Criteria Specification Registry

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Criteria Specification

ClinGen Cardiomyopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for TPM1 Version 1.0.0

Version : 1.0.0 Release Notes :

PS4 calculator link added.

Cardiomyopathy VCEP

Released

4/22/2024

15:45:10

Rules for TPM1

Gene: TPM1 (HGNC:12010) HGNC Name: tropomyosin 1 Transcripts: NM_001018005.2 Disease: hypertrophic cardiomyopathy (MONDO:0005045) Mode of Inheritance: Autosomal dominant inheritance

Criteria & Strength Specifications
PVS1 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease. Caveats: • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7). • Use caution interpreting LOF variants at the extreme 3’ end of a gene. • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact. • Use caution in the presence of multiple transcripts. Stand Alone Very Strong Strong Moderate Supporting Not Applicable Comments: Not currently applicable to TPM1. See PM4 for truncating variants that do NOT undergo NMD.
PS1 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Same amino acid change as a previously established pathogenic variant regardless of nucleotide change. Example: Val->Leu caused by either G>C or G>T in the same codon. Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level. Stand Alone Very Strong Strong No cardiomyopathy specifications. Apply as outlined by Richards et al. 2015¹. Example of when rule should NOT be applied. NM_000256.3(MYBPC3): c.2308G>A (p.Asp770Asn) has an established impact on splicing leading to nonsense mediated decay (NMD) and should not be used to provide evidence for other variants observed to result in the same amino acid change. Modification Type: No change Moderate Supporting Not Applicable
PS2 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary De novo (both maternity and paternity confirmed) in a patient with the disease and no family history. Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity. Stand Alone Very Strong Strong Refer to SVI guidance on number/combination of cases required based on phenotype specificity². For most cardiomyopathies, it is recommended to default to Phenotype consistency: “Phenotype consistent with gene but not highly specific”. Clinical judgment is required for shifting to a higher or lower category. For use as a STRONG or VERY STRONG criterion, ideally parents have been thoroughly clinically evaluated without evidence of cardiomyopathy (ideally using a combination of ECG and echocardiogram or cardiac MRI for maximum sensitivity). A family history consistent with de novo inheritance should not have any clinical signs or symptoms suggestive of cardiomyopathy in a 1^st or 2^nd degree relative, for example: Sudden death under 60 years of age Heart transplant Implantable cardiac defibrillator (ICD) under 60 years of age Features of cardiomyopathy (e.g., systolic dysfunction, hypertrophy, left ventricular enlargement in an individual without risk factors). Other related/overlapping cardiomyopathies Examples of non-suspicious family history may include non-specific clinical features (e.g., palpitations, syncope, borderline/inconclusive echocardiogram findings, heart attack if age appropriate and suspected to result from coronary artery disease), but every attempt should be made to clarify features. Generally, this criterion is only applicable in the ABSENCE of any other possible disease-causing variants. If other pathogenic or likely pathogenic variants are present, consider decreasing points assigned or overall weight. Modification Type: Disease-specific Moderate Supporting Not Applicable
PS3 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product. Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established. Stand Alone Very Strong Strong In vitro splicing assays (e.g., RNA studies) In vitro splicing assays may be considered as STRONG evidence, providing the following criteria are met. Prior knowledge of predominant transcripts in cardiac tissue Analysis undertaken using RNA extracted from cardiac tissue from the individual with the variant Analysis undertaken using RNA extracted from whole blood providing the relevant transcripts (isoforms) are expressed in blood and are at sufficient levels to assess splice disruption. Assay shows a clear, reproducible and convincing effect on splicing (i.e. a distinct splice product, present at a level comparable to the splice product from the wild-type allele), which is not observed in controls Confirmation of abnormal splice product by Sanger sequencing NOTE: Mini-gene assay in non-patient derived cell lines are NOT considered to provide STRONG evidence. NOTE: Whether to activate this rule needs to be reconciled with the variant spectrum and disease mechanism for the gene at hand (i.e., consider whether the effect is likely to lead to LOF or an in-frame alteration and whether this type of effect is expected to be disease causing) (Abou Tayoun et al. 2018³). Modification Type: Disease-specific Moderate In vivo models (e.g., variant knock-in animal models) Mammalian variant-specific knock-in animal models that produce a phenotype consistent with the clinical phenotype in humans (e.g., structural and/or functional cardiac abnormalities, premature death, arrhythmia) may be considered as MODERATE evidence NOTE: The following assays/models do NOT meet criteria Assays that are known to be associated with non-specific cardiac phenotypes (e.g., morpholino-induced pericardial edema in zebrafish) In vivo evidence that is not variant specific, such as whole gene alterations (i.e., cDNA or whole gene transgenic mice and whole or partial gene knock-out mice) Modification Type: Disease-specific Supporting In vitro assays (e.g., biochemical assays of myofilament function, motility assays, human iPSC-CM) While some in vitro assays may provide evidence that a variant in a cardiomyopathy gene has an effect on protein and/or myofilament function, at present, there are no validated “gold-standard” assays that are considered to reliably predict the clinical phenotype. As such, in the cardiomyopathy genes listed in these guidelines, data from individual in vitro studies are unlikely to meet the criteria required to assign this rule at more than SUPPORTING level. Modification Type: Disease-specific Instructions: Evaluation of studies/assays is required prior to application of functional evidence at any strength. Refer to SVI guidance for functional evidence (Brnich et al. 2020⁴). In the context of cardiomyopathy, very few functional assays currently meet criteria sufficient for application of this rule at a STRONG level. Examples of the types of study/assays that MAY be relevant are described, but further definition of cardiomyopathy models/assays is outside the scope of these guidelines. Not Applicable
