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To select the optimal combination of software and parameters included in our ensemble CNV approach, we conducted a number of benchmarking analyses deploying numerous detection algorithms on both real and simulated genomes. We utilized the well-characterized HG002 genome for benchmarking deletion calls obtained from the Genome in a Bottle (GIAB) consortium,22 which provides tier 1 benchmark regions of high-quality deletions that we utilized as our ground truth data set. For our simulation data, we used RSVsim to simulate deletions and duplications of a range of sizes at various genomic coordinates. Wgsim in the SAMtools package was used to create comparable GS reads similar to our study cohorts for further benchmarking and analysis. For sensitivity and precision evaluations, we used Truvari for HG002 deletion benchmarking and in-house scripts for accuracy metrics on simulated data.

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