Search

Your search keyword '"Maghzian O"' showing total 8 results
Start Over Author "Maghzian O"
8 results on '"Maghzian O"'

2. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence

Author

Hill, W. D., Marioni, R. E., Maghzian, O., Ritchie, S. J., Hagenaars, S. P., McIntosh, A. M., Gale, C. R., Davies, G., and Deary, I. J.

Subjects
Data Analysis, Male, Multifactorial Inheritance, Neurogenesis, Intelligence, Nerve Fibers, Myelinated, Polymorphism, Single Nucleotide, Cognition, Genetic Loci, Humans, Female, Genetic Predisposition to Disease, Immediate Communication, Genome-Wide Association Study
Abstract

Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence (rg = 0.70). We used these findings as foundations for our use of a novel approach—multi-trait analysis of genome-wide association studies (MTAG; Turley et al. 2017)—to combine two large genome-wide association studies (GWASs) of education and intelligence, increasing statistical power and resulting in the largest GWAS of intelligence yet reported. Our study had four goals: first, to facilitate the discovery of new genetic loci associated with intelligence; second, to add to our understanding of the biology of intelligence differences; third, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predicts phenotypic intelligence in an independent sample. By combining datasets using MTAG, our functional sample size increased from 199,242 participants to 248,482. We found 187 independent loci associated with intelligence, implicating 538 genes, using both SNP-based and gene-based GWAS. We found evidence that neurogenesis and myelination—as well as genes expressed in the synapse, and those involved in the regulation of the nervous system—may explain some of the biological differences in intelligence. The results of our combined analysis demonstrated the same pattern of genetic correlations as those from previous GWASs of intelligence, providing support for the meta-analysis of these genetically-related phenotypes.

Published
2018
Full Text
View/download PDF

3. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

Author

Lee, J.J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Galesloot, T.E., Kiemeney, L.A.L.M., Franke, B., Benjamin, D.J., Cesarini, D., Lee, J.J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Galesloot, T.E., Kiemeney, L.A.L.M., Franke, B., Benjamin, D.J., and Cesarini, D.

Abstract

Contains fulltext : 195595.pdf (publisher's version ) (Closed access), Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

Published
2018

4. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

Author

Lee, JJ, Wedow, R, Okbay, A, Kong, E, Maghzian, O, Zacher, M, Tuan Anh, N-V, Bowers, P, Sidorenko, J, Linner, RK, Fontana, MA, Kundu, T, Lee, C, Li, H, Li, R, Royer, R, Timshel, PN, Walters, RK, Willoughby, EA, Yengo, L, Alver, M, Bao, Y, Clark, DW, Day, FR, Furlotte, NA, Joshi, PK, Kemper, KE, Kleinman, A, Langenberg, C, Magi, R, Trampush, JW, Verma, SS, Wu, Y, Lam, Mei, Zhao, JH, Zheng, Z, Boardman, JD, Campbell, H, Freese, J, Harris, KM, Hayward, C, Herd, P, Kumari, M, Lencz, T, Luan, JA, Malhotra, AK, Metspalu, A, Milani, L, Ong, KK, Perry, JRB, Porteous, DJ, Ritchie, MD, Smart, MC, Smith, BH, Tung, JY, Wareham, NJ, Wilson, JF, Beauchamp, JP, Conley, DC, Esko, T, Lehrer, SF, Magnusson, PKE, Oskarsson, S, Pers, TH, Robinson, MR, Thom, K, Watson, C, Chabris, CF, Meyer, MN, Laibson, DI, Yang, Jiaqi, Johannesson, M, Koellinger, PD, Turley, P, Visscher, PM, Benjamin, DJ, Cesarini, D, Lee, JJ, Wedow, R, Okbay, A, Kong, E, Maghzian, O, Zacher, M, Tuan Anh, N-V, Bowers, P, Sidorenko, J, Linner, RK, Fontana, MA, Kundu, T, Lee, C, Li, H, Li, R, Royer, R, Timshel, PN, Walters, RK, Willoughby, EA, Yengo, L, Alver, M, Bao, Y, Clark, DW, Day, FR, Furlotte, NA, Joshi, PK, Kemper, KE, Kleinman, A, Langenberg, C, Magi, R, Trampush, JW, Verma, SS, Wu, Y, Lam, Mei, Zhao, JH, Zheng, Z, Boardman, JD, Campbell, H, Freese, J, Harris, KM, Hayward, C, Herd, P, Kumari, M, Lencz, T, Luan, JA, Malhotra, AK, Metspalu, A, Milani, L, Ong, KK, Perry, JRB, Porteous, DJ, Ritchie, MD, Smart, MC, Smith, BH, Tung, JY, Wareham, NJ, Wilson, JF, Beauchamp, JP, Conley, DC, Esko, T, Lehrer, SF, Magnusson, PKE, Oskarsson, S, Pers, TH, Robinson, MR, Thom, K, Watson, C, Chabris, CF, Meyer, MN, Laibson, DI, Yang, Jiaqi, Johannesson, M, Koellinger, PD, Turley, P, Visscher, PM, Benjamin, DJ, and Cesarini, D

