201
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Cheng S, Lyu J, Shi X, Wang K, Wang Z, Deng M, Sun B, Wang C. Rare variant association tests for ancestry-matched case-control data based on conditional logistic regression. Brief Bioinform 2022; 23:6502553. [PMID: 35021184 DOI: 10.1093/bib/bbab572] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/29/2021] [Accepted: 12/13/2021] [Indexed: 12/13/2022] Open
Abstract
With the increasing volume of human sequencing data available, analysis incorporating external controls becomes a popular and cost-effective approach to boost statistical power in disease association studies. To prevent spurious association due to population stratification, it is important to match the ancestry backgrounds of cases and controls. However, rare variant association tests based on a standard logistic regression model are conservative when all ancestry-matched strata have the same case-control ratio and might become anti-conservative when case-control ratio varies across strata. Under the conditional logistic regression (CLR) model, we propose a weighted burden test (CLR-Burden), a variance component test (CLR-SKAT) and a hybrid test (CLR-MiST). We show that the CLR model coupled with ancestry matching is a general approach to control for population stratification, regardless of the spatial distribution of disease risks. Through extensive simulation studies, we demonstrate that the CLR-based tests robustly control type 1 errors under different matching schemes and are more powerful than the standard Burden, SKAT and MiST tests. Furthermore, because CLR-based tests allow for different case-control ratios across strata, a full-matching scheme can be employed to efficiently utilize all available cases and controls to accelerate the discovery of disease associated genes.
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Affiliation(s)
- Shanshan Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P.R. China
| | - Jingjing Lyu
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P.R. China
| | - Xian Shi
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P.R. China
| | - Kai Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P.R. China
| | - Zengmiao Wang
- Center for Quantitative Biology, Peking University, Beijing 100871, P. R. China
| | - Minghua Deng
- Center for Quantitative Biology, Peking University, Beijing 100871, P. R. China.,LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, P. R. China.,Center for Statistical Sciences, Peking University, Beijing 100871, P. R. China
| | - Baoluo Sun
- Department of Statistics and Data Science, National University of Singapore, Singapore 117546, Singapore
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P.R. China.,Department of Orthopedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P.R. China
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202
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Khani M, Gibbons E, Bras J, Guerreiro R. Challenge accepted: uncovering the role of rare genetic variants in Alzheimer's disease. Mol Neurodegener 2022; 17:3. [PMID: 35000612 PMCID: PMC8744312 DOI: 10.1186/s13024-021-00505-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 12/06/2021] [Indexed: 12/11/2022] Open
Abstract
The search for rare variants in Alzheimer's disease (AD) is usually deemed a high-risk - high-reward situation. The challenges associated with this endeavor are real. Still, the application of genome-wide technologies to large numbers of cases and controls or to small, well-characterized families has started to be fruitful.Rare variants associated with AD have been shown to increase risk or cause disease, but also to protect against the development of AD. All of these can potentially be targeted for the development of new drugs.Multiple independent studies have now shown associations of rare variants in NOTCH3, TREM2, SORL1, ABCA7, BIN1, CLU, NCK2, AKAP9, UNC5C, PLCG2, and ABI3 with AD and suggested that they may influence disease via multiple mechanisms. These genes have reported functions in the immune system, lipid metabolism, synaptic plasticity, and apoptosis. However, the main pathway emerging from the collective of genes harboring rare variants associated with AD is the Aβ pathway. Associations of rare variants in dozens of other genes have also been proposed, but have not yet been replicated in independent studies. Replication of this type of findings is one of the challenges associated with studying rare variants in complex diseases, such as AD. In this review, we discuss some of these primary challenges as well as possible solutions.Integrative approaches, the availability of large datasets and databases, and the development of new analytical methodologies will continue to produce new genes harboring rare variability impacting AD. In the future, more extensive and more diverse genetic studies, as well as studies of deeply characterized families, will enhance our understanding of disease pathogenesis and put us on the correct path for the development of successful drugs.
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Affiliation(s)
- Marzieh Khani
- School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - Elizabeth Gibbons
- Department of Neurodegenerative Science, Van Andel Institute, 333 Bostwick Ave. N.E., Grand Rapids, Michigan 49503-2518 USA
| | - Jose Bras
- Department of Neurodegenerative Science, Van Andel Institute, 333 Bostwick Ave. N.E., Grand Rapids, Michigan 49503-2518 USA
- Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI USA
| | - Rita Guerreiro
- Department of Neurodegenerative Science, Van Andel Institute, 333 Bostwick Ave. N.E., Grand Rapids, Michigan 49503-2518 USA
- Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI USA
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203
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Wang J, Yu J, Lipka AE, Zhang Z. Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:63-80. [PMID: 35641759 DOI: 10.1007/978-1-0716-2237-7_5] [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] [Indexed: 06/15/2023]
Abstract
With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile-quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.
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Affiliation(s)
- Jiabo Wang
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, Sichuan, China.
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA
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204
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Wang Z, Choi SW, Chami N, Boerwinkle E, Fornage M, Redline S, Bis JC, Brody JA, Psaty BM, Kim W, McDonald MLN, Regan EA, Silverman EK, Liu CT, Vasan RS, Kalyani RR, Mathias RA, Yanek LR, Arnett DK, Justice AE, North KE, Kaplan R, Heckbert S, de Andrade M, Guo X, Lange LA, Rich S, Rotter JI, Ellinor PT, Lubitz SA, Blangero J, Shoemaker MB, Darbar D, Gladwin MT, Albert CM, Chasman DI, Jackson RD, Kooperberg C, Reiner AP, O’Reilly PF, Loos RJF. The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations. Front Endocrinol (Lausanne) 2022; 13:863893. [PMID: 35592775 PMCID: PMC9110787 DOI: 10.3389/fendo.2022.863893] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/11/2022] [Indexed: 01/05/2023] Open
Abstract
Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) ≥1%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRScommon) with a rare variant PRS (PRSrare) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m2), obesity (BMI ≥ 30 kg/m2), and extreme obesity (BMI ≥ 40 kg/m2). We built PRSscommon and PRSsrare using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRScommon explained 10.1% of variation in BMI, and 18.3% and 22.5% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRSrare explained 1.49%, and 2.97% and 3.68%, respectively. The PRSrare was associated with an increased risk of obesity and extreme obesity (ORobesity = 1.37 per SDPRS, Pobesity = 1.7x10-85; ORextremeobesity = 1.55 per SDPRS, Pextremeobesity = 3.8x10-40), which was attenuated, after adjusting for PRScommon (ORobesity = 1.08 per SDPRS, Pobesity = 9.8x10-6; ORextremeobesity= 1.09 per SDPRS, Pextremeobesity = 0.02). When PRSrare and PRScommon are combined, the increase in explained variance attributed to PRSrare was small (incremental Nagelkerke R2 = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRSrare to PRScommon provided little improvement to the prediction of obesity (PRSrare AUC = 0.591; PRScommon AUC = 0.708; PRScombined AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRSrare provides limited improvement over PRScommon in the prediction of obesity risk, based on these large populations.
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Affiliation(s)
- Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Shing Wan Choi
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York, NY, United States
| | - Nathalie Chami
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, United States
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Susan Redline
- Division of Sleep Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Wonji Kim
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Merry-Lynn N. McDonald
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Elizabeth A. Regan
- Division of Rheumatology, Department of Medicine, National Jewish Health, Denver, CO, United States
| | - Edwin K. Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Ramachandran S. Vasan
- National Heart, Lung and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, MA, United States
- Section of Preventive Medicine and Epidemiology, Evans Department of Medicine, Boston University School of Medicine, Boston, MA, United States
- Whitaker Cardiovascular Institute and Cardiology Section, Evans Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Rita R. Kalyani
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Rasika A. Mathias
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Lisa R. Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Donna K. Arnett
- College of Public Health, University of Kentucky, Lexington, KY, United States
| | - Anne E. Justice
- Department of Population Health Services, Geisinger Health, Danville, PA, United States
| | - Kari E. North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Susan R. Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA, United States
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anchutz Medical Camus, Aurora, CA, United States
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Patrick T. Ellinor
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - Steven A. Lubitz
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, United States
| | - M. Benjamin Shoemaker
- Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Dawood Darbar
- Division of Cardiology, University of Illinois at Chicago, Chicago, IL, United States
| | - Mark T. Gladwin
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Christine M. Albert
- Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Daniel I. Chasman
- Department of Medicine, Harvard Medical School, Boston, MA, United States
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Rebecca D. Jackson
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, The Ohio State University, Columbus, OH, United States
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Alexander P. Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, United States
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Paul F. O’Reilly
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York, NY, United States
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- *Correspondence: Ruth J. F. Loos,
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205
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Jiang L, Jiang H, Dai S, Chen Y, Song Y, Tang CSM, Pang SYY, Ho SL, Wang B, Garcia-Barcelo MM, Tam PKH, Cherny SS, Li MJ, Sham PC, Li M. Deviation from baseline mutation burden provides powerful and robust rare-variants association test for complex diseases. Nucleic Acids Res 2021; 50:e34. [PMID: 34931221 PMCID: PMC8989543 DOI: 10.1093/nar/gkab1234] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 11/19/2021] [Accepted: 12/04/2021] [Indexed: 02/07/2023] Open
Abstract
Identifying rare variants that contribute to complex diseases is challenging because of the low statistical power in current tests comparing cases with controls. Here, we propose a novel and powerful rare variants association test based on the deviation of the observed mutation burden of a gene in cases from a baseline predicted by a weighted recursive truncated negative-binomial regression (RUNNER) on genomic features available from public data. Simulation studies show that RUNNER is substantially more powerful than state-of-the-art rare variant association tests and has reasonable type 1 error rates even for stratified populations or in small samples. Applied to real case-control data, RUNNER recapitulates known genes of Hirschsprung disease and Alzheimer's disease missed by current methods and detects promising new candidate genes for both disorders. In a case-only study, RUNNER successfully detected a known causal gene of amyotrophic lateral sclerosis. The present study provides a powerful and robust method to identify susceptibility genes with rare risk variants for complex diseases.
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Affiliation(s)
- Lin Jiang
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Research Center of Medical Sciences, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Sun Yat-sen University, Guangzhou, China.,Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
| | - Hui Jiang
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Sun Yat-sen University, Guangzhou, China.,Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
| | - Sheng Dai
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Sun Yat-sen University, Guangzhou, China.,Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
| | - Ying Chen
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Sun Yat-sen University, Guangzhou, China.,Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
| | - Youqiang Song
- School of Biomedical Sciences, the University of Hong Kong, Hong Kong, SAR China.,State Key Laboratory of Brain and Cognitive Sciences, the University of Hong Kong, Hong Kong, SAR China
| | - Clara Sze-Man Tang
- Department of Surgery, the University of Hong Kong, Hong Kong, SAR China.,Dr. Li Dak-Sum Research Centre, The University of Hong Kong - Karolinska Institutet Collaboration in Regenerative Medicine, Hong Kong, SAR China
| | - Shirley Yin-Yu Pang
- Division of Neurology, Department of Medicine, the University of Hong Kong, Hong Kong, SAR China
| | - Shu-Leong Ho
- Division of Neurology, Department of Medicine, the University of Hong Kong, Hong Kong, SAR China
| | - Binbin Wang
- Department of Genetics, National Research Institute for Family Planning, Beijing, China
| | | | - Paul Kwong-Hang Tam
- Department of Surgery, the University of Hong Kong, Hong Kong, SAR China.,Dr. Li Dak-Sum Research Centre, The University of Hong Kong - Karolinska Institutet Collaboration in Regenerative Medicine, Hong Kong, SAR China.,Faculty of Medicine, Macau University of Science and Technology, Macau, SAR China
| | | | - Mulin Jun Li
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China
| | - Pak Chung Sham
- The Centre for PanorOmic Sciences, the University of Hong Kong, Hong Kong, SAR China.,State Key Laboratory of Brain and Cognitive Sciences, the University of Hong Kong, Hong Kong, SAR China.,Department of Psychiatry, the University of Hong Kong, Hong Kong, SAR China
| | - Miaoxin Li
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Sun Yat-sen University, Guangzhou, China.,Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.,The Centre for PanorOmic Sciences, the University of Hong Kong, Hong Kong, SAR China.,Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
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206
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Lu H, Qiao J, Shao Z, Wang T, Huang S, Zeng P. A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics. BMC Med 2021; 19:314. [PMID: 34895209 PMCID: PMC8667366 DOI: 10.1186/s12916-021-02186-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 11/10/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear. METHODS We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders. RESULTS We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders. CONCLUSIONS Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.
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Affiliation(s)
- Haojie Lu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jiahao Qiao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Zhonghe Shao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuiping Huang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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207
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Maus Esfahani N, Catchpoole D, Khan J, Kennedy PJ. MCKAT: a multi-dimensional copy number variant kernel association test. BMC Bioinformatics 2021; 22:588. [PMID: 34895138 PMCID: PMC8666084 DOI: 10.1186/s12859-021-04494-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 11/25/2021] [Indexed: 11/25/2022] Open
Abstract
Background Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods. Results We address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smaller p-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets. Conclusion A multi-dimensional copy number variant kernel association test can detect statistically significant associated CNV regions with any disease-related trait. MCKAT can provide biologists with CNV hot spots at the cytogenetic band level that CNVs on them may have a significant association with disease-related traits. Using MCKAT, biologists can narrow their investigation from the whole genome, including many genes and CNVs, to more specific cytogenetic bands that MCKAT identifies. Furthermore, MCKAT can help biologists detect significantly associated CNVs with disease-related traits across a patient group instead of examining each subject’s CNVs case by case.
