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Items: 1 to 20 of 138

1.

Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma.

Li L, Kabesch M, Bouzigon E, Demenais F, Farrall M, Moffatt MF, Lin X, Liang L.

Front Genet. 2013 May 31;4:103. doi: 10.3389/fgene.2013.00103. eCollection 2013.

2.

Association between Expression Quantitative Trait Loci and Metabolic Traits in Two Korean Populations.

Hong KW, Jeong SW, Chung M, Cho SB.

PLoS One. 2014 Dec 10;9(12):e114128. doi: 10.1371/journal.pone.0114128. eCollection 2014.

3.

Mapping of hepatic expression quantitative trait loci (eQTLs) in a Han Chinese population.

Wang X, Tang H, Teng M, Li Z, Li J, Fan J, Zhong L, Sun X, Xu J, Chen G, Chen D, Wang Z, Xing T, Zhang J, Huang L, Wang S, Peng X, Qin S, Shi Y, Peng Z.

J Med Genet. 2014 May;51(5):319-26. doi: 10.1136/jmedgenet-2013-102045. Epub 2014 Mar 24.

4.

Improving power of genome-wide association studies with weighted false discovery rate control and prioritized subset analysis.

Lin WY, Lee WC.

PLoS One. 2012;7(4):e33716. doi: 10.1371/journal.pone.0033716. Epub 2012 Apr 9.

5.

Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS.

Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ.

PLoS Genet. 2010 Apr 1;6(4):e1000888. doi: 10.1371/journal.pgen.1000888.

6.

Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs.

Brown CD, Mangravite LM, Engelhardt BE.

PLoS Genet. 2013;9(8):e1003649. doi: 10.1371/journal.pgen.1003649. Epub 2013 Aug 1.

7.

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.

8.

The use of genome-wide eQTL associations in lymphoblastoid cell lines to identify novel genetic pathways involved in complex traits.

Min JL, Taylor JM, Richards JB, Watts T, Pettersson FH, Broxholme J, Ahmadi KR, Surdulescu GL, Lowy E, Gieger C, Newton-Cheh C, Perola M, Soranzo N, Surakka I, Lindgren CM, Ragoussis J, Morris AP, Cardon LR, Spector TD, Zondervan KT.

PLoS One. 2011;6(7):e22070. doi: 10.1371/journal.pone.0022070. Epub 2011 Jul 15.

9.

JEPEG: a summary statistics based tool for gene-level joint testing of functional variants.

Lee D, Williamson VS, Bigdeli TB, Riley BP, Fanous AH, Vladimirov VI, Bacanu SA.

Bioinformatics. 2015 Apr 15;31(8):1176-82. doi: 10.1093/bioinformatics/btu816. Epub 2014 Dec 12.

10.

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.

11.

Expression quantitative trait loci (eQTL) mapping in Puerto Rican children.

Chen W, Brehm JM, Lin J, Wang T, Forno E, Acosta-Pérez E, Boutaoui N, Canino G, Celedón JC.

PLoS One. 2015 Mar 27;10(3):e0122464. doi: 10.1371/journal.pone.0122464. eCollection 2015.

12.

Covariate-modulated local false discovery rate for genome-wide association studies.

Zablocki RW, Schork AJ, Levine RA, Andreassen OA, Dale AM, Thompson WK.

Bioinformatics. 2014 Aug 1;30(15):2098-104. doi: 10.1093/bioinformatics/btu145. Epub 2014 Apr 7.

13.

Power and type I error rate of false discovery rate approaches in genome-wide association studies.

Yang Q, Cui J, Chazaro I, Cupples LA, Demissie S.

BMC Genet. 2005 Dec 30;6 Suppl 1:S134.

14.

Multiple testing in genome-wide association studies via hidden Markov models.

Wei Z, Sun W, Wang K, Hakonarson H.

Bioinformatics. 2009 Nov 1;25(21):2802-8. doi: 10.1093/bioinformatics/btp476. Epub 2009 Aug 4.

15.

Hidden Markov models for controlling false discovery rate in genome-wide association analysis.

Wei Z.

Methods Mol Biol. 2012;802:337-44. doi: 10.1007/978-1-61779-400-1_22.

PMID:
22130891
16.

Weighted multiple hypothesis testing procedures.

Kang G, Ye K, Liu N, Allison DB, Gao G.

Stat Appl Genet Mol Biol. 2009;8:Article23. doi: 10.2202/1544-6115.1437. Epub 2009 Apr 16.

17.

Lung eQTLs to help reveal the molecular underpinnings of asthma.

Hao K, Bossé Y, Nickle DC, Paré PD, Postma DS, Laviolette M, Sandford A, Hackett TL, Daley D, Hogg JC, Elliott WM, Couture C, Lamontagne M, Brandsma CA, van den Berge M, Koppelman G, Reicin AS, Nicholson DW, Malkov V, Derry JM, Suver C, Tsou JA, Kulkarni A, Zhang C, Vessey R, Opiteck GJ, Curtis SP, Timens W, Sin DD.

PLoS Genet. 2012;8(11):e1003029. doi: 10.1371/journal.pgen.1003029. Epub 2012 Nov 29. Erratum in: PLoS Genet. 2012 Dec;8(12). doi: 10.1371/annotation/80d53ac6-4f5d-4c34-b92b-3fec00d514ac.

18.

Genome-wide expression quantitative trait loci analysis in asthma.

Bossé Y.

Curr Opin Allergy Clin Immunol. 2013 Oct;13(5):487-94. doi: 10.1097/ACI.0b013e328364e951. Review.

PMID:
23945176
19.

Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation.

Croteau-Chonka DC, Rogers AJ, Raj T, McGeachie MJ, Qiu W, Ziniti JP, Stubbs BJ, Liang L, Martinez FD, Strunk RC, Lemanske RF Jr, Liu AH, Stranger BE, Carey VJ, Raby BA.

PLoS One. 2015 Oct 16;10(10):e0140758. doi: 10.1371/journal.pone.0140758. eCollection 2015.

20.

Analysis of SNPs with an effect on gene expression identifies UBE2L3 and BCL3 as potential new risk genes for Crohn's disease.

Fransen K, Visschedijk MC, van Sommeren S, Fu JY, Franke L, Festen EA, Stokkers PC, van Bodegraven AA, Crusius JB, Hommes DW, Zanen P, de Jong DJ, Wijmenga C, van Diemen CC, Weersma RK.

Hum Mol Genet. 2010 Sep 1;19(17):3482-8. doi: 10.1093/hmg/ddq264. Epub 2010 Jul 3.

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