نتایج جستجو برای: wide association study

تعداد نتایج: 4492963  

Journal: :Oncology 2010
Dezheng Huo Olufunmilayo I Olopade

As genome-wide association studies (GWAS) have opened the door to systematic discovery of genetic factors for complex diseases, including cancers, the clinical utility of the findings remains to be determined. This is elegantly discussed in the article in this issue of ONCOLOGY by Stadler et al. The authors rightfully caution against the use of “personal genomic tests” based on cancer GWAS resu...

2012
Stephen A. Stanhope Andrew D. Skol

In a two stage genome-wide association study (2S-GWAS), a sample of cases and controls is allocated into two groups, and genetic markers are analyzed sequentially with respect to these groups. For such studies, experimental design considerations have primarily focused on minimizing study cost as a function of the allocation of cases and controls to stages, subject to a constraint on the power t...

2011
Jantina de Vries Susan J Bull Ogobara Doumbo Muntaser Ibrahim Odile Mercereau-Puijalon Dominic Kwiatkowski Michael Parker

BACKGROUND Genome-wide association studies (GWAS) provide a powerful means of identifying genetic variants that play a role in common diseases. Such studies present important ethical challenges. An increasing number of GWAS is taking place in lower income countries and there is a pressing need to identify the particular ethical challenges arising in such contexts. In this paper, we draw upon th...

2014
Michael J. McGeachie George L. Clemmer Jessica Lasky-Su Amber Dahlin Benjamin A. Raby Scott T. Weiss

We show here that combining two existing genome wide association studies (GWAS) yields additional biologically relevant information, beyond that obtained by either GWAS separately. We propose Joint GWAS Analysis, a method that compares a pair of GWAS for similarity among the top SNP associations, top genes identified, gene functional clusters, and top biological pathways. We show that Joint GWA...

2015

There are several errors in mathematical expressions in theIntroduction under the subheading GWAS Plus: GCTA. In the final sentence of the second paragraph, the expression for the distribution of u is incorrect. The subscript n should be an m and the subscript epsilon should be a u. The correct distribution is: u∼Nmð0; IσuÞ. In the second sentence of the sixth paragraph, the expression for the ...

2013
C. T. Meira M.R.S. Fortes M. M. Farah L. R. Porto-Neto R. A. Curi S. S. Moore

Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Jaboticabal, SP 14884-900, Brazil The University of Queensland, Queensland Alliance for Agriculture and Food innovation, Centre for Animal Science, Brisbane, Qld 4062, Australia CSIRO Food Futures Flagship and Animal, Health and Food Sciences, 306 Carmody Road, St Lucia, QLD 4067, Australia Faculdade de Medicina Vete...

2015
Nada A. Al-Tassan Nicola Whiffin Fay J. Hosking Claire Palles Susan M. Farrington Sara E. Dobbins Rebecca Harris Maggie Gorman Albert Tenesa Brian F. Meyer Salma M. Wakil Ben Kinnersley Harry Campbell Lynn Martin Christopher G. Smith Shelley Idziaszczyk Ella Barclay Timothy S. Maughan Richard Kaplan Rachel Kerr David Kerr Daniel D. Buchanan Aung Ko Win John Hopper Mark Jenkins Noralane M. Lindor Polly A. Newcomb Steve Gallinger David Conti Fred Schumacher Graham Casey Malcolm G. Dunlop Ian P. Tomlinson Jeremy P. Cheadle Richard S. Houlston

Nada A. Al-Tassan, Nicola Whiffin, Fay J. Hosking, Claire Palles, Susan M. Farrington, Sara E. Dobbins, Rebecca Harris, Maggie Gorman, Albert Tenesa, Brian F. Meyer, Salma M. Wakil, Ben Kinnersley, Harry Campbell, Lynn Martin, Christopher G. Smith, Shelley Idziaszczyk, Ella Barclay, Timothy S. Maughan, Richard Kaplan, Rachel Kerr, David Kerr, Daniel D. Buchanan, Aung Ko Win, John Hopper, Mark J...

Journal: :Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 2015
Michael Lim Trevor Hastie

We introduce a method for learning pairwise interactions in a linear regression or logistic regression model in a manner that satisfies strong hierarchy: whenever an interaction is estimated to be nonzero, both its associated main effects are also included in the model. We motivate our approach by modeling pairwise interactions for categorical variables with arbitrary numbers of levels, and the...

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