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Items: 1 to 20 of 187

1.

A multivariate regression approach to association analysis of a quantitative trait network.

Kim S, Sohn KA, Xing EP.

Bioinformatics. 2009 Jun 15;25(12):i204-12. doi: 10.1093/bioinformatics/btp218.

2.

Statistical estimation of correlated genome associations to a quantitative trait network.

Kim S, Xing EP.

PLoS Genet. 2009 Aug;5(8):e1000587. doi: 10.1371/journal.pgen.1000587. Epub 2009 Aug 14.

3.

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.

4.
5.
6.

Learning gene networks under SNP perturbations using eQTL datasets.

Zhang L, Kim S.

PLoS Comput Biol. 2014 Feb 27;10(2):e1003420. doi: 10.1371/journal.pcbi.1003420. eCollection 2014 Feb. Erratum in: PLoS Comput Biol. 2014 Apr;10(4):e1003608.

7.

Graph-regularized dual Lasso for robust eQTL mapping.

Cheng W, Zhang X, Guo Z, Shi Y, Wang W.

Bioinformatics. 2014 Jun 15;30(12):i139-48. doi: 10.1093/bioinformatics/btu293.

8.

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
9.

A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits.

Marchetti-Bowick M, Yin J, Howrylak JA, Xing EP.

Bioinformatics. 2016 Oct 1;32(19):2903-10. doi: 10.1093/bioinformatics/btw347. Epub 2016 Jun 13.

PMID:
27296983
10.

Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood.

Lee SH, Yang J, Goddard ME, Visscher PM, Wray NR.

Bioinformatics. 2012 Oct 1;28(19):2540-2. Epub 2012 Jul 26.

11.

Haplotype-based quantitative trait mapping using a clustering algorithm.

Li J, Zhou Y, Elston RC.

BMC Bioinformatics. 2006 May 18;7:258.

12.

Effects of causal networks on the structure and stability of resource allocation trait correlations.

Gove RP, Chen W, Zweber NB, Erwin R, Rychtář J, Remington DL.

J Theor Biol. 2012 Jan 21;293:1-14. doi: 10.1016/j.jtbi.2011.09.034. Epub 2011 Oct 12.

PMID:
22004994
13.

Network-based group variable selection for detecting expression quantitative trait loci (eQTL).

Wang W, Zhang X.

BMC Bioinformatics. 2011 Jun 30;12:269. doi: 10.1186/1471-2105-12-269.

14.

Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes.

Wang Y, Goh W, Wong L, Montana G; Alzheimer's Disease Neuroimaging Initiative.

BMC Bioinformatics. 2013;14 Suppl 16:S6. doi: 10.1186/1471-2105-14-S16-S6. Epub 2013 Oct 22.

15.

An efficient approach to large-scale genotype-phenotype association analyses.

Yang R, Li H, Fu L, Liu Y.

Brief Bioinform. 2014 Sep;15(5):814-22. doi: 10.1093/bib/bbt061. Epub 2013 Aug 29.

PMID:
23990269
16.

A novel association test for multiple secondary phenotypes from a case-control GWAS.

Ray D, Basu S.

Genet Epidemiol. 2017 Jul;41(5):413-426. doi: 10.1002/gepi.22045. Epub 2017 Apr 10.

PMID:
28393390
17.

Network-based multiple locus linkage analysis of expression traits.

Pan W.

Bioinformatics. 2009 Jun 1;25(11):1390-6. doi: 10.1093/bioinformatics/btp177. Epub 2009 Mar 31.

18.

A network-driven approach for genome-wide association mapping.

Lee S, Kong S, Xing EP.

Bioinformatics. 2016 Jun 15;32(12):i164-i173. doi: 10.1093/bioinformatics/btw270.

19.

Poly-omic prediction of complex traits: OmicKriging.

Wheeler HE, Aquino-Michaels K, Gamazon ER, Trubetskoy VV, Dolan ME, Huang RS, Cox NJ, Im HK.

Genet Epidemiol. 2014 Jul;38(5):402-15. doi: 10.1002/gepi.21808. Epub 2014 May 2.

20.

A stochastic expectation and maximization algorithm for detecting quantitative trait-associated genes.

Zhan H, Chen X, Xu S.

Bioinformatics. 2011 Jan 1;27(1):63-9. doi: 10.1093/bioinformatics/btq558. Epub 2010 Oct 29.

PMID:
21037246

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