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Deng Q, Song C, Lin S. An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics. Eur J Hum Genet 2024; 32:681-690. [PMID: 37237036 PMCID: PMC11153499 DOI: 10.1038/s41431-023-01389-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 05/01/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. In contrast to individual-level data, GWAS summary statistics are usually publicly available, and thus methods using only summary statistics have greater usage. Although many methods have been developed for joint analysis of multiple traits using summary statistics, there are many issues, including inconsistent performance, computational inefficiency, and numerical problems when considering lots of traits. To address these challenges, we propose a multi-trait adaptive Fisher method for summary statistics (MTAFS), a computationally efficient method with robust power performance. We applied MTAFS to two sets of brain imaging derived phenotypes (IDPs) from the UK Biobank, including a set of 58 Volumetric IDPs and a set of 212 Area IDPs. Through annotation analysis, the underlying genes of the SNPs identified by MTAFS were found to exhibit higher expression and are significantly enriched in brain-related tissues. Together with results from a simulation study, MTAFS shows its advantage over existing multi-trait methods, with robust performance across a range of underlying settings. It controls type 1 error well and can efficiently handle a large number of traits.
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Affiliation(s)
- Qiaolan Deng
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
- Department of Statistics, College of Arts and Sciences, The Ohio State University, Columbus, OH, USA
| | - Chi Song
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Shili Lin
- Department of Statistics, College of Arts and Sciences, The Ohio State University, Columbus, OH, USA.
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2
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Chen Y, Zheng X, Liu C, Liu T, Lin S, Xie H, Zhang H, Shi J, Liu X, Bu Z, Guo S, Huang Z, Deng L, Shi H. Anthropometrics and cancer prognosis: a multicenter cohort study. Am J Clin Nutr 2024:S0002-9165(24)00481-7. [PMID: 38763424 DOI: 10.1016/j.ajcnut.2024.05.016] [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: 12/24/2023] [Revised: 03/21/2024] [Accepted: 05/01/2024] [Indexed: 05/21/2024] Open
Abstract
BACKGROUND Anthropometric indicators have been shown to be associated with the prognosis of patients with cancer. However, any single anthropometric index has limitation in predicting the prognosis. OBJECTIVES This study aimed to observe the predictive role of 7 anthropometric indicators based on body size on the prognosis of patients with cancer. METHODS A principal component analysis (PCA) on 7 anthropometric measurements: height, weight, BMI, hand grip strength (HGS), triceps skinfold thickness (TSF), mid-upper arm circumference (MAC), and calf circumference (CAC) was conducted. Principal components (PCs) were derived from this analysis. Cox regression analysis was used to investigate the association between the prognosis of patients with cancer and the PCs. Subgroups and sensitivity analyses were also conducted. RESULTS Through PCA, 4 distinct PCs were identified, collectively explaining 88.3% of the variance. PC1, primarily characterized by general obesity, exhibited a significant inverse association with risk of cancer-related death (adjusted hazard ratio [HR]: 0.86; 95% confidence interval [CI]: 0.83, 0.88). PC2 (short stature with high TSF) was not significantly associated with cancer prognosis. PC3 (high BMI coupled with low HGS) demonstrated a significant increase with risk of cancer-related death (adjusted HR: 1.08; 95% CI: 1.05, 1.11). PC4 (tall stature with high TSF) exhibited a significant association with increased cancer risk (adjusted HR: 1.05; 95% CI: 1.02, 1.07). These associations varied across different cancer stages. The stability of the results was confirmed through sensitivity analyses. CONCLUSIONS Different body sizes are associated with distinct prognostic outcomes in patients with cancer. The impact of BMI on prognosis is influenced by both HGS and subcutaneous fat. This finding may influence the clinical care of cancer and improve the survival of cancer patients.
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Affiliation(s)
- Yue Chen
- The Second Clinical Medical College of Wenzhou Medical University, Wenzhou, Zhejiang, China; Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Xin Zheng
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Chenan Liu
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Tong Liu
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Shiqi Lin
- The Second Clinical Medical College of Wenzhou Medical University, Wenzhou, Zhejiang, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Hailun Xie
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Heyang Zhang
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Jinyu Shi
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Xiaoyue Liu
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Zhaoting Bu
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Shubin Guo
- Emergency Medicine Clinical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Fengtai, China
| | - Zhenghui Huang
- Emergency Medicine Clinical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Fengtai, China
| | - Li Deng
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China.
| | - Hanping Shi
- The Second Clinical Medical College of Wenzhou Medical University, Wenzhou, Zhejiang, China; Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China.
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3
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Cao X, Zhang S, Sha Q. A novel method for multiple phenotype association studies based on genotype and phenotype network. PLoS Genet 2024; 20:e1011245. [PMID: 38728360 PMCID: PMC11111089 DOI: 10.1371/journal.pgen.1011245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 05/22/2024] [Accepted: 03/29/2024] [Indexed: 05/12/2024] Open
Abstract
Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.
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Affiliation(s)
- Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
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4
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Bass AJ, Bian S, Wingo AP, Wingo TS, Cutler DJ, Epstein MP. Identifying latent genetic interactions in genome-wide association studies using multiple traits. Genome Med 2024; 16:62. [PMID: 38664839 PMCID: PMC11044415 DOI: 10.1186/s13073-024-01329-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).
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Affiliation(s)
- Andrew J Bass
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA.
| | - Shijia Bian
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA
| | - Aliza P Wingo
- Department of Psychiatry, Emory University, Atlanta, GA, 30322, USA
| | - Thomas S Wingo
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
- Department of Neurology, Emory University, Atlanta, GA, 30322, USA
| | - David J Cutler
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
| | - Michael P Epstein
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA.
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5
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Rabe F, Smigielski L, Georgiadis F, Kallen N, Omlor W, Kirschner M, Cathomas F, Grünblatt E, Silverstein S, Blose B, Barthelmes D, Schaal K, Rubio J, Lencz T, Homan P. Genetic susceptibility to schizophrenia through neuroinflammatory pathways is associated with retinal thinning: Findings from the UK-Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.05.24305387. [PMID: 38633770 PMCID: PMC11023639 DOI: 10.1101/2024.04.05.24305387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
The human retina is part of the central nervous system and can be easily and non-invasively imaged with optical coherence tomography. While imaging the retina may provide insights on central nervous system-related disorders such as schizophrenia, a typical challenge are confounders often present in schizophrenia which may negatively impact retinal health. Here, we therefore aimed to investigate retinal changes in the context of common genetic variations conveying a risk of schizophrenia as measured by polygenic risk scores. We used population data from the UK Biobank, including White British and Irish individuals without diagnosed schizophrenia, and estimated a polygenic risk score for schizophrenia based on the newest genome-wide association study (PGC release 2022). We hypothesized that greater genetic susceptibility to schizophrenia is associated with retinal thinning, especially within the macula. To gain additional mechanistic insights, we conducted pathway-specific polygenic risk score associations analyses, focusing on gene pathways that are related to schizophrenia. Of 65484 individuals recruited, 48208 participants with available matching imaging-genetic data were included in the analysis of whom 22427 (53.48%) were female and 25781 (46.52%) were male. Our robust principal component regression results showed that polygenic risk scores for schizophrenia were associated with retinal thinning while controlling for confounding factors (b = -0.03, p = 0.007, pFWER = 0.01). Similarly, we found that polygenic risk for schizophrenia specific to neuroinflammation gene sets revealed significant associations with retinal thinning (b = -0.03, self-contained p = 0.041 (reflecting the level of association), competitive p = 0.05 (reflecting the level of enrichment)). These results go beyond previous studies suggesting a relationship between manifested schizophrenia and retinal phenotypes. They indicate that the retina is a mirror reflecting the genetic complexities of schizophrenia and that alterations observed in the retina of individuals with schizophrenia may be connected to an inherent genetic predisposition to neurodegenerative aspects of the condition. These associations also suggest the potential involvement of the neuroinflammatory pathway, with indications of genetic overlap with specific retinal phenotypes. The findings further indicate that this gene pathway in individuals with a high polygenic risk for schizophrenia could contribute through acute-phase proteins to structural changes in the retina.
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Affiliation(s)
- Finn Rabe
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Lukasz Smigielski
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland
| | - Foivos Georgiadis
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Nils Kallen
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Wolfgang Omlor
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Matthias Kirschner
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Flurin Cathomas
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Edna Grünblatt
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
- Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland
| | - Steven Silverstein
- Department of Psychiatry, University of Rochester Medical Center, Rochester, New York, USA
- Department of Ophthalmology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
- Center for Visual Science, University of Rochester, Rochester, New York, USA
| | - Brittany Blose
- Department of Psychiatry, University of Rochester Medical Center, Rochester, New York, USA
- Department of Ophthalmology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
- Center for Visual Science, University of Rochester, Rochester, New York, USA
| | - Daniel Barthelmes
- Department of Ophthalmology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Karen Schaal
- Department of Ophthalmology, Inselspital University Hospital Bern, Bern, Switzerland
| | - Jose Rubio
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Philipp Homan
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
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6
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Wang L, Babushkin N, Liu Z, Liu X. Trans-eQTL mapping in gene sets identifies network effects of genetic variants. CELL GENOMICS 2024; 4:100538. [PMID: 38565144 PMCID: PMC11019359 DOI: 10.1016/j.xgen.2024.100538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 12/08/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.
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Affiliation(s)
- Lili Wang
- The Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Nikita Babushkin
- Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Zhonghua Liu
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
| | - Xuanyao Liu
- The Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
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7
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Zhou Y, Cosentino J, Yun T, Biradar MI, Shreibati J, Lai D, Schwantes-An TH, Luben R, McCaw Z, Engmann J, Providencia R, Schmidt AF, Munroe P, Yang H, Carroll A, Khawaja AP, McLean CY, Behsaz B, Hormozdiari F. Utilizing multimodal AI to improve genetic analyses of cardiovascular traits. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304547. [PMID: 38562791 PMCID: PMC10984061 DOI: 10.1101/2024.03.19.24304547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Electronic health records, biobanks, and wearable biosensors contain multiple high-dimensional clinical data (HDCD) modalities (e.g., ECG, Photoplethysmography (PPG), and MRI) for each individual. Access to multimodal HDCD provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a novel multimodal deep learning method, M-REGLE, for discovering genetic associations from a joint representation of multiple complementary HDCD modalities. We showcase the effectiveness of this model by applying it to several cardiovascular modalities. M-REGLE jointly learns a lower representation (i.e., latent factors) of multimodal HDCD using a convolutional variational autoencoder, performs genome wide association studies (GWAS) on each latent factor, then combines the results to study the genetics of the underlying system. To validate the advantages of M-REGLE and multimodal learning, we apply it to common cardiovascular modalities (PPG and ECG), and compare its results to unimodal learning methods in which representations are learned from each data modality separately, but the downstream genetic analyses are performed on the combined unimodal representations. M-REGLE identifies 19.3% more loci on the 12-lead ECG dataset, 13.0% more loci on the ECG lead I + PPG dataset, and its genetic risk score significantly outperforms the unimodal risk score at predicting cardiac phenotypes, such as atrial fibrillation (Afib), in multiple biobanks.