PS4 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls. Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance. Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence. Stand Alone Very Strong Strong Currently few well-designed case-control studies have been performed for inherited cardiomyopathies. Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). Cohorts used in these analyses should meet the following criteria: The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype). When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded. This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing). The controls should not be derived from study populations that might be enriched for the specified disorder. The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls). The cohorts do not include closely related individuals (i.e., family members are not included in the case counts). The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once). The population diversity of the case and control cohorts are broadly similar. Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh et al.* 2017⁵, DECIPHER). To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended: STRONG evidence requires the lower bound of the 95% confidence interval (CI) around the odds ratio (OR) estimate to be ≥20 A PS4 calculator is available at www.cardiodb.org. If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered). RELEVANT PHENOTYPES: Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. Additional considerations for LVNC and end-stage HCM: Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson et al.* 2017⁶; Oechslin et al. 2017⁷; Hershberger et al. 2017⁸; Ross et al. 2020⁹), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies). HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants. Modification Type: Disease-specific Moderate Currently few well-designed case-control studies have been performed for inherited cardiomyopathies. Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). Cohorts used in these analyses should meet the following criteria: The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype). When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded. This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing). The controls should not be derived from study populations that might be enriched for the specified disorder. The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls). The cohorts do not include closely related individuals (i.e., family members are not included in the case counts). The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once). The population diversity of the case and control cohorts are broadly similar. Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh et al.* 2017⁵, DECIPHER). To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended: MODERATE evidence requires the lower bound of the 95% CI around the OR to be ≥10 A PS4 calculator is available at www.cardiodb.org. If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered). RELEVANT PHENOTYPES: Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. Additional considerations for LVNC and end-stage HCM: Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson et al.* 2017⁶; Oechslin et al. 2017⁷; Hershberger et al. 2017⁸; Ross et al. 2020⁹), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies). HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants. Modification Type: Disease-specific Supporting Currently few well-designed case-control studies have been performed for inherited cardiomyopathies. Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). Cohorts used in these analyses should meet the following criteria: The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype). When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded. This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing). The controls should not be derived from study populations that might be enriched for the specified disorder. The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls). The cohorts do not include closely related individuals (i.e., family members are not included in the case counts). The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once). The population diversity of the case and control cohorts are broadly similar. Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh et al.* 2017⁵, DECIPHER). To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended: SUPPORTING evidence requires the lower bound of the 95% CI around the OR to be ≥5 A PS4 calculator is available at www.cardiodb.org. If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered). RELEVANT PHENOTYPES: Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. Additional considerations for LVNC and end-stage HCM: Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson et al.* 2017⁶; Oechslin et al. 2017⁷; Hershberger et al. 2017⁸; Ross et al. 2020⁹), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies). HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants. Modification Type: Disease-specific Not Applicable
PM1 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation. Stand Alone Very Strong Strong Moderate Supporting Not Applicable Comments: Application of this rule takes into consideration empirical data quantifying levels of rare missense variant enrichment in HCM referral cohorts compared to population-based cohorts (Walsh et al. 2019 PMID:30696458). For TPM1, there is evidence for gene-level enrichment of rare missense variants (see PP2 specifications).