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Affiliation(s)
- Nastaran Maus Esfahani
- Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, Australia.
| | - Daniel Catchpoole
- Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, Australia.,The Tumour Bank, The Children's Hospital at Westmead, Sydney, Australia
| | - Javed Khan
- Center for Cancer Research, National Cancer Institute, Bethesda, USA
| | - Paul J Kennedy
- Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, Australia
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208
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Gu X, Chen Y, Wei Q, Hou Y, Cao B, Zhang L, Ou R, Lin J, Liu K, Zhao B, Shang H. Rare CYLD Variants in Chinese Patients With Amyotrophic Lateral Sclerosis. Front Genet 2021; 12:740052. [PMID: 34868212 PMCID: PMC8633398 DOI: 10.3389/fgene.2021.740052] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 10/14/2021] [Indexed: 02/05/2023] Open
Abstract
Background: CYLD Lysine 63 Deubiquitinase gene (CYLD) was recently identified to be a novel causative gene for frontal temporal dementia (FTD)-amyotrophic lateral sclerosis (ALS). In the current study, we aimed to (1) systematically screen the mutations of CYLD in a large cohort of Chinese ALS patients, (2) study the genotype–phenotype correlation, and (3) explore the role of CYLD in ALS via rare variants burden analysis. Methods: A total of 978 Chinese sporadic ALS (sALS) patients and 46 familial ALS (fALS) patients were sequenced with whole-exome sequencing and analyzed rare variants in CYLD with minor allele frequency <0.1%. Results: In total, seven rare missense variants in CYLD have been identified in 7 (0.72%) patients among 978 sALS patients. Two (4.3%) rare missense variants were identified among the 46 fALS cases, in which one patient was diagnosed as having comorbidity of ALS and progressive supranuclear palsy (PSP). Moreover, the burden analysis indicated no enrichment of rare variants in CYLD among patients with ALS. Conclusion: In conclusion, our study extended the genotype and phenotype of CYLD in ALS, but the pathogenicity of these variants needs to be further verified. Moreover, burden analysis argued against the role of CYLD in the pathogenesis of ALS. More studies from different ethnicities would be needed.
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Affiliation(s)
- Xiaojing Gu
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Disease Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yongping Chen
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Disease Center, West China Hospital, Sichuan University, Chengdu, China
| | - Qianqian Wei
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Disease Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yanbing Hou
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Disease Center, West China Hospital, Sichuan University, Chengdu, China
| | - Bei Cao
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Disease Center, West China Hospital, Sichuan University, Chengdu, China
| | - Lingyu Zhang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Disease Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ruwei Ou
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Disease Center, West China Hospital, Sichuan University, Chengdu, China
| | - Junyu Lin
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Disease Center, West China Hospital, Sichuan University, Chengdu, China
| | - Kuncheng Liu
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Disease Center, West China Hospital, Sichuan University, Chengdu, China
| | - Bi Zhao
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Disease Center, West China Hospital, Sichuan University, Chengdu, China
| | - Huifang Shang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Disease Center, West China Hospital, Sichuan University, Chengdu, China
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Venkataraman GR, DeBoever C, Tanigawa Y, Aguirre M, Ioannidis AG, Mostafavi H, Spencer CCA, Poterba T, Bustamante CD, Daly MJ, Pirinen M, Rivas MA. Bayesian model comparison for rare-variant association studies. Am J Hum Genet 2021; 108:2354-2367. [PMID: 34822764 PMCID: PMC8715195 DOI: 10.1016/j.ajhg.2021.11.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 11/02/2021] [Indexed: 12/12/2022] Open
Abstract
Whole-genome sequencing studies applied to large populations or biobanks with extensive phenotyping raise new analytic challenges. The need to consider many variants at a locus or group of genes simultaneously and the potential to study many correlated phenotypes with shared genetic architecture provide opportunities for discovery not addressed by the traditional one variant, one phenotype association study. Here, we introduce a Bayesian model comparison approach called MRP (multiple rare variants and phenotypes) for rare-variant association studies that considers correlation, scale, and direction of genetic effects across a group of genetic variants, phenotypes, and studies, requiring only summary statistic data. We apply our method to exome sequencing data (n = 184,698) across 2,019 traits from the UK Biobank, aggregating signals in genes. MRP demonstrates an ability to recover signals such as associations between PCSK9 and LDL cholesterol levels. We additionally find MRP effective in conducting meta-analyses in exome data. Non-biomarker findings include associations between MC1R and red hair color and skin color, IL17RA and monocyte count, and IQGAP2 and mean platelet volume. Finally, we apply MRP in a multi-phenotype setting; after clustering the 35 biomarker phenotypes based on genetic correlation estimates, we find that joint analysis of these phenotypes results in substantial power gains for gene-trait associations, such as in TNFRSF13B in one of the clusters containing diabetes- and lipid-related traits. Overall, we show that the MRP model comparison approach improves upon useful features from widely used meta-analysis approaches for rare-variant association analyses and prioritizes protective modifiers of disease risk.
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Affiliation(s)
| | - Christopher DeBoever
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Yosuke Tanigawa
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Matthew Aguirre
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | | | | | | | - Timothy Poterba
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Carlos D Bustamante
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Mark J Daly
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00014, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Department of Mathematics and Statistics, University of Helsinki, Helsinki 00014, Finland.
| | - Manuel A Rivas
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
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210
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Ho PJ, Khng AJ, Loh HW, Ho WK, Yip CH, Mohd-Taib NA, Tan VKM, Tan BKT, Tan SM, Tan EY, Lim SH, Jamaris S, Sim Y, Wong FY, Ngeow J, Lim EH, Tai MC, Wijaya EA, Lee SC, Chan CW, Buhari SA, Chan PMY, Chen JJC, Seah JCM, Lee WP, Mok CW, Lim GH, Woo E, Kim SW, Lee JW, Lee MH, Park SK, Dunning AM, Easton DF, Schmidt MK, Teo SH, Li J, Hartman M. Germline breast cancer susceptibility genes, tumor characteristics, and survival. Genome Med 2021; 13:185. [PMID: 34857041 PMCID: PMC8638193 DOI: 10.1186/s13073-021-00978-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 09/24/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Mutations in certain genes are known to increase breast cancer risk. We study the relevance of rare protein-truncating variants (PTVs) that may result in loss-of-function in breast cancer susceptibility genes on tumor characteristics and survival in 8852 breast cancer patients of Asian descent. METHODS Gene panel sequencing was performed for 34 known or suspected breast cancer predisposition genes, of which nine genes (ATM, BRCA1, BRCA2, CHEK2, PALB2, BARD1, RAD51C, RAD51D, and TP53) were associated with breast cancer risk. Associations between PTV carriership in one or more genes and tumor characteristics were examined using multinomial logistic regression. Ten-year overall survival was estimated using Cox regression models in 6477 breast cancer patients after excluding older patients (≥75years) and stage 0 and IV disease. RESULTS PTV9genes carriership (n = 690) was significantly associated (p < 0.001) with more aggressive tumor characteristics including high grade (poorly vs well-differentiated, odds ratio [95% confidence interval] 3.48 [2.35-5.17], moderately vs well-differentiated 2.33 [1.56-3.49]), as well as luminal B [HER-] and triple-negative subtypes (vs luminal A 2.15 [1.58-2.92] and 2.85 [2.17-3.73], respectively), adjusted for age at diagnosis, study, and ethnicity. Associations with grade and luminal B [HER2-] subtype remained significant after excluding BRCA1/2 carriers. PTV25genes carriership (n = 289, excluding carriers of the nine genes associated with breast cancer) was not associated with tumor characteristics. However, PTV25genes carriership, but not PTV9genes carriership, was suggested to be associated with worse 10-year overall survival (hazard ratio [CI] 1.63 [1.16-2.28]). CONCLUSIONS PTV9genes carriership is associated with more aggressive tumors. Variants in other genes might be associated with the survival of breast cancer patients. The finding that PTV carriership is not just associated with higher breast cancer risk, but also more severe and fatal forms of the disease, suggests that genetic testing has the potential to provide additional health information and help healthy individuals make screening decisions.
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Affiliation(s)
- Peh Joo Ho
- Genome Institute of Singapore, Human Genetics, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Alexis J. Khng
- Genome Institute of Singapore, Human Genetics, Singapore, Singapore
| | - Hui Wen Loh
- Genome Institute of Singapore, Human Genetics, Singapore, Singapore
| | - Weang-Kee Ho
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Malaysia
- Cancer Research Malaysia, 1 Jalan SS12/1A, 47500 Subang Jaya, Selangor Malaysia
| | - Cheng Har Yip
- Subang Jaya Medical Centre, Jalan SS 12/1A, 47500 Subang Jaya, Selangor Malaysia
| | - Nur Aishah Mohd-Taib
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- UM Cancer Research Institute, Kuala Lumpur, Malaysia
| | - Veronique Kiak Mien Tan
- Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
- Division of Surgical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Benita Kiat-Tee Tan
- Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
- Division of Surgical Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Department of General Surgery, Sengkang General Hospital, Singapore, Singapore
| | - Su-Ming Tan
- Division of Breast Surgery, Changi General Hospital, Singapore, Singapore
| | - Ern Yu Tan
- Department of General Surgery, Tan Tock Seng Hospital, Singapore, 308433 Singapore
- Lee Kong Chian School of Medicine, Singapore, Singapore
- Institute of Molecular and Cell Biology, Singapore, Singapore
| | - Swee Ho Lim
- KK Breast Department, KK Women’s and Children’s Hospital, Singapore, 229899 Singapore
| | - Suniza Jamaris
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- UM Cancer Research Institute, Kuala Lumpur, Malaysia
| | - Yirong Sim
- Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
- Division of Surgical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Fuh Yong Wong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Joanne Ngeow
- Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, Singapore
- Cancer Genetics Service, National Cancer Centre Singapore, Singapore, Singapore
- Oncology Academic Clinical Program, Duke NUS, Singapore, Singapore
| | - Elaine Hsuen Lim
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Mei Chee Tai
- Cancer Research Malaysia, 1 Jalan SS12/1A, 47500 Subang Jaya, Selangor Malaysia
| | | | - Soo Chin Lee
- Department of Hematology-oncology, National University Cancer Institute, National University Health System, Singapore, 119074 Singapore
| | - Ching Wan Chan
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
| | - Shaik Ahmad Buhari
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
| | - Patrick M. Y. Chan
- Department of General Surgery, Tan Tock Seng Hospital, Singapore, 308433 Singapore
| | - Juliana J. C. Chen
- Department of General Surgery, Tan Tock Seng Hospital, Singapore, 308433 Singapore
| | | | - Wai Peng Lee
- Division of Breast Surgery, Changi General Hospital, Singapore, Singapore
| | - Chi Wei Mok
- Division of Breast Surgery, Changi General Hospital, Singapore, Singapore
| | - Geok Hoon Lim
- KK Breast Department, KK Women’s and Children’s Hospital, Singapore, 229899 Singapore
| | - Evan Woo
- KK Breast Department, KK Women’s and Children’s Hospital, Singapore, 229899 Singapore
| | - Sung-Won Kim
- Department of Surgery, Breast Care Center, Daerim St. Mary’s Hospital, Seoul, Korea
| | - Jong Won Lee
- Department of Surgery, University of Ulsan College of Medicine and Asan Medical Center, Seoul, Republic of Korea
| | - Min Hyuk Lee
- Department of Surgery, Soonchunhyang University and Hospital, Seoul, Republic of Korea
| | - Sue K. Park
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Alison M. Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Marjanka K. Schmidt
- Division of Molecular Pathology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Soo-Hwang Teo
- Cancer Research Malaysia, 1 Jalan SS12/1A, 47500 Subang Jaya, Selangor Malaysia
- Department of Surgery, Faculty of Medicine, University of Malaya, Jalan Universiti, 50630 Kuala Lumpur, Malaysia
| | - Jingmei Li
- Genome Institute of Singapore, Human Genetics, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
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211
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Metovic A, Musanovic J, Ramic N, Lepara O, Secic D, Pepic E, Zec SL, Sljuka S. Population-genetic Aspects of Breast Cancers and Association with Rh Factor in Selected Sample. Med Arch 2021; 75:413-417. [PMID: 35169367 PMCID: PMC8802679 DOI: 10.5455/medarh.2021.75.413-417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 12/16/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Breast cancer in women is the second most common and accounts for approximately 18% of all malignant tumors in women worldwide. The etiology of breast cancer is not clear enough. Starting from the assumption that the manifestation of breast cancer may have a multifactorial model, this article compares the population-genetic structure of patients (experimental group) with the population-genetic structure of healthy population (control group). OBJECTIVE The aim of the study was to examine the possible genetic basis of the Rh factor relationship with selected homozygous-recessive traits of females with breast cancer, and to diagnose the probability (assess the risk) of developing the disease in healthy women by analyzing homozygous-recessive traits (HRT). METHODS This are an anthroposcopic-qualitative study that included two groups of subjects, experimental and control (a total of 80 subjects). An analysis of the percentages within each group was performed using the Chi-square test. The results are presented in tables, and the accepted level of significance is at the level of p <0.05. RESULTS In the group of Rh+ subjects, the correlation of this type of Rh factor with the breast cancer was proven, given the frequency of the phenotype of homozygous-recessive traits in them. A statistically significant difference was found for 4 traits, and three are also close to the set significance level. In subjects with Rh- factor, a statistically significant difference was found for only one trait (absence of mallets on the phalanges). CONCLUSION Although the number of subjects was relatively small, we can conclude that in the experimental group a higher frequency of recessive phenotypes for the examined traits was recorded, which indicates the genetic load of the subjects from this group. Correlation with Rh factor was observed in the case of subjects of the experimental group with Rh+ factor.
