401
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Dashti HS, Redline S, Saxena R. Polygenic risk score identifies associations between sleep duration and diseases determined from an electronic medical record biobank. Sleep 2019; 42:zsy247. [PMID: 30521049 PMCID: PMC6424085 DOI: 10.1093/sleep/zsy247] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 11/07/2018] [Accepted: 12/03/2018] [Indexed: 01/01/2023] Open
Abstract
STUDY OBJECTIVES We aimed to detect cross-sectional phenotype and polygenic risk score (PRS) associations between sleep duration and prevalent diseases using the Partners Biobank, a hospital-based cohort study linking electronic medical records (EMR) with genetic information. METHODS Disease prevalence was determined from EMR, and sleep duration was self-reported. A PRS for sleep duration was derived using 78 previously associated SNPs from genome-wide association studies (GWAS) for self-reported sleep duration. We tested for associations between (1) self-reported sleep duration and 22 prevalent diseases (n = 30 251), (2) the PRS and self-reported sleep duration (n = 6903), and (3) the PRS and the 22 prevalent diseases (n = 16 033). For observed PRS-disease associations, we tested causality using two-sample Mendelian randomization (MR). RESULTS In the age-, sex-, and race-adjusted model, U-shaped associations were observed for sleep duration and asthma, depression, hypertension, insomnia, obesity, obstructive sleep apnea, and type 2 diabetes, where both short and long sleepers had higher odds for these diseases than normal sleepers (p < 2.27 × 10-3). Next, we confirmed associations between the PRS and longer sleep duration (0.65 ± 0.19 SD minutes per effect allele; p = 7.32 × 10-04). The PRS collectively explained 1.4% of the phenotypic variance in sleep duration. After adjusting for age, sex, genotyping array, and principal components of ancestry, we observed that the PRS was also associated with congestive heart failure (CHF; p = 0.015), obesity (p = 0.019), hypertension (p = 0.039), restless legs syndrome (RLS; p = 0.041), and insomnia (p = 0.049). Associations were maintained following additional adjustment for obesity status, except for hypertension and insomnia. For all diseases, except RLS, carrying a higher genetic burden of the 78 sleep duration-increasing alleles (i.e. higher sleep duration PRS) associated with lower odds for prevalent disease. In MR, we estimated causal associations between genetically defined longer sleep duration with decreased risk of CHF (inverse variance weighted [IVW] OR per minute of sleep [95% CI] = 0.978 [0.961-0.996]; p = 0.019) and hypertension (IVW OR [95% CI] = 0.993 [0.986-1.000]; p = 0.049), and increased risk of RLS (IVW OR [95% CI] = 1.018 [1.000-1.036]; p = 0.045). CONCLUSIONS By validating the PRS for sleep duration and identifying cross-phenotype associations, we lay the groundwork for future investigations on the intersection between sleep, genetics, clinical measures, and diseases using large EMR datasets.
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Affiliation(s)
- Hassan S Dashti
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Susan Redline
- Departments of Medicine, Brigham and Women’s Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA
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402
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Kaur Y, Wang DX, Liu HY, Meyre D. Comprehensive identification of pleiotropic loci for body fat distribution using the NHGRI-EBI Catalog of published genome-wide association studies. Obes Rev 2019; 20:385-406. [PMID: 30565845 DOI: 10.1111/obr.12806] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 10/05/2018] [Accepted: 10/15/2018] [Indexed: 12/22/2022]
Abstract
We conducted a hypothesis-free cross-trait analysis for waist-to-hip ratio adjusted for body mass index (WHRadjBMI ) loci derived through genome-wide association studies (GWAS). Summary statistics from published GWAS were used to capture all WHRadjBMI single-nucleotide polymorphisms (SNPs), and their proxy SNPs were identified. These SNPs were used to extract cross-trait associations between WHRadjBMI SNPs and other traits through the NHGRI-EBI GWAS Catalog. Pathway analysis was conducted for pleiotropic WHRadjBMI SNPs. We found 160 WHRadjBMI SNPs and 3675 proxy SNPs. Cross-trait analysis identified 239 associations, of which 100 were for obesity traits. The remaining 139 associations were filtered down to 101 unique linkage disequilibrium block associations, which were grouped into 13 categories: lipids, red blood cell traits, white blood cell counts, inflammatory markers and autoimmune diseases, type 2 diabetes-related traits, adiponectin, cancers, blood pressure, height, neuropsychiatric disorders, electrocardiography changes, urea measurement, and others. The highest number of cross-trait associations were found for triglycerides (n = 10), high-density lipoprotein cholesterol (n = 9), and reticulocyte counts (n = 8). Pathway analysis for WHRadjBMI pleiotropic SNPs found immune function pathways as the top canonical pathways. Results from our original methodology indicate a novel genetic association between WHRadjBMI and reticulocyte counts and highlight the pleiotropy between abdominal obesity, immune pathways, and other traits.
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Affiliation(s)
- Yuvreet Kaur
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.,Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Dominic X Wang
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.,Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Hsin-Yen Liu
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.,Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
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403
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Chen HH, Petty LE, Bush W, Naj AC, Below JE. GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations. CURRENT GENETIC MEDICINE REPORTS 2019; 7:30-40. [PMID: 33312764 PMCID: PMC7731888 DOI: 10.1007/s40142-019-0159-z] [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] [Indexed: 10/27/2022]
Abstract
PURPOSE OF REVIEW Neurodegenerative diseases, neuropsychiatric disorders, and related traits have highly complex etiologies but are also highly heritable and identifying the causal genes and biological pathways underlying these traits may advance the development of treatments and preventive strategies. While many genome-wide association studies (GWAS) have successfully identified variants contributing to polygenic neurodegenerative and neuropsychiatric phenotypes including Alzheimer's disease (AD), schizophrenia (SCZ), and bipolar disorder (BPD) amongst others, interpreting the biological roles of significantly-associated variants in the genetic architecture of these traits remains a significant challenge. Here we review several 'omics' approaches which attempt to bridge the gap from associated genetic variants to phenotype by helping define the functional roles of GWAS loci in the development of neuropsychiatric disorders and traits. RECENT FINDINGS Several common 'omics' approaches have been applied to examine neuropsychiatric traits, such as nearest-gene mapping, trans-ethnic fine mapping, annotation enrichment analysis, transcriptomic analysis, and pathway analysis, and each of these approaches has strengths and limitations in providing insight into biological mechanisms. One popular emerging method is the examination of tissue-specific genetically-regulated gene expression (GReX), which aggregates the genetic variants' effects at the gene-level. Furthermore, proteomic, metabolomic, and microbiomic studies and phenome-wide association studies will further enhance our understanding of neuropsychiatric traits. SUMMARY GWAS has been applied to neuropsychiatric traits for a decade, but our understanding about the biological function of identified variants remains limited. Today, technological advancements have created analytical approaches for integrating transcriptomics, metabolomics, proteomics, pharmacology and toxicology as tools for understanding the functional roles of genetics variants. These data, as well as the broader clinical information provided by electronic health records, can provide additional insight and complement genomic analyses.
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Affiliation(s)
- Hung-Hsin Chen
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lauren E. Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - William Bush
- Institute for Computational Biology, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Adam C. Naj
- Department of Biostatistics, Epidemiology, and Informatics; Department of Pathology and Laboratory Medicine; Center for Clinical Epidemiology and Biostatistics; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer E. Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
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404
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Liu Z, Lin X. A Geometric Perspective on the Power of Principal Component Association Tests in Multiple Phenotype Studies. J Am Stat Assoc 2019; 114:975-990. [PMID: 31564761 DOI: 10.1080/01621459.2018.1513363] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Joint analysis of multiple phenotypes can increase statistical power in genetic association studies. Principal component analysis, as a popular dimension reduction method, especially when the number of phenotypes is high-dimensional, has been proposed to analyze multiple correlated phenotypes. It has been empirically observed that the first PC, which summarizes the largest amount of variance, can be less powerful than higher order PCs and other commonly used methods in detecting genetic association signals. In this paper, we investigate the properties of PCA-based multiple phenotype analysis from a geometric perspective by introducing a novel concept called principal angle. A particular PC is powerful if its principal angle is 0° and is powerless if its principal angle is 90°. Without prior knowledge about the true principal angle, each PC can be powerless. We propose linear, non-linear and data-adaptive omnibus tests by combining PCs. We demonstrate that the Wald test is a special quadratic PC-based test. We show that the omnibus PC test is robust and powerful in a wide range of scenarios. We study the properties of the proposed methods using power analysis and eigen-analysis. The subtle differences and close connections between these combined PC methods are illustrated graphically in terms of their rejection boundaries. Our proposed tests have convex acceptance regions and hence are admissible. The p-values for the proposed tests can be efficiently calculated analytically and the proposed tests have been implemented in a publicly available R package MPAT. We conduct simulation studies in both low and high dimensional settings with various signal vectors and correlation structures. We apply the proposed tests to the joint analysis of metabolic syndrome related phenotypes with data sets collected from four international consortia to demonstrate the effectiveness of the proposed combined PC testing procedures.
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Affiliation(s)
- Zhonghua Liu
- Department of Statistics and Actuarial Science, University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Xihong Lin
- Chair and Henry Pickering Walcott Professor of Biostatistics, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115
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405
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Martínez-Bueno M, Alarcón-Riquelme ME. Exploring Impact of Rare Variation in Systemic Lupus Erythematosus by a Genome Wide Imputation Approach. Front Immunol 2019; 10:258. [PMID: 30863397 PMCID: PMC6399402 DOI: 10.3389/fimmu.2019.00258] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 01/29/2019] [Indexed: 01/31/2023] Open
Abstract
The importance of low frequency and rare variation in complex disease genetics is difficult to estimate in patient populations. Genome-wide association studies are therefore, underpowered to detect rare variation. We have used a combined approach of genome-wide-based imputation with a highly stringent sequence kernel association (SKAT) test and a case-control burden test. We identified 98 candidate genes containing rare variation that in aggregate show association with SLE many of which have recognized immunological function, but also function and expression related to relevant tissues such as the joints, skin, blood or central nervous system. In addition we also find that there is a significant enrichment of genes annotated for disease-causing mutations in the OMIM database, suggesting that in complex diseases such as SLE, such mutations may be involved in subtle or combined phenotypes or could accelerate specific organ abnormalities found in the disease. We here provide an important resource of candidate genes for SLE.
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Affiliation(s)
- Manuel Martínez-Bueno
- Department of Medical Genomics, GENYO, Center for Genomics and Oncological Research Pfizer, University of Granada, Granada, Spain
| | - Marta E Alarcón-Riquelme
- Unit of Chronic Inflammation, Institute for Environmental Medicine, Karolinska Institute, Stockholm, Sweden
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406
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Dimou NL, Pantavou KG, Braliou GG, Bagos PG. Multivariate Methods for Meta-Analysis of Genetic Association Studies. Methods Mol Biol 2019; 1793:157-182. [PMID: 29876897 DOI: 10.1007/978-1-4939-7868-7_11] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.
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Affiliation(s)
- Niki L Dimou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.,Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Katerina G Pantavou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Georgia G Braliou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.
