Assessment of incidental findings in 232 whole-exome sequences from the Baylor–Hopkins Center for Mendelian Genomics

PurposeIn March 2013 the American College of Medical Genetics and Genomics published a list of 56 genes with the recommendation that pathogenic and likely pathogenic variants detected incidentally by clinical sequencing be reported to patients. As an initial step in determining the practical consequences of this recommendation in the research setting, we searched for variants in these genes in 232 whole-exome sequences from the Baylor-Hopkins Center for Mendelian Genomics.MethodsWe identified rare, nonsynonymous, and splicing single-nucleotide variants and insertions/deletions and assessed variant classification using the Human Gene Mutation, Emory, and ClinVar databases. We analyzed the burden of mutation in each of the 56 genes and determined which variants should be reported to patients.ResultsOur filtering resulted in 249 distinct variants, with a mean of 1.69 variants per individual. Half of these were novel missense mutations not classified by any of the three reference databases. Of 101 variants listed in the Human Gene Mutation Database, 48 were also in ClinVar and 3 were also in Emory; half of these shared variants were classified discordantly between databases. Some genes consistently had greater variation than others. In total, 0.86% of individuals had a reportable incidental variant.ConclusionThese observations demonstrate some current challenges of assessing phenotypic consequences of incidental variants for counseling patients.     

Evidence in the UK Biobank for the underdiagnosis of erythropoietic protoporphyria

PurposeErythropoietic protoporphyria (EPP), characterized by painful cutaneous photosensitivity, results from pathogenic variants in ferrochelatase (FECH). For 96% of patients, EPP results from coinheriting a rare pathogenic variant in trans of a common hypomorphic variant c.315-48T>C (minor allele frequency 0.05). The estimated prevalence of EPP derived from the number of diagnosed individuals in Europe is 0.00092%, but this may be conservative due to underdiagnosis. No study has estimated EPP prevalence using large genetic data sets.MethodsDisease-associated FECH variants were identified in the UK Biobank, a data set of 500,953 individuals including 49,960 exome sequences. EPP prevalence was then estimated. The association of FECH variants with EPP-related traits was assessed.ResultsAnalysis of pathogenic FECH variants in the UK Biobank provides evidence that EPP prevalence is 0.0059% (95% confidence interval [CI]: 0.0042–0.0076%), 1.7–3.0 times more common than previously thought in the UK. In homozygotes for the common c.315-48T>C FECH variant, there was a novel decrement in both erythrocyte mean corpuscular volume (MCV) and hemoglobin.ConclusionThe prevalence of EPP has been underestimated secondary to underdiagnosis. The common c.315-48T>C allele is associated with both MCV and hemoglobin, an association that could be important both for those with and without EPP.     

Reduced penetrance of pathogenic ACMG variants in a deeply phenotyped cohort study and evaluation of ClinVar classification over time

PurposeWe studied the penetrance of pathogenically classified variants in an elderly Dutch population from the Rotterdam Study, for which deep phenotyping is available. We screened the 59 actionable genes for which reporting of known pathogenic variants was recommended by the American College of Medical Genetics and Genomics (ACMG), and demonstrate that determining what constitutes a known pathogenic variant can be quite challenging.MethodsWe defined “known pathogenic” as classified pathogenic by both ClinVar and the Human Gene Mutation Database (HGMD). In 2628 individuals, we performed exome sequencing and identified known pathogenic variants. We investigated the clinical records of carriers and evaluated clinical events during 25 years of follow-up for evidence of variant pathogenicity.ResultsOf 3815 variants detected in the 59 ACMG genes, 17 variants were considered known pathogenic. For 14/17 variants the ClinVar classification had changed over time. Of 24 confirmed carriers of these variants, we observed at least one clinical event possibly caused by the variant in only three participants (13%).ConclusionWe show that the definition of “known pathogenic” is often unclear and should be approached carefully. Additionally variants marked as known pathogenic do not always have clinical impact on their carriers. Definition and classification of true (individual) expected pathogenic impact should be defined carefully.     

