Regional-specific calibration enables application of computational evidence for clinical classification of 5' cis-regulatory variants in Mendelian disease.

To date, clinical genetic testing for Mendelian disease variants has focused heavily on exonic coding and intronic gene regions. This multi-step study was undertaken to provide an evidence base for selecting and applying computational approaches for use in clinical classification of 5' cis-regulatory region variants. Curated datasets of clinically reported disease-causing 5' cis-regulatory region variants and variants from matched genomic regions in population controls were used to calibrate six bioinformatic tools as predictors of variant pathogenicity. Likelihood ratio estimates were aligned to code weights following ClinGen recommendations for application of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification scheme. Considering code assignment across all reference dataset variants, performance was best for CADD (81.2%) and REMM (81.5%). Optimized thresholds provided moderate evidence toward pathogenicity (CADD, REMM) and moderate (CADD) or supporting (REMM) evidence against pathogenicity. Both sensitivity and specificity of prediction were improved when further categorizing variants based on location in an EPDnew-defined promoter region. Combining predictions (CADD, REMM, and location in a promoter region) increased specificity at the expense of sensitivity. Importantly, the optimal CADD thresholds for assigning ACMG/AMP codes PP3 (≥10) and BP4 (≤8) were vastly different from recommendations for protein-coding variants (PP3 ≥25.3; BP4 ≤22.7); CADD <22.7 would incorrectly assign BP4 for >90% of reported disease-causing cis-regulatory region variants. Our results demonstrate the need to consider a tiered approach and tailored score thresholds to optimize bioinformatic impact prediction for clinical classification of 5' cis-regulatory region variants.

Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline ATM sequence variants.

The ClinGen Hereditary Breast, Ovarian, and Pancreatic Cancer (HBOP) Variant Curation Expert Panel (VCEP) is composed of internationally recognized experts in clinical genetics, molecular biology, and variant interpretation. This VCEP made specifications for the American College of Medical Genetics and Association for Molecular Pathology (ACMG/AMP) guidelines for the ataxia telangiectasia mutated (ATM) gene according to the ClinGen protocol. These gene-specific rules for ATM were modified from the ACMG/AMP guidelines and were tested against 33 ATM variants of various types and classifications in a pilot curation phase. The pilot revealed a majority agreement between the HBOP VCEP classifications and the ClinVar-deposited classifications. Six pilot variants had conflicting interpretations in ClinVar, and re-evaluation with the VCEP's ATM-specific rules resulted in four that were classified as benign, one as likely pathogenic, and one as a variant of uncertain significance (VUS) by the VCEP, improving the certainty of interpretations in the public domain. Overall, 28 of the 33 pilot variants were not VUS, leading to an 85% classification rate. The ClinGen-approved, modified rules demonstrated value for improved interpretation of variants in ATM.

Evidence-based recommendations for gene-specific ACMG/AMP variant classification from the ClinGen ENIGMA BRCA1 and BRCA2 Variant Curation Expert Panel.

The ENIGMA research consortium develops and applies methods to determine clinical significance of variants in hereditary breast and ovarian cancer genes. An ENIGMA BRCA1/2 classification sub-group, formed in 2015 as a ClinGen external expert panel, evolved into a ClinGen internal Variant Curation Expert Panel (VCEP) to align with Food and Drug Administration recognized processes for ClinVar contributions. The VCEP reviewed American College of Medical Genetics and Genomics/Association of Molecular Pathology (ACMG/AMP) classification criteria for relevance to interpreting BRCA1 and BRCA2 variants. Statistical methods were used to calibrate evidence strength for different data types. Pilot specifications were tested on 40 variants and documentation revised for clarity and ease of use. The original criterion descriptions for 13 evidence codes were considered non-applicable or overlapping with other criteria. Scenario of use was extended or re-purposed for eight codes. Extensive analysis and/or data review informed specification descriptions and weights for all codes. Specifications were applied to pilot variants with pre-existing ClinVar classification as follows: 13 uncertain significance or conflicting, 14 pathogenic and/or likely pathogenic, and 13 benign and/or likely benign. Review resolved classification for 11/13 uncertain significance or conflicting variants and retained or improved confidence in classification for the remaining variants. Alignment of pre-existing ENIGMA research classification processes with ACMG/AMP classification guidelines highlighted several gaps in the research processes and the baseline ACMG/AMP criteria. Calibration of evidence strength was key to justify utility and strength of different data types for gene-specific application. The gene-specific criteria demonstrated value for improving ACMG/AMP-aligned classification of BRCA1 and BRCA2 variants.

