Binary Time-Series Query Framework for Efficient Quantitative Trait Association Study

Quantitative trait association study examines the association between quantitative traits and genetic variants. As a promising tool, it has been widely applied to dissect the genetic basis of complex diseases. However, such study usually involves testing trillions of variant-trait pairs and demands intensive computational resources. Recently, several algorithms have been developed to improve its efficiency. In this paper, we propose a framework, Fabrique, which models quantitative trait association study as querying binary time-series and bridges the two seemly different problems. Specifically, in the proposed framework, genetic variants are treated as a database consisting of binary time-series. Finding trait-associated variants is equivalent to finding the nearest neighbors of the trait. For efficient query process, Fabrique partitions and normalizes the binary time-series, and estimates a tight upper bound for each group of time-series to prune the search space. Extensive experimental results demonstrate that Fabrique only needs to search a very small portion of the database to locate the target variants and significantly outperforms the state-of-the-art method. We also show that Fabrique can be applied to other binary time-series query problem in addition to the genetic association study.