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Affiliation(s)
| | | | | | - Mahantesh I Biradar
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | | | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Tae-Hwi Schwantes-An
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Robert Luben
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | - Zachary McCaw
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jorgen Engmann
- Center for Translational Genomics, Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, UK
| | - Rui Providencia
- Institute of Health Informatics Research, University College London, London, UK
- Electrophysiology Department, Barts Heart Centre, St. Bartholomew's Hospital, London, UK
| | - Amand Floriaan Schmidt
- Department of Cardiology; Amsterdam University Medical Centres, Amsterdam, The Netherlands
- Institute of Cardiovascular Science; University College London, London, UK
- Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, Netherlands
| | - Patricia Munroe
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Howard Yang
- Google Research, San Francisco CA, 94105 USA
| | | | - Anthony P Khawaja
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
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8
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Xie H, Cao X, Zhang S, Sha Q. Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer network. Bioinformatics 2023; 39:btad707. [PMID: 37991852 PMCID: PMC10697735 DOI: 10.1093/bioinformatics/btad707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 09/29/2023] [Accepted: 11/21/2023] [Indexed: 11/24/2023] Open
Abstract
MOTIVATION Genome-wide association studies is an essential tool for analyzing associations between phenotypes and single nucleotide polymorphisms (SNPs). Most of binary phenotypes in large biobanks are extremely unbalanced, which leads to inflated type I error rates for many widely used association tests for joint analysis of multiple phenotypes. In this article, we first propose a novel method to construct a Multi-Layer Network (MLN) using individuals with at least one case status among all phenotypes. Then, we introduce a computationally efficient community detection method to group phenotypes into disjoint clusters based on the MLN. Finally, we propose a novel approach, MLN with Omnibus (MLN-O), to jointly analyse the association between phenotypes and a SNP. MLN-O uses the score test to test the association of each merged phenotype in a cluster and a SNP, then uses the Omnibus test to obtain an overall test statistic to test the association between all phenotypes and a SNP. RESULTS We conduct extensive simulation studies to reveal that the proposed approach can control type I error rates and is more powerful than some existing methods. Meanwhile, we apply the proposed method to a real data set in the UK Biobank. Using phenotypes in Chapter XIII (Diseases of the musculoskeletal system and connective tissue) in the UK Biobank, we find that MLN-O identifies more significant SNPs than other methods we compare with. AVAILABILITY AND IMPLEMENTATION https://github.com/Hongjing-Xie/Multi-Layer-Network-with-Omnibus-MLN-O.
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Affiliation(s)
- Hongjing Xie
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, United States
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, United States
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, United States
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, United States
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9
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Chen S, Lin Z, Shen X, Li L, Pan W. Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data. Genet Epidemiol 2023; 47:585-599. [PMID: 37573486 PMCID: PMC10840616 DOI: 10.1002/gepi.22535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 06/19/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023]
Abstract
We propose structural equation models (SEMs) as a general framework to infer causal networks for metabolites and other complex traits. Traditionally SEMs are used only for individual-level data under the assumption that all instrumental variables (IVs) are valid. To overcome these limitations, we propose both one- and two-sample approaches for causal network inference based on SEMs that can: (1) perform causal analysis and discover causal relationships among multiple traits; (2) account for the possible presence of some invalid IVs; (3) allow for data analysis using only genome-wide association studies (GWAS) summary statistics when individual-level data are not available; (4) consider the possibility of bidirectional relationships between traits. Our method employs a simple stepwise selection to identify invalid IVs, thus avoiding false positives while possibly increasing true discoveries based on two-stage least squares (2SLS). We use both real GWAS data and simulated data to demonstrate the superior performance of our method over the standard 2SLS/SEMs. For real data analysis, our proposed approach is applied to a human blood metabolite GWAS summary data set to uncover putative causal relationships among the metabolites; we also identify some metabolites (putative) causal to Alzheimer's disease (AD), which, along with the inferred causal metabolite network, suggest some possible pathways of metabolites involved in AD.
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Affiliation(s)
- Siyi Chen
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455
| | - Zhaotong Lin
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, MN 55455
| | - Ling Li
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455
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Liang J, Pang L, Yang C, Long J, Liao Q, Tang P, Huang H, Wei H, Chen Q, Yang K, Liu T, Lv F, Liu S, Huang D, Qiu X. Effects of prenatal single and mixed bisphenol exposure on bone mineral density in preschool children: A population-based prospective cohort study. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 267:115665. [PMID: 37951091 DOI: 10.1016/j.ecoenv.2023.115665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/22/2023] [Accepted: 11/03/2023] [Indexed: 11/13/2023]
Abstract
Exposure to bisphenols can affect bone mineral density (BMD) in animals and humans. However, the effects of maternal exposure to bisphenols during pregnancy on bone health in preschool children remain unknown. We aimed to assess the effects of prenatal exposure to single and multiple bisphenols on bone health in preschool children. A total of 230 mother-child pairs were included in this study. Generalized linear regression, restricted cubic spline (RCS), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR) were utilized to assess the relationship between bisphenol levels and bone health in preschool children. Each natural log (Ln) unit increase in tetrabromobisphenol A was related to a 0.007 m/s (95 % CI: -0.015, 0.000) decrease in Ln-transformed speed of sound (SOS) among girls. Decreased BMD Z scores in preschool children were found only in the high bisphenol S exposure group (β = -0.568; 95 % CI: -1.087, -0.050) in boys. The risk of low BMD (BMDL) was significantly higher in the middle-exposure group (OR = 4.695; 95 % CI: 1.143, 24.381) and high-exposure group of BPS (OR = 6.165, 95 % CI: 1.445, 33.789) compared with the low-exposure group in boys. In girls, the risk of BMDL decreased with increasing bisphenol A concentration (OR = 0.413, 95 % CI: 0.215, 0.721). RCS analysis revealed a U-shaped nonlinear correlation between BPB concentration and BMDL in girls (P-overall = 0.011, P-nonlinear = 0.009). In PCA, a U-shaped dose-response relationship was found between PC2 and the risk of BMDL (P-overall = 0.048, P-nonlinear = 0.032), and a significant association was only noted in girls when stratified by sex. The BKMR model revealed a horizontal S-shaped curve relationship between bisphenol mixtures and BMDL in girls. The results indicated that prenatal exposure to single and mixed bisphenols can affect BMD in preschool children, exerting nonmonotonic and child sex-specific effects.
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Affiliation(s)
- Jun Liang
- Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Lixiang Pang
- Department of Sanitary Chemistry, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Chunxiu Yang
- The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi 530021, China
| | - Jinghua Long
- Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China; The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Qian Liao
- Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Peng Tang
- Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Huishen Huang
- Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Huanni Wei
- Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou 545006, China
| | - Qian Chen
- Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Kaiqi Yang
- Department of Sanitary Chemistry, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Tao Liu
- Huaihua Center for Disease Control and Prevention, Huaihua, Hunan 418000, China
| | - Fangfang Lv
- Department of Child and Adolescent Health & Maternal and Child Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Shun Liu
- Department of Child and Adolescent Health & Maternal and Child Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Dongping Huang
- Department of Sanitary Chemistry, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China.
| | - Xiaoqiang Qiu
- Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China.
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11
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St-Pierre J, Oualkacha K. A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes. Int J Biostat 2023; 19:369-387. [PMID: 36279152 PMCID: PMC10644254 DOI: 10.1515/ijb-2022-0010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 05/26/2022] [Accepted: 08/23/2022] [Indexed: 11/15/2022]
Abstract
In genome wide association studies (GWAS), researchers are often dealing with dichotomous and non-normally distributed traits, or a mixture of discrete-continuous traits. However, most of the current region-based methods rely on multivariate linear mixed models (mvLMMs) and assume a multivariate normal distribution for the phenotypes of interest. Hence, these methods are not applicable to disease or non-normally distributed traits. Therefore, there is a need to develop unified and flexible methods to study association between a set of (possibly rare) genetic variants and non-normal multivariate phenotypes. Copulas are multivariate distribution functions with uniform margins on the [0, 1] interval and they provide suitable models to deal with non-normality of errors in multivariate association studies. We propose a novel unified and flexible copula-based multivariate association test (CBMAT) for discovering association between a genetic region and a bivariate continuous, binary or mixed phenotype. We also derive a data-driven analytic p-value procedure of the proposed region-based score-type test. Through simulation studies, we demonstrate that CBMAT has well controlled type I error rates and higher power to detect associations compared with other existing methods, for discrete and non-normally distributed traits. At last, we apply CBMAT to detect the association between two genes located on chromosome 11 and several lipid levels measured on 1477 subjects from the ASLPAC study.
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Affiliation(s)
- Julien St-Pierre
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Karim Oualkacha
- Département de Mathématiques, Université du Québec à Montréal, Montreal, QC, Canada
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12
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Neumann A, Ohlei O, Küçükali F, Bos IJ, Timsina J, Vos S, Prokopenko D, Tijms BM, Andreasson U, Blennow K, Vandenberghe R, Scheltens P, Teunissen CE, Engelborghs S, Frisoni GB, Blin O, Richardson JC, Bordet R, Lleó A, Alcolea D, Popp J, Marsh TW, Gorijala P, Clark C, Peyratout G, Martinez-Lage P, Tainta M, Dobson RJB, Legido-Quigley C, Van Broeckhoven C, Tanzi RE, Ten Kate M, Lill CM, Barkhof F, Cruchaga C, Lovestone S, Streffer J, Zetterberg H, Visser PJ, Sleegers K, Bertram L. Multivariate GWAS of Alzheimer's disease CSF biomarker profiles implies GRIN2D in synaptic functioning. Genome Med 2023; 15:79. [PMID: 37794492 PMCID: PMC10548686 DOI: 10.1186/s13073-023-01233-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/12/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) of Alzheimer's disease (AD) have identified several risk loci, but many remain unknown. Cerebrospinal fluid (CSF) biomarkers may aid in gene discovery and we previously demonstrated that six CSF biomarkers (β-amyloid, total/phosphorylated tau, NfL, YKL-40, and neurogranin) cluster into five principal components (PC), each representing statistically independent biological processes. Here, we aimed to (1) identify common genetic variants associated with these CSF profiles, (2) assess the role of associated variants in AD pathophysiology, and (3) explore potential sex differences. METHODS We performed GWAS for each of the five biomarker PCs in two multi-center studies (EMIF-AD and ADNI). In total, 973 participants (n = 205 controls, n = 546 mild cognitive impairment, n = 222 AD) were analyzed for 7,433,949 common SNPs and 19,511 protein-coding genes. Structural equation models tested whether biomarker PCs mediate genetic risk effects on AD, and stratified and interaction models probed for sex-specific effects. RESULTS Five loci showed genome-wide significant association with CSF profiles, two were novel (rs145791381 [inflammation] and GRIN2D [synaptic functioning]) and three were previously described (APOE, TMEM106B, and CHI3L1). Follow-up analyses of the two novel signals in independent datasets only supported the GRIN2D locus, which contains several functionally interesting candidate genes. Mediation tests indicated that variants in APOE are associated with AD status via processes related to amyloid and tau pathology, while markers in TMEM106B and CHI3L1 are associated with AD only via neuronal injury/inflammation. Additionally, seven loci showed sex-specific associations with AD biomarkers. CONCLUSIONS These results suggest that pathway and sex-specific analyses can improve our understanding of AD genetics and may contribute to precision medicine.
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Affiliation(s)
- Alexander Neumann
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Olena Ohlei
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, V50.2M, Lübeck, 23562, Germany
| | - Fahri Küçükali
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Isabelle J Bos
- Netherlands Institute for Health Services Research, Utrecht, Netherlands
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
| | - Stephanie Vos
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Ulf Andreasson
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Neurology Service, University Hospital Leuven, Leuven, Belgium
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Memory Clinic, Universitair Ziekenhuis Brussel (UZ Brussel) and Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Giovanni B Frisoni
- Memory Center, Department of Rehabilitation and Geriatrics, Geneva University and University Hospitals, Geneva, Switzerland
| | - Oliver Blin
- Clinical Pharmacology & Pharmacovigilance Department, Marseille University Hospital, Marseille, France
| | | | - Régis Bordet
- Neuroscience & Cognition, CHU de Lille, University of Lille, Inserm, France
| | - Alberto Lleó
- Memory Unit, Neurology Department, Hospital de Sant Pau, Barcelona, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Daniel Alcolea
- Memory Unit, Neurology Department, Hospital de Sant Pau, Barcelona, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Julius Popp
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zurich, Switzerland
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Thomas W Marsh
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
- Division of Biology & Biomedical Sciences, Washington University in St. Louis, St Louis, MO, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
| | - Christopher Clark
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zurich, Switzerland
| | - Gwendoline Peyratout
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Pablo Martinez-Lage
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Mikel Tainta
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
- Zumarraga Hospital, Osakidetza, Integrated Health Organization (OSI) Goierri-Urola Garia, Basque Country, Spain
| | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, Boston, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Cristina Legido-Quigley
- Steno Diabetes Center, Copenhagen, Denmark
- Institute of Pharmaceutical Science, King's College London, London, UK
| | - Christine Van Broeckhoven
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neurodegenerative Brain Diseases Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Rudolph E Tanzi
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Mara Ten Kate
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands
| | - Christina M Lill
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, V50.2M, Lübeck, 23562, Germany
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College, London, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St Louis, MO, USA
| | - Simon Lovestone
- Janssen Medical Ltd, Wycombe, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Johannes Streffer
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- AC Immune SA, Lausanne, Switzerland
- Janssen R&D, LLC, Beerse, Belgium
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute, University College London, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Pieter Jelle Visser
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, Netherlands
| | - Kristel Sleegers
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, V50.2M, Lübeck, 23562, Germany.