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407
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Grau-Perez M, Agha G, Pang Y, Bermudez JD, Tellez-Plaza M. Mendelian Randomization and the Environmental Epigenetics of Health: a Systematic Review. Curr Environ Health Rep 2019; 6:38-51. [DOI: 10.1007/s40572-019-0226-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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408
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Parker MM, Lutz SM, Hobbs BD, Busch R, McDonald MN, Castaldi PJ, Beaty TH, Hokanson JE, Silverman EK, Cho MH. Assessing pleiotropy and mediation in genetic loci associated with chronic obstructive pulmonary disease. Genet Epidemiol 2019; 43:318-329. [PMID: 30740764 DOI: 10.1002/gepi.22192] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 09/10/2018] [Accepted: 10/10/2018] [Indexed: 12/14/2022]
Abstract
Genetic association studies have increasingly recognized variant effects on multiple phenotypes. Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease with environmental and genetic causes. Multiple genetic variants have been associated with COPD, many of which show significant associations to additional phenotypes. However, it is unknown if these associations represent biological pleiotropy or if they exist through correlation of related phenotypes ("mediated pleiotropy"). Using 6,670 subjects from the COPDGene study, we describe the association of known COPD susceptibility loci with other COPD-related phenotypes and distinguish if these act directly on the phenotypes (i.e., biological pleiotropy) or if the association is due to correlation (i.e., mediated pleiotropy). We identified additional associated phenotypes for 13 of 25 known COPD loci. Tests for pleiotropy between genotype and associated outcomes were significant for all loci. In cases of significant pleiotropy, we performed mediation analysis to test if SNPs had a direct association to phenotype. Most loci showed a mediated effect through the hypothesized causal pathway. However, many loci also had direct associations, suggesting causal explanations (i.e., emphysema leading to reduced lung function) are incomplete. Our results highlight the high degree of pleiotropy in complex disease-associated loci and provide novel insights into the mechanisms underlying COPD.
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Affiliation(s)
- Margaret M Parker
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Sharon M Lutz
- Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Denver, Colorado
| | - Brian D Hobbs
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Robert Busch
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - MerryLynn N McDonald
- Division of Pulmonary, Allergy, and Critical Care Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Peter J Castaldi
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts
| | - Terri H Beaty
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - John E Hokanson
- Department of Epidemiology, University of Colorado, Denver, Aurora, Colorado
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
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409
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Bartell JA, Sommer LM, Haagensen JAJ, Loch A, Espinosa R, Molin S, Johansen HK. Evolutionary highways to persistent bacterial infection. Nat Commun 2019; 10:629. [PMID: 30733448 PMCID: PMC6367392 DOI: 10.1038/s41467-019-08504-7] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 01/10/2019] [Indexed: 01/18/2023] Open
Abstract
Persistent infections require bacteria to evolve from their naïve colonization state by optimizing fitness in the host via simultaneous adaptation of multiple traits, which can obscure evolutionary trends and complicate infection management. Accordingly, here we screen 8 infection-relevant phenotypes of 443 longitudinal Pseudomonas aeruginosa isolates from 39 young cystic fibrosis patients over 10 years. Using statistical modeling, we map evolutionary trajectories and identify trait correlations accounting for patient-specific influences. By integrating previous genetic analyses of 474 isolates, we provide a window into early adaptation to the host, finding: (1) a 2–3 year timeline of rapid adaptation after colonization, (2) variant “naïve” and “adapted” states reflecting discordance between phenotypic and genetic adaptation, (3) adaptive trajectories leading to persistent infection via three distinct evolutionary modes, and (4) new associations between phenotypes and pathoadaptive mutations. Ultimately, we effectively deconvolute complex trait adaptation, offering a framework for evolutionary studies and precision medicine in clinical microbiology. The pathogen Pseudomonas aeruginosa undergoes complex trait adaptation within cystic fibrosis patients. Here, Bartell, Sommer, and colleagues use statistical modeling of longitudinal isolates to characterize the joint genetic and phenotypic evolutionary trajectories of P. aeruginosa within hosts.
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Affiliation(s)
- Jennifer A Bartell
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark.
| | - Lea M Sommer
- Department of Clinical Microbiology, Rigshospitalet, 2100, Copenhagen Ø, Denmark.
| | - Janus A J Haagensen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Anne Loch
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Rocio Espinosa
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Søren Molin
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Helle Krogh Johansen
- Department of Clinical Microbiology, Rigshospitalet, 2100, Copenhagen Ø, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen N, Denmark
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410
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Pei G, Sun H, Dai Y, Liu X, Zhao Z, Jia P. Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics. BMC Genomics 2019; 20:79. [PMID: 30712509 PMCID: PMC6360716 DOI: 10.1186/s12864-018-5373-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Background Genome-wide association studies (GWAS) have been successful in identifying disease-associated genetic variants. Recently, an increasing number of GWAS summary statistics have been made available to the research community, providing extensive repositories for studies of human complex diseases. In particular, cross-trait associations at the genetic level can be beneficial from large-scale GWAS summary statistics by using genetic variants that are associated with multiple traits. However, direct assessment of cross-trait associations using susceptibility loci has been challenging due to the complex genetic architectures in most diseases, calling for advantageous methods that could integrate functional interpretation and imply biological mechanisms. Results We developed an analytical framework for systematic integration of cross-trait associations. It incorporates two different approaches to detect enriched pathways and requires only summary statistics. We demonstrated the framework using 25 traits belonging to four phenotype groups. Our results revealed an average of 54 significantly associated pathways (ranged between 18 and 175) per trait. We further proved that pathway-based analysis provided increased power to estimate cross-trait associations compared to gene-level analysis. Based on Fisher’s Exact Test (FET), we identified a total of 24 (53) pairs of trait-trait association at adjusted pFET < 1 × 10− 3 (pFET < 0.01) among the 25 traits. Our trait-trait association network revealed not only many relationships among the traits within the same group but also novel relationships among traits from different groups, which warrants further investigation in future. Conclusions Our study revealed that risk variants for 25 different traits aggregated in particular biological pathways and that these pathways were frequently shared among traits. Our results confirmed known mechanisms and also suggested several novel insights into the etiology of multi-traits. Electronic supplementary material The online version of this article (10.1186/s12864-018-5373-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA
| | - Hua Sun
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA
| | - Xiaoming Liu
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA. .,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. .,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA.
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411
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Gupta V, Sachdeva MP, Walia GK. "Mendelian Randomization" Approach in Economic Assessment of Health Conditions. Front Public Health 2019; 7:2. [PMID: 30778381 PMCID: PMC6369183 DOI: 10.3389/fpubh.2019.00002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 01/04/2019] [Indexed: 01/30/2023] Open
Abstract
The increased prevalence of non-communicable chronic diseases (NCDs) is reflected in the rising economic burden of health conditions. Observational studies conducted in health economics research are detecting associations of NCDs or related risk factors with economic measures like health insurance, economic inequalities, accessibility of jobs, education, annual income, health expenditure, etc. The inferences of such relationships do not prove causation and are limited to associations which are many times influenced by confounding factors and reverse causation. Mendelian randomization (MR) approach is a useful method for exploring causal relations between modifiable risk factors and measures of health economics. The application of MR in economic assessment of health conditions has been started and is producing fruitful results.
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Affiliation(s)
- Vipin Gupta
- Department of Anthropology, University of Delhi, New Delhi, India
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412
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Dutta D, Scott L, Boehnke M, Lee S. Multi-SKAT: General framework to test for rare-variant association with multiple phenotypes. Genet Epidemiol 2019; 43:4-23. [PMID: 30298564 PMCID: PMC6330125 DOI: 10.1002/gepi.22156] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 07/12/2018] [Accepted: 07/15/2018] [Indexed: 12/13/2022]
Abstract
In genetic association analysis, a joint test of multiple distinct phenotypes can increase power to identify sets of trait-associated variants within genes or regions of interest. Existing multiphenotype tests for rare variants make specific assumptions about the patterns of association with underlying causal variants, and the violation of these assumptions can reduce power to detect association. Here, we develop a general framework for testing pleiotropic effects of rare variants on multiple continuous phenotypes using multivariate kernel regression (Multi-SKAT). Multi-SKAT models affect sizes of variants on the phenotypes through a kernel matrix and perform a variance component test of association. We show that many existing tests are equivalent to specific choices of kernel matrices with the Multi-SKAT framework. To increase power of detecting association across tests with different kernel matrices, we developed a fast and accurate approximation of the significance of the minimum observed P value across tests. To account for related individuals, our framework uses random effects for the kinship matrix. Using simulated data and amino acid and exome-array data from the METabolic Syndrome In Men (METSIM) study, we show that Multi-SKAT can improve power over single-phenotype SKAT-O test and existing multiple-phenotype tests, while maintaining Type I error rate.
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Affiliation(s)
- Diptavo Dutta
- Department of Biostatistics, University of Michigan Ann Arbor, Michigan, USA
- Center for Statistical Genetics, University of Michigan Ann Arbor, Michigan, USA
| | - Laura Scott
- Department of Biostatistics, University of Michigan Ann Arbor, Michigan, USA
- Center for Statistical Genetics, University of Michigan Ann Arbor, Michigan, USA
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan Ann Arbor, Michigan, USA
- Center for Statistical Genetics, University of Michigan Ann Arbor, Michigan, USA
| | - Seunggeun Lee
- Department of Biostatistics, University of Michigan Ann Arbor, Michigan, USA
- Center for Statistical Genetics, University of Michigan Ann Arbor, Michigan, USA
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413
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Precision medicine review: rare driver mutations and their biophysical classification. Biophys Rev 2019; 11:5-19. [PMID: 30610579 PMCID: PMC6381362 DOI: 10.1007/s12551-018-0496-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 12/18/2018] [Indexed: 02/07/2023] Open
Abstract
How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.
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414
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Liu HJ, Yan J. Crop genome-wide association study: a harvest of biological relevance. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:8-18. [PMID: 30368955 DOI: 10.1111/tpj.14139] [Citation(s) in RCA: 113] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/13/2018] [Accepted: 10/22/2018] [Indexed: 05/20/2023]
Abstract
With the advent of rapid genotyping and next-generation sequencing technologies, genome-wide association study (GWAS) has become a routine strategy for decoding genotype-phenotype associations in many species. More than 1000 such studies over the last decade have revealed substantial genotype-phenotype associations in crops and provided unparalleled opportunities to probe functional genomics. Beyond the many 'hits' obtained, this review summarizes recent efforts to increase our understanding of the genetic architecture of complex traits by focusing on non-main effects including epistasis, pleiotropy, and phenotypic plasticity. We also discuss how these achievements and the remaining gaps in our knowledge will guide future studies. Synthetic association is highlighted as leading to false causality, which is prevalent but largely underestimated. Furthermore, validation evidence is appealing for future GWAS, especially in the context of emerging genome-editing technologies.
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Affiliation(s)
- Hai-Jun Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
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415
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Hong EP, Rhee KH, Kim DH, Park JW. Identification of pleiotropic genetic variants affecting osteoporosis risk in a Korean elderly cohort. J Bone Miner Metab 2019; 37:43-52. [PMID: 29273888 DOI: 10.1007/s00774-017-0892-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 11/29/2017] [Indexed: 11/30/2022]
Abstract
Pleiotropy has important implications for understanding the genetic basis and risk assessment of osteoporosis. Our aim was to identify pleiotropic genetic variants associated with the development of osteoporosis and predict osteoporosis risk by leveraging pleiotropic variants. We evaluated the effects of 21 conventional risk factors and 185 single-nucleotide polymorphisms (SNPs) in 63 inflammation- and metabolism-related genes on osteoporosis risk in a community-based Korean cohort study of 1025 participants, the Hallym Aging Study. Ten nongenetic factors, including sex (female) and hematocrit level, and 12 SNPs across ten genes showed evidence of association with incident osteoporosis in 270 initially osteoporosis-free subjects who completed a 6-year follow up. Three gene variants, rs1801282 (PPARG-Pro12Ala, hazard ratio (HR) = 3.26, P = 0.008), rs1408282 (near EPHA7, HR = 1.87, P = 0.002), and rs2076212 (PNPLA3-Gly115Cys, HR = 2.24, P = 0.024), were associated with significant differences in survival among the three genotype groups (Pdiff = 0.042, 0.003, and 0.048, respectively). Individuals in the highest polygenic risk score tertile were 27.9 fold more likely to develop osteoporosis than those in the lowest tertile (P = 0.004). The PPARG gene in particular was a hub pleiotropic gene in the epistasis network. Our findings highlight pleiotropic modulations of metabolism- and inflammation-related genes in the development of osteoporosis and demonstrate the contribution of pleiotropic genetic variants in prediction of osteoporosis risk.
