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6 results on '"linkage disequilibrium score regression (LDSC)"'

3. Serum uric acid and risk of diabetic neuropathy: a genetic correlation and mendelian randomization study.

Author

Youqian Zhang, Zitian Tang, Ling Tong, Yang Wang, and Lin Li

Subjects
GENETIC correlations, DIABETIC neuropathies, LDL cholesterol, URIC acid, LINKAGE disequilibrium, INSULIN resistance
Abstract

Background: Previous observational studies have indicated an association between serum uric acid (SUA) and diabetic neuropathy (DN), but confounding factors and reverse causality have left the causality of this relationship uncertain. Methods: Univariate Mendelian randomization (MR), multivariate MR and linkage disequilibrium score (LDSC) regression analysis were utilized to assess the causal link between SUA and DN. Summary-level data for SUA were drawn from the CKDGen consortium, comprising 288,648 individuals, while DN data were obtained from the FinnGen consortium, with 2,843 cases and 271,817 controls. Causal effects were estimated primarily using inverse variance weighted (IVW) analysis, supplemented by four validation methods, with additional sensitivity analyses to evaluate pleiotropy, heterogeneity, and result robustness. Results: The LDSC analysis revealed a significant genetic correlation between SUA and DN (genetic correlation = 0.293, P = 2.60 × 10-5). The primary methodology IVW indicated that each increase of 1 mg/dL in SUA would increase DN risk by 17% (OR = 1.17, 95% CI 1.02-1.34, P = 0.02), while no causal relationship was found in reverse analysis (OR = 1.00, 95% CI 0.98~1.01, P = 0.97). Multivariate MR further identified that the partial effect of SUA on DN may be mediated by physical activity, low density lipoprotein cholesterol (LDL-C), insulin resistance (IR), and alcohol use. Conclusion: The study establishes a causal link between elevated SUA levels and an increased risk of DN, with no evidence for a reverse association. This underscores the need for a comprehensive strategy in DN management, integrating urate-lowering interventions with modulations of the aforementioned mediators. [ABSTRACT FROM AUTHOR]

Published
2023
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4. Estimation of Genetic Correlation Between Rheumatoid Arthritis and Multiple Sclerosis Using Summary Statistics from Genome-Wide Association Studies.

Author

Öztornacı, Ragıp Onur

Subjects
*GENETIC correlations, *RHEUMATOID arthritis, *MULTIPLE sclerosis, *CHRONIC diseases, *IMMUNE system
Abstract

Objective: Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic basis of diseases by examining millions of genetic variants across the genome. Rheumatoid arthritis (RA) and multiple sclerosis (MS) are chronic autoimmune diseases characterized by immune system dysregulation and inflammation. Investigating the genetic correlation between RA and MS can provide insights into shared genetic factors, potential mechanisms, and pathways underlying these complex disorders. The objective of this study was to compare different statistical methods to estimate the genetic correlation between RA and MS using GWAS summary statistics. Materials and Methods: To estimate single nucleotide polymorphism (SNP) heritability and genetic correlation, we utilized two popular methods: Linkage Disequilibrium Score Regression (LDSC) and Linkage Disequilibrium Adjusted Kinship (LDAK) models. Results: Our analysis revealed a significant, moderate, positive correlation between RA and MS using both LDSC and LDAK (LSDCMS-RA=0.448, LDAKMS-RA=0.387, SpearmanMS-RA=0.0262, p<0.001). Additionally, there were notable differences in heritability estimates between the two methods and the traits. The LDAK model demonstrated higher heritability estimates for the RA-MS relationship (h²MS-RA=0.314) compared to the LDSC (h²RA-RA=0.138). Conclusion: There is a significant positive genetic correlation between RA and MS, indicating a shared genetic component. Differential heritability estimates from LDAK and LDSC highlight the importance of the method. Genetic overlap informs common pathways and potential therapeutic targets. These findings contribute to the evidence of a moderately positive genetic correlation, emphasizing the need for further research and personalized approaches to managing autoimmune diseases. [ABSTRACT FROM AUTHOR]

