Genomic Signal Processing for Variant Detection in Diploid Parent-Child Trios

Structural variants (SVs) are rearrangements in the DNA sequence of members within the same species. Detecting SVs is challenging because most approaches suffer from high-false positive rates. In this work, we improve the accuracy of SV detection by exploiting familial relationships and the rare occurence of these rearrangements. Mathematically, we pose SV detection as a constrained optimization problem regularized by a sparsity promoting term. Furthermore, we generalize our previous methods in two ways. First, we consider a biologically realistic scenario of a parent-child-trio, where each individual may carry zero, one, or two copies of any potential SV. Second, we employ a novel block-coordinate descent approach with orthogonal projection to efficiently minimize the objective and to enforce feasibility within the biological constraint space. Numerical results using both simulated and real trios demonstrate that our proposed approach improves our ability to separate true SVs from false positives.