test whether volumetric variation in brain regions putatively associated with ADHD mediate the association between PGSs and ADHD outcomes. METHODS Data were from the Philadelphia Neurodevelopmental Cohort (N = 555). Multiple mediation models were tested to examine the indirect effects of two ADHD PGSs-one using a traditional computation involving clumping and thresholding and another using a functionally annotated approach (i.e., AnnoPred)-on ADHD inattention (IA) and hyperactivity-impulsivity (HI) symptoms, via gray matter volumes in the cingulate gyrus, angular gyrus, caudate, dorsolateral prefrontal cortex (DLPFC), and inferior temporal lobe. RESULTS A direct effect was detected between the AnnoPred ADHD PGS and IA symptoms in adolescents. No indirect effects via brain volumes were detected for either IA or HI symptoms. However, both ADHD PGSs were negatively associated with the DLPFC. CONCLUSIONS The AnnoPred ADHD PGS was a more developmentally specific predictor of adolescent IA symptoms compared to the traditional ADHD PGS. However, brain volumes did not mediate the effects of either a traditional or AnnoPred ADHD PGS on ADHD symptoms, suggesting that we may still be underpowered in clarifying brain-based biomarkers for ADHD using genetic measures.
Collapse
Affiliation(s)
- Quanfa He
- Department of Psychology, University of, Wisconsin-Madison, 1202 W. Johnson Street, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, USA
| | | | - Qi Zhang
- Department of Educational Psychology, University of Wisconsin-Madison, Madison, USA
| | - Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, USA
| | - Justin D Russell
- Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Ryan J Herringa
- Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, USA
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, USA
- Department of Statistics, University of Wisconsin-Madison, Madison, USA
| | - Brittany G Travers
- Waisman Center, University of Wisconsin-Madison, Madison, USA
- Department of Kinesiology, University of Wisconsin-Madison, Madison, USA
| | - James J Li
- Department of Psychology, University of, Wisconsin-Madison, 1202 W. Johnson Street, Madison, WI, 53706, USA.
- Waisman Center, University of Wisconsin-Madison, Madison, USA.
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, USA.
| |
Collapse
|
12
|
Ye Y, Noche RB, Szejko N, Both CP, Acosta JN, Leasure AC, Brown SC, Sheth KN, Gill TM, Zhao H, Falcone GJ. A genome-wide association study of frailty identifies significant genetic correlation with neuropsychiatric, cardiovascular, and inflammation pathways. GeroScience 2023; 45:2511-2523. [PMID: 36928559 PMCID: PMC10651618 DOI: 10.1007/s11357-023-00771-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
Frailty is an aging-related clinical phenotype defined as a state in which there is an increase in a person's vulnerability for dependency and/or mortality when exposed to a stressor. While underlying mechanisms leading to the occurrence of frailty are complex, the importance of genetic factors has not been fully investigated. We conducted a large-scale genome-wide association study (GWAS) of frailty, as defined by the five criteria (weight loss, exhaustion, physical activity, walking speed, and grip strength) captured in the Fried Frailty Score (FFS), in 386,565 European descent participants enrolled in the UK Biobank (mean age 57 [SD 8] years, 208,481 [54%] females). We identified 37 independent, novel loci associated with the FFS (p < 5 × 10-8), including seven loci without prior described associations with other traits. The variants associated with FFS were significantly enriched in brain tissues as well as aging-related pathways. Our post-GWAS bioinformatic analyses revealed significant genetic correlations between FFS and cardiovascular-, neurological-, and inflammation-related diseases/traits, and subsequent Mendelian Randomization analyses identified causal associations with chronic pain, obesity, diabetes, education-related traits, joint disorders, and depressive/neurological, metabolic, and respiratory diseases. The GWAS signals were replicated in the Health and Retirement Study (HRS, n = 9,720, mean age 73 [SD 7], 5,582 [57%] females), where the polygenic risk score built from UKB GWAS was significantly associated with the FFS in HRS individuals (OR per SD of the score 1.27, 95% CI 1.22-1.31, p = 1.3 × 10-11). These results provide new insight into the biology of frailty by comprehensively evaluating its genetic architecture.
Collapse
Affiliation(s)
- Yixuan Ye
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Rommell B Noche
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1004D, P.O. Box 20801, New Haven, CT, 06510, USA
| | - Natalia Szejko
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1004D, P.O. Box 20801, New Haven, CT, 06510, USA
- Department of Neurology, Medical University of Warsaw, Warsaw, Poland
- Department of Bioethics, Medical University of Warsaw, Warsaw, Poland
| | - Cameron P Both
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1004D, P.O. Box 20801, New Haven, CT, 06510, USA
| | - Julian N Acosta
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1004D, P.O. Box 20801, New Haven, CT, 06510, USA
| | - Audrey C Leasure
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1004D, P.O. Box 20801, New Haven, CT, 06510, USA
| | - Stacy C Brown
- University of Hawai'I, John A. Burns School of Medicine, Honolulu, HI, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1004D, P.O. Box 20801, New Haven, CT, 06510, USA
| | - Thomas M Gill
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Hongyu Zhao
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
- Department of Biostatistics, Yale School of Public Health, 60 College Street, P.O. Box 208034, New Haven, CT, 06520, USA.
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1004D, P.O. Box 20801, New Haven, CT, 06510, USA.
| |
Collapse
|
13
|
Dincer TU, Ernst J. Integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.14.549056. [PMID: 37503240 PMCID: PMC10369970 DOI: 10.1101/2023.07.14.549056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
We introduce ChromActivity, a computational framework for predicting and annotating regulatory activity across the genome through integration of multiple epigenomic maps and various functional characterization datasets. ChromActivity generates genomewide predictions of regulatory activity associated with each functional characterization dataset across many cell types based on available epigenomic data. It then for each cell type produces (1) ChromScoreHMM genome annotations based on the combinatorial and spatial patterns within these predictions and (2) ChromScore tracks of overall predicted regulatory activity. ChromActivity provides a resource for analyzing and interpreting the human regulatory genome across diverse cell types.
Collapse
Affiliation(s)
- Tevfik Umut Dincer
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, 90095, USA
- Department of Biological Chemistry, University of California, Los Angeles, CA, 90095, USA
| | - Jason Ernst
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, 90095, USA
- Department of Biological Chemistry, University of California, Los Angeles, CA, 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at University of California, Los Angeles, CA, 90095, USA
- Computer Science Department, University of California, Los Angeles, CA, 90095, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, CA, 90095, USA
- Department of Computational Medicine, University of California, Los Angeles, CA, 90095, USA
| |
Collapse
|
14
|
Deng Q, Gupta A, Jeon H, Nam JH, Yilmaz AS, Chang W, Pietrzak M, Li L, Kim HJ, Chung D. graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data. Front Genet 2023; 14:1079198. [PMID: 37501720 PMCID: PMC10370274 DOI: 10.3389/fgene.2023.1079198] [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: 10/25/2022] [Accepted: 06/21/2023] [Indexed: 07/29/2023] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants.
Collapse
Affiliation(s)
- Qiaolan Deng
- The Interdisciplinary PhD Program in Biostatistics, The Ohio State University, Columbus, OH, United States
| | - Arkobrato Gupta
- The Interdisciplinary PhD Program in Biostatistics, The Ohio State University, Columbus, OH, United States
| | - Hyeongseon Jeon
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States
| | - Jin Hyun Nam
- Division of Big Data Science, Korea University Sejong Campus, Sejong, Republic of Korea
| | - Ayse Selen Yilmaz
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Won Chang
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, United States
| | - Maciej Pietrzak
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Hang J. Kim
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, United States
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States
| |
Collapse
|
15
|
Liu W, Deng W, Chen M, Dong Z, Zhu B, Yu Z, Tang D, Sauler M, Lin C, Wain LV, Cho MH, Kaminski N, Zhao H. A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases. PLoS Genet 2023; 19:e1010825. [PMID: 37523391 PMCID: PMC10414598 DOI: 10.1371/journal.pgen.1010825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 08/10/2023] [Accepted: 06/12/2023] [Indexed: 08/02/2023] Open
Abstract
Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. In this manuscript, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed good statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.
Collapse
Affiliation(s)
- Wei Liu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Wenxuan Deng
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Ming Chen
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Zihan Dong
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Biqing Zhu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Zhaolong Yu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Daiwei Tang
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Maor Sauler
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Chen Lin
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Louise V. Wain
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Naftali Kaminski
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Hongyu Zhao
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| |
Collapse
|
16
|
Cheng Y, Dao C, Zhou H, Li B, Kember RL, Toikumo S, Zhao H, Gelernter J, Kranzler HR, Justice AC, Xu K. Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program. Transl Psychiatry 2023; 13:148. [PMID: 37147289 PMCID: PMC10162964 DOI: 10.1038/s41398-023-02409-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 05/07/2023] Open
Abstract
Smoking behaviors and alcohol use disorder (AUD), both moderately heritable traits, commonly co-occur in the general population. Single-trait genome-wide association studies (GWAS) have identified multiple loci for smoking and AUD. However, GWASs that have aimed to identify loci contributing to co-occurring smoking and AUD have used small samples and thus have not been highly informative. Applying multi-trait analysis of GWASs (MTAG), we conducted a joint GWAS of smoking and AUD with data from the Million Veteran Program (N = 318,694). By leveraging GWAS summary statistics for AUD, MTAG identified 21 genome-wide significant (GWS) loci associated with smoking initiation and 17 loci associated with smoking cessation compared to 16 and 8 loci, respectively, identified by single-trait GWAS. The novel loci for smoking behaviors identified by MTAG included those previously associated with psychiatric or substance use traits. Colocalization analysis identified 10 loci shared by AUD and smoking status traits, all of which achieved GWS in MTAG, including variants on SIX3, NCAM1, and near DRD2. Functional annotation of the MTAG variants highlighted biologically important regions on ZBTB20, DRD2, PPP6C, and GCKR that contribute to smoking behaviors. In contrast, MTAG of smoking behaviors and alcohol consumption (AC) did not enhance discovery compared with single-trait GWAS for smoking behaviors. We conclude that using MTAG to augment the power of GWAS enables the identification of novel genetic variants for commonly co-occuring phenotypes, providing new insights into their pleiotropic effects on smoking behavior and AUD.
Collapse
Affiliation(s)
- Youshu Cheng
- Yale School of Public Health, New Haven, CT, 06511, USA
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Cecilia Dao
- Yale School of Public Health, New Haven, CT, 06511, USA
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Hang Zhou
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA
- Yale School of Medicine, New Haven, CT, 06511, USA
| | - Boyang Li
- Yale School of Public Health, New Haven, CT, 06511, USA
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Rachel L Kember
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, 19104, USA
| | - Sylvanus Toikumo
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, 19104, USA
| | - Hongyu Zhao
- Yale School of Public Health, New Haven, CT, 06511, USA
- Yale School of Medicine, New Haven, CT, 06511, USA
| | - Joel Gelernter
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA
- Yale School of Medicine, New Haven, CT, 06511, USA
| | - Henry R Kranzler
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, 19104, USA
| | - Amy C Justice
- Yale School of Public Health, New Haven, CT, 06511, USA
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA
- Yale School of Medicine, New Haven, CT, 06511, USA
| | - Ke Xu
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA.
- Yale School of Medicine, New Haven, CT, 06511, USA.
| |
Collapse
|
17
|
Johnson EC, Kapoor M, Hatoum AS, Zhou H, Polimanti R, Wendt FR, Walters RK, Lai D, Kember RL, Hartz S, Meyers JL, Peterson RE, Ripke S, Bigdeli TB, Fanous AH, Pato CN, Pato MT, Goate AM, Kranzler HR, O'Donovan MC, Walters JTR, Gelernter J, Edenberg HJ, Agrawal A. Investigation of convergent and divergent genetic influences underlying schizophrenia and alcohol use disorder. Psychol Med 2023; 53:1196-1204. [PMID: 34231451 PMCID: PMC8738774 DOI: 10.1017/s003329172100266x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders. METHODS We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific. RESULTS We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001). CONCLUSIONS Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.
Collapse
Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Manav Kapoor
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Hang Zhou
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Frank R Wendt
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Raymond K Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Rachel L Kember
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Sarah Hartz
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jacquelyn L Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
- Henri Begleiter Neurodynamics Laboratory, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Roseann E Peterson
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Tim B Bigdeli
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Ayman H Fanous
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Carlos N Pato
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Michele T Pato
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Alison M Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Henry R Kranzler
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Michael C O'Donovan
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | - James T R Walters
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | - Joel Gelernter
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| |
Collapse
|
18
|
Zhang J, Zhao H. eQTL Studies: from Bulk Tissues to Single Cells. ARXIV 2023:arXiv:2302.11662v1. [PMID: 36866231 PMCID: PMC9980190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of certain genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies to date have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detections of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.
Collapse
Affiliation(s)
- Jingfei Zhang
- Information Systems and Operations Management, Emory University
| | - Hongyu Zhao
- Department of Biostatistics, Yale University
| |
Collapse
|
19
|
Yu X, Xiao J, Cai M, Jiao Y, Wan X, Liu J, Yang C. PALM: a powerful and adaptive latent model for prioritizing risk variants with functional annotations. Bioinformatics 2023; 39:7028484. [PMID: 36744920 PMCID: PMC9950853 DOI: 10.1093/bioinformatics/btad068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/12/2023] [Accepted: 02/03/2023] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION The findings from genome-wide association studies (GWASs) have greatly helped us to understand the genetic basis of human complex traits and diseases. Despite the tremendous progress, much effects are still needed to address several major challenges arising in GWAS. First, most GWAS hits are located in the non-coding region of human genome, and thus their biological functions largely remain unknown. Second, due to the polygenicity of human complex traits and diseases, many genetic risk variants with weak or moderate effects have not been identified yet. RESULTS To address the above challenges, we propose a powerful and adaptive latent model (PALM) to integrate cell-type/tissue-specific functional annotations with GWAS summary statistics. Unlike existing methods, which are mainly based on linear models, PALM leverages a tree ensemble to adaptively characterize non-linear relationship between functional annotations and the association status of genetic variants. To make PALM scalable to millions of variants and hundreds of functional annotations, we develop a functional gradient-based expectation-maximization algorithm, to fit the tree-based non-linear model in a stable manner. Through comprehensive simulation studies, we show that PALM not only controls false discovery rate well, but also improves statistical power of identifying risk variants. We also apply PALM to integrate summary statistics of 30 GWASs with 127 cell type/tissue-specific functional annotations. The results indicate that PALM can identify more risk variants as well as rank the importance of functional annotations, yielding better interpretation of GWAS results. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/YangLabHKUST/PALM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Xinyi Yu
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China.,Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Jiashun Xiao
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China.,Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Mingxuan Cai
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.,Department of Biostatistics, City University of Hong Kong, Hong Kong SAR, China
| | - Yuling Jiao
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China
| | - Jin Liu
- Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore.,School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen 518172, China
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| |
Collapse
|
20
|
Spanbauer C, Pan W. Sparse prediction informed by genetic annotations using the logit normal prior for Bayesian regression tree ensembles. Genet Epidemiol 2023; 47:26-44. [PMID: 36349692 PMCID: PMC9892284 DOI: 10.1002/gepi.22505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/08/2022] [Accepted: 09/21/2022] [Indexed: 11/11/2022]
Abstract
Using high-dimensional genetic variants such as single nucleotide polymorphisms (SNP) to predict complex diseases and traits has important applications in basic research and other clinical settings. For example, predicting gene expression is a necessary first step to identify (putative) causal genes in transcriptome-wide association studies. Due to weak signals, high-dimensionality, and linkage disequilibrium (correlation) among SNPs, building such a prediction model is challenging. However, functional annotations at the SNP level (e.g., as epigenomic data across multiple cell- or tissue-types) are available and could be used to inform predictor importance and aid in outcome prediction. Existing approaches to incorporate annotations have been based mainly on (generalized) linear models. Bayesian additive regression trees (BART), in contrast, is a reliable method to obtain high-quality nonlinear out of sample predictions without overfitting. Unfortunately, the default prior from BART may be too inflexible to handle sparse situations where the number of predictors approaches or surpasses the number of observations. Motivated by our real data application, this article proposes an alternative prior based on the logit normal distribution because it provides a framework that is adaptive to sparsity and can model informative functional annotations. It also provides a framework to incorporate prior information about the between SNP correlations. Computational details for carrying out inference are presented along with the results from a simulation study and a genome-wide prediction analysis of the Alzheimer's Disease Neuroimaging Initiative data.
