Abstract
The kernel machine-based regression is an efficient approach to region-based association analysis aimed at identification of rare genetic variants. However, this method is computationally complex. The running time of kernel-based association analysis becomes especially long for samples with genetic (sub) structures, thus increasing the need to develop new and effective methods, algorithms, and software packages. We have developed a new R-package called fast family-based sequence kernel association test (FFBSKAT) for analysis of quantitative traits in samples of related individuals. This software implements a score-based variance component test to assess the association of a given set of single nucleotide polymorphisms with a continuous phenotype. We compared the performance of our software with that of two existing software for family-based sequence kernel association testing, namely, ASKAT and famSKAT, using the Genetic Analysis Workshop 17 family sample. Results demonstrate that FFBSKAT is several times faster than other available programs. In addition, the calculations of the three-compared software were similarly accurate. With respect to the available analysis modes, we combined the advantages of both ASKAT and famSKAT and added new options to empower FFBSKAT users. The FFBSKAT package is fast, user-friendly, and provides an easy-to-use method to perform whole-exome kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The FFBSKAT package, along with its manual, is available for free download at http://mga.bionet.nsc.ru/soft/FFBSKAT/.
Highlights
The development of new and effective whole-exome and wholegenome resequencing technologies demands the establishment of powerful and computationally efficient statistical methods to test the associations between rare variants and complex traits
A set of rare variants in a region is replaced by a single genetic variable that is tested for association through conventional genome-wide association study (GWAS) methods [1,4,5,6]
We propose novel software called fast family-based sequence kernel association test (SKAT) (FFBSKAT), which is faster and offers more available analysis modes compared with adjusted SKAT (ASKAT) and famSKAT
Summary
The development of new and effective whole-exome and wholegenome resequencing technologies demands the establishment of powerful and computationally efficient statistical methods to test the associations between rare variants and complex traits. The computational complexity of regional association analysis based on the collapsing approach is similar to that of GWAS, where fast software packages have been developed even for structured samples (e.g., [7,8,9,10]).
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