Challenges and approaches to calibrating patient phenotype as evidence for cancer gene variant classification under ACMG/AMP guidelines.

Since first publication of the American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) variant classification guidelines, additional recommendations for application of certain criteria have been released (https://clinicalgenome.org/docs/), to improve their application in the diagnostic setting. However, none have addressed use of the PS4 and PP4 criteria, capturing patient presentation as evidence towards pathogenicity. Application of PS4 can be done through traditional case-control studies, or "proband counting" within or across clinical testing cohorts. Review of the existing PS4 and PP4 specifications for Hereditary Cancer Gene Variant Curation Expert Panels revealed substantial differences in the approach to defining specifications. Using BRCA1, BRCA2 and TP53 as exemplar genes, we calibrated different methods proposed for applying the "PS4 proband counting" criterion. For each approach, we considered limitations, non-independence with other ACMG/AMP criteria, broader applicability, and variability in results for different datasets. Our findings highlight inherent overlap of proband-counting methods with ACMG/AMP frequency codes, and the importance of calibration to derive dataset-specific code weights that can account for potential between-dataset differences in ascertainment and other factors. Our work emphasizes the advantages and generalizability of logistic regression analysis over simple proband-counting approaches to empirically determine the relative predictive capacity and weight of various personal clinical features in the context of multigene panel testing, for improved variant interpretation. We also provide a general protocol, including instructions for data formatting and a web-server for analysis of personal history parameters, to facilitate dataset-specific calibration analyses required to use such data for germline variant classification.

Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup.

The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) framework for classifying variants uses six evidence categories related to the splicing potential of variants: PVS1, PS3, PP3, BS3, BP4, and BP7. However, the lack of guidance on how to apply such codes has contributed to variation in the specifications developed by different Clinical Genome Resource (ClinGen) Variant Curation Expert Panels. The ClinGen Sequence Variant Interpretation Splicing Subgroup was established to refine recommendations for applying ACMG/AMP codes relating to splicing data and computational predictions. We utilized empirically derived splicing evidence to (1) determine the evidence weighting of splicing-related data and appropriate criteria code selection for general use, (2) outline a process for integrating splicing-related considerations when developing a gene-specific PVS1 decision tree, and (3) exemplify methodology to calibrate splice prediction tools. We propose repurposing the PVS1_Strength code to capture splicing assay data that provide experimental evidence for variants resulting in RNA transcript(s) with loss of function. Conversely, BP7 may be used to capture RNA results demonstrating no splicing impact for intronic and synonymous variants. We propose that the PS3/BS3 codes are applied only for well-established assays that measure functional impact not directly captured by RNA-splicing assays. We recommend the application of PS1 based on similarity of predicted RNA-splicing effects for a variant under assessment in comparison with a known pathogenic variant. The recommendations and approaches for consideration and evaluation of RNA-assay evidence described aim to help standardize variant pathogenicity classification processes when interpreting splicing-based evidence.

Australian Genomics: Outcomes of a 5-year national program to accelerate the integration of genomics in healthcare.

Australian Genomics is a national collaborative partnership of more than 100 organizations piloting a whole-of-system approach to integrating genomics into healthcare, based on federation principles. In the first five years of operation, Australian Genomics has evaluated the outcomes of genomic testing in more than 5,200 individuals across 19 rare disease and cancer flagship studies. Comprehensive analyses of the health economic, policy, ethical, legal, implementation and workforce implications of incorporating genomics in the Australian context have informed evidence-based change in policy and practice, resulting in national government funding and equity of access for a range of genomic tests. Simultaneously, Australian Genomics has built national skills, infrastructure, policy, and data resources to enable effective data sharing to drive discovery research and support improvements in clinical genomic delivery.