PM2 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium. Caveat: Population data for indels may be poorly called by next generation sequencing. Stand Alone Very Strong Strong Moderate Supporting The values used to calculate the PM2 thresholds were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/500 or lower), where the most frequent pathogenic variant accounts for no more than 2% of cases (e.g., has an allele frequency of ≤0.02 in cases based on the upper bound of 95% CI), and where the penetrance of a pathogenic variant is expected to be at least 50% (Kelly et al. 2018¹⁰). A threshold of ≤0.00004 in the subpopulation with the highest frequency when using the upper bound of the 95% CI activates this rule. Alternatively, this is equivalent to the variant NOT being observed more than once (≤1 allele) in gnomAD v.2.1.1 in one of the non-founder populations (e.g., absence required from the Other and Ashkenazi Jewish subpopulations). Applying a threshold of ≤0.00004 (upper bound of 95% CI of the allele frequency in gnomAD) is equivalent to the variant being seen in a single subpopulation and that subpopulation meets any of the following: Allele Count (AC) in Allele Number (AN) ≤1 in ≥120,000 ≤2 in ≥160,000 ≤3 in ≥195,000 ≤4 in ≥230,000 gnomAD is the preferred database for this calculation, but currently only displays the filtering allele frequency (FAF), which is equivalent to a lower bound estimate of the 95% CI, when the upper bound is what is needed. Confidence interval tools, such as Confit-de-MAF, can be used to determine the upper bound of the 95% CI of the observed allele frequency. Due to current technical limitations of next generation sequencing technologies, minor allele frequencies for complex variants (e.g., large indels) may not be accurately represented in population databases. Caution should be used when a variant is only identified, or over-represented, in one of the smaller gnomAD populations, as the gnomAD allele frequencies may not accurately represent the true population frequency. Population databases may contain affected or pre-symptomatic individuals for diseases with reduced penetrance/variable onset. Modification Type: Disease-specific Not Applicable
PM3 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary For recessive disorders, detected in trans with a pathogenic variant Note: This requires testing of parents (or offspring) to determine phase. Stand Alone Very Strong Strong Moderate Supporting Not Applicable Comments: While compound heterozygosity leading to a more severe phenotype has been documented, this rule was designed for traditional recessive inheritance. It is acknowledged that there is increasing evidence supporting that some of these genes/variants may also be recessive (e.g., MYL2, MYL3), but addressing those edge cases was outside the scope of this current guideline.
PM4 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants. Stand Alone Very Strong Strong Moderate Strength of rule should be carefully considered and may require downgrading to SUPPORTING based on the predicted impact of the variant, including the size of the deletion/insertion, its location, and conservation of the region. For genes where PVS1 is not applicable (i.e., where there is no evidence that pLOF variants cause disease), consider using this rule at MODERATE or SUPPORTING strength for truncating variants that do NOT undergo nonsense mediated decay (NMD). Modification Type: General recommendation Supporting Not Applicable
PM5 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before. Example: Arg156His is pathogenic; now you observe Arg156Cys. Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level. Stand Alone Very Strong Strong Moderate This criterion can be used at MODERATE if a different missense variant at the same codon has been classified as pathogenic using these modified guidelines without application of PM5. The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to SUPPORTING if the predicted impact is not expected to be equivalent or more severe. PM5 should not be combined with PM1. If both are applicable at MODERATE weight, use of PM5 is most appropriate since it is variant specific. Modification Type: General recommendation Supporting This criterion can be considered at SUPPORTING if a different missense variant at the same codon has been classified as likely pathogenic using these modified guidelines without application of PM5. The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as likely pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to NOT APPLICABLE if the predicted impact is not expected to be equivalent or more severe. PM5 should not be combined with PM1. The one with the higher strength should be applied, but if both are applicable at SUPPORTING weight, use of PM5 is most appropriate since it is variant specific. Modification Type: General recommendation Not Applicable
PM6 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Assumed de novo, but without confirmation of paternity and maternity. Stand Alone Very Strong Strong Moderate Refer to SVI guidance on number/combination of cases required based on phenotype specificity². For most cardiomyopathies, it is recommended to default to “phenotype consistent with gene but not highly specific”. Clinical judgment is required for shifting to a higher or lower phenotypic consistency. See PS2 for additional considerations. Modification Type: Disease-specific Supporting Not Applicable