- Centre for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway.
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13
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Bass AJ, Bian S, Wingo AP, Wingo TS, Cutler DJ, Epstein MP. Identifying latent genetic interactions in genome-wide association studies using multiple traits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557155. [PMID: 37745553 PMCID: PMC10515795 DOI: 10.1101/2023.09.11.557155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This 'missing' heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.
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Affiliation(s)
- Andrew J. Bass
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
| | - Shijia Bian
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
| | - Aliza P. Wingo
- Department of Psychiatry, Emory University, Atlanta, GA 30322, USA
| | - Thomas S. Wingo
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
- Department of Neurology, Emory University, Atlanta, GA 30322, USA
| | - David J. Cutler
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
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14
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Yun T, Cosentino J, Behsaz B, McCaw ZR, Hill D, Luben R, Lai D, Bates J, Yang H, Schwantes-An TH, Zhou Y, Khawaja AP, Carroll A, Hobbs BD, Cho MH, McLean CY, Hormozdiari F. Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.28.23289285. [PMID: 37163049 PMCID: PMC10168505 DOI: 10.1101/2023.04.28.23289285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
High-dimensional clinical data are becoming more accessible in biobank-scale datasets. However, effectively utilizing high-dimensional clinical data for genetic discovery remains challenging. Here we introduce a general deep learning-based framework, REpresentation learning for Genetic discovery on Low-dimensional Embeddings (REGLE), for discovering associations between genetic variants and high-dimensional clinical data. REGLE uses convolutional variational autoencoders to compute a non-linear, low-dimensional, disentangled embedding of the data with highly heritable individual components. REGLE can incorporate expert-defined or clinical features and provides a framework to create accurate disease-specific polygenic risk scores (PRS) in datasets which have minimal expert phenotyping. We apply REGLE to both respiratory and circulatory systems: spirograms which measure lung function and photoplethysmograms (PPG) which measure blood volume changes. Genome-wide association studies on REGLE embeddings identify more genome-wide significant loci than existing methods and replicate known loci for both spirograms and PPG, demonstrating the generality of the framework. Furthermore, these embeddings are associated with overall survival. Finally, we construct a set of PRSs that improve predictive performance of asthma, chronic obstructive pulmonary disease, hypertension, and systolic blood pressure in multiple biobanks. Thus, REGLE embeddings can quantify clinically relevant features that are not currently captured in a standardized or automated way.
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Affiliation(s)
| | | | | | | | - Davin Hill
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 94304, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Robert Luben
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - John Bates
- Verily Life Sciences, South San Francisco, CA 94080, USA
| | | | - Tae-Hwi Schwantes-An
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Division of Cardiology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | | | - Anthony P. Khawaja
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | | | - Brian D. Hobbs
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
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15
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Roth K, Pröll-Cornelissen MJ, Henne H, Appel AK, Schellander K, Tholen E, Große-Brinkhaus C. Multivariate genome-wide associations for immune traits in two maternal pig lines. BMC Genomics 2023; 24:492. [PMID: 37641029 PMCID: PMC10463314 DOI: 10.1186/s12864-023-09594-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/16/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Immune traits are considered to serve as potential biomarkers for pig's health. Medium to high heritabilities have been observed for some of the immune traits suggesting genetic variability of these phenotypes. Consideration of previously established genetic correlations between immune traits can be used to identify pleiotropic genetic markers. Therefore, genome-wide association study (GWAS) approaches are required to explore the joint genetic foundation for health biomarkers. Usually, GWAS explores phenotypes in a univariate (uv), trait-by-trait manner. Besides two uv GWAS methods, four multivariate (mv) GWAS approaches were applied on combinations out of 22 immune traits for Landrace (LR) and Large White (LW) pig lines. RESULTS In total 433 (LR: 351, LW: 82) associations were identified with the uv approach implemented in PLINK and a Bayesian linear regression uv approach (BIMBAM) software. Single Nucleotide Polymorphisms (SNPs) that were identified with both uv approaches (n = 32) were mostly associated with immune traits such as haptoglobin, red blood cell characteristics and cytokines, and were located in protein-coding genes. Mv GWAS approaches detected 647 associations for different mv immune trait combinations which were summarized to 133 Quantitative Trait Loci (QTL). SNPs for different trait combinations (n = 66) were detected with more than one mv method. Most of these SNPs are associated with red blood cell related immune trait combinations. Functional annotation of these QTL revealed 453 immune-relevant protein-coding genes. With uv methods shared markers were not observed between the breeds, whereas mv approaches were able to detect two conjoint SNPs for LR and LW. Due to unmapped positions for these markers, their functional annotation was not clarified. CONCLUSIONS This study evaluated the joint genetic background of immune traits in LR and LW piglets through the application of various uv and mv GWAS approaches. In comparison to uv methods, mv methodologies identified more significant associations, which might reflect the pleiotropic background of the immune system more accurately. In genetic research of complex traits, the SNP effects are generally small. Furthermore, one genetic variant can affect several correlated immune traits at the same time, termed pleiotropy. As mv GWAS methods consider strong dependencies among traits, the power to detect SNPs can be boosted. Both methods revealed immune-relevant potential candidate genes. Our results indicate that one single test is not able to detect all the different types of genetic effects in the most powerful manner and therefore, the methods should be applied complementary.
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Affiliation(s)
- Katharina Roth
- Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115, Bonn, Germany
| | | | - Hubert Henne
- BHZP GmbH, An der Wassermühle 8, 21368, Dahlenburg-Ellringen, Germany
| | | | - Karl Schellander
- Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115, Bonn, Germany
| | - Ernst Tholen
- Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115, Bonn, Germany
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16
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Zhang Y, Jiang X, Mentzer AJ, McVean G, Lunter G. Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank. CELL GENOMICS 2023; 3:100371. [PMID: 37601973 PMCID: PMC10435382 DOI: 10.1016/j.xgen.2023.100371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 05/04/2023] [Accepted: 07/07/2023] [Indexed: 08/22/2023]
Abstract
Many diseases show patterns of co-occurrence, possibly driven by systemic dysregulation of underlying processes affecting multiple traits. We have developed a method (treeLFA) for identifying such multimorbidities from routine health-care data, which combines topic modeling with an informative prior derived from medical ontology. We apply treeLFA to UK Biobank data and identify a variety of topics representing multimorbidity clusters, including a healthy topic. We find that loci identified using topic weights as traits in a genome-wide association study (GWAS) analysis, which we validated with a range of approaches, only partially overlap with loci from GWASs on constituent single diseases. We also show that treeLFA improves upon existing methods like latent Dirichlet allocation in various ways. Overall, our findings indicate that topic models can characterize multimorbidity patterns and that genetic analysis of these patterns can provide insight into the etiology of complex traits that cannot be determined from the analysis of constituent traits alone.
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Affiliation(s)
- Yidong Zhang
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100006, China
| | - Xilin Jiang
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge CB2 0SR, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge CB2 0BB, UK
| | - Alexander J. Mentzer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Gil McVean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Gerton Lunter
- MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen 9700 RB, the Netherlands
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17
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Asp ME, Thanh MTH, Dutta S, Comstock JA, Welch RD, Patteson AE. Mechanobiology as a tool for addressing the genotype-to-phenotype problem in microbiology. BIOPHYSICS REVIEWS 2023; 4:021304. [PMID: 38504926 PMCID: PMC10903382 DOI: 10.1063/5.0142121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 04/03/2023] [Indexed: 03/21/2024]
Abstract
The central hypothesis of the genotype-phenotype relationship is that the phenotype of a developing organism (i.e., its set of observable attributes) depends on its genome and the environment. However, as we learn more about the genetics and biochemistry of living systems, our understanding does not fully extend to the complex multiscale nature of how cells move, interact, and organize; this gap in understanding is referred to as the genotype-to-phenotype problem. The physics of soft matter sets the background on which living organisms evolved, and the cell environment is a strong determinant of cell phenotype. This inevitably leads to challenges as the full function of many genes, and the diversity of cellular behaviors cannot be assessed without wide screens of environmental conditions. Cellular mechanobiology is an emerging field that provides methodologies to understand how cells integrate chemical and physical environmental stress and signals, and how they are transduced to control cell function. Biofilm forming bacteria represent an attractive model because they are fast growing, genetically malleable and can display sophisticated self-organizing developmental behaviors similar to those found in higher organisms. Here, we propose mechanobiology as a new area of study in prokaryotic systems and describe its potential for unveiling new links between an organism's genome and phenome.
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18
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Ahn E, Prom LK, Magill C. Multi-Trait Genome-Wide Association Studies of Sorghum bicolor Regarding Resistance to Anthracnose, Downy Mildew, Grain Mold and Head Smut. Pathogens 2023; 12:779. [PMID: 37375469 DOI: 10.3390/pathogens12060779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/18/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
Multivariate linear mixed models (mvLMMs) are widely applied for genome-wide association studies (GWAS) to detect genetic variants affecting multiple traits with correlations and/or different plant growth stages. Subsets of multiple sorghum populations, including the Sorghum Association Panel (SAP), the Sorghum Mini Core Collection and the Senegalese sorghum population, have been screened against various sorghum diseases such as anthracnose, downy mildew, grain mold and head smut. Still, these studies were generally performed in a univariate framework. In this study, we performed GWAS based on the principal components of defense-related multi-traits against the fungal diseases, identifying new potential SNPs (S04_51771351, S02_66200847, S09_47938177, S08_7370058, S03_72625166, S07_17951013, S04_66666642 and S08_51886715) associated with sorghum's defense against these diseases.
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Affiliation(s)
- Ezekiel Ahn
- USDA-ARS Plant Science Research Unit, St. Paul, MN 55108, USA
| | - Louis K Prom
- USDA-ARS Southern Plains Agricultural Research Center, College Station, TX 77845, USA
| | - Clint Magill
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX 77843, USA
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19
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Skougaard M, Ditlev SB, Søndergaard MF, Kristensen LE. Cytokine Signatures in Psoriatic Arthritis Patients Indicate Different Phenotypic Traits Comparing Responders and Non-Responders of IL-17A and TNFα Inhibitors. Int J Mol Sci 2023; 24:ijms24076343. [PMID: 37047315 PMCID: PMC10093817 DOI: 10.3390/ijms24076343] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/22/2023] [Accepted: 03/24/2023] [Indexed: 03/30/2023] Open
Abstract
This study aimed to explore the dynamic interactions between 32 cytokines and biomarkers in Psoriatic Arthritis (PsA) patients to compare cytokine signatures of treatment responders and non-responders. Biomarkers were measured before and after four months of treatment in 39 PsA patients initiating either Tumor Necrosis Factor alpha inhibitor (TNFi) or Interleukin-17A inhibitor (IL-17Ai). Response to treatment was defined by the composite measure, Disease Activity in Psoriatic Arthritis (DAPSA). A two-component principal component analysis (PCA) was implemented to describe cytokine signatures comparing DAPSA50 responders and non-responders. The cytokine signature of TNFi responders was driven by the correlated cytokines interferon γ (IFNγ) and IL-6, additionally associated with IL-12/IL-23p40, TNFα, and CRP, while the cytokine signature of TNFi non-responders was driven by the correlated cytokines IL-15, IL-8, and IFNγ. IL-17Ai responders were characterized by contributions of strongly correlated Th17 inflammatory cytokines, IL-17A, IL-12/IL-23p40, IL-22 to the cytokine signature, whereas IL-17A and IL-12/IL-23p40 did not demonstrate significant contribution in IL-17Ai non-responders. Based on PCA results it was possible to differentiate DAPSA50 responders and non-responders to treatment, endorsing additional examination of cytokine interaction models in PsA patients and supporting further PsA patient immune stratification to improve individualized treatment of PsA patients.
