An R package "VariABEL" for genome-wide searching of potentially interacting loci by testing genotypic variance heterogeneity
1 Department of Epidemiology, Erasmus MC, Rotterdam, 3000 CA, The Netherlands
2 Department of Biostatistics, Erasmus MC, Rotterdam, 3000 CA, The Netherlands
3 Recombination and Segregation laboratory, Institute of Cytology and Genetics SD RAS, Novosibirsk, 630090, Russia
Citation and License
BMC Genetics 2012, 13:4 doi:10.1186/1471-2156-13-4Published: 24 January 2012
Hundreds of new loci have been discovered by genome-wide association studies of human traits. These studies mostly focused on associations between single locus and a trait. Interactions between genes and between genes and environmental factors are of interest as they can improve our understanding of the genetic background underlying complex traits. Genome-wide testing of complex genetic models is a computationally demanding task. Moreover, testing of such models leads to multiple comparison problems that reduce the probability of new findings. Assuming that the genetic model underlying a complex trait can include hundreds of genes and environmental factors, testing of these models in genome-wide association studies represent substantial difficulties.
We and Pare with colleagues (2010) developed a method allowing to overcome such difficulties. The method is based on the fact that loci which are involved in interactions can show genotypic variance heterogeneity of a trait. Genome-wide testing of such heterogeneity can be a fast scanning approach which can point to the interacting genetic variants.
In this work we present a new method, SVLM, allowing for variance heterogeneity analysis of imputed genetic variation. Type I error and power of this test are investigated and contracted with these of the Levene's test. We also present an R package, VariABEL, implementing existing and newly developed tests.
Variance heterogeneity analysis is a promising method for detection of potentially interacting loci. New method and software package developed in this work will facilitate such analysis in genome-wide context.