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Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

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., De 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., Scheftner, W.A., Sigurdsson, E., Schork, N.J., Schatzberg, A.F., Baekvad-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., 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., De 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., Quested, D.J., Rossin, L., Sanders, A.R., Shi, J., Sklar, P., St Clair, D., Stroup, T.S., Van Os, J., Wiersma, D. and Zammit, S. (2018) Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits. Nature Communications, 9 (989). ISSN 2041-1723

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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. © 2018 The Author(s).

Item Type: Article
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The full-text of the published article can be accessed via the Official URL.

Uncontrolled Keywords: genetic risk prediction, genome-wide association studies, genetic correlations, predictive medicine, statistical methods, quantitative traits
Subjects: R Medicine > R Medicine (General)
Divisions: Academic Departments > Institute of Health and Society
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Copyright Info: Open Access, Creative Commons Attribution 4.0 International License
Depositing User: Katherine Gordon-Smith
Date Deposited: 26 Feb 2019 14:42
Last Modified: 11 Apr 2019 08:04
URI: https://eprints.worc.ac.uk/id/eprint/7649

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