Helicobacter pylori, Homologous-Recombination Genes, and Gastric Cancer.

BACKGROUND: Helicobacter pylori infection is a well-known risk factor for gastric cancer. However, the contribution of germline pathogenic variants in cancer-predisposing genes and their effect, when combined with H. pylori infection, on the risk of gastric cancer has not been widely evaluated. METHODS: We evaluated the association between germline pathogenic variants in 27 cancer-predisposing genes and the risk of gastric cancer in a sample of 10,426 patients with gastric cancer and 38,153 controls from BioBank Japan. We also assessed the combined effect of pathogenic variants and H. pylori infection status on the risk of gastric cancer and calculated the cumulative risk in 1433 patients with gastric cancer and 5997 controls from the Hospital-based Epidemiologic Research Program at Aichi Cancer Center (HERPACC). RESULTS: Germline pathogenic variants in nine genes (APC, ATM, BRCA1, BRCA2, CDH1, MLH1, MSH2, MSH6, and PALB2) were associated with the risk of gastric cancer. We found an interaction between H. pylori infection and pathogenic variants in homologous-recombination genes with respect to the risk of gastric cancer in the sample from HERPACC (relative excess risk due to the interaction, 16.01; 95% confidence interval [CI], 2.22 to 29.81; P = 0.02). At 85 years of age, persons with H. pylori infection and a pathogenic variant had a higher cumulative risk of gastric cancer than noncarriers infected with H. pylori (45.5% [95% CI, 20.7 to 62.6] vs. 14.4% [95% CI, 12.2 to 16.6]). CONCLUSIONS: H. pylori infection modified the risk of gastric cancer associated with germline pathogenic variants in homologous-recombination genes. (Funded by the Japan Agency for Medical Research and Development and others.).

Clinical implications of VUS reclassification in a single-centre series from application of ACMG/AMP classification rules specified for BRCA1/2.

BACKGROUND: BRCA1/2 testing is crucial to guide clinical decisions in patients with hereditary breast/ovarian cancer, but detection of variants of uncertain significance (VUSs) prevents proper management of carriers. The ENIGMA (Evidence-based Network for the Interpretation of Germline Mutant Alleles) BRCA1/2 Variant Curation Expert Panel (VCEP) has recently developed BRCA1/2 variant classification guidelines consistent with ClinGen processes, specified against the ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular-Pathology) classification framework. METHODS: The ClinGen-approved BRCA1/2-specified ACMG/AMP classification guidelines were applied to BRCA1/2 VUSs identified from 2011 to 2022 in a series of patients, retrieving information from the VCEP documentation, public databases, literature and ENIGMA unpublished data. Then, we critically re-evaluated carrier families based on new results and checked consistency of updated classification with main sources for clinical interpretation of BRCA1/2 variants. RESULTS: Among 166 VUSs detected in 231 index cases, 135 (81.3%) found in 197 index cases were classified by applying BRCA1/2-specified ACMG/AMP criteria: 128 (94.8%) as Benign/Likely Benign and 7 (5.2%) as Pathogenic/Likely Pathogenic. The average time from the first report as 'VUS' to classification using this approach was 49.4 months. Considering that 15 of these variants found in 64 families had already been internally reclassified prior to this work, this study provided 121 new reclassifications among the 151 (80.1%) remaining VUSs, relevant to 133/167 (79.6%) families. CONCLUSIONS: These results demonstrated the effectiveness of new BRCA1/2 ACMG/AMP classification guidelines for VUS classification within a clinical cohort, and their important clinical impact. Furthermore, they suggested a cadence of no more than 3 years for regular review of VUSs, which however requires time, expertise and resources.

Male with an apparently normal phenotype carrying a BRCA1 exon 20 duplication in trans to a BRCA1 frameshift variant.

