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Identification and characterisation of germline variants in an Irish cohort with breast cancer

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  • معلومة اضافية
    • Contributors:
      Morris, Derek W.; Miller, Nicola; Kerin, Michael J.; National Breast Cancer Research Institute; Health Research Board; Monkstown Hospital Foundation
    • بيانات النشر:
      NUI Galway
    • الموضوع:
      2020
    • Collection:
      National University of Ireland (NUI), Galway: ARAN
    • نبذة مختصرة :
      Breast cancer is the most common female malignancy worldwide, and the second most common cause of cancer-related deaths in women. Twin studies estimate that up to one third of breast cancers are the result of hereditary factors; approximately 50% of the inherited genetic predisposition to the disease is yet to be fully elucidated. Next-generation sequencing (NGS) is a rapidly evolving technology that enables sequencing of many genes across multiple samples simultaneously. NGS, particularly multi-gene panels, is an accessible and practical option for clinicians and research groups investigating the genetic basis of inherited disease. However, variant interpretation remains a significant challenge, particularly in genes with an ill-defined association to disease. This research focused on the development of a custom NGS multi-gene panel to investigate the prevalence of variants in putative breast cancer susceptibility genes in Irish women with breast cancer, and to assess the pathogenicity of variants based on in silico predictions and biochemical analysis. A custom multi-gene panel was designed comprising 282 genes, including known breast cancer susceptibility genes, along with candidate genes identified by extensive literature review. Germline DNA was obtained from patients affected by breast cancer (n=91) and ethnically matched controls (n=77). Bioinformatic analysis was conducted in-line with GATK best practices. Variant annotation was performed with SnpEff, VEP, and ANNOVAR. Loss-of-function variants were verified using the UCSC Genome Browser. Missense variants were prioritised using five in silico algorithms. Population frequency and variant classification data were obtained from public databases. Common (MAF>1%) and benign variants were removed from analysis. Analysis was restricted to genes appearing on clinical breast ± ovarian cancer panels (n=83). Novel variants were prioritised for biochemical characterisation by the availability of 3D protein structures and functional assays. Recombinant wildtype ...
    • Relation:
      http://hdl.handle.net/10379/15778
    • Rights:
      Attribution-NonCommercial-NoDerivs 3.0 Ireland ; https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
    • الرقم المعرف:
      edsbas.B7A28F2