Collapse
Affiliation(s)
- Charles Spanbauer
- Division of Biostatistics, University of Minnesota, MN, USA,Corresponding author;
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, MN, USA
| | - The Alzheimer’s Disease Neuroimaging Initiative
- Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
| |
Collapse
|
21
|
Panyard DJ, Deming YK, Darst BF, Van Hulle CA, Zetterberg H, Blennow K, Kollmorgen G, Suridjan I, Carlsson CM, Johnson SC, Asthana S, Engelman CD, Lu Q. Liver-Specific Polygenic Risk Score Is Associated with Alzheimer's Disease Diagnosis. J Alzheimers Dis 2023; 92:395-409. [PMID: 36744333 PMCID: PMC10050104 DOI: 10.3233/jad-220599] [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] [Indexed: 02/05/2023]
Abstract
BACKGROUND Our understanding of the pathophysiology underlying Alzheimer's disease (AD) has benefited from genomic analyses, including those that leverage polygenic risk score (PRS) models of disease. The use of functional annotation has been able to improve the power of genomic models. OBJECTIVE We sought to leverage genomic functional annotations to build tissue-specific AD PRS models and study their relationship with AD and its biomarkers. METHODS We built 13 tissue-specific AD PRS and studied the scores' relationships with AD diagnosis, cerebrospinal fluid (CSF) amyloid, CSF tau, and other CSF biomarkers in two longitudinal cohort studies of AD. RESULTS The AD PRS model that was most predictive of AD diagnosis (even without APOE) was the liver AD PRS: n = 1,115; odds ratio = 2.15 (1.67-2.78), p = 3.62×10-9. The liver AD PRS was also statistically significantly associated with cerebrospinal fluid biomarker evidence of amyloid-β (Aβ42:Aβ40 ratio, p = 3.53×10-6) and the phosphorylated tau:amyloid-β ratio (p = 1.45×10-5). CONCLUSION These findings provide further evidence of the role of the liver-functional genome in AD and the benefits of incorporating functional annotation into genomic research.
Collapse
Affiliation(s)
- Daniel J. Panyard
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Yuetiva K. Deming
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Burcu F. Darst
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States of America
| | - Carol A. Van Hulle
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | | | - Cynthia M. Carlsson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sanjay Asthana
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Corinne D. Engelman
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI 53726, United States of America
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53706, United States of America
| |
Collapse
|
22
|
Kim DY, Kim JS, Seo YS, Park WY, Kim BH, Hong EH, Kim JY, Cho SJ, Rhee HY, Kim A, Kim KY, Oh DJ, Chung WK. Evaluation of Efficacy and Safety Using Low Dose Radiation Therapy with Alzheimer's Disease: A Protocol for Multicenter Phase II Clinical Trial. J Alzheimers Dis 2023; 95:1263-1272. [PMID: 37638435 PMCID: PMC10578208 DOI: 10.3233/jad-230241] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/12/2023] [Indexed: 08/29/2023]
Abstract
BACKGROUND Alzheimer's disease (AD), the most common cause of dementia, is a neurodegenerative disease resulting from extracellular and intracellular deposits of amyloid-β (Aβ) and neurofibrillary tangles in the brain. Although many clinical studies evaluating pharmacological approaches have been conducted, most have shown disappointing results; thus, innovative strategies other than drugs have been actively attempted. OBJECTIVE This study aims to explore low-dose radiation therapy (LDRT) for the treatment of patients with AD based on preclinical evidence, case reports, and a small pilot trial in humans. METHODS This study is a phase II, multicenter, prospective, single-blinded, randomized controlled trial that will evaluate the efficacy and safety of LDRT to the whole brain using a linear accelerator in patients with mild AD. Sixty participants will be randomly assigned to three groups: experimental I (24 cGy/6 fractions), experimental II (300 cGy/6 fractions), or sham RT group (0 cGy/6 fractions). During LDRT and follow-up visits after LDRT, possible adverse events will be assessed by the physician's interview and neurological examinations. Furthermore, the effectiveness of LDRT will be measured using neurocognitive function tests and imaging tools at 6 and 12 months after LDRT. We will also monitor the alterations in cytokines, Aβ42/Aβ40 ratio, and tau levels in plasma. Our primary endpoint is the change in cognitive function test scores estimated by the Alzheimer's Disease Assessment Scale-Korea compared to baseline after 6 months of LDRT. CONCLUSIONS This study is registered at ClinicalTrials.gov [NCT05635968] and is currently recruiting patients. This study will provide evidence that LDRT is a new treatment strategy for AD.
Collapse
Affiliation(s)
- Dong-Yun Kim
- Department of Radiation Oncology, Kyunghee University Hospital at Gangdong, Seoul, Korea
| | - Jae Sik Kim
- Department of Radiation Oncology, Soonchunhyang University Seoul Hospital, Seoul, Korea
| | - Young-Seok Seo
- Department of Radiation Oncology, Chungbuk National University Hospital, Cheongju, Korea
| | - Woo-Yoon Park
- Department of Radiation Oncology, Chungbuk National University Hospital, Cheongju, Korea
| | - Byoung Hyuck Kim
- Department of Radiation Oncology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea
| | - Eun-Hee Hong
- Radiation Health Research Institute, Korea Hydro & Nuclear Power Co Ltd., Seoul, Korea
| | - Ji Young Kim
- Radiation Health Research Institute, Korea Hydro & Nuclear Power Co Ltd., Seoul, Korea
| | - Seong-Jun Cho
- Radiation Health Research Institute, Korea Hydro & Nuclear Power Co Ltd., Seoul, Korea
| | - Hak Young Rhee
- Department of Neurology, Kyunghee University Hospital at Gangdong, Seoul, Korea
| | - Aryun Kim
- Department of Neurology, Chungbuk National University Hospital, Cheongju, Korea
| | - Keun You Kim
- Department of Psychiatry, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea
| | - Dae Jong Oh
- Workplace Mental Health Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Weon Kuu Chung
- Department of Radiation Oncology, Kyunghee University Hospital at Gangdong, Seoul, Korea
| |
Collapse
|
23
|
Romero-Molina C, Garretti F, Andrews SJ, Marcora E, Goate AM. Microglial efferocytosis: Diving into the Alzheimer's disease gene pool. Neuron 2022; 110:3513-3533. [PMID: 36327897 DOI: 10.1016/j.neuron.2022.10.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/04/2022] [Accepted: 10/07/2022] [Indexed: 11/07/2022]
Abstract
Genome-wide association studies and functional genomics studies have linked specific cell types, genes, and pathways to Alzheimer's disease (AD) risk. In particular, AD risk alleles primarily affect the abundance or structure, and thus the activity, of genes expressed in macrophages, strongly implicating microglia (the brain-resident macrophages) in the etiology of AD. These genes converge on pathways (endocytosis/phagocytosis, cholesterol metabolism, and immune response) with critical roles in core macrophage functions such as efferocytosis. Here, we review these pathways, highlighting relevant genes identified in the latest AD genetics and genomics studies, and describe how they may contribute to AD pathogenesis. Investigating the functional impact of AD-associated variants and genes in microglia is essential for elucidating disease risk mechanisms and developing effective therapeutic approaches.
Collapse
Affiliation(s)
- Carmen Romero-Molina
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Francesca Garretti
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shea J Andrews
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Edoardo Marcora
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Alison M Goate
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| |
Collapse
|
24
|
Xi X, Li H, Chen S, Lv T, Ma T, Jiang R, Zhang P, Wong WH, Zhang X. Unfolding the genotype-to-phenotype black box of cardiovascular diseases through cross-scale modeling. iScience 2022; 25:104790. [PMID: 35992073 PMCID: PMC9386115 DOI: 10.1016/j.isci.2022.104790] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/26/2022] [Accepted: 07/14/2022] [Indexed: 12/01/2022] Open
Abstract
Complex traits such as cardiovascular diseases (CVD) are the results of complicated processes jointly affected by genetic and environmental factors. Genome-wide association studies (GWAS) identified genetic variants associated with diseases but usually did not reveal the underlying mechanisms. There could be many intermediate steps at epigenetic, transcriptomic, and cellular scales inside the black box of genotype-phenotype associations. In this article, we present a machine-learning-based cross-scale framework GRPath to decipher putative causal paths (pcPaths) from genetic variants to disease phenotypes by integrating multiple omics data. Applying GRPath on CVD, we identified 646 and 549 pcPaths linking putative causal regions, variants, and gene expressions in specific cell types for two types of heart failure, respectively. The findings suggest new understandings of coronary heart disease. Our work promoted the modeling of tissue- and cell type-specific cross-scale regulation to uncover mechanisms behind disease-associated variants, and provided new findings on the molecular mechanisms of CVD. We defined one type of cross-scale genotype-to-phenotype regulation path We designed a framework GRPath to uncover putative regulation paths for diseases GRPath helped uncover molecular mechanisms for two major types of heart failure
Collapse
Affiliation(s)
- Xi Xi
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing 100084, China
| | - Haochen Li
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Shengquan Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing 100084, China
| | - Tingting Lv
- Department of Cardiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - Tianxing Ma
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing 100084, China
| | - Rui Jiang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing 100084, China
| | - Ping Zhang
- Department of Cardiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - Wing Hung Wong
- Departments of Statistics and Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing 100084, China
- School of Medicine, Tsinghua University, Beijing 100084, China
- Corresponding author
| |
Collapse
|
25
|
Gene-lifestyle interactions in the genomics of human complex traits. Eur J Hum Genet 2022; 30:730-739. [PMID: 35314805 PMCID: PMC9178041 DOI: 10.1038/s41431-022-01045-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 12/22/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies.
Collapse
|
26
|
Abdalla M, Abdalla M. A general framework for predicting the transcriptomic consequences of non-coding variation and small molecules. PLoS Comput Biol 2022; 18:e1010028. [PMID: 35421087 PMCID: PMC9041867 DOI: 10.1371/journal.pcbi.1010028] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 04/26/2022] [Accepted: 03/16/2022] [Indexed: 11/18/2022] Open
Abstract
Genome wide association studies (GWASs) for complex traits have implicated thousands of genetic loci. Most GWAS-nominated variants lie in noncoding regions, complicating the systematic translation of these findings into functional understanding. Here, we leverage convolutional neural networks to assist in this challenge. Our computational framework, peaBrain, models the transcriptional machinery of a tissue as a two-stage process: first, predicting the mean tissue specific abundance of all genes and second, incorporating the transcriptomic consequences of genotype variation to predict individual abundance on a subject-by-subject basis. We demonstrate that peaBrain accounts for the majority (>50%) of variance observed in mean transcript abundance across most tissues and outperforms regularized linear models in predicting the consequences of individual genotype variation. We highlight the validity of the peaBrain model by calculating non-coding impact scores that correlate with nucleotide evolutionary constraint that are also predictive of disease-associated variation and allele-specific transcription factor binding. We further show how these tissue-specific peaBrain scores can be leveraged to pinpoint functional tissues underlying complex traits, outperforming methods that depend on colocalization of eQTL and GWAS signals. We subsequently: (a) derive continuous dense embeddings of genes for downstream applications; (b) highlight the utility of the model in predicting transcriptomic impact of small molecules and shRNA (on par with in vitro experimental replication of external test sets); (c) explore how peaBrain can be used to model difficult-to-study processes (such as neural induction); and (d) identify putatively functional eQTLs that are missed by high-throughput experimental approaches.
Collapse
Affiliation(s)
- Moustafa Abdalla
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
- Computational Statistics and Machine Learning, Department of Statistics, University of Oxford, Oxford, United Kingdom
- Department of Surgery, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail: (MA); (MA)
| | - Mohamed Abdalla
- Vector Institute for Artificial Intelligence, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- * E-mail: (MA); (MA)
| |
Collapse
|
27
|
Khatiwada A, Wolf BJ, Yilmaz AS, Ramos PS, Pietrzak M, Lawson A, Hunt KJ, Kim HJ, Chung D. GPA-Tree: statistical approach for functional-annotation-tree-guided prioritization of GWAS results. Bioinformatics 2022; 38:1067-1074. [PMID: 34849578 PMCID: PMC10060690 DOI: 10.1093/bioinformatics/btab802] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 10/09/2021] [Accepted: 11/23/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION In spite of great success of genome-wide association studies (GWAS), multiple challenges still remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), each with small or moderate effect sizes. Second, our understanding of the functional mechanisms through which genetic variants are associated with complex traits is still limited. To address these challenges, we propose GPA-Tree and it simultaneously implements association mapping and identifies key combinations of functional annotations related to risk-associated SNPs by combining a decision tree algorithm with a hierarchical modeling framework. RESULTS First, we implemented simulation studies to evaluate the proposed GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs and identifying the true combinations of functional annotations with high accuracy. Second, we applied GPA-Tree to a systemic lupus erythematosus (SLE) GWAS and functional annotation data including GenoSkyline and GenoSkylinePlus. The results from GPA-Tree highlight the dysregulation of blood immune cells, including but not limited to primary B, memory helper T, regulatory T, neutrophils and CD8+ memory T cells in SLE. These results demonstrate that GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits. AVAILABILITY AND IMPLEMENTATION The GPATree software is available at https://dongjunchung.github.io/GPATree/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Aastha Khatiwada
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
- Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, CO 80206, USA
| | - Bethany J Wolf
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Ayse Selen Yilmaz
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
| | - Paula S Ramos
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
- Department of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Maciej Pietrzak
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
| | - Andrew Lawson
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Kelly J Hunt
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Hang J Kim
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
| |
Collapse
|
28
|
Hutchinson A, Reales G, Willis T, Wallace C. Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR. PLoS Genet 2021; 17:e1009853. [PMID: 34669738 PMCID: PMC8559959 DOI: 10.1371/journal.pgen.1009853] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/01/2021] [Accepted: 09/30/2021] [Indexed: 12/15/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions ("Flexible cFDR"). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.
Collapse
Affiliation(s)
- Anna Hutchinson
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Guillermo Reales
- Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), University of Cambridge, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Thomas Willis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), University of Cambridge, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| |
Collapse
|
29
|
Huang D, Zhou Y, Yi X, Fan X, Wang J, Yao H, Sham PC, Hao J, Chen K, Li MJ. VannoPortal: multiscale functional annotation of human genetic variants for interrogating molecular mechanism of traits and diseases. Nucleic Acids Res 2021; 50:D1408-D1416. [PMID: 34570217 PMCID: PMC8728305 DOI: 10.1093/nar/gkab853] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 09/05/2021] [Accepted: 09/14/2021] [Indexed: 12/16/2022] Open
Abstract
Interpreting the molecular mechanism of genomic variations and their causal relationship with diseases/traits are important and challenging problems in the human genetic study. To provide comprehensive and context-specific variant annotations for biologists and clinicians, here, by systematically integrating over 4TB genomic/epigenomic profiles and frequently-used annotation databases from various biological domains, we develop a variant annotation database, called VannoPortal. In general, the database has following major features: (i) systematically integrates 40 genome-wide variant annotations and prediction scores regarding allele frequency, linkage disequilibrium, evolutionary signature, disease/trait association, tissue/cell type-specific epigenome, base-wise functional prediction, allelic imbalance and pathogenicity; (ii) equips with our recent novel index system and parallel random-sweep searching algorithms for efficient management of backend databases and information extraction; (iii) greatly expands context-dependent variant annotation to incorporate large-scale epigenomic maps and regulatory profiles (such as EpiMap) across over 33 tissue/cell types; (iv) compiles many genome-scale base-wise prediction scores for regulatory/pathogenic variant classification beyond protein-coding region; (v) enables fast retrieval and direct comparison of functional evidence among linked variants using highly interactive web panel in addition to plain table; (vi) introduces many visualization functions for more efficient identification and interpretation of functional variants in single web page. VannoPortal is freely available at http://mulinlab.org/vportal.
