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Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases.

Aschard H, Chen J, Cornelis MC, Chibnik LB, Karlson EW, Kraft P.

Am J Hum Genet. 2012 Jun 8;90(6):962-72. doi: 10.1016/j.ajhg.2012.04.017. Erratum in: Am J Hum Genet. 2012 Jun 8;90(6):1116.


Association of environmental and genetic factors and gene-environment interactions with risk of developing rheumatoid arthritis.

Karlson EW, Ding B, Keenan BT, Liao K, Costenbader KH, Klareskog L, Alfredsson L, Chibnik LB.

Arthritis Care Res (Hoboken). 2013 Jul;65(7):1147-56. doi: 10.1002/acr.22005.


Variation in predictive ability of common genetic variants by established strata: the example of breast cancer and age.

Aschard H, Zaitlen N, Lindström S, Kraft P.

Epidemiology. 2015 Jan;26(1):51-8. doi: 10.1097/EDE.0000000000000195.


How accurate can genetic predictions be?

Dreyfuss JM, Levner D, Galagan JE, Church GM, Ramoni MF.

BMC Genomics. 2012 Jul 24;13:340. doi: 10.1186/1471-2164-13-340.


A multiclass likelihood ratio approach for genetic risk prediction allowing for phenotypic heterogeneity.

Wen Y, Lu Q.

Genet Epidemiol. 2013 Nov;37(7):715-25. doi: 10.1002/gepi.21751.


Bagging optimal ROC curve method for predictive genetic tests, with an application for rheumatoid arthritis.

Lu Q, Cui Y, Ye C, Wei C, Elston RC.

J Biopharm Stat. 2010 Mar;20(2):401-14. doi: 10.1080/10543400903572811.


Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities.

Mittag F, Büchel F, Saad M, Jahn A, Schulte C, Bochdanovits Z, Simón-Sánchez J, Nalls MA, Keller M, Hernandez DG, Gibbs JR, Lesage S, Brice A, Heutink P, Martinez M, Wood NW, Hardy J, Singleton AB, Zell A, Gasser T, Sharma M; International Parkinson’s Disease Genomics Consortium..

Hum Mutat. 2012 Dec;33(12):1708-18. doi: 10.1002/humu.22161.


From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes.

Wei Z, Wang K, Qu HQ, Zhang H, Bradfield J, Kim C, Frackleton E, Hou C, Glessner JT, Chiavacci R, Stanley C, Monos D, Grant SF, Polychronakos C, Hakonarson H.

PLoS Genet. 2009 Oct;5(10):e1000678. doi: 10.1371/journal.pgen.1000678.


The value of genetic information for diabetes risk prediction - differences according to sex, age, family history and obesity.

Mühlenbruch K, Jeppesen C, Joost HG, Boeing H, Schulze MB.

PLoS One. 2013;8(5):e64307. doi: 10.1371/journal.pone.0064307. Erratum in: PLoS One. 2013;8(9). doi:10.1371/annotation/65bd3a11-b821-4f10-88d2-29b69a730f21.


Investigation on cardiovascular risk prediction using genetic information.

Pu LN, Zhao Z, Zhang YT.

IEEE Trans Inf Technol Biomed. 2012 Sep;16(5):795-808. Review.


A genetic risk predictor for breast cancer using a combination of low-penetrance polymorphisms in a Japanese population.

Sueta A, Ito H, Kawase T, Hirose K, Hosono S, Yatabe Y, Tajima K, Tanaka H, Iwata H, Iwase H, Matsuo K.

Breast Cancer Res Treat. 2012 Apr;132(2):711-21. doi: 10.1007/s10549-011-1904-5.


A novel approach to simulate gene-environment interactions in complex diseases.

Amato R, Pinelli M, D'Andrea D, Miele G, Nicodemi M, Raiconi G, Cocozza S.

BMC Bioinformatics. 2010 Jan 5;11:8. doi: 10.1186/1471-2105-11-8.


Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review.

Bao W, Hu FB, Rong S, Rong Y, Bowers K, Schisterman EF, Liu L, Zhang C.

Am J Epidemiol. 2013 Oct 15;178(8):1197-207. doi: 10.1093/aje/kwt123. Review.


Pretest prediction of BRCA1 or BRCA2 mutation by risk counselors and the computer model BRCAPRO.

Euhus DM, Smith KC, Robinson L, Stucky A, Olopade OI, Cummings S, Garber JE, Chittenden A, Mills GB, Rieger P, Esserman L, Crawford B, Hughes KS, Roche CA, Ganz PA, Seldon J, Fabian CJ, Klemp J, Tomlinson G.

J Natl Cancer Inst. 2002 Jun 5;94(11):844-51.


Comparison of six statistics of genetic association regarding their ability to discriminate between causal variants and genetically linked markers.

Lorenzo Bermejo J, Garcia Perez A, Brandt A, Hemminki K, Matthews AG.

Hum Hered. 2011;72(2):142-52. doi: 10.1159/000332006.


Genome-wide investigation of gene-environment interactions in colorectal cancer.

Siegert S, Hampe J, Schafmayer C, von Schönfels W, Egberts JH, Försti A, Chen B, Lascorz J, Hemminki K, Franke A, Nothnagel M, Nöthlings U, Krawczak M.

Hum Genet. 2013 Feb;132(2):219-31. doi: 10.1007/s00439-012-1239-2.


Ability to predict breast cancer in Asian women using a polygenic susceptibility model.

Hartman M, Suo C, Lim WY, Miao H, Teo YY, Chia KS.

Breast Cancer Res Treat. 2011 Jun;127(3):805-12. doi: 10.1007/s10549-010-1279-z.


Genome-wide association study for type 2 diabetes: clinical applications.

Lyssenko V, Groop L.

Curr Opin Lipidol. 2009 Apr;20(2):87-91. doi: 10.1097/MOL.0b013e32832923af. Review.

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