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Affiliation(s)
- Eun Pyo Hong
- Department of Medical Genetics, College of Medicine, Hallym University, 1 Hallymdaehak-gil, Chuncheon, Gangwon-do, 24252, Republic of Korea
| | - Ka Hyun Rhee
- Department of Medicine, College of Medicine, Hallym University, Chuncheon, Republic of Korea
| | - Dong Hyun Kim
- Department of Social and Preventive Medicine, College of Medicine, Hallym University, Chuncheon, Republic of Korea
- Hallym Research Institute of Clinical Epidemiology, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea
| | - Ji Wan Park
- Department of Medical Genetics, College of Medicine, Hallym University, 1 Hallymdaehak-gil, Chuncheon, Gangwon-do, 24252, Republic of Korea.
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416
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Zhang X, Veturi Y, Verma S, Bone W, Verma A, Lucas A, Hebbring S, Denny JC, Stanaway IB, Jarvik GP, Crosslin D, Larson EB, Rasmussen-Torvik L, Pendergrass SA, Smoller JW, Hakonarson H, Sleiman P, Weng C, Fasel D, Wei WQ, Kullo I, Schaid D, Chung WK, Ritchie MD. Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2019; 24:272-283. [PMID: 30864329 PMCID: PMC6457436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The link between cardiovascular diseases and neurological disorders has been widely observed in the aging population. Disease prevention and treatment rely on understanding the potential genetic nexus of multiple diseases in these categories. In this study, we were interested in detecting pleiotropy, or the phenomenon in which a genetic variant influences more than one phenotype. Marker-phenotype association approaches can be grouped into univariate, bivariate, and multivariate categories based on the number of phenotypes considered at one time. Here we applied one statistical method per category followed by an eQTL colocalization analysis to identify potential pleiotropic variants that contribute to the link between cardiovascular and neurological diseases. We performed our analyses on ~530,000 common SNPs coupled with 65 electronic health record (EHR)-based phenotypes in 43,870 unrelated European adults from the Electronic Medical Records and Genomics (eMERGE) network. There were 31 variants identified by all three methods that showed significant associations across late onset cardiac- and neurologic- diseases. We further investigated functional implications of gene expression on the detected "lead SNPs" via colocalization analysis, providing a deeper understanding of the discovered associations. In summary, we present the framework and landscape for detecting potential pleiotropy using univariate, bivariate, multivariate, and colocalization methods. Further exploration of these potentially pleiotropic genetic variants will work toward understanding disease causing mechanisms across cardiovascular and neurological diseases and may assist in considering disease prevention as well as drug repositioning in future research.
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Affiliation(s)
- Xinyuan Zhang
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA*Authors contributed equally to this work
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417
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Quan M, Du Q, Xiao L, Lu W, Wang L, Xie J, Song Y, Xu B, Zhang D. Genetic architecture underlying the lignin biosynthesis pathway involves noncoding RNAs and transcription factors for growth and wood properties in Populus. PLANT BIOTECHNOLOGY JOURNAL 2019; 17:302-315. [PMID: 29947466 PMCID: PMC6330548 DOI: 10.1111/pbi.12978] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Revised: 06/20/2018] [Accepted: 06/24/2018] [Indexed: 05/18/2023]
Abstract
Lignin provides structural support in perennial woody plants and is a complex phenolic polymer derived from phenylpropanoid pathway. Lignin biosynthesis is regulated by coordinated networks involving transcription factors (TFs), microRNAs (miRNAs) and long noncoding RNAs (lncRNAs). However, the genetic networks underlying the lignin biosynthesis pathway for tree growth and wood properties remain unknown. Here, we used association genetics (additive, dominant and epistasis) and expression quantitative trait nucleotide (eQTN) mapping to decipher the genetic networks for tree growth and wood properties in 435 unrelated individuals of Populus tomentosa. We detected 124 significant associations (P ≤ 6.89E-05) for 10 growth and wood property traits using 30 265 single nucleotide polymorphisms from 203 lignin biosynthetic genes, 81 TF genes, 36 miRNA genes and 71 lncRNA loci, implying their common roles in wood formation. Epistasis analysis uncovered 745 significant pairwise interactions, which helped to construct proposed genetic networks of lignin biosynthesis pathway and found that these regulators might affect phenotypes by linking two lignin biosynthetic genes. eQTNs were used to interpret how causal genes contributed to phenotypes. Lastly, we investigated the possible functions of the genes encoding 4-coumarate: CoA ligase and cinnamate-4-hydroxylase in wood traits using epistasis, eQTN mapping and enzymatic activity assays. Our study provides new insights into the lignin biosynthesis pathway in poplar and enables the novel genetic factors as biomarkers for facilitating genetic improvement of trees.
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Affiliation(s)
- Mingyang Quan
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Qingzhang Du
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Liang Xiao
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Wenjie Lu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Longxin Wang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Jianbo Xie
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Yuepeng Song
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Baohua Xu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Deqiang Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
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418
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Tudorache C, Slabbekoorn H, Robbers Y, Hin E, Meijer JH, Spaink HP, Schaaf MJM. Biological clock function is linked to proactive and reactive personality types. BMC Biol 2018; 16:148. [PMID: 30577878 PMCID: PMC6303931 DOI: 10.1186/s12915-018-0618-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 12/04/2018] [Indexed: 12/16/2022] Open
Abstract
Background Many physiological processes in our body are controlled by the biological clock and show circadian rhythmicity. It is generally accepted that a robust rhythm is a prerequisite for optimal functioning and that a lack of rhythmicity can contribute to the pathogenesis of various diseases. Here, we tested in a heterogeneous laboratory zebrafish population whether and how variation in the rhythmicity of the biological clock is associated with the coping styles of individual animals, as assessed in a behavioural assay to reliably measure this along a continuum between proactive and reactive extremes. Results Using RNA sequencing on brain samples, we demonstrated a prominent difference in the expression level of genes involved in the biological clock between proactive and reactive individuals. Subsequently, we tested whether this correlation between gene expression and coping style was due to a consistent change in the level of clock gene expression or to a phase shift or to altered amplitude of the circadian rhythm of gene expression. Our data show a remarkable individual variation in amplitude of the clock gene expression rhythms, which was also reflected in the fluctuating concentrations of melatonin and cortisol, and locomotor activity. This variation in rhythmicity showed a strong correlation with the coping style of the individual, ranging from robust rhythms with large amplitudes in proactive fish to a complete absence of rhythmicity in reactive fish. The rhythmicity of the proactive fish decreased when challenged with constant light conditions whereas the rhythmicity of reactive individuals was not altered. Conclusion These results shed new light on the role of the biological clock by demonstrating that large variation in circadian rhythmicity of individuals may occur within populations. The observed correlation between coping style and circadian rhythmicity suggests that the level of rhythmicity forms an integral part of proactive or reactive coping styles. Electronic supplementary material The online version of this article (10.1186/s12915-018-0618-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Hans Slabbekoorn
- Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Yuri Robbers
- Molecular Cell Biology, Leiden University Medical Center, Leiden, The Netherlands
| | - Eline Hin
- Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Johanna H Meijer
- Molecular Cell Biology, Leiden University Medical Center, Leiden, The Netherlands
| | - Herman P Spaink
- Institute of Biology, Leiden University, Leiden, The Netherlands
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419
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PCA-Based Multiple-Trait GWAS Analysis: A Powerful Model for Exploring Pleiotropy. Animals (Basel) 2018; 8:ani8120239. [PMID: 30562943 PMCID: PMC6316348 DOI: 10.3390/ani8120239] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 11/26/2018] [Accepted: 11/28/2018] [Indexed: 11/16/2022] Open
Abstract
Principal component analysis (PCA) is a potential approach that can be applied in multiple-trait genome-wide association studies (GWAS) to explore pleiotropy, as well as increase the power of quantitative trait loci (QTL) detection. In this study, the relationship of test single nucleotide polymorphisms (SNPs) was determined between single-trait GWAS and PCA-based GWAS. We found that the estimated pleiotropic quantitative trait nucleotides (QTNs) β * ^ were in most cases larger than the single-trait model estimations ( β 1 ^ and β 2 ^ ). Analysis using the simulated data showed that PCA-based multiple-trait GWAS has improved statistical power for detecting QTL compared to single-trait GWAS. For the minor allele frequency (MAF), when the MAF of QTNs was greater than 0.2, the PCA-based model had a significant advantage in detecting the pleiotropic QTNs, but when its MAF was reduced from 0.2 to 0, the advantage began to disappear. In addition, as the linkage disequilibrium (LD) of the pleiotropic QTNs decreased, its detection ability declined in the co-localization effect model. Furthermore, on the real data of 1141 Simmental cattle, we applied the PCA model to the multiple-trait GWAS analysis and identified a QTL that was consistent with a candidate gene, MCHR2, which was associated with presoma muscle development in cattle. In summary, PCA-based multiple-trait GWAS is an efficient model for exploring pleiotropic QTNs in quantitative traits.
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420
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Fadason T, Schierding W, Lumley T, O'Sullivan JM. Chromatin interactions and expression quantitative trait loci reveal genetic drivers of multimorbidities. Nat Commun 2018; 9:5198. [PMID: 30518762 PMCID: PMC6281603 DOI: 10.1038/s41467-018-07692-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 11/14/2018] [Indexed: 02/06/2023] Open
Abstract
Clinical studies of non-communicable diseases identify multimorbidities that suggest a common set of predisposing factors. Despite the fact that humans have ~24,000 genes, we do not understand the genetic pathways that contribute to the development of multimorbid non-communicable disease. Here we create a multimorbidity atlas of traits based on pleiotropy of spatially regulated genes. Using chromatin interaction and expression Quantitative Trait Loci (eQTL) data, we analyse 20,782 variants (p < 5 × 10-6) associated with 1351 phenotypes to identify 16,248 putative spatial eQTL-eGene pairs that are involved in 76,013 short- and long-range regulatory interactions (FDR < 0.05) in different human tissues. Convex biclustering of spatial eGenes that are shared among phenotypes identifies complex interrelationships between nominally different phenotype-associated SNPs. Our approach enables the simultaneous elucidation of variant interactions with target genes that are drivers of multimorbidity, and those that contribute to unique phenotype associated characteristics.
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Affiliation(s)
- Tayaza Fadason
- The Liggins Institute, The University of Auckland, Auckland, 1023, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, 1010, New Zealand
| | - William Schierding
- The Liggins Institute, The University of Auckland, Auckland, 1023, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, 1010, New Zealand
| | - Thomas Lumley
- The Department of Biostatistics, The University of Auckland, Auckland, 1010, New Zealand
| | - Justin M O'Sullivan
- The Liggins Institute, The University of Auckland, Auckland, 1023, New Zealand.
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, 1010, New Zealand.
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421
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Stephan Y, Sutin AR, Luchetti M, Caille P, Terracciano A. Polygenic Score for Alzheimer Disease and cognition: The mediating role of personality. J Psychiatr Res 2018; 107:110-113. [PMID: 30384091 PMCID: PMC6346269 DOI: 10.1016/j.jpsychires.2018.10.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 09/03/2018] [Accepted: 10/22/2018] [Indexed: 01/19/2023]
Abstract
Alzheimer's disease (AD) polygenic risk score (PGS) is associated with lower cognitive functioning even among older individuals without dementia. We tested the hypothesis that personality traits mediate the association between AD genetic risk and cognitive functioning. Participants (N > 7,000, aged 50-99 years old) from the Health and Retirement Study were genotyped and completed personality and cognition tests at baseline. Cognition was assessed again four years later. Bootstrap analysis revealed that a higher AD polygenic risk score was associated with lower cognitive scores at baseline through higher neuroticism, lower conscientiousness, and lower levels of the industriousness facet of conscientiousness. In addition, a higher polygenic score for AD was associated with decline in cognition over four years in part through higher neuroticism and lower conscientiousness. The findings support the hypothesis that the genetic vulnerability for AD contributes to cognitive functioning in part through its association with personality traits.
