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Your search keyword '"Peyrot, WJ"' showing total 8 results
Start Over Author "Peyrot, WJ" Topic genome-wide association study
8 results on '"Peyrot, WJ"'

1. On the interpretation of transcriptome-wide association studies.

Author

de Leeuw C, Werme J, Savage JE, Peyrot WJ, and Posthuma D

Subjects
Chromosome Mapping, Computer Simulation, Data Analysis, Genome-Wide Association Study, Transcriptome genetics
Abstract

Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a genetic relationship between gene expression and a phenotype, but this interpretation is not consistent with the null hypothesis that is evaluated in the traditional TWAS framework. In this study we provide a mathematical outline of this TWAS framework, and elucidate what interpretations are warranted given the null hypothesis it actually tests. We then use both simulations and real data analysis to assess the implications of misinterpreting TWAS results as indicative of a genetic relationship between gene expression and the phenotype. Our simulation results show considerably inflated type 1 error rates for TWAS when interpreted this way, with 41% of significant TWAS associations detected in the real data analysis found to have insufficient statistical evidence to infer such a relationship. This demonstrates that in current implementations, TWAS cannot reliably be used to investigate genetic relationships between gene expression and a phenotype, but that local genetic correlation analysis can serve as a potential alternative., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 de Leeuw et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Published
2023
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3. Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores.

Author

Weissbrod O, Kanai M, Shi H, Gazal S, Peyrot WJ, Khera AV, Okada Y, Martin AR, Finucane HK, and Price AL

Subjects
Humans, Linkage Disequilibrium, Polymorphism, Single Nucleotide genetics, Risk Factors, Genome-Wide Association Study, Multifactorial Inheritance genetics
Abstract

Polygenic risk scores suffer reduced accuracy in non-European populations, exacerbating health disparities. We propose PolyPred, a method that improves cross-population polygenic risk scores by combining two predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing linkage disequilibrium differences, and BOLT-LMM, a published predictor. When a large training sample is available in the non-European target population, we propose PolyPred + , which further incorporates the non-European training data. We applied PolyPred to 49 diseases/traits in four UK Biobank populations using UK Biobank British training data, and observed relative improvements versus BOLT-LMM ranging from +7% in south Asians to +32% in Africans, consistent with simulations. We applied PolyPred + to 23 diseases/traits in UK Biobank east Asians using both UK Biobank British and Biobank Japan training data, and observed improvements of +24% versus BOLT-LMM and +12% versus PolyPred. Summary statistics-based analogs of PolyPred and PolyPred + attained similar improvements., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Published
2022
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4. Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank.

Author

Coleman JRI, Peyrot WJ, Purves KL, Davis KAS, Rayner C, Choi SW, Hübel C, Gaspar HA, Kan C, Van der Auwera S, Adams MJ, Lyall DM, Choi KW, Dunn EC, Vassos E, Danese A, Maughan B, Grabe HJ, Lewis CM, O'Reilly PF, McIntosh AM, Smith DJ, Wray NR, Hotopf M, Eley TC, and Breen G

Subjects
Adult, Aged, Female, Humans, Male, Middle Aged, United Kingdom epidemiology, Waist Circumference, Databases, Factual, Depressive Disorder, Major epidemiology, Depressive Disorder, Major genetics, Gene-Environment Interaction, Genetic Predisposition to Disease genetics, Genome-Wide Association Study, Psychological Trauma epidemiology, Self Report
Abstract

Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD stratified by reported trauma exposure (final sample size range: 24,094-92,957). The SNP-based heritability of MDD with reported trauma exposure (24%) was greater than MDD without reported trauma exposure (12%). Simulations showed that this is not confounded by the strong, positive genetic correlation observed between MDD and reported trauma exposure. We also observed that the genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (r g  = 0.24, p = 1.8 × 10 -7 versus r g  = -0.05, p = 0.39 in individuals not reporting trauma exposure, difference p = 2.3 × 10 -4 ). Our results suggest that the genetic contribution to MDD is greater when reported trauma is present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.

Published
2020
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5. Genetic correlations of polygenic disease traits: from theory to practice.

Author

van Rheenen W, Peyrot WJ, Schork AJ, Lee SH, and Wray NR

Subjects
Humans, Models, Genetic, Multifactorial Inheritance, Phenotype, Disease, Genome-Wide Association Study
Abstract

The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.

Published
2019
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6. Genome-wide association study identifies 74 loci associated with educational attainment.