Collapse
Affiliation(s)
- Dandan Huang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Yao Zhou
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xianfu Yi
- School of Biomedical Engineering, Tianjin Medical University, Tianjin 300070, China
| | - Xutong Fan
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Jianhua Wang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Hongcheng Yao
- Centre for PanorOmic Sciences-Genomics and Bioinformatics Cores, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Pak Chung Sham
- Centre for PanorOmic Sciences-Genomics and Bioinformatics Cores, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Jihui Hao
- Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China
| | - Mulin Jun Li
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China.,Department of Epidemiology and Biostatistics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China
| |
Collapse
|
30
|
Colbert SMC, Funkhouser SA, Johnson EC, Morrison CL, Hoeffer CA, Friedman NP, Ehringer MA, Evans LM. Novel characterization of the multivariate genetic architecture of internalizing psychopathology and alcohol use. Am J Med Genet B Neuropsychiatr Genet 2021; 186:353-366. [PMID: 34569141 PMCID: PMC8556277 DOI: 10.1002/ajmg.b.32874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/12/2021] [Accepted: 09/03/2021] [Indexed: 12/21/2022]
Abstract
Genetic correlations suggest that the genetic relationship of alcohol use with internalizing psychopathology depends on the measure of alcohol use. Problematic alcohol use (PAU) is positively genetically correlated with internalizing psychopathology, whereas alcohol consumption ranges from not significantly correlated to moderately negatively correlated with internalizing psychopathology. To explore these different genetic relationships of internalizing psychopathology with alcohol use, we performed a multivariate genome-wide association study of four correlated factors (internalizing psychopathology, PAU, quantity of alcohol consumption, and frequency of alcohol consumption) and then assessed genome-wide and local genetic covariance between these factors. We identified 14 significant regions of local, largely positive, genetic covariance between PAU and internalizing psychopathology and 12 regions of significant local genetic covariance (including both positive and negative genetic covariance) between consumption factors and internalizing psychopathology. Partitioned genetic covariance among functional annotations suggested that brain tissues contribute significantly to positive genetic covariance between internalizing psychopathology and PAU but not to the genetic covariance between internalizing psychopathology and quantity or frequency of alcohol consumption. We hypothesize that genome-wide genetic correlations between alcohol use and psychiatric traits may not capture the more complex shared or divergent genetic architectures at the locus or tissue specific level. This study highlights the complexity of genetic architectures of alcohol use and internalizing psychopathology, and the differing shared genetics of internalizing disorders with PAU compared to consumption.
Collapse
Affiliation(s)
- Sarah M. C. Colbert
- Institute for Behavioral Genetics, University of Colorado Boulder
- Department of Ecology and Evolutionary Biology, University of Colorado Boulder
| | | | - Emma C. Johnson
- Department of Psychiatry, Washington University School of Medicine
| | - Claire L. Morrison
- Institute for Behavioral Genetics, University of Colorado Boulder
- Department of Psychology and Neuroscience, University of Colorado Boulder
| | - Charles A. Hoeffer
- Institute for Behavioral Genetics, University of Colorado Boulder
- Department of Integrative Physiology, University of Colorado Boulder
| | - Naomi P. Friedman
- Institute for Behavioral Genetics, University of Colorado Boulder
- Department of Psychology and Neuroscience, University of Colorado Boulder
| | - Marissa A. Ehringer
- Institute for Behavioral Genetics, University of Colorado Boulder
- Department of Integrative Physiology, University of Colorado Boulder
| | - Luke M. Evans
- Institute for Behavioral Genetics, University of Colorado Boulder
- Department of Ecology and Evolutionary Biology, University of Colorado Boulder
| |
Collapse
|
31
|
Hao X, Wang K, Dai C, Ding Z, Yang W, Wang C, Cheng S. Integrative analysis of scRNA-seq and GWAS data pinpoints periportal hepatocytes as the relevant liver cell types for blood lipids. Hum Mol Genet 2021; 29:3145-3153. [PMID: 32821946 DOI: 10.1093/hmg/ddaa188] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 08/10/2020] [Accepted: 08/18/2020] [Indexed: 12/22/2022] Open
Abstract
Liver, a heterogeneous tissue consisting of various cell types, is known to be relevant for blood lipid traits. By integrating summary statistics from genome-wide association studies (GWAS) of lipid traits and single-cell transcriptome data of the liver, we sought to identify specific cell types in the liver that were most relevant for blood lipid levels. We conducted differential expression analyses for 40 cell types from human and mouse livers in order to construct the cell-type specifically expressed gene sets, which we refer to as construction of the liver cell-type specifically expressed gene sets (CT-SEGS). Under the assumption that CT-SEGS represented specific functions of each cell type, we applied stratified linkage disequilibrium score regression to determine cell types that were most relevant for complex traits and diseases. We first confirmed the validity of this method (of delineating functionally relevant cell types) by identifying the immune cell types as relevant for autoimmune diseases. We further showed that lipid GWAS signals were enriched in the human and mouse periportal hepatocytes. Our results provide important information to facilitate future cellular studies of the metabolic mechanism affecting blood lipid levels.
Collapse
Affiliation(s)
- Xingjie Hao
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health
| | - Kai Wang
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health
| | - Chengguqiu Dai
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health
| | | | - Wei Yang
- Department of Nutrition and Food Hygiene, School of Public Health
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health.,Department of Orthopedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Shanshan Cheng
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health
| |
Collapse
|
32
|
Welzenbach J, Hammond NL, Nikolić M, Thieme F, Ishorst N, Leslie EJ, Weinberg SM, Beaty TH, Marazita ML, Mangold E, Knapp M, Cotney J, Rada-Iglesias A, Dixon MJ, Ludwig KU. Integrative approaches generate insights into the architecture of non-syndromic cleft lip with or without cleft palate. HGG ADVANCES 2021; 2:100038. [PMID: 35047836 PMCID: PMC8756534 DOI: 10.1016/j.xhgg.2021.100038] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 05/27/2021] [Indexed: 12/15/2022] Open
Abstract
Non-syndromic cleft lip with or without cleft palate (nsCL/P) is a common congenital facial malformation with a multifactorial etiology. Genome-wide association studies (GWASs) have identified multiple genetic risk loci. However, functional interpretation of these loci is hampered by the underrepresentation in public resources of systematic functional maps representative of human embryonic facial development. To generate novel insights into the etiology of nsCL/P, we leveraged published GWAS data on nsCL/P as well as available chromatin modification and expression data on mid-facial development. Our analyses identified five novel risk loci, prioritized candidate target genes within associated regions, and highlighted distinct pathways. Furthermore, the results suggest the presence of distinct regulatory effects of nsCL/P risk variants throughout mid-facial development and shed light on its regulatory architecture. Our integrated data provide a platform to advance hypothesis-driven molecular investigations of nsCL/P and other human facial defects.
Collapse
Affiliation(s)
- Julia Welzenbach
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Nigel L. Hammond
- Faculty of Biology, Medicine, and Health, Manchester Academic Health Sciences Centre, University of Manchester, Manchester M13 9PT, UK
| | - Miloš Nikolić
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Frederic Thieme
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Nina Ishorst
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Elizabeth J. Leslie
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Seth M. Weinberg
- Center for Craniofacial and Dental Genetics, Department of Oral Biology, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Terri H. Beaty
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Mary L. Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral Biology, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry and Clinical and Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Elisabeth Mangold
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Michael Knapp
- Institute of Medical Biometry, Informatics, and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Justin Cotney
- Department of Genetics and Genome Sciences, UConn Health, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA
| | - Alvaro Rada-Iglesias
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Cluster of Excellence Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Institute of Biomedicine and Biotechnology of Cantabria (IBBTEC), University of Cantabria, Cantabria, Spain
| | - Michael J. Dixon
- Faculty of Biology, Medicine, and Health, Manchester Academic Health Sciences Centre, University of Manchester, Manchester M13 9PT, UK
| | - Kerstin U. Ludwig
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| |
Collapse
|
33
|
Zekavat SM, Lin SH, Bick AG, Liu A, Paruchuri K, Wang C, Uddin MM, Ye Y, Yu Z, Liu X, Kamatani Y, Bhattacharya R, Pirruccello JP, Pampana A, Loh PR, Kohli P, McCarroll SA, Kiryluk K, Neale B, Ionita-Laza I, Engels EA, Brown DW, Smoller JW, Green R, Karlson EW, Lebo M, Ellinor PT, Weiss ST, Daly MJ, Terao C, Zhao H, Ebert BL, Reilly MP, Ganna A, Machiela MJ, Genovese G, Natarajan P. Hematopoietic mosaic chromosomal alterations increase the risk for diverse types of infection. Nat Med 2021; 27:1012-1024. [PMID: 34099924 PMCID: PMC8245201 DOI: 10.1038/s41591-021-01371-0] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 04/23/2021] [Indexed: 12/13/2022]
Abstract
Age is the dominant risk factor for infectious diseases, but the mechanisms linking age to infectious disease risk are incompletely understood. Age-related mosaic chromosomal alterations (mCAs) detected from genotyping of blood-derived DNA, are structural somatic variants indicative of clonal hematopoiesis, and are associated with aberrant leukocyte cell counts, hematological malignancy, and mortality. Here, we show that mCAs predispose to diverse types of infections. We analyzed mCAs from 768,762 individuals without hematological cancer at the time of DNA acquisition across five biobanks. Expanded autosomal mCAs were associated with diverse incident infections (hazard ratio (HR) 1.25; 95% confidence interval (CI) = 1.15-1.36; P = 1.8 × 10-7), including sepsis (HR 2.68; 95% CI = 2.25-3.19; P = 3.1 × 10-28), pneumonia (HR 1.76; 95% CI = 1.53-2.03; P = 2.3 × 10-15), digestive system infections (HR 1.51; 95% CI = 1.32-1.73; P = 2.2 × 10-9) and genitourinary infections (HR 1.25; 95% CI = 1.11-1.41; P = 3.7 × 10-4). A genome-wide association study of expanded mCAs identified 63 loci, which were enriched at transcriptional regulatory sites for immune cells. These results suggest that mCAs are a marker of impaired immunity and confer increased predisposition to infections.
Collapse
Affiliation(s)
- Seyedeh M Zekavat
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Shu-Hong Lin
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Alexander G Bick
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Aoxing Liu
- Institute for Molecular Medicine Finland, Helsinki, Finland
| | - Kaavya Paruchuri
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Chen Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York City, NY, USA
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Md Mesbah Uddin
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Yixuan Ye
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Zhaolong Yu
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
| | - Romit Bhattacharya
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - James P Pirruccello
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Akhil Pampana
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Po-Ru Loh
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Puja Kohli
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Vertex Pharmaceuticals, Boston, MA, USA
| | - Steven A McCarroll
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
- Irving Institute for Clinical and Translational Research, Columbia University, New York City, NY, USA
| | - Benjamin Neale
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York City, NY, USA
| | - Eric A Engels
- Infections and Immunoepidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Derek W Brown
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Jordan W Smoller
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Robert Green
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Elizabeth W Karlson
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA
| | - Matthew Lebo
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Laboratory for Molecular Medicine, Partners Healthcare, Cambridge, MA, USA
| | - Patrick T Ellinor
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Scott T Weiss
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Mark J Daly
- Institute for Molecular Medicine Finland, Helsinki, Finland
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Hongyu Zhao
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Benjamin L Ebert
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Howard Hughes Medical Institute, Boston, MA, USA
| | - Muredach P Reilly
- Irving Institute for Clinical and Translational Research, Columbia University, New York City, NY, USA
- Division of Cardiology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Andrea Ganna
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Institute for Molecular Medicine Finland, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Mitchell J Machiela
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Giulio Genovese
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Pradeep Natarajan
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
34
|
Guo H, Li JJ, Lu Q, Hou L. Detecting local genetic correlations with scan statistics. Nat Commun 2021; 12:2033. [PMID: 33795679 PMCID: PMC8016883 DOI: 10.1038/s41467-021-22334-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 03/08/2021] [Indexed: 02/06/2023] Open
Abstract
Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to seven phenotypically distinct but genetically correlated neuropsychiatric traits, we identify 227 non-overlapping genome regions associated with multiple traits, including multiple hub regions showing concordant effects on five or more traits. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.
Collapse
Affiliation(s)
- Hanmin Guo
- Center for Statistical Science, Tsinghua University, Beijing, China
- Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - James J Li
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
| | - Lin Hou
- Center for Statistical Science, Tsinghua University, Beijing, China.
- Department of Industrial Engineering, Tsinghua University, Beijing, China.
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China.
| |
Collapse
|
35
|
Andersen MS, Bandres-Ciga S, Reynolds RH, Hardy J, Ryten M, Krohn L, Gan-Or Z, Holtman IR, Pihlstrøm L. Heritability Enrichment Implicates Microglia in Parkinson's Disease Pathogenesis. Ann Neurol 2021; 89:942-951. [PMID: 33502028 PMCID: PMC9017316 DOI: 10.1002/ana.26032] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/12/2021] [Accepted: 01/24/2021] [Indexed: 12/29/2022]
Abstract
Objective: Understanding how different parts of the immune system contribute to pathogenesis in Parkinson’s disease is a burning challenge with important therapeutic implications. We studied enrichment of common variant heritability for Parkinson’s disease stratified by immune and brain cell types. Methods: We used summary statistics from the most recent meta-analysis of genomewide association studies in Parkinson’s disease and partitioned heritability using linkage disequilibrium score regression, stratified for specific cell types, as defined by open chromatin regions. We also validated enrichment results using a polygenic risk score approach and intersected disease-associated variants with epigenetic data and expression quantitative loci to nominate and explore a putative microglial locus. Results: We found significant enrichment of Parkinson’s disease risk heritability in open chromatin regions of microglia and monocytes. Genomic annotations overlapped substantially between these 2 cell types, and only the enrichment signal for microglia remained significant in a joint model. We present evidence suggesting P2RY12, a key microglial gene and target for the antithrombotic agent clopidogrel, as the likely driver of a significant Parkinson’s disease association signal on chromosome 3. Interpretation: Our results provide further support for the importance of immune mechanisms in Parkinson’s disease pathogenesis, highlight microglial dysregulation as a contributing etiological factor, and nominate a targetable microglial gene candidate as a pathogenic player. Immune processes can be modulated by therapy, with potentially important clinical implications for future treatment in Parkinson’s disease.