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Affiliation(s)
- Azra Metovic
- Department of Medical Biology with Human genetics, Faculty of Medicine, University of Sarajevo, Bosnia and Herzegovina
| | - Jasmin Musanovic
- Department of Medical Biology with Human genetics, Faculty of Medicine, University of Sarajevo, Bosnia and Herzegovina
| | - Nerman Ramic
- Health Centre, City of Vitez, Bosnia and Herzegovina
| | - Orhan Lepara
- Department of Physiology, Faculty of Medicine, University of Sarajevo, Bosnia and Herzegovina
| | - Damir Secic
- Department of Patophysiology, Faculty of Medicine, University of Sarajevo, Bosnia and Herzegovina
| | - Esad Pepic
- Department of Patophysiology, Faculty of Medicine, University of Sarajevo, Bosnia and Herzegovina
| | - Svjetlana Loga Zec
- Department of Pharmacology, Faculty of Medicine, University of Sarajevo, Bosnia and Herzegovina
| | - Senad Sljuka
- Department of General Biology, Faculty of Medicine, University of Sarajevo, Bosnia and Herzegovina
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Viehweger A, Blumenscheit C, Lippmann N, Wyres KL, Brandt C, Hans JB, Hölzer M, Irber L, Gatermann S, Lübbert C, Pletz MW, Holt KE, König B. Context-aware genomic surveillance reveals hidden transmission of a carbapenemase-producing Klebsiella pneumoniae. Microb Genom 2021; 7:000741. [PMID: 34913861 PMCID: PMC8767333 DOI: 10.1099/mgen.0.000741] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 11/04/2021] [Indexed: 01/18/2023] Open
Abstract
Genomic surveillance can inform effective public health responses to pathogen outbreaks. However, integration of non-local data is rarely done. We investigate two large hospital outbreaks of a carbapenemase-carrying Klebsiella pneumoniae strain in Germany and show the value of contextual data. By screening about 10 000 genomes, over 400 000 metagenomes and two culture collections using in silico and in vitro methods, we identify a total of 415 closely related genomes reported in 28 studies. We identify the relationship between the two outbreaks through time-dated phylogeny, including their respective origin. One of the outbreaks presents extensive hidden transmission, with descendant isolates only identified in other studies. We then leverage the genome collection from this meta-analysis to identify genes under positive selection. We thereby identify an inner membrane transporter (ynjC) with a putative role in colistin resistance. Contextual data from other sources can thus enhance local genomic surveillance at multiple levels and should be integrated by default when available.
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Affiliation(s)
- Adrian Viehweger
- Institute of Medical Microbiology and Virology, University Hospital Leipzig, Leipzig, Germany
| | | | - Norman Lippmann
- Institute of Medical Microbiology and Virology, University Hospital Leipzig, Leipzig, Germany
| | - Kelly L. Wyres
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Australia
| | - Christian Brandt
- Institute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Germany
| | - Jörg B. Hans
- National Reference Center for multidrug-resistant Gram-negative bacteria, Department for Medical Microbiology, Ruhr-University Bochum, Bochum, Germany
| | - Martin Hölzer
- Methodology and Research Infrastructure, MF1 Bioinformatics, Robert Koch Institute, Berlin, Germany
| | - Luiz Irber
- Department of Population Health and Reproduction, University of California, Davis, Davis, California, USA
| | - Sören Gatermann
- National Reference Center for multidrug-resistant Gram-negative bacteria, Department for Medical Microbiology, Ruhr-University Bochum, Bochum, Germany
| | - Christoph Lübbert
- Division of Infectious Diseases and Tropical Medicine, Department of Medicine II, University Hospital Leipzig, Leipzig, Germany
| | - Mathias W. Pletz
- Institute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Germany
| | - Kathryn E. Holt
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Australia
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, UK
| | - Brigitte König
- Institute of Medical Microbiology and Virology, University Hospital Leipzig, Leipzig, Germany
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213
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Wang Y, Zhu M, Ma H, Shen H. Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention. MEDICAL REVIEW (BERLIN, GERMANY) 2021; 1:129-149. [PMID: 37724297 PMCID: PMC10471106 DOI: 10.1515/mr-2021-0025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 12/13/2021] [Indexed: 09/20/2023]
Abstract
Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual's genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.
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Affiliation(s)
- Yuzhuo Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
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Davitte JM, Stott-Miller M, Ehm MG, Cunnington MC, Reynolds RF. Integration of Real-World Data and Genetics to Support Target Identification and Validation. Clin Pharmacol Ther 2021; 111:63-76. [PMID: 34818443 DOI: 10.1002/cpt.2477] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 10/06/2021] [Accepted: 10/27/2021] [Indexed: 01/01/2023]
Abstract
Even modest improvements in the probability of success of selecting drug targets which are ultimately approved can substantially reduce the costs of research and development. Drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. A key enabler of identifying and validating these genetically validated targets is access to association results from genome-wide genotyping, whole-exome sequencing, and whole-genome sequencing studies with observable traits (often diseases) across large numbers of individuals. Today, linkage between genotype and real-world data (RWD) provides significant opportunities to not only increase the statistical power of genome-wide association studies by ascertaining additional cases for diseases of interest, but also to improve diversity and coverage of association studies across the disease phenome. As RWD-genetics linked resources continue to grow in diversity of participants, breadth of data captured, length of observation, and number of participants, there is a greater need to leverage the experience of RWD experts, clinicians, and highly experienced geneticists together to understand which lessons and frameworks from general research using RWD sources are relevant to improve genetics-driven drug discovery and development. This paper describes new challenges and opportunities for phenotypes enabled by diverse RWD sources, considerations in the use of RWD phenotypes for disease gene identification across the disease phenome, and challenges and opportunities in leveraging RWD phenotypes in target validation. The paper concludes with views on the future directions for phenotype development using RWD, and key questions requiring further research and development to advance this nascent field.
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Affiliation(s)
| | | | | | | | - Robert F Reynolds
- GlaxoSmithKline, New York, New York, USA.,Tulane School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
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Guan Z, Shen R, Begg CB. Exome-Wide Pan-Cancer Analysis of Germline Variants in 8,719 Individuals Finds Little Evidence of Rare Variant Associations. Hum Hered 2021; 86:34-44. [PMID: 34718237 DOI: 10.1159/000519355] [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: 12/21/2020] [Accepted: 08/30/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants. The "rare variant hypothesis" proposes that much of the missing heritability lies in rare variants that cannot reliably be detected by linkage analysis or GWAS. Until recently, high sequencing costs have precluded extensive surveys of rare variants, but technological advances have now made it possible to analyze rare variants on a much greater scale. OBJECTIVES In this study, we investigated associations between rare variants and 14 cancer types. METHODS We ran association tests using whole-exome sequencing data from The Cancer Genome Atlas (TCGA) and validated the findings using data from the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG). RESULTS We identified four significant associations in TCGA, only one of which was replicated in PCAWG (BRCA1 and ovarian cancer). CONCLUSIONS Our results provide little evidence in favor of the rare variant hypothesis. Much larger sample sizes may be needed to detect undiscovered rare cancer variants.
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Affiliation(s)
- Zoe Guan
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Ronglai Shen
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Colin B Begg
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
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216
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Sun TH, Shao YHJ, Mao CL, Hung MN, Lo YY, Ko TM, Hsiao TH. A Novel Quality-Control Procedure to Improve the Accuracy of Rare Variant Calling in SNP Arrays. Front Genet 2021; 12:736390. [PMID: 34764980 PMCID: PMC8577504 DOI: 10.3389/fgene.2021.736390] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 09/21/2021] [Indexed: 12/16/2022] Open
Abstract
Background: Single-nucleotide polymorphism (SNP) arrays are an ideal technology for genotyping genetic variants in mass screening. However, using SNP arrays to detect rare variants [with a minor allele frequency (MAF) of <1%] is still a challenge because of noise signals and batch effects. An approach that improves the genotyping quality is needed for clinical applications. Methods: We developed a quality-control procedure for rare variants which integrates different algorithms, filters, and experiments to increase the accuracy of variant calling. Using data from the TWB 2.0 custom Axiom array, we adopted an advanced normalization adjustment to prevent false calls caused by splitting the cluster and a rare het adjustment which decreases false calls in rare variants. The concordance of allelic frequencies from array data was compared to those from sequencing datasets of Taiwanese. Finally, genotyping results were used to detect familial hypercholesterolemia (FH), thrombophilia (TH), and maturity-onset diabetes of the young (MODY) to assess the performance in disease screening. All heterozygous calls were verified by Sanger sequencing or qPCR. The positive predictive value (PPV) of each step was estimated to evaluate the performance of our procedure. Results: We analyzed SNP array data from 43,433 individuals, which interrogated 267,247 rare variants. The advanced normalization and rare het adjustment methods adjusted genotyping calling of 168,134 variants (96.49%). We further removed 3916 probesets which were discordant in MAFs between the SNP array and sequencing data. The PPV for detecting pathogenic variants with 0.01%10,000 are available. The results demonstrated our procedure could perform correct genotype calling of rare variants. It provides a solution of pathogenic variant detection through SNP array. The approach brings tremendous promise for implementing precision medicine in medical practice.
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Affiliation(s)
- Ting-Hsuan Sun
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Yu-Hsuan Joni Shao
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| | - Chien-Lin Mao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Miao-Neng Hung
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Yi-Yun Lo
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Tai-Ming Ko
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Tzu-Hung Hsiao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Public Health, Fu Jen Catholic University, New Taipei City, Taiwan
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan
- Research Center for Biomedical Science and Engineering, National Tsing Hua University, Hsinchu, Taiwan
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217
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Malik R, Beaufort N, Frerich S, Gesierich B, Georgakis MK, Rannikmäe K, Ferguson AC, Haffner C, Traylor M, Ehrmann M, Sudlow CLM, Dichgans M. Whole-exome sequencing reveals a role of HTRA1 and EGFL8 in brain white matter hyperintensities. Brain 2021; 144:2670-2682. [PMID: 34626176 PMCID: PMC8557338 DOI: 10.1093/brain/awab253] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/01/2021] [Accepted: 06/19/2021] [Indexed: 11/13/2022] Open
Abstract
White matter hyperintensities (WMH) are among the most common radiological abnormalities in the ageing population and an established risk factor for stroke and dementia. While common variant association studies have revealed multiple genetic loci with an influence on their volume, the contribution of rare variants to the WMH burden in the general population remains largely unexplored. We conducted a comprehensive analysis of this burden in the UK Biobank using publicly available whole-exome sequencing data (n up to 17 830) and found a splice-site variant in GBE1, encoding 1,4-alpha-glucan branching enzyme 1, to be associated with lower white matter burden on an exome-wide level [c.691+2T>C, β = -0.74, standard error (SE) = 0.13, P = 9.7 × 10-9]. Applying whole-exome gene-based burden tests, we found damaging missense and loss-of-function variants in HTRA1 (frequency of 1 in 275 in the UK Biobank population) to associate with an increased WMH volume (P = 5.5 × 10-6, false discovery rate = 0.04). HTRA1 encodes a secreted serine protease implicated in familial forms of small vessel disease. Domain-specific burden tests revealed that the association with WMH volume was restricted to rare variants in the protease domain (amino acids 204-364; β = 0.79, SE = 0.14, P = 9.4 × 10-8). The frequency of such variants in the UK Biobank population was 1 in 450. The WMH volume was brought forward by ∼11 years in carriers of a rare protease domain variant. A comparison with the effect size of established risk factors for WMH burden revealed that the presence of a rare variant in the HTRA1 protease domain corresponded to a larger effect than meeting the criteria for hypertension (β = 0.26, SE = 0.02, P = 2.9 × 10-59) or being in the upper 99.8% percentile of the distribution of a polygenic risk score based on common genetic variants (β = 0.44, SE = 0.14, P = 0.002). In biochemical experiments, most (6/9) of the identified protease domain variants resulted in markedly reduced protease activity. We further found EGFL8, which showed suggestive evidence for association with WMH volume (P = 1.5 × 10-4, false discovery rate = 0.22) in gene burden tests, to be a direct substrate of HTRA1 and to be preferentially expressed in cerebral arterioles and arteries. In a phenome-wide association study mapping ICD-10 diagnoses to 741 standardized Phecodes, rare variants in the HTRA1 protease domain were associated with multiple neurological and non-neurological conditions including migraine with aura (odds ratio = 12.24, 95%CI: 2.54-35.25; P = 8.3 × 10-5]. Collectively, these findings highlight an important role of rare genetic variation and the HTRA1 protease in determining WMH burden in the general population.
