Genetic Variant Detection Over Generations: Sparsity-Constrained Optimization Using Block-Coordinate Descent

Structural variants (SVs) are rearrangements of regions in an individual’s genome signal. SVs are an important source of genetic diversity and disease in humans and other mammalian species. The SV detection process is susceptible to sequencing and mapping errors, especially when the average number of reads supporting each variant is low (i.e. low-coverage settings), which leads to high false-positive rates. Besides their rarity in the human genome, they are shared between related individuals. Thus, it’s advantageous to devise algorithms that focus on close relatives. In this paper, we develop a constrained-optimization method to detect germline SVs in genetic signals by considering multiple related people. First, we exploit familial relationships by considering a biologically realistic scenario of three generations of related individuals (a grandparent, a parent, and a child). Second, we pose the problem as a constrained optimization problem regularized by a sparsity-promoting penalty. Our framework demonstrates improvements in predicting SVs in related individuals and uncovering true SVs from false positives on both simulated and real genetic signals from the 1000 Genomes Project with low coverage. Further, our block-coordinate descent approach produces results with equal accuracy to the 3D projections of the solution, demonstrating feasibility for more complex and higher-dimensional pedigrees.