  Research on the localization of the genetic basis associated with diseases or
traits has been widely conducted in the last a few decades. Scan methods have
been developed for region-based analysis in whole-genome association studies,
helping us better understand how genetics influences human diseases or traits,
especially when the aggregated effects of multiple causal variants are present.
In this paper, we propose a fast and effective algorithm coupling with
high-dimensional test for simultaneously detecting multiple signal regions,
which is distinct from existing methods using scan or knockoff statistics. The
idea is to conduct binary splitting with re-search and arrangement based on a
sequence of dynamic critical values to increase detection accuracy and reduce
computation. Theoretical and empirical studies demonstrate that our approach
enjoys favorable theoretical guarantees with fewer restrictions and exhibits
superior numerical performance with faster computation. Utilizing the UK
Biobank data to identify the genetic regions related to breast cancer, we
confirm previous findings and meanwhile, identify a number of new regions which
suggest strong association with risk of breast cancer and deserve further
investigation.