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Affiliation(s)
- Rainer Malik
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Nathalie Beaufort
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Simon Frerich
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Marios K Georgakis
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Kristiina Rannikmäe
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh EH16 4TL, UK
| | - Amy C Ferguson
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh EH16 4TL, UK
| | - Christof Haffner
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Matthew Traylor
- Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK
- The Barts Heart Centre and NIHR Barts Biomedical Research Centre - Barts Health NHS Trust, The William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Michael Ehrmann
- Center of Medical Biotechnology, Faculty of Biology, University Duisburg-Essen, Essen 45141, Germany
- School of Biosciences, Cardiff University, Cardiff CF10 3AX, UK
| | - Cathie L M Sudlow
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh EH16 4TL, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4TL, UK
- Health Data Research UK Scotland, University of Edinburgh, Edinburgh EH16 4TL, UK
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
- Munich Cluster for Systems Neurology, Munich 81377, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich 81377, Germany
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218
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Gu X, Hou Y, Chen Y, Ou R, Cao B, Wei Q, Zhang L, Song W, Zhao B, Wu Y, Shang H. Comprehensive Analysis of LIN28A in Chinese Patients With Early Onset Parkinson's Disease. Front Genet 2021; 12:740096. [PMID: 34733315 PMCID: PMC8558378 DOI: 10.3389/fgene.2021.740096] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 09/30/2021] [Indexed: 02/05/2023] Open
Abstract
A loss-of-function variant in Lin-28 Homolog A gene (LIN28A p. R192G, rs558060339) has been identified in two East Asian ancestry patients with early-onset PD (EOPD). Functional studies revealed that such a variant could lead to developmental defects and PD-related phenotype, and the phenotypes could be rescued after correction of the variant. The aim of the study was to screen the variants of LIN28A in Chinese patients with EOPD. A total of 682 EOPD patients were sequenced with whole exome sequencing and the coding and flanking region of LIN28A were analyzed. We identified a rare coding variant, p. P182L, of LIN28A in a Chinese patient with EOPD. Moreover, we also found a 3'-UTR polymorphism (rs4659441) to be associated with an increased risk for PD. However, our rare variant burden analysis did not support a role for LIN28A as a major causal gene for PD.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Huifang Shang
- Laboratory of Neurodegenerative Disorders, Rare Disease Center, Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
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219
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McMahon A, Lewis E, Buniello A, Cerezo M, Hall P, Sollis E, Parkinson H, Hindorff LA, Harris LW, MacArthur JA. Sequencing-based genome-wide association studies reporting standards. CELL GENOMICS 2021; 1:100005. [PMID: 34870259 PMCID: PMC8637874 DOI: 10.1016/j.xgen.2021.100005] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Genome sequencing has recently become a viable genotyping technology for use in genome-wide association studies (GWASs), offering the potential to analyze a broader range of genome-wide variation, including rare variants. To survey current standards, we assessed the content and quality of reporting of statistical methods, analyses, results, and datasets in 167 exome- or genome-wide-sequencing-based GWAS publications published from 2014 to 2020; 81% of publications included tests of aggregate association across multiple variants, with multiple test models frequently used. We observed a lack of standardized terms and incomplete reporting of datasets, particularly for variants analyzed in aggregate tests. We also find a lower frequency of sharing of summary statistics compared with array-based GWASs. Reporting standards and increased data sharing are required to ensure sequencing-based association study data are findable, interoperable, accessible, and reusable (FAIR). To support that, we recommend adopting the standard terminology of sequencing-based GWAS (seqGWAS). Further, we recommend that single-variant analyses be reported following the same standards and conventions as standard array-based GWASs and be shared in the GWAS Catalog. We also provide initial recommended standards for aggregate analyses metadata and summary statistics.
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Affiliation(s)
- Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK,Corresponding author
| | - Elizabeth Lewis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Annalisa Buniello
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Maria Cerezo
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Peggy Hall
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Elliot Sollis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK,Corresponding author
| | - Lucia A. Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Laura W. Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Jacqueline A.L. MacArthur
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK,BHF Data Science Centre, Health Data Research UK, London, UK
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220
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Lu H, Wei Y, Jiang Z, Zhang J, Wang T, Huang S, Zeng P. Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies. J Transl Med 2021; 19:418. [PMID: 34627275 PMCID: PMC8502405 DOI: 10.1186/s12967-021-03090-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 09/26/2021] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Integrating functional annotations into SNP-set association studies has been proven a powerful analysis strategy. Statistical methods for such integration have been developed for continuous and binary phenotypes; however, the SNP-set integrative approaches for time-to-event or survival outcomes are lacking. METHODS We here propose IEHC, an integrative eQTL (expression quantitative trait loci) hierarchical Cox regression, for SNP-set based survival association analysis by modeling effect sizes of genetic variants as a function of eQTL via a hierarchical manner. Three p-values combination tests are developed to examine the joint effects of eQTL and genetic variants after a novel decorrelated modification of statistics for the two components. An omnibus test (IEHC-ACAT) is further adapted to aggregate the strengths of all available tests. RESULTS Simulations demonstrated that the IEHC joint tests were more powerful if both eQTL and genetic variants contributed to association signal, while IEHC-ACAT was robust and often outperformed other approaches across various simulation scenarios. When applying IEHC to ten TCGA cancers by incorporating eQTL from relevant tissues of GTEx, we revealed that substantial correlations existed between the two types of effect sizes of genetic variants from TCGA and GTEx, and identified 21 (9 unique) cancer-associated genes which would otherwise be missed by approaches not incorporating eQTL. CONCLUSION IEHC represents a flexible, robust, and powerful approach to integrate functional omics information to enhance the power of identifying association signals for the survival risk of complex human cancers.
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Affiliation(s)
- Haojie Lu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yongyue Wei
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China
| | - Zhou Jiang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jinhui Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuiping Huang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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221
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Denault WRP, Gjessing HK, Juodakis J, Jacobsson B, Jugessur A. Wavelet Screening: a novel approach to analyzing GWAS data. BMC Bioinformatics 2021; 22:484. [PMID: 34620077 PMCID: PMC8499521 DOI: 10.1186/s12859-021-04356-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
Background Traditional methods for single-variant genome-wide association study (GWAS) incur a substantial multiple-testing burden because of the need to test for associations with a vast number of single-nucleotide polymorphisms (SNPs) simultaneously. Further, by ignoring more complex joint effects of nearby SNPs within a given region, these methods fail to consider the genomic context of an association with the outcome. Results To address these shortcomings, we present a more powerful method for GWAS, coined ‘Wavelet Screening’ (WS), that greatly reduces the number of tests to be performed. This is achieved through the use of a sliding-window approach based on wavelets to sequentially screen the entire genome for associations. Wavelets are oscillatory functions that are useful for analyzing the local frequency and time behavior of signals. The signals can then be divided into different scale components and analyzed separately. In the current setting, we consider a sequence of SNPs as a genetic signal, and for each screened region, we transform the genetic signal into the wavelet space. The null and alternative hypotheses are modeled using the posterior distribution of the wavelet coefficients. WS is enhanced by using additional information from the regression coefficients and by taking advantage of the pyramidal structure of wavelets. When faced with more complex genetic signals than single-SNP associations, we show via simulations that WS provides a substantial gain in power compared to both the traditional GWAS modeling and another popular regional association test called SNP-set (Sequence) Kernel Association Test (SKAT). To demonstrate feasibility, we applied WS to a large Norwegian cohort (N=8006) with genotypes and information available on gestational duration. Conclusions WS is a powerful and versatile approach to analyzing whole-genome data and lends itself easily to investigating various omics data types. Given its broader focus on the genomic context of an association, WS may provide additional insight into trait etiology by revealing genes and loci that might have been missed by previous efforts.
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Affiliation(s)
- William R P Denault
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway. .,Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway. .,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.
| | - Håkon K Gjessing
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Julius Juodakis
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway.,Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Bo Jacobsson
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway.,Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Astanand Jugessur
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway.,Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
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222
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Aloui C, Hervé D, Marenne G, Savenier F, Le Guennec K, Bergametti F, Verdura E, Ludwig TE, Lebenberg J, Jabeur W, Morel H, Coste T, Demarquay G, Bachoumas P, Cogez J, Mathey G, Bernard E, Chabriat H, Génin E, Tournier-Lasserve E. End-Truncated LAMB1 Causes a Hippocampal Memory Defect and a Leukoencephalopathy. Ann Neurol 2021; 90:962-975. [PMID: 34606115 DOI: 10.1002/ana.26242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 10/01/2021] [Accepted: 10/02/2021] [Indexed: 12/21/2022]
Abstract
OBJECTIVE The majority of patients with a familial cerebral small vessel disease (CSVD) referred for molecular screening do not show pathogenic variants in known genes. In this study, we aimed to identify novel CSVD causal genes. METHODS We performed a gene-based collapsing test of rare protein-truncating variants identified in exome data of 258 unrelated CSVD patients of an ethnically matched control cohort and of 2 publicly available large-scale databases, gnomAD and TOPMed. Western blotting was used to investigate the functional consequences of variants. Clinical and magnetic resonance imaging features of mutated patients were characterized. RESULTS We showed that LAMB1 truncating variants escaping nonsense-mediated messenger RNA decay are strongly overrepresented in CSVD patients, reaching genome-wide significance (p < 5 × 10-8 ). Using 2 antibodies recognizing the N- and C-terminal parts of LAMB1, we showed that truncated forms of LAMB1 are expressed in the endogenous fibroblasts of patients and trapped in the cytosol. These variants are associated with a novel phenotype characterized by the association of a hippocampal type episodic memory defect and a diffuse vascular leukoencephalopathy. INTERPRETATION These findings are important for diagnosis and clinical care, to avoid unnecessary and sometimes invasive investigations, and also from a mechanistic point of view to understand the role of extracellular matrix proteins in neuronal homeostasis. ANN NEUROL 2021;90:962-975.
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Affiliation(s)
- Chaker Aloui
- Université de Paris, INSERM UMR 1141 NeuroDiderot, Paris, France
| | - Dominique Hervé
- Université de Paris, INSERM UMR 1141 NeuroDiderot, Paris, France.,AP-HP, Groupe Hospitalier Saint-Louis Lariboisière-Fernand-Widal, Service de Neurologie, Centre de Référence des Maladies Vasculaires Rares du Cerveau et de l'Œil (CERVCO), Paris, France
| | - Gaelle Marenne
- Université de Brest, Inserm, EFS, CHU Brest, UMR 1078, GGB, Brest, France
| | - Florian Savenier
- Université de Paris, INSERM UMR 1141 NeuroDiderot, Paris, France
| | - Kilan Le Guennec
- Université de Paris, INSERM UMR 1141 NeuroDiderot, Paris, France
| | | | - Edgard Verdura
- Université de Paris, INSERM UMR 1141 NeuroDiderot, Paris, France
| | - Thomas E Ludwig
- Université de Brest, Inserm, EFS, CHU Brest, UMR 1078, GGB, Brest, France
| | | | - Waliyde Jabeur
- Université de Paris, INSERM UMR 1141 NeuroDiderot, Paris, France
| | - Hélène Morel
- Université de Paris, INSERM UMR 1141 NeuroDiderot, Paris, France.,AP-HP, Service de Génétique Moléculaire Neurovasculaire, Hôpital Saint-Louis, Paris, France
| | - Thibault Coste
- Université de Paris, INSERM UMR 1141 NeuroDiderot, Paris, France.,AP-HP, Service de Génétique Moléculaire Neurovasculaire, Hôpital Saint-Louis, Paris, France
| | - Geneviève Demarquay
- Hôpital Neurologique, Hospices Civils de Lyon, Lyon Neuroscience Research Center (CRNL), Brain Dynamics and Cognition Team (Dycog), INSERM U1028, CNRS UMR5292, Lyon, France
| | | | - Julien Cogez
- CHU Caen, Department of Neurology, CHU de Caen Côte de Nacre, Caen, France
| | | | - Emilien Bernard
- Department of Neurology, Hôpital Neurologique Pierre Wertheimer, Hospices Civils de Lyon, Bron, France.,Institut NeuroMyoGène, INSERM-CNRS-UMR, Université Claude Bernard, Lyon, France
| | | | - Hugues Chabriat
- Université de Paris, INSERM UMR 1141 NeuroDiderot, Paris, France.,AP-HP, Groupe Hospitalier Saint-Louis Lariboisière-Fernand-Widal, Service de Neurologie, Centre de Référence des Maladies Vasculaires Rares du Cerveau et de l'Œil (CERVCO), Paris, France
| | - Emmanuelle Génin
- Université de Brest, Inserm, EFS, CHU Brest, UMR 1078, GGB, Brest, France
| | - Elisabeth Tournier-Lasserve
- Université de Paris, INSERM UMR 1141 NeuroDiderot, Paris, France.,AP-HP, Service de Génétique Moléculaire Neurovasculaire, Hôpital Saint-Louis, Paris, France
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223
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Lyra DH, Griffiths CA, Watson A, Joynson R, Molero G, Igna AA, Hassani-Pak K, Reynolds MP, Hall A, Paul MJ. Gene-based mapping of trehalose biosynthetic pathway genes reveals association with source- and sink-related yield traits in a spring wheat panel. Food Energy Secur 2021; 10:e292. [PMID: 34594548 PMCID: PMC8459250 DOI: 10.1002/fes3.292] [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] [Revised: 04/12/2021] [Accepted: 04/12/2021] [Indexed: 12/11/2022] Open
Abstract
Trehalose 6‐phosphate (T6P) signalling regulates carbon use and allocation and is a target to improve crop yields. However, the specific contributions of trehalose phosphate synthase (TPS) and trehalose phosphate phosphatase (TPP) genes to source‐ and sink‐related traits remain largely unknown. We used enrichment capture sequencing on TPS and TPP genes to estimate and partition the genetic variation of yield‐related traits in a spring wheat (Triticum aestivum) breeding panel specifically built to capture the diversity across the 75,000 CIMMYT wheat cultivar collection. Twelve phenotypes were correlated to variation in TPS and TPP genes including plant height and biomass (source), spikelets per spike, spike growth and grain filling traits (sink) which showed indications of both positive and negative gene selection. Individual genes explained proportions of heritability for biomass and grain‐related traits. Three TPS1 homologues were particularly significant for trait variation. Epistatic interactions were found within and between the TPS and TPP gene families for both plant height and grain‐related traits. Gene‐based prediction improved predictive ability for grain weight when gene effects were combined with the whole‐genome markers. Our study has generated a wealth of information on natural variation of TPS and TPP genes related to yield potential which confirms the role for T6P in resource allocation and in affecting traits such as grain number and size confirming other studies which now opens up the possibility of harnessing natural genetic variation more widely to better understand the contribution of native genes to yield traits for incorporation into breeding programmes.
