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Items: 1 to 20 of 67

1.

kruX: matrix-based non-parametric eQTL discovery.

Qi J, Asl HF, Björkegren J, Michoel T.

BMC Bioinformatics. 2014 Jan 14;15:11. doi: 10.1186/1471-2105-15-11.

2.

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. Epub 2012 Feb 21.

3.

Matrix eQTL: ultra fast eQTL analysis via large matrix operations.

Shabalin AA.

Bioinformatics. 2012 May 15;28(10):1353-8. doi: 10.1093/bioinformatics/bts163. Epub 2012 Apr 6.

4.

Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression quantitative trait loci study.

Zhang X, Huang S, Sun W, Wang W.

Genetics. 2012 Apr;190(4):1511-20. doi: 10.1534/genetics.111.137737. Epub 2012 Jan 31.

5.

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.

6.

Simultaneous inferences based on empirical Bayes methods and false discovery rates ineQTL data analysis.

Chakraborty A, Jiang G, Boustani M, Liu Y, Skaar T, Li L.

BMC Genomics. 2013;14 Suppl 8:S8. doi: 10.1186/1471-2164-14-S8-S8. Epub 2013 Dec 9.

7.

Leveraging input and output structures for joint mapping of epistatic and marginal eQTLs.

Lee S, Xing EP.

Bioinformatics. 2012 Jun 15;28(12):i137-46. doi: 10.1093/bioinformatics/bts227.

8.

QRank: a novel quantile regression tool for eQTL discovery.

Song X, Li G, Zhou Z, Wang X, Ionita-Laza I, Wei Y.

Bioinformatics. 2017 Jul 15;33(14):2123-2130. doi: 10.1093/bioinformatics/btx119.

9.

Adaptive linear rank tests for eQTL studies.

Szymczak S, Scheinhardt MO, Zeller T, Wild PS, Blankenberg S, Ziegler A.

Stat Med. 2013 Feb 10;32(3):524-37. doi: 10.1002/sim.5593. Epub 2012 Aug 30.

10.

Large-scale East-Asian eQTL mapping reveals novel candidate genes for LD mapping and the genomic landscape of transcriptional effects of sequence variants.

Narahara M, Higasa K, Nakamura S, Tabara Y, Kawaguchi T, Ishii M, Matsubara K, Matsuda F, Yamada R.

PLoS One. 2014 Jun 23;9(6):e100924. doi: 10.1371/journal.pone.0100924. eCollection 2014.

11.

Fast eQTL Analysis for Twin Studies.

Yin Z, Xia K, Chung W, Sullivan PF, Zou F.

Genet Epidemiol. 2015 Jul;39(5):357-65. doi: 10.1002/gepi.21900. Epub 2015 Apr 10.

12.

FastMap: fast eQTL mapping in homozygous populations.

Gatti DM, Shabalin AA, Lam TC, Wright FA, Rusyn I, Nobel AB.

Bioinformatics. 2009 Feb 15;25(4):482-9. doi: 10.1093/bioinformatics/btn648. Epub 2008 Dec 17.

13.

Methodological aspects of the genetic dissection of gene expression.

Carlborg O, De Koning DJ, Manly KF, Chesler E, Williams RW, Haley CS.

Bioinformatics. 2005 May 15;21(10):2383-93.

PMID:
15613385
14.

Impact of common regulatory single-nucleotide variants on gene expression profiles in whole blood.

Mehta D, Heim K, Herder C, Carstensen M, Eckstein G, Schurmann C, Homuth G, Nauck M, Völker U, Roden M, Illig T, Gieger C, Meitinger T, Prokisch H.

Eur J Hum Genet. 2013 Jan;21(1):48-54. doi: 10.1038/ejhg.2012.106. Epub 2012 Jun 13.

15.

A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.

Stegle O, Parts L, Durbin R, Winn J.

PLoS Comput Biol. 2010 May 6;6(5):e1000770. doi: 10.1371/journal.pcbi.1000770.

16.

A Flexible Computational Framework Using R and Map-Reduce for Permutation Tests of Massive Genetic Analysis of Complex Traits.

Mahjani B, Toor S, Nettelblad C, Holmgren S.

IEEE/ACM Trans Comput Biol Bioinform. 2017 Mar-Apr;14(2):381-392. doi: 10.1109/TCBB.2016.2527639. Epub 2016 Feb 11.

PMID:
26887003
17.

Accurate and fast multiple-testing correction in eQTL studies.

Sul JH, Raj T, de Jong S, de Bakker PI, Raychaudhuri S, Ophoff RA, Stranger BE, Eskin E, Han B.

Am J Hum Genet. 2015 Jun 4;96(6):857-68. doi: 10.1016/j.ajhg.2015.04.012. Epub 2015 May 28.

18.

Meta-eQTL: a tool set for flexible eQTL meta-analysis.

Di Narzo AF, Cheng H, Lu J, Hao K.

BMC Bioinformatics. 2014 Nov 28;15:392. doi: 10.1186/s12859-014-0392-0.

19.

SNiPer-HD: improved genotype calling accuracy by an expectation-maximization algorithm for high-density SNP arrays.

Hua J, Craig DW, Brun M, Webster J, Zismann V, Tembe W, Joshipura K, Huentelman MJ, Dougherty ER, Stephan DA.

Bioinformatics. 2007 Jan 1;23(1):57-63. Epub 2006 Oct 24.

PMID:
17062589
20.

MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues.

Lewin A, Saadi H, Peters JE, Moreno-Moral A, Lee JC, Smith KG, Petretto E, Bottolo L, Richardson S.

Bioinformatics. 2016 Feb 15;32(4):523-32. doi: 10.1093/bioinformatics/btv568. Epub 2015 Oct 26.

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