1. Improving genetic prediction by leveraging genetic correlations among human diseases and traits
- Author
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Maier, R. M., Zhu, Z., Lee, S. H., Trzaskowski, M., Ruderfer, D. M., Stahl, E. A., Ripke, S., Wray, N. R., Yang, J., Visscher, P. M., Robinson, M. R., Forstner, A. J., Mcquillin, A., Trubetskoy, V., Wang, W., Wang, Y., Coleman, J. R. I., Gaspar, H. A., Leeuw, C. A., Whitehead Pavlides, J. M., Olde Loohuis, L. M., Pers, T. H., Lee, P. H., Charney, A. W., Dobbyn, A. L., Huckins, L., Boocock, J., Giambartolomei, C., Roussos, P., Mullins, N., Awasthi, S., Agerbo, E., Als, T. D., Pedersen, C. B., Grove, J., Kupka, R., Regeer, E. J., Anjorin, A., Casas, M., Mahon, P. B., Allardyce, J., Escott-Price, V., Forty, L., Fraser, C., Kogevinas, M., Frank, J., Streit, F., Strohmaier, J., Treutlein, J., Witt, S. H., Kennedy, J. L., Strauss, J. S., Garnham, J., O Donovan, C., Slaney, C., Steinberg, S., Thorgeirsson, T. E., Hautzinger, M., Steffens, M., Perlis, R. H., Sánchez-Mora, C., Hipolito, M., Lawson, W. B., Nwulia, E. A., Levy, S. E., Foroud, T. M., Jamain, S., Young, A. H., Mckay, J. D., Albani, D., Zandi, P., Potash, J. B., Zhang, P., Raymond Depaulo, J., Bergen, S. E., Juréus, A., Karlsson, R., Kandaswamy, R., Mcguffin, P., Rivera, M., Lissowska, J., Cruceanu, C., Lucae, S., Cervantes, P., Budde, M., Gade, K., Heilbronner, U., Pedersen, M. G., Morris, D. W., Weickert, C. S., Weickert, T. W., Macintyre, D. J., Lawrence, J., Elvsåshagen, T., Smeland, O. B., Djurovic, S., Xi, S., Green, E. K., Czerski, P. M., Hauser, J., Xu, W., Vedder, H., Oruc, L., Spijker, A. T., Gordon, S. D., Medland, S. E., Curtis, D., Mühleisen, T. W., Badner, J. A., Scheftner, W. A., Sigurdsson, E., Schork, N. J., Schatzberg, A. F., Bækvad-Hansen, M., Bybjerg-Grauholm, J., Hansen, C. S., Knowles, J. A., Szelinger, S., Montgomery, G. W., Boks, M., Adolfsson, A. N., Hoffmann, P., Bauer, M., Pfennig, A., Leber, M., Kittel-Schneider, S., Reif, A., Del-Favero, J., Fischer, S. B., Herms, S., Reinbold, C. S., Degenhardt, F., Koller, A. C., Maaser, A., Ori, A. P. S., Dale, A. M., Fan, C. C., Greenwood, T. A., Nievergelt, C. M., Shehktman, T., Shilling, P. D., Byerley, W., Bunney, W., Alliey-Rodriguez, N., Clarke, T. K., Liu, C., Coryell, W., Akil, H., Burmeister, M., Flickinger, M., Li, J. Z., Mcinnis, M. G., Meng, F., Thompson, R. C., Watson, S. J., Zollner, S., Guan, W., Green, M. J., Craig, D., Sobell, J. L., Milani, L., Gordon-Smith, Katherine, Knott, Sarah, Perry, Amy, Parra, J. G., Mayoral, F., Rivas, F., Rice, J. P., Barchas, J. D., Børglum, A. D., Mortensen, P. B., Mors, O., Grigoroiu-Serbanescu, M., Bellivier, F., Etain, B., Leboyer, M., Ramos-Quiroga, J. A., Agartz, I., Amin, F., Azevedo, M. H., Bass, N., Black, D. W., Blackwood, D. H. R., Bruggeman, R., Buccola, N. G., Choudhury, K., Cloninger, C. R., Corvin, A., Craddock, N., Daly, M. J., Datta, S., Donohoe, G. J., Duan, J., Dudbridge, F., Fanous, A., Freedman, R., Freimer, N. B., Friedl, M., Gill, M., Gurling, H., Haan, L., Hamshere, M. L., Hartmann, A. M., Holmans, P. A., Kahn, R. S., Keller, M. C., Kenny, E., Kirov, G. K., Krabbendam, L., Krasucki, R., Lencz, T., Levinson, D. F., Lieberman, J. A., Lin, D. -Y, Linszen, D. H., Magnusson, P. K. E., Maier, W., Malhotra, A. K., Mattheisen, M., Mattingsdal, M., Mccarroll, S. A., Medeiros, H., Melle, I., Milanova, V., Myin-Germeys, I., Neale, B. M., Ophoff, R. A., Owen, M. J., Pimm, J., Purcell, S. M., Puri, V., Digby Quested, Rossin, L., Sanders, A. R., Shi, J., Sklar, P., St Clair, D., Stroup, T. S., Os, J., Wiersma, D., Zammit, S., Maier, Robert M, Zhu, Zhihong, Lee, Sang Hong, Trzaskowski, Maciej, Ruderfer, Douglas M, Stahl, Eli A, Ripke, Stephan, Wray, Naomi R, Yang, Jian, Visscher, Peter M, Robinson, Matthew R, Bipolar Disorder Working Grp Psy, Schizophrenia Working Grp Psychiat, Complex Trait Genetics, Amsterdam Neuroscience - Complex Trait Genetics, APH - Mental Health, ANS - Complex Trait Genetics, Adult Psychiatry, and Psychiatry
- Subjects
0301 basic medicine ,Bipolar Disorder ,Chemistry(all) ,Science ,General Physics and Astronomy ,Genomics ,Genome-wide association study ,Computational biology ,Biology ,Physics and Astronomy(all) ,Risk Assessment ,Biochemistry ,General Biochemistry, Genetics and Molecular Biology ,Article ,predictive medicine ,quantitative trait ,03 medical and health sciences ,0302 clinical medicine ,SDG 3 - Good Health and Well-being ,Pleiotropy ,Genetic Pleiotropy ,Humans ,Genetic Predisposition to Disease ,lcsh:Science ,Multidisciplinary ,Models, Statistical ,Bipolar Disorder/genetics ,Genome-Wide Association Study ,Schizophrenia/genetics ,Biochemistry, Genetics and Molecular Biology(all) ,General Chemistry ,Precision medicine ,R1 ,Biobank ,3. Good health ,genome wide association studies ,030104 developmental biology ,Trait ,Schizophrenia ,statistical methods ,lcsh:Q ,Risk assessment ,030217 neurology & neurosurgery ,Genetics and Molecular Biology(all) - Abstract
Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait., Genetic prediction of complex traits so far has limited accuracy because of insufficient understanding of the genetic risk. Here, Maier et al. develop an improved method for trait prediction that makes use of genetic correlations between traits and apply it to summary statistics of psychiatric diseases.
- Published
- 2018
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