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Affiliation(s)
- Danilo H Lyra
- Computational & Analytical Sciences Rothamsted Research Harpenden UK
| | | | - Amy Watson
- Plant Sciences Rothamsted Research Harpenden UK
| | | | - Gemma Molero
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT) Texcoco Mexico
| | | | | | - Matthew P Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT) Texcoco Mexico
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224
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Kendall KM, Van Assche E, Andlauer TFM, Choi KW, Luykx JJ, Schulte EC, Lu Y. The genetic basis of major depression. Psychol Med 2021; 51:2217-2230. [PMID: 33682643 DOI: 10.1017/s0033291721000441] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Major depressive disorder (MDD) is a common, debilitating, phenotypically heterogeneous disorder with heritability ranges from 30% to 50%. Compared to other psychiatric disorders, its high prevalence, moderate heritability, and strong polygenicity have posed major challenges for gene-mapping in MDD. Studies of common genetic variation in MDD, driven by large international collaborations such as the Psychiatric Genomics Consortium, have confirmed the highly polygenic nature of the disorder and implicated over 100 genetic risk loci to date. Rare copy number variants associated with MDD risk were also recently identified. The goal of this review is to present a broad picture of our current understanding of the epidemiology, genetic epidemiology, molecular genetics, and gene-environment interplay in MDD. Insights into the impact of genetic factors on the aetiology of this complex disorder hold great promise for improving clinical care.
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Affiliation(s)
- K M Kendall
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - E Van Assche
- Department of Psychiatry, University of Muenster, Muenster, Germany
| | - T F M Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - K W Choi
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA02114, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA02114, USA
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA02115, USA
| | - J J Luykx
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Outpatient Second Opinion Clinic, GGNet Mental Health, Warnsveld, The Netherlands
| | - E C Schulte
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Y Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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225
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Liu L, Kiryluk K. Coding Variants in Susceptibility to Diabetic Kidney Disease. J Am Soc Nephrol 2021; 32:2397-2399. [PMID: 34599034 PMCID: PMC8722810 DOI: 10.1681/asn.2021081088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Affiliation(s)
- Lili Liu
- Division of Nephrology, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York
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226
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Dilliott AA, Abdelhady A, Sunderland KM, Farhan SMK, Abrahao A, Binns MA, Black SE, Borrie M, Casaubon LK, Dowlatshahi D, Finger E, Fischer CE, Frank A, Freedman M, Grimes D, Hassan A, Jog M, Kumar S, Kwan D, Lang AE, Mandzia J, Masellis M, McIntyre AD, Pasternak SH, Pollock BG, Rajji TK, Rogaeva E, Sahlas DJ, Saposnik G, Sato C, Seitz D, Shoesmith C, Steeves TDL, Swartz RH, Tan B, Tang-Wai DF, Tartaglia MC, Turnbull J, Zinman L, Hegele RA. Contribution of rare variant associations to neurodegenerative disease presentation. NPJ Genom Med 2021; 6:80. [PMID: 34584092 PMCID: PMC8478934 DOI: 10.1038/s41525-021-00243-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/27/2021] [Indexed: 12/25/2022] Open
Abstract
Genetic factors contribute to neurodegenerative diseases, with high heritability estimates across diagnoses; however, a large portion of the genetic influence remains poorly understood. Many previous studies have attempted to fill the gaps by performing linkage analyses and association studies in individual disease cohorts, but have failed to consider the clinical and pathological overlap observed across neurodegenerative diseases and the potential for genetic overlap between the phenotypes. Here, we leveraged rare variant association analyses (RVAAs) to elucidate the genetic overlap among multiple neurodegenerative diagnoses, including Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia (FTD), mild cognitive impairment, and Parkinson's disease (PD), as well as cerebrovascular disease, using the data generated with a custom-designed neurodegenerative disease gene panel in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). As expected, only ~3% of ONDRI participants harboured a monogenic variant likely driving their disease presentation. Yet, when genes were binned based on previous disease associations, we observed an enrichment of putative loss of function variants in PD genes across all ONDRI cohorts. Further, individual gene-based RVAA identified significant enrichment of rare, nonsynonymous variants in PARK2 in the FTD cohort, and in NOTCH3 in the PD cohort. The results indicate that there may be greater heterogeneity in the genetic factors contributing to neurodegeneration than previously appreciated. Although the mechanisms by which these genes contribute to disease presentation must be further explored, we hypothesize they may be a result of rare variants of moderate phenotypic effect contributing to overlapping pathology and clinical features observed across neurodegenerative diagnoses.
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Affiliation(s)
- Allison A Dilliott
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
- Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
| | - Abdalla Abdelhady
- Department of Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Kelly M Sunderland
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Sali M K Farhan
- Departments of Neurology and Neurosurgery, and Human Genetics, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Agessandro Abrahao
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre and University of Toronto, Toronto, ON, Canada
| | - Malcolm A Binns
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Sandra E Black
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre and University of Toronto, Toronto, ON, Canada
- LCCampbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program Sunnybrook Health Sciences Research Program, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Michael Borrie
- St. Joseph's Health Care Centre, London, ON, Canada
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Leanne K Casaubon
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, ON, Canada
- University Health Network Stroke Program, Toronto Western Hospital, Toronto, ON, Canada
| | - Dar Dowlatshahi
- Department of Medicine, University of Ottawa, Ottawa, ON, Canada
- Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
| | - Corinne E Fischer
- Keenan Research Centre for Biomedical Research, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - Andrew Frank
- Department of Medicine, University of Ottawa, Ottawa, ON, Canada
- Bruyère Research Institute, Ottawa, ON, Canada
| | - Morris Freedman
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
- Division of Neurology, Department of Medicine, Baycrest Health Sciences, Mt. Sinai Hospital and University of Toronto, Toronto, ON, Canada
| | - David Grimes
- Department of Medicine, University of Ottawa, Ottawa, ON, Canada
- Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Ayman Hassan
- Thunder Bay Regional Research Institute and Northern Ontario School of Medicine, Thunder Bay, ON, Canada
| | - Mandar Jog
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- London Health Sciences Centre, London, ON, Canada
| | - Sanjeev Kumar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Donna Kwan
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Anthony E Lang
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, ON, Canada
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, ON, Canada
| | - Jennifer Mandzia
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Mario Masellis
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, ON, Canada
- Cognitive & Movement Disorders Clinic and L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Science Program, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Adam D McIntyre
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Stephen H Pasternak
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Cognitive Neurology and Alzheimer's Disease Research Centre, Parkwood Institute, St. Joseph's Health Care, London, ON, Canada
| | - Bruce G Pollock
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Tarek K Rajji
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry and Toronto Dementia Research Alliance, University of Toronto, Toronto, ON, Canada
| | - Ekaterina Rogaeva
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | | | - Gustavo Saposnik
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
- Clinical Outcomes and Decision Neuroscience Unit, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Christine Sato
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Dallas Seitz
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | | | - Thomas D L Steeves
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, ON, Canada
- Division of Neurology, St. Michael's Hospital, Toronto, ON, Canada
| | - Richard H Swartz
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre and University of Toronto, Toronto, ON, Canada
- LCCampbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program Sunnybrook Health Sciences Research Program, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, ON, Canada
| | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - David F Tang-Wai
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, Toronto Western Hospital, Toronto, ON, Canada
- University Health Network Memory Clinic, Toronto Western Hospital, Toronto, ON, Canada
| | - Maria C Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - John Turnbull
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Lorne Zinman
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre and University of Toronto, Toronto, ON, Canada
| | - Robert A Hegele
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
- Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
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227
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Controlling for human population stratification in rare variant association studies. Sci Rep 2021; 11:19015. [PMID: 34561511 PMCID: PMC8463695 DOI: 10.1038/s41598-021-98370-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 08/25/2021] [Indexed: 12/05/2022] Open
Abstract
Population stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these approaches generally focused on limited types of structure and large sample sizes. We investigated the properties of several correction methods through a large simulation study using real exome data, and several within- and between-continent stratification scenarios. We considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. Large samples showed that accounting for stratification was more difficult with a continental than with a worldwide structure. When considering a sample of 50 cases, an inflation of type-I-errors was observed with PCs for small numbers of controls (≤ 100), and with LMMs for large numbers of controls (≥ 1000). We also tested a novel local permutation method (LocPerm), which maintained a correct type-I-error in all situations. Powers were equivalent for all approaches pointing out that the key issue is to properly control type-I-errors. Finally, we found that power of analyses including small numbers of cases can be increased, by adding a large panel of external controls, provided an appropriate stratification correction was used.
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228
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Gu X, Hou Y, Chen Y, Ou R, Cao B, Wei Q, Zhang L, Song W, Zhao B, Wu Y, Li C, Shang H. Enrichment of rare variants in E3 ubiquitin ligase genes in Early onset Parkinson's disease. Neurobiol Aging 2021; 109:273-278. [PMID: 34544586 DOI: 10.1016/j.neurobiolaging.2021.08.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/15/2021] [Accepted: 08/18/2021] [Indexed: 02/08/2023]
Abstract
Altered ubiquitin signaling and disrupted protein quality control have been implicated in the pathogenesis of PD. The aim of the study was to systematically examine the overlaps between E3 ubiquitin ligase genes and early onset PD (EOPD). A total of 695 EOPD patients were analyzed aggregate burden for rare variants (MAF <0.001 and MAF <0.0001) in a total of 44 E3 ubiquitin ligase genes causing disorders involved in the nervous system. There was significant enrichment of the rare and rare damaging variants in the E3 ubiquitin ligase genes in EOPD patients. Detailly, in the gene-based level, the strongest associations were found in HERC1, IRF2BPL, KMT2D, RAPSN, RLIM, RNF168 and RNF216. Our findings highlighted the importance of UPS mechanism in the pathogenesis of PD from the genetic perspective. Moreover, our study also expanded the susceptible gene spectrum for PD.
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Affiliation(s)
- Xiaojing Gu
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yanbing Hou
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yongping Chen
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ruwei Ou
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Bei Cao
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qianqian Wei
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lingyu Zhang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei Song
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Bi Zhao
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ying Wu
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chunyu Li
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Huifang Shang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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229
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Park J, Packard EA, Levin MG, Judy RL, Damrauer SM, Day SM, Ritchie MD, Rader DJ. A genome-first approach to rare variants in hypertrophic cardiomyopathy genes MYBPC3 and MYH7 in a medical biobank. Hum Mol Genet 2021; 31:827-837. [PMID: 34542152 DOI: 10.1093/hmg/ddab249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/24/2021] [Accepted: 08/19/2021] [Indexed: 11/14/2022] Open
Abstract
'Genome-first' approaches to analyzing rare variants can reveal new insights into human biology and disease. Because pathogenic variants are often rare, new discovery requires aggregating rare coding variants into 'gene burdens' for sufficient power. However, a major challenge is deciding which variants to include in gene burden tests. Pathogenic variants in MYBPC3 and MYH7 are well-known causes of hypertrophic cardiomyopathy (HCM), and focusing on these 'positive control' genes in a genome-first approach could help inform variant selection methods and gene burdening strategies for other genes and diseases. Integrating exome sequences with electronic health records among 41 759 participants in the Penn Medicine BioBank, we evaluated the performance of aggregating predicted loss-of-function (pLOF) and/or predicted deleterious missense (pDM) variants in MYBPC3 and MYH7 for gene burden phenome-wide association studies (PheWAS). The approach to grouping rare variants for these two genes produced very different results: pLOFs but not pDM variants in MYBPC3 were strongly associated with HCM, whereas the opposite was true for MYH7. Detailed review of clinical charts revealed that only 38.5% of patients with HCM diagnoses carrying an HCM-associated variant in MYBPC3 or MYH7 had a clinical genetic test result. Additionally, 26.7% of MYBPC3 pLOF carriers without HCM diagnoses had clear evidence of left atrial enlargement and/or septal/LV hypertrophy on echocardiography. Our study shows the importance of evaluating both pLOF and pDM variants for gene burden testing in future studies to uncover novel gene-disease relationships and identify new pathogenic loss-of-function variants across the human genome through genome-first analyses of healthcare-based populations.
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Affiliation(s)
- Joseph Park
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Elizabeth A Packard
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael G Levin
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Renae L Judy
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sharlene M Day
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Daniel J Rader
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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230
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Liu X, Tian G, Liu Z. Identification of novel genes for triple-negative breast cancer with semiparametric gene-based analysis. J Appl Stat 2021; 50:691-702. [PMID: 36819073 PMCID: PMC9930760 DOI: 10.1080/02664763.2021.1973387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Triple-negative breast cancer (TNBC) is generally considered an aggressive breast cancer subtype associated with poor prognostic outcomes. Up to now, the molecular and cellular mechanisms underlying TNBC pathology have not been fully understood. In this manuscript, we propose a novel semiparametric model with kernel for gene-based analysis with a breast cancer GWAS data. The software of SPMGBA (semiparametric method for gene-based analysis) in MATLAB is available at GitHub (https://github.com/zliu3/SPMGBA). Genetic signatures associated with breast cancer are discovered. We further validate the prognostic power of the identified genes with a large cohort of expression data from the European Genome-Phenome Archive, and discover that SEL1L is associated with the overall survival of TNBC with the p-value of .0002. We conclude that gene SEL1L is down-regulated in TNBC and the expression of SEL1L is positively associated with patient survival.
