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Your search keyword '"Oskarsson, Sven"' showing total 5 results
Start Over Author "Oskarsson, Sven" Publisher escholarship, university of california
5 results on '"Oskarsson, Sven"'

1. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Author

Howe, Laurence J, Nivard, Michel G, Morris, Tim T, Hansen, Ailin F, Rasheed, Humaira, Cho, Yoonsu, Chittoor, Geetha, Ahlskog, Rafael, Lind, Penelope A, Palviainen, Teemu, van der Zee, Matthijs D, Cheesman, Rosa, Mangino, Massimo, Wang, Yunzhang, Li, Shuai, Klaric, Lucija, Ratliff, Scott M, Bielak, Lawrence F, Nygaard, Marianne, Giannelis, Alexandros, Willoughby, Emily A, Reynolds, Chandra A, Balbona, Jared V, Andreassen, Ole A, Ask, Helga, Baras, Aris, Bauer, Christopher R, Boomsma, Dorret I, Campbell, Archie, Campbell, Harry, Chen, Zhengming, Christofidou, Paraskevi, Corfield, Elizabeth, Dahm, Christina C, Dokuru, Deepika R, Evans, Luke M, de Geus, Eco JC, Giddaluru, Sudheer, Gordon, Scott D, Harden, K Paige, Hill, W David, Hughes, Amanda, Kerr, Shona M, Kim, Yongkang, Kweon, Hyeokmoon, Latvala, Antti, Lawlor, Deborah A, Li, Liming, Lin, Kuang, Magnus, Per, Magnusson, Patrik KE, Mallard, Travis T, Martikainen, Pekka, Mills, Melinda C, Njølstad, Pål Rasmus, Overton, John D, Pedersen, Nancy L, Porteous, David J, Reid, Jeffrey, Silventoinen, Karri, Southey, Melissa C, Stoltenberg, Camilla, Tucker-Drob, Elliot M, Wright, Margaret J, Hewitt, John K, Keller, Matthew C, Stallings, Michael C, Lee, James J, Christensen, Kaare, Kardia, Sharon LR, Peyser, Patricia A, Smith, Jennifer A, Wilson, James F, Hopper, John L, Hägg, Sara, Spector, Tim D, Pingault, Jean-Baptiste, Plomin, Robert, Havdahl, Alexandra, Bartels, Meike, Martin, Nicholas G, Oskarsson, Sven, Justice, Anne E, Millwood, Iona Y, Hveem, Kristian, Naess, Øyvind, Willer, Cristen J, Åsvold, Bjørn Olav, Koellinger, Philipp D, Kaprio, Jaakko, Medland, Sarah E, Walters, Robin G, Benjamin, Daniel J, Turley, Patrick, Evans, David M, Davey Smith, George, Hayward, Caroline, Brumpton, Ben, Hemani, Gibran, and Davies, Neil M

Subjects
Human Genome, Genetics, Pediatric, 2.1 Biological and endogenous factors, Aetiology, Mental health, Generic health relevance, Genome-Wide Association Study, Humans, Mendelian Randomization Analysis, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Social Science Genetic Association Consortium, Within Family Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

Published
2022

2. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Author

Okbay, Aysu, Wu, Yeda, Wang, Nancy, Jayashankar, Hariharan, Bennett, Michael, Nehzati, Seyed Moeen, Sidorenko, Julia, Kweon, Hyeokmoon, Goldman, Grant, Gjorgjieva, Tamara, Jiang, Yunxuan, Hicks, Barry, Tian, Chao, Hinds, David A, Ahlskog, Rafael, Magnusson, Patrik KE, Oskarsson, Sven, Hayward, Caroline, Campbell, Archie, Porteous, David J, Freese, Jeremy, Herd, Pamela, Watson, Chelsea, Jala, Jonathan, Conley, Dalton, Koellinger, Philipp D, Johannesson, Magnus, Laibson, David, Meyer, Michelle N, Lee, James J, Kong, Augustine, Yengo, Loic, Cesarini, David, Turley, Patrick, Visscher, Peter M, Beauchamp, Jonathan P, Benjamin, Daniel J, and Young, Alexander I

Subjects
Human Genome, Genetics, Pediatric, Aetiology, 2.1 Biological and endogenous factors, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, 23andMe Research Team, Social Science Genetic Association Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

Published
2022

3. Resource profile and user guide of the Polygenic Index Repository

Author

Becker, Joel, Burik, Casper AP, Goldman, Grant, Wang, Nancy, Jayashankar, Hariharan, Bennett, Michael, Belsky, Daniel W, Karlsson Linnér, Richard, Ahlskog, Rafael, Kleinman, Aaron, Hinds, David A, Caspi, Avshalom, Corcoran, David L, Moffitt, Terrie E, Poulton, Richie, Sugden, Karen, Williams, Benjamin S, Harris, Kathleen Mullan, Steptoe, Andrew, Ajnakina, Olesya, Milani, Lili, Esko, Tõnu, Iacono, William G, McGue, Matt, Magnusson, Patrik KE, Mallard, Travis T, Harden, K Paige, Tucker-Drob, Elliot M, Herd, Pamela, Freese, Jeremy, Young, Alexander, Beauchamp, Jonathan P, Koellinger, Philipp D, Oskarsson, Sven, Johannesson, Magnus, Visscher, Peter M, Meyer, Michelle N, Laibson, David, Cesarini, David, Benjamin, Daniel J, Turley, Patrick, and Okbay, Aysu

