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Sparse genomic structural variant detection: Exploiting parent-child relatedness for signal recovery

Structural variants (SVs) - rearrangements of an individuals' genome - are an important source of heterogeneity in human and other mammalian species. Typically, SVs are identified by comparing fragments of DNA from a test genome to a known reference genome, but errors in both the sequencing and the noisy mapping process contribute to high false positive rates. When multiple related individuals are studied, their relatedness offers a constraint to improve the signal of true SVs. We develop a computational method to predict SVs given genomic DNA from a child and both parents. We demonstrate that enforcing relatedness between individuals and constraining our solution with a sparsity-promoting ℓ1 penalty (since SV instances should be rare) results in improved performance. We present results on both simulated genomes as well as two-sequenced parent-child trios from the 1000 Genomes Project.