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Items: 1 to 20 of 89

1.

Analysis of multiple phenotypes in genome-wide genetic mapping studies.

Suo C, Toulopoulou T, Bramon E, Walshe M, Picchioni M, Murray R, Ott J.

BMC Bioinformatics. 2013 May 2;14:151. doi: 10.1186/1471-2105-14-151.

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.

Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies.

Aschard H, Vilhjálmsson BJ, Greliche N, Morange PE, Trégouët DA, Kraft P.

Am J Hum Genet. 2014 May 1;94(5):662-76. doi: 10.1016/j.ajhg.2014.03.016. Epub 2014 Apr 17.

4.

Genome-wide pathway association studies of multiple correlated quantitative phenotypes using principle component analyses.

Zhang F, Guo X, Wu S, Han J, Liu Y, Shen H, Deng HW.

PLoS One. 2012;7(12):e53320. doi: 10.1371/journal.pone.0053320. Epub 2012 Dec 28.

5.

Multivariate phenotype association analysis by marker-set kernel machine regression.

Maity A, Sullivan PF, Tzeng JY.

Genet Epidemiol. 2012 Nov;36(7):686-95. doi: 10.1002/gepi.21663. Epub 2012 Aug 16.

6.

Genome-wide association studies with high-dimensional phenotypes.

Marttinen P, Gillberg J, Havulinna A, Corander J, Kaski S.

Stat Appl Genet Mol Biol. 2013 Aug;12(4):413-31. doi: 10.1515/sagmb-2012-0032.

PMID:
23759510
7.

MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS.

O'Reilly PF, Hoggart CJ, Pomyen Y, Calboli FC, Elliott P, Jarvelin MR, Coin LJ.

PLoS One. 2012;7(5):e34861. doi: 10.1371/journal.pone.0034861. Epub 2012 May 2.

8.

Multiple phenotypes in genome-wide genetic mapping studies.

Ott J, Wang J.

Protein Cell. 2011 Jul;2(7):519-22. doi: 10.1007/s13238-011-1059-5. Epub 2011 Jun 6. Review.

9.

Nonmetric multidimensional scaling corrects for population structure in association mapping with different sample types.

Zhu C, Yu J.

Genetics. 2009 Jul;182(3):875-88. doi: 10.1534/genetics.108.098863. Epub 2009 May 4.

10.
11.

PSEA: Phenotype Set Enrichment Analysis--a new method for analysis of multiple phenotypes.

Ried JS, Döring A, Oexle K, Meisinger C, Winkelmann J, Klopp N, Meitinger T, Peters A, Suhre K, Wichmann HE, Gieger C.

Genet Epidemiol. 2012 Apr;36(3):244-52. doi: 10.1002/gepi.21617.

PMID:
22714936
12.

Identifying the genetic variation of gene expression using gene sets: application of novel gene Set eQTL approach to PharmGKB and KEGG.

Abo R, Jenkins GD, Wang L, Fridley BL.

PLoS One. 2012;7(8):e43301. doi: 10.1371/journal.pone.0043301. Epub 2012 Aug 14.

13.

Analyze multivariate phenotypes in genetic association studies by combining univariate association tests.

Yang Q, Wu H, Guo CY, Fox CS.

Genet Epidemiol. 2010 Jul;34(5):444-54. doi: 10.1002/gepi.20497.

14.

Genome-wide association mapping of agronomic traits in sugar beet.

Würschum T, Maurer HP, Kraft T, Janssen G, Nilsson C, Reif JC.

Theor Appl Genet. 2011 Nov;123(7):1121-31. doi: 10.1007/s00122-011-1653-1. Epub 2011 Jul 15.

PMID:
21761161
15.

Fine-scale genetic mapping using independent component analysis.

Dawy Z, Sarkis M, Hagenauer J, Mueller JC.

IEEE/ACM Trans Comput Biol Bioinform. 2008 Jul-Sep;5(3):448-60. doi: 10.1109/TCBB.2007.1072.

PMID:
18670047
16.

Experimental approaches for identifying schizophrenia risk genes.

Mantripragada KK, Carroll LS, Williams NM.

Curr Top Behav Neurosci. 2010;4:587-610. Review.

PMID:
21312414
17.

A principal components-based clustering method to identify variants associated with complex traits.

Black MH, Watanabe RM.

Hum Hered. 2011;71(1):50-8. doi: 10.1159/000323567. Epub 2011 Mar 10.

18.

Sparse principal component analysis for identifying ancestry-informative markers in genome-wide association studies.

Lee S, Epstein MP, Duncan R, Lin X.

Genet Epidemiol. 2012 May;36(4):293-302. doi: 10.1002/gepi.21621. Epub 2012 Apr 16.

19.

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
20.

Increased power of mixed models facilitates association mapping of 10 loci for metabolic traits in an isolated population.

Kenny EE, Kim M, Gusev A, Lowe JK, Salit J, Smith JG, Kovvali S, Kang HM, Newton-Cheh C, Daly MJ, Stoffel M, Altshuler DM, Friedman JM, Eskin E, Breslow JL, Pe'er I.

Hum Mol Genet. 2011 Feb 15;20(4):827-39. doi: 10.1093/hmg/ddq510. Epub 2010 Nov 30.

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