AVADA: toward automated pathogenic variant evidence retrieval directly from the full-text literature

PurposeBoth monogenic pathogenic variant cataloging and clinical patient diagnosis start with variant-level evidence retrieval followed by expert evidence integration in search of diagnostic variants and genes. Here, we try to accelerate pathogenic variant evidence retrieval by an automatic approach.MethodsAutomatic VAriant evidence DAtabase (AVADA) is a novel machine learning tool that uses natural language processing to automatically identify pathogenic genetic variant evidence in full-text primary literature about monogenic disease and convert it to genomic coordinates.ResultsAVADA automatically retrieved almost 60% of likely disease-causing variants deposited in the Human Gene Mutation Database (HGMD), a 4.4-fold improvement over the current best open source automated variant extractor. AVADA contains over 60,000 likely disease-causing variants that are in HGMD but not in ClinVar. AVADA also highlights the challenges of automated variant mapping and pathogenicity curation. However, when combined with manual validation, on 245 diagnosed patients, AVADA provides valuable evidence for an additional 18 diagnostic variants, on top of ClinVar’s 21, versus only 2 using the best current automated approach.ConclusionAVADA advances automated retrieval of pathogenic monogenic variant evidence from full-text literature. Far from perfect, but much faster than PubMed/Google Scholar search, careful curation of AVADA-retrieved evidence can aid both database curation and patient diagnosis.     

The Spectrum of Genetic Variants Associated with the Development of Monogenic Obesity in Qatar

IntroductionMonogenic obesity (MO) is a rare genetic disease characterized by severe early-onset obesity in affected individuals. Previous genetic studies revealed 8 definitive genes for monogenic non-syndromic obesity; many were discovered in consanguineous populations. Here, we examined MO in the Qatari population, whose population is largely consanguineous (54%) and characterized by extensive obesity (45%).MethodsWhole genome sequencing data of Qatar Biobank samples from 250 subjects with obesity and 250 subjects with normal weight, obtained in association with the Qatar Genome Programme, were searched for genetic variants in the genes known to be associated with MO (i.e., LEP, LEPR, POMC, PCSK1, MC3R, MC4R, MRAP2, and ADCY3). The impact of the variants identified was investigated utilizing in silico tools for prediction in combination with protein visualization by PyMOL.ResultsWe identified potential MO variants in more than 5% of the cases in our cohort. We revealed 11 rare variants in 6 of the genes targeted, including two disease-causing variants in MC4R and MRAP2, all of which were heterozygous. Moreover, enrichment of a heterozygous ADCY3 variant (c.1658C>T; p.A553V) appeared to cause severe obesity in an autosomal dominant manner.ConclusionThese findings highlight the importance of implementing routine testing for genetic variants that predispose for MO in Qatar. Clearly, additional studies of this nature on populations not yet examined are required. At the same time, functional investigations, both in vitro and in vivo, are necessary in order to better understand the role of the variants identified in the pathogenesis of obesity.     

Polygenic risk score as a possible tool for identifying familial monogenic causes of complex diseases

PurposeThe study aimed to evaluate whether polygenic risk scores could be helpful in addition to family history for triaging individuals to undergo deep-depth diagnostic sequencing for identifying monogenic causes of complex diseases.MethodsAmong 44,550 exome-sequenced European ancestry UK Biobank participants, we identified individuals with a clinically reported or computationally predicted monogenic pathogenic variant for breast cancer, bowel cancer, heart disease, diabetes, or Alzheimer disease. We derived polygenic risk scores for these diseases. We tested whether a polygenic risk score could identify rare pathogenic variant heterozygotes among individuals with a parental disease history.ResultsMonogenic causes of complex diseases were more prevalent among individuals with a parental disease history than in the rest of the population. Polygenic risk scores showed moderate discriminative power to identify familial monogenic causes. For instance, we showed that prescreening the patients with a polygenic risk score for type 2 diabetes can prioritize individuals to undergo diagnostic sequencing for monogenic diabetes variants and reduce needs for such sequencing by up to 37%.ConclusionAmong individuals with a family history of complex diseases, those with a low polygenic risk score are more likely to have monogenic causes of the disease and could be prioritized to undergo genetic testing.     