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Affiliation(s)
- Marie Skougaard
- The Parker Institute, Bispebjerg and Frederiksberg Hospital, Nordre Fasanvej 57, 2000 Frederiksberg, Denmark
- Copenhagen Center for Translational Research, Bispebjerg and Frederiksberg Hospital, Bispebjerg Bakke 23, 2400 Copenhagen, Denmark
- Department of Clinical Immunology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus, Denmark
- Correspondence:
| | - Sisse Bolm Ditlev
- Copenhagen Center for Translational Research, Bispebjerg and Frederiksberg Hospital, Bispebjerg Bakke 23, 2400 Copenhagen, Denmark
| | - Magnus Friis Søndergaard
- Copenhagen Center for Translational Research, Bispebjerg and Frederiksberg Hospital, Bispebjerg Bakke 23, 2400 Copenhagen, Denmark
| | - Lars Erik Kristensen
- The Parker Institute, Bispebjerg and Frederiksberg Hospital, Nordre Fasanvej 57, 2000 Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, 2200 Copenhagen, Denmark
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20
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Xie H, Cao X, Zhang S, Sha Q. Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies. Genet Epidemiol 2023; 47:185-197. [PMID: 36691904 DOI: 10.1002/gepi.22513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 11/16/2022] [Accepted: 01/11/2023] [Indexed: 01/25/2023]
Abstract
In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In this research, we develop a method to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. We first group multiple phenotypes into different clusters based on a hierarchical clustering method, then we merge phenotypes in each cluster into a single phenotype. In each cluster, we use the saddlepoint approximation to estimate the p value of an association test between the merged phenotype and a single nucleotide polymorphism (SNP) which eliminates the issue of inflated type I error rate of the test for extremely unbalanced case-control phenotypes. Finally, we use the Cauchy combination method to obtain an integrated p value for all clusters to test the association between multiple phenotypes and a SNP. We use extensive simulation studies to evaluate the performance of the proposed approach. The results show that the proposed approach can control type I error rate very well and is more powerful than other available methods. We also apply the proposed approach to phenotypes in category IX (diseases of the circulatory system) in the UK Biobank. We find that the proposed approach can identify more significant SNPs than the other viable methods we compared with.
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Affiliation(s)
- Hongjing Xie
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
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21
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Reinert S. Quantitative genetics of pleiotropy and its potential for plant sciences. JOURNAL OF PLANT PHYSIOLOGY 2022; 276:153784. [PMID: 35944292 DOI: 10.1016/j.jplph.2022.153784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Stephan Reinert
- Friedrich-Alexander-University Erlangen-Nürnberg, Department of Biology, Division of Biochemistry, Biocomputing Lab, Staudtstraße 5, 91058, Erlangen, Germany.
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22
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Kirchler M, Konigorski S, Norden M, Meltendorf C, Kloft M, Schurmann C, Lippert C. transferGWAS: GWAS of images using deep transfer learning. Bioinformatics 2022; 38:3621-3628. [PMID: 35640976 DOI: 10.1093/bioinformatics/btac369] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 05/05/2022] [Accepted: 05/26/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Medical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations. RESULTS We validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases. AVAILABILITY AND IMPLEMENTATION Our method is implemented in Python and available at https://github.com/mkirchler/transferGWAS/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Matthias Kirchler
- Digital Health-Machine Learning Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.,Department of Computer Science, TU Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Stefan Konigorski
- Digital Health-Machine Learning Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Matthias Norden
- Digital Health & Personalized Medicine Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.,Department of Anesthesiology and Intensive Care Medicine, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Christian Meltendorf
- Department of Electrical Engineering - Mechatronics - Optometry, Beuth University of Applied Sciences Berlin, 13353 Berlin, Germany
| | - Marius Kloft
- Department of Computer Science, TU Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Claudia Schurmann
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Digital Health & Personalized Medicine Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany
| | - Christoph Lippert
- Digital Health-Machine Learning Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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23
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Lyu C, Huang M, Liu N, Chen Z, Lupo PJ, Tycko B, Witte JS, Hobbs CA, Li M. Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci. Bioinformatics 2022; 38:3853-3862. [PMID: 35781319 PMCID: PMC9364381 DOI: 10.1093/bioinformatics/btac443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION CpG sites within the same genomic region often share similar methylation patterns and tend to be co-regulated by multiple genetic variants that may interact with one another. RESULTS We propose a multi-trait methylation random field (multi-MRF) method to evaluate the joint association between a set of CpG sites and a set of genetic variants. The proposed method has several advantages. First, it is a multi-trait method that allows flexible correlation structures between neighboring CpG sites (e.g. distance-based correlation). Second, it is also a multi-locus method that integrates the effect of multiple common and rare genetic variants. Third, it models the methylation traits with a beta distribution to characterize their bimodal and interval properties. Through simulations, we demonstrated that the proposed method had improved power over some existing methods under various disease scenarios. We further illustrated the proposed method via an application to a study of congenital heart defects (CHDs) with 83 cardiac tissue samples. Our results suggested that gene BACE2, a methylation quantitative trait locus (QTL) candidate, colocalized with expression QTLs in artery tibial and harbored genetic variants with nominal significant associations in two genome-wide association studies of CHD. AVAILABILITY AND IMPLEMENTATION https://github.com/chenlyu2656/Multi-MRF. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chen Lyu
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN 47405, USA,Department of Population Health, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Manyan Huang
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN 47405, USA
| | - Nianjun Liu
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN 47405, USA
| | - Zhongxue Chen
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN 47405, USA
| | - Philip J Lupo
- Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Benjamin Tycko
- Center for Discovery and Innovation, Nutley, NJ 07110, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA,Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA
| | - Charlotte A Hobbs
- Rady Children’s Institute for Genomic Medicine, San Diego, CA 92123, USA
| | - Ming Li
- To whom correspondence should be addressed.
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24
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Higgins-Chen AT, Thrush KL, Wang Y, Minteer CJ, Kuo PL, Wang M, Niimi P, Sturm G, Lin J, Moore AZ, Bandinelli S, Vinkers CH, Vermetten E, Rutten BPF, Geuze E, Okhuijsen-Pfeifer C, van der Horst MZ, Schreiter S, Gutwinski S, Luykx JJ, Picard M, Ferrucci L, Crimmins EM, Boks MP, Hägg S, Hu-Seliger TT, Levine ME. A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking. NATURE AGING 2022; 2:644-661. [PMID: 36277076 PMCID: PMC9586209 DOI: 10.1038/s43587-022-00248-2] [Citation(s) in RCA: 89] [Impact Index Per Article: 44.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 06/08/2022] [Indexed: 01/09/2023]
Abstract
Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data, but this data can be surprisingly unreliable. Here we show technical noise produces deviations up to 9 years between replicates for six prominent epigenetic clocks, limiting their utility. We present a computational solution to bolster reliability, calculating principal components from CpG-level data as input for biological age prediction. Our retrained principal-component versions of six clocks show agreement between most replicates within 1.5 years, improved detection of clock associations and intervention effects, and reliable longitudinal trajectories in vivo and in vitro. This method entails only one additional step compared to traditional clocks, requires no replicates or prior knowledge of CpG reliabilities for training, and can be applied to any existing or future epigenetic biomarker. The high reliability of principal component-based clocks is critical for applications to personalized medicine, longitudinal tracking, in vitro studies, and clinical trials of aging interventions.
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Affiliation(s)
- Albert T Higgins-Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Kyra L Thrush
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Yunzhang Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Pei-Lun Kuo
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Meng Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Peter Niimi
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Gabriel Sturm
- Departments of Psychiatry and Neurology, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, United States
- New York State Psychiatric Institute, New York, NY United States
| | - Jue Lin
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, United States
| | - Ann Zenobia Moore
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | | | - Christiaan H Vinkers
- Department of Psychiatry, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Eric Vermetten
- Department Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - Bart P F Rutten
- School for Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Elbert Geuze
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
- Brain Research & Innovation Centre, Ministry of Defence, Utrecht, the Netherlands
| | - Cynthia Okhuijsen-Pfeifer
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Marte Z van der Horst
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
- Second Opinion Outpatient Clinic, GGNet Mental Health, Warnsveld, The Netherlands
| | - Stefanie Schreiter
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Stefan Gutwinski
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Jurjen J Luykx
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
- Second Opinion Outpatient Clinic, GGNet Mental Health, Warnsveld, The Netherlands
| | - Martin Picard
- Departments of Psychiatry and Neurology, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, United States
- New York State Psychiatric Institute, New York, NY United States
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Eileen M Crimmins
- Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Marco P Boks
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Morgan E Levine
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
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25
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Vernet R, Matran R, Zerimech F, Madore AM, Lavoie ME, Gagnon PA, Mohamdi H, Margaritte-Jeannin P, Siroux V, Dizier MH, Demenais F, Laprise C, Nadif R, Bouzigon E. Identification of novel genes influencing eosinophil-specific protein levels in asthma families. J Allergy Clin Immunol 2022; 150:1168-1177. [PMID: 35671886 DOI: 10.1016/j.jaci.2022.05.017] [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: 10/27/2021] [Revised: 05/19/2022] [Accepted: 05/25/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Eosinophils play a key role in the asthma allergic response by releasing cytotoxic molecules such as eosinophil cationic protein (ECP) and eosinophil-derived neurotoxin (EDN) that generate epithelium damages. OBJECTIVE To identify genetic variants influencing ECP and EDN levels in asthma-ascertained families. METHODS We performed univariate and bivariate genome-wide association analyses of ECP and EDN levels in 1,018 subjects from EGEA study with follow-up in 153 subjects from SLSJ study and combined the results of these two studies through meta-analysis. We then conducted Bayesian statistical fine-mapping together with quantitative trait locus and functional annotation analyses to identify the most likely functional genetic variants and candidate genes. RESULTS We identified five genome-wide significant loci (P<5x10-8) including seven distinct signals associated with ECP and/or EDN levels. The genes targeted by our fine-mapping and functional search include RNASE2 and RNASE3 (14q11) which encode EDN and ECP respectively and four other genes which regulate ECP/EDN levels. These four genes were the following: JAK1 (1p31) a transcription factor with a key role in the immune response and a potential therapeutic target for eosinophilic asthma, ARHGAP25 (2p13) involved in leukocyte recruitment to inflammatory sites, NDUFA4 (7p21) encoding a component of the mitochondrial respiratory chain and involved in cellular response to stress and CTSL (9q22) involved in immune response, extra-cellular remodeling and allergic inflammation. CONCLUSION This study demonstrates that the analysis of specific phenotypes produced by eosinophils allows identifying genes with a major role in allergic response and inflammation and offering potential therapeutic targets for asthma.
