Abstract
The widespread availability of next-generation sequencing (NGS) has transformed our understanding of human genetic variation and its impact on human health. This chapter describes the most common DNA sequencing technologies available to research and clinical laboratories today, and resources for interpreting the functional impact of genetic variants identified with these technologies. Targeted genetic capture techniques were developed to dramatically decrease the cost of determining variant genotypes, although as prices decrease many of the advantages of targeted experiments diminish. Targeted whole exome sequencing is used to identify variants that alter the amino acid sequence of a protein. Sequencing can also be performed for the whole genome, enabling the identification of variants that fall within non-coding regions, which might alter the expression of a gene by disrupting regulatory sequences such as transcription factor binding sites. Careful consideration of the study design, particularly the use of prior knowledge about the phenotype in question, family history, and the availability of affected and unaffected family members, increases the chances that meaningful results are obtained. Identifying variants in the short, error-prone sequencing reads generated by modern technologies is challenging, although software packages exist that mitigate the most common types of errors. The most difficult task in analyzing results is interpreting the functional impact of putative variants and differentiating between clinically reportable variants and variants of unknown significance (VUS) or variants within genes of unknown significance (GUS). Additional information, such as population allele frequencies, genetic inheritance patterns, and functional genomics data, can help to identify the variants most likely to be involved in disease pathogenesis.
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Dexheimer, P.J., Kaufman, K.M., Weirauch, M.T. (2016). Genetic Technologies and Causal Variant Discovery. In: Hutton, J. (eds) Pediatric Biomedical Informatics. Translational Bioinformatics, vol 10. Springer, Singapore. https://doi.org/10.1007/978-981-10-1104-7_14
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DOI: https://doi.org/10.1007/978-981-10-1104-7_14
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