Collapse
Affiliation(s)
- Maren Stolp Andersen
- Department of Neurology, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sara Bandres-Ciga
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Regina H Reynolds
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London, UK.,NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK.,Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, University College London, London, UK
| | - John Hardy
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London, UK.,UK Dementia Research Institute at UCL and Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK.,Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, London, UK.,UCL Movement Disorders Centre, University College London, London, UK.,Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Mina Ryten
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London, UK.,NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK.,Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, University College London, London, UK
| | - Lynne Krohn
- Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.,Department of Human Genetics, McGill University, Montreal, Québec, Canada
| | - Ziv Gan-Or
- Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.,Department of Human Genetics, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada
| | - Inge R Holtman
- Department of Biomedical Sciences of Cells & Systems, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | | |
Collapse
|
36
|
Schwartzentruber J, Cooper S, Liu JZ, Barrio-Hernandez I, Bello E, Kumasaka N, Young AMH, Franklin RJM, Johnson T, Estrada K, Gaffney DJ, Beltrao P, Bassett A. Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer's disease risk genes. Nat Genet 2021; 53:392-402. [PMID: 33589840 PMCID: PMC7610386 DOI: 10.1038/s41588-020-00776-w] [Citation(s) in RCA: 231] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 12/23/2020] [Indexed: 01/30/2023]
Abstract
Genome-wide association studies have discovered numerous genomic loci associated with Alzheimer's disease (AD); yet the causal genes and variants are incompletely identified. We performed an updated genome-wide AD meta-analysis, which identified 37 risk loci, including new associations near CCDC6, TSPAN14, NCK2 and SPRED2. Using three SNP-level fine-mapping methods, we identified 21 SNPs with >50% probability each of being causally involved in AD risk and others strongly suggested by functional annotation. We followed this with colocalization analyses across 109 gene expression quantitative trait loci datasets and prioritization of genes by using protein interaction networks and tissue-specific expression. Combining this information into a quantitative score, we found that evidence converged on likely causal genes, including the above four genes, and those at previously discovered AD loci, including BIN1, APH1B, PTK2B, PILRA and CASS4.
Collapse
Affiliation(s)
- Jeremy Schwartzentruber
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
- Open Targets, Wellcome Genome Campus, Cambridge, UK.
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
| | - Sarah Cooper
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | - Inigo Barrio-Hernandez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - Erica Bello
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | - Adam M H Young
- Wellcome-Medical Research Council Cambridge Stem Cell Institute, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Robin J M Franklin
- Wellcome-Medical Research Council Cambridge Stem Cell Institute, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Toby Johnson
- Target Sciences-R&D, GSK Medicines Research Centre, Stevenage, UK
| | | | - Daniel J Gaffney
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Genomics Plc, Oxford, UK
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - Andrew Bassett
- Open Targets, Wellcome Genome Campus, Cambridge, UK.
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
| |
Collapse
|
37
|
Zhu H, Shang L, Zhou X. A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types. Front Genet 2021; 11:587887. [PMID: 33584792 PMCID: PMC7874162 DOI: 10.3389/fgene.2020.587887] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 12/30/2020] [Indexed: 11/17/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified and replicated many genetic variants that are associated with diseases and disease-related complex traits. However, the biological mechanisms underlying these identified associations remain largely elusive. Exploring the biological mechanisms underlying these associations requires identifying trait-relevant tissues and cell types, as genetic variants likely influence complex traits in a tissue- and cell type-specific manner. Recently, several statistical methods have been developed to integrate genomic data with GWASs for identifying trait-relevant tissues and cell types. These methods often rely on different genomic information and use different statistical models for trait-tissue relevance inference. Here, we present a comprehensive technical review to summarize ten existing methods for trait-tissue relevance inference. These methods make use of different genomic information that include functional annotation information, expression quantitative trait loci information, genetically regulated gene expression information, as well as gene co-expression network information. These methods also use different statistical models that range from linear mixed models to covariance network models. We hope that this review can serve as a useful reference both for methodologists who develop methods and for applied analysts who apply these methods for identifying trait relevant tissues and cell types.
Collapse
Affiliation(s)
- Huanhuan Zhu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Lulu Shang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States
| |
Collapse
|
38
|
Munn‐Chernoff MA, Johnson EC, Chou Y, Coleman JR, Thornton LM, Walters RK, Yilmaz Z, Baker JH, Hübel C, Gordon S, Medland SE, Watson HJ, Gaspar HA, Bryois J, Hinney A, Leppä VM, Mattheisen M, Ripke S, Yao S, Giusti‐Rodríguez P, Hanscombe KB, Adan RA, Alfredsson L, Ando T, Andreassen OA, Berrettini WH, Boehm I, Boni C, Boraska Perica V, Buehren K, Burghardt R, Cassina M, Cichon S, Clementi M, Cone RD, Courtet P, Crow S, Crowley JJ, Danner UN, Davis OS, Zwaan M, Dedoussis G, Degortes D, DeSocio JE, Dick DM, Dikeos D, Dina C, Dmitrzak‐Weglarz M, Docampo E, Duncan LE, Egberts K, Ehrlich S, Escaramís G, Esko T, Estivill X, Farmer A, Favaro A, Fernández‐Aranda F, Fichter MM, Fischer K, Föcker M, Foretova L, Forstner AJ, Forzan M, Franklin CS, Gallinger S, Giegling I, Giuranna J, Gonidakis F, Gorwood P, Gratacos Mayora M, Guillaume S, Guo Y, Hakonarson H, Hatzikotoulas K, Hauser J, Hebebrand J, Helder SG, Herms S, Herpertz‐Dahlmann B, Herzog W, Huckins LM, Hudson JI, Imgart H, Inoko H, Janout V, Jiménez‐Murcia S, Julià A, Kalsi G, Kaminská D, Karhunen L, Karwautz A, Kas MJ, Kennedy JL, Keski‐Rahkonen A, Kiezebrink K, Kim Y, Klump KL, Knudsen GPS, La Via MC, Le Hellard S, Levitan RD, Li D, Lilenfeld L, Lin BD, Lissowska J, Luykx J, Magistretti PJ, Maj M, Mannik K, Marsal S, Marshall CR, Mattingsdal M, McDevitt S, McGuffin P, Metspalu A, Meulenbelt I, Micali N, Mitchell K, Monteleone AM, Monteleone P, Nacmias B, Navratilova M, Ntalla I, O'Toole JK, Ophoff RA, Padyukov L, Palotie A, Pantel J, Papezova H, Pinto D, Rabionet R, Raevuori A, Ramoz N, Reichborn‐Kjennerud T, Ricca V, Ripatti S, Ritschel F, Roberts M, Rotondo A, Rujescu D, Rybakowski F, Santonastaso P, Scherag A, Scherer SW, Schmidt U, Schork NJ, Schosser A, Seitz J, Slachtova L, Slagboom PE, Slof‐Op't Landt MC, Slopien A, Sorbi S, Świątkowska B, Szatkiewicz JP, Tachmazidou I, Tenconi E, Tortorella A, Tozzi F, Treasure J, Tsitsika A, Tyszkiewicz‐Nwafor M, Tziouvas K, Elburg AA, Furth EF, Wagner G, Walton E, Widen E, Zeggini E, Zerwas S, Zipfel S, Bergen AW, Boden JM, Brandt H, Crawford S, Halmi KA, Horwood LJ, Johnson C, Kaplan AS, Kaye WH, Mitchell J, Olsen CM, Pearson JF, Pedersen NL, Strober M, Werge T, Whiteman DC, Woodside DB, Grove J, Henders AK, Larsen JT, Parker R, Petersen LV, Jordan J, Kennedy MA, Birgegård A, Lichtenstein P, Norring C, Landén M, Mortensen PB, Polimanti R, McClintick JN, Adkins AE, Aliev F, Bacanu S, Batzler A, Bertelsen S, Biernacka JM, Bigdeli TB, Chen L, Clarke T, Degenhardt F, Docherty AR, Edwards AC, Foo JC, Fox L, Frank J, Hack LM, Hartmann AM, Hartz SM, Heilmann‐Heimbach S, Hodgkinson C, Hoffmann P, Hottenga J, Konte B, Lahti J, Lahti‐Pulkkinen M, Lai D, Ligthart L, Loukola A, Maher BS, Mbarek H, McIntosh AM, McQueen MB, Meyers JL, Milaneschi Y, Palviainen T, Peterson RE, Ryu E, Saccone NL, Salvatore JE, Sanchez‐Roige S, Schwandt M, Sherva R, Streit F, Strohmaier J, Thomas N, Wang J, Webb BT, Wedow R, Wetherill L, Wills AG, Zhou H, Boardman JD, Chen D, Choi D, Copeland WE, Culverhouse RC, Dahmen N, Degenhardt L, Domingue BW, Frye MA, Gäebel W, Hayward C, Ising M, Keyes M, Kiefer F, Koller G, Kramer J, Kuperman S, Lucae S, Lynskey MT, Maier W, Mann K, Männistö S, Müller‐Myhsok B, Murray AD, Nurnberger JI, Preuss U, Räikkönen K, Reynolds MD, Ridinger M, Scherbaum N, Schuckit MA, Soyka M, Treutlein J, Witt SH, Wodarz N, Zill P, Adkins DE, Boomsma DI, Bierut LJ, Brown SA, Bucholz KK, Costello EJ, Wit H, Diazgranados N, Eriksson JG, Farrer LA, Foroud TM, Gillespie NA, Goate AM, Goldman D, Grucza RA, Hancock DB, Harris KM, Hesselbrock V, Hewitt JK, Hopfer CJ, Iacono WG, Johnson EO, Karpyak VM, Kendler KS, Kranzler HR, Krauter K, Lind PA, McGue M, MacKillop J, Madden PA, Maes HH, Magnusson PK, Nelson EC, Nöthen MM, Palmer AA, Penninx BW, Porjesz B, Rice JP, Rietschel M, Riley BP, Rose RJ, Shen P, Silberg J, Stallings MC, Tarter RE, Vanyukov MM, Vrieze S, Wall TL, Whitfield JB, Zhao H, Neale BM, Wade TD, Heath AC, Montgomery GW, Martin NG, Sullivan PF, Kaprio J, Breen G, Gelernter J, Edenberg HJ, Bulik CM, Agrawal A. Shared genetic risk between eating disorder‐ and substance‐use‐related phenotypes: Evidence from genome‐wide association studies. Addict Biol 2021; 26:e12880. [DOI: 10.1111/adb.12880] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 12/09/2019] [Accepted: 01/13/2020] [Indexed: 02/01/2023]
Affiliation(s)
- Melissa A. Munn‐Chernoff
- Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
| | - Emma C. Johnson
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| | - Yi‐Ling Chou
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| | - Jonathan R.I. Coleman
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
- National Institute for Health Research Biomedical Research Centre King's College London and South London and Maudsley National Health Service Trust London UK
| | - Laura M. Thornton
- Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
| | - Raymond K. Walters
- Analytic and Translational Genetics Unit, Department of Medicine Massachusetts General Hospital and Harvard Medical School Boston Massachusetts USA
- Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts USA
| | - Zeynep Yilmaz
- Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
- Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
| | - Jessica H. Baker
- Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
| | - Christopher Hübel
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
- National Institute for Health Research Biomedical Research Centre King's College London and South London and Maudsley National Health Service Trust London UK
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden
| | - Scott Gordon
- QIMR Berghofer Medical Research Institute Brisbane Queensland Australia
| | - Sarah E. Medland
- QIMR Berghofer Medical Research Institute Brisbane Queensland Australia
| | - Hunna J. Watson
- Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
- School of Psychology Curtin University Perth Western Australia Australia
- School of Paediatrics and Child Health University of Western Australia Perth Western Australia Australia
| | - Héléna A. Gaspar
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
- National Institute for Health Research Biomedical Research Centre King's College London and South London and Maudsley National Health Service Trust London UK
| | - Julien Bryois
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden
| | - Anke Hinney
- Department of Child and Adolescent Psychiatry University Hospital Essen, University of Duisburg‐Essen Essen Germany
| | - Virpi M. Leppä
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden
| | - Manuel Mattheisen
- Department of Biomedicine Aarhus University Aarhus Denmark
- Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden
- Center for Psychiatry Research, Stockholm Health Care Services Stockholm County Council Stockholm Sweden
- Department of Psychiatry, Psychosomatics and Psychotherapy University of Würzburg Germany
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Department of Medicine Massachusetts General Hospital and Harvard Medical School Boston Massachusetts USA
- Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts USA
- Department of Psychiatry and Psychotherapy Charité ‐ Universitätsmedizin Berlin Germany
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden
| | - Paola Giusti‐Rodríguez
- Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
| | - Ken B. Hanscombe
- Department of Medical and Molecular Genetics King's College London, Guy's Hospital London UK
| | - Roger A.H. Adan
- Department of Translational Neuroscience, Brain Center Rudolf Magnus University Medical Center Utrecht Utrecht The Netherlands
- Center for Eating Disorders Rintveld Altrecht Mental Health Institute Zeist The Netherlands
- Sahlgrenska Academy University of Gothenburg Gothenburg Sweden
| | - Lars Alfredsson
- Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden
| | - Tetsuya Ando
- Department of Behavioral Medicine, National Institute of Mental Health National Center of Neurology and Psychiatry Kodaira Tokyo Japan
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, NORMENT Centre University of Oslo, Oslo University Hospital Oslo Norway
| | - Wade H. Berrettini
- Department of Psychiatry, Center for Neurobiology and Behavior University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USA
| | - Ilka Boehm
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine Technische Universität Dresden Dresden Germany
| | - Claudette Boni
- Centre of Psychiatry and Neuroscience INSERM U894 Paris France
| | - Vesna Boraska Perica
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton Cambridge UK
- Department of Medical Biology, School of Medicine University of Split Split Croatia
| | - Katharina Buehren
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy RWTH Aachen University Aachen Germany
| | | | - Matteo Cassina
- Clinical Genetics Unit, Department of Woman and Child Health University of Padova Italy
| | - Sven Cichon
- Institute of Medical Genetics and Pathology University Hospital Basel Basel Switzerland
- Department of Biomedicine University of Basel Basel Switzerland
- Institute of Neuroscience and Medicine (INM‐1) Research Center Juelich Germany
| | - Maurizio Clementi
- Clinical Genetics Unit, Department of Woman and Child Health University of Padova Italy
| | - Roger D. Cone
- Department of Molecular and Integrative Physiology, Life Sciences Institute University of Michigan Ann Arbor Michigan USA
| | - Philippe Courtet
- Department of Emergency Psychiatry and Post‐Acute Care, CHRU Montpellier University of Montpellier Montpellier France
| | - Scott Crow
- Department of Psychiatry University of Minnesota Minneapolis Minnesota USA
| | - James J. Crowley
- Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
- Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden
| | - Unna N. Danner
- Altrecht Eating Disorders Rintveld Altrecht Mental Health Institute Zeist The Netherlands
| | - Oliver S.P. Davis
- MRC Integrative Epidemiology Unit University of Bristol Bristol UK
- School of Social and Community Medicine University of Bristol Bristol UK
| | - Martina Zwaan
- Department of Psychosomatic Medicine and Psychotherapy Hannover Medical School Hannover Germany
| | - George Dedoussis
- Department of Nutrition and Dietetics Harokopio University Athens Greece
| | | | | | - Danielle M. Dick
- Department of Psychology Virginia Commonwealth University Richmond Virginia USA
- College Behavioral and Emotional Health Institute Virginia Commonwealth University Richmond Virginia USA
- Department of Human & Molecular Genetics Virginia Commonwealth University Richmond Virginia USA
| | - Dimitris Dikeos
- Department of Psychiatry, Athens University Medical School Athens University Athens Greece
| | - Christian Dina
- l'institut du thorax INSERM, CNRS, Univ Nantes Nantes France
| | | | - Elisa Docampo
- Barcelona Institute of Science and Technology Barcelona Spain
- Universitat Pompeu Fabra Barcelona Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP) Barcelona Spain
| | - Laramie E. Duncan
- Department of Psychiatry and Behavioral Sciences Stanford University Stanford California USA
| | - Karin Egberts
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Centre for Mental Health University Hospital of Würzburg Würzburg Germany
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine Technische Universität Dresden Dresden Germany
| | - Geòrgia Escaramís
- Barcelona Institute of Science and Technology Barcelona Spain
- Universitat Pompeu Fabra Barcelona Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP) Barcelona Spain
| | - Tõnu Esko
- Estonian Genome Center University of Tartu Tartu Estonia
- Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge Massachusetts USA
| | - Xavier Estivill
