PURPOSE: To evaluate whether levels of corneal subbasal nerve fiber length (SNFL) in dry eye disease (DED) could prognosticate the level of improvement in signs and symptoms after treatment. DESIGN: Phase IV, double-masked, randomized clinical trial. PARTICIPANTS: Sixty patients with meibomian gland dysfunction-associated DED and 27 age-matched controls. METHODS: Patients with DED were randomized to receive topical artificial tears, loteprednol etabonate 0.5%, or loteprednol etabonate 0.5%/tobramycin 0.3% twice daily for 4 weeks. At baseline, in vivo confocal microscopy of central cornea was performed in both eyes. Patients with DED were divided into 2 subgroups: those with low baseline SNFL and those with near-normal baseline SNFL for this purpose (the cutoff point: the mean SNFL in controls minus 2 standard deviations). Clinical signs and symptoms at baseline and after 4 weeks of treatment were compared between the subgroups with low and near-normal SNFL for all therapeutic groups. MAIN OUTCOME MEASURES: Symptom questionnaires, corneal fluorescein staining (CFS), conjunctival staining with lissamine green, tear break-up time, Schirmer's test, and SNFL. RESULTS: In patients with DED, baseline SNFL (17.06±5.78 mm/mm(2)) was significantly lower than in controls (23.68±3.42 mm/mm(2), P = 0.001). In the artificial tear and loteprednol groups, although no significant improvement in any sign or symptom was noted in patients with low baseline SNFL (<16.84 mm/mm(2)), subjects with near-normal baseline SNFL (≥16.84 mm/mm(2)) showed significant improvement in both symptoms and CFS score (all P < 0.05). In the loteprednol/tobramycin group, no significant change was evident for any sign or symptom in either subgroup of low or near-normal baseline SNFL. CONCLUSIONS: Significant improvements in CFS and patient symptomatology after DED treatment were evident only in the subgroup with near-normal corneal SNFL. Consideration of SNFL may assist in explaining the variability of patients' response to DED therapy.

PURPOSE: Next-generation sequencing-based methods are being adopted broadly for genetic diagnostic testing, but the performance characteristics of these techniques with regard to test accuracy and reproducibility have not been fully defined. METHODS: We developed a targeted enrichment and next-generation sequencing approach for genetic diagnostic testing of patients with inherited eye disorders, including inherited retinal degenerations, optic atrophy, and glaucoma. In preparation for providing this genetic eye disease (GEDi) test on a CLIA-certified basis, we performed experiments to measure the sensitivity, specificity, and reproducibility, as well as the clinical sensitivity, of the test. RESULTS: The GEDi test is highly reproducible and accurate, with sensitivity and specificity of 97.9 and 100%, respectively, for single-nucleotide variant detection. The sensitivity for variant detection was notably better than the 88.3% achieved by whole-exome sequencing using the same metrics, because of better coverage of targeted genes in the GEDi test as compared with a commercially available exome capture set. Prospective testing of 192 patients with inherited retinal degenerations indicated that the clinical sensitivity of the GEDi test is high, with a diagnostic rate of 51%. CONCLUSION: Based on quantified performance metrics, the data suggest that selective targeted enrichment is preferable to whole-exome sequencing for genetic diagnostic testing.Genet Med 17 4, 253-261.

MOTIVATION: All current mitochondrial haplogroup classification tools require variants to be detected from an alignment with the reference sequence and to be properly named according to the canonical nomenclature standards for describing mitochondrial variants, before they can be compared with the haplogroup determining polymorphisms. With the emergence of high-throughput sequencing technologies and hence greater availability of mitochondrial genome sequences, there is a strong need for an automated haplogroup classification tool that is alignment-free and agnostic to reference sequence. RESULTS: We have developed a novel mitochondrial genome haplogroup-defining algorithm using a k-mer approach namely Phy-Mer. Phy-Mer performs equally well as the leading haplogroup classifier, HaploGrep, while avoiding the errors that may occur when preparing variants to required formats and notations. We have further expanded Phy-Mer functionality such that next-generation sequencing data can be used directly as input. AVAILABILITY AND IMPLEMENTATION: Phy-Mer is publicly available under the GNU Affero General Public License v3.0 on GitHub (https://github.com/danielnavarrogomez/phy-mer). CONTACT: Xiaowu_Gai@meei.harvard.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.