Published
2023
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5. Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood

Author

Guiyan Ni, Gerhard Moser, Naomi R. Wray, S. Hong Lee, Stephan Ripke, Benjamin M. Neale, Aiden Corvin, James T.R. Walters, Kai-How Farh, Peter A. Holmans, Phil Lee, Brendan Bulik-Sullivan, David A. Collier, Hailiang Huang, Tune H. Pers, Ingrid Agartz, Esben Agerbo, Margot Albus, Madeline Alexander, Farooq Amin, Silviu A. Bacanu, Martin Begemann, Richard A. Belliveau, Judit Bene, Sarah E. Bergen, Elizabeth Bevilacqua, Tim B. Bigdeli, Donald W. Black, Richard Bruggeman, Nancy G. Buccola, Randy L. Buckner, William Byerley, Wiepke Cahn, Guiqing Cai, Dominique Campion, Rita M. Cantor, Vaughan J. Carr, Noa Carrera, Stanley V. Catts, Kimberly D. Chambert, Raymond C.K. Chan, Ronald Y.L. Chen, Eric Y.H. Chen, Wei Cheng, Eric F.C. Cheung, Siow Ann Chong, C. Robert Cloninger, David Cohen, Nadine Cohen, Paul Cormican, Nick Craddock, James J. Crowley, David Curtis, Michael Davidson, Kenneth L. Davis, Franziska Degenhardt, Jurgen Del Favero, Ditte Demontis, Dimitris Dikeos, Timothy Dinan, Srdjan Djurovic, Gary Donohoe, Elodie Drapeau, Jubao Duan, Frank Dudbridge, Naser Durmishi, Peter Eichhammer, Johan Eriksson, Valentina Escott-Price, Laurent Essioux, Ayman H. Fanous, Martilias S. Farrell, Josef Frank, Lude Franke, Robert Freedman, Nelson B. Freimer, Marion Friedl, Joseph I. Friedman, Menachem Fromer, Giulio Genovese, Lyudmila Georgieva, Ina Giegling, Paola Giusti-Rodríguez, Stephanie Godard, Jacqueline I. Goldstein, Vera Golimbet, Srihari Gopal, Jacob Gratten, Lieuwe de Haan, Christian Hammer, Marian L. Hamshere, Mark Hansen, Thomas Hansen, Vahram Haroutunian, Annette M. Hartmann, Frans A. Henskens, Stefan Herms, Joel N. Hirschhorn, Per Hoffmann, Andrea Hofman, Mads V. Hollegaard, David M. Hougaard, Masashi Ikeda, Inge Joa, Antonio Juliá, René S. Kahn, Luba Kalaydjieva, Sena Karachanak-Yankova, Juha Karjalainen, David Kavanagh, Matthew C. Keller, James L. Kennedy, Andrey Khrunin, Yunjung Kim, Janis Klovins, James A. Knowles, Bettina Konte, Vaidutis Kucinskas, Zita Ausrele Kucinskiene, Hana Kuzelova-Ptackova, Anna K. Kähler, Claudine Laurent, Jimmy Lee Chee Keong, Sophie E. Legge, Bernard Lerer, Miaoxin Li, Tao Li, Kung-Yee Liang, Jeffrey Lieberman, Svetlana Limborska, Carmel M. Loughland, Jan Lubinski, Jouko Lönnqvist, Milan Macek, Patrik K.E. Magnusson, Brion S. Maher, Wolfgang Maier, Jacques Mallet, Sara Marsal, Manuel Mattheisen, Morten Mattingsda, Robert W. McCarley, Colm McDonald, Andrew M. McIntosh, Sandra Meier, Carin J. Meijer, Bela Melegh, Ingrid Melle, Raquelle I. Mesholam-Gately, Andres Metspalu, Patricia T. Michie, Lili Milani, Vihra Milanova, Younes Mokrab, Derek W. Morris, Ole Mors, Kieran C. Murphy, Robin