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422
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Dai JY, Peters U, Wang X, Kocarnik J, Chang-Claude J, Slattery ML, Chan A, Lemire M, Berndt SI, Casey G, Song M, Jenkins MA, Brenner H, Thrift AP, White E, Hsu L. Diagnostics for Pleiotropy in Mendelian Randomization Studies: Global and Individual Tests for Direct Effects. Am J Epidemiol 2018; 187:2672-2680. [PMID: 30188971 PMCID: PMC6269243 DOI: 10.1093/aje/kwy177] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2017] [Revised: 05/03/2018] [Accepted: 05/09/2018] [Indexed: 12/29/2022] Open
Abstract
Diagnosing pleiotropy is critical for assessing the validity of Mendelian randomization (MR) analyses. The popular MR-Egger method evaluates whether there is evidence of bias-generating pleiotropy among a set of candidate genetic instrumental variables. In this article, we propose a statistical method-global and individual tests for direct effects (GLIDE)-for systematically evaluating pleiotropy among the set of genetic variants (e.g., single nucleotide polymorphisms (SNPs)) used for MR. As a global test, simulation experiments suggest that GLIDE is nearly uniformly more powerful than the MR-Egger method. As a sensitivity analysis, GLIDE is capable of detecting outliers in individual variant-level pleiotropy, in order to obtain a refined set of genetic instrumental variables. We used GLIDE to analyze both body mass index and height for associations with colorectal cancer risk in data from the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry (multiple studies). Among the body mass index-associated SNPs and the height-associated SNPs, several individual variants showed evidence of pleiotropy. Removal of these potentially pleiotropic SNPs resulted in attenuation of respective estimates of the causal effects. In summary, the proposed GLIDE method is useful for sensitivity analyses and improves the validity of MR.
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Affiliation(s)
- James Y Dai
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
| | - Xiaoyu Wang
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jonathan Kocarnik
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Genetic Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, Utah
| | - Andrew Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Mathieu Lemire
- Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Graham Casey
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California
| | - Mingyang Song
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Mark A Jenkins
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center and National Center for Tumor Diseases, Heidelberg, Germany
- German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Aaron P Thrift
- Department of Medicine and Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Emily White
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
| | - Li Hsu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
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423
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Reddon H, Patel Y, Turcotte M, Pigeyre M, Meyre D. Revisiting the evolutionary origins of obesity: lazy versus peppy-thrifty genotype hypothesis. Obes Rev 2018; 19:1525-1543. [PMID: 30261552 DOI: 10.1111/obr.12742] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 06/26/2018] [Accepted: 07/01/2018] [Indexed: 12/31/2022]
Abstract
The recent global obesity epidemic is attributed to major societal and environmental changes, such as excessive energy intake and sedentary lifestyle. However, exposure to 'obesogenic' environments does not necessarily result in obesity at the individual level, as 40-75% of body mass index variation in population is attributed to genetic differences. The thrifty genotype theory posits that genetic variants promoting efficient food sequestering and optimal deposition of fat during periods of food abundance were evolutionarily advantageous for the early hunter-gatherer and were positively selected. However, the thrifty genotype is likely too simplistic and fails to provide a justification for the complex distribution of obesity predisposing gene variants and for the broad range of body mass index observed in diverse ethnic groups. This review proposes that gene pleiotropy may better account for the variability in the distribution of obesity susceptibility alleles across modern populations. We outline the lazy-thrifty versus peppy-thrifty genotype hypothesis and detail the body of evidence in the literature in support of this novel concept. Future population genetics and mathematical modelling studies that account for pleiotropy may further improve our understanding of the evolutionary origins of the current obesity epidemic.
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Affiliation(s)
- H Reddon
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada
| | - Y Patel
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada
| | - M Turcotte
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada
| | - M Pigeyre
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
| | - D Meyre
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
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424
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Abstract
According to the Sasang theory, humans can be categorized into one of the four Sasang constitution (SC) types. The four SC types are Tae-Yang (TY), Tae-Eum (TE), So-Yang (SY), and So-Eum (SE), which are determined mainly on the basis of anthropometric characteristics, personality, and the balance of the physiological functions of the major organ systems. There is a growing recognition in the complementary and alternative medicine area that SC types have the potential to be a useful scientific tool for the prevention, diagnosis, and treatment of diseases (Cooper, Evidence Based Complementary and Alternative Medicine, Vol. 6 (Suppl. 1), 2009, pp. 1–3). The main purposes of the present study are to estimate genetic and environmental influences on SC types, and to explore genetic and environmental correlations that affect phenotypic associations among the SC types. In total, 1,742 (365 monozygotic male, 173 dizygotic male, 675 monozygotic female, 271 dizygotic female, and 258 opposite-sex dizygotic) twins (mean age = 19.1 ± 3.1 year) completed a Sasang questionnaire. Univariate and multivariate model-fitting analyses were performed. Total (additive and non-additive) genetic influences were 71% for males and 81% for females in TE, 70% for males and 71% for females in SE, and 47% for both sexes in SY. Non-additive genetic effects were substantial, and shared environmental influences were negligible in most SC types. Multivariate model-fitting analysis revealed that non-additive genetic and individual-specific environmental correlations between TE and SE were -0.92 (95% CI [-0.89, -0.93]) and -0.62 (95% CI [-0.57, -0.68]), respectively. The corresponding estimates were -0.55 (95% CI [-0.48, -0.61]) and -0.44 (95% CI [-0.37, -0.51]) between TE and SY and 0.19 (95% CI [0.09, 0.29]) and -0.40 (95% CI [-0.32, -0.47]) between SE and SY. These results suggest that the phenotypic associations among SC types may be mediated by pleiotropic mechanism of genes.
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425
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Diogo D, Tian C, Franklin CS, Alanne-Kinnunen M, March M, Spencer CCA, Vangjeli C, Weale ME, Mattsson H, Kilpeläinen E, Sleiman PMA, Reilly DF, McElwee J, Maranville JC, Chatterjee AK, Bhandari A, Nguyen KDH, Estrada K, Reeve MP, Hutz J, Bing N, John S, MacArthur DG, Salomaa V, Ripatti S, Hakonarson H, Daly MJ, Palotie A, Hinds DA, Donnelly P, Fox CS, Day-Williams AG, Plenge RM, Runz H. Phenome-wide association studies across large population cohorts support drug target validation. Nat Commun 2018; 9:4285. [PMID: 30327483 PMCID: PMC6191429 DOI: 10.1038/s41467-018-06540-3] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 09/05/2018] [Indexed: 12/12/2022] Open
Abstract
Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.
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Affiliation(s)
| | - Chao Tian
- 23andMe Inc, Mountain View, CA, 94041, USA
| | | | - Mervi Alanne-Kinnunen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014, Helsinki, Finland
| | - Michael March
- The Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | | | | | - Hannele Mattsson
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014, Helsinki, Finland
- National Institute for Health and Welfare, FI-00271, Helsinki, Finland
| | - Elina Kilpeläinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014, Helsinki, Finland
| | - Patrick M A Sleiman
- The Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Joshua McElwee
- Merck Sharp & Dohme, Boston, MA, 02115, USA
- Nimbus Therapeutics, Cambridge, MA, 02139, USA
| | - Joseph C Maranville
- Merck Sharp & Dohme, Boston, MA, 02115, USA
- Celgene, Cambridge, MA, 02140, USA
| | - Arnaub K Chatterjee
- Merck Sharp & Dohme, Boston, MA, 02115, USA
- McKinsey & Co., Boston, MA, 02210, USA
| | - Aman Bhandari
- Merck Sharp & Dohme, Boston, MA, 02115, USA
- Vertex Pharmaceuticals, Boston, MA, 02210, USA
| | | | - Karol Estrada
- Biogen, Research and Early Development, Cambridge, MA, 02142, USA
| | | | | | - Nan Bing
- Pfizer, Cambridge, MA, 02139, USA
| | - Sally John
- Biogen, Research and Early Development, Cambridge, MA, 02142, USA
| | - Daniel G MacArthur
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Veikko Salomaa
- National Institute for Health and Welfare, FI-00271, Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Hakon Hakonarson
- The Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mark J Daly
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | | | | | | | - Aaron G Day-Williams
- Merck Sharp & Dohme, Boston, MA, 02115, USA
- Biogen, Research and Early Development, Cambridge, MA, 02142, USA
| | - Robert M Plenge
- Merck Sharp & Dohme, Boston, MA, 02115, USA
- Celgene, Cambridge, MA, 02140, USA
| | - Heiko Runz
- Merck Sharp & Dohme, Boston, MA, 02115, USA.
- Biogen, Research and Early Development, Cambridge, MA, 02142, USA.
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426
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Genetic pleiotropy between mood disorders, metabolic, and endocrine traits in a multigenerational pedigree. Transl Psychiatry 2018; 8:218. [PMID: 30315151 PMCID: PMC6185949 DOI: 10.1038/s41398-018-0226-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 05/10/2018] [Accepted: 07/14/2018] [Indexed: 12/15/2022] Open
Abstract
Bipolar disorder (BD) is a mental disorder characterized by alternating periods of depression and mania. Individuals with BD have higher levels of early mortality than the general population, and a substantial proportion of this is due to increased risk for comorbid diseases. To identify the molecular events that underlie BD and related medical comorbidities, we generated imputed whole-genome sequence data using a population-specific reference panel for an extended multigenerational Old Order Amish pedigree (n = 394), segregating BD and related disorders. First, we investigated all putative disease-causing variants at known Mendelian disease loci present in this pedigree. Second, we performed genomic profiling using polygenic risk scores (PRS) to establish each individual's risk for several complex diseases. We identified a set of Mendelian variants that co-occur in individuals with BD more frequently than their unaffected family members, including the R3527Q mutation in APOB associated with hypercholesterolemia. Using PRS, we demonstrated that BD individuals from this pedigree were enriched for the same common risk alleles for BD as the general population (β = 0.416, p = 6 × 10-4). Furthermore, we find evidence for a common genetic etiology between BD risk and polygenic risk for clinical autoimmune thyroid disease (p = 1 × 10-4), diabetes (p = 1 × 10-3), and lipid traits such as triglyceride levels (p = 3 × 10-4) in the pedigree. We identify genomic regions that contribute to the differences between BD individuals and unaffected family members by calculating local genetic risk for independent LD blocks. Our findings provide evidence for the extensive genetic pleiotropy that can drive epidemiological findings of comorbidities between diseases and other complex traits.
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427
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Selzam S, Coleman JRI, Caspi A, Moffitt TE, Plomin R. A polygenic p factor for major psychiatric disorders. Transl Psychiatry 2018; 8:205. [PMID: 30279410 PMCID: PMC6168558 DOI: 10.1038/s41398-018-0217-4] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 07/16/2018] [Indexed: 12/28/2022] Open
Abstract
It has recently been proposed that a single dimension, called the p factor, can capture a person's liability to mental disorder. Relevant to the p hypothesis, recent genetic research has found surprisingly high genetic correlations between pairs of psychiatric disorders. Here, for the first time, we compare genetic correlations from different methods and examine their support for a genetic p factor. We tested the hypothesis of a genetic p factor by applying principal component analysis to matrices of genetic correlations between major psychiatric disorders estimated by three methods-family study, genome-wide complex trait analysis, and linkage-disequilibrium score regression-and on a matrix of polygenic score correlations constructed for each individual in a UK-representative sample of 7 026 unrelated individuals. All disorders loaded positively on a first unrotated principal component, which accounted for 57, 43, 35, and 22% of the variance respectively for the four methods. Our results showed that all four methods provided strong support for a genetic p factor that represents the pinnacle of the hierarchical genetic architecture of psychopathology.
