Browsing by Subject "Clinical genomics"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemEmbargo
    Automated sequence variant classification tool for DNA diagnostics
    (2024-07) İnan, R. Arda
    Advancements in DNA sequencing rapidly improved our understanding of the genome in recent years. Today, these advances are revealing thousands of genetic variants that are still waiting to be deciphered. Establishing the association between genetic variation and diseases enables us to better appreciate the biology of diseases and to develop effective therapeutical solutions. To this end, clinical organizations such as American College of Medical Genetics and Genomics (ACMG) developed standards and guidelines to interpret sequence variants. Clinical Genomics Resource (ClinGen) provided further specifications to the guidelines to improve the interpretations. However, implementation of the guidelines takes considerable time and requires substantial expertise in clinical genetics. Available computational tools to automate the process (i) do not comprehensively describe how their frameworks function, (ii) fail to completely follow the latest specifications and (iii) lack high consistency with variant classifications manually performed by experts. Here, this work presents automated ACMGbased variant classifier (AAVC), which computationally interprets sequence variants based on the ACMG Guidelines and the ClinGen Specifications by aggregating information from large public databases and in silico prediction tools including BayesDel, ClinVar, Ensembl, gnomAD, PhyloP, RepeatMasker, SpliceAI and UniProt. The tool demonstrates a high concordance (99.67%) with FDA-approved variant classification database, reveals more than two hundred novel variants in clinically actionable genes in the Turkish Variome and reclassifies at least 57,000 inconclusive variants in ClinVar as pathogenic or likely pathogenic. The work provides a comprehensive framework to enable rapid and accurate interpretation of sequence variants by the ACMG Standards.
  • Thumbnail Image
    ItemOpen Access
    Privacy-preserving genomic testing in the clinic: a model using HIV treatment
    (Nature Publishing Group, 2016) Mclaren, P. J.; Raisaro, J. L.; Aouri, M.; Rotger, M.; Ayday, E.; Bartha, I.; Delgado, M. B.; Vallet, Y.; Günthard, H. F.; Cavassini, M.; Furrer, H.; Doco-Lecompte, T.; Marzolini, C.; Schmid, P.; Di Benedetto, C.; Decosterd, L. A.; Fellay, J.; Hubaux, Jean-Pierre; Telenti A.
    Purpose:The implementation of genomic-based medicine is hindered by unresolved questions regarding data privacy and delivery of interpreted results to health-care practitioners. We used DNA-based prediction of HIV-related outcomes as a model to explore critical issues in clinical genomics.Methods:We genotyped 4,149 markers in HIV-positive individuals. Variants allowed for prediction of 17 traits relevant to HIV medical care, inference of patient ancestry, and imputation of human leukocyte antigen (HLA) types. Genetic data were processed under a privacy-preserving framework using homomorphic encryption, and clinical reports describing potentially actionable results were delivered to health-care providers.Results:A total of 230 patients were included in the study. We demonstrated the feasibility of encrypting a large number of genetic markers, inferring patient ancestry, computing monogenic and polygenic trait risks, and reporting results under privacy-preserving conditions. The average execution time of a multimarker test on encrypted data was 865 ms on a standard computer. The proportion of tests returning potentially actionable genetic results ranged from 0 to 54%.Conclusions:The model of implementation presented herein informs on strategies to deliver genomic test results for clinical care. Data encryption to ensure privacy helps to build patient trust, a key requirement on the road to genomic-based medicine.