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Affiliation(s)
- Raphaël Vernet
- Université Paris Cité, INSERM, UMR 1124, Group of Genomic Epidemiology and Multifactorial Diseases, Paris, France
| | - Régis Matran
- Université Lille, CHU Lille, Institut Pasteur de Lille, ULR 4483, F-59000 Lille, France
| | - Farid Zerimech
- Université Lille, CHU Lille, Institut Pasteur de Lille, EA 4483 - IMPECS, F-59000 Lille, France
| | - Anne-Marie Madore
- Basic Sciences department, Université du Québec à Chicoutimi, Saguenay, Québec, Canada, Centre intersectoriel en santé durable, Université du Québec à Chicoutimi, Saguenay, Québec, Canada
| | - Marie-Eve Lavoie
- Basic Sciences department, Université du Québec à Chicoutimi, Saguenay, Québec, Canada, Centre intersectoriel en santé durable, Université du Québec à Chicoutimi, Saguenay, Québec, Canada
| | - Pierre-Alexandre Gagnon
- Basic Sciences department, Université du Québec à Chicoutimi, Saguenay, Québec, Canada, Centre intersectoriel en santé durable, Université du Québec à Chicoutimi, Saguenay, Québec, Canada
| | - Hamida Mohamdi
- Université Paris Cité, INSERM, UMR 1124, Group of Genomic Epidemiology and Multifactorial Diseases, Paris, France
| | - Patricia Margaritte-Jeannin
- Université Paris Cité, INSERM, UMR 1124, Group of Genomic Epidemiology and Multifactorial Diseases, Paris, France
| | - Valérie Siroux
- Inserm, Université Grenoble Alpes, CNRS, IAB, Team of Environmental Epidemiology Applied to the Development and Respiratory Health, Grenoble, France
| | - Marie-Hélène Dizier
- Université Paris Cité, INSERM, UMR 1124, Group of Genomic Epidemiology and Multifactorial Diseases, Paris, France
| | - Florence Demenais
- Université Paris Cité, INSERM, UMR 1124, Group of Genomic Epidemiology and Multifactorial Diseases, Paris, France
| | - Catherine Laprise
- Basic Sciences department, Université du Québec à Chicoutimi, Saguenay, Québec, Canada, Centre intersectoriel en santé durable, Université du Québec à Chicoutimi, Saguenay, Québec, Canada
| | - Rachel Nadif
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, Equipe d'Epidémiologie Respiratoire Intégrative, CESP, 94807, Villejuif, France
| | - Emmanuelle Bouzigon
- Université Paris Cité, INSERM, UMR 1124, Group of Genomic Epidemiology and Multifactorial Diseases, Paris, France.
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26
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Nakabuye M, Kamiza AB, Soremekun O, Machipisa T, Cohen E, Pirie F, Nashiru O, Young E, Sandhu MS, Motala AA, Chikowore T, Fatumo S. Genetic loci implicated in meta-analysis of body shape in Africans. Nutr Metab Cardiovasc Dis 2022; 32:1511-1518. [PMID: 35461751 DOI: 10.1016/j.numecd.2022.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 02/02/2022] [Accepted: 03/07/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND AND AIMS Obesity is one of the leading causes of non-communicable diseases (NCD). Thus, NCD risk varies in obese individuals based on the location of their fat depots; while subcutaneous adiposity is protective, visceral adiposity increases NCD risk. Although, previously anthropometric traits have been used to quantify body shape in low-income settings, there is no consensus on how it should be assessed. Hence, there is a growing interest to evaluate body shape derived from the principal component analysis (PCA) of anthropometric traits; however, this is yet to be explored in individuals of African ancestry whose body shape is different from those of Europeans. We set out to capture body shape in its multidimensional structure and examine the association between genetic variants and body shape in individuals of African ancestry. METHOD AND RESULTS We performed a genome-wide association study (GWAS) for body shape derived from PCA analysis of anthropometric traits in the Ugandan General Population Cohort (GPC, n = 6407) and the South African Zulu Cohort (SZC, n = 2595), followed by a GWAS meta-analysis to assess the genetic variants associated with body shape. We identified variants in FGF12, GRM8, TLX1NB and TRAP1 to be associated with body shape. These genes were different from the genes been associated with BMI, height, weight, WC and waist-hip ration in continental Africans. Notably, we also observed that a standard deviation change in body shape was associated with an increase in blood pressure and blood lipids. CONCLUSION Variants associated with body shape, as a composite variable might be different for those of individual anthropometric traits. Larger studies are required to further explore these phenomena.
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Affiliation(s)
- Mariam Nakabuye
- The African Computational Genomics (TACG) Research Group, Medical Research Council/Uganda Virus Research Institute (MRC/UVRI) and London School of Hygiene and Tropical Medicine (LSHTM), Entebbe, Uganda
| | - Abram Bunya Kamiza
- The African Computational Genomics (TACG) Research Group, Medical Research Council/Uganda Virus Research Institute (MRC/UVRI) and London School of Hygiene and Tropical Medicine (LSHTM), Entebbe, Uganda; Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Opeyemi Soremekun
- The African Computational Genomics (TACG) Research Group, Medical Research Council/Uganda Virus Research Institute (MRC/UVRI) and London School of Hygiene and Tropical Medicine (LSHTM), Entebbe, Uganda
| | - Tafadzwa Machipisa
- Department of Medicine, University of Cape Town & Groote Schuur Hospital, Cape Town, South Africa; Population Health Research Institute (PHRI) & the Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, 1280 Main Street West, Hamilton ON L8S 4K1, Canada
| | - Emmanuel Cohen
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; CNRS, UMR 7206 « Eco-anthropologie », Muséum National d'Histoire Naturelle, France
| | - Fraser Pirie
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, 4013 South Africa
| | - Oyekanmi Nashiru
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | | | - Manjinder S Sandhu
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom
| | - Ayesha A Motala
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, 4013 South Africa
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, Medical Research Council/Uganda Virus Research Institute (MRC/UVRI) and London School of Hygiene and Tropical Medicine (LSHTM), Entebbe, Uganda; H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria; Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine (LSHTM), United Kingdom.
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27
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Fan CC, Loughnan R, Makowski C, Pecheva D, Chen CH, Hagler DJ, Thompson WK, Parker N, van der Meer D, Frei O, Andreassen OA, Dale AM. Multivariate genome-wide association study on tissue-sensitive diffusion metrics highlights pathways that shape the human brain. Nat Commun 2022; 13:2423. [PMID: 35505052 PMCID: PMC9065144 DOI: 10.1038/s41467-022-30110-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 04/12/2022] [Indexed: 11/12/2022] Open
Abstract
The molecular determinants of tissue composition of the human brain remain largely unknown. Recent genome-wide association studies (GWAS) on this topic have had limited success due to methodological constraints. Here, we apply advanced whole-brain analyses on multi-shell diffusion imaging data and multivariate GWAS to two large scale imaging genetic datasets (UK Biobank and the Adolescent Brain Cognitive Development study) to identify and validate genetic association signals. We discover 503 unique genetic loci that have impact on multiple regions of human brain. Among them, more than 79% are validated in either of two large-scale independent imaging datasets. Key molecular pathways involved in axonal growth, astrocyte-mediated neuroinflammation, and synaptogenesis during development are found to significantly impact the measured variations in tissue-specific imaging features. Our results shed new light on the biological determinants of brain tissue composition and their potential overlap with the genetic basis of neuropsychiatric disorders.
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Affiliation(s)
- Chun Chieh Fan
- Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla, CA, USA. .,Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA. .,Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA.
| | - Robert Loughnan
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Carolina Makowski
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Diliana Pecheva
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Chi-Hua Chen
- Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Donald J Hagler
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Wesley K Thompson
- Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Nadine Parker
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Oleksandr Frei
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA.,Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.,Department of Neuroscience, University of California, San Diego, La Jolla, CA, USA
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28
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Wang M, Zhang S, Sha Q. A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS. PLoS One 2022; 17:e0260911. [PMID: 35482827 PMCID: PMC9049312 DOI: 10.1371/journal.pone.0260911] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 04/13/2022] [Indexed: 11/18/2022] Open
Abstract
There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared.
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Affiliation(s)
- Meida Wang
- Mathematical Sciences, Michigan Technological University, Houghton, MI, United States of America
| | - Shuanglin Zhang
- Mathematical Sciences, Michigan Technological University, Houghton, MI, United States of America
| | - Qiuying Sha
- Mathematical Sciences, Michigan Technological University, Houghton, MI, United States of America
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29
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Fu L, Wang Y, Li T, Yang S, Hu YQ. A Novel Hierarchical Clustering Approach for Joint Analysis of Multiple Phenotypes Uncovers Obesity Variants Based on ARIC. Front Genet 2022; 13:791920. [PMID: 35391794 PMCID: PMC8981031 DOI: 10.3389/fgene.2022.791920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/27/2022] [Indexed: 12/02/2022] Open
Abstract
Genome-wide association studies (GWASs) have successfully discovered numerous variants underlying various diseases. Generally, one-phenotype one-variant association study in GWASs is not efficient in identifying variants with weak effects, indicating that more signals have not been identified yet. Nowadays, jointly analyzing multiple phenotypes has been recognized as an important approach to elevate the statistical power for identifying weak genetic variants on complex diseases, shedding new light on potential biological mechanisms. Therefore, hierarchical clustering based on different methods for calculating correlation coefficients (HCDC) is developed to synchronously analyze multiple phenotypes in association studies. There are two steps involved in HCDC. First, a clustering approach based on the similarity matrix between two groups of phenotypes is applied to choose a representative phenotype in each cluster. Then, we use existing methods to estimate the genetic associations with the representative phenotypes rather than the individual phenotypes in every cluster. A variety of simulations are conducted to demonstrate the capacity of HCDC for boosting power. As a consequence, existing methods embedding HCDC are either more powerful or comparable with those of without embedding HCDC in most scenarios. Additionally, the application of obesity-related phenotypes from Atherosclerosis Risk in Communities via existing methods with HCDC uncovered several associated variants. Among these, UQCC1-rs1570004 is reported as a significant obesity signal for the first time, whose differential expression in subcutaneous fat, visceral fat, and muscle tissue is worthy of further functional studies.
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Affiliation(s)
- Liwan Fu
- Center for Non-communicable Disease Management, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China.,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yuquan Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Tingting Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Siqian Yang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yue-Qing Hu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China.,Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
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30
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Highland HM, Wojcik GL, Graff M, Nishimura KK, Hodonsky CJ, Baldassari AR, Cote AC, Cheng I, Gignoux CR, Tao R, Li Y, Boerwinkle E, Fornage M, Haessler J, Hindorff LA, Hu Y, Justice AE, Lin BM, Lin D, Stram DO, Haiman CA, Kooperberg C, Le Marchand L, Matise TC, Kenny EE, Carlson CS, Stahl EA, Avery CL, North KE, Ambite JL, Buyske S, Loos RJ, Peters U, Young KL, Bien SA, Huckins LM. Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits. Am J Hum Genet 2022; 109:669-679. [PMID: 35263625 PMCID: PMC9069067 DOI: 10.1016/j.ajhg.2022.02.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/15/2022] [Indexed: 02/06/2023] Open
Abstract
One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.
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Affiliation(s)
- Heather M Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Katherine K Nishimura
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Chani J Hodonsky
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Antoine R Baldassari
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Alanna C Cote
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yuqing Li
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Myriam Fornage
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA; Brown Foundation Institute for Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Jeffrey Haessler
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Lucia A Hindorff
- Division of Genomic Medicine, NIH National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Yao Hu
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health System, Danville, PA 17822, USA
| | - Bridget M Lin
- Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Danyu Lin
- Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Daniel O Stram
- Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Christopher A Haiman
- Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Charles Kooperberg
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; School of Public Health, University of Washington, Seattle, WA 98195, USA
| | | | - Tara C Matise
- Genetics, Rutgers University, New Brunswick, NJ 08901-8554, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Christopher S Carlson
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Eli A Stahl
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Christy L Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Jose Luis Ambite
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Steven Buyske
- Statistics, Rutgers University, New Brunswick, NJ 08901-8554, USA
| | - Ruth J Loos
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ulrike Peters
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; School of Public Health, University of Washington, Seattle, WA 98195, USA
| | - Kristin L Young
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Stephanie A Bien
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Laura M Huckins
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Mental Illness Research, Education and Clinical Centers, James J. Peters Department of Veterans Affairs Medical Center, Bronx, NY 14068, USA.