M. Murray, Inez Myin-Germeys, Bertram Müller-Myhsok, Mari Nelis, Igor Nenadic, Deborah A. Nertney, Gerald Nestadt, Kristin K. Nicodemus, Liene Nikitina-Zake, Laura Nisenbaum, Annelie Nordin, Eadbhard O’Callaghan, Colm O’Dushlaine, F. Anthony O’Neill, Sang-Yun Oh, Ann Olinc, Line Olsen, Jim Van Os, Christos Pantelis, George N. Papadimitriou, Sergi Papio, Elena Parkhomenko, Michele T. Pato, Tiina Paunio, Milica Pejovic-Milovancevic, Diana O. Perkins, Olli Pietiläinenl, Jonathan Pimm, Andrew J. Pocklington, John Powell, Alkes Price, Ann E. Pulver, Shaun M. Purcell, Digby Quested, Henrik B. Rasmussen, Abraham Reichenberg, Mark A. Reimers, Alexander L. Richards, Joshua L. Roffman, Panos Roussos, Douglas M. Ruderfer, Veikko Salomaa, Alan R. Sanders, Ulrich Schall, Christian R. Schubert, Thomas G. Schulze, Sibylle G. Schwab, Edward M. Scolnick, Rodney J. Scott, Larry J. Seidman, Jianxin Shi, Engilbert Sigurdsson, Teimuraz Silagadze, Jeremy M. Silverman, Kang Sim, Petr Slominsky, Jordan W. Smoller, Hon-Cheong So, Chris C.A. Spencer, Eli A. Stah, Hreinn Stefansson, Stacy Steinberg, Elisabeth Stogmann, Richard E. Straub, Eric Strengman, Jana Strohmaier, T. Scott Stroup, Mythily Subramaniam, Jaana Suvisaari, Dragan M. Svrakic, Jin P. Szatkiewicz, Erik Söderman, Srinivas Thirumalai, Draga Toncheva, Sarah Tosato, Juha Veijola, John Waddington, Dermot Walsh, Dai Wang, Qiang Wang, Bradley T. Webb, Mark Weiser, Dieter B. Wildenauer, Nigel M. Williams, Stephanie Williams, Stephanie H. Witt, Aaron R. Wolen, Emily H.M. Wong, Brandon K. Wormley, Hualin Simon Xi, Clement C. Zai, Xuebin Zheng, Fritz Zimprich, Kari Stefansson, Peter M. Visscher, Rolf Adolfsson, Ole A. Andreassen, Douglas H.R. Blackwood, Elvira Bramon, Joseph D. Buxbaum, Anders D. Børglum, Sven Cichon, Ariel Darvasi, Enrico Domenici, Hannelore Ehrenreich, Tõnu Esko, Pablo V. Gejman, Michael Gill, Hugh Gurling, Christina M. Hultman, Nakao Iwata, Assen V. Jablensky, Erik G. Jönsson, Kenneth S. Kendler, George Kirov, Jo Knight, Todd Lencz, Douglas F. Levinson, Qingqin S. Li, Jianjun Liu, Anil K. Malhotra, Steven A. McCarrol, Andrew McQuillin, Jennifer L. Moran, Preben B. Mortensen, Bryan J. Mowry, Markus M. Nöthen, Roel A. Ophoff, Michael J. Owen, Aarno Palotie, Carlos N. Pato, Tracey L. Petryshen, Danielle Posthuma, Marcella Rietsche, Brien P. Riley, Dan Rujescu, Pak C. Sham, Pamela Sklar, David St Clair, Daniel R. Weinberger, Jens R. Wendland, Thomas Werge, Mark J. Daly, Patrick F. Sullivan, Michael C. O’Donovan, APH - Mental Health, ANS - Complex Trait Genetics, Adult Psychiatry, Ni, Guiyan, Moser, Gerhard, Wray, Naomi R, Lee, S Hong, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Amsterdam Neuroscience - Complex Trait Genetics, Complex Trait Genetics, Clinicum, Department of Psychiatry, and HUS Psychiatry

Subjects
0301 basic medicine, Linkage disequilibrium, Schizophrenia/genetics, INFORMATION, Restricted maximum likelihood, Inheritance Patterns, linkage disequilibrium score regression, Bioinformatics, Medical and Health Sciences, 3124 Neurology and psychiatry, Linkage Disequilibrium, biasedness, 0302 clinical medicine, Statistics, Databases, Genetic, WIDE ASSOCIATION, Genetics(clinical), PARTITIONING HERITABILITY, Genetics (clinical), Genetics & Heredity, education.field_of_study, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Likelihood Functions, Genome, Body Height/genetics, accuracy, Regression analysis, Single Nucleotide, Biological Sciences, Polymorphism, Single Nucleotide/genetics, genetic correlation, Regression, STATISTICS, genomic restricted maximum likelihood, Mental Health, Phenotype, Regression Analysis, COMPLEX HUMAN TRAITS, Single Nucleotide/genetics, Human, Adult, SUSCEPTIBILITY LOCI, Genotype, SNP heritability, body mass index, genome-wide SNPs, height, schizophrenia, Population, Haplotypes/genetics, Biology, Genetic correlation, Polymorphism, Single Nucleotide, 03 medical and health sciences, Databases, Genetic, ddc:570, Report, Genetics, Humans, Linkage Disequilibrium/genetics, Computer Simulation, Polymorphism, education, linkage disequilibrium score regression (LDSC), Genome, Human, Human Genome, genetic architecture, Genetic architecture, Body Height, Brain Disorders, Inheritance Patterns/genetics, BODY-MASS INDEX, 030104 developmental biology, Haplotypes, Sample size determination, Schizophrenia, 3111 Biomedicine, HUMAN HEIGHT, 030217 neurology & neurosurgery
Abstract

J. Lönnqvist on työryhmän Psychiat Genomics Consortium jäsen. Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on similar to 150,000 individuals give a higher accuracy than LDSC estimates based on similar to 400,000 individuals (from combinedmeta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.

Published
2017
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6. Genetic Contributions to Multivariate Data-Driven Brain Networks Constructed via Source-Based Morphometry.

Author

Rodrigue AL, Alexander-Bloch AF, Knowles EEM, Mathias SR, Mollon J, Koenis MMG, Perrone-Bizzozero NI, Almasy L, Turner JA, Calhoun VD, and Glahn DC

Subjects
Adult, Aged, Bipolar Disorder genetics, Bipolar Disorder physiopathology, Depressive Disorder, Major genetics, Depressive Disorder, Major physiopathology, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Multivariate Analysis, Principal Component Analysis, Schizophrenia genetics, Schizophrenia physiopathology, Brain physiopathology, Brain Mapping methods, Genetic Association Studies, Nerve Net physiopathology
Abstract

Identifying genetic factors underlying neuroanatomical variation has been difficult. Traditional methods have used brain regions from predetermined parcellation schemes as phenotypes for genetic analyses, although these parcellations often do not reflect brain function and/or do not account for covariance between regions. We proposed that network-based phenotypes derived via source-based morphometry (SBM) may provide additional insight into the genetic architecture of neuroanatomy given its data-driven approach and consideration of covariance between voxels. We found that anatomical SBM networks constructed on ~ 20 000 individuals from the UK Biobank were heritable and shared functionally meaningful genetic overlap with each other. We additionally identified 27 unique genetic loci that contributed to one or more SBM networks. Both GWA and genetic correlation results indicated complex patterns of pleiotropy and polygenicity similar to other complex traits. Lastly, we found genetic overlap between a network related to the default mode and schizophrenia, a disorder commonly associated with neuroanatomic alterations., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Published
2020
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