PP1 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease. Note: May be used as stronger evidence with increasing segregation data. Stand Alone Very Strong Strong Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at ≥7 segregations (LOD score of 2.1) for STRONG. Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)¹¹ can be considered: STRONG evidence requires ≥5 segregations (LOD score of 1.5) Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance. Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis. Important considerations include: Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant. If linkage disequilibrium is a concern, consider downgrading strength of segregation. Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1. Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. Caution is needed when distantly related (≥3^rd degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease). Modification Type: Disease-specific Moderate Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at ≥5 segregations (LOD score of 1.5) for MODERATE. Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)¹¹ can be considered: MODERATE evidence requires ≥4 segregations (LOD score of 1.2) Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance. Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis. Important considerations include: Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant. If linkage disequilibrium is a concern, consider downgrading strength of segregation. Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below). Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. Caution is needed when distantly related (≥3^rd degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease). Modification Type: Disease-specific Supporting Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at ≥3 segregations (LOD score of 0.9) for SUPPORTING. The thresholds as proposed by Jarvik and Browning (2016)¹¹ are the same at ≥3 segregations (LOD score of 0.9) for supporting. Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance. Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis. Important considerations include: Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant. If linkage disequilibrium is a concern, consider downgrading strength of segregation. Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below). Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. Caution is needed when distantly related (≥3^rd degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease). Modification Type: Disease-specific Not Applicable
PP2 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease. Stand Alone Very Strong Strong Moderate Supporting Application of this rule takes into consideration empirical data quantifying levels of rare missense variant enrichment in HCM referral cohorts compared to population-based cohorts (Walsh et al. 2019¹²) rather than the missense constraint score in gnomAD. On the basis of data from Walsh et al. 2019¹², PP2 is currently only applicable to TPM1 for HCM (transcripts ENST00000403994 and NM_001018005.2). Data from HCM case cohorts was used to derive these cluster regions. Therefore, this rule should NOT be applied when additional evidence for the variant supports that the variant causes a phenotype other than HCM (e.g., variant seen in multiple DCM cases). Enrichment was not observed for DCM in any genes. Modification Type: Disease-specific,Gene-specific Not Applicable Comments: Application of this rule takes into consideration empirical data quantifying levels of rare missense variant enrichment in HCM referral cohorts compared to population-based cohorts (Walsh et al. 2019 PMID:30696458) rather than the missense constraint score in gnomAD. For TNNI3, there is evidence for regional enrichment of rare missense variants (see PM1 specifications).
PP3 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.). Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant. Stand Alone Very Strong Strong Moderate Supporting As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors. Use of REVEL (Ioannidis et al. 2016¹³) is recommended at thresholds of ≥0.70 for PP3. Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data. Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions. SpliceAI ¹⁴ is recommended for evaluation of predicted splice impacts. Modification Type: Disease-specific Not Applicable
PP4 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology. Stand Alone Very Strong Strong Moderate Supporting Not Applicable Comments: Inherited cardiomyopathies have high locus heterogeneity as well as non-genetic etiologies.
PP5
Original ACMG Summary Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation. Not Applicable This criterion is not for use as recommended by the ClinGen Sequence Variant Interpretation VCEP Review Committee. PubMed : 29543229
BA1 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium. Stand Alone Allele frequency is ≥0.001 based on the filtering allele frequency (FAF) in gnomAD in the subpopulation with the highest frequency (popmax). The values used to calculate the BA1 threshold were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/300 or lower). The threshold is applicable when assessing variants in the context of autosomal dominant cardiomyopathy. gnomAD is the preferred database for this calculation. If a subpopulation specific FAF other than the popmax is needed, this value can be calculated using the AlleleFrequencyApp on the CardioDB website. Using the Inverse AF tab, enter in the population size and the number of alleles identified and it will calculate the FAF. Set confidence to 0.95 (95%). If the FAF is ≥0.001, this rule can be applied. The FAF by platform (e.g., exome vs. genome; v.2.1.1 vs. v.3.1.1) should be considered, the larger population is most likely to have the most accurate representation of “true” population allele frequency. Caution is needed when considering any population cohorts that are smaller than the smallest subpopulations within gnomAD v.2.1.1 (e.g., ~5000 individuals or ~10,000 alleles). Despite this conservative nature of this threshold and approach, in smaller cohorts, the observed allele frequency may less accurately reflect the true allele frequency. Traditionally, once a variant is classified as Benign, it is rarely re-evaluated and so the highest confidence is needed to establish that classification on an allele frequency alone. Modification Type: Disease-specific Very Strong Strong Moderate Supporting Not Applicable
BS1 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Allele frequency is greater than expected for disorder. Stand Alone Very Strong Strong Allele frequency is ≥0.0001 for TPM1 based on the filtering allele frequency (FAF) in gnomAD in the subpopulation with the highest frequency (popmax). Criterion BS1 may only be used as standalone evidence to classify a variant as Likely Benign in the absence of conflicting data. See SVI guidance (Tavtigian et al. 2018¹⁵; Tavtigian et al. 2020¹⁶). See BA1 for additional specifications that also apply to BS1. Modification Type: Disease-specific,Gene-specific Moderate Supporting Not Applicable
