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Items: 1 to 20 of 94

1.

Population substructure and control selection in genome-wide association studies.

Yu K, Wang Z, Li Q, Wacholder S, Hunter DJ, Hoover RN, Chanock S, Thomas G.

PLoS One. 2008 Jul 2;3(7):e2551. doi: 10.1371/journal.pone.0002551.

2.

Genetic background comparison using distance-based regression, with applications in population stratification evaluation and adjustment.

Li Q, Wacholder S, Hunter DJ, Hoover RN, Chanock S, Thomas G, Yu K.

Genet Epidemiol. 2009 Jul;33(5):432-41. doi: 10.1002/gepi.20396.

3.

Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies.

Duggal P, Gillanders EM, Holmes TN, Bailey-Wilson JE.

BMC Genomics. 2008 Oct 31;9:516. doi: 10.1186/1471-2164-9-516.

4.

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.

5.

Principal-component analysis for assessment of population stratification in mitochondrial medical genetics.

Biffi A, Anderson CD, Nalls MA, Rahman R, Sonni A, Cortellini L, Rost NS, Matarin M, Hernandez DG, Plourde A, de Bakker PI, Ross OA, Greenberg SM, Furie KL, Meschia JF, Singleton AB, Saxena R, Rosand J.

Am J Hum Genet. 2010 Jun 11;86(6):904-17. doi: 10.1016/j.ajhg.2010.05.005.

6.

Association between Prostinogen (KLK15) genetic variants and prostate cancer risk and aggressiveness in Australia and a meta-analysis of GWAS data.

Batra J, Lose F, O'Mara T, Marquart L, Stephens C, Alexander K, Srinivasan S, Eeles RA, Easton DF, Al Olama AA, Kote-Jarai Z, Guy M, Muir K, Lophatananon A, Rahman AA, Neal DE, Hamdy FC, Donovan JL, Chambers S, Gardiner RA, Aitken J, Yaxley J, Kedda MA, Clements JA, Spurdle AB.

PLoS One. 2011;6(11):e26527. doi: 10.1371/journal.pone.0026527.

7.

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.

8.

SNP selection and multidimensional scaling to quantify population structure.

Miclaus K, Wolfinger R, Czika W.

Genet Epidemiol. 2009 Sep;33(6):488-96. doi: 10.1002/gepi.20401.

PMID:
19194989
9.

Genetic variants associated with idiopathic pulmonary fibrosis susceptibility and mortality: a genome-wide association study.

Noth I, Zhang Y, Ma SF, Flores C, Barber M, Huang Y, Broderick SM, Wade MS, Hysi P, Scuirba J, Richards TJ, Juan-Guardela BM, Vij R, Han MK, Martinez FJ, Kossen K, Seiwert SD, Christie JD, Nicolae D, Kaminski N, Garcia JG.

Lancet Respir Med. 2013 Jun;1(4):309-17. doi: 10.1016/S2213-2600(13)70045-6.

10.

[Analysis of population stratification using random SNPs in genome-wide association studies].

Cao ZF, Ma CX, Wang L, Cai B.

Yi Chuan. 2010 Sep;32(9):921-8. Chinese.

PMID:
20870613
11.

Using principal components of genetic variation for robust and powerful detection of gene-gene interactions in case-control and case-only studies.

Bhattacharjee S, Wang Z, Ciampa J, Kraft P, Chanock S, Yu K, Chatterjee N.

Am J Hum Genet. 2010 Mar 12;86(3):331-42. doi: 10.1016/j.ajhg.2010.01.026.

12.

Breast cancer prediction using genome wide single nucleotide polymorphism data.

Hajiloo M, Damavandi B, Hooshsadat M, Sangi F, Mackey JR, Cass CE, Greiner R, Damaraju S.

BMC Bioinformatics. 2013;14 Suppl 13:S3. doi: 10.1186/1471-2105-14-S13-S3.

13.

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.

14.

Uncovering networks from genome-wide association studies via circular genomic permutation.

Cabrera CP, Navarro P, Huffman JE, Wright AF, Hayward C, Campbell H, Wilson JF, Rudan I, Hastie ND, Vitart V, Haley CS.

G3 (Bethesda). 2012 Sep;2(9):1067-75. doi: 10.1534/g3.112.002618.

15.

Accounting for multiple comparisons in a genome-wide association study (GWAS).

Johnson RC, Nelson GW, Troyer JL, Lautenberger JA, Kessing BD, Winkler CA, O'Brien SJ.

BMC Genomics. 2010 Dec 22;11:724. doi: 10.1186/1471-2164-11-724.

16.

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
17.
18.

LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J; Schizophrenia Working Group of the Psychiatric Genomics Consortium., Patterson N, Daly MJ, Price AL, Neale BM.

Nat Genet. 2015 Mar;47(3):291-5. doi: 10.1038/ng.3211.

19.

Artifact due to differential error when cases and controls are imputed from different platforms.

Sinnott JA, Kraft P.

Hum Genet. 2012 Jan;131(1):111-9. doi: 10.1007/s00439-011-1054-1.

20.

In search of causal variants: refining disease association signals using cross-population contrasts.

Saccone NL, Saccone SF, Goate AM, Grucza RA, Hinrichs AL, Rice JP, Bierut LJ.

BMC Genet. 2008 Aug 29;9:58. doi: 10.1186/1471-2156-9-58.

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