Acute Intermittent Porphyria: Predicted Pathogenicity of HMBS Variants Indicates Extremely Low Penetrance of the Autosomal Dominant Disease

Acute intermittent porphyria results from hydroxymethylbilane synthase (HMBS) mutations that markedly decrease HMBS enzymatic activity. This dominant disease is diagnosed when heterozygotes have life‐threatening acute attacks, while most heterozygotes remain asymptomatic and undiagnosed. Although >400 HMBS mutations have been reported, the prevalence of pathogenic HMBS mutations in genomic/exomic databases, and the actual disease penetrance are unknown. Thus, we interrogated genomic/exomic databases, identified non‐synonymous variants (NSVs) and consensus splice‐site variants (CSSVs) in various demographic/racial groups, and determined the NSV's pathogenicity by prediction algorithms and in vitro expression assays. Caucasians had the most: 58 NSVs and two CSSVs among ～92,000 alleles, a 0.00575 combined allele frequency. In silico algorithms predicted 14 out of 58 NSVs as “likely‐pathogenic.” In vitro expression identified 10 out of 58 NSVs as likely‐pathogenic (seven predicted in silico), which together with two CSSVs had a combined allele frequency of 0.00056. Notably, six presumably pathogenic mutations/NSVs in the Human Gene Mutation Database were benign. Compared with the recent prevalence estimate of symptomatic European heterozygotes (～0.000005), the prevalence of likely‐pathogenic HMBS mutations among Caucasians was >100 times more frequent. Thus, the estimated penetrance of acute attacks was ～1% of heterozygotes with likely‐pathogenic mutations, highlighting the importance of predisposing/protective genes and environmental modifiers that precipitate/prevent the attacks.     

Evaluating the quality of Marfan genotype–phenotype correlations in existing FBN1 databases

BackgroundGenetic FBN1 testing is pivotal for confirming the clinical diagnosis of Marfan syndrome. In an effort to evaluate variant causality, FBN1 databases are often used. We evaluated the current databases regarding FBN1 variants and validated associated phenotype records with a new Marfan syndrome geno-phenotyping tool called the Marfan score.Methods and resultsWe evaluated four databases (UMD-FBN1, ClinVar, the Human Gene Mutation Database (HGMD), and Uniprot) containing 2,250 FBN1 variants supported by 4,904 records presented in 307 references. The Marfan score calculated for phenotype data from the records quantified variant associations with Marfan syndrome phenotype. We calculated a Marfan score for 1,283 variants, of which we confirmed the database diagnosis of Marfan syndrome in 77.1%. This represented only 35.8% of the total registered variants; 18.5–33.3% (UMD-FBN1 versus HGMD) of variants associated with Marfan syndrome in the databases could not be confirmed by the recorded phenotype.ConclusionFBN1 databases can be imprecise and incomplete. Data should be used with caution when evaluating FBN1 variants. At present, the UMD-FBN1 database seems to be the biggest and best curated; therefore, it is the most comprehensive database. However, the need for better genotype–phenotype curated databases is evident, and we hereby present such a database.Genet Med advance online publication 01 December 2016     

Difficulties in diagnosing Marfan syndrome using current FBN1 databases

PurposeThe diagnostic criteria of Marfan syndrome (MFS) highlight the importance of a FBN1 mutation test in diagnosing MFS. As genetic sequencing becomes better, cheaper, and more accessible, the expected increase in the number of genetic tests will become evident, resulting in numerous genetic variants that need to be evaluated for disease-causing effects based on database information. The aim of this study was to evaluate genetic variants in four databases and review the relevant literature.MethodsWe assessed background data on 23 common variants registered in ESP6500 and classified as causing MFS in the Human Gene Mutation Database (HGMD). We evaluated data in four variant databases (HGMD, UMD-FBN1, ClinVar, and UniProt) according to the diagnostic criteria for MFS and compared the results with the classification of each variant in the four databases.ResultsNone of the 23 variants was clearly associated with MFS, even though all classifications in the databases stated otherwise.ConclusionA genetic diagnosis of MFS cannot reliably be based on current variant databases because they contain incorrectly interpreted conclusions on variants. Variants must be evaluated by time-consuming review of the background material in the databases and by combining these data with expert knowledge on MFS. This is a major problem because we expect even more genetic test results in the near future as a result of the reduced cost and process time for next-generation sequencing.     