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Affiliation(s)
- Saskia Selzam
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Jonathan R. I. Coleman
- 0000 0001 2322 6764grid.13097.3cMRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK ,0000 0000 9439 0839grid.37640.36NIHR Biomedical Research Centre for Mental Health, South London and Maudsley NHS Trust, London, UK
| | - Avshalom Caspi
- 0000 0001 2322 6764grid.13097.3cMRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK ,0000 0004 1936 7961grid.26009.3dDepartment of Psychology and Neuroscience, Duke University, Durham, USA ,0000 0004 1936 7961grid.26009.3dCenter for Genomic and Computational Biology, Duke University, Durham, USA ,0000000100241216grid.189509.cDepartment of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, USA
| | - Terrie E. Moffitt
- 0000 0001 2322 6764grid.13097.3cMRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK ,0000 0004 1936 7961grid.26009.3dDepartment of Psychology and Neuroscience, Duke University, Durham, USA ,0000 0004 1936 7961grid.26009.3dCenter for Genomic and Computational Biology, Duke University, Durham, USA ,0000000100241216grid.189509.cDepartment of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, USA
| | - Robert Plomin
- 0000 0001 2322 6764grid.13097.3cMRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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428
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Qi G, Chatterjee N. Heritability informed power optimization (HIPO) leads to enhanced detection of genetic associations across multiple traits. PLoS Genet 2018; 14:e1007549. [PMID: 30289880 PMCID: PMC6192650 DOI: 10.1371/journal.pgen.1007549] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Revised: 10/17/2018] [Accepted: 07/09/2018] [Indexed: 12/31/2022] Open
Abstract
Genome-wide association studies have shown that pleiotropy is a common phenomenon that can potentially be exploited for enhanced detection of susceptibility loci. We propose heritability informed power optimization (HIPO) for conducting powerful pleiotropic analysis using summary-level association statistics. We find optimal linear combinations of association coefficients across traits that are expected to maximize non-centrality parameter for the underlying test statistics, taking into account estimates of heritability, sample size variations and overlaps across the traits. Simulation studies show that the proposed method has correct type I error, robust to population stratification and leads to desired genome-wide enrichment of association signals. Application of the proposed method to publicly available data for three groups of genetically related traits, lipids (N = 188,577), psychiatric diseases (Ncase = 33,332, Ncontrol = 27,888) and social science traits (N ranging between 161,460 to 298,420 across individual traits) increased the number of genome-wide significant loci by 12%, 200% and 50%, respectively, compared to those found by analysis of individual traits. Evidence of replication is present for many of these loci in subsequent larger studies for individual traits. HIPO can potentially be extended to high-dimensional phenotypes as a way of dimension reduction to maximize power for subsequent genetic association testing.
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Affiliation(s)
- Guanghao Qi
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Department of Oncology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
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429
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Chen J, Wu JS, Mize T, Shui D, Chen X. Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits. J Neuroimmune Pharmacol 2018; 13:532-540. [PMID: 30276764 DOI: 10.1007/s11481-018-9811-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 09/12/2018] [Indexed: 01/03/2023]
Abstract
Schizophrenia is genetically heterogeneous and comorbid with many conditions. In this study, we explored polygenic scores (PGSs) from genetically related conditions and traits to predict schizophrenia diagnosis using both logistic regression and deep neural network (DNN) models. We used the combined Molecular Genetics of Schizophrenia and Swedish Schizophrenia Case Control Study (MGS + SSCCS) data for training and testing the models, and used the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) data as independent validation. We screened 28 conditions and traits comorbid with schizophrenia to identify traits as potential predictors and used LASSO regression to select predictors for model construction. We investigated how PGS calculation influenced model performance. We found that the inclusion of comorbid traits improved model performance and PGSs calculated from two traits were more generalizable in independent validation. With a DNN model using 19 PGS predictors, we accomplished a prediction accuracy of 0.813 and an AUC of 0.905 in the MGS + SSCCS data. When this model was validated with the CATIE data, it achieved an accuracy of 0.721 and AUC of 0.747. Our results indicate that PGSs alone may not be sufficient to predict schizophrenia accurately and the inclusion of behavioral and clinical data may be necessary for more accurate prediction model.
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Affiliation(s)
- Jingchun Chen
- Nevada institute of personalized medicine, University of Nevada Las Vegas, Las Vegas, NV, 89154-4009, USA
| | - Jian-Shing Wu
- Nevada institute of personalized medicine, University of Nevada Las Vegas, Las Vegas, NV, 89154-4009, USA
| | - Travis Mize
- Department of Psychology, University of Nevada Las Vegas, 4505 S. Maryland Parkway, Las Vegas, NV, 89154-4009, USA
| | - Dandan Shui
- Nevada institute of personalized medicine, University of Nevada Las Vegas, Las Vegas, NV, 89154-4009, USA
| | - Xiangning Chen
- Nevada institute of personalized medicine, University of Nevada Las Vegas, Las Vegas, NV, 89154-4009, USA. .,Department of Psychology, University of Nevada Las Vegas, 4505 S. Maryland Parkway, Las Vegas, NV, 89154-4009, USA.
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430
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Smoller JW. The use of electronic health records for psychiatric phenotyping and genomics. Am J Med Genet B Neuropsychiatr Genet 2018; 177:601-612. [PMID: 28557243 PMCID: PMC6440216 DOI: 10.1002/ajmg.b.32548] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2017] [Accepted: 04/20/2017] [Indexed: 12/22/2022]
Abstract
The widespread adoption of electronic health record (EHRs) in healthcare systems has created a vast and continuously growing resource of clinical data and provides new opportunities for population-based research. In particular, the linking of EHRs to biospecimens and genomic data in biobanks may help address what has become a rate-limiting study for genetic research: the need for large sample sizes. The principal roadblock to capitalizing on these resources is the need to establish the validity of phenotypes extracted from the EHR. For psychiatric genetic research, this represents a particular challenge given that diagnosis is based on patient reports and clinician observations that may not be well-captured in billing codes or narrative records. This review addresses the opportunities and pitfalls in EHR-based phenotyping with a focus on their application to psychiatric genetic research. A growing number of studies have demonstrated that diagnostic algorithms with high positive predictive value can be derived from EHRs, especially when structured data are supplemented by text mining approaches. Such algorithms enable semi-automated phenotyping for large-scale case-control studies. In addition, the scale and scope of EHR databases have been used successfully to identify phenotypic subgroups and derive algorithms for longitudinal risk prediction. EHR-based genomics are particularly well-suited to rapid look-up replication of putative risk genes, studies of pleiotropy (phenomewide association studies or PheWAS), investigations of genetic networks and overlap across the phenome, and pharmacogenomic research. EHR phenotyping has been relatively under-utilized in psychiatric genomic research but may become a key component of efforts to advance precision psychiatry.
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Affiliation(s)
- Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
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431
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Smeland OB, Andreassen OA. How can genetics help understand the relationship between cognitive dysfunction and schizophrenia? Scand J Psychol 2018; 59:26-31. [PMID: 29356008 DOI: 10.1111/sjop.12407] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 08/14/2017] [Indexed: 01/05/2023]
Abstract
Despite the consistent finding that cognitive dysfunction is a core characteristic of schizophrenia (SCZ), little is known about the underlying pathophysiology. Recent progress in human genetics, driven by large genome-wide association studies (GWAS), has provided new data about the genetic architecture of complex human traits, including cognition and SCZ. Novel analytical tools have provided unprecedented opportunities to leverage the large amount of information from GWAS. Here we review the latest findings related to genetic architecture and risk genes of SCZ and cognitive functions, and recent findings of overlapping genetic factors. The recent GWAS of SCZ implicate over 100 risk gene loci, each with a small effect. A similar genetic architecture seems to be present in cognitive domains, suggesting that these phenotypes are highly polygenic. Further, GWAS have revealed more than 20 gene loci associated with cognitive traits, including intelligence, general cognition (g-factor), reaction time and verbal-numerical reasoning. Several gene loci have been implicated in educational attainment, a proxy measure of cognitive function. Recently, overlapping gene loci were found between education and SCZ, and between SCZ and cognitive traits, suggesting common genetic risk between SCZ and cognitive dysfunction. Mathematical modeling of GWAS of cognition and SCZ indicate that only a fraction of the heritability is identified. The evidence suggests a polygenic architecture for SCZ and cognitive functions, and a large degree of shared genetic risk. This indicates novel molecular genetic mechanisms and strengthens the notion that SCZ is more likely a part of the normal distribution and not a separate entity.
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Affiliation(s)
- Olav B Smeland
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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432
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Wang T, Xue X, Xie X, Ye K, Zhu X, Elston RC. Adjustment for covariates using summary statistics of genome-wide association studies. Genet Epidemiol 2018; 42:812-825. [PMID: 30238496 DOI: 10.1002/gepi.22148] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 06/18/2018] [Accepted: 06/19/2018] [Indexed: 01/09/2023]
Abstract
Linear regression is a standard approach to identify genetic variants associated with continuous traits in genome-wide association studies (GWAS). In a standard epidemiology study, linear regression is often performed with adjustment for covariates to estimate the independent effect of a predictor variable or to improve statistical power by reducing residual variability. However, it is problematic to adjust for heritable covariates in genetic association analysis. Here, we propose a new method that utilizes summary statistics of the covariate from additional samples for reducing the residual variability and hence improves statistical power. Our simulation study showed that the proposed methodology can maintain a good control of Type I error and can achieve much higher power than a simple linear regression. The method is illustrated by an application to the GWAS results from the Genetic Investigation of Anthropometric Traits consortium.
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Affiliation(s)
- Tao Wang
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, New York, New York
| | - Xiaonan Xue
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, New York, New York
| | - Xianhong Xie
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, New York, New York
| | - Kenny Ye
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, New York, New York
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Robert C Elston
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio
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433
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Wang X, Boekstegers F, Brinster R. Methods and results from the genome-wide association group at GAW20. BMC Genet 2018; 19:79. [PMID: 30255814 PMCID: PMC6157187 DOI: 10.1186/s12863-018-0649-0] [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] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND This paper summarizes the contributions from the Genome-wide Association Study group (GWAS group) of the GAW20. The GWAS group contributions focused on topics such as association tests, phenotype imputation, and application of empirical kinships. The goals of the GWAS group contributions were varied. A real or a simulated data set based on the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was employed by different methods. Different outcomes and covariates were considered, and quality control procedures varied throughout the contributions. RESULTS The consideration of heritability and family structure played a major role in some contributions. The inclusion of family information and adaptive weights based on data were found to improve power in genome-wide association studies. It was proven that gene-level approaches are more powerful than single-marker analysis. Other contributions focused on the comparison between pedigree-based kinship and empirical kinship matrices, and investigated similar results in heritability estimation, association mapping, and genomic prediction. A new approach for linkage mapping of triglyceride levels was able to identify a novel linkage signal. CONCLUSIONS This summary paper reports on promising statistical approaches and findings of the members of the GWAS group applied on real and simulated data which encompass the current topics of epigenetic and pharmacogenomics.
