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Items: 1 to 20 of 131

1.

Comparison of statistics in association tests of genetic markers for survival outcomes.

Mendolia F, Klein JP, Petersdorf EW, Malkki M, Wang T.

Stat Med. 2014 Feb 28;33(5):828-44. doi: 10.1002/sim.5982.

2.

Set-based tests for genetic association in longitudinal studies.

He Z, Zhang M, Lee S, Smith JA, Guo X, Palmas W, Kardia SL, Diez Roux AV, Mukherjee B.

Biometrics. 2015 Sep;71(3):606-15. doi: 10.1111/biom.12310.

3.

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.

Fan R, Wang Y, Yan Q, Ding Y, Weeks DE, Lu Z, Ren H, Cook RJ, Xiong M, Swaroop A, Chew EY, Chen W.

Genet Epidemiol. 2016 Feb;40(2):133-43. doi: 10.1002/gepi.21947.

4.

Linear score tests for variance components in linear mixed models and applications to genetic association studies.

Qu L, Guennel T, Marshall SL.

Biometrics. 2013 Dec;69(4):883-92. doi: 10.1111/biom.12095.

PMID:
24328714
5.

Modeling and testing for joint association using a genetic random field model.

He Z, Zhang M, Zhan X, Lu Q.

Biometrics. 2014 Sep;70(3):471-9. doi: 10.1111/biom.12160.

6.

A Monte Carlo approach for change-point detection in the Cox proportional hazards model.

Liu M, Lu W, Shao Y.

Stat Med. 2008 Aug 30;27(19):3894-909. doi: 10.1002/sim.3214.

PMID:
18254131
7.

Sequence kernel association test for survival traits.

Chen H, Lumley T, Brody J, Heard-Costa NL, Fox CS, Cupples LA, Dupuis J.

Genet Epidemiol. 2014 Apr;38(3):191-7. doi: 10.1002/gepi.21791.

8.
9.

Identifying genetic marker sets associated with phenotypes via an efficient adaptive score test.

Cai T, Lin X, Carroll RJ.

Biostatistics. 2012 Sep;13(4):776-90. doi: 10.1093/biostatistics/kxs015.

10.

SNP Set Association Testing for Survival Outcomes in the Presence of Intrafamilial Correlation.

Leclerc M; Consortium of Investigators of Modifiers of BRCA1/2., Simard J, Lakhal-Chaieb L.

Genet Epidemiol. 2015 Sep;39(6):406-14. doi: 10.1002/gepi.21914.

PMID:
26282997
11.

Adaptively weighted association statistics.

LeBlanc M, Kooperberg C.

Genet Epidemiol. 2009 Jul;33(5):442-52. doi: 10.1002/gepi.20397.

12.

Resampling-based multiple hypothesis testing procedures for genetic case-control association studies.

Chen BE, Sakoda LC, Hsing AW, Rosenberg PS.

Genet Epidemiol. 2006 Sep;30(6):495-507.

PMID:
16755536
13.

Nonparametric evaluation of quantitative traits in population-based association studies when the genetic model is unknown.

Konietschke F, Libiger O, Hothorn LA.

PLoS One. 2012;7(2):e31242. doi: 10.1371/journal.pone.0031242.

14.

Detection of inappropriate samples in association studies by an IBS-based method considering linkage disequilibrium between genetic markers.

Andoh M, Sato Y, Sakamoto H, Yoshida T, Ohtaki M.

J Hum Genet. 2010 Jul;55(7):436-40. doi: 10.1038/jhg.2010.43.

PMID:
20448652
15.

A pseudo-R2 measure for selecting genomic markers with crossing hazards functions.

Rouam S, Moreau T, Bro√ęt P.

BMC Med Res Methodol. 2011 Mar 15;11:28. doi: 10.1186/1471-2288-11-28.

16.

Circumventing multiple testing: a multilocus Monte Carlo approach to testing for association.

McIntyre LM, Martin ER, Simonsen KL, Kaplan NL.

Genet Epidemiol. 2000 Jul;19(1):18-29.

PMID:
10861894
17.

A novel method for identifying SNP disease association based on maximal information coefficient.

Liu HM, Rao N, Yang D, Yang L, Li Y, Ou F.

Genet Mol Res. 2014 Dec 19;13(4):10863-77. doi: 10.4238/2014.December.19.7.

18.

Trend tests for genetic association using population-based cross-sectional complex survey data.

She D, Li Y, Zhang H, Graubard BI, Li Z.

Biostatistics. 2010 Jan;11(1):48-56. doi: 10.1093/biostatistics/kxp035.

PMID:
19745160
19.

Association studies with imputed variants using expectation-maximization likelihood-ratio tests.

Huang KC, Sun W, Wu Y, Chen M, Mohlke KL, Lange LA, Li Y.

PLoS One. 2014 Nov 10;9(11):e110679. doi: 10.1371/journal.pone.0110679.

20.

A robust distribution-free test for genetic association studies of quantitative traits.

Kozlitina J, Schucany WR.

Stat Appl Genet Mol Biol. 2015 Nov;14(5):443-64. doi: 10.1515/sagmb-2014-0050.

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