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Affiliation(s)
- Xiaotong Liu
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Guoliang Tian
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Zhenqiu Liu
- Department of Public Health Sciences, Pennsylvania State University, Hershey, PA, USA, Zhenqiu Liu Department of Public Health Sciences, Pennsylvania State University, Hershey, PA17033, USA
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231
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Fores-Martos J, Cervera-Vidal R, Sierra-Roca J, Lozano-Asencio C, Fedele V, Cornelissen S, Edvarsen H, Tadeo-Cervera I, Eroles P, Lluch A, Tabares-Seisdedos R, Falcó A, Van't Veer LJ, Schmidt M, Quigley DA, Børresen-Dale AL, Kristensen VN, Balmain A, Climent J. Circadian PERformance in breast cancer: a germline and somatic genetic study of PER3 VNTR polymorphisms and gene co-expression. NPJ Breast Cancer 2021; 7:118. [PMID: 34508103 PMCID: PMC8433453 DOI: 10.1038/s41523-021-00329-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 08/16/2021] [Indexed: 11/30/2022] Open
Abstract
Polymorphisms in the PER3 gene have been associated with several human disease phenotypes, including sleep disorders and cancer. In particular, the long allele of a variable number of tandem repeat (VNTR) polymorphism has been previously linked to an increased risk of breast cancer. Here we carried out a combined germline and somatic genetic analysis of the role of the PER3VNRT polymorphism in breast cancer. The combined data from 8284 individuals showed a non-significant trend towards increased breast cancer risk in the 5-repeat allele homozygous carriers (OR = 1.17, 95% CI: 0.97–1.42). We observed allelic imbalance at the PER3 locus in matched blood and tumor DNA samples, showing a significant retention of the long variant (risk) allele in tumor samples, and a preferential loss of the short repetition allele (p = 0.0005). Gene co-expression analysis in healthy and tumoral breast tissue samples uncovered significant associations between PER3 expression levels with those from genes which belong to several cancer-associated pathways. Finally, relapse-free survival (RFS) analysis showed that low expression levels of PER3 were linked to a significant lower RSF in luminal A (p = 3 × 10−12) but not in the rest of breast cancer subtypes.
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Affiliation(s)
- Jaume Fores-Martos
- ESI International Chair at CEU-UCH, CEU Universities, Valencia, Spain.,Biomedical Research Networking Center of Mental Health (CIBERSAM), Madrid, Spain
| | | | | | - Carlos Lozano-Asencio
- INCLIVA Research Institute. Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Vita Fedele
- Digestive Molecular Clinical Oncology Research Unit, Section of Medical Oncology, Department of Medicine, University of Verona, Verona, Italy
| | - Sten Cornelissen
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Hege Edvarsen
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
| | - Irene Tadeo-Cervera
- Departamento de Producción y Sanidad Animal, Salud Pública Veterinaria y Ciencia y Tecnología de los Alimentos. Facultad de Veterinaria, Universidad CEU Cardenal Herrera. CEU Universities, Valencia, Spain
| | - Pilar Eroles
- INCLIVA Research Institute. Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Ana Lluch
- INCLIVA Research Institute. Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Rafa Tabares-Seisdedos
- Biomedical Research Networking Center of Mental Health (CIBERSAM), Madrid, Spain.,Department of Medicine, Faculty of Medicine, University of Valencia, Valencia, Spain
| | - Antonio Falcó
- ESI International Chair at CEU-UCH, CEU Universities, Valencia, Spain.,Departamento de Matemáticas, Física y Ciencias Tecnológicas, Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera, CEU Universities, Valencia, Spain
| | - Laura J Van't Veer
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Marjanka Schmidt
- Departamento de Producción y Sanidad Animal, Salud Pública Veterinaria y Ciencia y Tecnología de los Alimentos. Facultad de Veterinaria, Universidad CEU Cardenal Herrera. CEU Universities, Valencia, Spain
| | - David A Quigley
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA.,Departments of Urology and Epidemiology & Biostatistics, University of California San Francisco, Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA
| | - Anne-Lise Børresen-Dale
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
| | - Vessela N Kristensen
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
| | - Allan Balmain
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Joan Climent
- ESI International Chair at CEU-UCH, CEU Universities, Valencia, Spain. .,INCLIVA Research Institute. Hospital Clínico Universitario de Valencia, Valencia, Spain. .,Departamento de Producción y Sanidad Animal, Salud Pública Veterinaria y Ciencia y Tecnología de los Alimentos. Facultad de Veterinaria, Universidad CEU Cardenal Herrera. CEU Universities, Valencia, Spain.
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232
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Jin X, Shi G. Variance-component-based meta-analysis of gene-environment interactions for rare variants. G3-GENES GENOMES GENETICS 2021; 11:6298593. [PMID: 34544119 PMCID: PMC8661424 DOI: 10.1093/g3journal/jkab203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022]
Abstract
Complex diseases are often caused by interplay between genetic and environmental factors. Existing gene-environment interaction (G × E) tests for rare variants largely focus on detecting gene-based G × E effects in a single study; thus, their statistical power is limited by the sample size of the study. Meta-analysis methods that synthesize summary statistics of G × E effects from multiple studies for rare variants are still limited. Based on variance component models, we propose four meta-analysis methods of testing G × E effects for rare variants: HOM-INT-FIX, HET-INT-FIX, HOM-INT-RAN, and HET-INT-RAN. Our methods consider homogeneous or heterogeneous G × E effects across studies and treat the main genetic effect as either fixed or random. Through simulations, we show that the empirical distributions of the four meta-statistics under the null hypothesis align with their expected theoretical distributions. When the interaction effect is homogeneous across studies, HOM-INT-FIX and HOM-INT-RAN have as much statistical power as a pooled analysis conducted on a single interaction test with individual-level data from all studies. When the interaction effect is heterogeneous across studies, HET-INT-FIX and HET-INT-RAN provide higher power than pooled analysis. Our methods are further validated via testing 12 candidate gene-age interactions in blood pressure traits using whole-exome sequencing data from UK Biobank.
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Affiliation(s)
- Xiaoqin Jin
- State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China
| | - Gang Shi
- State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China
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233
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Kleine-Levin syndrome is associated with birth difficulties and genetic variants in the TRANK1 gene loci. Proc Natl Acad Sci U S A 2021; 118:2005753118. [PMID: 33737391 DOI: 10.1073/pnas.2005753118] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Kleine-Levin syndrome (KLS) is a rare disorder characterized by severe episodic hypersomnia, with cognitive impairment accompanied by apathy or disinhibition. Pathophysiology is unknown, although imaging studies indicate decreased activity in hypothalamic/thalamic areas during episodes. Familial occurrence is increased, and risk is associated with reports of a difficult birth. We conducted a worldwide case-control genome-wide association study in 673 KLS cases collected over 14 y, and ethnically matched 15,341 control individuals. We found a strong genome-wide significant association (rs71947865, Odds Ratio [OR] = 1.48, P = 8.6 × 10-9) within the 3'region of TRANK1 gene locus, previously associated with bipolar disorder and schizophrenia. Strikingly, KLS cases with rs71947865 variant had significantly increased reports of a difficult birth. As perinatal outcomes have dramatically improved over the last 40 y, we further stratified our sample by birth years and found that recent cases had a significantly reduced rs71947865 association. While the rs71947865 association did not replicate in the entire follow-up sample of 171 KLS cases, rs71947865 was significantly associated with KLS in the subset follow-up sample of 59 KLS cases who reported birth difficulties (OR = 1.54, P = 0.01). Genetic liability of KLS as explained by polygenic risk scores was increased (pseudo R 2 = 0.15; P < 2.0 × 10-22 at P = 0.5 threshold) in the follow-up sample. Pathway analysis of genetic associations identified enrichment of circadian regulation pathway genes in KLS cases. Our results suggest links between KLS, circadian regulation, and bipolar disorder, and indicate that the TRANK1 polymorphisms in conjunction with reported birth difficulties may predispose to KLS.
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234
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Lin JR, Sin-Chan P, Napolioni V, Torres GG, Mitra J, Zhang Q, Jabalameli MR, Wang Z, Nguyen N, Gao T, Laudes M, Görg S, Franke A, Nebel A, Greicius MD, Atzmon G, Ye K, Gorbunova V, Ladiges WC, Shuldiner AR, Niedernhofer LJ, Robbins PD, Milman S, Suh Y, Vijg J, Barzilai N, Zhang ZD. Rare genetic coding variants associated with human longevity and protection against age-related diseases. NATURE AGING 2021; 1:783-794. [PMID: 37117627 DOI: 10.1038/s43587-021-00108-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 08/05/2021] [Indexed: 12/18/2022]
Abstract
Extreme longevity in humans has a strong genetic component, but whether this involves genetic variation in the same longevity pathways as found in model organisms is unclear. Using whole-exome sequences of a large cohort of Ashkenazi Jewish centenarians to examine enrichment for rare coding variants, we found most longevity-associated rare coding variants converge upon conserved insulin/insulin-like growth factor 1 signaling and AMP-activating protein kinase signaling pathways. Centenarians have a number of pathogenic rare coding variants similar to control individuals, suggesting that rare variants detected in the conserved longevity pathways are protective against age-related pathology. Indeed, we detected a pro-longevity effect of rare coding variants in the Wnt signaling pathway on individuals harboring the known common risk allele APOE4. The genetic component of extreme human longevity constitutes, at least in part, rare coding variants in pathways that protect against aging, including those that control longevity in model organisms.
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Affiliation(s)
- Jhih-Rong Lin
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | | | - Valerio Napolioni
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
| | | | - Joydeep Mitra
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Quanwei Zhang
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - M Reza Jabalameli
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Zhen Wang
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Tina Gao
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
| | - Matthias Laudes
- Division of Endocrinology, Diabetes and Clinical Nutrition, Department of Internal Medicine I, Kiel University, Kiel, Germany
| | - Siegfried Görg
- Institute of Transfusion Medicine, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Almut Nebel
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Michael D Greicius
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Gil Atzmon
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
- Department of Biology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel
| | - Kenny Ye
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, New York, NY, USA
| | - Vera Gorbunova
- Department of Biology, University of Rochester, Rochester, NY, USA
| | - Warren C Ladiges
- Department of Comparative Medicine, School of Medicine, University of Washington, Seattle, WA, USA
| | | | - Laura J Niedernhofer
- Institute on the Biology of Aging and Metabolism and Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Paul D Robbins
- Institute on the Biology of Aging and Metabolism and Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Sofiya Milman
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
| | - Yousin Suh
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY, USA
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Nir Barzilai
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
| | - Zhengdong D Zhang
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA.
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235
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Faul JD, Kho M, Zhao W, Rumfelt KE, Yu M, Mitchell C, Smith JA. Trans-ethnic Meta-analysis of Interactions between Genetics and Early Life Socioeconomic Context on Memory Performance and Decline in Older Americans. J Gerontol A Biol Sci Med Sci 2021; 77:2248-2256. [PMID: 34448475 DOI: 10.1093/gerona/glab255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Indexed: 11/14/2022] Open
Abstract
Later life cognitive function is influenced by genetics as well as early- and later-life socioeconomic context. However, few studies have examined the interaction between genetics and early childhood factors. Using gene-based tests (iSKAT/iSKAT-O), we examined whether common and/or rare exonic variants in 39 gene regions previously associated with cognitive performance, dementia, and related traits had an interaction with childhood socioeconomic context (parental education and financial strain) on memory performance or decline in European ancestry (EA, N=10,468) and African ancestry (AA, N=2,252) participants from the Health and Retirement Study. Of the 39 genes, 22 in EA and 19 in AA had nominally significant interactions with at least one childhood socioeconomic measure on memory performance and/or decline; however, all but one (father's education by SLC24A4 in AA) were not significant after multiple testing correction (FDR <0.05). In trans-ethnic meta-analysis, two genes interacted with childhood socioeconomic context (FDR <0.05): mother's education by MS4A4A on memory performance, and father's education by SLC24A4 on memory decline. Both interactions remained significant (p<0.05) after adjusting for respondent's own educational attainment, APOE ε4 status, lifestyle factors, BMI, and comorbidities. For both interactions in EA and AA, the genetic effect was stronger in participants with low parental education. Examination of common and rare variants in genes discovered through GWAS shows that childhood context may interact with key gene regions to jointly impact later life memory function and decline. Genetic effects may be more salient for those with lower childhood socioeconomic status.
