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Items: 1 to 20 of 147


Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data.

Shi J, Park JH, Duan J, Berndt ST, Moy W, Yu K, Song L, Wheeler W, Hua X, Silverman D, Garcia-Closas M, Hsiung CA, Figueroa JD, Cortessis VK, Malats N, Karagas MR, Vineis P, Chang IS, Lin D, Zhou B, Seow A, Matsuo K, Hong YC, Caporaso NE, Wolpin B, Jacobs E, Petersen GM, Klein AP, Li D, Risch H, Sanders AR, Hsu L, Schoen RE, Brenner H; MGS (Molecular Genetics of Schizophrenia) GWAS Consortium; GECCO (The Genetics and Epidemiology of Colorectal Cancer Consortium); GAME-ON/TRICL (Transdisciplinary Research in Cancer of the Lung) GWAS Consortium; PRACTICAL (PRostate cancer AssoCiation group To Investigate Cancer Associated aLterations) Consortium; PanScan Consortium; GAME-ON/ELLIPSE Consortium, Stolzenberg-Solomon R, Gejman P, Lan Q, Rothman N, Amundadottir LT, Landi MT, Levinson DF, Chanock SJ, Chatterjee N.

PLoS Genet. 2016 Dec 30;12(12):e1006493. doi: 10.1371/journal.pgen.1006493. eCollection 2016 Dec.


Combining common genetic variants and non-genetic risk factors to predict risk of cutaneous melanoma.

Gu F, Chen TH, Pfeiffer RM, Fargnoli MC, Calista D, Ghiorzo P, Peris K, Puig S, Menin C, De Nicolo A, Rodolfo M, Pellegrini C, Pastorino L, Evangelou E, Zhang T, Hua X, DellaValle CT, Timothy Bishop D, MacGregor S, Iles MI, Law MH, Cust A, Brown KM, Stratigos AJ, Nagore E, Chanock S, Shi J, Consortium MM, Consortium M, Landi MT.

Hum Mol Genet. 2018 Dec 1;27(23):4145-4156. doi: 10.1093/hmg/ddy282.


Testing for polygenic effects in genome-wide association studies.

Pan W, Chen YM, Wei P.

Genet Epidemiol. 2015 May;39(4):306-16. doi: 10.1002/gepi.21899. Epub 2015 Apr 6.


A flexible genome-wide bootstrap method that accounts for ranking and threshold-selection bias in GWAS interpretation and replication study design.

Faye LL, Sun L, Dimitromanolakis A, Bull SB.

Stat Med. 2011 Jul 10;30(15):1898-912. doi: 10.1002/sim.4228. Epub 2011 May 3.


Estimating the total number of susceptibility variants underlying complex diseases from genome-wide association studies.

So HC, Yip BH, Sham PC.

PLoS One. 2010 Nov 17;5(11):e13898. doi: 10.1371/journal.pone.0013898.


Polygenic prediction via Bayesian regression and continuous shrinkage priors.

Ge T, Chen CY, Ni Y, Feng YA, Smoller JW.

Nat Commun. 2019 Apr 16;10(1):1776. doi: 10.1038/s41467-019-09718-5.


Statistical correction of the Winner's Curse explains replication variability in quantitative trait genome-wide association studies.

Palmer C, Pe'er I.

PLoS Genet. 2017 Jul 17;13(7):e1006916. doi: 10.1371/journal.pgen.1006916. eCollection 2017 Jul.


POLARIS: Polygenic LD-adjusted risk score approach for set-based analysis of GWAS data.

Baker E, Schmidt KM, Sims R, O'Donovan MC, Williams J, Holmans P, Escott-Price V, Consortium WTG.

Genet Epidemiol. 2018 Jun;42(4):366-377. doi: 10.1002/gepi.22117. Epub 2018 Mar 12.


Power, false discovery rate and Winner's Curse in eQTL studies.

Huang QQ, Ritchie SC, Brozynska M, Inouye M.

Nucleic Acids Res. 2018 Dec 14;46(22):e133. doi: 10.1093/nar/gky780.


Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies.

Zhong H, Prentice RL.

Biostatistics. 2008 Oct;9(4):621-34. doi: 10.1093/biostatistics/kxn001. Epub 2008 Feb 28.


Improving polygenic risk prediction from summary statistics by an empirical Bayes approach.

So HC, Sham PC.

Sci Rep. 2017 Feb 1;7:41262. doi: 10.1038/srep41262.


Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits.

Zhang Y, Qi G, Park JH, Chatterjee N.

Nat Genet. 2018 Sep;50(9):1318-1326. doi: 10.1038/s41588-018-0193-x. Epub 2018 Aug 13.


BR-squared: a practical solution to the winner's curse in genome-wide scans.

Sun L, Dimitromanolakis A, Faye LL, Paterson AD, Waggott D; DCCT/EDIC Research Group, Bull SB.

Hum Genet. 2011 May;129(5):545-52. doi: 10.1007/s00439-011-0948-2. Epub 2011 Jan 19.


Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk.

Simonson MA, Wills AG, Keller MC, McQueen MB.

BMC Med Genet. 2011 Oct 26;12:146. doi: 10.1186/1471-2350-12-146.


Local True Discovery Rate Weighted Polygenic Scores Using GWAS Summary Data.

Mak TS, Kwan JS, Campbell DD, Sham PC.

Behav Genet. 2016 Jul;46(4):573-82. doi: 10.1007/s10519-015-9770-2. Epub 2016 Jan 9.


Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.

Hu Y, Lu Q, Liu W, Zhang Y, Li M, Zhao H.

PLoS Genet. 2017 Jun 9;13(6):e1006836. doi: 10.1371/journal.pgen.1006836. eCollection 2017 Jun.


Exploiting Linkage Disequilibrium for Ultrahigh-Dimensional Genome-Wide Data with an Integrated Statistical Approach.

Carlsen M, Fu G, Bushman S, Corcoran C.

Genetics. 2016 Feb;202(2):411-26. doi: 10.1534/genetics.115.179507. Epub 2015 Dec 12.


Contrasting the Genetic Architecture of 30 Complex Traits from Summary Association Data.

Shi H, Kichaev G, Pasaniuc B.

Am J Hum Genet. 2016 Jul 7;99(1):139-53. doi: 10.1016/j.ajhg.2016.05.013. Epub 2016 Jun 23.


A simple yet accurate correction for winner's curse can predict signals discovered in much larger genome scans.

Bigdeli TB, Lee D, Webb BT, Riley BP, Vladimirov VI, Fanous AH, Kendler KS, Bacanu SA.

Bioinformatics. 2016 Sep 1;32(17):2598-603. doi: 10.1093/bioinformatics/btw303. Epub 2016 May 13.


Resampling to Address the Winner's Curse in Genetic Association Analysis of Time to Event.

Poirier JG, Faye LL, Dimitromanolakis A, Paterson AD, Sun L, Bull SB.

Genet Epidemiol. 2015 Nov;39(7):518-28. doi: 10.1002/gepi.21920. Epub 2015 Sep 28.

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