Allele frequency of variants reported to cause adenine phosphoribosyltransferase deficiency

Adenine phosphoribosyltransferase deficiency is a rare, autosomal recessive disorder of purine metabolism that causes nephrolithiasis and progressive chronic kidney disease. The small number of reported cases indicates an extremely low prevalence, although it has been suggested that missed diagnoses may play a role. We assessed the prevalence of APRT deficiency based on the frequency of causally-related APRT sequence variants in a diverse set of large genomic databases. A thorough search was carried out for all APRT variants that have been confirmed as pathogenic under recessive mode of inheritance, and the frequency of the identified variants examined in six population genomic databases: the deCODE genetics database, the UK Biobank, the 100,000 Genomes Project, the Genome Aggregation Database, the Human Genetic Variation Database and the Korean Variant Archive. The estimated frequency of homozygous genotypes was calculated using the Hardy-Weinberg equation. Sixty-two pathogenic APRT variants were identified, including six novel variants. Most common were the missense variants c.407T>C (p.(Met136Thr)) in Japan and c.194A>T (p.(Asp65Val)) in Iceland, as well as the splice-site variant c.400 + 2dup (p.(Ala108Glufs*3)) in the European population. Twenty-nine variants were detected in at least one of the six genomic databases. The highest cumulative minor allele frequency (cMAF) of pathogenic variants outside of Japan and Iceland was observed in the Irish population (0.2%), though no APRT deficiency cases have been reported in Ireland. The large number of cases in Japan and Iceland is consistent with a founder effect in these populations. There is no evidence for widespread underdiagnosis based on the current analysis.Subject terms: Genomics, Medical research     

TRIP12基因c.40C>T杂合变异致智力障碍一例

目的对1例智力障碍患者的TRIP12基因进行变异分析,明确其致病原因。方法应用全外显子测序及Sanger测序验证,并对其家系成员进行验证。结果测序结果显示先证者TRIP12基因存在c.40C>T(p.Arg14X)杂合无义变异,其父母该位点未检测到变异,为新发变异(de novo),查阅人类基因变异数据库未见该变异类型报道。根据美国医学遗传学及基因组学学会遗传变异解读指南,c.40C>T变异判定为致病性变异(PVS1+PS2+PP3)。结论这例患者为TRIP12基因c.40C>T杂合变异导致的常染色体显性遗传智力障碍。     

Population data improves variant interpretation in autosomal dominant polycystic kidney disease

PurposeAutosomal dominant polycystic kidney disease (ADPKD) is a common adult-onset monogenic disorder, with prevalence of 1/1000. Population databases including ExAC have improved pathogenic variant prioritization in many diseases. Due to pseudogene homology of PKD1, the predominant ADPKD disease gene, and the variable disease severity and age of onset, we aimed to investigate the utility of ExAC for variant assessment in ADPKD.MethodsWe assessed coverage and variant quality in the ExAC cohort and combined allele frequency and age data from the ExAC database (n = 60,706) with curated variants from 2000 ADPKD pedigrees (ADPKD Mutation Database).ResultsSeventy-six percent of PKD1 and PKD2 were sequenced adequately for variant discovery and variant quality was high in ExAC. In ExAC, we identified 25 truncating and 393 previously reported disease-causing variants in PKD1 and PKD2, 6.9-fold higher than expected. Fifty-four different variants, previously classified as disease-causing, were observed in ≥5 participants in ExAC.ConclusionOur study demonstrates that many previously implicated disease-causing variants are too common, challenging their pathogenicity, or penetrance. The presence of protein-truncating variants in older participants in ExAC demonstrates the complexity of variant classification and highlights need for further study of prevalence and penetrance of this common monogenic disease.     

Next-generation sequencing of 12 obesity genes in a Portuguese cohort of patients with overweight and obesity

We examined 12 monogenic obesity genes in 72 Portuguese individuals with overweight and obesity (class 1 and class 2), some of which with suspected genetic obesity, to identify known or unknown potential obesity variants. Genomic DNA was analyzed for variants in genes LEP, LEPR, MC4R, POMC, PCSK1, BDNF, NTRK2, SIM1, SH2B1, UCP3, GCG and ADCY3 through next generation sequencing (NGS). The impact of the rare variants was investigated in the ClinVar database and using in silico tools for prediction of pathogenicity. Four potential pathogenic missense variants were detected at the heterozygous state in five individuals: two in the ADCY3 gene, NM_004036.5:c.1153G > A (p.Val385Ile) (rs756783003) and NM_004036.5:c.1222G > A (p.Gly408Arg) (rs201606553), one in gene SH2B1, NM_001145795.1:c.127C > A (p.Arg43Ser) (rs547678855), and the fourth in gene POMC NM_000939.4:c.706C > G (p.Arg236Gly) (rs28932472), which was found in two individuals. Moreover, six rare variants near splicing sites were also identified, as well as eight rare synonymous variants. In summary, some potential pathogenic rare missense variants were identified, two of them in ADCY3 gene, the most recently identified gene as having a role in monogenic obesity. Further analysis should be performed to confirm the clinical relevance of these variants.     

