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Supplemental Digital Content is Available in the Text.In this report, we describe a challenging case of autoimmune retinopathy mimicking heritable photoreceptor degeneration in a patient with common variable immune deficiency and investigate the humoral and cell-mediated branches of the immune system in this patient to better understand the mechanism of immune-mediated photoreceptor damage in this disease.

Retinal vascular and structural changes, particularly outside of the central macula, are not well characterized in X-linked retinoschisis (XLRS). We aim to describe wide-field swept-source OCT (SS-OCT) and swept-source OCT angiography (SS-OCTA) findings in XLRS.

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Suicidal behavior has been shown to have a heritable component that is partly driven by psychiatric disorders [Brent and Mann, 2005]. However, there is also an independent factor contributing to the heritability of suicidal behavior. We previously conducted a genome‐wide association study (GWAS) of bipolar suicide attempters and bipolar non‐attempters to assess this independent factor [Willour et al., 2012]. This GWAS implicated glutamatergic neurotransmission in attempted suicide. In the current study, we have conducted a targeted next‐generation sequencing study of the glutamatergic N‐methyl‐D‐aspartate (NMDA) receptor, neurexin, and neuroligin gene families in 476 bipolar suicide attempters and 473 bipolar non‐attempters. The goal of this study was to gather sequence information from coding and regulatory regions of these glutamatergic genes to identify variants associated with attempted suicide. We identified 186 coding variants and 4,298 regulatory variants predicted to be functional in these genes. No individual variants were overrepresented in cases or controls to a degree that was statistically significant after correction for multiple testing. Additionally, none of the gene‐level results were statistically significant following correction. While this study provides no direct support for a role of the examined glutamatergic candidate genes, further sequencing in expanded gene sets and datasets will be required to ultimately determine whether genetic variation in glutamatergic signaling influences suicidal behavior. © 2016 Wiley Periodicals, Inc.

Objective: To present audiometric data in 3 dimensions by considering age as an addition dimension.Methods: Audioprofile surfaces (APSs) were fitted to a set of audiograms by plotting each measurement of an audiogram as an independent point in 3 dimensions with the x, y, and z axes representing frequency, hearing loss in dB, and age, respectively.Results: Using the Java-based APS viewer as a standalone application, APSs were pre-computed for 34 loci. By selecting APSs for the appropriate genetic locus, a clinician can compare this APS-generated average surface to a specific patient’s audiogram.Conclusion: Audioprofile surfaces provide an easily interpreted visual representation of a person’s hearing acuity relative to others with the same genetic cause of hearing loss. Audioprofile surfaces will support the generation and testing of sophisticated hypotheses to further refine our understanding of the biology of hearing.

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.

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Purpose: Gene identification in small families segregating autosomal dominant sensorineural hearing loss presents a significant challenge. To address this challenge, we have developed a machine learning-based software tool, AudioGene v2.0, to prioritize candidate genes for mutation screening based on audioprofiling.Methods: We analyzed audiometric data from a cohort of American families with high-frequency autosomal dominant sensorineural hearing loss. Those families predicted to have a DFNA2 audioprofile by AudioGene v2.0 were screened for mutations in the KCNQ4 gene.Results: Two novel missense mutations and a stop mutation were detected in three American families predicted to have DFNA2-related deafness for a positive predictive value of 6.3%. The false negative rate was 0%. The missense mutations were located in the channel pore region and the stop mutation was in transmembrane domain S5. The latter is the first DFNA2-causing stop mutation reported in KCNQ4.Conclusions: Our data suggest that the N-terminal end of the P-loop is crucial in maintaining the integrity of the KCNQ4 channel pore and AudioGene audioprofile analysis can effectively prioritize genes for mutation screening in small families segregating high-frequency autosomal dominant sensorineural hearing loss. AudioGene software will be made freely available to clinicians and researchers once it has been fully validated.

Gene identification in small families segregating autosomal dominant sensorineural hearing loss presents a significant challenge. To address this challenge, we have developed a machine learning-based software tool, AudioGene v2.0, to prioritize candidate genes for mutation screening based on audioprofiling.

The article presents the poem "Dream Racing Through My Head," by Adam DeLuca. First Line: Right before the dawn; Last Line: For I found that my dream was dead.