BS2 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age. Stand Alone Very Strong Strong Moderate Supporting Not Applicable Comments: Inherited cardiomyopathies generally display reduced penetrance, variable expressivity, and adult-onset.
BS3 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing. Stand Alone Very Strong Strong See PS3 specifications. Modification Type: Disease-specific Moderate See PS3 specifications. Modification Type: Disease-specific Supporting See PS3 specifications. Modification Type: Disease-specific Instructions: Evaluation of studies/assays is required prior to application of functional evidence at any strength. Refer to SVI guidance for functional evidence (Brnich et al. 2020⁴). In the context of cardiomyopathy, very few functional assays currently meet criteria sufficient for application of this rule at a STRONG level. Examples of the types of study/assays that MAY be relevant are described, but further definition of cardiomyopathy models/assays is outside the scope of these guidelines. Not Applicable
BS4 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Lack of segregation in affected members of a family. Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation. Stand Alone Very Strong Strong Any non-segregations should be carefully evaluated to rule out a phenocopy or the presence of a second disease-causing variant before considering it as conflicting or benign evidence. The presence of “phenocopies” (e.g., athlete’s heart, hypertensive heart disease, ischemic cardiomyopathy, alcoholic cardiomyopathy, diabetic cardiomyopathy) can mimic non-segregation (i.e., lack of segregation) among affected individuals. Families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent ‘non-segregation’. Because of these possibilities, multiple (≥2) non-segregations that are highly unlikely to be phenocopies or due to alternate variants (e.g., those without a possible alternate cause) are required to apply this rule. A higher number of non-segregations is necessary for instances where alternative causes are possible (e.g., non-segregation in a sibling with childhood onset cardiomyopathy versus a grandparent with hypertension and HCM). Careful consideration of the above points is required when using this data as conflicting evidence, especially when overall evidence supports likely pathogenic or pathogenic. Modification Type: Disease-specific Moderate Supporting Not Applicable
BP1 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Missense variant in a gene for which primarily truncating variants are known to cause disease. Stand Alone Very Strong Strong Moderate Supporting Not Applicable Comments: For the current genes where null variants are a known mechanism, pathogenic missense variants have also been reported.
BP2 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern. Stand Alone Very Strong Strong Moderate Supporting Other variants must be pathogenic as defined by these specifications. Testing of parents or other informative relatives is often required to determine cis/trans status. If a variant is seen in trans (or as double heterozygous) with another pathogenic variant in ≥2 cases and the phenotype is not more severe than when either of the two variants are seen in isolation, this rule may be applied (i.e., high confidence this variant is NOT contributing to disease). <1% of cases of HCM have >1 pathogenic or likely pathogenic variant (0.6%; Alfares et al. 2015¹⁷). This rule cannot be applied when the variant has only been observed in cis with a pathogenic variant as its significance in isolation is unknown in this scenario. Caution is needed if using this criterion as a primary piece of evidence for classifying a variant as likely benign/benign (i.e., only 2 SUPPORTING criteria are sufficient for a likely benign classification). Modification Type: Disease-specific Not Applicable
BP3 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary In frame-deletions/insertions in a repetitive region without a known function. Stand Alone Very Strong Strong Moderate Supporting Not Applicable Comments: Not applicable to the current genes.
BP4 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc) Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant. Stand Alone Very Strong Strong Moderate Supporting As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors. Use of REVEL (Ioannidis et al. 2016¹³) is recommended at thresholds of ≤0.40 for BP4. Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data. Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions. SpliceAI ¹⁴ is recommended for evaluation of predicted splice impacts. Modification Type: Disease-specific Not Applicable
BP5 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary Variant found in a case with an alternate molecular basis for disease. Stand Alone Very Strong Strong Moderate Supporting Not Applicable Comments: Co-occurrence with an established pathogenic or likely pathogenic variant for a non-cardiomyopathy related disease does not reduce the likelihood that a variant is independently disease-causing for cardiomyopathy.
BP6
Original ACMG Summary Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation. Not Applicable This criterion is not for use as recommended by the ClinGen Sequence Variant Interpretation VCEP Review Committee. PubMed : 29543229
BP7 PVS1 PS1 PS2 PS3 PS4 PM1 PM2 PM3 PM4 PM5 PM6 PP1 PP2 PP3 PP4 PP5 BA1 BS1 BS2 BS3 BS4 BP1 BP2 BP3 BP4 BP5 BP6 BP7 Files References
Original ACMG Summary A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved. Stand Alone Very Strong Strong Moderate Supporting Also applicable to intronic variants outside the splice consensus sequence (-4 and +7 outward) for which splicing prediction algorithms predict no impact to the splice consensus sequence NOR the creation of a new splice site AND the nucleotide is not highly conserved. Rule can be combined with BP4 to make a variant likely benign per Richards et al. 2015¹. Modification Type: General recommendation Not Applicable