Estimating diagnostic noise in panel-based genomic analysis

PurposeGene panels with a series of strict variant filtering rules are often used for clinical analysis of exomes and genomes. Panel sizes vary, affecting the test’s sensitivity and specificity. We investigated the background rate of candidate variants in a population setting using gene panels developed to diagnose a range of heterogeneous monogenic diseases.MethodsWe used the Gene2Phenotype database with the Variant Effect Predictor plugin to identify rare nonsynonymous variants in exome sequence data from 200,643 individuals in UK Biobank. We evaluated 5 clinically curated gene panels of varying sizes (50-1700 genes).ResultsBigger gene panels resulted in more prioritized variants, varying from an average of approximately 0.3 to 3.5 variants per person. The number of individuals with prioritized variants varied linearly with coding sequence length for monoallelic genes (～300 individuals per 1000 base pairs) and quadratically for biallelic genes, with notable outliers.ConclusionAlthough large gene panels may be the best strategy to maximize diagnostic yield in genetically heterogeneous diseases, they frequently prioritize likely benign variants requiring follow up. Most individuals have ≥1 rare nonsynonymous variant in panels containing >500 disease genes. Extreme caution should be applied when interpreting candidate variants, particularly in the absence of relevant phenotypes.     

Systematic large-scale assessment of the genetic architecture of left ventricular noncompaction reveals diverse etiologies

PurposeTo characterize the genetic architecture of left ventricular noncompaction (LVNC) and investigate the extent to which it may represent a distinct pathology or a secondary phenotype associated with other cardiac diseases.MethodsWe performed rare variant association analysis with 840 LVNC cases and 125,748 gnomAD population controls, and compared results to similar analyses on dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM).ResultsWe observed substantial genetic overlap indicating that LVNC often represents a phenotypic variation of DCM or HCM. In contrast, truncating variants in MYH7, ACTN2, and PRDM16 were uniquely associated with LVNC and may reflect a distinct LVNC etiology. In particular, MYH7 truncating variants (MYH7tv), generally considered nonpathogenic for cardiomyopathies, were 20-fold enriched in LVNC cases over controls. MYH7tv heterozygotes identified in the UK Biobank and healthy volunteer cohorts also displayed significantly greater noncompaction compared with matched controls. RYR2 exon deletions and HCN4 transmembrane variants were also enriched in LVNC, supporting prior reports of association with arrhythmogenic LVNC phenotypes.ConclusionLVNC is characterized by substantial genetic overlap with DCM/HCM but is also associated with distinct noncompaction and arrhythmia etiologies. These results will enable enhanced application of LVNC genetic testing and help to distinguish pathological from physiological noncompaction.     

A framework for automated gene selection in genomic applications

PurposeAn efficient framework to identify disease-associated genes is needed to evaluate genomic data for both individuals with an unknown disease etiology and those undergoing genomic screening. Here, we propose a framework for gene selection used in genomic analyses, including applications limited to genes with strong or established evidence levels and applications including genes with less or emerging evidence of disease association.MethodsWe extracted genes with evidence for gene–disease association from the Human Gene Mutation Database, OMIM, and ClinVar to build a comprehensive gene list of 6,145 genes. Next, we applied stringent filters in conjunction with computationally curated evidence (DisGeNET) to create a restrictive list limited to 3,929 genes with stronger disease associations.ResultsWhen compared to manual gene curation efforts, including the Clinical Genome Resource, genes with strong or definitive disease associations are included in both gene lists at high percentages, while genes with limited evidence are largely removed. We further confirmed the utility of this approach in identifying pathogenic and likely pathogenic variants in 45 genomes.ConclusionOur approach efficiently creates highly sensitive gene lists for genomic applications, while remaining dynamic and updatable, enabling time savings in genomic applications.     

