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Items: 1 to 20 of 533

1.

Comprehensive evaluation of imputation performance in African Americans.

Chanda P, Yuhki N, Li M, Bader JS, Hartz A, Boerwinkle E, Kao WH, Arking DE.

J Hum Genet. 2012 Jul;57(7):411-21. doi: 10.1038/jhg.2012.43. Epub 2012 May 31.

2.

Assessment of genotype imputation performance using 1000 Genomes in African American studies.

Hancock DB, Levy JL, Gaddis NC, Bierut LJ, Saccone NL, Page GP, Johnson EO.

PLoS One. 2012;7(11):e50610. doi: 10.1371/journal.pone.0050610. Epub 2012 Nov 30.

3.

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.

4.

Genotype imputation for African Americans using data from HapMap phase II versus 1000 genomes projects.

Sung YJ, Gu CC, Tiwari HK, Arnett DK, Broeckel U, Rao DC.

Genet Epidemiol. 2012 Jul;36(5):508-16. doi: 10.1002/gepi.21647. Epub 2012 May 29.

5.

Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs.

Krithika S, Valladares-Salgado A, Peralta J, Escobedo-de La Peña J, Kumate-Rodríguez J, Cruz M, Parra EJ.

BMC Med Genomics. 2012 May 1;5:12. doi: 10.1186/1755-8794-5-12.

6.

Effect of genome-wide genotyping and reference panels on rare variants imputation.

Zheng HF, Ladouceur M, Greenwood CM, Richards JB.

J Genet Genomics. 2012 Oct 20;39(10):545-50. doi: 10.1016/j.jgg.2012.07.002. Epub 2012 Jul 24.

PMID:
23089364
7.

Performance of genotype imputations using data from the 1000 Genomes Project.

Sung YJ, Wang L, Rankinen T, Bouchard C, Rao DC.

Hum Hered. 2012;73(1):18-25. doi: 10.1159/000334084. Epub 2011 Dec 30.

8.

Performance of genotype imputation for low frequency and rare variants from the 1000 genomes.

Zheng HF, Rong JJ, Liu M, Han F, Zhang XW, Richards JB, Wang L.

PLoS One. 2015 Jan 26;10(1):e0116487. doi: 10.1371/journal.pone.0116487. eCollection 2015.

9.

Genotype imputation of Metabochip SNPs using a study-specific reference panel of ~4,000 haplotypes in African Americans from the Women's Health Initiative.

Liu EY, Buyske S, Aragaki AK, Peters U, Boerwinkle E, Carlson C, Carty C, Crawford DC, Haessler J, Hindorff LA, Marchand LL, Manolio TA, Matise T, Wang W, Kooperberg C, North KE, Li Y.

Genet Epidemiol. 2012 Feb;36(2):107-17. doi: 10.1002/gepi.21603.

10.

Founder population-specific HapMap panel increases power in GWA studies through improved imputation accuracy and CNV tagging.

Surakka I, Kristiansson K, Anttila V, Inouye M, Barnes C, Moutsianas L, Salomaa V, Daly M, Palotie A, Peltonen L, Ripatti S.

Genome Res. 2010 Oct;20(10):1344-51. doi: 10.1101/gr.106534.110. Epub 2010 Sep 1.

11.

Imputation reliability on DNA biallelic markers for drug metabolism studies.

Mijatovic V, Iacobucci I, Sazzini M, Xumerle L, Mori A, Pignatti PF, Martinelli G, Malerba G.

BMC Bioinformatics. 2012;13 Suppl 14:S7. doi: 10.1186/1471-2105-13-S14-S7. Epub 2012 Sep 7.

12.

Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy.

Johnson EO, Hancock DB, Levy JL, Gaddis NC, Saccone NL, Bierut LJ, Page GP.

Hum Genet. 2013 May;132(5):509-22. doi: 10.1007/s00439-013-1266-7. Epub 2013 Jan 22.

13.

The effect of reference panels and software tools on genotype imputation.

Nho K, Shen L, Kim S, Swaminathan S, Risacher SL, Saykin AJ; Alzheimer’s Disease Neuroimaging Initiative (ADNI).

AMIA Annu Symp Proc. 2011;2011:1013-8. Epub 2011 Oct 22.

14.

A flexible and accurate genotype imputation method for the next generation of genome-wide association studies.

Howie BN, Donnelly P, Marchini J.

PLoS Genet. 2009 Jun;5(6):e1000529. doi: 10.1371/journal.pgen.1000529. Epub 2009 Jun 19.

15.

Impact of genetic similarity on imputation accuracy.

Roshyara NR, Scholz M.

BMC Genet. 2015 Jul 22;16:90. doi: 10.1186/s12863-015-0248-2.

16.

Practical considerations for imputation of untyped markers in admixed populations.

Shriner D, Adeyemo A, Chen G, Rotimi CN.

Genet Epidemiol. 2010 Apr;34(3):258-65. doi: 10.1002/gepi.20457.

17.

Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle.

van Binsbergen R, Bink MC, Calus MP, van Eeuwijk FA, Hayes BJ, Hulsegge I, Veerkamp RF.

Genet Sel Evol. 2014 Jul 15;46:41. doi: 10.1186/1297-9686-46-41.

18.

Accuracy of imputation using the most common sires as reference population in layer chickens.

Heidaritabar M, Calus MP, Vereijken A, Groenen MA, Bastiaansen JW.

BMC Genet. 2015 Aug 18;16:101. doi: 10.1186/s12863-015-0253-5.

19.

Rare variant genotype imputation with thousands of study-specific whole-genome sequences: implications for cost-effective study designs.

Pistis G, Porcu E, Vrieze SI, Sidore C, Steri M, Danjou F, Busonero F, Mulas A, Zoledziewska M, Maschio A, Brennan C, Lai S, Miller MB, Marcelli M, Urru MF, Pitzalis M, Lyons RH, Kang HM, Jones CM, Angius A, Iacono WG, Schlessinger D, McGue M, Cucca F, Abecasis GR, Sanna S.

Eur J Hum Genet. 2015 Jul;23(7):975-83. doi: 10.1038/ejhg.2014.216. Epub 2014 Oct 8.

20.

Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets.

Li MX, Yeung JM, Cherny SS, Sham PC.

Hum Genet. 2012 May;131(5):747-56. doi: 10.1007/s00439-011-1118-2. Epub 2011 Dec 6.

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