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Approach to Cohort-Wide Re-Analysis of Exome Data in 1000 Individuals with Neurodevelopmental Disorders.

Insa HalfmeyerTobias BartolomaeusBernt PoppMaximilian RadtkeTobias HelmsJulia HentschelDenny PoppRami Abou Jamra
Published in: Genes (2022)
The re-analysis of nondiagnostic exome sequencing (ES) has the potential to increase diagnostic yields in individuals with rare diseases, but its implementation in the daily routines of laboratories is limited due to restricted capacities. Here, we describe a systematic approach to re-analyse the ES data of a cohort consisting of 1040 diagnostic and nondiagnostic samples. We applied a strict filter cascade to reveal the most promising single-nucleotide variants (SNVs) of the whole cohort, which led to an average of 0.77 variants per individual that had to be manually evaluated. This variant set revealed seven novel diagnoses (0.8% of all nondiagnostic cases) and two secondary findings. Thirteen additional variants were identified by a scientific approach prior to this re-analysis and were also present in this variant set. This resulted in a total increase in the diagnostic yield of 2.3%. The filter cascade was optimised during the course of the study and finally resulted in sensitivity of 85%. After applying the filter cascade, our re-analysis took 20 h and enabled a workflow that can be used repeatedly. This work is intended to provide a practical recommendation for other laboratories wishing to introduce a resource-efficient re-analysis strategy into their clinical routine.
Keyphrases
  • copy number
  • primary care
  • single cell
  • machine learning
  • big data
  • dna methylation
  • genome wide
  • artificial intelligence
  • data analysis