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Affiliation(s)
- Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI
| | - Minjung Kho
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Kalee E Rumfelt
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Miao Yu
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Colter Mitchell
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI
| | - Jennifer A Smith
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI.,Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
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236
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Chen Y, Graf L, Chen T, Liao Q, Bai T, Petric PP, Zhu W, Yang L, Dong J, Lu J, Chen Y, Shen J, Haller O, Staeheli P, Kochs G, Wang D, Schwemmle M, Shu Y. Rare variant MX1 alleles increase human susceptibility to zoonotic H7N9 influenza virus. Science 2021; 373:918-922. [PMID: 34413236 DOI: 10.1126/science.abg5953] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 07/19/2021] [Indexed: 12/14/2022]
Abstract
Zoonotic avian influenza A virus (IAV) infections are rare. Sustained transmission of these IAVs between humans has not been observed, suggesting a role for host genes. We used whole-genome sequencing to compare avian IAV H7N9 patients with healthy controls and observed a strong association between H7N9 infection and rare, heterozygous single-nucleotide variants in the MX1 gene. MX1 codes for myxovirus resistance protein A (MxA), an interferon-induced antiviral guanosine triphosphatase known to control IAV infections in transgenic mice. Most of the MxA variants identified lost the ability to inhibit avian IAVs, including H7N9, in transfected human cell lines. Nearly all of the inactive MxA variants exerted a dominant-negative effect on the antiviral function of wild-type MxA, suggesting an MxA null phenotype in heterozygous carriers. Our study provides genetic evidence for a crucial role of the MX1-based antiviral defense in controlling zoonotic IAV infections in humans.
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Affiliation(s)
- Yongkun Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Laura Graf
- Institute of Virology, Medical Center - University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Tao Chen
- Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Qijun Liao
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Tian Bai
- Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Philipp P Petric
- Institute of Virology, Medical Center - University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Spemann Graduate School of Biology and Medicine, University of Freiburg, Freiburg, Germany
| | - Wenfei Zhu
- Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Lei Yang
- Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jie Dong
- Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jian Lu
- Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | | | | | - Otto Haller
- Institute of Virology, Medical Center - University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Peter Staeheli
- Institute of Virology, Medical Center - University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Georg Kochs
- Institute of Virology, Medical Center - University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dayan Wang
- Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Martin Schwemmle
- Institute of Virology, Medical Center - University of Freiburg, Freiburg, Germany. .,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Yuelong Shu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China. .,Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
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237
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Mullaert J, Bouaziz M, Seeleuthner Y, Bigio B, Casanova JL, Alcaïs A, Abel L, Cobat A. Taking population stratification into account by local permutations in rare-variant association studies on small samples. Genet Epidemiol 2021; 45:821-829. [PMID: 34402542 DOI: 10.1002/gepi.22426] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 06/07/2021] [Accepted: 07/15/2021] [Indexed: 11/08/2022]
Abstract
Many methods for rare variant association studies require permutations to assess the significance of tests. Standard permutations assume that all individuals are exchangeable and do not take population stratification (PS), a known confounding factor in genetic studies, into account. We propose a novel strategy, LocPerm, in which individual phenotypes are permuted only with their closest ancestry-based neighbors. We performed a simulation study, focusing on small samples, to evaluate and compare LocPerm with standard permutations and classical adjustment on first principal components. Under the null hypothesis, LocPerm was the only method providing an acceptable type I error, regardless of sample size and level of stratification. The power of LocPerm was similar to that of standard permutation in the absence of PS, and remained stable in different PS scenarios. We conclude that LocPerm is a method of choice for taking PS and/or small sample size into account in rare variant association studies.
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Affiliation(s)
- Jimmy Mullaert
- Université de Paris, IAME, INSERM, Paris, France.,AP-HP, Hôpital Bichat, DEBRC, Paris, France.,Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France
| | - Matthieu Bouaziz
- Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.,Université de Paris, Imagine Institute, Paris, EU, France
| | - Yoann Seeleuthner
- Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.,Université de Paris, Imagine Institute, Paris, EU, France
| | - Benedetta Bigio
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, New York, USA
| | - Jean-Laurent Casanova
- Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.,Université de Paris, Imagine Institute, Paris, EU, France.,St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, New York, USA.,Howard Hughes Medical Institute, New York, New York, USA
| | - Alexandre Alcaïs
- Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.,Université de Paris, Imagine Institute, Paris, EU, France
| | - Laurent Abel
- Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.,Université de Paris, Imagine Institute, Paris, EU, France.,St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, New York, USA
| | - Aurélie Cobat
- Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.,Université de Paris, Imagine Institute, Paris, EU, France
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238
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Abstract
Bacterial genome-wide association studies (bGWAS) capture associations between genomic variation and phenotypic variation. Convergence-based bGWAS methods identify genomic mutations that occur independently multiple times on the phylogenetic tree in the presence of phenotypic variation more often than is expected by chance. This work introduces hogwash, an open source R package that implements three algorithms for convergence-based bGWAS. Hogwash additionally contains two burden testing approaches to perform gene or pathway analysis to improve power and increase convergence detection for related but weakly penetrant genotypes. To identify optimal use cases, we applied hogwash to data simulated with a variety of phylogenetic signals and convergence distributions. These simulated data are publicly available and contain the relevant metadata regarding convergence and phylogenetic signal for each phenotype and genotype. Hogwash is available for download from GitHub.
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Affiliation(s)
- Katie Saund
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
| | - Evan S Snitkin
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA.,Department of Internal Medicine, Division of Infectious Diseases, University of Michigan, Ann Arbor, Michigan, USA
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239
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Calculation of Fetal Fraction for Non-Invasive Prenatal Testing. BIOTECH 2021; 10:biotech10030017. [PMID: 35822771 PMCID: PMC9245487 DOI: 10.3390/biotech10030017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 07/21/2021] [Accepted: 07/26/2021] [Indexed: 12/05/2022] Open
Abstract
Estimating the fetal fraction of DNA in a pregnant mother’s blood is a risk-free, non-invasive way of predicting fetal aneuploidy. It is a rapidly developing field of study, offering researchers a plethora of different complementary methods. Such methods include examining the differences in methylation profiles between the fetus and the mother. Others include calculating the average allele frequency based on the difference in genotype of a number of single-nucleotide polymorphisms. Differences in the length distribution of DNA fragments between the mother and the fetus as well as measuring the proportion of DNA reads mapping to the Y chromosome also constitute fetal fraction estimation methods. The advantages and disadvantages of each of these main method types are discussed. Moreover, several well-known fetal fraction estimation methods, such as SeqFF, are described and compared with other methods. These methods are amenable to not only the estimation of fetal fraction but also paternity, cancer, and transplantation monitoring studies. NIPT is safe, and should aneuploidy be detected, this information can help parents prepare mentally and emotionally for the birth of a special needs child.
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240
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Sheng Z, Liu Y, Li P, Qin J. Likelihood ratio test for genetic association study with case–control data under Probit model. J Appl Stat 2021; 49:3717-3731. [DOI: 10.1080/02664763.2021.1962261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Zhen Sheng
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science, MOE, Shanghai, People's Republic of China
- School of Statistics, East China Normal University, Shanghai, People's Republic of China
| | - Yukun Liu
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science, MOE, Shanghai, People's Republic of China
- School of Statistics, East China Normal University, Shanghai, People's Republic of China
| | - Pengfei Li
- Department of Statistics and Actuarial Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Jing Qin
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
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241
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Dinneen TJ, Ghrálaigh FN, Walsh R, Lopez LM, Gallagher L. How does genetic variation modify ND-CNV phenotypes? Trends Genet 2021; 38:140-151. [PMID: 34364706 DOI: 10.1016/j.tig.2021.07.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/30/2021] [Accepted: 07/06/2021] [Indexed: 02/05/2023]
Abstract
Rare copy-number variants (CNVs) associated with neurodevelopmental disorders (NDDs), i.e., ND-CNVs, provide an insight into the neurobiology of NDDs and, potentially, a link between biology and clinical outcomes. However, ND-CNVs are characterised by incomplete penetrance resulting in heterogeneous carrier phenotypes, ranging from non-affected to multimorbid psychiatric, neurological, and physical phenotypes. Recent evidence indicates that other variants in the genome, or 'other hits', may partially explain the variable expressivity of ND-CNVs. These may be other rare variants or the aggregated effects of common variants that modify NDD risk. Here we discuss the recent findings, current questions, and future challenges relating to other hits research in the context of ND-CNVs and their potential for improved clinical diagnostics and therapeutics for ND-CNV carriers.
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Affiliation(s)
- Thomas J Dinneen
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland.
| | - Fiana Ní Ghrálaigh
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland; Department of Biology, National University of Ireland Maynooth, Maynooth, Ireland
| | - Ruth Walsh
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - Lorna M Lopez
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland; Department of Biology, National University of Ireland Maynooth, Maynooth, Ireland
| | - Louise Gallagher
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland.
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242
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Lacaze P, Riaz M, Sebra R, Hooper AJ, Pang J, Tiller J, Polekhina G, Tonkin A, Reid C, Zoungas S, Murray AM, Nicholls S, Watts G, Schadt E, McNeil JJ. Protective lipid-lowering variants in healthy older individuals without coronary heart disease. Open Heart 2021; 8:openhrt-2021-001710. [PMID: 34341098 PMCID: PMC8330577 DOI: 10.1136/openhrt-2021-001710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 07/13/2021] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVE Genetic variants that disrupt the function of the PCSK9 (proprotein convertase subtilisin kexin type 9) and APOB (apolipoprotein B)genes result in lower serum low-density lipoprotein cholesterol (LDL-C) levels and subsequently confer protection against coronary heart disease (CHD). The objective of this study was to measure the prevalence and selective advantage of such variants among healthy older individuals without a history of CHD. METHODS We performed targeted sequencing of the PCSK9 and APOB genes in 13 131 healthy individuals without CHD aged 70 years or older enrolled into the ASPirin in Reducing Events in the Elderly trial. We detected variants in the PCSK9 and APOB genes with predicted loss-of-function. We associated variant carrier status with serum LDL-C and total cholesterol (TC) levels at the time of study enrolment, adjusting for statin use. RESULTS We detected 22 different rare PCSK9/APOB candidate variants with putative lipid-lowering effect, carried by 104 participants (carrier rate 1 in 126). Serum LDL-C and TC concentrations for rare PCSK9/APOB variant carriers were consistently lower than non-carriers. Rare variant carrier status was associated with 19.4 mg/dL (14.6%) lower LDL-C, compared with non-carriers (p≤0.001, adjusted for statin use). Statin prescriptions were less prevalent in rare variant carriers (16%) than non-carriers (35%). The more common PCSK9 R46L variant (rs11591147-T) was associated with 15.5 mg/dL (11.8%) lower LDL-C in heterozygotes, and 25.2 mg/dL (19.2%) lower LDL-C in homozygotes (both p≤0.001). CONCLUSIONS Lipid-lowering genetic variants are carried by healthy older individuals and contribute to CHD-free survival. TRIAL REGISTRATION NUMBER NCT01038583.
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Affiliation(s)
- Paul Lacaze
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Moeen Riaz
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Amanda J Hooper
- School of Medicine, Faculty of Medicine and Health Sciences, The University of Western Australia, Perth, Western Australia, Australia.,Department of Clinical Biochemistry, PathWest Laboratory Medicine WA, Royal Perth Hospital and Fiona Stanley Hospital Network, Royal Perth Hospital, Perth, Western Australia, Australia
| | - Jing Pang
- School of Medicine, Faculty of Medicine and Health Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Jane Tiller
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Galina Polekhina
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Andrew Tonkin
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Chris Reid
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.,School of Public Health, Curtin University, Perth, Western Australia, Australia
| | - Sophia Zoungas
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Anne M Murray
- Berman Center for Outcomes and Clinical Research, Hennepin Healthcare Research Institute, Hennepin Healthcare, Minneapolis, Minnesota, USA
| | - Stephen Nicholls
- Monash Cardiovascular Research Centre, Monash University and MonashHeart, Monash Health, Clayton, Victoria, Australia
| | - Gerald Watts
- Department of Clinical Biochemistry, PathWest Laboratory Medicine WA, Royal Perth Hospital and Fiona Stanley Hospital Network, Royal Perth Hospital, Perth, Western Australia, Australia.,Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, Western Australia, Australia
| | - Eric Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - John J McNeil
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Forrest IS, Chaudhary K, Vy HMT, Bafna S, Kim S, Won HH, Loos RJ, Cho J, Pasquale LR, Nadkarni GN, Rocheleau G, Do R. Genetic pleiotropy of ERCC6 loss-of-function and deleterious missense variants links retinal dystrophy, arrhythmia, and immunodeficiency in diverse ancestries. Hum Mutat 2021; 42:969-977. [PMID: 34005834 PMCID: PMC8295228 DOI: 10.1002/humu.24220] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 04/27/2021] [Accepted: 05/15/2021] [Indexed: 11/08/2022]
Abstract
Biobanks with exomes linked to electronic health records (EHRs) enable the study of genetic pleiotropy between rare variants and seemingly disparate diseases. We performed robust clinical phenotyping of rare, putatively deleterious variants (loss-of-function [LoF] and deleterious missense variants) in ERCC6, a gene implicated in inherited retinal disease. We analyzed 213,084 exomes, along with a targeted set of retinal, cardiac, and immune phenotypes from two large-scale EHR-linked biobanks. In the primary analysis, a burden of deleterious variants in ERCC6 was strongly associated with (1) retinal disorders; (2) cardiac and electrocardiogram perturbations; and (3) immunodeficiency and decreased immunoglobulin levels. Meta-analysis of results from the BioMe Biobank and UK Biobank showed a significant association of deleterious ERCC6 burden with retinal dystrophy (odds ratio [OR] = 2.6, 95% confidence interval [CI]: 1.5-4.6; p = 8.7 × 10-4 ), atypical atrial flutter (OR = 3.5, 95% CI: 1.9-6.5; p = 6.2 × 10-5 ), arrhythmia (OR = 1.5, 95% CI: 1.2-2.0; p = 2.7 × 10-3 ), and lymphocyte immunodeficiency (OR = 3.8, 95% CI: 2.1-6.8; p = 5.0 × 10-6 ). Carriers of ERCC6 LoF variants who lacked a diagnosis of these conditions exhibited increased symptoms, indicating underdiagnosis. These results reveal a unique genetic link among retinal, cardiac, and immune disorders and underscore the value of EHR-linked biobanks in assessing the full clinical profile of carriers of rare variants.
