Challenges in Founding and Developing Medical School Student-Run Asylum Clinics.

In response to the rapidly rising number of asylum applications, student-run asylum clinics (SRACs) designed to provide pro bono forensic medical evaluations have emerged at medical schools across the United States. Distinct from traditional student-run clinics in the services they provide and in their operational models, SRACs face a unique set of challenges. This study aims to identify the common challenges in building SRACs and to collect insights to inform a structured approach to collaborative problem-solving. This study gathered data from online surveys and semi-structured phone interviews with representative medical student SRAC leaders. 14 clinics participated in the 2017 online survey, 15 clinics in the 2018 online survey, and eight clinics in the 2018-2019 phone interviews. We identified common challenges in five areas: volunteer recruitment, clinic operations, case demand, institutional support, and leadership. SRACs stand to benefit from ongoing extramural collaborations to overcome shared challenges.

A suite of automated sequence analyses reduces the number of candidate deleterious variants and reveals a difference between probands and unaffected siblings.

PURPOSE: Develop an automated exome analysis workflow that can produce a very small number of candidate variants yet still detect different numbers of deleterious variants between probands and unaffected siblings. METHODS: Ninety-seven outbred nuclear families from the Undiagnosed Diseases Program/Network included single probands and the corresponding unaffected sibling(s). Single-nucleotide polymorphism (SNP) chip and exome analyses were performed on all, with proband and unaffected sibling considered independently as the target. The total burden of candidate genetic variants was summed for probands and siblings over all considered disease models. RESULTS: Exome analysis workflow include automated programs for ethnicity-matched genotype calling, salvage pathway for Mendelian inconsistency, compound heterozygous recessive detection, BAM file regional curation, population frequency filtering, pedigree-aware BAM file noise evaluation, and exon deletion filtration. This workflow relied heavily on BAM file analysis. A greater average pathogenic variant number was found compared with unaffected siblings. This was significant (p < 0.05) when using published recommended thresholds, and implies that causal variants are retained in many probands' lists. CONCLUSION: Using Mendelian and non-Mendelian models, this agnostic exome analysis shows a difference between a small group of probands and their unaffected siblings. This workflow produces candidate lists small enough to pursue with laboratory validation.