BACKGROUND: Reports of dual carriers of pathogenic BRCA1 variants in trans are extremely rare, and so far, most individuals have been associated with a Fanconi Anemia-like phenotype. METHODS: We identified two families with a BRCA1 in-frame exon 20 duplication (Ex20dup). In one male individual, the variant was in trans with the BRCA1 frameshift variant c.2475delC p.(Asp825Glufs*21). We performed splicing analysis and used a transcription activation domain (TAD) assay to assess the functional impact of Ex20dup. We collected pedigrees and mapped the breakpoints of the duplication by long- and short-read genome sequencing. In addition, we performed a mitomycin C (MMC) assay from the dual carrier using cultured lymphoblastoid cells. RESULTS: Genome sequencing and RNA analysis revealed the BRCA1 exon 20 duplication to be in tandem. The duplication was expressed without skipping any one of the two exon 20 copies, resulting in a lack of wild-type transcripts from this allele. TAD assay indicated that the Ex20dup variant has a functional level similar to the well-known moderate penetrant pathogenic BRCA1 variant c.5096G > A p.(Arg1699Gln). MMC assay of the dual carrier indicated a slightly impaired chromosomal repair ability. CONCLUSIONS: This is the first reported case where two BRCA1 variants with demonstrated functional impact are identified in trans in a male patient with an apparently normal clinical phenotype and no BRCA1-associated cancer. The results pinpoint a minimum necessary BRCA1 protein activity to avoid a Fanconi Anemia-like phenotype in compound heterozygous status and yet still predispose carriers to hormone-related cancers. These findings urge caution when counseling families regarding potential Fanconi Anemia risk. Furthermore, prudence should be taken when classifying individual variants as benign based on co-occurrence in trans with well-established pathogenic variants.

SpliceAI-10k calculator for the prediction of pseudoexonization, intron retention, and exon deletion.

SUMMARY: SpliceAI is a widely used splicing prediction tool and its most common application relies on the maximum delta score to assign variant impact on splicing. We developed the SpliceAI-10k calculator (SAI-10k-calc) to extend use of this tool to predict: the splicing aberration type including pseudoexonization, intron retention, partial exon deletion, and (multi)exon skipping using a 10 kb analysis window; the size of inserted or deleted sequence; the effect on reading frame; and the altered amino acid sequence. SAI-10k-calc has 95% sensitivity and 96% specificity for predicting variants that impact splicing, computed from a control dataset of 1212 single-nucleotide variants (SNVs) with curated splicing assay results. Notably, it has high performance (≥84% accuracy) for predicting pseudoexon and partial intron retention. The automated amino acid sequence prediction allows for efficient identification of variants that are expected to result in mRNA nonsense-mediated decay or translation of truncated proteins. AVAILABILITY AND IMPLEMENTATION: SAI-10k-calc is implemented in R (https://github.com/adavi4/SAI-10k-calc) and also available as a Microsoft Excel spreadsheet. Users can adjust the default thresholds to suit their target performance values.

Reporting clinically relevant genetic variants unrelated to genomic test requests: a survey of Australian clinical laboratory policies and practices.

Approaches to reporting clinically important genetic findings unrelated to the initial test request vary internationally. We sought to investigate practices regarding the management and return of these findings in Australia. Australian clinically accredited genetic testing laboratories were surveyed in 2017 and 2020 regarding their opinions on issues relating to the return of clinically important genetic findings unrelated to the initial test request. Responses were collated and analysed for 15 laboratories in 2017, and 17 laboratories in 2020. Content analysis was also performed on seven laboratory policies in 2020. Analysis showed that overall there was a lack of consensus about the terminology used to describe such findings and reporting practices across different testing contexts. A clear exception was that no laboratories were actively searching for a list of medically actionable genes (eg, American College of Medical Genetics and Genomics secondary findings gene list). Laboratory policies showed little consistency in the documentation of issues related to the handling of these findings. These findings indicate a need for Australian-specific policy guidance that covers all aspects of clinically important genetic findings unrelated to the initial test request. We present recommendations for consideration when developing laboratory policies.

CHEK2 is not a Li-Fraumeni syndrome gene: time to update public resources.

The gene-disease relationship for CHEK2 remains listed as 'Li-Fraumeni syndrome 2' in public resources such as OMIM and MONDO, despite published evidence to the contrary, causing frustration among Li-Fraumeni syndrome (LFS) clinical experts. Here, we compared personal cancer characteristics of 2095 CHEK2 and 248 TP53 pathogenic variant carriers undergoing multigene panel testing at Ambry Genetics against 15 135 individuals with no known pathogenic variant. Our results from a within-cohort logistic regression approach highlight obvious differences between clinical presentation of TP53 and CHEK2 pathogenic variant carriers, with no evidence of CHEK2 being associated with any of the TP53-related core LFS cancers. These findings emphasise the need to replace 'Li-Fraumeni syndrome 2' as the CHEK2-associated disease name, thereby limiting potential confusion.

Optimising clinical care through CDH1-specific germline variant curation: improvement of clinical assertions and updated curation guidelines.

BACKGROUND: Germline pathogenic variants in CDH1 are associated with increased risk of diffuse gastric cancer and lobular breast cancer. Risk reduction strategies include consideration of prophylactic surgery, thereby making accurate interpretation of germline CDH1 variants critical for physicians deciding on these procedures. The Clinical Genome Resource (ClinGen) CDH1 Variant Curation Expert Panel (VCEP) developed specifications for CDH1 variant curation with a goal to resolve variants of uncertain significance (VUS) and with ClinVar conflicting interpretations and continues to update these specifications. METHODS: CDH1 variant classification specifications were modified based on updated genetic testing clinical criteria, new recommendations from ClinGen and expert knowledge from ongoing CDH1 variant curations. The CDH1 VCEP reviewed 273 variants using updated CDH1 specifications and incorporated published and unpublished data provided by diagnostic laboratories. RESULTS: Updated CDH1-specific interpretation guidelines include 11 major modifications since the initial specifications from 2018. Using the refined guidelines, 97% (36 of 37) of variants with ClinVar conflicting interpretations were resolved to benign, likely benign, likely pathogenic or pathogenic, and 35% (15 of 43) of VUS were resolved to benign or likely benign. Overall, 88% (239 of 273) of curated variants had non-VUS classifications. To date, variants classified as pathogenic are either nonsense, frameshift, splicing, or affecting the translation initiation codon, and the only missense variants classified as pathogenic or likely pathogenic have been shown to affect splicing. CONCLUSIONS: The development and evolution of CDH1-specific criteria by the expert panel resulted in decreased uncertain and conflicting interpretations of variants in this clinically actionable gene, which can ultimately lead to more effective clinical management recommendations.

Catastrophic chemotherapy toxicity leading to diagnosis of Fanconi anaemia due to FANCD1/BRCA2 during adulthood: description of an emerging phenotype.

Fanconi anaemia due to biallelic loss of BRCA2 (Fanconi anaemia subtype D1) is traditionally diagnosed during childhood with cancer rates historically reported as 97% by 5.2 years. This report describes an adult woman with a history of primary ovarian failure, who was diagnosed with gastrointestinal adenocarcinoma and BRCA2-associated Fanconi anaemia at 23 years of age, only after she suffered severe chemotherapy toxicity. The diagnostic challenges include atypical presentation, initial false-negative chromosome fragility testing and variant classification. It highlights gastrointestinal adenocarcinoma as a consideration for adults with biallelic BRCA2 pathogenic variants with implications for surveillance. After over 4 years, the patient has no evidence of gastrointestinal cancer recurrence although the tumour was initially considered only borderline resectable. The use of platinum-based chemotherapy, to which heterozygous BRCA2 carriers are known to respond, may have had a beneficial anticancer effect, but caution is advised given its extreme immediate toxicity at standard dosing. Fanconi anaemia should be considered as a cause for women with primary ovarian failure of unknown cause and referral to cancer genetic services recommended when there is a family history of cancer in the hereditary breast/ovarian cancer spectrum.

Estimating the proportion of pathogenic variants from breast cancer case-control data: Application to calibration of ACMG/AMP variant classification criteria.

