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ABSTRACT: Background
Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different "omics" layers. Existing tools only consider single-nucleotide polymorphism (SNP)-SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables.Results
We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Compariso
SUBMITTER: Molnos S
PROVIDER: S-EPMC5622569 | biostudies-literature | 2017 Sep
REPOSITORIES: biostudies-literature