Author

Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, Turley P, Chen GB, Emilsson V, Meddens SF, Oskarsson S, Pickrell JK, Thom K, Timshel P, de Vlaming R, Abdellaoui A, Ahluwalia TS, Bacelis J, Baumbach C, Bjornsdottir G, Brandsma JH, Pina Concas M, Derringer J, Furlotte NA, Galesloot TE, Girotto G, Gupta R, Hall LM, Harris SE, Hofer E, Horikoshi M, Huffman JE, Kaasik K, Kalafati IP, Karlsson R, Kong A, Lahti J, van der Lee SJ, deLeeuw C, Lind PA, Lindgren KO, Liu T, Mangino M, Marten J, Mihailov E, Miller MB, van der Most PJ, Oldmeadow C, Payton A, Pervjakova N, Peyrot WJ, Qian Y, Raitakari O, Rueedi R, Salvi E, Schmidt B, Schraut KE, Shi J, Smith AV, Poot RA, St Pourcain B, Teumer A, Thorleifsson G, Verweij N, Vuckovic D, Wellmann J, Westra HJ, Yang J, Zhao W, Zhu Z, Alizadeh BZ, Amin N, Bakshi A, Baumeister SE, Biino G, Bønnelykke K, Boyle PA, Campbell H, Cappuccio FP, Davies G, De Neve JE, Deloukas P, Demuth I, Ding J, Eibich P, Eisele L, Eklund N, Evans DM, Faul JD, Feitosa MF, Forstner AJ, Gandin I, Gunnarsson B, Halldórsson BV, Harris TB, Heath AC, Hocking LJ, Holliday EG, Homuth G, Horan MA, Hottenga JJ, de Jager PL, Joshi PK, Jugessur A, Kaakinen MA, Kähönen M, Kanoni S, Keltigangas-Järvinen L, Kiemeney LA, Kolcic I, Koskinen S, Kraja AT, Kroh M, Kutalik Z, Latvala A, Launer LJ, Lebreton MP, Levinson DF, Lichtenstein P, Lichtner P, Liewald DC, Loukola A, Madden PA, Mägi R, Mäki-Opas T, Marioni RE, Marques-Vidal P, Meddens GA, McMahon G, Meisinger C, Meitinger T, Milaneschi Y, Milani L, Montgomery GW, Myhre R, Nelson CP, Nyholt DR, Ollier WE, Palotie A, Paternoster L, Pedersen NL, Petrovic KE, Porteous DJ, Räikkönen K, Ring SM, Robino A, Rostapshova O, Rudan I, Rustichini A, Salomaa V, Sanders AR, Sarin AP, Schmidt H, Scott RJ, Smith BH, Smith JA, Staessen JA, Steinhagen-Thiessen E, Strauch K, Terracciano A, Tobin MD, Ulivi S, Vaccargiu S, Quaye L, van Rooij FJ, Venturini C, Vinkhuyzen AA, Völker U, Völzke H, Vonk JM, Vozzi D, Waage J, Ware EB, Willemsen G, Attia JR, Bennett DA, Berger K, Bertram L, Bisgaard H, Boomsma DI, Borecki IB, Bültmann U, Chabris CF, Cucca F, Cusi D, Deary IJ, Dedoussis GV, van Duijn CM, Eriksson JG, Franke B, Franke L, Gasparini P, Gejman PV, Gieger C, Grabe HJ, Gratten J, Groenen PJ, Gudnason V, van der Harst P, Hayward C, Hinds DA, Hoffmann W, Hyppönen E, Iacono WG, Jacobsson B, Järvelin MR, Jöckel KH, Kaprio J, Kardia SL, Lehtimäki T, Lehrer SF, Magnusson PK, Martin NG, McGue M, Metspalu A, Pendleton N, Penninx BW, Perola M, Pirastu N, Pirastu M, Polasek O, Posthuma D, Power C, Province MA, Samani NJ, Schlessinger D, Schmidt R, Sørensen TI, Spector TD, Stefansson K, Thorsteinsdottir U, Thurik AR, Timpson NJ, Tiemeier H, Tung JY, Uitterlinden AG, Vitart V, Vollenweider P, Weir DR, Wilson JF, Wright AF, Conley DC, Krueger RF, Davey Smith G, Hofman A, Laibson DI, Medland SE, Meyer MN, Yang J, Johannesson M, Visscher PM, Esko T, Koellinger PD, Cesarini D, and Benjamin DJ

Subjects
Alzheimer Disease genetics, Bipolar Disorder genetics, Cognition, Computational Biology, Gene-Environment Interaction, Humans, Molecular Sequence Annotation, Schizophrenia genetics, United Kingdom, Brain metabolism, Educational Status, Fetus metabolism, Gene Expression Regulation genetics, Genome-Wide Association Study, Polymorphism, Single Nucleotide genetics
Abstract

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

Published
2016
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7. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment.