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31
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Neumann A, Küçükali F, Bos I, Vos SJB, Engelborghs S, De Pooter T, Joris G, De Rijk P, De Roeck E, Tsolaki M, Verhey F, Martinez-Lage P, Tainta M, Frisoni G, Blin O, Richardson J, Bordet R, Scheltens P, Popp J, Peyratout G, Johannsen P, Frölich L, Vandenberghe R, Freund-Levi Y, Streffer J, Lovestone S, Legido-Quigley C, Ten Kate M, Barkhof F, Strazisar M, Zetterberg H, Bertram L, Visser PJ, van Broeckhoven C, Sleegers K. Rare variants in IFFO1, DTNB, NLRC3 and SLC22A10 associate with Alzheimer's disease CSF profile of neuronal injury and inflammation. Mol Psychiatry 2022; 27:1990-1999. [PMID: 35173266 PMCID: PMC9126805 DOI: 10.1038/s41380-022-01437-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/04/2021] [Accepted: 01/05/2022] [Indexed: 11/30/2022]
Abstract
Alzheimer's disease (AD) biomarkers represent several neurodegenerative processes, such as synaptic dysfunction, neuronal inflammation and injury, as well as amyloid pathology. We performed an exome-wide rare variant analysis of six AD biomarkers (β-amyloid, total/phosphorylated tau, NfL, YKL-40, and Neurogranin) to discover genes associated with these markers. Genetic and biomarker information was available for 480 participants from two studies: EMIF-AD and ADNI. We applied a principal component (PC) analysis to derive biomarkers combinations, which represent statistically independent biological processes. We then tested whether rare variants in 9576 protein-coding genes associate with these PCs using a Meta-SKAT test. We also tested whether the PCs are intermediary to gene effects on AD symptoms with a SMUT test. One PC loaded on NfL and YKL-40, indicators of neuronal injury and inflammation. Four genes were associated with this PC: IFFO1, DTNB, NLRC3, and SLC22A10. Mediation tests suggest, that these genes also affect dementia symptoms via inflammation/injury. We also observed an association between a PC loading on Neurogranin, a marker for synaptic functioning, with GABBR2 and CASZ1, but no mediation effects. The results suggest that rare variants in IFFO1, DTNB, NLRC3, and SLC22A10 heighten susceptibility to neuronal injury and inflammation, potentially by altering cytoskeleton structure and immune activity disinhibition, resulting in an elevated dementia risk. GABBR2 and CASZ1 were associated with synaptic functioning, but mediation analyses suggest that the effect of these two genes on synaptic functioning is not consequential for AD development.
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Affiliation(s)
- Alexander Neumann
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium.
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
| | - Fahri Küçükali
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Isabelle Bos
- Netherlands Institute for Health Services Research, Utrecht, the Netherlands
| | - Stephanie J B Vos
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Memory Clinic, Universitair Ziekenhuis Brussel (UZ Brussel) and Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Tim De Pooter
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Geert Joris
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Peter De Rijk
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Ellen De Roeck
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Magda Tsolaki
- 1st Department of Neurology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Makedonia, Thessaloniki, Greece
| | - Frans Verhey
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
- Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Pablo Martinez-Lage
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Mikel Tainta
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Giovanni Frisoni
- Department of Psychiatry, Faculty of Medicine, Geneva University Hospitals, Geneva, Switzerland
- RCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Oliver Blin
- Clinical Pharmacology & Pharmacovigilance Department, Marseille University Hospital, Marseille, France
| | - Jill Richardson
- Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevanage, UK
| | - Régis Bordet
- Neuroscience & Cognition, CHU de Lille, University of Lille, Inserm, France
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, the Netherlands
| | - Julius Popp
- Department of Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland
- Old Age Psychiatry, Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Gwendoline Peyratout
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Peter Johannsen
- Clinical Drug Development, Novo Nordisk, Copenhagen, Denmark
| | - Lutz Frölich
- Department of Geriatric Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Yvonne Freund-Levi
- Center for Alzheimer Research, Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society Karolinska Institute Stockholm Sweden, Stockholm, Sweden
- School of Medical Sciences Örebro, University Örebro, Örebro, Sweden
| | - Johannes Streffer
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Simon Lovestone
- Department of Psychiatry, University of Oxford, Oxford, UK
- Janssen Medical Ltd, High Wycombe, UK
| | - Cristina Legido-Quigley
- Steno Diabetes Center, Copenhagen, Denmark
- Institute of Pharmaceutical Sciences, King's College London, London, UK
| | - Mara Ten Kate
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Mojca Strazisar
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute, University College London, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
- Centre for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Pieter Jelle Visser
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, the Netherlands
| | - Christine van Broeckhoven
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neurodegenerative Brain Diseases Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Kristel Sleegers
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
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32
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Zhang L, Du F, Wang Y, Li Y, Yang C, Li S, Huang X, Wang C. Oil fingerprint identification technology using a simplified set of biomarkers selected based on principal component difference. CAN J CHEM ENG 2022. [DOI: 10.1002/cjce.24213] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Lujun Zhang
- Department of Physics and Optoelectronic Engineering Weifang University Weifang China
| | - Fei Du
- Department of Physics and Optoelectronic Engineering Weifang University Weifang China
| | - Yan Wang
- Department of Physics and Optoelectronic Engineering Weifang University Weifang China
| | - Yingde Li
- Department of Physics and Optoelectronic Engineering Weifang University Weifang China
| | - Chun Yang
- Emergencies Science and Technology Section (ESTS), Science and Technology Branch, Environment and Climate Change Canada Ottawa Ontario Canada
| | - Sensen Li
- Science and Technology on Electro‐Optical Information Security Control Laboratory Tianjin China
| | - Xiaodong Huang
- Department of Physics and Optoelectronic Engineering Weifang University Weifang China
| | - Chunyan Wang
- Department of Physics and Optoelectronic Engineering Weifang University Weifang China
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33
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Aguate FM, Vazquez AI, Merriman TR, de Los Campos G. Mapping pleiotropic loci using a fast-sequential testing algorithm. Eur J Hum Genet 2021; 29:1762-1773. [PMID: 34145383 PMCID: PMC8633382 DOI: 10.1038/s41431-021-00911-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/27/2021] [Accepted: 05/19/2021] [Indexed: 02/07/2023] Open
Abstract
Pleiotropy (i.e., genes with effects on multiple traits) leads to genetic correlations between traits and contributes to the development of many syndromes. Identifying variants with pleiotropic effects on multiple health-related traits can improve the biological understanding of gene action and disease etiology, and can help to advance disease-risk prediction. Sequential testing is a powerful approach for mapping genes with pleiotropic effects. However, the existing methods and the available software do not scale to analyses involving millions of SNPs and large datasets. This has limited the adoption of sequential testing for pleiotropy mapping at large scale. In this study, we present a sequential test and software that can be used to test pleiotropy in large systems of traits with biobank-sized data. Using simulations, we show that the methods implemented in the software are powerful and have adequate type-I error rate control. To demonstrate the use of the methods and software, we present a whole-genome scan in search of loci with pleiotropic effects on seven traits related to metabolic syndrome (MetS) using UK-Biobank data (n~300 K distantly related white European participants). We found abundant pleiotropy and report 170, 44, and 18 genomic regions harboring SNPs with pleiotropic effects in at least two, three, and four of the seven traits, respectively. We validate our results using previous studies documented in the GWAS-catalog and using data from GTEx. Our results confirm previously reported loci and lead to several novel discoveries that link MetS-related traits through plausible biological pathways.
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Affiliation(s)
- Fernando M Aguate
- Department of Epidemiology & Biostatistics, IQ - Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.
| | - Ana I Vazquez
- Department of Epidemiology & Biostatistics, IQ - Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Tony R Merriman
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Gustavo de Los Campos
- Department of Epidemiology & Biostatistics, IQ - Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.
- Department of Statistics & Probability, Michigan State University, East Lansing, MI, USA.
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34
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Liu W, Xu Y, Wang A, Huang T, Liu Z. The eigen higher criticism and eigen Berk–Jones tests for multiple trait association studies based on GWAS summary statistics. Genet Epidemiol 2021; 46:89-104. [DOI: 10.1002/gepi.22439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/10/2021] [Accepted: 10/21/2021] [Indexed: 11/11/2022]
Affiliation(s)
- Wei Liu
- Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong SAR China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences Xi'an Jiaotong University Health Science Center Xi'an China
| | - Yuyang Xu
- Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong SAR China
| | - Anqi Wang
- Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong SAR China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health Peking University Beijing China
- Institute for Artificial Intelligence, Center for Intelligent Public Health Peking University Beijing China
- Key Laboratory of Molecular Cardiovascular Diseases, Peking University Ministry of Education Beijing China
| | - Zhonghua Liu
- Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong SAR China
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35
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Julienne H, Laville V, McCaw ZR, He Z, Guillemot V, Lasry C, Ziyatdinov A, Nerin C, Vaysse A, Lechat P, Ménager H, Le Goff W, Dube MP, Kraft P, Ionita-Laza I, Vilhjálmsson BJ, Aschard H. Multitrait GWAS to connect disease variants and biological mechanisms. PLoS Genet 2021; 17:e1009713. [PMID: 34460823 PMCID: PMC8437297 DOI: 10.1371/journal.pgen.1009713] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 09/13/2021] [Accepted: 07/12/2021] [Indexed: 12/30/2022] Open
Abstract
Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials.
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Affiliation(s)
- Hanna Julienne
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Vincent Laville
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Zachary R. McCaw
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, United States of America
| | - Vincent Guillemot
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Carla Lasry
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Andrey Ziyatdinov
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Cyril Nerin
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Amaury Vaysse
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Pierre Lechat
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Hervé Ménager
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Wilfried Le Goff
- Sorbonne Université, INSERM, Institute of Cardiometabolism and Nutrition (ICAN), UMR_S 1166, Paris, France
| | - Marie-Pierre Dube
- Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal Heart Institute, Montreal, Canada
- Université de Montréal, Faculty of Medicine, Department of medicine, Université de Montréal, Montreal, Canada
| | - Peter Kraft
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Columbia University, New York, New York, United States of America
| | - Bjarni J. Vilhjálmsson
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Paris, France
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
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36
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Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies. NPJ Digit Med 2021; 4:116. [PMID: 34302027 PMCID: PMC8302667 DOI: 10.1038/s41746-021-00488-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 05/06/2021] [Indexed: 12/30/2022] Open
Abstract
Labeling clinical data from electronic health records (EHR) in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Furthermore, existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies in case definitions across different algorithms. We describe here quantitative disease risk scores based on almost unsupervised methods that require minimal input from clinicians, can be applied to large datasets, and alleviate some of the main weaknesses of existing phenotyping algorithms. We show applications to phenotypic data on approximately 100,000 individuals in eMERGE, and focus on several complex diseases, including Chronic Kidney Disease, Coronary Artery Disease, Type 2 Diabetes, Heart Failure, and a few others. We demonstrate that relative to existing approaches, the proposed methods have higher prediction accuracy, can better identify phenotypic features relevant to the disease under consideration, can perform better at clinical risk stratification, and can identify undiagnosed cases based on phenotypic features available in the EHR. Using genetic data from the eMERGE-seq panel that includes sequencing data for 109 genes on 21,363 individuals from multiple ethnicities, we also show how the new quantitative disease risk scores help improve the power of genetic association studies relative to the standard use of disease phenotypes. The results demonstrate the effectiveness of quantitative disease risk scores derived from rich phenotypic EHR databases to provide a more meaningful characterization of clinical risk for diseases of interest beyond the prevalent binary (case-control) classification.
