A systematic approach to assessing the clinical significance of genetic variants

Clin Genet. 2013 Nov;84(5):453-63. doi: 10.1111/cge.12257.

Abstract

Molecular genetic testing informs diagnosis, prognosis, and risk assessment for patients and their family members. Recent advances in low-cost, high-throughput DNA sequencing and computing technologies have enabled the rapid expansion of genetic test content, resulting in dramatically increased numbers of DNA variants identified per test. To address this challenge, our laboratory has developed a systematic approach to thorough and efficient assessments of variants for pathogenicity determination. We first search for existing data in publications and databases including internal, collaborative and public resources. We then perform full evidence-based assessments through statistical analyses of observations in the general population and disease cohorts, evaluation of experimental data from in vivo or in vitro studies, and computational predictions of potential impacts of each variant. Finally, we weigh all evidence to reach an overall conclusion on the potential for each variant to be disease causing. In this report, we highlight the principles of variant assessment, address the caveats and pitfalls, and provide examples to illustrate the process. By sharing our experience and providing a framework for variant assessment, including access to a freely available customizable tool, we hope to help move towards standardized and consistent approaches to variant assessment.

Keywords: (4−9) clinical interpretation; gain-of-function (GOF); genetic variant; loss of function (LOF); next-generation sequencing (NGS); sequence analysis; variant assessment; variant of uncertain significance (VUS).

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Base Sequence
  • Databases, Genetic
  • Decision Trees
  • Female
  • Genetic Testing*
  • Genetic Variation*
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Male
  • Molecular Sequence Data
  • Prognosis
  • RNA, Messenger / genetics*
  • Risk Assessment
  • Software*

Substances

  • RNA, Messenger