My name is Karen Wain. I'm a genetic counselor at Geisinger and a member of
the ClinGen education workgroup. This presentation will provide an overview of
the current ACMG AMP sequence variant interpretation guidelines, and ongoing
efforts within ClinGen to further optimize and direct the use of specific
evidence criteria for variant interpretation. In 2015, the American
College of Medical Genetics and Genomics, ACMG, and the Association for Molecular
Pathology, AMP, published a joint consensus recommendation for standards
and guidelines for the interpretation of sequence variation. The overarching goal
of these guidelines, in addition to providing a consistent standard, was to
create a framework to determine if a variant within a gene that has a
definitive role in a Mendelian disorder is pathogenic for that specific disorder.
These guidelines are also intended to reduce the number of variants that are
reported as being disease-causing that have insufficient evidence. It's
important to note that this framework as written is not designed to consider
variants in genes with minimal evidence supporting their association to a
Mendelian disease, which are often referred to as "genes of uncertain
significance." To accomplish this, the guidelines define 28 criteria that
address various types and levels of evidence that either support a
pathogenic classification for a given variant with levels ranging from
supporting to very strong, or support a benign classification, with supporting
strong, and standalone evidence levels. This figure provides a visual of the
different criteria outlined as part of this guideline, and how they are grouped
into different types of evidence. Evidence is categorized by strength,
which is identified by color. The table to the right shows how different
combinations of evidence of different strengths are used to determine a
classification. For example, a variant can be classified
as pathogenic based on one very strong and two moderate criteria being met, or by
having two strong criteria being met. A variant can be classified as benign if
the standalone minor allele frequency criteria is met, or if two strong benign
criteria are met. Now we will focus on general explanations for each specific
type of evidence or line in the table above. Additional webinars that provide
more depth on the use of these types of evidence for variant
interpretation are available in the ClinGen YouTube channel. Population
data refers to the use of genomic data from large groups of individuals, either
patients or controls. Population databases can provide the frequency of
an allele, or genetic variant, in these populations. If case and control
populations are available in adequate size, then case control studies can be
performed to determine if a variant is present in affected individuals
statistically more often than in controls. This is considered strong
evidence that a variant is pathogenic, abbreviated as PS4. However, large
datasets from the general population, which are often derived from merging
multiple smaller cohorts, can inform variants interpretation if a variant is
very rare or absent, PM2, or if it is very common or more frequent than we
would expect for a particular disorder.
The computational and predicted data category refers to the use of
computational prediction or in silico models such as SIFT, PolyPhen-2, and
others which provide predictions of the likelihood that some types of genetic
variants would be disruptive in a resulting protein product. These models
are often limited to missense variants, though some models provide predictions
about splicing or inframe deletions as well. Computational
predictions are considered to be at the lowest level of strength supporting. We
also make predictions based on our knowledge of the mechanism of disease
associated with a gene and a disorder such as whether haploinsufficiency is the
mechanism of disease, and based on which types of variants are more likely to be
pathogenic. The PVS1, criteria which is the only one considered to be very
strong, can be applied to variants that are predicted to cause loss of function
in a gene where loss of function is a known mechanism of disease. Novel
nucleotide substitutions that impact amino acid residues which have been
reported previously as pathogenic are also considered as either moderate or
strong criteria designated above as PM5 and PS1. Functional data typically
refers to in vitro or in vivo studies that have characterized the domains,
functions, and active properties of gene products, typically proteins. There are
many types of functional experiments, and the appropriate approaches depend on the
protein and disease. Most of this work is research grade, as opposed to clinically-
validated assays. The strongest functional data comes from studies of a
specific variant to determine if that particular variant shows a deleterious
effect or not, designated as PS3 and BS3. If the functional impact of a
particular variant has not been studied but it is located in a mutational
hotspot or well-studied functional domain of a protein that does not also
contain benign variation, this is considered moderate evidence, PM1.
Population databases are useful to determine the presence of benign
variation and are necessary to apply PP2, a supporting criterion which may
apply for a missense variant in a gene that does not harbor expected levels of
missense variation.
Whether a variant segregates with disease in a family is a classic
clinical genetics approach to informing variant interpretation. In general, if it
does not segregate with disease, this is strong evidence that the variant is
benign. If it does segregate with disease, it supports a pathogenic classification.