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37
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Determination of k-mer density in a DNA sequence and subsequent cluster formation algorithm based on the application of electronic filter. Sci Rep 2021; 11:13701. [PMID: 34211040 PMCID: PMC8249421 DOI: 10.1038/s41598-021-93154-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023] Open
Abstract
We describe a novel algorithm for information recovery from DNA sequences by using a digital filter. This work proposes a three-part algorithm to decide the k-mer or q-gram word density. Employing a finite impulse response digital filter, one can calculate the sequence's k-mer or q-gram word density. Further principal component analysis is used on word density distribution to analyze the dissimilarity between sequences. A dissimilarity matrix is thus formed and shows the appearance of cluster formation. This cluster formation is constructed based on the alignment-free sequence method. Furthermore, the clusters are used to build phylogenetic relations. The cluster algorithm is in good agreement with alignment-based algorithms. The present algorithm is simple and requires less time for computation than other currently available algorithms. We tested the algorithm using beta hemoglobin coding sequences (HBB) of 10 different species and 18 primate mitochondria genome (mtDNA) sequences.
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38
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Selection indexes using principal component analysis for reproductive, beef and milk traits in Simmental cattle. Trop Anim Health Prod 2021; 53:378. [PMID: 34185177 DOI: 10.1007/s11250-021-02815-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/18/2021] [Indexed: 10/21/2022]
Abstract
Selection indexes in dual-purpose cattle should include beef, milk and reproductive traits. The principal component analysis is a multivariate technique that allows researchers to explore relationships between explanatory variables and traits of interest. The objective of this study was to construct selection indexes for tropical dual-purpose Simmental cattle based on principal components. The evaluated traits were weight at 8 months of age; age at first calving; cumulative first-lactation milk yield at 60, 150, 210 and 305 days; and first calving interval. The selection indexes were estimated as the sum of the products of the estimated breeding values for the seven traits times their respective eigenvectors for the first three principal components. The three selection indexes from principal components analysis generated favourable expected genetic progress for all the traits. However, a selection index with a high expected genetic progress for all traits could not be obtained. The principal component analysis allows breeders to have a selection index that simultaneously improves milk, beef and reproductive traits in dual-purpose Simmental cattle. Because a selection index yielding high expected genetic progress for all traits could not be achieved, the decision to use a specific selection index will depend on the specific conditions of the market, the local needs and the farmer preference.
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39
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Fu L, Wang Y, Li T, Hu YQ. A Novel Approach Integrating Hierarchical Clustering and Weighted Combination for Association Study of Multiple Phenotypes and a Genetic Variant. Front Genet 2021; 12:654804. [PMID: 34220938 PMCID: PMC8249926 DOI: 10.3389/fgene.2021.654804] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 04/20/2021] [Indexed: 11/26/2022] Open
Abstract
As a pivotal research tool, genome-wide association study has successfully identified numerous genetic variants underlying distinct diseases. However, these identified genetic variants only explain a small proportion of the phenotypic variation for certain diseases, suggesting that there are still more genetic signals to be detected. One of the reasons may be that one-phenotype one-variant association study is not so efficient in detecting variants of weak effects. Nowadays, it is increasingly worth noting that joint analysis of multiple phenotypes may boost the statistical power to detect pathogenic variants with weak genetic effects on complex diseases, providing more clues for their underlying biology mechanisms. So a Weighted Combination of multiple phenotypes following Hierarchical Clustering method (WCHC) is proposed for simultaneously analyzing multiple phenotypes in association studies. A series of simulations are conducted, and the results show that WCHC is either the most powerful method or comparable with the most powerful competitor in most of the simulation scenarios. Additionally, we evaluated the performance of WCHC in its application to the obesity-related phenotypes from Atherosclerosis Risk in Communities, and several associated variants are reported.
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Affiliation(s)
- Liwan Fu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China.,Center for Non-communicable Disease Management, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Yuquan Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Tingting Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yue-Qing Hu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China.,Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
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40
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A genetic polymorphism that is associated with mitochondrial energy metabolism increases risk of fibromyalgia. Pain 2021; 161:2860-2871. [PMID: 32658146 DOI: 10.1097/j.pain.0000000000001996] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Alterations in cellular energy metabolism have been implicated in chronic pain, suggesting a role for mitochondrial DNA. Previous studies reported associations of a limited number of mitochondrial DNA polymorphisms with specific pain conditions. In this study, we examined the full mitochondrial genomes of people with a variety of chronic pain conditions. A discovery cohort consisting of 609 participants either with or without a complex persistent pain conditions (CPPCs) was examined. Mitochondrial DNA was subjected to deep sequencing for identification of rare mutations, common variants, haplogroups, and heteroplasmy associated with 5 CPPCs: episodic migraine, irritable bowel syndrome, fibromyalgia, vulvar vestibulitis, or temporomandibular disorders. The strongest association found was the presence of the C allele at the single nucleotide polymorphism m.2352T>C (rs28358579) that significantly increased the risk for fibromyalgia (odds ratio [OR] = 4.6, P = 4.3 × 10). This relationship was even stronger in women (OR = 5.1, P = 2.8 × 10), and m.2352T>C was associated with all other CPPCs in a consistent risk-increasing fashion. This finding was replicated in another cohort (OR = 4.3, P = 2.6 × 10) of the Orofacial Pain: Prospective Evaluation and Risk Assessment study consisting of 1754 female participants. To gain insight into the cellular consequences of the associated genetic variability, we conducted an assay testing metabolic reprogramming in human cell lines with defined genotypes. The minor allele C was associated with decreased mitochondrial membrane potential under conditions where oxidative phosphorylation is required, indicating a role of oxidative phosphorylation in pathophysiology of chronic pain. Our results suggest that cellular energy metabolism, modulated by m.2352T>C, contributes to fibromyalgia and possibly other chronic pain conditions.
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41
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Feng H, Mancuso N, Gusev A, Majumdar A, Major M, Pasaniuc B, Kraft P. Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies. PLoS Genet 2021; 17:e1008973. [PMID: 33831007 PMCID: PMC8057593 DOI: 10.1371/journal.pgen.1008973] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 04/20/2021] [Accepted: 03/16/2021] [Indexed: 12/17/2022] Open
Abstract
Transcriptome-wide association studies (TWAS) test the association between traits and genetically predicted gene expression levels. The power of a TWAS depends in part on the strength of the correlation between a genetic predictor of gene expression and the causally relevant gene expression values. Consequently, TWAS power can be low when expression quantitative trait locus (eQTL) data used to train the genetic predictors have small sample sizes, or when data from causally relevant tissues are not available. Here, we propose to address these issues by integrating multiple tissues in the TWAS using sparse canonical correlation analysis (sCCA). We show that sCCA-TWAS combined with single-tissue TWAS using an aggregate Cauchy association test (ACAT) outperforms traditional single-tissue TWAS. In empirically motivated simulations, the sCCA+ACAT approach yielded the highest power to detect a gene associated with phenotype, even when expression in the causal tissue was not directly measured, while controlling the Type I error when there is no association between gene expression and phenotype. For example, when gene expression explains 2% of the variability in outcome, and the GWAS sample size is 20,000, the average power difference between the ACAT combined test of sCCA features and single-tissue, versus single-tissue combined with Generalized Berk-Jones (GBJ) method, single-tissue combined with S-MultiXcan, UTMOST, or summarizing cross-tissue expression patterns using Principal Component Analysis (PCA) approaches was 5%, 8%, 5% and 38%, respectively. The gain in power is likely due to sCCA cross-tissue features being more likely to be detectably heritable. When applied to publicly available summary statistics from 10 complex traits, the sCCA+ACAT test was able to increase the number of testable genes and identify on average an additional 400 additional gene-trait associations that single-trait TWAS missed. Our results suggest that aggregating eQTL data across multiple tissues using sCCA can improve the sensitivity of TWAS while controlling for the false positive rate.
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Affiliation(s)
- Helian Feng
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Genetics, Brigham & Women’s Hospital, Boston, MA, United States of America
- Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Arunabha Majumdar
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Megan Major
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, California, United States of America
| | - Bogdan Pasaniuc
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
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42
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Chen Y, Wu H, Yang W, Zhao W, Tong C. Multivariate linear mixed model enhanced the power of identifying genome-wide association to poplar tree heights in a randomized complete block design. G3-GENES GENOMES GENETICS 2021; 11:6064171. [PMID: 33604666 PMCID: PMC8022933 DOI: 10.1093/g3journal/jkaa053] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 11/01/2020] [Indexed: 01/09/2023]
Abstract
With the advances in high-throughput sequencing technologies, it is not difficult to extract tens of thousands of single-nucleotide polymorphisms (SNPs) across many individuals in a fast and cheap way, making it possible to perform genome-wide association studies (GWAS) of quantitative traits in outbred forest trees. It is very valuable to apply traditional breeding experiments in GWAS for identifying genome variants associated with ecologically and economically important traits in Populus. Here, we reported a GWAS of tree height measured at multiple time points from a randomized complete block design (RCBD), which was established with clones from an F1 hybrid population of Populus deltoides and Populus simonii. A total of 22,670 SNPs across 172 clones in the RCBD were obtained with restriction site-associated DNA sequencing (RADseq) technology. The multivariate mixed linear model was applied by incorporating the pedigree relationship matrix of individuals to test the association of each SNP to the tree heights over 8 time points. Consequently, 41 SNPs were identified significantly associated with the tree height under the P-value threshold determined by Bonferroni correction at the significant level of 0.01. These SNPs were distributed on all but two chromosomes (Chr02 and Chr18) and explained the phenotypic variance ranged from 0.26% to 2.64%, amounting to 63.68% in total. Comparison with previous mapping studies for poplar height as well as the candidate genes of these detected SNPs were also investigated. We therefore showed that the application of multivariate linear mixed model to the longitudinal phenotypic data from the traditional breeding experimental design facilitated to identify far more genome-wide variants for tree height in poplar. The significant SNPs identified in this study would enhance understanding of molecular mechanism for growth traits and would accelerate marker-assisted breeding programs in Populus.
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Affiliation(s)
- Yuhua Chen
- Key Laboratory of Forest Genetics & Biotechnology of Ministry of Education, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China.,School of Animal Science and Technology, Jingling Institute of Technology, Nanjing 210038, China
| | - Hainan Wu
- Key Laboratory of Forest Genetics & Biotechnology of Ministry of Education, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Wenguo Yang
- Key Laboratory of Forest Genetics & Biotechnology of Ministry of Education, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Wei Zhao
- Key Laboratory of Forest Genetics & Biotechnology of Ministry of Education, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Chunfa Tong
- Key Laboratory of Forest Genetics & Biotechnology of Ministry of Education, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
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43
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Associating Multivariate Traits with Genetic Variants Using Collapsing and Kernel Methods with Pedigree- or Population-Based Studies. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:8812282. [PMID: 33628328 PMCID: PMC7889379 DOI: 10.1155/2021/8812282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 01/02/2021] [Accepted: 01/08/2021] [Indexed: 11/18/2022]
Abstract
In genetic association analysis, several relevant phenotypes or multivariate traits with different types of components are usually collected to study complex or multifactorial diseases. Over the past few years, jointly testing for association between multivariate traits and multiple genetic variants has become more popular because it can increase statistical power to identify causal genes in pedigree- or population-based studies. However, most of the existing methods mainly focus on testing genetic variants associated with multiple continuous phenotypes. In this investigation, we develop a framework for identifying the pleiotropic effects of genetic variants on multivariate traits by using collapsing and kernel methods with pedigree- or population-structured data. The proposed framework is applicable to the burden test, the kernel test, and the omnibus test for autosomes and the X chromosome. The proposed multivariate trait association methods can accommodate continuous phenotypes or binary phenotypes and further can adjust for covariates. Simulation studies show that the performance of our methods is satisfactory with respect to the empirical type I error rates and power rates in comparison with the existing methods.
