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GWAR: robust analysis and meta-analysis of genome-wide association studies
Author(s) -
Niki Dimou,
Konstantinos D. Tsirigos,
Arne Elofsson,
Pantelis G. Bagos
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx008
Subject(s) - computer science , statistic , genome wide association study , context (archaeology) , data mining , power analysis , meta analysis , type i and type ii errors , statistical power , statistics , mathematics , algorithm , medicine , biology , paleontology , biochemistry , cryptography , genotype , single nucleotide polymorphism , gene
In the context of genome-wide association studies (GWAS), there is a variety of statistical techniques in order to conduct the analysis, but, in most cases, the underlying genetic model is usually unknown. Under these circumstances, the classical Cochran-Armitage trend test (CATT) is suboptimal. Robust procedures that maximize the power and preserve the nominal type I error rate are preferable. Moreover, performing a meta-analysis using robust procedures is of great interest and has never been addressed in the past. The primary goal of this work is to implement several robust methods for analysis and meta-analysis in the statistical package Stata and subsequently to make the software available to the scientific community.

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