Assessing statistical significance in multivariable genome wide association analysis.

ABSTRACT:

Motivation

Although Genome Wide Association Studies (GWAS) genotype a very large number of single nucleotide polymorphisms (SNPs), the data are often analyzed one SNP at a time. The low predictive power of single SNPs, coupled with the high significance threshold needed to correct for multiple testing, greatly decreases the power of GWAS.

Results

We propose a procedure in which all the SNPs are analyzed in a multiple generalized linear model, and we show its use for extremely high-dimensional datasets. Our method yields P-values for assessing significance of single SNPs or groups of SNPs while controlling for all other SNPs and the family wise error rate (FWER). Thus, our method tests whether or not a SNP carries any additional information about the phenotype beyond that ava

SUBMITTER: Buzdugan L 

PROVIDER: S-EPMC4920127 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature