There are a number of types of evidence that are referred to in the genomic medicine field. Below are some examples of evidence and related frameworks for assessing that evidence.
In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published an updated standard and guideline for the classification of genomic sequence variants. This guideline provides direction on how to combine clinical, genetic, population, and functional evidence with expert review to classify variants into 1 of 5 categories – Pathogenic, Likely Pathogenic, Uncertain, Likely Benign and Benign (Richards et al. 2015). The paper also describes some of the sources of clinical, population and functional evidence and criteria for classifying pathogenic variants based on the strength of the evidence (very strong, strong, moderate, supporting).
These ACMG/AMP Standards and Guidelines were developed primarily as a resource for clinical laboratory geneticists. However, they are also used by other groups involved in variant curation such as ClinGen, a US National Institutes of Health (NIH)-funded resource dedicated to building a central resource that defines the clinical relevance of genes and variants. ClinGen’s Gene-Disease Clinical Validity Curation is a process by which the strength of evidence supporting or refuting a claim that variation in a particular gene causes a particular disease is assessed. Here a framework describes the evidence supporting a gene-disease association in a semiquantitative measurement based on the strength of evidence of a gene-disease relationship with classifications being “Definitive,” “Strong,” “Moderate,” “Limited,” “No Reported Evidence,” or “Conflicting Evidence.” (Strande et al. 2017).
Another example of the use of evidence comes from the CDC Office of Public Health Genomics (OPHG) which conducts horizon scanning to identify and track the progress of genomic tests as they move from research into clinical and public health practice. As an aid to organizing these horizon scanning results, OPHG ranks genomic tests, and family health history applications, by levels of evidence. The approach is described in Dotson et al.2014 and results in the assignment of genomic applications to tiers defined by availability of synthesised evidence. The classification scheme stratifies applications into three categories:
Tier 1/Green - have a base of synthesized evidence that supports implementation in practice.
Tier 2/Yellow - have synthesized evidence that is insufficient to support their implementation in routine practice. Nevertheless, the evidence may be useful for informing selective use strategies (such as in clinical trials) through individual clinical, or public health policy, decision making.
Tier 3/Red - either (i) have synthesized evidence that supports recommendations against or discourages use, or (ii) no relevant synthesized evidence is available.
The Tier Table Database is intended to act as a guide to the amount of evidence relevant to particular genes or tests. The contents of the database is not a comprehensive listing of genetic tests by tier level but is a useful starting point for identifying relevant evidence sources.
Resources on Evidence
S. Richards et al., Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics In Medicine 17, 405 (2015).
N. T. Strande et al., Evaluating the Clinical Validity of Gene-Disease Associations: An Evidence-Based Framework Developed by the Clinical Genome Resource. Am J Hum Genet 100, 895-906 (2017).
W. D. Dotson et al., Prioritizing genomic applications for action by level of evidence: a horizon-scanning method. Clin Pharmacol Ther 95, 394-402 (2014).
D. L. Doyle et al., Proposed outcomes measures for state public health genomic programs. Genetics in Medicine 20, 995-1003 (2018).