In the last two decades, complex traits have become the main focus of genetic studies. The hypothesis that both rare and common variants are associated with complex traits is increasingly being discussed. Family‐based association studies using relatively large pedigrees are suitable for both rare and common variant identification. Because of the high cost of sequencing technologies, imputation methods are important for increasing the amount of information at low cost. A recent family‐based imputation method, Genotype Imputation Given Inheritance (GIGI), is able to handle large pedigrees and accurately impute rare variants, but does less well for common variants where population‐based methods perform better. Here, we propose a flexible approach to combine imputation data from both family‐ and population‐based methods. We also extend the Sequence Kernel Association Test for Rare and Common variants (SKAT‐RC), originally proposed for data from unrelated subjects, to family data in order to make use of such imputed data. We call this extension “famSKAT‐RC.” We compare the performance of famSKAT‐RC and several other existing burden and kernel association tests. In simulated pedigree sequence data, our results show an increase of imputation accuracy from use of our combining approach. Also, they show an increase of power of the association tests with this approach over the use of either family‐ or population‐based imputation methods alone, in the context of rare and common variants. Moreover, our results show better performance of famSKAT‐RC compared to the other considered tests, in most scenarios investigated here.
Availability: famSKATRC is also available as
Qatar Genome Programme is an initiative that aims to use the latest DNA sequencing technology to establish a genome map of the local population. It uses a collection of samples and data from Qatar Biobank participants to identify genotype-phenotype associations relevant to the Qatari population. This will provide unique insights that would enable the development of personalized healthcare in Qatar.
QCRI Bioinformatics group is part of the QGP consortium and is involved in solving how the Whole Genome Sequencing Reveals the Germline Landscape of Cancer-Susceptibility Genes Variation in Qataris population.