  Motivation: Exome or targeted sequencing data exerts analytical challenge to
test single nucleotide polymorphisms (SNPs) with extremely small minor allele
frequency (MAF). Various rare variant tests were proposed to increase power by
aggregating SNPs within a fixed genomic region, such as a gene or pathway.
However, a gene could contain from several to thousands of markers, and not all
of them may be related to the phenotype. Combining functional and
non-functional SNPs in arbitrary genomic region could impair the testing power.
Results: We propose a Zoom-Focus algorithm (ZFA) to locate the optimal testing
region within a given genomic region, as a wrapper function to be applied in
conjunction with rare variant association tests. In the first Zooming step, a
given genomic region is partitioned by order of two, and the best partition is
located within all partition levels. In the next Focusing step, boundaries of
the zoomed region are refined. Simulation studies showed that ZFA substantially
enhanced the statistical power of rare variant tests by over 10 folds,
including the WSS, SKAT and W-test. The algorithm is applied on real exome
sequencing data of hypertensive disorder, and identified biologically relevant
genetic markers to metabolic disorder that are undiscoverable by testing using
full gene. The proposed algorithm is an efficient and powerful tool to increase
the effectiveness of rare variant association tests for exome sequencing
datasets of complex disorder.