Author

Rietveld CA, Medland SE, Derringer J, Yang J, Esko T, Martin NW, Westra HJ, Shakhbazov K, Abdellaoui A, Agrawal A, Albrecht E, Alizadeh BZ, Amin N, Barnard J, Baumeister SE, Benke KS, Bielak LF, Boatman JA, Boyle PA, Davies G, de Leeuw C, Eklund N, Evans DS, Ferhmann R, Fischer K, Gieger C, Gjessing HK, Hägg S, Harris JR, Hayward C, Holzapfel C, Ibrahim-Verbaas CA, Ingelsson E, Jacobsson B, Joshi PK, Jugessur A, Kaakinen M, Kanoni S, Karjalainen J, Kolcic I, Kristiansson K, Kutalik Z, Lahti J, Lee SH, Lin P, Lind PA, Liu Y, Lohman K, Loitfelder M, McMahon G, Vidal PM, Meirelles O, Milani L, Myhre R, Nuotio ML, Oldmeadow CJ, Petrovic KE, Peyrot WJ, Polasek O, Quaye L, Reinmaa E, Rice JP, Rizzi TS, Schmidt H, Schmidt R, Smith AV, Smith JA, Tanaka T, Terracciano A, van der Loos MJ, Vitart V, Völzke H, Wellmann J, Yu L, Zhao W, Allik J, Attia JR, Bandinelli S, Bastardot F, Beauchamp J, Bennett DA, Berger K, Bierut LJ, Boomsma DI, Bültmann U, Campbell H, Chabris CF, Cherkas L, Chung MK, Cucca F, de Andrade M, De Jager PL, De Neve JE, Deary IJ, Dedoussis GV, Deloukas P, Dimitriou M, Eiríksdóttir G, Elderson MF, Eriksson JG, Evans DM, Faul JD, Ferrucci L, Garcia ME, Grönberg H, Guðnason V, Hall P, Harris JM, Harris TB, Hastie ND, Heath AC, Hernandez DG, Hoffmann W, Hofman A, Holle R, Holliday EG, Hottenga JJ, Iacono WG, Illig T, Järvelin MR, Kähönen M, Kaprio J, Kirkpatrick RM, Kowgier M, Latvala A, Launer LJ, Lawlor DA, Lehtimäki T, Li J, Lichtenstein P, Lichtner P, Liewald DC, Madden PA, Magnusson PK, Mäkinen TE, Masala M, McGue M, Metspalu A, Mielck A, Miller MB, Montgomery GW, Mukherjee S, Nyholt DR, Oostra BA, Palmer LJ, Palotie A, Penninx BW, Perola M, Peyser PA, Preisig M, Räikkönen K, Raitakari OT, Realo A, Ring SM, Ripatti S, Rivadeneira F, Rudan I, Rustichini A, Salomaa V, Sarin AP, Schlessinger D, Scott RJ, Snieder H, St Pourcain B, Starr JM, Sul JH, Surakka I, Svento R, Teumer A, Tiemeier H, van Rooij FJ, Van Wagoner DR, Vartiainen E, Viikari J, Vollenweider P, Vonk JM, Waeber G, Weir DR, Wichmann HE, Widen E, Willemsen G, Wilson JF, Wright AF, Conley D, Davey-Smith G, Franke L, Groenen PJ, Hofman A, Johannesson M, Kardia SL, Krueger RF, Laibson D, Martin NG, Meyer MN, Posthuma D, Thurik AR, Timpson NJ, Uitterlinden AG, van Duijn CM, Visscher PM, Benjamin DJ, Cesarini D, and Koellinger PD

Subjects
Cognition, Endophenotypes, Female, Genetic Loci, Humans, Male, Multifactorial Inheritance, Educational Status, Genome-Wide Association Study, Polymorphism, Single Nucleotide
Abstract

A genome-wide association study (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R(2) ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics.

