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Items: 1 to 20 of 78

1.

A powerful and efficient set test for genetic markers that handles confounders.

Listgarten J, Lippert C, Kang EY, Xiang J, Kadie CM, Heckerman D.

Bioinformatics. 2013 Jun 15;29(12):1526-33. doi: 10.1093/bioinformatics/btt177. Epub 2013 Apr 18.

2.

Greater power and computational efficiency for kernel-based association testing of sets of genetic variants.

Lippert C, Xiang J, Horta D, Widmer C, Kadie C, Heckerman D, Listgarten J.

Bioinformatics. 2014 Nov 15;30(22):3206-14. doi: 10.1093/bioinformatics/btu504. Epub 2014 Jul 29.

3.

Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning.

Padhukasahasram B, Reddy CK, Levin AM, Burchard EG, Williams LK.

PLoS One. 2015 Nov 30;10(11):e0143489. doi: 10.1371/journal.pone.0143489. eCollection 2015.

4.

Further improvements to linear mixed models for genome-wide association studies.

Widmer C, Lippert C, Weissbrod O, Fusi N, Kadie C, Davidson R, Listgarten J, Heckerman D.

Sci Rep. 2014 Nov 12;4:6874. doi: 10.1038/srep06874.

5.

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.

6.

A mixed two-stage method for detecting interactions in genomewide association studies.

Zuo Y, Kang G.

J Theor Biol. 2010 Feb 21;262(4):576-83. doi: 10.1016/j.jtbi.2009.10.029. Epub 2009 Nov 6.

PMID:
19896954
7.

Pathway analysis comparison using Crohn's disease genome wide association studies.

Ballard D, Abraham C, Cho J, Zhao H.

BMC Med Genomics. 2010 Jun 28;3:25. doi: 10.1186/1755-8794-3-25.

8.

Unified tests for fine-scale mapping and identifying sparse high-dimensional sequence associations.

Cao S, Qin H, Gossmann A, Deng HW, Wang YP.

Bioinformatics. 2016 Feb 1;32(3):330-7. doi: 10.1093/bioinformatics/btv586. Epub 2015 Oct 12.

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. Epub 2012 Jun 25.

10.

Adjusted sequence kernel association test for rare variants controlling for cryptic and family relatedness.

Oualkacha K, Dastani Z, Li R, Cingolani PE, Spector TD, Hammond CJ, Richards JB, Ciampi A, Greenwood CM.

Genet Epidemiol. 2013 May;37(4):366-76. doi: 10.1002/gepi.21725. Epub 2013 Mar 25.

PMID:
23529756
11.

Conditional random fields for fast, large-scale genome-wide association studies.

Huang JC, Meek C, Kadie C, Heckerman D.

PLoS One. 2011;6(7):e21591. doi: 10.1371/journal.pone.0021591. Epub 2011 Jul 12.

12.

FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics.

Huang J, Wang K, Wei P, Liu X, Liu X, Tan K, Boerwinkle E, Potash JB, Han S.

Genetics. 2016 Mar;202(3):919-29. doi: 10.1534/genetics.115.185009. Epub 2016 Jan 15.

13.

Blindly using Wald's test can miss rare disease-causal variants in case-control association studies.

Xing G, Lin CY, Wooding SP, Xing C.

Ann Hum Genet. 2012 Mar;76(2):168-77. doi: 10.1111/j.1469-1809.2011.00700.x. Epub 2012 Jan 18.

14.

Efficient p-value estimation in massively parallel testing problems.

Kustra R, Shi X, Murdoch DJ, Greenwood CM, Rangrej J.

Biostatistics. 2008 Oct;9(4):601-12. doi: 10.1093/biostatistics/kxm053. Epub 2008 Feb 27.

15.

A SUPER powerful method for genome wide association study.

Wang Q, Tian F, Pan Y, Buckler ES, Zhang Z.

PLoS One. 2014 Sep 23;9(9):e107684. doi: 10.1371/journal.pone.0107684. eCollection 2014.

16.

Efficient set tests for the genetic analysis of correlated traits.

Casale FP, Rakitsch B, Lippert C, Stegle O.

Nat Methods. 2015 Aug;12(8):755-8. doi: 10.1038/nmeth.3439. Epub 2015 Jun 15.

PMID:
26076425
17.

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
18.

A new expectation-maximization statistical test for case-control association studies considering rare variants obtained by high-throughput sequencing.

Gordon D, Finch SJ, De La Vega FM.

Hum Hered. 2011;71(2):113-25. doi: 10.1159/000325590. Epub 2011 Jul 6. Erratum in: Hum Hered. 2011;72(1):53. De La Vega, Francisco [corrected to De La Vega, Francisco M].

PMID:
21734402
19.

Gene, region and pathway level analyses in whole-genome studies.

De la Cruz O, Wen X, Ke B, Song M, Nicolae DL.

Genet Epidemiol. 2010 Apr;34(3):222-231. doi: 10.1002/gepi.20452.

20.

M(3): an improved SNP calling algorithm for Illumina BeadArray data.

Li G, Gelernter J, Kranzler HR, Zhao H.

Bioinformatics. 2012 Feb 1;28(3):358-65. doi: 10.1093/bioinformatics/btr673. Epub 2011 Dec 8.

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