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Affiliation(s)
- Xuexia Wang
- University of North Texas, GAB 459, 1155 Union Circle #311430, Denton, TX 76203 USA
| | - Felix Boekstegers
- Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany
| | - Regina Brinster
- Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany
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434
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Zhang Z, Ma P, Li Q, Xiao Q, Sun H, Olasege BS, Wang Q, Pan Y. Exploring the Genetic Correlation Between Growth and Immunity Based on Summary Statistics of Genome-Wide Association Studies. Front Genet 2018; 9:393. [PMID: 30271426 PMCID: PMC6149433 DOI: 10.3389/fgene.2018.00393] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 08/29/2018] [Indexed: 01/09/2023] Open
Abstract
The relationship between growth and immune phenotypes has been presented in the context of physiology and energy allocation theory, but has rarely been explained genetically in humans. As more summary statistics of genome-wide association studies (GWAS) become available, it is increasingly possible to explore the genetic relationship between traits at the level of genome-wide summary statistics. In this study, publicly available summary statistics of growth and immune related traits were used to evaluate the genetic correlation coefficients between immune and growth traits, as well as the cause and effect relationship between them. In addition, pleiotropic variants and KEGG pathways were identified. As a result, we found negative correlations between birthweight and immune cell count phenotypes, a positive correlation between childhood head circumference and eosinophil counts (EO), and positive or negative correlations between childhood body mass index and immune phenotypes. Statistically significant negative effects of immune cell count phenotypes on human height, and a slight but significant negative influence of human height on allergic disease were also observed. A total of 98 genomic regions were identified as containing variants potentially related to both immunity and growth. Some variants, such as rs3184504 located in SH2B3, rs13107325 in SLC39A8, and rs1260326 located in GCKR, which have been identified to be pleiotropic SNPs among other traits, were found to also be related to growth and immune traits in this study. Meanwhile, the most frequent overlapping KEGG pathways between growth and immune phenotypes were autoimmune related pathways. Pleiotropic pathways such as the adipocytokine signaling pathway and JAK-STAT signaling pathway were also identified to be significant. The results of this study indicate the complex genetic relationship between growth and immune phenotypes, and reveal the genetic background of their correlation in the context of pleiotropy.
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Affiliation(s)
- Zhe Zhang
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Peipei Ma
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Qiumeng Li
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Qian Xiao
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Hao Sun
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Babatunde Shittu Olasege
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Qishan Wang
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Yuchun Pan
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
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Suri P, Palmer MR, Tsepilov YA, Freidin MB, Boer CG, Yau MS, Evans DS, Gelemanovic A, Bartz TM, Nethander M, Arbeeva L, Karssen L, Neogi T, Campbell A, Mellstrom D, Ohlsson C, Marshall LM, Orwoll E, Uitterlinden A, Rotter JI, Lauc G, Psaty BM, Karlsson MK, Lane NE, Jarvik GP, Polasek O, Hochberg M, Jordan JM, Van Meurs JBJ, Jackson R, Nielson CM, Mitchell BD, Smith BH, Hayward C, Smith NL, Aulchenko YS, Williams FMK. Genome-wide meta-analysis of 158,000 individuals of European ancestry identifies three loci associated with chronic back pain. PLoS Genet 2018; 14:e1007601. [PMID: 30261039 PMCID: PMC6159857 DOI: 10.1371/journal.pgen.1007601] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 08/02/2018] [Indexed: 01/07/2023] Open
Abstract
Back pain is the #1 cause of years lived with disability worldwide, yet surprisingly little is known regarding the biology underlying this symptom. We conducted a genome-wide association study (GWAS) meta-analysis of chronic back pain (CBP). Adults of European ancestry were included from 15 cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and from the UK Biobank interim data release. CBP cases were defined as those reporting back pain present for ≥3-6 months; non-cases were included as comparisons ("controls"). Each cohort conducted genotyping using commercially available arrays followed by imputation. GWAS used logistic regression models with additive genetic effects, adjusting for age, sex, study-specific covariates, and population substructure. The threshold for genome-wide significance in the fixed-effect inverse-variance weighted meta-analysis was p<5×10(-8). Suggestive (p<5×10(-7)) and genome-wide significant (p<5×10(-8)) variants were carried forward for replication or further investigation in the remaining UK Biobank participants not included in the discovery sample. The discovery sample comprised 158,025 individuals, including 29,531 CBP cases. A genome-wide significant association was found for the intronic variant rs12310519 in SOX5 (OR 1.08, p = 7.2×10(-10)). This was subsequently replicated in 283,752 UK Biobank participants not included in the discovery sample, including 50,915 cases (OR 1.06, p = 5.3×10(-11)), and exceeded genome-wide significance in joint meta-analysis (OR 1.07, p = 4.5×10(-19)). We found suggestive associations at three other loci in the discovery sample, two of which exceeded genome-wide significance in joint meta-analysis: an intergenic variant, rs7833174, located between CCDC26 and GSDMC (OR 1.05, p = 4.4×10(-13)), and an intronic variant, rs4384683, in DCC (OR 0.97, p = 2.4×10(-10)). In this first reported meta-analysis of GWAS for CBP, we identified and replicated a genetic locus associated with CBP (SOX5). We also identified 2 other loci that reached genome-wide significance in a 2-stage joint meta-analysis (CCDC26/GSDMC and DCC).
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Affiliation(s)
- Pradeep Suri
- Seattle Epidemiologic Research and Information Center (ERIC), Department of Veterans Affairs Office of Research and Development, Seattle, Washington, United States of America
- Division of Rehabilitation Care Services, VA Puget Sound Health Care System, Seattle, Washington, United States of America
- Department of Rehabilitation Medicine, University of Washington, Seattle, Washington, United States of America
| | - Melody R. Palmer
- Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Yakov A. Tsepilov
- Polyomica, ‘s-Hertogenbosch, the Netherlands
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, Novosibirsk, Russia
- Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics SD RAS, Novosibirsk, Russia
| | - Maxim B. Freidin
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Cindy G. Boer
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Michelle S. Yau
- Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, United States of America
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Daniel S. Evans
- California Pacific Medical Center Research Institute, San Francisco, California, United States of America
| | - Andrea Gelemanovic
- Department of Public Health, University of Split Medical School, Split, Croatia
| | - Traci M. Bartz
- Cardiovascular Health Research Unit and Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Maria Nethander
- Department of Medicine, University of Göteborg, Göteborg, Sweden
| | - Liubov Arbeeva
- Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | | | - Tuhina Neogi
- Clinical Epidemiology Unit, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Dan Mellstrom
- Geriatric Medicine, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Sweden
| | - Claes Ohlsson
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Göteborg, Sweden
| | - Lynn M. Marshall
- Department of Orthopedics and Rehabilitation, Oregon Health and Science University, Portland, Oregon, United States of America
| | - Eric Orwoll
- Department of Medicine, Oregon Health and Science University, Portland, Oregon, United States of America
| | - Andre Uitterlinden
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California, United States of America
- Division of Genomic Outcomes, Departments of Pediatrics and Medicine, Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Gordan Lauc
- Genos Ltd, Osijek, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit and Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Health Services, University of Washington, Seattle, Washington, United States of America
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, United States of America
| | - Magnus K. Karlsson
- Department of Orthopedics, Skane University Hospital, Lund University, Malmö, Sweden
| | - Nancy E. Lane
- Departments of Medicine and Rheumatology, University of California Davis, Sacramento, California, United States of America
| | - Gail P. Jarvik
- Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Ozren Polasek
- Department of Public Health, University of Split Medical School, Split, Croatia
- Hospital “Sveti Ivan”, Zagreb, Croatia
| | - Marc Hochberg
- Departments of Medicine and Epidemiology, University of Maryland, Baltimore, Maryland, United States of America
| | - Joanne M. Jordan
- Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | | | - Rebecca Jackson
- Department of Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Carrie M. Nielson
- School of Public Health, Oregon Health and Science University, Portland, Oregon, United States of America
| | - Braxton D. Mitchell
- Departments of Medicine and Epidemiology, University of Maryland, Baltimore, Maryland, United States of America
- Geriatric Research, Education and Clinical Center, Veterans Affairs Medical Center, Baltimore, Maryland, United States of America
| | - Blair H. Smith
- Division of Population Health Sciences, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, United Kingdom
| | - Nicholas L. Smith
- Seattle Epidemiologic Research and Information Center (ERIC), Department of Veterans Affairs Office of Research and Development, Seattle, Washington, United States of America
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, United States of America
| | | | - Frances M. K. Williams
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
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436
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Chen B, Chen J, Du Q, Zhou D, Wang L, Xie J, Li Y, Zhang D. Genetic variants in microRNA biogenesis genes as novel indicators for secondary growth in Populus. THE NEW PHYTOLOGIST 2018; 219:1263-1282. [PMID: 29916214 DOI: 10.1111/nph.15262] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 05/06/2018] [Indexed: 05/21/2023]
Abstract
MicroRNAs (miRNAs) function as key regulators of complex traits, but how genetic alterations in miRNA biogenesis genes (miRBGs) affect quantitative variation has not been elucidated. We conducted transcript analyses and association genetics to investigate how miRBGs, miRNA genes (MIRNAs) and their respective targets contribute to secondary growth in a natural population of 435 Populus tomentosa individuals. This analysis identified 29 843 common single-nucleotide polymorphisms (SNPs; frequency > 0.10) within 682 genes (80 miRBGs, 152 MIRNAs, and 457 miRNA targets). Single-SNP association analysis found SNPs in 234 candidate genes exhibited significant additive/dominant effects on phenotypes. Among these, specific candidates that associated with the same traits produced 791 miRBG-MIRNA-target combinations, suggesting possible genetic miRBG-MIRNA and MIRNA-target interactions, providing an important clue for the regulatory mechanisms of miRBGs. Multi-SNP association found 4672 epistatic pairs involving 578 genes that showed significant associations with traits and identified 106 miRBG-MIRNA-target combinations. Two multi-hierarchical networks were constructed based on correlations of miRBG-miRNA and miRNA-target expression to further probe the mechanisms of trait diversity underlying changes in miRBGs. Our study opens avenues for the investigation of miRNA function in perennial plants and underscored miRBGs as potentially modulating quantitative variation in traits.
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Affiliation(s)
- Beibei Chen
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
| | - Jinhui Chen
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
| | - Qingzhang Du
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
| | - Daling Zhou
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
| | - Longxin Wang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
| | - Jianbo Xie
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
| | - Ying Li
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
| | - Deqiang Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, China
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437
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Burgess S, Foley CN, Zuber V. Inferring Causal Relationships Between Risk Factors and Outcomes from Genome-Wide Association Study Data. Annu Rev Genomics Hum Genet 2018; 19:303-327. [PMID: 29709202 PMCID: PMC6481551 DOI: 10.1146/annurev-genom-083117-021731] [Citation(s) in RCA: 153] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
An observational correlation between a suspected risk factor and an outcome does not necessarily imply that interventions on levels of the risk factor will have a causal impact on the outcome (correlation is not causation). If genetic variants associated with the risk factor are also associated with the outcome, then this increases the plausibility that the risk factor is a causal determinant of the outcome. However, if the genetic variants in the analysis do not have a specific biological link to the risk factor, then causal claims can be spurious. We review the Mendelian randomization paradigm for making causal inferences using genetic variants. We consider monogenic analysis, in which genetic variants are taken from a single gene region, and polygenic analysis, which includes variants from multiple regions. We focus on answering two questions: When can Mendelian randomization be used to make reliable causal inferences, and when can it be used to make relevant causal inferences?