Published
2013
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8. The association between lower educational attainment and depression owing to shared genetic effects?: Results in ~25,000 subjects

Author

Patricia A. Peyser, Jessica D. Faul, Patrik K. E. Magnusson, Nicholas D. Hastie, Beate St Pourcain, Marcus Ising, Gérard Waeber, Behrooz Z. Alizadeh, Judith M. Vonk, Lawrence F. Bielak, Sang Hong Lee, Wouter J. Peyrot, Thomas Illig, M. M. Weissman, Nicholas J. Timpson, George Dedoussis, Nicholas G. Martin, Tomi E. Mäkinen, Jorma Viikari, Lili Milani, Harold Snieder, Laura J. Bierut, A. C. Heath, Reinhold E. Schmidt, Mariza de Andrade, Vilmundur Gudnason, K. Petrovic, Robert M. Kirkpatrick, Marcela González Gross, William G. Iacono, Michelle N. Meyer, Henry Völzke, Marisa Loitfelder, Maria Dimitriou, Lude Franke, Robert F. Krueger, E. J. C. G. van den Oord, Sven Cichon, Michael Conlon O'Donovan, Ian W. Craig, Shawn N. Murphy, Danielle Posthuma, Brenda W.J.H. Penninx, Aarno Palotie, Roy Thurik, Panos Deloukas, Matt McGue, M. Preisig, Patricia A. Boyle, Osorio Meirelles, Ben A. Oostra, Klaus Berger, G. M. Montgomery, Sharon L.R. Kardia, Peter K. Joshi, K. Stefansson, Paul Lichtenstein, Andrew Heath, Andrea Schulz, Dena G. Hernandez, Debbie A Lawlor, S. P. Hamilton, James B. Potash, Z. Kutalik, Elisabeth Widen, Emil L. Sigurdsson, Rudolf S N Fehrmann, Matthias Nauck, Mikael Landén, Kurt Lohman, S.D. Gordon, Lefkos T. Middleton, Caroline Hayward, Anjali K. Henders, Philipp Koellinger, Jeffrey A. Boatman, G van Grootheest, M. Daly, Jian Yang, Peter Vollenweider, Penelope A. Lind, Stacy Steinberg, Frank J. A. van Rooij, Florian Holsboer, Hkon K. Gjessing, Erkki Vartiainen, Magnus Johannesson, Jingmei Li, David Laibson, Henrik Grönberg, Tõnu Esko, Ivana Kolcic, Niina Eklund, Kelly S. Benke, Henning Tiemeier, Isaac S. Kohane, Nicolas W. Martin, Ronny Myhre, Frans G. Zitman, Arpana Agrawal, James F. Wilson, Michael R. Barnes, Lei Yu, Thorgeir E. Thorgeirsson, Franois Bastardot, Katri Räikkönen, William Lawson, Willem A. Nolen, M. Rietschel, René Breuer, Bertram Müller-Myhsok, James A. Knowles, Grant W. Montgomery, Eva Reinmaa, Rudolf Uher, Andreas Mielck, Luigi Ferrucci, S. E. Medland, Yuri Milaneschi, Philip L. De Jager, Manfred Uhr, A. E. Farmer, Cornelia M. van Duijn, Samuli Ripatti, Marja-Liisa Nuotio, Manuel Mattheisen, Sebastian E. Baumeister, David R. Van Wagoner, Martin Preisig, Fernando Rivadeneira, Peter Lichtner, Christopher Oldmeadow, Hreinn Stefansson, Ian B. Hickie, Darina Czamara, Elizabeth G. Holliday, Astanand Jugessur, Carla A. Ibrahim-Verbaas, Jaime Derringer, Vivian S. Gainer, P. Muglia, Daniel J. Benjamin, Patrick K.E. Magnusson, Patience J. Gallagher, Jennifer A. Smith, Lynn Cherkas, Pamela A. F. Madden, David A. Bennett, Zoltán Kutalik, George Davey-Smith, Gudny Eiriksdottir, Jens Treutlein, N. Craddock, Juliette Harris, Antti Latvala, Roy H. Perlis, Markus M. Noethen, Jan-Emmanuel De Neve, Stanley I. Shyn, J.H. Smit, Dalton Conley, Adriaan Hofman, Jari Lahti, Patrick J. F. Groenen, Jüri Allik, Albert V. Smith, Ozren Polasek, Susan M. Ring, Thomas Bettecken, Michele L. Pergadia, Patrick J. McGrath, Katherine E. Tansey, Stephan Ripke, Hogni Oskarsson, Peng Lin, Douglas F. Levinson, Matthijs J. H. M. van der Loos, Melissa E. Garcia, Jonathan P. Beauchamp, Rodney J. Scott, Zhihong Zhu, Michel Guipponi, Lyle J. Palmer, Alexander Teumer, William Coryell, Stefan Kloiber, Gonneke Willemsen, John Frank, Victor M. Castro, Andrew M. McIntosh, John M. Starr, Antonio Terracciano, Mika Kähönen, Marco Masala, Markus Perola, André G. Uitterlinden, Sutapa