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44
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Ning Z, Tsepilov YA, Sharapov SZ, Wang Z, Grishenko AK, Feng X, Shirali M, Joshi PK, Wilson JF, Pawitan Y, Haley CS, Aulchenko YS, Shen X. Nontrivial Replication of Loci Detected by Multi-Trait Methods. Front Genet 2021; 12:627989. [PMID: 33613642 PMCID: PMC7886991 DOI: 10.3389/fgene.2021.627989] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/04/2021] [Indexed: 11/21/2022] Open
Abstract
The ever-growing genome-wide association studies (GWAS) have revealed widespread pleiotropy. To exploit this, various methods that jointly consider associations of a genetic variant with multiple traits have been developed. Most efforts have been made concerning improving GWAS discovery power. However, how to replicate these discovered pleiotropic loci has yet to be discussed thoroughly. Unlike a single-trait scenario, multi-trait replication is not trivial considering the underlying genotype-multi-phenotype map of the associations. Here, we evaluate four methods for replicating multi-trait associations, corresponding to four levels of replication strength. Weak replication cannot justify pleiotropic genetic effects, whereas strong replication using our developed correlation methods can inform consistent pleiotropic genetic effects across the discovery and replication samples. We provide a protocol for replicating multi-trait genetic associations in practice. The described methods are implemented in the free and open-source R package MultiABEL.
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Affiliation(s)
- Zheng Ning
- Biostatistics Group, School of Life Sciences and School of Ecology, Sun Yat-sen University, Guangzhou, China.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Yakov A Tsepilov
- Division of Biology, Novosibirsk State University, Novosibirsk, Russia.,Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | | | - Zhipeng Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Bioinformatics Center, Northeast Agricultural University, Harbin, China
| | | | - Xiao Feng
- Biostatistics Group, School of Life Sciences and School of Ecology, Sun Yat-sen University, Guangzhou, China
| | - Masoud Shirali
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - James F Wilson
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom.,Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Chris S Haley
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom
| | - Yurii S Aulchenko
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.,PolyOmica, 's-Hertogenbosch, Netherlands
| | - Xia Shen
- Biostatistics Group, School of Life Sciences and School of Ecology, Sun Yat-sen University, Guangzhou, China.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom.,Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
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Li Y, Ma Y, Wang K, Zhang M, Wang Y, Liu X, Hao M, Yin X, Liang M, Zhang H, Wang X, Chen X, Zhang Y, Duan W, Kang L, Qiao B, Wang J, Jin L. Using Composite Phenotypes to Reveal Hidden Physiological Heterogeneity in High-Altitude Acclimatization in a Chinese Han Longitudinal Cohort. PHENOMICS (CHAM, SWITZERLAND) 2021; 1:3-14. [PMID: 36939745 PMCID: PMC9584130 DOI: 10.1007/s43657-020-00005-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 10/16/2020] [Accepted: 10/26/2020] [Indexed: 11/30/2022]
Abstract
Altitude acclimatization is a human physiological process of adjusting to the decreased oxygen availability. Since several physiological processes are involved and their correlations are complicated, the analyses of single traits are insufficient in revealing the complex mechanism of high-altitude acclimatization. In this study, we examined these physiological responses as the composite phenotypes that are represented by a linear combination of physiological traits. We developed a strategy that combines both spectral clustering and partial least squares path modeling (PLSPM) to define composite phenotypes based on a cohort study of 883 Chinese Han males. In addition, we captured 14 composite phenotypes from 28 physiological traits of high-altitude acclimatization. Using these composite phenotypes, we applied k-means clustering to reveal hidden population physiological heterogeneity in high-altitude acclimatization. Furthermore, we employed multivariate linear regression to systematically model (Models 1 and 2) oxygen saturation (SpO2) changes in high-altitude acclimatization and evaluated model fitness performance. Composite phenotypes based on Model 2 fit better than single trait-based Model 1 in all measurement indices. This new strategy of using composite phenotypes may be potentially employed as a general strategy for complex traits research such as genetic loci discovery and analyses of phenomics.
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Affiliation(s)
- Yi Li
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438 China
- Institute for Six-Sector Economy, Fudan University, Shanghai, 200433 China
| | - Yanyun Ma
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, 200438 China
- Institute for Six-Sector Economy, Fudan University, Shanghai, 200433 China
| | - Kun Wang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, 200438 China
| | - Menghan Zhang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438 China
| | - Yi Wang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, 200438 China
| | - Xiaoyu Liu
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438 China
| | - Meng Hao
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, 200438 China
| | - Xianhong Yin
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438 China
| | - Meng Liang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, 200438 China
| | - Hui Zhang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438 China
| | - Xiaofeng Wang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438 China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438 China
- Fudan-Taizhou Institute of Health Sciences, Taizhou, 225300 China
| | - Yao Zhang
- Key Laboratory of High Altitude Environment and Genes Related To Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, 712082 China
| | - Wenyuan Duan
- Institute of Cardiovascular Disease, Shandong Provincial Western Hospital, Jinan, Shandong 250022 China
| | - Longli Kang
- Key Laboratory of High Altitude Environment and Genes Related To Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, 712082 China
| | - Bin Qiao
- Institute of Cardiovascular Disease, Shandong Provincial Western Hospital, Jinan, Shandong 250022 China
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438 China
- Institute for Six-Sector Economy, Fudan University, Shanghai, 200433 China
- Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Beijing, 100730 China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438 China
- Institute for Six-Sector Economy, Fudan University, Shanghai, 200433 China
- Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Beijing, 100730 China
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Chen L, Zhou Y. A fast and powerful aggregated Cauchy association test for joint analysis of multiple phenotypes. Genes Genomics 2021; 43:69-77. [PMID: 33432394 DOI: 10.1007/s13258-020-01034-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 12/23/2020] [Indexed: 11/27/2022]
Abstract
BACKGROUND Pleiotropy is a widespread phenomenon in complex human diseases. Jointly analyzing multiple phenotypes can improve power performance of detecting genetic variants and uncover the underlying genetic mechanism. OBJECTIVE This study aims to detect the association between genetic variants in a genomic region and multiple phenotypes. METHODS We develop the aggregated Cauchy association test to detect the association between rare variants in a genomic region and multiple phenotypes (abbreviated as "Multi-ACAT"). Multi-ACAT first detects the association between each rare variant and multiple phenotypes based on reverse regression and obtains variant-level p-values, then takes linear combination of transformed p-values as the test statistic which approximately follows Cauchy distribution under the null hypothesis. RESULTS Extensive simulation studies show that when the proportion of causal variants in a genomic region is extremely small, Multi-ACAT is more powerful than the other several methods and is robust to bi-directional effects of causal variants. Finally, we illustrate our proposed method by analyzing two phenotypes [systolic blood pressure (SBP) and diastolic blood pressure (DBP)] from Genetic Analysis Workshop 19 (GAW19). CONCLUSION The Multi-ACAT computes extremely fast, does not consider complex distributions of multiple correlated phenotypes, and can be applied to the case with noise phenotypes.
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Affiliation(s)
- Lili Chen
- School of Mathematical Sciences, Heilongjiang University, No. 74 Xuefu Road, Nangang District, Harbin, 150080, People's Republic of China
| | - Yajing Zhou
- School of Mathematical Sciences, Heilongjiang University, No. 74 Xuefu Road, Nangang District, Harbin, 150080, People's Republic of China.
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Wen Y, Lu Q. An optimal kernel-based multivariate U-statistic to test for associations with multiple phenotypes. Biostatistics 2020; 23:705-720. [PMID: 33108446 DOI: 10.1093/biostatistics/kxaa049] [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: 07/10/2020] [Revised: 09/24/2020] [Accepted: 10/03/2020] [Indexed: 11/13/2022] Open
Abstract
Set-based analysis that jointly considers multiple predictors in a group has been broadly conducted for association tests. However, their power can be sensitive to the distribution of phenotypes, and the underlying relationships between predictors and outcomes. Moreover, most of the set-based methods are designed for single-trait analysis, making it hard to explore the pleiotropic effect and borrow information when multiple phenotypes are available. Here, we propose a kernel-based multivariate U-statistics (KMU) that is robust and powerful in testing the association between a set of predictors and multiple outcomes. We employed a rank-based kernel function for the outcomes, which makes our method robust to various outcome distributions. Rather than selecting a single kernel, our test statistics is built based on multiple kernels selected in a data-driven manner, and thus is capable of capturing various complex relationships between predictors and outcomes. The asymptotic properties of our test statistics have been developed. Through simulations, we have demonstrated that KMU has controlled type I error and higher power than its counterparts. We further showed its practical utility by analyzing a whole genome sequencing data from Alzheimer's Disease Neuroimaging Initiative study, where novel genes have been detected to be associated with imaging phenotypes.
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Affiliation(s)
- Y Wen
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Qing Lu
- Department of Biostatistics, College of Public Health, University of Florida, Gainesville, FL, USA
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Gao C, Sha Q, Zhang S, Zhang K. MF-TOWmuT: Testing an optimally weighted combination of common and rare variants with multiple traits using family data. Genet Epidemiol 2020; 45:64-81. [PMID: 33047835 DOI: 10.1002/gepi.22355] [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: 03/24/2020] [Revised: 08/03/2020] [Accepted: 08/18/2020] [Indexed: 11/11/2022]
Abstract
With rapid advancements of sequencing technologies and accumulations of electronic health records, a large number of genetic variants and multiple correlated human complex traits have become available in many genetic association studies. Thus, it becomes necessary and important to develop new methods that can jointly analyze the association between multiple genetic variants and multiple traits. Compared with methods that only use a single marker or trait, the joint analysis of multiple genetic variants and multiple traits is more powerful since such an analysis can fully incorporate the correlation structure of genetic variants and/or traits and their mutual dependence patterns. However, most of existing methods that simultaneously analyze multiple genetic variants and multiple traits are only applicable to unrelated samples. We develop a new method called MF-TOWmuT to detect association of multiple phenotypes and multiple genetic variants in a genomic region with family samples. MF-TOWmuT is based on an optimally weighted combination of variants. Our method can be applied to both rare and common variants and both qualitative and quantitative traits. Our simulation results show that (1) the type I error of MF-TOWmuT is preserved; (2) MF-TOWmuT outperforms two existing methods such as Multiple Family-based Quasi-Likelihood Score Test and Multivariate Family-based Rare Variant Association Test in terms of power. We also illustrate the usefulness of MF-TOWmuT by analyzing genotypic and phenotipic data from the Genetics of Kidneys in Diabetes study. R program is available at https://github.com/gaochengPRC/MF-TOWmuT.
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Affiliation(s)
- Cheng Gao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
| | - Kui Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
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Rice BR, Fernandes SB, Lipka AE. Multi-Trait Genome-Wide Association Studies Reveal Loci Associated with Maize Inflorescence and Leaf Architecture. PLANT & CELL PHYSIOLOGY 2020; 61:1427-1437. [PMID: 32186727 DOI: 10.1093/pcp/pcaa039] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 03/17/2020] [Indexed: 05/23/2023]
Abstract
Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.
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Affiliation(s)
- Brian R Rice
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
| | | | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
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50
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Bu D, Yang Q, Meng Z, Zhang S, Li Q. Truncated tests for combining evidence of summary statistics. Genet Epidemiol 2020; 44:687-701. [DOI: 10.1002/gepi.22330] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 04/24/2020] [Accepted: 06/01/2020] [Indexed: 12/15/2022]
Affiliation(s)
- Deliang Bu
- School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China
- Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China
| | - Qinglong Yang
- School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan China
| | - Zhen Meng
- LSC, NCMIS, Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China
| | - Sanguo Zhang
- School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China
- Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China
| | - Qizhai Li
- School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China
- LSC, NCMIS, Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China
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