June 1, 2021

Screening for causative structural variants in neurological disorders using long-read sequencing

Author(s): Ekholm, J. and Kujawa, S. and Tsai, Y. Greenberg, D. and Hon, T. and Eng, K. Wenger, A. and Tseng, E. and Wang, J. and Jarosz, M. and Giorda, K. and Clark, T.

Over the past decades neurological disorders have been extensively studied producing a large number of candidate genomic regions and candidate genes. The SNPs identified in these studies rarely represent the true disease-related functional variants. However, more recently a shift in focus from SNPs to larger structural variants has yielded breakthroughs in our understanding of neurological disorders.Here we have developed candidate gene screening methods that combine enrichment of long DNA fragments with long-read sequencing that is optimized for structural variation discovery. We have also developed a novel, amplification-free enrichment technique using the CRISPR/Cas9 system to target genomic regions.We sequenced gDNA and full-length cDNA extracted from the temporal lobe for two Alzheimer’s patients for 35 GWAS candidate genes. The multi-kilobase long reads allowed for phasing across the genes and detection of a broad range of genomic variants including SNPs to multi-kilobase insertions, deletions and inversions. In the full-length cDNA data we detected differential allelic isoform complexity, novel exons as well as transcript isoforms. By combining the gDNA data with full-length isoform characterization allows to build a more comprehensive view of the underlying biological disease mechanisms in Alzheimer’s disease. Using the novel PCR-free CRISPR-Cas9 enrichment method we screened several genes including the hexanucleotide repeat expansion C9ORF72 that is associated with 40% of familiar ALS cases. This method excludes any PCR bias or errors from an otherwise hard to amplify region as well as preserves the basemodication in a single molecule fashion which allows you to capture mosaicism present in the sample.

Organization: PacBio
Year: 2017

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