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, United Kingdom;
- Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge CB1 8RN, United Kingdom
| | - Christopher N Foley
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, United Kingdom;
| | - Verena Zuber
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, United Kingdom;
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438
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Slob EAW, Groenen PJF, Thurik AR, Rietveld CA. A note on the use of Egger regression in Mendelian randomization studies. Int J Epidemiol 2018; 46:2094-2097. [PMID: 29025040 DOI: 10.1093/ije/dyx191] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Eric A W Slob
- Erasmus University Rotterdam Institute for Behavior and Biology.,Department of Applied Economics
| | - Patrick J F Groenen
- Erasmus University Rotterdam Institute for Behavior and Biology.,Econometric Institute, Erasmus School of Economics, Rotterdam, The Netherlands
| | - A Roy Thurik
- Erasmus University Rotterdam Institute for Behavior and Biology.,Department of Applied Economics.,Montpellier Business School, Montpellier, France
| | - Cornelius A Rietveld
- Erasmus University Rotterdam Institute for Behavior and Biology.,Department of Applied Economics
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439
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Abstract
Schizophrenia is a severe psychiatric disorder of complex etiology. Immune processes have long been proposed to contribute to the development of schizophrenia, and accumulating evidence supports immune involvement in at least a subset of cases. In recent years, large-scale genetic studies have provided new insights into the role of the immune system in this disease. Here, we provide an overview of the immunogenetic architecture of schizophrenia based on findings from genome-wide association studies (GWAS). First, we review individual immune loci identified in secondary analyses of GWAS, which implicate over 30 genes expressed in both immune and brain cells. The function of the proteins encoded by these immune candidates highlight the role of the complement system, along with regulation of apoptosis in both immune and neuronal cells. Next, we review hypothesis-free pathway analyses which have so far been inconclusive with respect to identifying immune pathways involved in schizophrenia. Finally, we explore the genetic overlap between schizophrenia and immune-mediated diseases. Although there have been some inconsistencies across studies, genome-wide pleiotropy has been reported between schizophrenia and Crohn's disease, multiple sclerosis, rheumatoid arthritis, systemic lupus erythematosus, type 1 diabetes, and ulcerative colitis. Overall, there are multiple lines of evidence supporting the role of immune genes in schizophrenia. Current evidence suggests that specific immune pathways are involved-likely those with dual functions in the central nervous system. Future studies focused on further elucidating the relevant pathways hold the potential to identify novel biomarkers and therapeutic targets for schizophrenia.
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Affiliation(s)
- Jennie G Pouget
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
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440
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Wang L, Pittman KJ, Barker JR, Salinas RE, Stanaway IB, Williams GD, Carroll RJ, Balmat T, Ingham A, Gopalakrishnan AM, Gibbs KD, Antonia AL, Heitman J, Lee SC, Jarvik GP, Denny JC, Horner SM, DeLong MR, Valdivia RH, Crosslin DR, Ko DC. An Atlas of Genetic Variation Linking Pathogen-Induced Cellular Traits to Human Disease. Cell Host Microbe 2018; 24:308-323.e6. [PMID: 30092202 PMCID: PMC6093297 DOI: 10.1016/j.chom.2018.07.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 05/28/2018] [Accepted: 07/05/2018] [Indexed: 12/18/2022]
Abstract
Pathogens have been a strong driving force for natural selection. Therefore, understanding how human genetic differences impact infection-related cellular traits can mechanistically link genetic variation to disease susceptibility. Here we report the Hi-HOST Phenome Project (H2P2): a catalog of cellular genome-wide association studies (GWAS) comprising 79 infection-related phenotypes in response to 8 pathogens in 528 lymphoblastoid cell lines. Seventeen loci surpass genome-wide significance for infection-associated phenotypes ranging from pathogen replication to cytokine production. We combined H2P2 with clinical association data from patients to identify a SNP near CXCL10 as a risk factor for inflammatory bowel disease. A SNP in the transcriptional repressor ZBTB20 demonstrated pleiotropy, likely through suppression of multiple target genes, and was associated with viral hepatitis. These data are available on a web portal to facilitate interpreting human genome variation through the lens of cell biology and should serve as a rich resource for the research community.
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Affiliation(s)
- Liuyang Wang
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Kelly J Pittman
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Jeffrey R Barker
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Raul E Salinas
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Ian B Stanaway
- Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Graham D Williams
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN 37212, USA
| | - Tom Balmat
- Social Science Research Institute, Duke University, Durham, NC 27710, USA
| | - Andy Ingham
- Duke Research Computing, Duke University, Durham, NC 27710, USA
| | - Anusha M Gopalakrishnan
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Kyle D Gibbs
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Alejandro L Antonia
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Joseph Heitman
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA; Division of Infectious Diseases, Department of Medicine, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Soo Chan Lee
- South Texas Center for Emerging Infectious Diseases (STCEID), Department of Biology, College of Sciences, the University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Gail P Jarvik
- Department of Medicine, Division of Medical Genetics, School of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN 37212, USA
| | - Stacy M Horner
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA; Division of Infectious Diseases, Department of Medicine, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Mark R DeLong
- Duke Research Computing, Duke University, Durham, NC 27710, USA
| | - Raphael H Valdivia
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - David R Crosslin
- Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Dennis C Ko
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA; Division of Infectious Diseases, Department of Medicine, School of Medicine, Duke University, Durham, NC 27710, USA.
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441
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Luijk R, Dekkers KF, van Iterson M, Arindrarto W, Claringbould A, Hop P, Boomsma DI, van Duijn CM, van Greevenbroek MMJ, Veldink JH, Wijmenga C, Franke L, 't Hoen PAC, Jansen R, van Meurs J, Mei H, Slagboom PE, Heijmans BT, van Zwet EW. Genome-wide identification of directed gene networks using large-scale population genomics data. Nat Commun 2018; 9:3097. [PMID: 30082726 PMCID: PMC6079029 DOI: 10.1038/s41467-018-05452-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 07/04/2018] [Indexed: 12/31/2022] Open
Abstract
Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. Here, we develop a method for the large-scale inference of gene-gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). Analysis of genotype and whole-blood RNA-sequencing data from 3072 individuals identified 49 genes as drivers of downstream transcriptional changes (Wald P < 7 × 10-10), among which transcription factors were overrepresented (Fisher's P = 3.3 × 10-7). Our analysis suggests new gene functions and targets, including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6600 genes with a genetic instrument can be explored individually using a web-based browser.
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Affiliation(s)
- René Luijk
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Zuid-Holland, 2333 ZC, The Netherlands
| | - Koen F Dekkers
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Zuid-Holland, 2333 ZC, The Netherlands
| | - Maarten van Iterson
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Zuid-Holland, 2333 ZC, The Netherlands
| | - Wibowo Arindrarto
- Sequence Analysis Support Core, Leiden University Medical Center, Leiden, Zuid-Holland, 2333 ZC, The Netherlands
| | - Annique Claringbould
- Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, 9713 AV, The Netherlands
| | - Paul Hop
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Zuid-Holland, 2333 ZC, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, 1081 TB, The Netherlands
| | - Cornelia M van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, ErasmusMC, Rotterdam, 3015 GE, The Netherlands
| | - Marleen M J van Greevenbroek
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, 6211 LK, The Netherlands
- School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, 6229 ER, The Netherlands
| | - Jan H Veldink
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, 3584 CG, The Netherlands
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, 9713 AV, The Netherlands
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, 9713 AV, The Netherlands
| | - Peter A C 't Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, Zuid-Holland, 2333 ZC, The Netherlands
| | - Rick Jansen
- Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, 1081 HV, The Netherlands
| | - Joyce van Meurs
- Department of Internal Medicine, ErasmusMC, Rotterdam, 3015 CE, The Netherlands
| | - Hailiang Mei
- Sequence Analysis Support Core, Leiden University Medical Center, Leiden, Zuid-Holland, 2333 ZC, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Zuid-Holland, 2333 ZC, The Netherlands
| | - Bastiaan T Heijmans
- Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Zuid-Holland, 2333 ZC, The Netherlands.
| | - Erik W van Zwet
- Medical Statistics Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Zuid-Holland, 2333 ZC, The Netherlands.
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442
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Liu J, Wan X, Wang C, Yang C, Zhou X, Yang C. LLR: a latent low-rank approach to colocalizing genetic risk variants in multiple GWAS. Bioinformatics 2018; 33:3878-3886. [PMID: 28961754 DOI: 10.1093/bioinformatics/btx512] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Accepted: 08/09/2017] [Indexed: 12/30/2022] Open
Abstract
Motivation Genome-wide association studies (GWAS), which genotype millions of single nucleotide polymorphisms (SNPs) in thousands of individuals, are widely used to identify the risk SNPs underlying complex human phenotypes (quantitative traits or diseases). Most conventional statistical methods in GWAS only investigate one phenotype at a time. However, an increasing number of reports suggest the ubiquity of pleiotropy, i.e. many complex phenotypes sharing common genetic bases. This motivated us to leverage pleiotropy to develop new statistical approaches to joint analysis of multiple GWAS. Results In this study, we propose a latent low-rank (LLR) approach to colocalizing genetic risk variants using summary statistics. In the presence of pleiotropy, there exist risk loci that affect multiple phenotypes. To leverage pleiotropy, we introduce a low-rank structure to modulate the probabilities of the latent association statuses between loci and phenotypes. Regarding the computational efficiency of LLR, a novel expectation-maximization-path (EM-path) algorithm has been developed to greatly reduce the computational cost and facilitate model selection and inference. We demonstrate the advantages of LLR over competing approaches through simulation studies and joint analysis of 18 GWAS datasets. Availability and implementation The LLR software is available on https://sites.google.com/site/liujin810822. Contact macyang@ust.hk.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jin Liu
- Center for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Xiang Wan
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Chaolong Wang
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | | | - Xiaowei Zhou
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Can Yang
- Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, China.,Department of Mathematics, Hong Kong Baptist University, Hong Kong, China
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443
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Zhou X, Cheung CL, Karasugi T, Karppinen J, Samartzis D, Hsu YH, Mak TSH, Song YQ, Chiba K, Kawaguchi Y, Li Y, Chan D, Cheung KMC, Ikegawa S, Cheah KSE, Sham PC. Trans-Ethnic Polygenic Analysis Supports Genetic Overlaps of Lumbar Disc Degeneration With Height, Body Mass Index, and Bone Mineral Density. Front Genet 2018; 9:267. [PMID: 30127800 PMCID: PMC6088183 DOI: 10.3389/fgene.2018.00267] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 07/02/2018] [Indexed: 01/08/2023] Open
Abstract
Lumbar disc degeneration (LDD) is age-related break-down in the fibrocartilaginous joints between lumbar vertebrae. It is a major cause of low back pain and is conventionally assessed by magnetic resonance imaging (MRI). Like most other complex traits, LDD is likely polygenic and influenced by both genetic and environmental factors. However, genome-wide association studies (GWASs) of LDD have uncovered few susceptibility loci due to the limited sample size. Previous epidemiology studies of LDD also reported multiple heritable risk factors, including height, body mass index (BMI), bone mineral density (BMD), lipid levels, etc. Genetics can help elucidate causality between traits and suggest loci with pleiotropic effects. One such approach is polygenic score (PGS) which summarizes the effect of multiple variants by the summation of alleles weighted by estimated effects from GWAS. To investigate genetic overlaps of LDD and related heritable risk factors, we calculated the PGS of height, BMI, BMD and lipid levels in a Chinese population-based cohort with spine MRI examination and a Japanese case-control cohort of lumbar disc herniation (LDH) requiring surgery. Because most large-scale GWASs were done in European populations, PGS of corresponding traits were created using weights from European GWASs. We calibrated their prediction performance in independent Chinese samples, then tested associations with MRI-derived LDD scores and LDH affection status. The PGS of height, BMI, BMD and lipid levels were strongly associated with respective phenotypes in Chinese, but phenotype variances explained were lower than in Europeans which would reduce the power to detect genetic overlaps. Despite of this, the PGS of BMI and lumbar spine BMD were significantly associated with LDD scores; and the PGS of height was associated with the increased the liability of LDH. Furthermore, linkage disequilibrium score regression suggested that, osteoarthritis, another degenerative disorder that shares common features with LDD, also showed genetic correlations with height, BMI and BMD. The findings suggest a common key contribution of biomechanical stress to the pathogenesis of LDD and will direct the future search for pleiotropic genes.
