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Ascia Eskin

Publications and source records attributed to Ascia Eskin.

2 recordsLinked to original sources

Online Self-Report Data for Duchenne Muscular Dystrophy confirms natural history and can be used to assess for therapeutic benefits

To assess the utility of online patient self-report outcomes in a rare disease, we attempted to observe the effects of corticosteroids in delaying age at fulltime wheelchair use in Duchenne muscular dystrophy (DMD) using data from 1,057 males from DuchenneConnect, an online registry. Data collected were compared to prior natural history data in regard to age at diagnosis, mutation spectrum, and age at loss of ambulation. Because registrants reported differences in steroid and other medication usage, as well as age and ambulation status, we could explore these data for correlations with age at loss of ambulation. Using multivariate analysis, current steroid usage was the most significant and largest independent predictor of improved wheelchair-free survival. Thus, these online self-report data were sufficient to retrospectively observe that current steroid use by patients with DMD is associated with a delay in loss of ambulation. Comparing commonly used steroid drugs, deflazacort prolonged ambulation longer than prednisone (median 14 years and 13 years, respectively). Further, use of Vitamin D and Coenzyme Q10, insurance status, and age at diagnosis after 4 years were also significant, but smaller, independent predictors of longer wheelchair-free survival. Nine other common supplements were also individually tested but had lower study power. This study demonstrates the utility of DuchenneConnect data to observe therapeutic differences, and highlights needs for improvement in quality and quantity of patient-report data, which may allow exploration of drug/therapeutic practice combinations impractical to study in clinical trial settings. Further, with the low barrier to participation, we anticipate substantial growth in the dataset in the coming years.

Physiology

Leveraging ancestry to improve causal variant identification in exome sequencing for monogenic disorders

Recent breakthroughs in exome sequencing technology have made possible the identification of many causal variants of monogenic disorders. Although extremely powerful when closely related individuals (e.g. child and parents) are simultaneously sequenced, exome sequencing of individual only cases is often unsuccessful due to the large number of variants that need to be followed-up for functional validation. Many approaches remove from consideration common variants above a given frequency threshold (e.g. 1%), and then prioritize the remaining variants according to their allele frequency, functional, structural and conservation properties. In this work, we present methods that leverage the genetic structure of different populations while accounting for the finite sample size of the reference panels to improve the variant filtering step. Using simulations and real exome data from individuals with monogenic disorders, we show that our methods significantly reduce the number of variants to be followed-up (e.g. a 36% reduction from an average 418 variants per exome when ancestry is ignored to 267 when ancestry is taken into account for case-only sequenced individuals). Most importantly our proposed approaches are well calibrated with respect to the probability of filtering out a true causal variant (i.e. false negative rate, FNR), whereas existing approaches are susceptible to high FNR when reference panel sizes are limited.

Genetics