The ACMG SF v3.0 gene list increases returnable variant detection by 22% when compared with v2.0 in the ClinSeq cohort

PurposeThe American College of Medical Genetics and Genomics (ACMG) recommends the return of pathogenic and likely pathogenic (P/LP) secondary findings from exome and genome sequencing. The latest version (ACMG secondary finding [SF] v3.0) includes 14 additional genes. We interrogated the ClinSeq cohort for variants in these genes to determine the additional yield in unselected individuals.MethodsExome data from 1473 individuals (60% White, 34% African American or Black, 6% other) were analyzed. We restricted our analyses to coding variants; +1,+2,–1, and –2 splice site variants; and the pathogenic GAA variant, NM_000152.5:c.-32-13T>G. Variants were assessed with slightly modified ACMG/Association of Molecular Pathology guidelines.ResultsA total of 25 P/LP variants were identified. In total, 7 individuals had P/LP variants in genes recommended for return of heterozygous variants, namely HNF1A (1), PALB2 (3), TMEM127 (1), and TTN (2). In total, 4 individuals had a homozygous variant in a gene recommended for biallelic variant return, namely HFE, NM_000410.3(HFE):c.845G>A p.Cys282Tyr. A total of 17 P/LP variants were identified in the heterozygous state in genes recommended only for biallelic variant reporting and were not returned. The frequency of returnable P/LP variants did not significantly differ by race.ConclusionUsing the ACMG SF v3.0, the returnable P/LP variant frequency increased in the ClinSeq cohort by 22%, from 3.4% (n = 50, ACMG SF v2.0) to 4.1% (n = 61, ACMG SF v3.0).     

Carrier screening in the Mexican Jewish community using a pan-ethnic expanded carrier screening NGS panel

PurposeThe Mexican Jewish community (MJC) is a previously uncharacterized, genetically isolated group composed of Ashkenazi and Sephardi-Mizrahi Jews who migrated in the early 1900s. We aimed to determine the heterozygote frequency of disease-causing variants in 302 genes in this population.MethodsWe conducted a cross-sectional study of the MJC involving individuals representing Ashkenazi Jews, Sephardi-Mizrahi Jews, or mixed-ancestry Jews. We offered saliva-based preconception pan-ethnic expanded carrier screening, which examined 302 genes. We analyzed heterozygote frequencies of pathogenic/likely pathogenic variants and compared them with those in the Genome Aggregation Database (gnomAD).ResultsWe recruited 208 participants. The carrier screening results showed that 72.1% were heterozygous for at least 1 severe disease-causing variant in 1 of the genes analyzed. The most common genes with severe disease-causing variants were CFTR (16.8% of participants), MEFV (11.5%), WNT10A (6.7%), and GBA (6.7%). The allele frequencies were compared with those in the gnomAD; 85% of variant frequencies were statistically different from those found in gnomAD (P <.05). Finally, 6% of couples were at risk of having a child with a severe disorder.ConclusionThe heterozygote frequency of at least 1 severe disease-causing variant in the MJC was 72.1%. The use of carrier screening in the MJC and other understudied populations could help parents make more informed decisions.     

Integration of functional assay data results provides strong evidence for classification of hundreds of BRCA1 variants of uncertain significance

PurposeBRCA1 pathogenic variant heterozygotes are at a substantially increased risk for breast and ovarian cancer. The widespread uptake of testing has led to a significant increase in the detection of missense variants in BRCA1, the vast majority of which are variants of uncertain clinical significance (VUS), posing a challenge to genetic counseling. Here, we harness a wealth of functional data for thousands of variants to aid in variant classification.MethodsWe have collected, curated, and harmonized functional data for 2701 missense variants representing 24.5% of possible missense variants in BRCA1. Results were harmonized across studies by converting data into binary categorical variables (functional impact versus no functional impact). Using a panel of reference variants we identified a subset of assays with high sensitivity and specificity (≥80%) and apply the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant interpretation guidelines to assign evidence criteria for classification.ResultsIntegration of data from validated assays provided ACMG/AMP evidence criteria in favor of pathogenicity for 297 variants or against pathogenicity for 2058 representing 96.2% of current VUS functionally assessed. We also explore discordant results and identify limitations in the approach.ConclusionHigh quality functional data are available for BRCA1 missense variants and provide evidence for classification of 2355 VUS according to their pathogenicity.