Progressive rod-cone degeneration (prcd) is a late-onset, autosomal recessive photoreceptor degeneration of dogs and a homolog for some forms of human retinitis pigmentosa (RP). Previously, the disease-relevant interval was reduced to a 106-kb region on CFA9, and a common phenotype-specific haplotype was identified in all affected dogs from several different breeds and breed varieties. Screening of a canine retinal EST library identified partial cDNAs for novel candidate genes in the disease-relevant interval. The complete cDNA of one of these, PRCD, was cloned in dog, human, and mouse. The gene codes for a 54-amino-acid (aa) protein in dog and human and a 53-aa protein in the mouse; the first 24 aa, coded for by exon 1, are highly conserved in 14 vertebrate species. A homozygous mutation (TGC --> TAC) in the second codon shows complete concordance with the disorder in 18 different dog breeds/breed varieties tested. The same homozygous mutation was identified in a human patient from Bangladesh with autosomal recessive RP. Expression studies support the predominant expression of this gene in the retina, with equal expression in the retinal pigment epithelium, photoreceptor, and ganglion cell layers. This study provides strong evidence that a mutation in the novel gene PRCD is the cause of autosomal recessive retinal degeneration in both dogs and humans.

The authors propose these two spontaneous mutations in the canine VMD2 gene, which cause cmr, as the first naturally occurring animal model of BMD. Further development of the cmr models will permit elucidation of the complex molecular mechanism of these retinopathies and the development of potential therapies.

PurposeTo characterize macular ganglion cell layer (GCL) changes with age and provide a framework to assess changes in ocular disease. This study used data clustering to analyze macular GCL patterns from optical coherence tomography (OCT) in a large cohort of subjects without ocular disease.MethodsSingle eyes of 201 patients evaluated at the Centre for Eye Health (Sydney, Australia) were retrospectively enrolled (age range, 20–85); 8 × 8 grid locations obtained from Spectralis OCT macular scans were analyzed with unsupervised classification into statistically separable classes sharing common GCL thickness and change with age. The resulting classes and gridwise data were fitted with linear and segmented linear regression curves. Additionally, normalized data were analyzed to determine regression as a percentage. Accuracy of each model was examined through comparison of predicted 50-year-old equivalent macular GCL thickness for the entire cohort to a true 50-year-old reference cohort.ResultsPattern recognition clustered GCL thickness across the macula into five to eight spatially concentric classes. F-test demonstrated segmented linear regression to be the most appropriate model for macular GCL change. The pattern recognition–derived and normalized model revealed less difference between the predicted macular GCL thickness and the reference cohort (average ± SD 0.19 ± 0.92 and −0.30 ± 0.61 μm) than a gridwise model (average ± SD 0.62 ± 1.43 μm).ConclusionsPattern recognition successfully identified statistically separable macular areas that undergo a segmented linear reduction with age. This regression model better predicted macular GCL thickness. The various unique spatial patterns revealed by pattern recognition combined with core GCL thickness data provide a framework to analyze GCL loss in ocular disease.

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PurposeTo determine the locus of test locations that exhibit statistically similar age-related decline in sensitivity to light increments and age-corrected contrast sensitivity isocontours (CSIs) across the central visual field (VF). We compared these CSIs with test point clusters used by the Glaucoma Hemifield Test (GHT).MethodsSixty healthy observers underwent testing on the Humphrey Field Analyzer 30-2 test grid using Goldmann (G) stimulus sizes I-V. Age-correction factors for GI-V were determined using linear regression analysis. Pattern recognition analysis was used to cluster test locations across the VF exhibiting equal age-related sensitivity decline (age-related CSIs), and points of equal age-corrected sensitivity (age-corrected CSIs) for GI-V.ResultsThere was a small but significant test size–dependent sensitivity decline with age, with smaller stimuli declining more rapidly. Age-related decline in sensitivity was more rapid in the periphery. A greater number of unique age-related CSIs was revealed when using smaller stimuli, particularly in the mid-periphery. Cluster analysis of age-corrected sensitivity thresholds revealed unique CSIs for GI-V, with smaller stimuli having a greater number of unique clusters. Zones examined by the GHT consisted of test locations that did not necessarily belong to the same CSI, particularly in the periphery.ConclusionsCluster analysis reveals statistically significant groups of test locations within the 30-2 test grid exhibiting the same age-related decline. CSIs facilitate pooling of sensitivities to reduce the variability of individual test locations. These CSIs could guide future structure-function and alternate hemifield asymmetry analyses by comparing matched areas of similar sensitivity signatures.