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Abstrait

Closing the diganostic gap in rare genetic conditions

Lamia Mestek Boukhibar

The diagnosis of rare genetic diseases has seen a leap in recent years thanks to technological advances in next generation sequencing. Government and private initiatives facilitate the adoption of such technologies both in clinical and research settings, which has advanced our understanding of rare genetic conditions. However, the diagnostic yield (30-60%) reflects the need for further approcahes to address this diagnostic gap. Lack of diagnosis is problematic at all levels and is mainly translated into missed opprtunities of coordinated care and potential therapies. Therore, despite the promissing achievements in both diagnosis methodologies and novel therapies, many patients remain undiagnosed. To address this unmet clinical need, we put together a framework that can easily be adopted in clinical and research seetings to narrow the diagnosic gap. This diagnotic gap workflow is a multidisciplinary approach based on data sharing, data mining, functional work and and up to date biobase. Here we explain the diagnostic gap workflow, and give an example of how it has led to improved diagnostic rate in a number of critically ill infants who remained without a diagnosis despite having had whole genome sequencing. We propose this workflow can seemlessly be implemented without the need for sophsticated infrastructure.