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Affiliation(s)
- Xueya Zhou
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong.,Department of Systems Biology, Department of Pediatrics, Columbia University Medical Center, New York, NY, United States
| | - Ching-Lung Cheung
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong.,Li Ka Shing Faculty of Medicine, Center for Genomic Sciences, The University of Hong Kong, Hong Kong, Hong Kong
| | - Tatsuki Karasugi
- Department of Orthopaedic Surgery, Faculty of Life Sciences, Kumamoto University, Kumamoto City, Japan
| | - Jaro Karppinen
- Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Dino Samartzis
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Yi-Hsiang Hsu
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, United States.,Harvard Medical School, Boston, MA, United States.,Molecular and Integrative Physiological Sciences Program, Harvard School of Public Health, Boston, MA, United States
| | - Timothy Shin-Heng Mak
- Li Ka Shing Faculty of Medicine, Center for Genomic Sciences, The University of Hong Kong, Hong Kong, Hong Kong
| | - You-Qiang Song
- Li Ka Shing Faculty of Medicine, Center for Genomic Sciences, The University of Hong Kong, Hong Kong, Hong Kong.,Li Ka Shing Faculty of Medicine, School of Biomedical Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Kazuhiro Chiba
- Department of Orthopedic Surgery, National Defense Medical College, Tokorozawa, Saitama, Japan
| | - Yoshiharu Kawaguchi
- Department of Orthopaedic Surgery, Toyama University, Toyama Prefecture, Japan
| | - Yan Li
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Danny Chan
- Li Ka Shing Faculty of Medicine, School of Biomedical Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Kenneth Man-Chee Cheung
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Shiro Ikegawa
- Laboratory of Bone and Joint Diseases, Center for Integrative Medical Sciences, RIKEN, Tokyo, Japan
| | - Kathryn Song-Eng Cheah
- Li Ka Shing Faculty of Medicine, School of Biomedical Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Pak Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong.,Li Ka Shing Faculty of Medicine, Center for Genomic Sciences, The University of Hong Kong, Hong Kong, Hong Kong
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444
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Wedow R, Zacher M, Huibregtse BM, Harris KM, Domingue BW, Boardman JD. Education, Smoking, and Cohort Change: Forwarding a Multidimensional Theory of the Environmental Moderation of Genetic Effects. AMERICAN SOCIOLOGICAL REVIEW 2018; 83:802-832. [PMID: 31534265 PMCID: PMC6750804 DOI: 10.1177/0003122418785368] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We introduce a genetic correlation by environment interaction model [(rG)xE] which allows for social environmental moderation of the genetic relationship between two traits. To empirically demonstrate the significance of the (rG)xE perspective, we use genome wide information from respondents of the Health and Retirement Study (HRS; n = 8,181; birth years 1920-1959) and the National Longitudinal Study of Adolescent to Adult Health (Add Health; n = 4,347; birth years 1974-1983) to examine whether the genetic correlation (rG) between education and smoking has increased over historical time. Genetic correlation estimates (rGHRS = -0.357; rGAdd Health = -0.729) support this hypothesis. Using polygenic scores for educational attainment, we show that this is not due to latent indicators of intellectual capacity, and we highlight the importance of education itself as an explanation of the increasing genetic correlation. Analyses based on contextual variation the milieus of the Add Health respondents corroborate key elements of the birth cohort analyses. We argue that the increasing overlap with respect to genes associated with educational attainment and smoking is a fundamentally social process involving complex process of selection based on observable behaviors that may be linked to genotype.
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Affiliation(s)
- Robbee Wedow
- Department of Sociology, University of Colorado, Boulder, Colorado
- Health and Society Program and Population Program, Institute of Behavioral Science, University of Colorado, Boulder, Colorado
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado
- Social Science Genetic Association Consortium (SSGAC)
- Direct correspondence to Robbee Wedow, Institute of Behavioral Science University of Colorado Boulder, 1440 15th Street, Boulder, CO 80302,
| | - Meghan Zacher
- Social Science Genetic Association Consortium (SSGAC)
- Department of Sociology, Harvard University, Cambridge, Massachusetts
| | - Brooke M. Huibregtse
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado
- Department of Psychology, University of Colorado, Boulder, Colorado
| | - Kathleen Mullan Harris
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Benjamin W. Domingue
- Health and Society Program and Population Program, Institute of Behavioral Science, University of Colorado, Boulder, Colorado
- Graduate School of Education, Stanford University, Stanford, California
| | - Jason D. Boardman
- Department of Sociology, University of Colorado, Boulder, Colorado
- Health and Society Program and Population Program, Institute of Behavioral Science, University of Colorado, Boulder, Colorado
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado
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445
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van Kippersluis H, Rietveld CA. Pleiotropy-robust Mendelian randomization. Int J Epidemiol 2018; 47:1279-1288. [PMID: 28338774 PMCID: PMC6124631 DOI: 10.1093/ije/dyx002] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/07/2017] [Indexed: 01/15/2023] Open
Abstract
Background The potential of Mendelian randomization studies is rapidly expanding due to: (i) the growing power of genome-wide association study (GWAS) meta-analyses to detect genetic variants associated with several exposures; and (ii) the increasing availability of these genetic variants in large-scale surveys. However, without a proper biological understanding of the pleiotropic working of genetic variants, a fundamental assumption of Mendelian randomization (the exclusion restriction) can always be contested. Methods We build upon and synthesize recent advances in the literature on instrumental variables (IVs) estimation that test and relax the exclusion restriction. Our pleiotropy-robust Mendelian randomization (PRMR) method first estimates the degree of pleiotropy, and in turn corrects for it. If (i) a subsample exists for which the genetic variants do not affect the exposure; (ii) the selection into this subsample is not a joint consequence of the IV and the outcome; (iii) pleiotropic effects are homogeneous, PRMR obtains unbiased estimates of causal effects. Results Simulations show that existing MR methods produce biased estimators for realistic forms of pleiotropy. Under the aforementioned assumptions, PRMR produces unbiased estimators. We illustrate the practical use of PRMR by estimating the causal effect of: (i) tobacco exposure on body mass index (BMI); (ii) prostate cancer on self-reported health; and (iii) educational attainment on BMI in the UK Biobank data. Conclusions PRMR allows for instrumental variables that violate the exclusion restriction due to pleiotropy, and it corrects for pleiotropy in the estimation of the causal effect. If the degree of pleiotropy is unknown, PRMR can still be used as a sensitivity analysis.
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Affiliation(s)
- Hans van Kippersluis
- Erasmus School of Economics, Erasmus University Rotterdam, The Netherlands
- Department of Economics, Chinese University of Hong Kong, Hong Kong SAR
- Tinbergen Institute, Amsterdam, The Netherlands
| | - Cornelius A Rietveld
- Erasmus School of Economics, Erasmus University Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus University Rotterdam, The Netherlands
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446
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Leveraging multiple gene networks to prioritize GWAS candidate genes via network representation learning. Methods 2018; 145:41-50. [DOI: 10.1016/j.ymeth.2018.06.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 04/10/2018] [Accepted: 06/01/2018] [Indexed: 12/20/2022] Open
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447
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Noordam R, Oudt CH, Bos MM, Smit RAJ, van Heemst D. High-sensitivity C-reactive protein, low-grade systemic inflammation and type 2 diabetes mellitus: A two-sample Mendelian randomization study. Nutr Metab Cardiovasc Dis 2018; 28:795-802. [PMID: 29753585 DOI: 10.1016/j.numecd.2018.03.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 03/12/2018] [Accepted: 03/13/2018] [Indexed: 01/26/2023]
Abstract
BACKGROUND AND AIMS The role of inflammation in type 2 diabetes mellitus (T2D) remains unclear. We investigated the associations of high sensitivity C-reactive protein (hsCRP) concentration with T2D and glycemic traits using two-sample Mendelian Randomization. METHODS AND RESULTS We used publically available summary-statistics data from genome-wide association studies on T2D (DIAGRAM: 12 171 cases; 56 862 controls) and glycemic traits (MAGIC: 46 186 participants without diabetes mellitus). We combined the effects of the genetic instrumental variables through inverse-variance weighting (IVW), and MR-Egger regression and weighted-median estimation as sensitivity analyses which take into account potential violations (e.g., directional pleiotropy) of the assumptions of instrumental variable analyses. Analyses were conducted using 15 known hsCRP genetic instruments among which 6 instruments are hsCRP specific and not involved in inflammatory processes beyond hsCRP concentration regulation. Though we found no association between the combined effect of the genetic instrumental variables for hsCRP and T2D with IVW (odds ratio per 1 ln [hsCRP in mg/L]: 1.15; 95% confidence interval: 0.93, 1.42), we found associations for T2D with MR-Egger regression and weighted-median estimation (odds ratio with 95% confidence interval per 1 ln [hsCRP in mg/L], MR-Egger regression: 1.29; 1.08, 1.49; weighted-median estimator: 1.21; 1.02, 1.39). We found no association with T2D for the combination of hsCRP-specific genetic instruments nor did we found associations with glycemic traits in any of the analyses. CONCLUSION Evidence was provided for a potential causal association between hsCRP and T2D, but only after considering directional pleiotropy. However, hsCRP was not causally associated with glycemic traits.
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Affiliation(s)
- R Noordam
- Department of Internal Medicine, Section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands.
| | - C H Oudt
- Department of Internal Medicine, Section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - M M Bos
- Department of Internal Medicine, Section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - R A J Smit
- Department of Internal Medicine, Section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands; Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - D van Heemst
- Department of Internal Medicine, Section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
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448
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Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet 2018; 27:R195-R208. [PMID: 29771313 PMCID: PMC6061876 DOI: 10.1093/hmg/ddy163] [Citation(s) in RCA: 790] [Impact Index Per Article: 131.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 04/26/2018] [Accepted: 04/30/2018] [Indexed: 02/06/2023] Open
Abstract
Pleiotropy, the phenomenon of a single genetic variant influencing multiple traits, is likely widespread in the human genome. If pleiotropy arises because the single nucleotide polymorphism (SNP) influences one trait, which in turn influences another ('vertical pleiotropy'), then Mendelian randomization (MR) can be used to estimate the causal influence between the traits. Of prime focus among the many limitations to MR is the unprovable assumption that apparent pleiotropic associations are mediated by the exposure (i.e. reflect vertical pleiotropy), and do not arise due to SNPs influencing the two traits through independent pathways ('horizontal pleiotropy'). The burgeoning treasure trove of genetic associations yielded through genome wide association studies makes for a tantalizing prospect of phenome-wide causal inference. Recent years have seen substantial attention devoted to the problem of horizontal pleiotropy, and in this review we outline how newly developed methods can be used together to improve the reliability of MR.
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Affiliation(s)
- Gibran Hemani
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol
| | - Jack Bowden
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol
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449
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Banking on Polygenicity to Disentangle Psychiatric Comorbidity. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:577-578. [PMID: 30047475 DOI: 10.1016/j.bpsc.2018.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 05/23/2018] [Indexed: 11/20/2022]
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450
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Wang Z, Sha Q, Fang S, Zhang K, Zhang S. Testing an optimally weighted combination of common and/or rare variants with multiple traits. PLoS One 2018; 13:e0201186. [PMID: 30048520 PMCID: PMC6062080 DOI: 10.1371/journal.pone.0201186] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Accepted: 07/10/2018] [Indexed: 12/25/2022] Open
Abstract
Recently, joint analysis of multiple traits has become popular because it can increase statistical power to identify genetic variants associated with complex diseases. In addition, there is increasing evidence indicating that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single genetic variant. However, these methods by analyzing one variant at a time may not be ideal for rare variant association studies because of the allelic heterogeneity as well as the extreme rarity of rare variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study.
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Affiliation(s)
- Zhenchuan Wang
- 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
| | - Shurong Fang
- Department of Mathematics and Computer Science, John Carroll University, University Heights, Ohio, United States of America
| | - Kui Zhang
- 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
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