- Barcelona Institute of Science and Technology Barcelona Spain
- Universitat Pompeu Fabra Barcelona Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP) Barcelona Spain
- Genomics and Disease, Bioinformatics and Genomics Programme Centre for Genomic Regulation Barcelona Spain
| | - Anne Farmer
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
| | - Angela Favaro
- Department of Neurosciences University of Padova Padova Italy
| | - Fernando Fernández‐Aranda
- Department of Psychiatry University Hospital of Bellvitge –IDIBELL and CIBERobn Barcelona Spain
- Department of Clinical Sciences, School of Medicine University of Barcelona Barcelona Spain
| | - Manfred M. Fichter
- Department of Psychiatry and Psychotherapy Ludwig‐Maximilians‐University Munich Germany
- Schön Klinik Roseneck affiliated with the Medical Faculty of the University of Munich Munich Germany
| | - Krista Fischer
- Estonian Genome Center University of Tartu Tartu Estonia
| | - Manuel Föcker
- Department of Child and Adolescent Psychiatry University of Münster Münster Germany
| | - Lenka Foretova
- Department of Cancer, Epidemiology and Genetics Masaryk Memorial Cancer Institute Brno Czech Republic
| | - Andreas J. Forstner
- Department of Biomedicine University of Basel Basel Switzerland
- Centre for Human Genetics University of Marburg Marburg Germany
- Institute of Human Genetics School of Medicine & University Hospital Bonn, University of Bonn Bonn Germany
- Department of Psychiatry (UPK) University of Basel Basel Switzerland
| | - Monica Forzan
- Clinical Genetics Unit, Department of Woman and Child Health University of Padova Italy
| | | | - Steven Gallinger
- Department of Surgery, Faculty of Medicine University of Toronto Toronto Ontario Canada
| | - Ina Giegling
- Department of Psychiatry, Psychotherapy and Psychosomatics Martin‐Luther‐University Halle‐Wittenberg Halle (Saale) Germany
| | - Johanna Giuranna
- Department of Child and Adolescent Psychiatry University Hospital Essen, University of Duisburg‐Essen Essen Germany
| | - Fragiskos Gonidakis
- 1st Psychiatric Department National and Kapodistrian University of Athens, Medical School, Eginition Hospital Athens Greece
| | - Philip Gorwood
- Institute of Psychiatry and Neuroscience of Paris INSERM U1266 Paris France
- CMME (GHU Paris Psychiatrie et Neurosciences), Paris Descartes University Paris France
| | - Monica Gratacos Mayora
- Barcelona Institute of Science and Technology Barcelona Spain
- Universitat Pompeu Fabra Barcelona Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP) Barcelona Spain
| | - Sébastien Guillaume
- Department of Emergency Psychiatry and Post‐Acute Care, CHRU Montpellier University of Montpellier Montpellier France
| | - Yiran Guo
- Center for Applied Genomics Children's Hospital of Philadelphia Philadelphia Pennsylvania USA
| | - Hakon Hakonarson
- Center for Applied Genomics Children's Hospital of Philadelphia Philadelphia Pennsylvania USA
- Department of Pediatrics University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USA
| | - Konstantinos Hatzikotoulas
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton Cambridge UK
- Institute of Translational Genomics, Helmholtz Zentrum München ‐ German Research Centre for Environmental Health Neuherberg Germany
| | - Joanna Hauser
- Department of Adult Psychiatry Poznan University of Medical Sciences Poznan Poland
| | - Johannes Hebebrand
- Department of Child and Adolescent Psychiatry University Hospital Essen, University of Duisburg‐Essen Essen Germany
| | - Sietske G. Helder
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
- Zorg op Orde Delft The Netherlands
| | - Stefan Herms
- Institute of Medical Genetics and Pathology University Hospital Basel Basel Switzerland
- Department of Biomedicine University of Basel Basel Switzerland
| | - Beate Herpertz‐Dahlmann
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy RWTH Aachen University Aachen Germany
| | - Wolfgang Herzog
- Department of General Internal Medicine and Psychosomatics Heidelberg University Hospital, Heidelberg University Heidelberg Germany
| | - Laura M. Huckins
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton Cambridge UK
- Department of Psychiatry, and Genetics and Genomics Sciences, Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York New York USA
| | - James I. Hudson
- Biological Psychiatry Laboratory McLean Hospital/Harvard Medical School Boston Massachusetts USA
| | - Hartmut Imgart
- Eating Disorders Unit Parklandklinik Bad Wildungen Germany
| | - Hidetoshi Inoko
- Department of Molecular Life Science, Division of Basic Medical Science and Molecular Medicine, School of Medicine Tokai University Isehara Japan
| | - Vladimir Janout
- Faculty of Health Sciences Palacky University Olomouc Czech Republic
| | - Susana Jiménez‐Murcia
- Department of Psychiatry University Hospital of Bellvitge –IDIBELL and CIBERobn Barcelona Spain
- Department of Clinical Sciences, School of Medicine University of Barcelona Barcelona Spain
| | - Antonio Julià
- Rheumatology Research Group Vall d'Hebron Research Institute Barcelona Spain
| | - Gursharan Kalsi
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
| | - Deborah Kaminská
- Department of Psychiatry, First Faculty of Medicine Charles University Prague Czech Republic
| | - Leila Karhunen
- Department of Clinical Nutrition, Institute of Public Health and Clinical Nutrition University of Eastern Finland Kuopio Finland
| | - Andreas Karwautz
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry Medical University of Vienna Vienna Austria
| | - Martien J.H. Kas
- Department of Translational Neuroscience, Brain Center Rudolf Magnus University Medical Center Utrecht Utrecht The Netherlands
- Groningen Institute for Evolutionary Life Sciences University of Groningen Groningen The Netherlands
| | - James L. Kennedy
- Centre for Addiction and Mental Health Toronto Ontario Canada
- Institute of Medical Science University of Toronto Toronto Ontario Canada
- Department of Psychiatry University of Toronto Toronto Ontario Canada
| | | | - Kirsty Kiezebrink
- Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition University of Aberdeen Aberdeen UK
| | - Youl‐Ri Kim
- Department of Psychiatry Seoul Paik Hospital, Inje University Seoul Korea
| | - Kelly L. Klump
- Department of Psychology Michigan State University East Lansing Michigan USA
| | | | - Maria C. La Via
- Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
| | - Stephanie Le Hellard
- Department of Clinical Science, Norwegian Centre for Mental Disorders Research (NORMENT) University of Bergen Bergen Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine Haukeland University Hospital Bergen Norway
- Department of Clinical Medicine, Laboratory Building Haukeland University Hospital Bergen Norway
| | - Robert D. Levitan
- Centre for Addiction and Mental Health Toronto Ontario Canada
- Institute of Medical Science University of Toronto Toronto Ontario Canada
- Department of Psychiatry University of Toronto Toronto Ontario Canada
| | - Dong Li
- Center for Applied Genomics Children's Hospital of Philadelphia Philadelphia Pennsylvania USA
| | - Lisa Lilenfeld
- The Chicago School of Professional Psychology, Washington DC Campus Washington District of Columbia USA
| | - Bochao Danae Lin
- Department of Translational Neuroscience, Brain Center Rudolf Magnus University Medical Center Utrecht Utrecht The Netherlands
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention M Skłodowska‐Curie Cancer Center ‐ Oncology Center Warsaw Poland
| | - Jurjen Luykx
- Department of Translational Neuroscience, Brain Center Rudolf Magnus University Medical Center Utrecht Utrecht The Netherlands
| | - Pierre J. Magistretti
- BESE Division King Abdullah University of Science and Technology Thuwal Saudi Arabia
- Department of Psychiatry University of Lausanne‐University Hospital of Lausanne (UNIL‐CHUV) Lausanne Switzerland
| | - Mario Maj
- Department of Psychiatry University of Campania "Luigi Vanvitelli" Naples Italy
| | - Katrin Mannik
- Estonian Genome Center University of Tartu Tartu Estonia
- Center for Integrative Genomics University of Lausanne Lausanne Switzerland
| | - Sara Marsal
- Rheumatology Research Group Vall d'Hebron Research Institute Barcelona Spain
| | - Christian R. Marshall
- Department of Paediatric Laboratory Medicine, Division of Genome Diagnostics The Hospital for Sick Children Toronto Ontario Canada
| | - Morten Mattingsdal
- NORMENT KG Jebsen Centre, Division of Mental Health and Addiction University of Oslo, Oslo University Hospital Oslo Norway
| | - Sara McDevitt
- Department of Psychiatry University College Cork Cork Ireland
- Eist Linn Adolescent Unit, Bessborough Health Service Executive South Cork Ireland
| | - Peter McGuffin
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
| | - Andres Metspalu
- Estonian Genome Center University of Tartu Tartu Estonia
- Institute of Molecular and Cell Biology University of Tartu Tartu Estonia
| | - Ingrid Meulenbelt
- Molecular Epidemiology Section (Department of Biomedical Datasciences) Leiden University Medical Centre Leiden The Netherlands
| | - Nadia Micali
- Department of Psychiatry, Faculty of Medicine University of Geneva Geneva Switzerland
- Division of Child and Adolescent Psychiatry Geneva University Hospital Geneva Switzerland
| | - Karen Mitchell
- National Center for PTSD VA Boston Healthcare System Boston Massachusetts USA
- Department of Psychiatry Boston University School of Medicine Boston Massachusetts USA
| | | | - Palmiero Monteleone
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana" University of Salerno Salerno Italy
| | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA) University of Florence Florence Italy
| | - Marie Navratilova
- Department of Cancer, Epidemiology and Genetics Masaryk Memorial Cancer Institute Brno Czech Republic
| | - Ioanna Ntalla
- Department of Nutrition and Dietetics Harokopio University Athens Greece
| | | | - Roel A. Ophoff
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior University of California Los Angeles Los Angeles California USA
- Department of Psychiatry, Erasmus MC University Medical Center Rotterdam Rotterdam The Netherlands
| | - Leonid Padyukov
- Department of Medicine, Center for Molecular Medicine, Division of Rheumatology Karolinska Institutet and Karolinska University Hospital Stockholm Sweden
| | - Aarno Palotie
- Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge Massachusetts USA
- Institute for Molecular Medicine FIMM, HiLIFE University of Helsinki Helsinki Finland
- Center for Human Genome Research Massachusetts General Hospital Boston Massachusetts USA
| | - Jacques Pantel
- Centre of Psychiatry and Neuroscience INSERM U894 Paris France
| | - Hana Papezova
- Department of Psychiatry, First Faculty of Medicine Charles University Prague Czech Republic
| | - Dalila Pinto
- Department of Psychiatry, and Genetics and Genomics Sciences, Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York New York USA
| | - Raquel Rabionet
- Saint Joan de Déu Research Institute Saint Joan de Déu Barcelona Children's Hospital Barcelona Spain
- Institute of Biomedicine (IBUB) University of Barcelona Barcelona Spain
- Department of Genetics, Microbiology and Statistics University of Barcelona Barcelona Spain
| | - Anu Raevuori
- Department of Public Health University of Helsinki Helsinki Finland
| | - Nicolas Ramoz
- Institute of Psychiatry and Neuroscience of Paris INSERM U1266 Paris France
| | - Ted Reichborn‐Kjennerud
- Department of Mental Disorders Norwegian Institute of Public Health Oslo Norway
- Institute of Clinical Medicine University of Oslo Oslo Norway
| | - Valdo Ricca
- Department of Health Science University of Florence Florence Italy
| | - Samuli Ripatti
- Department of Biometry University of Helsinki Helsinki Finland
| | - Franziska Ritschel
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine Technische Universität Dresden Dresden Germany
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, Eating Disorders Research and Treatment Center Technische Universität Dresden Dresden Germany
| | - Marion Roberts
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
| | - Alessandro Rotondo
- Department of Psychiatry, Neurobiology, Pharmacology, and Biotechnologies University of Pisa Pisa Italy
| | - Dan Rujescu
- Department of Psychiatry, Psychotherapy and Psychosomatics Martin‐Luther‐University Halle‐Wittenberg Halle (Saale) Germany
| | - Filip Rybakowski
- Department of Psychiatry Poznan University of Medical Sciences Poznan Poland
| | - Paolo Santonastaso
- Department of Neurosciences, Padua Neuroscience Center University of Padova Padova Italy
| | - André Scherag
- Institute of Medical Statistics, Computer and Data Sciences Jena University Hospital Jena Germany
| | - Stephen W. Scherer
- Department of Genetics and Genomic Biology The Hospital for Sick Children Toronto Ontario Canada
- McLaughlin Centre University of Toronto Toronto Ontario Canada
| | - Ulrike Schmidt
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
| | | | - Alexandra Schosser
- Department of Psychiatry and Psychotherapy Medical University of Vienna Vienna Austria
| | - Jochen Seitz
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy RWTH Aachen University Aachen Germany
| | - Lenka Slachtova
- Department of Pediatrics and Center of Applied Genomics, First Faculty of Medicine Charles University Prague Czech Republic
| | - P. Eline Slagboom
- Molecular Epidemiology Section (Department of Medical Statistics) Leiden University Medical Centre Leiden The Netherlands
| | - Margarita C.T. Slof‐Op't Landt
- Center for Eating Disorders Ursula Rivierduinen Leiden The Netherlands
- Department of Psychiatry Leiden University Medical Centre Leiden The Netherlands
| | - Agnieszka Slopien
- Department of Child and Adolescent Psychiatry Poznan University of Medical Sciences Poznan Poland
| | - Sandro Sorbi
- Department of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA) University of Florence Florence Italy
- IRCCS Fondazione Don Carlo Gnocchi Florence Italy
| | - Beata Świątkowska
- Department of Environmental Epidemiology Nofer Institute of Occupational Medicine Lodz Poland
| | - Jin P. Szatkiewicz
- Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
| | | | - Elena Tenconi
- Department of Neurosciences University of Padova Padova Italy
| | - Alfonso Tortorella
- Department of Psychiatry University of Naples SUN Naples Italy
- Department of Psychiatry University of Perugia Perugia Italy
| | - Federica Tozzi
- Brain Sciences Department Stremble Ventures Limassol Cyprus
| | - Janet Treasure
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
| | - Artemis Tsitsika
- Adolescent Health Unit, Second Department of Pediatrics "P. & A. Kyriakou" Children's Hospital, University of Athens Athens Greece
| | - Marta Tyszkiewicz‐Nwafor
- Department of Child and Adolescent Psychiatry Poznan University of Medical Sciences Poznan Poland
| | - Konstantinos Tziouvas
- Pediatric Intensive Care Unit "P. & A. Kyriakou" Children's Hospital, University of Athens Athens Greece
| | - Annemarie A. Elburg
- Center for Eating Disorders Rintveld Altrecht Mental Health Institute Zeist The Netherlands
- Faculty of Social and Behavioral Sciences Utrecht University Utrecht The Netherlands
| | - Eric F. Furth
- Center for Eating Disorders Ursula Rivierduinen Leiden The Netherlands
- Department of Psychiatry Leiden University Medical Centre Leiden The Netherlands
| | - Gudrun Wagner
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry Medical University of Vienna Vienna Austria
| | - Esther Walton
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine Technische Universität Dresden Dresden Germany
| | - Elisabeth Widen
- Institute for Molecular Medicine FIMM, HiLIFE University of Helsinki Helsinki Finland
| | - Eleftheria Zeggini
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton Cambridge UK
- Institute of Translational Genomics, Helmholtz Zentrum München ‐ German Research Centre for Environmental Health Neuherberg Germany
| | - Stephanie Zerwas
- Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
| | - Stephan Zipfel
- Department of Internal Medicine VI, Psychosomatic Medicine and Psychotherapy University Medical Hospital Tuebingen Tuebingen Germany
| | - Andrew W. Bergen
- BioRealm, LLC Walnut California USA
- Oregon Research Institute Eugene Oregon USA
| | - Joseph M. Boden
- Christchurch Health and Development Study University of Otago Christchurch New Zealand
| | - Harry Brandt
- The Center for Eating Disorders at Sheppard Pratt Baltimore Maryland USA
| | - Steven Crawford
- The Center for Eating Disorders at Sheppard Pratt Baltimore Maryland USA
| | - Katherine A. Halmi
- Department of Psychiatry Weill Cornell Medical College New York New York USA
| | - L. John Horwood
- Christchurch Health and Development Study University of Otago Christchurch New Zealand
| | | | - Allan S. Kaplan
- Centre for Addiction and Mental Health Toronto Ontario Canada
- Institute of Medical Science University of Toronto Toronto Ontario Canada
- Department of Psychiatry University of Toronto Toronto Ontario Canada
| | - Walter H. Kaye
- Department of Psychiatry University of California San Diego La Jolla California USA
| | - James Mitchell
- Department of Psychiatry and Behavioral Science University of North Dakota School of Medicine and Health Sciences Fargo North Dakota USA
| | - Catherine M. Olsen
- Population Health Department QIMR Berghofer Medical Research Institute Brisbane Queensland Australia
| | - John F. Pearson
- Biostatistics and Computational Biology Unit University of Otago Christchurch New Zealand
| | - Nancy L. Pedersen