Published
2018

5. Author Correction: Multi-trait analysis of genome-wide association summary statistics using MTAG.

Author

Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, Nguyen-Viet TA, Wedow R, Zacher M, Furlotte NA, Magnusson P, Oskarsson S, Johannesson M, Visscher PM, Laibson D, Cesarini D, Neale BM, and Benjamin DJ

Abstract

In the version of the paper initially published, no competing interests were declared. The 'Competing interests' statement should have stated that B.M.N. is on the Scientific Advisory Board of Deep Genomics. The error has been corrected in the HTML and PDF versions of the article.

Published
2019
Full Text
View/download PDF

7. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.

Author

Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, Nguyen-Viet TA, Bowers P, Sidorenko J, Karlsson Linnér R, Fontana MA, Kundu T, Lee C, Li H, Li R, Royer R, Timshel PN, Walters RK, Willoughby EA, Yengo L, Alver M, Bao Y, Clark DW, Day FR, Furlotte NA, Joshi PK, Kemper KE, Kleinman A, Langenberg C, Mägi R, Trampush JW, Verma SS, Wu Y, Lam M, Zhao JH, Zheng Z, Boardman JD, Campbell H, Freese J, Harris KM, Hayward C, Herd P, Kumari M, Lencz T, Luan J, Malhotra AK, Metspalu A, Milani L, Ong KK, Perry JRB, Porteous DJ, Ritchie MD, Smart MC, Smith BH, Tung JY, Wareham NJ, Wilson JF, Beauchamp JP, Conley DC, Esko T, Lehrer SF, Magnusson PKE, Oskarsson S, Pers TH, Robinson MR, Thom K, Watson C, Chabris CF, Meyer MN, Laibson DI, Yang J, Johannesson M, Koellinger PD, Turley P, Visscher PM, Benjamin DJ, and Cesarini D

Subjects
Adult, Aged, Aged, 80 and over, Cohort Studies, Educational Status, Female, Genome-Wide Association Study methods, Humans, Male, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Multifactorial Inheritance
Abstract

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

Published
2018
Full Text
View/download PDF

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

Author

Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, Nguyen-Viet TA, Wedow R, Zacher M, Furlotte NA, Magnusson P, Oskarsson S, Johannesson M, Visscher PM, Laibson D, Cesarini D, Neale BM, and Benjamin DJ

Subjects
Algorithms, Datasets as Topic statistics & numerical data, Depression epidemiology, Depression genetics, Diagnostic Self Evaluation, Genetic Association Studies methods, Genetic Association Studies statistics & numerical data, Health statistics & numerical data, Humans, Meta-Analysis as Topic, Neuroticism, Phenotype, Polymorphism, Single Nucleotide, Data Interpretation, Statistical, Genome-Wide Association Study methods, Genome-Wide Association Study statistics & numerical data, Multifactorial Inheritance, Quantitative Trait Loci genetics
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
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results