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Items: 1 to 20 of 46

1.

Enhancing the discovery of rare disease variants through hierarchical modeling.

Chen GK.

BMC Proc. 2011 Nov 29;5 Suppl 9:S16. doi: 10.1186/1753-6561-5-S9-S16.

2.

Using the posterior distribution of deviance to measure evidence of association for rare susceptibility variants.

Lorenzo-Bermejo J, Beckmann L, Chang-Claude J, Fischer C.

BMC Proc. 2011 Nov 29;5 Suppl 9:S38. doi: 10.1186/1753-6561-5-S9-S38.

3.

Bayesian latent variable collapsing model for detecting rare variant interaction effect in twin study.

He L, Sillanpää MJ, Ripatti S, Pitkäniemi J.

Genet Epidemiol. 2014 May;38(4):310-24. doi: 10.1002/gepi.21804. Epub 2014 Apr 9.

PMID:
24719390
4.

Hierarchical Bayesian model for rare variant association analysis integrating genotype uncertainty in human sequence data.

He L, Pitkäniemi J, Sarin AP, Salomaa V, Sillanpää MJ, Ripatti S.

Genet Epidemiol. 2015 Feb;39(2):89-100. doi: 10.1002/gepi.21871. Epub 2014 Nov 13.

PMID:
25395270
5.

Incorporating biological information into association studies of sequencing data.

Chen GK, Wei P, DeStefano AL.

Genet Epidemiol. 2011;35 Suppl 1:S29-34. doi: 10.1002/gepi.20646. Erratum in: Genet Epidemiol. 2012 Apr;36(3):292. Chen, Gary [corrected to Chen, Gary K].

6.

A comparison of two collapsing methods in different approaches.

Dering C, Schillert A, König IR, Ziegler A.

BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S8. doi: 10.1186/1753-6561-8-S1-S8. eCollection 2014 Jun 17.

7.

Integrative analysis of functional genomic annotations and sequencing data to identify rare causal variants via hierarchical modeling.

Capanu M, Ionita-Laza I.

Front Genet. 2015 May 8;6:17. doi: 10.3389/fgene.2015.00176. eCollection 2015 May 8.

8.

Identifying rare and common variants with Bayesian variable selection.

Oh C.

BMC Proc. 2016 Oct 18;10(Suppl 7):379-384. eCollection 2016 Oct 18.

9.

Whole genome sequence analysis of the simulated systolic blood pressure in Genetic Analysis Workshop 18 family data: long-term average and collapsing methods.

Sung YJ, Basson J, Rao DC.

BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S12. doi: 10.1186/1753-6561-8-S1-S12. eCollection 2014 Jun 17.

10.

A novel genome-information content-based statistic for genome-wide association analysis designed for next-generation sequencing data.

Luo L, Zhu Y, Xiong M.

J Comput Biol. 2012 Jun;19(6):731-44. doi: 10.1089/cmb.2012.0035. Epub 2012 May 31.

11.

Detecting association of rare and common variants by testing an optimally weighted combination of variants.

Sha Q, Wang X, Wang X, Zhang S.

Genet Epidemiol. 2012 Sep;36(6):561-71. doi: 10.1002/gepi.21649. Epub 2012 Jun 19.

PMID:
22714994
12.

Combining effects from rare and common genetic variants in an exome-wide association study of sequence data.

Aschard H, Qiu W, Pasaniuc B, Zaitlen N, Cho MH, Carey V.

BMC Proc. 2011 Nov 29;5 Suppl 9:S44. doi: 10.1186/1753-6561-5-S9-S44.

13.

BioBin: a bioinformatics tool for automating the binning of rare variants using publicly available biological knowledge.

Moore CB, Wallace JR, Frase AT, Pendergrass SA, Ritchie MD.

BMC Med Genomics. 2013;6 Suppl 2:S6. doi: 10.1186/1755-8794-6-S2-S6. Epub 2013 May 7.

14.

Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents.

Coull BA, Bobb JF, Wellenius GA, Kioumourtzoglou MA, Mittleman MA, Koutrakis P, Godleski JJ.

Res Rep Health Eff Inst. 2015 Jun;(183 Pt 1-2):5-50.

PMID:
26333238
15.

Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model.

Bueno Filho JS, Morota G, Tran Q, Maenner MJ, Vera-Cala LM, Engelman CD, Meyers KJ.

BMC Proc. 2011 Nov 29;5 Suppl 9:S93. doi: 10.1186/1753-6561-5-S9-S93.

16.

Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification.

Wolahan SM, Hirt D, Glenn TC.

In: Kobeissy FH, editor. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. Boca Raton (FL): CRC Press/Taylor & Francis; 2015. Chapter 25.

17.

New insights into old methods for identifying causal rare variants.

Wang H, Huang CH, Lo SH, Zheng T, Hu I.

BMC Proc. 2011 Nov 29;5 Suppl 9:S50. doi: 10.1186/1753-6561-5-S9-S50.

18.

A W-test collapsing method for rare-variant association testing in exome sequencing data.

Sun R, Weng H, Hu I, Guo J, Wu WK, Zee BC, Wang MH.

Genet Epidemiol. 2016 Nov;40(7):591-596. doi: 10.1002/gepi.22000. Epub 2016 Aug 16.

PMID:
27531462
19.

Detecting functional rare variants by collapsing and incorporating functional annotation in Genetic Analysis Workshop 17 mini-exome data.

Yan X, Li L, Lee JS, Zheng W, Ferguson J, Zhao H.

BMC Proc. 2011 Nov 29;5 Suppl 9:S27. doi: 10.1186/1753-6561-5-S9-S27.

20.

A scalable, knowledge-based analysis framework for genetic association studies.

Baurley JW, Conti DV.

BMC Bioinformatics. 2013 Oct 23;14:312. doi: 10.1186/1471-2105-14-312.

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