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Affiliation(s)
- Iain S. Forrest
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kumardeep Chaudhary
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ha My T. Vy
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shantanu Bafna
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Soyeon Kim
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Ruth J.F. Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Judy Cho
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Louis R. Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Eye and Vision Research Institute, New York Eye and Ear Infirmary of Mount Sinai, New York, NY, USA
| | - Girish N. Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ghislain Rocheleau
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ron Do
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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244
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Althouse AD, Below JE, Claggett BL, Cox NJ, de Lemos JA, Deo RC, Duval S, Hachamovitch R, Kaul S, Keith SW, Secemsky E, Teixeira-Pinto A, Roger VL. Recommendations for Statistical Reporting in Cardiovascular Medicine: A Special Report From the American Heart Association. Circulation 2021; 144:e70-e91. [PMID: 34032474 DOI: 10.1161/circulationaha.121.055393] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Statistical analyses are a crucial component of the biomedical research process and are necessary to draw inferences from biomedical research data. The application of sound statistical methodology is a prerequisite for publication in the American Heart Association (AHA) journal portfolio. The objective of this document is to summarize key aspects of statistical reporting that might be most relevant to the authors, reviewers, and readership of AHA journals. The AHA Scientific Publication Committee convened a task force to inventory existing statistical standards for publication in biomedical journals and to identify approaches suitable for the AHA journal portfolio. The experts on the task force were selected by the AHA Scientific Publication Committee, who identified 12 key topics that serve as the section headers for this document. For each topic, the members of the writing group identified relevant references and evaluated them as a resource to make the standards summarized herein. Each section was independently reviewed by an expert reviewer who was not part of the task force. Expert reviewers were also permitted to comment on other sections if they chose. Differences of opinion were adjudicated by consensus. The standards presented in this report are intended to serve as a guide for high-quality reporting of statistical analyses methods and results.
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Affiliation(s)
- Andrew D Althouse
- Center for Research on Health Care Data Center, Division of General Internal Medicine, University of Pittsburgh, PA (A.D.A.)
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN (J.E.B., N.J.C.)
| | - Brian L Claggett
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA (B.L.C., R.C.D.)
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN (J.E.B., N.J.C.)
| | - James A de Lemos
- Division of Cardiology, University of Texas Southwestern Medical Center, Dallas (J.A.d.L.)
| | - Rahul C Deo
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA (B.L.C., R.C.D.)
| | - Sue Duval
- Cardiovascular Division, University of Minnesota Medical School, Minneapolis (S.D.)
| | - Rory Hachamovitch
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic Foundation, OH (R.H.)
| | - Sanjay Kaul
- Department of Cardiology, Cedars-Sinai Medical Center, and the David Geffen School of Medicine, University of California, Los Angeles (S.K.)
| | - Scott W Keith
- Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College of Thomas Jefferson University, Philadelphia, PA (S.W.K.)
| | - Eric Secemsky
- Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA (E.S.)
| | - Armando Teixeira-Pinto
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Australia (A.T.-P.)
| | - Veronique L Roger
- Department of Cardiovascular Diseases Medicine, Mayo Clinic College of Medicine, Rochester, MN (V.L.R.)
- now with Epidemiology and Community Health Branch National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD (V.L.R.)
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245
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McInnes G, Yee SW, Pershad Y, Altman RB. Genomewide Association Studies in Pharmacogenomics. Clin Pharmacol Ther 2021; 110:637-648. [PMID: 34185318 PMCID: PMC8376796 DOI: 10.1002/cpt.2349] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/15/2021] [Indexed: 12/24/2022]
Abstract
The increasing availability of genotype data linked with information about drug-response phenotypes has enabled genomewide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics (PGx) faces unique challenges. In this review, we analyze the last decade of GWAS in PGx. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future PGx discoveries.
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Affiliation(s)
- Gregory McInnes
- Biomedical Informatics Training Program, Stanford University, Stanford, California, USA
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, California, USA
| | - Yash Pershad
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, California, USA.,Departments of Genetics, Medicine, Biomedical Data Science, Stanford, California, USA
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246
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Lv J, Tu S, Xu L. Detection of Phenotype-Related Mutations of COVID-19 via the Whole Genomic Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1242-1249. [PMID: 33417561 PMCID: PMC8769011 DOI: 10.1109/tcbb.2021.3049836] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 11/30/2020] [Accepted: 01/03/2021] [Indexed: 06/12/2023]
Abstract
The coronavirus disease 2019 (COVID-19) epidemic continues to spread rapidly around the world and nearly 20 millions people are infected. This paper utilises both single-locus analysis and joint-SNPs analysis for detection of significant single nucleotide polymorphisms (SNPs) in the phenotypes of symptomatic versus asymptomatic, the early collection time versus the late collection time, the old versus the young, and the male versus the female. Also, this paper analyses the relationship between any two SNPs via linkage disequilibrium analysis, and visualises the patterns of cumulative mutations of SNPs over collection time. The results are in three folds. First, the SNP which locates at the nucleotide position 4321 is found to be an independent significant locus associated with all the first three phenotypes. Moreover, 12 significant SNPs are found in the first two studies. Second, gene orf1ab containing SNP-4321 is detected to be significantly associated with the first three phenotypes, and the three genes S, ORF3a, and N, are detected to be significant in the first two phenotypes. Third, some of the detected genes or SNPs are related to the SARS-COV-2 as supported by literature survey, which indicates that the results here may be helpful for further investigation.
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Affiliation(s)
- Jinxiong Lv
- Center for Cognitive Machines and Computational Health (CMaCH)Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai200240China
| | - Shikui Tu
- Center for Cognitive Machines and Computational Health (CMaCH)Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai200240China
| | - Lei Xu
- Center for Cognitive Machines and Computational Health (CMaCH)Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai200240China
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247
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Park JY, Fiecas M. Permutation-based inference for spatially localized signals in longitudinal MRI data. Neuroimage 2021; 239:118312. [PMID: 34182099 DOI: 10.1016/j.neuroimage.2021.118312] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 05/11/2021] [Accepted: 06/23/2021] [Indexed: 10/21/2022] Open
Abstract
Alzheimer's disease is a neurodegenerative disease in which the degree of cortical atrophy in specific structures of the brain serves as a useful imaging biomarker. Recent approaches using linear mixed effects (LME) models in longitudinal neuroimaging have been powerful and flexible in investigating the temporal trajectories of cortical thickness. However, massive-univariate analysis, a simplified approach that obtains a summary statistic (e.g., a p-value) for every vertex along the cortex, is insufficient to model cortical atrophy because it does not account for spatial similarities of the signals in neighboring locations. In this article, we develop a permutation-based inference procedure to detect spatial clusters of vertices showing statistically significant differences in the rates of cortical atrophy. The proposed method, called SpLoc, uses spatial information to combine the signals adaptively across neighboring vertices, yielding high statistical power while controlling family-wise error rate (FWER) accurately. When we reject the global null hypothesis, we use a cluster selection algorithm to detect the spatial clusters of significant vertices. We validate our method using simulation studies and apply it to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data to show its superior performance over existing methods. An R package for implementing SpLoc is publicly available.
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Affiliation(s)
- Jun Young Park
- Department of Statistical Sciences and Department of Psychology, University of Toronto, Toronto, ON M5S, Canada.
| | - Mark Fiecas
- Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN 55455, U.S.A
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248
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Bi W, Lee S. Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data. Front Genet 2021; 12:682638. [PMID: 34211504 PMCID: PMC8239389 DOI: 10.3389/fgene.2021.682638] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/17/2021] [Indexed: 02/05/2023] Open
Abstract
With the advances in genotyping technologies and electronic health records (EHRs), large biobanks have been great resources to identify novel genetic associations and gene-environment interactions on a genome-wide and even a phenome-wide scale. To date, several phenome-wide association studies (PheWAS) have been performed on biobank data, which provides comprehensive insights into many aspects of human genetics and biology. Although inspiring, PheWAS on large-scale biobank data encounters new challenges including computational burden, unbalanced phenotypic distribution, and genetic relationship. In this paper, we first discuss these new challenges and their potential impact on data analysis. Then, we summarize approaches that are scalable and robust in GWAS and PheWAS. This review can serve as a practical guide for geneticists, epidemiologists, and other medical researchers to identify genetic variations associated with health-related phenotypes in large-scale biobank data analysis. Meanwhile, it can also help statisticians to gain a comprehensive and up-to-date understanding of the current technical tool development.
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Affiliation(s)
- Wenjian Bi
- Department of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States
| | - Seunggeun Lee
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
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249
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Kolmykov S, Vasiliev G, Osadchuk L, Kleschev M, Osadchuk A. Whole-Exome Sequencing Analysis of Human Semen Quality in Russian Multiethnic Population. Front Genet 2021; 12:662846. [PMID: 34178030 PMCID: PMC8232892 DOI: 10.3389/fgene.2021.662846] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/19/2021] [Indexed: 01/12/2023] Open
Abstract
The global trend toward the reduction of human spermatogenic function observed in many countries, including Russia, raised the problem of extensive screening and monitoring of male fertility and elucidation of its genetic and ethnic mechanisms. Recently, whole-exome sequencing (WES) was developed as a powerful tool for genetic analysis of complex traits. We present here the first Russian WES study for identification of new genes associated with semen quality. The experimental 3 × 2 design of the WES study was based on the analysis of 157 samples including three ethnic groups—Slavs (59), Buryats (n = 49), and Yakuts (n = 49), and two different semen quality groups—pathozoospermia (n = 95) and normospermia (n = 62). Additionally, our WES study group was negative for complete AZF microdeletions of the Y-chromosome. The normospermia group included men with normal sperm parameters in accordance with the WHO-recommended reference limit. The pathozoospermia group included men with impaired semen quality, namely, with any combined parameters of sperm concentration <15 × 106/ml, and/or progressive motility <32%, and/or normal morphology <4%. The WES was performed for all 157 samples. Subsequent calling and filtering of variants were carried out according to the GATK Best Practices recommendations. On the genotyping stage, the samples were combined into four cohorts: three sets corresponded to three ethnic groups, and the fourth set contained all the 157 whole-exome samples. Association of the obtained polymorphisms with semen quality parameters was investigated using the χ2 test. To prioritize the obtained variants associated with pathozoospermia, their effects were determined using Ensembl Variant Effect Predictor. Moreover, polymorphisms located in genes expressed in the testis were revealed based on the genomic annotation. As a result, the nine potential SNP markers rs6971091, rs557806, rs610308, rs556052, rs1289658, rs278981, rs1129172, rs12268007, and rs17228441 were selected for subsequent verification on our previously collected population sample (about 1,500 males). The selected variants located in seven genes FAM71F1, PPP1R15A, TRIM45, PRAME, RBM47, WDFY4, and FSIP2 that are expressed in the testis and play an important role in cell proliferation, meiosis, and apoptosis.
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Affiliation(s)
- Semyon Kolmykov
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia.,Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia
| | - Gennady Vasiliev
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia
| | - Ludmila Osadchuk
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia
| | - Maxim Kleschev
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia
| | - Alexander Osadchuk
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia
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250
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Ausmees L, Talts M, Allik J, Vainik U, Sikka TT, Nikopensius T, Esko T, Realo A. Taking risks to feel excitement: Detailed personality profile and genetic associations. EUROPEAN JOURNAL OF PERSONALITY 2021. [DOI: 10.1177/08902070211019242] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This study mapped the personality and genetics of risky excitement-seekers focusing on skydiving behavior. We compared 298 skydivers to 298 demographically matched controls across the NEO Personality Inventory-3 domains, facets, and 240 items. The most significant item-level effects were aggregated into a poly-item score of skydiving-associated personality markers (Study 1), where higher scores describe individuals who enjoy risky situations but have no self-control issues. The skydiving-associated personality marker score was associated with greater physical activity, higher rate of traumatic injuries, and better mental health in a sample of 3558 adults (Study 2). From genetic perspective, we associated skydiving behavior with 19 candidate variants that have previously been linked to excitement-seeking (Study 1). Polymorphisms in the SERT gene were the strongest predictors of skydiving, but the false discovery rate-adjusted (FDR-adjusted) p-values were non-significant. In Study 2, we predicted the skydiving-associated personality marker score and E5: Excitement-seeking from multiple risk-taking polygenic scores, using publicly available summary data from genome-wide association studies. While E5: Excitement-seeking was most strongly predicted by general risk tolerance and risky behaviors’ polygenic scores, the skydiving-associated personality marker score was most strongly associated with the adventurousness polygenic scores. Phenotypic and polygenic scores associations suggest that skydiving is a specific—perhaps more functional—form of excitement-seeking, which may nevertheless lead to physical injuries.
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Affiliation(s)
- Liisi Ausmees
- Institute of Psychology, University of Tartu, Estonia
| | - Maie Talts
- Institute of Psychology, University of Tartu, Estonia
| | - Jüri Allik
- Institute of Psychology, University of Tartu, Estonia
- Estonian Academy of Sciences, Estonia
| | - Uku Vainik
- Institute of Psychology, University of Tartu, Estonia
- Montreal Neurological Institute, McGill University, Canada
| | | | | | - Tõnu Esko
- Institute of Genomics, University of Tartu, Estonia
| | - Anu Realo
- Institute of Psychology, University of Tartu, Estonia
- Department of Psychology, University of Warwick, UK
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