Maier RM, Zhu Z, Lee SH, Trzaskowski M, Ruderfer DM, Stahl EA, Ripke S, Wray NR, Yang J, Visscher PM, Robinson MR, Forstner AJ, Mcquillin A, Trubetskoy V, Wang W, Wang Y, Coleman JR, Gaspar HA, De Leeuw CA, Whitehead Pavlides JM, Olde Loohuis LM, Pers TH, Lee PH, Charney AW, Dobbyn AL, Huckins L, Boocock J, Giambartolomei C, Roussos P, Mullins N, Awasthi S, Agerbo E, Als TD, Pedersen CB, Grove J, Kupka R, Regeer EJ, Anjorin A, Casas M, Mahon PB, Allardyce J, Escott-Price V, Forty L, Fraser C, Kogevinas M, Frank J, Streit F, Strohmaier J, Treutlein J, Witt SH, Kennedy JL, Strauss JS, Garnham J, O'donovan C, Slaney C, Steinberg S, Thorgeirsson TE, Hautzinger M, Steffens M, Perlis RH, Sánchez-Mora C, Hipolito M, Lawson WB, Nwulia EA, Levy SE, Foroud TM, Jamain S, Young AH, Mckay JD, Albani D, Zandi P, Potash JB, Zhang P, Raymond Depaulo J, Bergen SE, 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 MG, Morris DW, Weickert CS, Weickert TW, Macintyre DJ, Lawrence J, Elvsåshagen T, Smeland OB, Djurovic S, Xi S, Green EK, Czerski PM, Hauser J, Xu W, Vedder H, Oruc L, Spijker AT, Gordon SD, Medland SE, Curtis D, Mühleisen TW, Badner J, Scheftner WA, Sigurdsson E, Schork NJ, Schatzberg AF, Bækvad-Hansen M, Bybjerg-Grauholm J, Hansen CS, Knowles JA, Szelinger S, Montgomery GW, Boks M, Adolfsson AN, Hoffmann P, Bauer M, Pfennig A, Leber M, Kittel-Schneider S, Reif A, Del-Favero J, Fischer SB, Herms S, Reinbold CS, Degenhardt F, Koller AC, Maaser A, Ori A, Dale AM, Fan CC, Greenwood TA, Nievergelt CM, Shehktman T, Shilling PD, Byerley W, Bunney W, Alliey-Rodriguez N, Clarke TK, Liu C, Coryell W, Akil H, Burmeister M, Flickinger M, Li JZ, Mcinnis MG, Meng F, Thompson RC, Watson SJ, Zollner S, Guan W, Green MJ, Craig D, Sobell JL, Milani L, Gordon-Smith K, Knott SV, Perry A, Parra JG, Mayoral F, Rivas F, Rice JP, Barchas JD, Børglum AD, Mortensen PB, Mors O, Grigoroiu-Serbanescu M, Bellivier F, Etain B, Leboyer M, Ramos-Quiroga JA, Agartz I, Amin F, Azevedo MH, Bass N, Black DW, Blackwood DH, Bruggeman R, Buccola NG, Choudhury K, Cloninger CR, Corvin A, Craddock N, Daly MJ, Datta S, Donohoe GJ, Duan J, Dudbridge F, Fanous A, Freedman R, Freimer NB, Friedl M, Gill M, Gurling H, De Haan L, Hamshere ML, Hartmann AM, Holmans PA, Kahn RS, Keller MC, Kenny E, Kirov GK, Krabbendam L, Krasucki R, Lencz T, Levinson DF, Lieberman JA, Lin DY, Linszen DH, Magnusson PK, Maier W, Malhotra AK, Mattheisen M, Mattingsdal M, Mccarroll SA, Medeiros H, Melle I, Milanova V, Myin-Germeys I, Neale BM, Ophoff RA, Owen MJ, Pimm J, Purcell SM, Puri V, Quested DJ, Rossin L, Sanders AR, Shi J, Sklar P, St Clair D, Stroup TS, Van Os J, Wiersma D, Zammit S (2018)
Publication Type: Journal article
Publication year: 2018
Book Volume: 9
Article Number: 989
Journal Issue: 1
DOI: 10.1038/s41467-017-02769-6
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.
APA:
Maier, R.M., Zhu, Z., Lee, S.H., Trzaskowski, M., Ruderfer, D.M., Stahl, E.A.,... Zammit, S. (2018). Improving genetic prediction by leveraging genetic correlations among human diseases and traits. Nature Communications, 9(1). https://doi.org/10.1038/s41467-017-02769-6
MLA:
Maier, Robert M., et al. "Improving genetic prediction by leveraging genetic correlations among human diseases and traits." Nature Communications 9.1 (2018).
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