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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Among all the organelles in an animal cell, only mitochondria have their own independent genomes. Animal mitochondrial DNA is a double-stranded, closed-circular molecule with around 20,000 base pairs. Mitochondrial DNA is unique in that one of its two strands, the heavy, or H, -strand is guanine rich, whereas the complementary strand is cytosine rich and called the light, or L, -strand. Compared to nuclear DNA, mitochondrial DNA has a very low percentage of non-coding regions and is marked by...
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EHR based Genetic Testing Knowledge Base (iGTKB) Development.

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    We developed an electronic health record (EHR) based genetic testing knowledge base (iGTKB) to improve clinical workflows. This system recommends genetic tests based on patient data and clinical evidence.

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    Area of Science:

    • Medical Informatics
    • Genetics
    • Clinical Decision Support

    Background:

    • Clinical integration of genetic tests is hindered by suboptimal workflows.
    • An intelligent system is needed to recommend genetic tests based on clinical evidence.
    • The individualized Genetic Testing Knowledge Base (iGTKB) was developed using EHR data.

    Purpose of the Study:

    • To develop an EHR-based knowledge base for genetic testing.
    • To integrate clinical evidence for genetic test recommendations.
    • To support the development of an automated genetic testing recommendation system.

    Main Methods:

    • Extracted genetic testing information and patient EHR data.
    • Utilized Natural Language Processing (NLP) for clinical feature annotation.
    • Prioritized clinical features using odds ratios for knowledge base construction.

    Main Results:

    • Developed iGTKB incorporating 28 dominant clinical features for five genetic tests.
    • Annotated 243 clinical features from Human Phenotype Ontology (HPO).
    • Deposited genetic tests, disorders, and features with odds ratios into iGTKB.

    Conclusions:

    • An EHR-based genetic testing knowledge base (iGTKB) was successfully developed.
    • iGTKB will be integrated into the Genetic Testing Ontology (GTO).
    • This facilitates the development of the iGenetics recommendation system.