Subjects
Epidemiology, Health Sciences, 2.6 Resources and infrastructure (aetiology), Aetiology, Data Analysis, Databases, Genetic, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Polygenic score, Polygenic index, Repository, Measurement error, 23andMe Research Group, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Published
2021

4. Multi-trait analysis of genome-wide association summary statistics using MTAG

Author

Turley, Patrick, Walters, Raymond K, Maghzian, Omeed, Okbay, Aysu, Lee, James J, Fontana, Mark Alan, Nguyen-Viet, Tuan Anh, Wedow, Robbee, Zacher, Meghan, Furlotte, Nicholas A, 23andMe Research Team, Social Science Genetic Association Consortium, Magnusson, Patrik, Oskarsson, Sven, Johannesson, Magnus, Visscher, Peter M, Laibson, David, Cesarini, David, Neale, Benjamin M, and Benjamin, Daniel J

Subjects
Biological Sciences, Genetics, Human Genome, Algorithms, Data Interpretation, Statistical, Datasets as Topic, Depression, Diagnostic Self Evaluation, Genetic Association Studies, Genome-Wide Association Study, Health, Humans, Meta-Analysis as Topic, Multifactorial Inheritance, Neuroticism, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, 23andMe Research Team, Social Science Genetic Association Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

Published
2018

5. Genome-wide association study identifies 74 loci associated with educational attainment

Author

Okbay, Aysu, Beauchamp, Jonathan P, Fontana, Mark Alan, Lee, James J, Pers, Tune H, Rietveld, Cornelius A, Turley, Patrick, Chen, Guo-Bo, Emilsson, Valur, Meddens, S Fleur W, Oskarsson, Sven, Pickrell, Joseph K, Thom, Kevin, Timshel, Pascal, de Vlaming, Ronald, Abdellaoui, Abdel, Ahluwalia, Tarunveer S, Bacelis, Jonas, Baumbach, Clemens, Bjornsdottir, Gyda, Brandsma, Johannes H, Pina Concas, Maria, Derringer, Jaime, Furlotte, Nicholas A, Galesloot, Tessel E, Girotto, Giorgia, Gupta, Richa, Hall, Leanne M, Harris, Sarah E, Hofer, Edith, Horikoshi, Momoko, Huffman, Jennifer E, Kaasik, Kadri, Kalafati, Ioanna P, Karlsson, Robert, Kong, Augustine, Lahti, Jari, Lee, Sven J van der, deLeeuw, Christiaan, Lind, Penelope A, Lindgren, Karl-Oskar, Liu, Tian, Mangino, Massimo, Marten, Jonathan, Mihailov, Evelin, Miller, Michael B, van der Most, Peter J, Oldmeadow, Christopher, Payton, Antony, Pervjakova, Natalia, Peyrot, Wouter J, Qian, Yong, Raitakari, Olli, Rueedi, Rico, Salvi, Erika, Schmidt, Börge, Schraut, Katharina E, Shi, Jianxin, Smith, Albert V, Poot, Raymond A, St Pourcain, Beate, Teumer, Alexander, Thorleifsson, Gudmar, Verweij, Niek, Vuckovic, Dragana, Wellmann, Juergen, Westra, Harm-Jan, Yang, Jingyun, Zhao, Wei, Zhu, Zhihong, Alizadeh, Behrooz Z, Amin, Najaf, Bakshi, Andrew, Baumeister, Sebastian E, Biino, Ginevra, Bønnelykke, Klaus, Boyle, Patricia A, Campbell, Harry, Cappuccio, Francesco P, Davies, Gail, De Neve, Jan-Emmanuel, Deloukas, Panos, Demuth, Ilja, Ding, Jun, Eibich, Peter, Eisele, Lewin, Eklund, Niina, Evans, David M, Faul, Jessica D, Feitosa, Mary F, Forstner, Andreas J, Gandin, Ilaria, Gunnarsson, Bjarni, Halldórsson, Bjarni V, Harris, Tamara B, Heath, Andrew C, Hocking, Lynne J, Holliday, Elizabeth G, Homuth, Georg, and Horan, Michael A

Subjects
Biological Sciences, Genetics, Epidemiology, Health Sciences, Statistics, Mathematical Sciences, Human Genome, Clinical Research, Alzheimer Disease, Bipolar Disorder, Brain, Cognition, Computational Biology, Educational Status, Fetus, Gene Expression Regulation, Gene-Environment Interaction, Genome-Wide Association Study, Humans, Molecular Sequence Annotation, Polymorphism, Single Nucleotide, Schizophrenia, United Kingdom, LifeLines Cohort Study, General Science & Technology
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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