Early intervention for newborns who are deaf or hard-of-hearing leads to improved language, communication, and social–emotional outcomes. Universal physiologic newborn hearing screening has been widely implemented across the United States with the goal of identifying newborns who are deaf or hard-of-hearing, thereby reducing time to diagnosis and intervention. The current physiologic newborn hearing screen is generally successful in accomplishing its goals but improvements could be made. In the past ten years, genetic testing has emerged as the most important etiological diagnostic test for evaluation of children with deafness and congenital cytomegalovirus has been recognized as a major cause of childhood deafness that may be treatable. A comprehensive newborn hearing screen that includes physiologic, genetic, and cytomegalovirus testing would have multiple benefits, including (1) identifying newborns with deafness missed by the current physiologic screen, (2) providing etiologic information, and (3) possibly decreasing the number of children lost to follow up. We present a framework for integrating limited genetic testing and cytomegalovirus screening into the current physiologic newborn hearing screening. We identify needed areas of research and include an overview of genome sequencing, which we believe will become available over the next decade as a complement to universal physiologic newborn hearing screening.

To develop and validate VisCap, a software program targeted to clinical laboratories for inference and visualization of germ-line copy-number variants (CNVs) from targeted next-generation sequencing data. VisCap calculates the fraction of overall sequence coverage assigned to genomic intervals and computes log2 ratios of these values to the median of reference samples profiled using the same test configuration. Candidate CNVs are called when log2 ratios exceed user-defined thresholds. We optimized VisCap using 14 cases with known CNVs, followed by prospective analysis of 1,104 cases referred for diagnostic DNA sequencing. To verify calls in the prospective cohort, we used droplet digital polymerase chain reaction (PCR) to confirm 10/27 candidate CNVs and 72/72 copy-neutral genomic regions scored by VisCap. We also used a genome-wide bead array to confirm the absence of CNV calls across panels applied to 10 cases. To improve specificity, we instituted a visual scoring system that enabled experienced reviewers to differentiate true-positive from false-positive calls with minimal impact on laboratory workflow. VisCap is a sensitive method for inferring CNVs from targeted sequence data from targeted gene panels. Visual scoring of data underlying CNV calls is a critical step to reduce false-positive calls for follow-up testing.Genet Med 18 7, 712-719.Genetics in Medicine (2016); 18 7, 712-719. doi:10.1038/gim.2015.156.