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden
| | - Michael Strober
- Department of Psychiatry and Biobehavioral Science, Semel Institute for Neuroscience and Human Behavior University of California Los Angeles Los Angeles California USA
- David Geffen School of Medicine University of California Los Angeles Los Angeles California USA
| | - Thomas Werge
- Department of Clinical Medicine University of Copenhagen Copenhagen Denmark
| | - David C. Whiteman
- Population Health Department QIMR Berghofer Medical Research Institute Brisbane Queensland Australia
| | - D. Blake Woodside
- Institute of Medical Science University of Toronto Toronto Ontario Canada
- Department of Psychiatry University of Toronto Toronto Ontario Canada
- Centre for Mental Health University Health Network Toronto Ontario Canada
- Program for Eating Disorders University Health Network Toronto Ontario Canada
| | - Jakob Grove
- Department of Biomedicine Aarhus University Aarhus Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) Aarhus Denmark
- Centre for Integrative Sequencing, iSEQ Aarhus University Aarhus Denmark
- Bioinformatics Research Centre Aarhus University Aarhus Denmark
| | - Anjali K. Henders
- Institute for Molecular Bioscience University of Queensland Brisbane Queensland Australia
| | - Janne T. Larsen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) Aarhus Denmark
- National Centre for Register‐Based Research, Aarhus BSS Aarhus University Aarhus Denmark
- Centre for Integrated Register‐based Research (CIRRAU) Aarhus University Aarhus Denmark
| | - Richard Parker
- QIMR Berghofer Medical Research Institute Brisbane Queensland Australia
| | - Liselotte V. Petersen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) Aarhus Denmark
- National Centre for Register‐Based Research, Aarhus BSS Aarhus University Aarhus Denmark
- Centre for Integrated Register‐based Research (CIRRAU) Aarhus University Aarhus Denmark
| | - Jennifer Jordan
- Department of Psychological Medicine University of Otago Christchurch New Zealand
- Canterbury District Health Board Christchurch New Zealand
| | - Martin A. Kennedy
- Department of Pathology and Biomedical Science University of Otago Christchurch New Zealand
| | - Andreas Birgegård
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden
- Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden
- Center for Psychiatry Research, Stockholm Health Care Services Stockholm County Council Stockholm Sweden
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden
| | - Claes Norring
- Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden
- Center for Psychiatry Research, Stockholm Health Care Services Stockholm County Council Stockholm Sweden
| | - Mikael Landén
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology The Sahlgrenska Academy at the University of Gothenburg Gothenburg Sweden
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) Aarhus Denmark
- National Centre for Register‐Based Research, Aarhus BSS Aarhus University Aarhus Denmark
- Centre for Integrated Register‐based Research (CIRRAU) Aarhus University Aarhus Denmark
| | - Renato Polimanti
- Department of Psychiatry, Division of Human Genetics Yale School of Medicine New Haven Connecticut USA
- Veterans Affairs Connecticut Healthcare System West Haven Connecticut USA
| | - Jeanette N. McClintick
- Department of Biochemistry and Molecular Biology Indiana University School of Medicine Indianapolis Indiana USA
| | - Amy E. Adkins
- Department of Psychology Virginia Commonwealth University Richmond Virginia USA
- College Behavioral and Emotional Health Institute Virginia Commonwealth University Richmond Virginia USA
| | - Fazil Aliev
- Department of Psychology Virginia Commonwealth University Richmond Virginia USA
- Faculty of Business Karabuk University Karabuk Turkey
| | - Silviu‐Alin Bacanu
- Virginia Commonwealth University Alcohol Research Center Virginia Commonwealth University Richmond Virginia USA
- Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University Richmond Virginia USA
- Department of Psychiatry Virginia Commonwealth University Richmond Virginia USA
| | - Anthony Batzler
- Psychiatric Genomics and Pharmacogenomics Program Mayo Clinic Rochester Minnesota USA
| | - Sarah Bertelsen
- Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York USA
| | - Joanna M. Biernacka
- Department of Health Sciences Research Mayo Clinic Rochester Minnesota USA
- Department of Psychiatry and Psychology Mayo Clinic Rochester Minnesota USA
| | - Tim B. Bigdeli
- Department of Psychiatry and Behavioral Sciences State University of New York Downstate Medical Center Brooklyn New York USA
| | - Li‐Shiun Chen
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| | | | - Franziska Degenhardt
- Institute of Human Genetics University of Bonn School of Medicine & University Hospital Bonn Bonn Germany
| | - Anna R. Docherty
- Department of Psychiatry University of Utah Salt Lake City Utah USA
| | - Alexis C. Edwards
- Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University Richmond Virginia USA
- Department of Psychiatry Virginia Commonwealth University Richmond Virginia USA
| | - Jerome C. Foo
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim Heidelberg University Mannheim Germany
| | - Louis Fox
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim Heidelberg University Mannheim Germany
| | - Laura M. Hack
- Department of Psychiatry and Behavioral Sciences Stanford University Stanford California USA
| | - Annette M. Hartmann
- Department of Psychiatry, Psychotherapy and Psychosomatics Martin‐Luther‐University Halle‐Wittenberg Halle (Saale) Germany
| | - Sarah M. Hartz
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| | - Stefanie Heilmann‐Heimbach
- Institute of Human Genetics University of Bonn School of Medicine & University Hospital Bonn Bonn Germany
| | | | - Per Hoffmann
- Institute of Medical Genetics and Pathology University Hospital Basel Basel Switzerland
- Institute of Human Genetics School of Medicine & University Hospital Bonn, University of Bonn Bonn Germany
- Human Genomics Research Group, Department of Biomedicine University of Basel Basel Switzerland
| | - Jouke‐Jan Hottenga
- Department of Biological Psychology, Amsterdam Public Health Research Institute Vrije Universiteit Amsterdam Amsterdam The Netherlands
| | - Bettina Konte
- Department of Psychiatry, Psychotherapy and Psychosomatics Martin‐Luther‐University Halle‐Wittenberg Halle (Saale) Germany
| | - Jari Lahti
- Turku Institute for Advanced Studies University of Turku Turku Finland
| | | | - Dongbing Lai
- Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis Indiana USA
| | - Lannie Ligthart
- Department of Biological Psychology, Amsterdam Public Health Research Institute Vrije Universiteit Amsterdam Amsterdam The Netherlands
| | - Anu Loukola
- Institute for Molecular Medicine FIMM, HiLIFE University of Helsinki Helsinki Finland
| | - Brion S. Maher
- Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA
| | - Hamdi Mbarek
- Department of Biological Psychology, Amsterdam Public Health Research Institute Vrije Universiteit Amsterdam Amsterdam The Netherlands
| | - Andrew M. McIntosh
- Division of Psychiatry, Centre for Cognitive Ageing and Cognitive Epidemiology University of Edinburgh Edinburgh UK
| | - Matthew B. McQueen
- Department of Integrative Physiology University of Colorado Boulder Boulder Colorado USA
| | - Jacquelyn L. Meyers
- Department of Psychiatry and Behavioral Sciences, Henri Begleiter Neurodynamics Laboratory SUNY Downstate Medical Center Brooklyn New York USA
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Public Health Research Institute VU University Medical Center/GGz inGeest Amsterdam The Netherlands
| | - Teemu Palviainen
- Institute for Molecular Medicine FIMM, HiLIFE University of Helsinki Helsinki Finland
| | - Roseann E. Peterson
- Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University Richmond Virginia USA
- Department of Psychiatry Virginia Commonwealth University Richmond Virginia USA
| | - Euijung Ryu
- Department of Health Sciences Research Mayo Clinic Rochester Minnesota USA
| | - Nancy L. Saccone
- Department of Genetics Washington University School of Medicine Saint Louis Missouri USA
| | - Jessica E. Salvatore
- Department of Psychology Virginia Commonwealth University Richmond Virginia USA
- Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University Richmond Virginia USA
- Department of Psychiatry Virginia Commonwealth University Richmond Virginia USA
| | - Sandra Sanchez‐Roige
- Department of Psychiatry University of California San Diego La Jolla California USA
| | | | - Richard Sherva
- Department of Medicine (Biomedical Genetics) Boston University School of Medicine Boston Massachusetts USA
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim Heidelberg University Mannheim Germany
| | - Jana Strohmaier
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim Heidelberg University Mannheim Germany
| | - Nathaniel Thomas
- Department of Psychology Virginia Commonwealth University Richmond Virginia USA
- College Behavioral and Emotional Health Institute Virginia Commonwealth University Richmond Virginia USA
| | - Jen‐Chyong Wang
- Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York USA
| | - Bradley T. Webb
- Virginia Commonwealth University Alcohol Research Center Virginia Commonwealth University Richmond Virginia USA
- Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University Richmond Virginia USA
- Department of Psychiatry Virginia Commonwealth University Richmond Virginia USA
| | - Robbee Wedow
- Analytic and Translational Genetics Unit, Department of Medicine Massachusetts General Hospital and Harvard Medical School Boston Massachusetts USA
- Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health Harvard University Cambridge Massachusetts USA
- Department of Sociology Harvard University Cambridge Massachusetts USA
| | - Leah Wetherill
- Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis Indiana USA
| | - Amanda G. Wills
- Department of Pharmacology University of Colorado School of Medicine Aurora Colorado USA
| | - Hang Zhou
- Department of Psychiatry, Division of Human Genetics Yale School of Medicine New Haven Connecticut USA
- Veterans Affairs Connecticut Healthcare System West Haven Connecticut USA
| | - Jason D. Boardman
- Institute of Behavioral Science University of Colorado Boulder Colorado USA
- Department of Sociology University of Colorado Boulder Colorado USA
| | - Danfeng Chen
- Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts USA
| | - Doo‐Sup Choi
- Department of Molecular Pharmacology and Experimental Therapeutics Mayo Clinic Rochester Minnesota USA
| | - William E. Copeland
- Department of Psychiatry University of Vermont Medical Center Burlington Vermont USA
| | - Robert C. Culverhouse
- Department of Medicine, Division of Biostatistics Washington University School of Medicine Saint Louis Missouri USA
| | - Norbert Dahmen
- Department of Psychiatry University of Mainz Mainz Germany
| | - Louisa Degenhardt
- National Drug and Alcohol Research Centre University of New South Wales Sydney New South Wales Australia
| | - Benjamin W. Domingue
- Stanford University Graduate School of Education Stanford University Stanford California USA
| | - Mark A. Frye
- Department of Psychiatry and Psychology Mayo Clinic Rochester Minnesota USA
| | - Wolfgang Gäebel
- Department of Psychiatry and Psychotherapy University of Düsseldorf Duesseldorf Germany
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine University of Edinburgh Edinburgh UK
| | - Marcus Ising
- Max‐Planck‐Institute of Psychiatry Munich Germany
| | - Margaret Keyes
- Department of Psychology University of Minnesota Minneapolis Minnesota USA
| | - Falk Kiefer
- Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim Heidelberg University Mannheim Germany
| | - Gabriele Koller
- Department of Psychiatry and Psychotherapy University Hospital, LMU Munich Munich Germany
| | - John Kramer
- Department of Psychiatry University of Iowa Roy J and Lucille A Carver College of Medicine Iowa City Iowa USA
| | - Samuel Kuperman
- Department of Psychiatry University of Iowa Roy J and Lucille A Carver College of Medicine Iowa City Iowa USA
| | | | - Michael T. Lynskey
- Addictions Department, Institute of Psychiatry, Psychology & Neuroscience King's College London London UK
| | - Wolfgang Maier
- Department of Psychiatry University of Bonn Bonn Germany
| | - Karl Mann
- Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim Heidelberg University Mannheim Germany
| | - Satu Männistö
- Department of Public Health Solutions National Institute for Health and Welfare Helsinki Finland
| | - Bertram Müller‐Myhsok
- Department of Statistical Genetics Max‐Planck‐Institute of Psychiatry München Germany
| | - Alison D. Murray
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences & Nutrition University of Aberdeen Foresterhill Aberdeen UK
| | - John I. Nurnberger
- Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis Indiana USA
- Department of Psychiatry Indiana University School of Medicine Indianapolis Indiana USA
| | - Ulrich Preuss
- Department of Psychiatry, Psychotherapy and Psychosomatics Martin‐Luther‐University Halle‐Wittenberg Herborn Germany
- Department of Psychiatry and Psychotherapy Vitos Hospital Herborn Herborn Germany
| | - Katri Räikkönen
- Department of Psychology and Logopedics University of Helsinki Helsinki Finland
| | | | - Monika Ridinger
- Department of Psychiatry and Psychotherapy University of Regensburg Psychiatric Health Care Aargau Regensburg Germany
| | - Norbert Scherbaum
- Department of Psychiatry and Psychotherapy and Department of Addictive Behaviour and Addiction Medicine, Medical Faculty LVR‐Hospital Essen, University of Duisburg‐Essen Essen Germany
| | - Marc A. Schuckit
- Department of Psychiatry University of California San Diego La Jolla California USA
| | - Michael Soyka
- Medical Park Chiemseeblick in Bernau‐Felden Ludwig‐Maximilians‐University Bernau am Chiemsee Germany
- Psychiatric Hospital, Ludwig‐Maximilians‐University Bernau am Chiemsee Germany
| | - Jens Treutlein
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim Heidelberg University Mannheim Germany
| | - Stephanie H. Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim Heidelberg University Mannheim Germany
| | - Norbert Wodarz
- Department of Psychiatry and Psychotherapy University of Regensburg Regensburg Germany
| | - Peter Zill
- Department of Psychiatry Psychiatric Hospital, Ludwig‐Maximilians‐University Munich Germany
| | - Daniel E. Adkins
- Department of Psychiatry University of Utah Salt Lake City Utah USA
- Department of Sociology University of Utah Salt Lake City Utah USA
| | - Dorret I. Boomsma
- Department of Biological Psychology, Amsterdam Public Health Research Institute Vrije Universiteit Amsterdam Amsterdam The Netherlands
| | - Laura J. Bierut
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| | - Sandra A. Brown
- Department of Psychiatry University of California San Diego La Jolla California USA
- Department of Psychology University of California San Diego La Jolla California USA
| | - Kathleen K. Bucholz
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| | - E. Jane Costello
- Department of Psychiatry and Behavioral Sciences Duke University Medical Center Durham North Carolina USA
| | - Harriet Wit
- Department of Psychiatry and Behavioral Neuroscience University of Chicago Chicago Illinois USA
| | | | - Johan G. Eriksson
- Department of General Practice and Primary Health Care University of Helsinki Helsinki Finland
- National Institute for Health and Welfare Helsinki Finland
| | - Lindsay A. Farrer
- Department of Medicine (Biomedical Genetics) Boston University School of Medicine Boston Massachusetts USA
- Department of Neurology Boston University School of Medicine Boston Massachusetts USA
- Department of Ophthalmology Boston University School of Medicine Boston Massachusetts USA
- Department of Epidemiology, School of Public Health Boston University Boston Massachusetts USA
- Department of Biostatistics, School of Public Health Boston University Boston Massachusetts USA
| | - Tatiana M. Foroud
- Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis Indiana USA
| | - Nathan A. Gillespie
- Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University Richmond Virginia USA
| | - Alison M. Goate
- Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York USA
| | - David Goldman
- Laboratory of Neurogenetics NIH/NIAAA Bethesda Maryland USA
- Office of the Clinical Director NIH/NIAAA Besthesda Maryland USA
| | - Richard A. Grucza
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| | - Dana B. Hancock
- Center for Omics Discovery and Epidemiology, Behavioral Health Research Division RTI International Research Triangle Park North Carolina USA
| | - Kathleen Mullan Harris
- Department of Sociology University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
- Carolina Population Center University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
| | - Victor Hesselbrock
- Department of Psychiatry University of Connecticut School of Medicine Farmington Connecticut USA
| | - John K. Hewitt
- Institute for Behavioral Genetics University of Colorado Boulder Boulder Colorado USA
| | | | - William G. Iacono
- Department of Psychology University of Minnesota Minneapolis Minnesota USA
| | - Eric O. Johnson
- Center for Omics Discovery and Epidemiology, Behavioral Health Research Division RTI International Research Triangle Park North Carolina USA
- Fellow Program RTI International Research Triangle Park North Carolina USA
| | - Victor M. Karpyak