Mukherjee, Alexander Viktorin, Lenore J. Launer, Elisabeth B. Binder, William A. Scheftner, Christel M. Middeldorp, D. H. R. Blackwood, I. Jones, Thais S. Rizzi, A. Teumer, Cornelius A. Rietveld, Aldo Rustichini, Guy Lewis, Susan L. Slager, David M. Evans, Dorret I. Boomsma, Harry Campbell, Susanne Churchill, Johan G. Eriksson, Alan F. Wright, Dan V. Iosifescu, W. Maier, Francesco Cucca, Federica Tozzi, David R. Weir, Eva Albrecht, L. Milani, Jennifer R. Harris, Min A. Jhun, Marjo-Riitta Järvelin, Martin F. Elderson, Ute Bültmann, Olli T. Raitakari, Konstantin Shakhbazov, Krista Fischer, Thomas G. Schulze, T. Jung-Ying, P. Lichtenstein, Terho Lethimäki, Jeffrey B. Weilburg, Rolf Holle, Bo Jacobsson, Pedro Marques Vidal, Jordan W. Smoller, Stavroula Kanoni, Kati Kristiansson, Sergey Goryachev, Michael Steffens, Peter M. Visscher, Toshiko Tanaka, Donald J. MacIntyre, Witte J.G. Hoogendijk, David Schlessinger, Ian J. Deary, Harm-Jan Westra, Erik Ingelsson, E.J.C. de Geus, Franziska Degenhardt, Lydia Quaye, John Barnard, David C. Liewald, John P. Rice, Christopher F. Chabris, P. McGuffin, Tamara B. Harris, C. M. Lewis, Gail Davies, Enda M. Byrne, H.-Erich Wichmann, Sara Hägg, David Cesarini, Najaf Amin, Juha Karjalainen, Dale R. Nyholt, Christian Gieger, Per Hall, Ania Korszun, Neale Bm, Wei Zhao, Abdel Abdellaoui, Andres Metspalu, Christina Holzapfel, Jae Hoon Sul, Christiaan de Leeuw, Antti-Pekka Sarin, Ida Surakka, Veikko Salomaa, Mina K. Chung, N. L. Pedersen, Gerome Breen, P. A. F. Madden, Martin A. Kohli, J Kaprio, John Attia, Jing Shi, Gibran Hemani, Rauli Svento, Veronique Vitart, Susanne Lucae, L. A. Jones, Jouke-Jan Hottenga, Daniel S. Evans, Hans-Jörgen Grabe, Yongmei Liu, Danyu Lin, Albert Hofman, George McMahon, Naomi R. Wray, Stefan Herms, Stefania Bandinelli, W. Hoffmann, P.F. Sullivan, Susanne Hoefels, Michael B. Miller, Alan W. 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Subjects
Netherlands Twin Register (NTR), Male, Genome-wide association study, Logistic regression, Cohort Studies, Odds Ratio, pleiotropic genetic effects, Netherlands, Psychiatry, education.field_of_study, Likelihood Functions, Single Nucleotide, Middle Aged, Psychiatry and Mental health, educational attainment, depression, Major depressive disorder, Educational Status, Regression Analysis, Female, Psychology, Adult, Estonia, medicine.medical_specialty, Biochemistry & Molecular Biology, Aged, Depressive Disorder, Major/epidemiology, Depressive Disorder, Major/genetics, Depressive Disorder, Major/psychology, Estonia/epidemiology, Gene-Environment Interaction, Genetic Association Studies, Genotype, Humans, Netherlands/epidemiology, Polymorphism, Single Nucleotide/genetics, Psychiatric Status Rating Scales, Concordance, Population, SNP, Single-nucleotide polymorphism, Genetic correlation, behavioral disciplines and activities, Polymorphism, Single Nucleotide, Article, Cellular and Molecular Neuroscience, mental disorders, medicine, Polymorphism, education, Molecular Biology, Depressive Disorder, Depressive Disorder, Major, major depressive disorder, ta1184, Neurosciences, Major, Odds ratio, medicine.disease, ta3124, Neurosciences & Neurology, polymorphisms, Demography
Abstract

An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14 949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15 138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884 105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on similar to 120 000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status. Refereed/Peer-reviewed

Published
2015
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