Rules for Combining Criteria

Pathogenic

≥ 2 Strong (PS1, PS2, PS3, PS4, PP1_Strong)

1 Strong (PS1, PS2, PS3, PS4, PP1_Strong) AND ≥ 3 Moderate (PS3_Moderate, PS4_Moderate, PM4, PM5, PM6, PP1_Moderate)

1 Strong (PS1, PS2, PS3, PS4, PP1_Strong) AND 2 Moderate (PS3_Moderate, PS4_Moderate, PM4, PM5, PM6, PP1_Moderate) AND ≥ 2 Supporting (PS3_Supporting, PS4_Supporting, PM2_Supporting, PM5_Supporting, PP1, PP2, PP3)

1 Strong (PS1, PS2, PS3, PS4, PP1_Strong) AND 1 Moderate (PS3_Moderate, PS4_Moderate, PM4, PM5, PM6, PP1_Moderate) AND ≥ 4 Supporting (PS3_Supporting, PS4_Supporting, PM2_Supporting, PM5_Supporting, PP1, PP2, PP3)

Likely Pathogenic

1 Strong (PS1, PS2, PS3, PS4, PP1_Strong) AND 1 Moderate (PS3_Moderate, PS4_Moderate, PM4, PM5, PM6, PP1_Moderate)

1 Strong (PS1, PS2, PS3, PS4, PP1_Strong) AND ≥ 2 Supporting (PS3_Supporting, PS4_Supporting, PM2_Supporting, PM5_Supporting, PP1, PP2, PP3)

≥ 3 Moderate (PS3_Moderate, PS4_Moderate, PM4, PM5, PM6, PP1_Moderate)

2 Moderate (PS3_Moderate, PS4_Moderate, PM4, PM5, PM6, PP1_Moderate) AND ≥ 2 Supporting (PS3_Supporting, PS4_Supporting, PM2_Supporting, PM5_Supporting, PP1, PP2, PP3)

