Greenwood CMT - Search Results

1

Estimating the effects of copy-number variants on intelligence using hierarchical Bayesian models.

Jiang L, Huguet G, Schramm C, Ciampi A, Main A, Passo C, Jean-Louis M, Auger M, Schumann G, Porteous D, Jacquemont S, Greenwood CMT. Jiang L, et al. Among authors: greenwood cmt. Genet Epidemiol. 2020 Nov;44(8):825-840. doi: 10.1002/gepi.22344. Epub 2020 Aug 11. Genet Epidemiol. 2020. PMID: 32783248

2

A tree-based model for allele-sharing-based linkage analysis in human complex diseases.

Xu W, Schulze TG, DePaulo JR, Bull SB, McMahon FJ, Greenwood CM. Xu W, et al. Genet Epidemiol. 2006 Feb;30(2):155-69. doi: 10.1002/gepi.20135. Genet Epidemiol. 2006. PMID: 16385469

3

Multiple regression methods show great potential for rare variant association tests.

Xu C, Ladouceur M, Dastani Z, Richards JB, Ciampi A, Greenwood CM. Xu C, et al. PLoS One. 2012;7(8):e41694. doi: 10.1371/journal.pone.0041694. Epub 2012 Aug 8. PLoS One. 2012. PMID: 22916111 Free PMC article.

4

Adjusted sequence kernel association test for rare variants controlling for cryptic and family relatedness.

Oualkacha K, Dastani Z, Li R, Cingolani PE, Spector TD, Hammond CJ, Richards JB, Ciampi A, Greenwood CM. Oualkacha K, et al. Genet Epidemiol. 2013 May;37(4):366-76. doi: 10.1002/gepi.21725. Epub 2013 Mar 25. Genet Epidemiol. 2013. PMID: 23529756

5

Exploring the potential benefits of stratified false discovery rates for region-based testing of association with rare genetic variation.

Xu C, Ciampi A, Greenwood CM; UK10K Consortium. Xu C, et al. Front Genet. 2014 Jan 29;5:11. doi: 10.3389/fgene.2014.00011. eCollection 2014. Front Genet. 2014. PMID: 24523729 Free PMC article.

6

Estimating genome-wide significance for whole-genome sequencing studies.

Xu C, Tachmazidou I, Walter K, Ciampi A, Zeggini E, Greenwood CM; UK10K Consortium. Xu C, et al. Genet Epidemiol. 2014 May;38(4):281-90. doi: 10.1002/gepi.21797. Epub 2014 Feb 14. Genet Epidemiol. 2014. PMID: 24676807 Free PMC article.

7

funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types.

Oros Klein K, Grinek S, Bernatsky S, Bouchard L, Ciampi A, Colmegna I, Fortin JP, Gao L, Hivert MF, Hudson M, Kobor MS, Labbe A, MacIsaac JL, Meaney MJ, Morin AM, O'Donnell KJ, Pastinen T, Van Ijzendoorn MH, Voisin G, Greenwood CM. Oros Klein K, et al. Bioinformatics. 2016 Feb 15;32(4):593-5. doi: 10.1093/bioinformatics/btv615. Epub 2015 Oct 24. Bioinformatics. 2016. PMID: 26500152 Free PMC article.

8

A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects.

Sun J, Oualkacha K, Forgetta V, Zheng HF, Brent Richards J, Ciampi A, Greenwood CM; UK10K Consortium. Sun J, et al. Eur J Hum Genet. 2016 Aug;24(9):1344-51. doi: 10.1038/ejhg.2016.8. Epub 2016 Feb 10. Eur J Hum Genet. 2016. PMID: 26860061 Free PMC article.

9

Principal component of explained variance: An efficient and optimal data dimension reduction framework for association studies.

Turgeon M, Oualkacha K, Ciampi A, Miftah H, Dehghan G, Zanke BW, Benedet AL, Rosa-Neto P, Greenwood CM, Labbe A; Alzheimer’s Disease Neuroimaging Initiative. Turgeon M, et al. Stat Methods Med Res. 2018 May;27(5):1331-1350. doi: 10.1177/0962280216660128. Epub 2016 Jul 26. Stat Methods Med Res. 2018. PMID: 27460538

10

Joint analysis of multiple blood pressure phenotypes in GAW19 data by using a multivariate rare-variant association test.

Sun J, Bhatnagar SR, Oualkacha K, Ciampi A, Greenwood CM. Sun J, et al. BMC Proc. 2016 Oct 18;10(Suppl 7):309-313. doi: 10.1186/s12919-016-0048-3. eCollection 2016. BMC Proc. 2016. PMID: 27980654 Free PMC article.

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