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Wilk, M. A.

Publications and source records attributed to Wilk, M. A..

2 recordsLinked to original sources

Retinal and Cortical Determinants of Cortical Magnification in Human Albinism

The human fovea lies at the center of the retina and supports high-acuity vision. In normal visual system development, foveal acuity is correlated with both a high density of cone photoreceptors at this location and a magnified retinotopic representation of the fovea in the visual cortex. Both cone density and the cortical area dedicated to each degree of visual space--the latter known as the cortical magnification function--steadily decline with increasing eccentricity from the fovea. In albinism, peak cone density at the fovea and visual acuity are reduced but appear to be normal in the periphery, thus providing a model to explore the correlation between retinal structure, cortical structure, and behavior. Here, we used adaptive optics scanning light ophthalmoscopy to assess retinal cone density and functional magnetic resonance imaging to measure cortical magnification in primary visual cortex of normal controls and individuals with albinism. We find that retinotopic organization is more varied in albinism than previously appreciated, yet cortical magnification outside the fovea is similar to that in controls. Moreover, cortical magnification in albinism and controls exceeds that which might be predicted based on cone density alone, suggesting that reduced foveal cone density in the albinotic retina may be partially counteracted by central connectivity. Together, these results emphasize that central as well as retinal factors must be included to provide a complete picture of aberrant structure and function in genetic conditions such as albinism.

neuroscience

VarSight: Prioritizing Clinically Reported Variants with Binary Classification Algorithms

MotivationIn genomic medicine for rare disease patients, the primary goal is to identify one or more variants that cause their disease. Typically, this is done through filtering and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance. ResultsWe tested the application of classification algorithms that ingest variant predictions along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network. We treated the classifiers as variant prioritization systems and compared them to another variant prioritization algorithm and two single-measure controls. We showed that these classifiers outperformed the other methods with the best classifier ranking 73% of all reported variants and 97% of reported pathogenic variants in the top 20. AvailabilityThe scripts used to generate results presented in this paper are available at https://github.com/HudsonAlpha/VarSight. Contactjholt@hudsonalpha.org

bioinformatics