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Updated: Feb 6, 2026

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia
Published on: June 15, 2011
Insights Into de novo Mutation Variation in Lithuanian Exome
Laura Pranckėnienė1, Audronė Jakaitienė1, Laima Ambrozaitytė1
1Department of Human and Medical Genetics, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.
Abstract:
In the last decade, one of the biggest challenges in genomics research has been to distinguish definitive pathogenic variants from all likely pathogenic variants identified by next-generation sequencing. This task is particularly complex because of our lack of knowledge regarding overall genome variation and pathogenicity of the variants. Therefore, obtaining sufficient information about genome variants in the general population is necessary as such data could be used for the interpretation of de novo mutations (DNMs) in the context of patient's phenotype in cases of sporadic genetic disease. In this study, data from whole-exome sequencing of the general population in Lithuania were directly examined. In total, 84 (VarScan) and 95 (VarSeqTM) DNMs were identified and validated using different algorithms. Thirty-nine of these mutations were considered likely to be pathogenic based on gene function, evolutionary conservation, and mutation impact. The mutation rate estimated per position pair per generation was 2.74 × 10-8 [95% CI: 2.24 × 10-8-3.35 × 10-8] (VarScan) and 2.4 × 10-8 [95% CI: 1.96 × 10-8-2.99 × 10-8] (VarSeqTM), with 1.77 × 10-8 [95% CI: 6.03 × 10-9-5.2 × 10-8] de novo indels per position per generation. The rate of germline DNMs in the Lithuanian population and the effects of the genomic and epigenetic context on DNM formation were calculated for the first time in this study, providing a basis for further analysis of DNMs in individuals with genetic diseases. Considering these findings, additional studies in patient groups with genetic diseases with unclear etiology may facilitate our ability to distinguish certain pathogenic or adaptive DNMs from tolerated background DNMs and to reliably identify disease-causing DNMs by their properties through direct observation.
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