A note on optimal sampling strategy for structural variant detection using optical mapping

Structural variants compose the majority of human genetic variation, but are difficult to accurately assess using current genomic sequencing technologies. Optical mapping technologies, which measure the size of chromosomal fragments between labeled markers, offer an alternative approach. As these technologies mature toward becoming clinical tools, there is a need to develop an approach for determining the optimal strategy for sampling biological material in order to detect a structural variant at some threshold. Here we develop an optimization approach using a simple, yet realistic, model of the genomic mapping process using a hypergeometric distribution and probabilistic concentration inequalities. Our approach is both computationally and analytically tractable and includes a novel approach to getting tail bounds of hypergeometric distribution. We show that if a genomic mapping technology can sample most of the chromosomal fragments within a sample, comparatively little biological material is needed to detect a variant at high confidence.