The more segregation data available from multiple family members and from
multiple independent families, the stronger the evidence. Of course we also
need to consider the possibility of phenocopies, particularly for diseases
that are more common in the general population. If a variant is determined to
be de novo, it is considered more likely to be pathogenic. However, this is not
enough evidence to conclude pathogenicity alone since all
individuals are expected to harbor small numbers of de novo variants.This
evidence is also considered stronger if both paternity and maternity are
confirmed. This is most often done clinically through trio analysis for
exome or genome sequencing, since the bioinformatic processing of samples
also confirms biological relatedness. If paternity and maternity are not
confirmed, which is often encountered when parental testing is completed after
a variant is identified in the proband, then de novo evidence is considered
moderate. Allelic data refers to whether a variant is in cis or trans with a
known pathogenic variant. Cis means a variant is on the same gene
homologue, maternal or paternal, as another variant, and trans means two
variants are on different homologs. For a dominant disorder, finding a novel
variant in trans with a pathogenic variant is considered supporting
evidence that the novel variant is benign,
particularly for disorders that results in embryonic lethality or very
severe phenotypes with biallelic variants. And finding a novel variant
in cis with a pathogenic variant, regardless of inheritance type, is also
supportive of benign, especially if it has been observed in cis with
different pathogenic variants. However, for recessive disorders, identifying a
novel variant in trans with a pathogenic variant if the patient's phenotype makes
sense with that disorder is considered moderate evidence toward pathogenicity.
Variants can be deposited in various clinical and research databases which
can differ in terms of active curation, clinical human genetics expertise, and
purposes. Searching these databases is an important component of identifying
evidence for variant interpretation. When the ACMG AMP guidelines were first
published, the use of these resources was included at the supporting level. This
was intended to allow for internal data or knowledge that couldn't be made
available for independent review to be incorporated into variant interpretation.
However, using this criteria was problematic and can result in double-
counting of other types of evidence and other errors. Therefore, currently these
criteria are not used by most clinical laboratories and have been recommended
for removal. The databases themselves are still helpful if they provide evidence
that can be independently evaluated and applied elsewhere in the ACMG AMP
framework. Finally, two additional criteria are considered in the other
data category. If a variant is identified in a patient who has a confirmed
alternative explanation for their phenotype, then the variant is less
likely to be causative, and this is supportive evidence toward it being
benign. Multiple observations of this may be necessary since it is possible for a
patient to have two disorders. On the other hand, if
a novel variant is identified in a gene that is highly associated with a
phenotype present in the patient or the family history, this is supportive of a
pathogenic interpretation. Caution is needed when applying this criterion,
however, to avoid a certain bias since many of the clinical indications for
genomic testing are not very specific and could be associated with many
different genes such as global developmental delays or autistic
features. The ACMG AMP sequence variant interpretation guidelines were clearly
intended to be general, and the authors recommended future modifications to
optimize and refine the framework. several of the ClinGen clinical domain
working groups are actively working toward adapting the ACMG AMP guidelines
to make gene-disease specific recommendations. This was first achieved
for MYH7-associated hereditary cardiomyopathies which established gene-
specific population allele frequency cut- offs, provided guidance on how to upgrade
or downgrade the strength of evidence, defined applicable functional studies,
and removed criteria from the original guideline that do not apply to MYH7.
Similar recommendations have recently been published for RASopathies,
inborn errors of metabolism, hearing loss, and for the PTEN and CDH1 genes. Other
general optimization efforts have also provided guidance that might be
applicable across many disorders, or are general improvements. A recent
publication in the ClinGen and ClinVar special edition of human mutation
specifically provides guidance on how to modify the strength of PVS1 under
certain circumstances. As this table shows, it may be
appropriate to downgrade the strength of this evidence
depending on how established the mechanism of disease is for a gene-
disease pair. There are also other variant and gene-specific
characteristics which may warrant downgrading PVS1 to strong or
moderate evidence. A detailed flowchart of these circumstances is provided in
the manuscript. Additionally, other recent publications have described the
development of a more quantitative variant interpretation framework that is
based on the 2015 ACMG AMP framework, discusses in-depth and disease-focused
considerations for the use of population allele frequency data as a standalone
criterion for benign classification, and recommending that their reputable source
criteria be removed from the framework.
The ClinGen sequence variant interpretation working group is also
developing recommendations to modify the strength of specific criteria depending
on different factors. These tables, which are available on the ClinGen website,
provide guidance on increasing or decreasing the strength of de novo
evidence for a variant, taking into account whether the de novo status is confirmed
or assumed, whether the patient's phenotype is consistent with the
disorder, and the number of de novo observations. And overtime, additional
recommendations are expected with respect to other criteria. You can access
all of these publications and recommendations on the ClinGen
Sequence Variant Interpretation working group website. This is an important
resource to be aware of as additional ClinGen recommendations for variant
interpretation practice become available.
I hope this has been a useful introduction to the current ACMG AMP
sequence variant interpretation guidelines and ongoing improvement
efforts. Please visit www.clinicalgenome.org for additional educational resources, and to learn more about ClinGen initiatives.
Our funding sources are listed at the bottom. Please feel free to contact
ClinGen by email at clingen@clinicalgenome.org with questions or feedback.
Thank you.