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Items: 1 to 20 of 83

1.

Incorporating prior knowledge to increase the power of genome-wide association studies.

Petersen A, Spratt J, Tintle NL.

Methods Mol Biol. 2013;1019:519-41. doi: 10.1007/978-1-62703-447-0_25. Review.

PMID:
23756909
2.

Accuracy of genome-wide imputation of untyped markers and impacts on statistical power for association studies.

Hao K, Chudin E, McElwee J, Schadt EE.

BMC Genet. 2009 Jun 16;10:27. doi: 10.1186/1471-2156-10-27.

3.

SNP-based pathway enrichment analysis for genome-wide association studies.

Weng L, Macciardi F, Subramanian A, Guffanti G, Potkin SG, Yu Z, Xie X.

BMC Bioinformatics. 2011 Apr 15;12:99. doi: 10.1186/1471-2105-12-99.

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Strategies for pathway analysis from GWAS data.

Yaspan BL, Veatch OJ.

Curr Protoc Hum Genet. 2011 Oct;Chapter 1:Unit1.20. doi: 10.1002/0471142905.hg0120s71.

PMID:
21975938
7.
8.

Pathway-based analysis using reduced gene subsets in genome-wide association studies.

Zhao J, Gupta S, Seielstad M, Liu J, Thalamuthu A.

BMC Bioinformatics. 2011 Jan 12;12:17. doi: 10.1186/1471-2105-12-17.

9.

Design considerations for genetic linkage and association studies.

Nsengimana J, Bishop DT.

Methods Mol Biol. 2012;850:237-62. doi: 10.1007/978-1-61779-555-8_13.

PMID:
22307702
10.

On the analysis of copy-number variations in genome-wide association studies: a translation of the family-based association test.

Ionita-Laza I, Perry GH, Raby BA, Klanderman B, Lee C, Laird NM, Weiss ST, Lange C.

Genet Epidemiol. 2008 Apr;32(3):273-84. doi: 10.1002/gepi.20302.

PMID:
18228561
11.

Association mapping and significance estimation via the coalescent.

Kimmel G, Karp RM, Jordan MI, Halperin E.

Am J Hum Genet. 2008 Dec;83(6):675-83. doi: 10.1016/j.ajhg.2008.10.017.

12.

Software comparison for evaluating genomic copy number variation for Affymetrix 6.0 SNP array platform.

Eckel-Passow JE, Atkinson EJ, Maharjan S, Kardia SL, de Andrade M.

BMC Bioinformatics. 2011 May 31;12:220. doi: 10.1186/1471-2105-12-220.

13.

Finding novel genes by testing G × E interactions in a genome-wide association study.

Gauderman WJ, Zhang P, Morrison JL, Lewinger JP.

Genet Epidemiol. 2013 Sep;37(6):603-13. doi: 10.1002/gepi.21748.

14.

Alternative methods for H1 simulations in genome-wide association studies.

Perduca V, Sinoquet C, Mourad R, Nuel G.

Hum Hered. 2012;73(2):95-104. doi: 10.1159/000336194.

15.

Inclusion of a priori information in genome-wide association analysis.

Tintle N, Lantieri F, Lebrec J, Sohns M, Ballard D, Bickeböller H.

Genet Epidemiol. 2009;33 Suppl 1:S74-80. doi: 10.1002/gepi.20476.

16.

GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation.

Chung D, Yang C, Li C, Gelernter J, Zhao H.

PLoS Genet. 2014 Nov 13;10(11):e1004787. doi: 10.1371/journal.pgen.1004787.

17.

Robust relationship inference in genome-wide association studies.

Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM.

Bioinformatics. 2010 Nov 15;26(22):2867-73. doi: 10.1093/bioinformatics/btq559.

18.

Efficiently identifying significant associations in genome-wide association studies.

Kostem E, Eskin E.

J Comput Biol. 2013 Oct;20(10):817-30. doi: 10.1089/cmb.2013.0087.

19.

Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from GWAS data.

Liu Y, Maxwell S, Feng T, Zhu X, Elston RC, Koyutürk M, Chance MR.

BMC Syst Biol. 2012;6 Suppl 3:S15. doi: 10.1186/1752-0509-6-S3-S15.

20.

A knowledge-based weighting framework to boost the power of genome-wide association studies.

Li MX, Sham PC, Cherny SS, Song YQ.

PLoS One. 2010 Dec 31;5(12):e14480. doi: 10.1371/journal.pone.0014480.

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