- Department of Psychiatry and Psychology Mayo Clinic Rochester Minnesota USA
| | - Kenneth S. Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University Richmond Virginia USA
- Department of Psychiatry Virginia Commonwealth University Richmond Virginia USA
| | - Henry R. Kranzler
- Center for Studies of Addiction University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USA
- VISN 4 MIRECC Crescenz VAMC Philadelphia Pennsylvania USA
| | - Kenneth Krauter
- Department of Molecular, Cellular, and Developmental Biology University of Colorado Boulder Boulder Colorado USA
| | - Penelope A. Lind
- QIMR Berghofer Medical Research Institute Brisbane Queensland Australia
| | - Matt McGue
- Department of Psychology University of Minnesota Minneapolis Minnesota USA
| | - James MacKillop
- Peter Boris Centre for Addictions Research McMaster University/St. Joseph's Healthcare Hamilton Hamilton Ontario Canada
- Michael G. DeGroote Centre for Medicinal Cannabis Research McMaster University/St. Joseph's Healthcare Hamilton Hamilton Ontario Canada
| | - Pamela A.F. Madden
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| | - Hermine H. Maes
- Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University Richmond Virginia USA
| | - Patrik K.E. Magnusson
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden
| | - Elliot C. Nelson
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| | - Markus M. Nöthen
- Institute of Human Genetics University of Bonn School of Medicine & University Hospital Bonn Bonn Germany
| | - Abraham A. Palmer
- Department of Psychiatry University of California San Diego La Jolla California USA
- Institute for Genomic Medicine University of California San Diego La Jolla California USA
| | - Brenda W.J.H. Penninx
- Department of Psychiatry, Amsterdam UMC VU University and GGZinGeest Amsterdam The Netherlands
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral Sciences, Henri Begleiter Neurodynamics Laboratory SUNY Downstate Medical Center Brooklyn New York USA
| | - John P. Rice
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim Heidelberg University Mannheim Germany
| | - Brien P. Riley
- Virginia Commonwealth University Alcohol Research Center Virginia Commonwealth University Richmond Virginia USA
- Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University Richmond Virginia USA
- Department of Psychiatry Virginia Commonwealth University Richmond Virginia USA
| | - Richard J. Rose
- Department of Psychological & Brain Sciences Indiana University Bloomington Bloomington Indiana USA
| | - Pei‐Hong Shen
- Laboratory of Neurogenetics NIH/NIAAA Bethesda Maryland USA
| | - Judy Silberg
- Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University Richmond Virginia USA
- Department of Psychiatry Virginia Commonwealth University Richmond Virginia USA
| | - Michael C. Stallings
- Institute for Behavioral Genetics University of Colorado Boulder Boulder Colorado USA
| | - Ralph E. Tarter
- School of Pharmacy University of Pittsburgh Pittsburgh Pennsylvania USA
| | | | - Scott Vrieze
- Department of Psychology University of Minnesota Minneapolis Minnesota USA
| | - Tamara L. Wall
- Department of Psychiatry University of California San Diego La Jolla California USA
| | - John B. Whitfield
- QIMR Berghofer Medical Research Institute Brisbane Queensland Australia
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health Yale University New Haven Connecticut USA
| | - Benjamin M. Neale
- Analytic and Translational Genetics Unit, Department of Medicine Massachusetts General Hospital and Harvard Medical School Boston Massachusetts USA
- Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts USA
| | - Tracey D. Wade
- School of Psychology Flinders University Adelaide South Australia Australia
| | - Andrew C. Heath
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| | - Grant W. Montgomery
- QIMR Berghofer Medical Research Institute Brisbane Queensland Australia
- Institute for Molecular Bioscience University of Queensland Brisbane Queensland Australia
- Queensland Brain Institute University of Queensland Brisbane Queensland Australia
| | | | - Patrick F. Sullivan
- Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
- Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden
| | - Jaakko Kaprio
- Department of Public Health University of Helsinki Helsinki Finland
- Institute for Molecular Medicine FIMM, HiLIFE University of Helsinki Helsinki Finland
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
- National Institute for Health Research Biomedical Research Centre King's College London and South London and Maudsley National Health Service Trust London UK
| | - Joel Gelernter
- Department of Psychiatry, Division of Human Genetics Yale School of Medicine New Haven Connecticut USA
- Veterans Affairs Connecticut Healthcare System West Haven Connecticut USA
- Department of Genetics Yale School of Medicine New Haven Connecticut USA
- Department of Neuroscience Yale School of Medicine New Haven Connecticut USA
| | - Howard J. Edenberg
- Department of Biochemistry and Molecular Biology Indiana University School of Medicine Indianapolis Indiana USA
- Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis Indiana USA
| | - Cynthia M. Bulik
- Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden
- Department of Nutrition University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
| | - Arpana Agrawal
- Department of Psychiatry Washington University School of Medicine Saint Louis Missouri USA
| |
Collapse
|
39
|
Li B, Dong J, Yu J, Fan Y, Shang L, Zhou X, Bai Y. Pinpointing miRNA and genes enrichment over trait-relevant tissue network in Genome-Wide Association Studies. BMC Med Genomics 2020; 13:191. [PMID: 33371893 PMCID: PMC7771066 DOI: 10.1186/s12920-020-00830-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 11/17/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Understanding gene regulation is important but difficult. Elucidating tissue-specific gene regulation mechanism is even more challenging and requires gene co-expression network assembled from protein-protein interaction, transcription factor and gene binding, and post-transcriptional regulation (e.g., miRNA targeting) information. The miRNA binding affinity could therefore be changed by SNP(s) located at the 3' untranslated regions (3'UTR) of the target messenger RNA (mRNA) which miRNA(s) interacts with. Genome-wide association study (GWAS) has reported significant numbers of loci hosting SNPs associated with many traits. The goal of this study is to pinpoint GWAS functional variants located in 3'UTRs and elucidate if the genes harboring these variants along with their targeting miRNAs are associated with genetic traits relevant to certain tissues. METHODS By applying MIGWAS, CoCoNet, ANNOVAR, and DAVID bioinformatics software and utilizing the gene expression database (e.g. GTEx data) to study GWAS summary statistics for 43 traits from 28 GWAS studies, we have identified a list of miRNAs and targeted genes harboring 3'UTR variants, which could contribute to trait-relevant tissue over miRNA-target gene network. RESULTS Our result demonstrated that strong association between traits and tissues exists, and in particular, the Primary Biliary Cirrhosis (PBC) trait has the most significant p-value for all 180 tissues among all 43 traits used for this study. We reported SNPs located in 3'UTR regions of genes (SFMBT2, ZC3HAV1, and UGT3A1) targeted by miRNAs for PBC trait and its tissue association network. After employing Gene Ontology (GO) analysis for PBC trait, we have also identified a very important miRNA targeted gene over miRNA-target gene network, PFKL, which encodes the liver subunit of an enzyme. CONCLUSIONS The non-coding variants identified from GWAS studies are casually assumed to be not critical to translated protein product. However, 3' untranslated regions (3'UTRs) of genes harbor variants can often change the binding affinity of targeting miRNAs playing important roles in protein translation degree. Our study has shown that GWAS variants could play important roles on miRNA-target gene networks by contributing the association between traits and tissues. Our analysis expands our knowledge on trait-relevant tissue network and paves way for future human disease studies.
Collapse
Affiliation(s)
- Binze Li
- Bellaire High School, 5100 Maple St, Bellaire, TX, 77401, USA
| | - Julian Dong
- Northville High School, 45700 Six Mile Road, Northville, MI, 48168, USA
| | - Jiaqi Yu
- College Preparatory School, 6100 Broadway, Oakland, CA, 94618, USA
| | - Yuqi Fan
- The Master's Academy, 1500 Lukas Ln, Oviedo, FL, 32765, USA
| | - Lulu Shang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.,Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yongsheng Bai
- Department of Biology, Eastern Michigan University, Ypsilanti, MI, 48197, USA. .,Next-Gen Intelligent Science Training, Ann Arbor, MI, 48105, USA.
| |
Collapse
|
40
|
Zekavat SM, Lin SH, Bick AG, Liu A, Paruchuri K, Uddin MM, Ye Y, Yu Z, Liu X, Kamatani Y, Pirruccello JP, Pampana A, Loh PR, Kohli P, McCarroll SA, Neale B, Engels EA, Brown DW, Smoller JW, Green R, Karlson EW, Lebo M, Ellinor PT, Weiss ST, Daly MJ, Terao C, Zhao H, Ebert BL, Ganna A, Machiela MJ, Genovese G, Natarajan P. Hematopoietic mosaic chromosomal alterations and risk for infection among 767,891 individuals without blood cancer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.11.12.20230821. [PMID: 33236019 PMCID: PMC7685330 DOI: 10.1101/2020.11.12.20230821] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Age is the dominant risk factor for infectious diseases, but the mechanisms linking the two are incompletely understood1,2. Age-related mosaic chromosomal alterations (mCAs) detected from blood-derived DNA genotyping, are structural somatic variants associated with aberrant leukocyte cell counts, hematological malignancy, and mortality3-11. Whether mCAs represent independent risk factors for infection is unknown. Here we use genome-wide genotyping of blood DNA to show that mCAs predispose to diverse infectious diseases. We analyzed mCAs from 767,891 individuals without hematological cancer at DNA acquisition across four countries. Expanded mCA (cell fraction >10%) prevalence approached 4% by 60 years of age and was associated with diverse incident infections, including sepsis, pneumonia, and coronavirus disease 2019 (COVID-19) hospitalization. A genome-wide association study of expanded mCAs identified 63 significant loci. Germline genetic alleles associated with expanded mCAs were enriched at transcriptional regulatory sites for immune cells. Our results link mCAs with impaired immunity and predisposition to infections. Furthermore, these findings may also have important implications for the ongoing COVID-19 pandemic, particularly in prioritizing individual preventive strategies and evaluating immunization responses.
Collapse
Affiliation(s)
- Seyedeh M. Zekavat
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
| | - Shu-Hong Lin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Alexander G. Bick
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center
| | - Aoxing Liu
- Institute for Molecular Medicine Finland, Helsinki, Finland
| | - Kaavya Paruchuri
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Md Mesbah Uddin
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
| | - Yixuan Ye
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT
| | - Zhaolong Yu
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT
| | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
| | - James P. Pirruccello
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Akhil Pampana
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
| | - Po-Ru Loh
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Puja Kohli
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA
- Vertex Pharmaceuticals, Boston, MA
| | - Steven A. McCarroll
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Genetics, Harvard Medical School, Boston, MA
| | - Benjamin Neale
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
| | - Eric A. Engels
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Derek W. Brown
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Jordan W. Smoller
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Robert Green
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Elizabeth W. Karlson
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Boston, MA
| | - Matthew Lebo
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Laboratory for Molecular Medicine, Partners Healthcare, Cambridge, MA
| | - Patrick T. Ellinor
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Scott T. Weiss
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Mark J. Daly
- Institute for Molecular Medicine Finland, Helsinki, Finland
| | | | | | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Hongyu Zhao
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT
- Department of Biostatistics, Yale School of Public Health, New Haven, CT
| | - Benjamin L. Ebert
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Howard Hughes Medical Institute, Boston, MA, USA
| | | | - Andrea Ganna
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Institute for Molecular Medicine Finland, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
| | - Mitchell J. Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Giulio Genovese
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Pradeep Natarajan
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| |
Collapse
|
41
|
Natarajan P, Zekavat S, Lin SH, Bick A, Liu A, Paruchuri K, Uddin MM, Ye Y, Yu Z, Liu X, Kamatani Y, Pirruccello J, Pampana A, Loh PR, Kohli P, McCarroll S, Neale B, Engels E, Brown D, Smoller J, Green R, Karlson E, Lebo M, Ellinor P, Weiss S, Daly M, Terao C, Zhao H, Ebert B, Machiela M, Genovese G. Hematopoietic mosaic chromosomal alterations and risk for infection among 767,891 individuals without blood cancer. RESEARCH SQUARE 2020. [PMID: 33236004 PMCID: PMC7685327 DOI: 10.21203/rs.3.rs-100817/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Age is the dominant risk factor for infectious diseases, but the mechanisms linking the two are incompletely understood1,2. Age-related mosaic chromosomal alterations (mCAs) detected from blood-derived DNA genotyping, are structural somatic variants associated with aberrant leukocyte cell counts, hematological malignancy, and mortality3-11. Whether mCAs represent independent risk factors for infection is unknown. Here we use genome-wide genotyping of blood DNA to show that mCAs predispose to diverse infectious diseases. We analyzed mCAs from 767,891 individuals without hematological cancer at DNA acquisition across four countries. Expanded mCA (cell fraction >10%) prevalence approached 4% by 60 years of age and was associated with diverse incident infections, including sepsis, pneumonia, and coronavirus disease 2019 (COVID-19) hospitalization. A genome-wide association study of expanded mCAs identified 63 significant loci. Germline genetic alleles associated with expanded mCAs were enriched at transcriptional regulatory sites for immune cells. Our results link mCAs with impaired immunity and predisposition to infections. Furthermore, these findings may also have important implications for the ongoing COVID-19 pandemic, particularly in prioritizing individual preventive strategies and evaluating immunization responses.
Collapse
|
42
|
Liu W, Li M, Zhang W, Zhou G, Wu X, Wang J, Lu Q, Zhao H. Leveraging functional annotation to identify genes associated with complex diseases. PLoS Comput Biol 2020; 16:e1008315. [PMID: 33137096 PMCID: PMC7660930 DOI: 10.1371/journal.pcbi.1008315] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 11/12/2020] [Accepted: 09/05/2020] [Indexed: 02/06/2023] Open
Abstract
To increase statistical power to identify genes associated with complex traits, a number of transcriptome-wide association study (TWAS) methods have been proposed using gene expression as a mediating trait linking genetic variations and diseases. These methods first predict expression levels based on inferred expression quantitative trait loci (eQTLs) and then identify expression-mediated genetic effects on diseases by associating phenotypes with predicted expression levels. The success of these methods critically depends on the identification of eQTLs, which may not be functional in the corresponding tissue, due to linkage disequilibrium (LD) and the correlation of gene expression between tissues. Here, we introduce a new method called T-GEN (Transcriptome-mediated identification of disease-associated Genes with Epigenetic aNnotation) to identify disease-associated genes leveraging epigenetic information. Through prioritizing SNPs with tissue-specific epigenetic annotation, T-GEN can better identify SNPs that are both statistically predictive and biologically functional. We found that a significantly higher percentage (an increase of 18.7% to 47.2%) of eQTLs identified by T-GEN are inferred to be functional by ChromHMM and more are deleterious based on their Combined Annotation Dependent Depletion (CADD) scores. Applying T-GEN to 207 complex traits, we were able to identify more trait-associated genes (ranging from 7.7% to 102%) than those from existing methods. Among the identified genes associated with these traits, T-GEN can better identify genes with high (>0.99) pLI scores compared to other methods. When T-GEN was applied to late-onset Alzheimer's disease, we identified 96 genes located at 15 loci, including two novel loci not implicated in previous GWAS. We further replicated 50 genes in an independent GWAS, including one of the two novel loci.
