Exploration of a diversity of computational and statistical measures of association for genome-wide genetic studies.

ABSTRACT:

Background

The principal line of investigation in Genome Wide Association Studies (GWAS) is the identification of main effects, that is individual Single Nucleotide Polymorphisms (SNPs) which are associated with the trait of interest, independent of other factors. A variety of methods have been proposed to this end, mostly statistical in nature and differing in assumptions and type of model employed. Moreover, for a given model, there may be multiple choices for the SNP genotype encoding. As an alternative to statistical methods, machine learning methods are often applicable. Typically, for a given GWAS, a single approach is selected and utilized to identify potential SNPs of interest. Even when multiple GWAS are combined through meta-analyses within a consortium, each GWAS is typi

SUBMITTER: Manduchi E 

PROVIDER: S-EPMC6617598 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature