Must love rules: an insider’s guide to variant sciences

Unknown significance. Quite possibly the two most dreaded words in clinical genetics. To some these two words should seldom be used let alone act as qualifiers for testing results. What are the rules of assessment? How do laboratories determine what constitutes enough evidence to say that a variant, previously known as mutation, is of known significance?  

World Genome by Richard Ricciardi (used under a CC attribution

World Genome by Richard Ricciardi (used under a CC attribution

Details. The field of variant sciences requires individuals who love to hunt for the devil in the details. If searching for such a candidate were akin to dating, these would be my criteria: inquisitive mind, with a penchant for evidence finding, preferably passionate about connecting dots, comfortable with ambiguity. Must love rules. The truth is: there are a lot of rules in assessing variants of unknown clinical significance. Every laboratory that reports out molecular results has a system, with either sophisticated algorithms and software packages or plain old spreadsheets and reams of papers, that allows them to deliver consistent results on every variant they come across.

Standards. In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) issued updated guidelines to provide standards for the classification of sequence variants. These guidelines came at just the right time: with advances in next generation sequencing, the ability to more efficiently read through patient samples from one end of the genome to the next yields an exceedingly large number of variants, often unique to that particular individual. To put this in perspective, a patient undergoing whole exome sequencing may have roughly 50-60K variants identified, 95% of which will be single nucleotide polymorphisms (SNPs) easily filtered out using a bioinformatics pipeline leaving ~3,000 variants to be assessed. How do diagnostic laboratories do this?

Data. What the ACMG/AMP guidelines offer to the casual reader is a framework for the thought process that is used by diagnostic laboratories when classifying variants identified through DNA sequencing. The classification of variants relies on a weighted, evidence-based process that first evaluates all the available information for each variant identified in a patient and then “buckets” the variants into categories that appear on a report. The new guidelines detail four levels of evidence (very strong, strong, moderate and supporting), which apply to two broad categories (pathogenic/likely pathogenic and benign/ likely benign). Each of these levels of evidence further breaks down into combinations which provide overall weight for pathogenicity. The art of variant science relies upon the expert and professional judgment of the laboratory scientist to apply the rules to each line of evidence when evaluating the full spectrum of information curated for each variant seen in a patient. Each laboratory has developed a classification and reporting structure that is loosely or stringently based on these guiding principles. Within this framework are eight lines of evidence:

Lines of Evidence


Population data Observation of variant in individuals (controls, patients, etc)
Computational/Predictive data Type of variant (silent, null, in-frame indels, etc), in silico predictions
Functional data Well-controlled study elucidating both normal function of gene/protein and impact of variant on protein function
Segregation data Observation of variant in affected individual and/or unaffected individual; or lack thereof
De novo data Variant not inherited from either parent with parental identity confirmed (or not)
Allelic data Observation of variant occurring with otherwise disease-causing or benign variants
Other data Phenotype, family history, co-occurrence with disease-causing variant in other gene, etc
Other databases Unpublished data from reputable sources

Relevance. The ACMG guidelines propose a 5-tiered approach to translate the evidence into consumer-friendly vernacular:

Reporting language


Pathogenic Causing disease
Likely pathogenic Combination of major and minor lines of evidence are sufficient to establish a relationship between the variant and disease-state but insufficient to classify as pathogenic
Uncertain significance Insufficient or lacking evidence and/or contradictory evidence for pathogenicity
Likely benign Combination of major and minor lines of evidence are sufficient to refute a relationship between the variant and disease-state but insufficient to classify as benign
Benign Variant of normal (wildtype), not causing disease

Clinical practice. So – you have received results for a patient containing variants of unknown clinical significance, now what? How do you interpret the results in the clinical context? What are the next steps toward clarifying the classification of a variant? How do you communicate the results to your patient? Read EpiGC’s next post on May 20th to learn more!

Khalida Liaquat

Khalida Liaquat is a member of EpiGC and a licensed laboratory genetic counselor at Quest Diagnostics. Prior to joining the laboratory, Khalida served as a clinical prenatal, pediatric and adult genetic counselor at Kings County Hospital Center and Woodhull Medical Center in Brooklyn, NY. At Quest Diagnostics, Khalida specializes in molecular genetic testing for the Athena Diagnostics laboratory, focusing on the areas of neuromuscular diseases, endocrinology and nephrology.

Khalida earned her bachelor’s degree in Biology from Carleton University in Ottawa, Canada and her master’s degree in Human Genetics from Sarah Lawrence College.