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Research for Inherited Unknown Genetic Conditions

Unknown genetic conditions can be identified by Exome sequencing, also known as whole exome sequencing (WES or WXS), is a technique for sequencing all the expressed genes in a genome (known as the exome). It consists of first selecting only the subset of DNA that encodes proteins (known as exons) and then sequencing that DNA using any high-throughput DNA sequencing technology. Humans have about 180,000 exons, constituting about 1% of the human genome, or approximately 30 million base pairs. The goal of this approach is to identify genetic variation that is responsible for both Mendelian and common diseases such as Miller syndrome and Alzheimer’s disease without the high costs associated with whole-genome sequencing.

Many patients with genetic diseases are not given a specific diagnosis. The standard of practice involves the recognition of specific phenotypic or radiographic features or biopsy findings in addition to the analysis of metabolites, genomic tests such as karyotyping or array-based comparative genomic hybridization or the selection of candidate-gene tests, including single-gene analyses and gene-panel tests. The majority of patients remain without a diagnosis. The lack of a diagnosis can have considerable adverse effects for patients and their families, including failure to identify potential treatments, failure to recognize the risk of recurrence in subsequent pregnancies, and failure to provide anticipatory guidance and prognosis. A long-term search for a genetic diagnosis, referred to as the “diagnostic odyssey,” also has implications for societal medical expenditures, with unsuccessful attempts consuming limited resources. However with Whole Exon Sequencing this problem can be solved to mush extent.

  • Identifies variants across a wide range of applications.
  • Achieves comprehensive coverage of coding regions.
  • Provides a cost-effective alternative to whole-genome sequencing (4–5 Gb of sequencing per exome compared to ~90 Gb per whole human genome).
  • Produces a smaller, more manageable data set for faster, easier analysis compared to whole-genome approaches.