Collapse
Affiliation(s)
- Wei Liu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States of America
| | - Mo Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States of America
| | - Wenfeng Zhang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States of America
| | - Geyu Zhou
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States of America
| | - Xing Wu
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, United States of America
| | - Jiawei Wang
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States of America
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WI, United States of America
- Department of Statistics, University of Wisconsin-Madison, WI, United States of America
- Center for Demography of Health and Aging, University of Wisconsin-Madison, WI, United States of America
| | - Hongyu Zhao
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States of America
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States of America
- Department of Genetics, Yale School of Medicine, New Haven, CT, United States of America
| |
Collapse
|
43
|
Xu K, Li B, McGinnis KA, Vickers-Smith R, Dao C, Sun N, Kember RL, Zhou H, Becker WC, Gelernter J, Kranzler HR, Zhao H, Justice AC. Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals. Nat Commun 2020; 11:5302. [PMID: 33082346 PMCID: PMC7598939 DOI: 10.1038/s41467-020-18489-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 08/20/2020] [Indexed: 12/12/2022] Open
Abstract
Here we report a large genome-wide association study (GWAS) for longitudinal smoking phenotypes in 286,118 individuals from the Million Veteran Program (MVP) where we identified 18 loci for smoking trajectory of current versus never in European Americans, one locus in African Americans, and one in Hispanic Americans. Functional annotations prioritized several dozen genes where significant loci co-localized with either expression quantitative trait loci or chromatin interactions. The smoking trajectories were genetically correlated with 209 complex traits, for 33 of which smoking was either a causal or a consequential factor. We also performed European-ancestry meta-analyses for smoking status in the MVP and GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN) (Ntotal = 842,717) and identified 99 loci for smoking initiation and 13 loci for smoking cessation. Overall, this large GWAS of longitudinal smoking phenotype in multiple populations, combined with a meta-GWAS for smoking status, adds new insights into the genetic vulnerability for smoking behavior. Genome-wide association studies (GWASs) for cigarette smoking have identified several hundred loci that account for a small proportion of the overall genetic risk. Here, the authors report a large GWAS for smoking trajectories and meta-analysis for smoking status, finding multiple plausible loci.
Collapse
Affiliation(s)
- Ke Xu
- Yale School of Medicine, New Haven, CT, 06511, USA.,VA Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Boyang Li
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA.,Yale School of Public Health, New Haven, CT, 06511, USA
| | | | | | - Cecilia Dao
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA.,Yale School of Public Health, New Haven, CT, 06511, USA
| | - Ning Sun
- Yale School of Public Health, New Haven, CT, 06511, USA
| | - Rachel L Kember
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.,Crescenz Veterans Affairs Medical Center, Philadelphia, PA, 19104, USA
| | - Hang Zhou
- Yale School of Medicine, New Haven, CT, 06511, USA.,VA Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - William C Becker
- Yale School of Medicine, New Haven, CT, 06511, USA.,VA Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Joel Gelernter
- Yale School of Medicine, New Haven, CT, 06511, USA.,VA Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Henry R Kranzler
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.,Crescenz Veterans Affairs Medical Center, Philadelphia, PA, 19104, USA
| | - Hongyu Zhao
- Yale School of Medicine, New Haven, CT, 06511, USA.,Yale School of Public Health, New Haven, CT, 06511, USA
| | - Amy C Justice
- Yale School of Medicine, New Haven, CT, 06511, USA. .,VA Connecticut Healthcare System, West Haven, CT, 06516, USA.
| | | |
Collapse
|
44
|
Huang D, Yi X, Zhou Y, Yao H, Xu H, Wang J, Zhang S, Nong W, Wang P, Shi L, Xuan C, Li M, Wang J, Li W, Kwan HS, Sham PC, Wang K, Li MJ. Ultrafast and scalable variant annotation and prioritization with big functional genomics data. Genome Res 2020; 30:1789-1801. [PMID: 33060171 PMCID: PMC7706736 DOI: 10.1101/gr.267997.120] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 09/22/2020] [Indexed: 02/06/2023]
Abstract
The advances of large-scale genomics studies have enabled compilation of cell type–specific, genome-wide DNA functional elements at high resolution. With the growing volume of functional annotation data and sequencing variants, existing variant annotation algorithms lack the efficiency and scalability to process big genomic data, particularly when annotating whole-genome sequencing variants against a huge database with billions of genomic features. Here, we develop VarNote to rapidly annotate genome-scale variants in large and complex functional annotation resources. Equipped with a novel index system and a parallel random-sweep searching algorithm, VarNote shows substantial performance improvements (two to three orders of magnitude) over existing algorithms at different scales. It supports both region-based and allele-specific annotations and introduces advanced functions for the flexible extraction of annotations. By integrating massive base-wise and context-dependent annotations in the VarNote framework, we introduce three efficient and accurate pipelines to prioritize the causal regulatory variants for common diseases, Mendelian disorders, and cancers.
Collapse
Affiliation(s)
- Dandan Huang
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xianfu Yi
- School of Biomedical Engineering, Tianjin Medical University, Tianjin 300070, China
| | - Yao Zhou
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Hongcheng Yao
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Hang Xu
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China.,School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Jianhua Wang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Shijie Zhang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Wenyan Nong
- School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR 999077, China
| | - Panwen Wang
- Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, Arizona 85259, USA
| | - Lei Shi
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Chenghao Xuan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Miaoxin Li
- Center for Genome Research, Center for Precision Medicine, Zhongshan School of Medicine, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Junwen Wang
- Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, Arizona 85259, USA
| | - Weidong Li
- Department of Genetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Hoi Shan Kwan
- School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR 999077, China
| | - Pak Chung Sham
- Centre of Genomics Sciences, Departments of Psychiatry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA
| | - Mulin Jun Li
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China.,Department of Epidemiology and Biostatistics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China
| |
Collapse
|
45
|
Ming J, Wang T, Yang C. LPM: a latent probit model to characterize the relationship among complex traits using summary statistics from multiple GWASs and functional annotations. Bioinformatics 2020; 36:2506-2514. [PMID: 31860024 DOI: 10.1093/bioinformatics/btz947] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Revised: 12/13/2019] [Accepted: 12/18/2019] [Indexed: 12/21/2022] Open
Abstract
MOTIVATION Much effort has been made toward understanding the genetic architecture of complex traits and diseases. In the past decade, fruitful GWAS findings have highlighted the important role of regulatory variants and pervasive pleiotropy. Because of the accumulation of GWAS data on a wide range of phenotypes and high-quality functional annotations in different cell types, it is timely to develop a statistical framework to explore the genetic architecture of human complex traits by integrating rich data resources. RESULTS In this study, we propose a unified statistical approach, aiming to characterize relationship among complex traits, and prioritize risk variants by leveraging regulatory information collected in functional annotations. Specifically, we consider a latent probit model (LPM) to integrate summary-level GWAS data and functional annotations. The developed computational framework not only makes LPM scalable to hundreds of annotations and phenotypes but also ensures its statistically guaranteed accuracy. Through comprehensive simulation studies, we evaluated LPM's performance and compared it with related methods. Then, we applied it to analyze 44 GWASs with 9 genic category annotations and 127 cell-type specific functional annotations. The results demonstrate the benefits of LPM and gain insights of genetic architecture of complex traits. AVAILABILITY AND IMPLEMENTATION The LPM package, all simulation codes and real datasets in this study are available at https://github.com/mingjingsi/LPM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Jingsi Ming
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Tao Wang
- Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China.,MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| |
Collapse
|
46
|
Neuner SM, Tcw J, Goate AM. Genetic architecture of Alzheimer's disease. Neurobiol Dis 2020; 143:104976. [PMID: 32565066 PMCID: PMC7409822 DOI: 10.1016/j.nbd.2020.104976] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 05/30/2020] [Accepted: 06/13/2020] [Indexed: 02/06/2023] Open
Abstract
Advances in genetic and genomic technologies over the last thirty years have greatly enhanced our knowledge concerning the genetic architecture of Alzheimer's disease (AD). Several genes including APP, PSEN1, PSEN2, and APOE have been shown to exhibit large effects on disease susceptibility, with the remaining risk loci having much smaller effects on AD risk. Notably, common genetic variants impacting AD are not randomly distributed across the genome. Instead, these variants are enriched within regulatory elements active in human myeloid cells, and to a lesser extent liver cells, implicating these cell and tissue types as critical to disease etiology. Integrative approaches are emerging as highly effective for identifying the specific target genes through which AD risk variants act and will likely yield important insights related to potential therapeutic targets in the coming years. In the future, additional consideration of sex- and ethnicity-specific contributions to risk as well as the contribution of complex gene-gene and gene-environment interactions will likely be necessary to further improve our understanding of AD genetic architecture.
Collapse
Affiliation(s)
- Sarah M Neuner
- Nash Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Julia Tcw
- Nash Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Alison M Goate
- Nash Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA.
| |
Collapse
|
47
|
A method for scoring the cell type-specific impacts of noncoding variants in personal genomes. Proc Natl Acad Sci U S A 2020; 117:21364-21372. [PMID: 32817564 PMCID: PMC7474608 DOI: 10.1073/pnas.1922703117] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Here we use the expression and accessibility data from a diverse set of cell types to learn a model for the dependence of the accessibility of a regulatory element on its DNA sequence and TF expression. Using GTEx samples with WGS data, we show that the noncoding variants predicted to affect accessibility are more strongly associated with the expression of nearby genes. To interpret a personal genome, we combine the sequence information with context-specific TF expression to prioritize variants and regulatory elements in any genomic region of interest. This approach should be helpful in the study of risk loci previously identified by GWAS. Results from analysis of height and WGS data from the GTEx project support this hypothesis. A person’s genome typically contains millions of variants which represent the differences between this personal genome and the reference human genome. The interpretation of these variants, i.e., the assessment of their potential impact on a person’s phenotype, is currently of great interest in human genetics and medicine. We have developed a prioritization tool called OpenCausal which takes as inputs 1) a personal genome and 2) a reference context-specific TF expression profile and returns a list of noncoding variants prioritized according to their impact on chromatin accessibility for any given genomic region of interest. We applied OpenCausal to 6,430 samples across 18 tissues derived from the GTEx project and found that the variants prioritized by OpenCausal are highly enriched for eQTLs and caQTLs. We further propose a strategy to integrate the predicted open scores with genome-wide association studies (GWAS) data to prioritize putative causal variants and regulatory elements for a given risk locus (i.e., fine-mapping analysis). As an initial example, we applied this method to a GWAS dataset of human height and found that the prioritized putative variants and elements are correlated with the phenotype (i.e., heights of individuals) better than others.
Collapse
|
48
|
Abstract
Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges.
Collapse
Affiliation(s)
- Ning Sun
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520, USA
| |
Collapse
|
49
|
Low-Dose Ionizing Radiation Modulates Microglia Phenotypes in the Models of Alzheimer's Disease. Int J Mol Sci 2020; 21:ijms21124532. [PMID: 32630597 PMCID: PMC7353052 DOI: 10.3390/ijms21124532] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 06/24/2020] [Accepted: 06/24/2020] [Indexed: 12/17/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common type of dementia. AD involves major pathologies such as amyloid-β (Aβ) plaques and neurofibrillary tangles in the brain. During the progression of AD, microglia can be polarized from anti-inflammatory M2 to pro-inflammatory M1 phenotype. The activation of triggering receptor expressed on myeloid cells 2 (TREM2) may result in microglia phenotype switching from M1 to M2, which finally attenuated Aβ deposition and memory loss in AD. Low-dose ionizing radiation (LDIR) is known to ameliorate Aβ pathology and cognitive deficits in AD; however, the therapeutic mechanisms of LDIR against AD-related pathology have been little studied. First, we reconfirm that LDIR (two Gy per fraction for five times)-treated six-month 5XFAD mice exhibited (1) the reduction of Aβ deposition, as reflected by thioflavins S staining, and (2) the improvement of cognitive deficits, as revealed by Morris water maze test, compared to sham-exposed 5XFAD mice. To elucidate the mechanisms of LDIR-induced inhibition of Aβ accumulation and memory loss in AD, we examined whether LDIR regulates the microglial phenotype through the examination of levels of M1 and M2 cytokines in 5XFAD mice. In addition, we investigated the direct effects of LDIR on lipopolysaccharide (LPS)-induced production and secretion of M1/M2 cytokines in the BV-2 microglial cells. In the LPS- and LDIR-treated BV-2 cells, the M2 phenotypic marker CD206 was significantly increased, compared with LPS- and sham-treated BV-2 cells. Finally, the effect of LDIR on M2 polarization was confirmed by detection of increased expression of TREM2 in LPS-induced BV2 cells. These results suggest that LDIR directly induced phenotype switching from M1 to M2 in the brain with AD. Taken together, our results indicated that LDIR modulates LPS- and Aβ-induced neuroinflammation by promoting M2 polarization via TREM2 expression, and has beneficial effects in the AD-related pathology such as Aβ deposition and memory loss.
Collapse
|
50
|
Bocher O, Génin E. Rare variant association testing in the non-coding genome. Hum Genet 2020; 139:1345-1362. [PMID: 32500240 DOI: 10.1007/s00439-020-02190-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 05/29/2020] [Indexed: 12/25/2022]
Abstract
The development of next-generation sequencing technologies has opened-up some new possibilities to explore the contribution of genetic variants to human diseases and in particular that of rare variants. Statistical methods have been developed to test for association with rare variants that require the definition of testing units and, in these testing units, the selection of qualifying variants to include in the test. In the coding regions of the genome, testing units are usually the different genes and qualifying variants are selected based on their functional effects on the encoded proteins. Extending these tests to the non-coding regions of the genome is challenging. Testing units are difficult to define as the non-coding genome organisation is still rather unknown. Qualifying variants are difficult to select as the functional impact of non-coding variants on gene expression is hard to predict. These difficulties could explain why very few investigators so far have analysed the non-coding parts of their whole genome sequencing data. These non-coding parts yet represent the vast majority of the genome and some studies suggest that they could play a major role in disease susceptibility. In this review, we discuss recent experimental and statistical developments to gain knowledge on the non-coding genome and how this knowledge could be used to include rare non-coding variants in association tests. We describe the few studies that have considered variants from the non-coding genome in association tests and how they managed to define testing units and select qualifying variants.
Collapse
Affiliation(s)
- Ozvan Bocher
- Génétique, Génomique Fonctionnelle Et Biotechnologies, Faculté de Médecine, Univ Brest, Inserm, Inserm UMR1078, Bâtiment E-IBRBS 2ieme étage, 22 avenue Camille Desmoulins, 29238, Brest Cedex 3, France.
| | - Emmanuelle Génin
- Génétique, Génomique Fonctionnelle Et Biotechnologies, Faculté de Médecine, Univ Brest, Inserm, Inserm UMR1078, Bâtiment E-IBRBS 2ieme étage, 22 avenue Camille Desmoulins, 29238, Brest Cedex 3, France.
- CHU Brest, Brest, France.
| |
Collapse
|