1 Moderate (PS3_Moderate, PS4_Moderate, PM4, PM5, PM6, PP1_Moderate) AND ≥ 4 Supporting (PS3_Supporting, PS4_Supporting, PM2_Supporting, PM5_Supporting, PP1, PP2, PP3)

1 Strong (PS1, PS2, PS3, PS4, PP1_Strong) AND 2 Moderate (PS3_Moderate, PS4_Moderate, PM4, PM5, PM6, PP1_Moderate)

Benign

≥ 2 Strong (BS1, BS3, BS4)

1 Stand Alone (BA1)

Likely Benign

1 Strong (BS1, BS3, BS4) AND 1 Supporting (BS3_Supporting, BP2, BP4, BP7)

≥ 2 Supporting (BS3_Supporting, BP2, BP4, BP7)

Files & Images

CM-VCEP PS4 Example Scenarios: Three example scenarios for application of the new PS4 specification.

References

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2. https://clinicalgenome.org/working-groups/sequence-variant-interpretation/

3. Abou Tayoun AN Pesaran T et al. Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion. Hum Mutat (2018) 39 (11) p. 1517-1524. 10.1002/humu.23626 30192042

4. Brnich SE Abou Tayoun AN et al. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med (2019) 12 (1) p. 3. 10.1186/s13073-019-0690-2 31892348

5. Walsh R Thomson KL et al. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet Med (2017) 19 (2) p. 192-203. 10.1038/gim.2016.90 27532257

6. Anderson RH Jensen B et al. Key Questions Relating to Left Ventricular Noncompaction Cardiomyopathy: Is the Emperor Still Wearing Any Clothes? Can J Cardiol (2017) 33 (6) p. 747-757. 10.1016/j.cjca.2017.01.017 28395867

7. Oechslin E Jenni R Nosology of Noncompaction Cardiomyopathy: The Emperor Still Wears Clothes! Can J Cardiol (2017) 33 (6) p. 701-704. 10.1016/j.cjca.2017.04.003 28545618

8. Hershberger RE Morales A et al. Is Left Ventricular Noncompaction a Trait, Phenotype, or Disease? The Evidence Points to Phenotype. Circ Cardiovasc Genet (2017) 10 (6) 10.1161/CIRCGENETICS.117.001968 29212902

9. Ross SB Jones K et al. A systematic review and meta-analysis of the prevalence of left ventricular non-compaction in adults. Eur Heart J (2020) 41 (14) p. 1428-1436. 10.1093/eurheartj/ehz317 31143950

10. Kelly MA Caleshu C et al. Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen's Inherited Cardiomyopathy Expert Panel. Genet Med (2018) 20 (3) p. 351-359. 10.1038/gim.2017.218 29300372

11. Jarvik GP Browning BL Consideration of Cosegregation in the Pathogenicity Classification of Genomic Variants. Am J Hum Genet (2016) 98 (6) p. 1077-1081. 10.1016/j.ajhg.2016.04.003 27236918

12. Walsh R Mazzarotto F et al. Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: the case of hypertrophic cardiomyopathy. Genome Med (2019) 11 (1) p. 5. 10.1186/s13073-019-0616-z 30696458

13. Ioannidis NM Rothstein JH et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet (2016) 99 (4) p. 877-885. 10.1016/j.ajhg.2016.08.016 27666373

14. Jaganathan K Kyriazopoulou Panagiotopoulou S et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell (2019) 176 (3) p. 535-548.e24. 10.1016/j.cell.2018.12.015 30661751

15. Tavtigian SV Greenblatt MS et al. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genet Med (2018) 20 (9) p. 1054-1060. 10.1038/gim.2017.210 29300386

16. Tavtigian SV Harrison SM et al. Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum Mutat (2020) 41 (10) p. 1734-1737. 10.1002/humu.24088 32720330

17. Alfares AA Kelly MA et al. Results of clinical genetic testing of 2,912 probands with hypertrophic cardiomyopathy: expanded panels offer limited additional sensitivity. Genet Med (2015) 17 (11) p. 880-8. 10.1038/gim.2014.205 25611685