For genes with reliable estimates of disease risk associated with loss-of-function variants, case-control data can be used to estimate the proportion of variants of typical risk effect for defined groups of variants, of relevance for variant classification. A calculation was derived for a maximum likelihood estimate of the proportion of pathogenic variants of typical effect from case-control data and applied to rare variant counts for ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C, and RAD51D from published breast cancer studies: BEACCON (5770 familial cases and 5741 controls) and breast cancer risk after diagnostic sequencing (60,466 familial and population-based cases and 53,461 controls). There was significant evidence of pathogenic variants among rare noncoding variants, in particular deeper intronic variants, for BRCA1 (13%, p = 8.3 × 10-7 ), BRCA2 (6%, p = 0.016) and PALB2 (13%, p = 0.001). The estimated proportion of pathogenic missense variants varied markedly between genes, generally with enrichment in familial cases, for example, 9% for BRCA2 versus 60%-90% for CHEK2. Stratifying missense variants by position indicated that, for most genes, location within a functional domain significantly predicted pathogenicity, whereas location outside domains provided robust evidence against pathogenicity. Our approach provides novel insights into the spectrum of pathogenic variants of specific breast cancer genes and has wider application to inform gene-focused specifications of American College of Medical Genetics and Genomics (ACMG)/Association of Molecular Pathology (AMP) codes for variant curation.

Analysis of hereditary cancer gene variant classifications from ClinVar indicates a need for regular reassessment of clinical assertions.

The clinical classification of variants may change with new information, however, there is limited guidance on how often significant changes in variant classification occur. We used ClinVar to examine how variant classification changes over time. We developed a custom parser and accessed variant data from ClinVar between January 2015 and July 2021. The ClinVar-assigned "aggregate" classification of variants in 121 hereditary cancer genes was harmonized across releases to align to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology terms. Aggregate classification categories were grouped as: benign/likely benign (B/LB); likely pathogenic/pathogenic (LP/P); variant of uncertain significance (VUS); conflicting interpretations of pathogenicity (Conflicting); or Other. We profiled changes in aggregate variant classification between consecutive semi-annual ClinVar releases. The proportion of variants that changed aggregate classification between semi-annual ClinVar releases ranged from 0.6% to 6.4%. The most frequent changes were "VUS to conflicting," "other to LP/P," and "B/LB to Conflicting." A limited number of variants changed aggregate classification from "LP/P to B/LB," or vice versa. Our analysis indicates need for regular reassessment of clinical variant interpretations. The parser developed for this project will facilitate extraction of relevant interpretation data from ClinVar.

Predictive functional assay-based classification of PMS2 variants in Lynch syndrome.

The large majority of germline alterations identified in the DNA mismatch repair (MMR) gene PMS2, a low-penetrance gene for the cancer predisposition Lynch syndrome, represent variants of uncertain significance (VUS). The inability to classify most VUS interferes with personalized healthcare. The complete in vitro MMR activity (CIMRA) assay, that only requires sequence information on the VUS, provides a functional analysis-based quantitative tool to improve the classification of VUS in MMR proteins. To derive a formula that translates CIMRA assay results into the odds of pathogenicity (OddsPath) for VUS in PMS2 we used a set of clinically classified PMS2 variants supplemented by inactivating variants that were generated by an in cellulo genetic screen, as proxies for cancer-predisposing variants. Validation of this OddsPath revealed high predictive values for benign and predisposing PMS2 VUS. We conclude that the OddsPath provides an integral metric that, following the other, higher penetrance, MMR proteins MSH2, MSH6 and MLH1 can be incorporated as strong evidence type into the upcoming criteria for MMR gene VUS classification of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP).

SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effects on mRNA splicing.

Modeling splicing is essential for tackling the challenge of variant interpretation as each nucleotide variation can be pathogenic by affecting pre-mRNA splicing via disruption/creation of splicing motifs such as 5'/3' splice sites, branch sites, or splicing regulatory elements. Unfortunately, most in silico tools focus on a specific type of splicing motif, which is why we developed the Splicing Prediction Pipeline (SPiP) to perform, in one single bioinformatic analysis based on a machine learning approach, a comprehensive assessment of the variant effect on different splicing motifs. We gathered a curated set of 4616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. The Bayesian analysis provided us with the number of control variants, that is, variants without impact on splicing, to mimic the deluge of variants from high-throughput sequencing data. Results show that SPiP can deal with the diversity of splicing alterations, with 83.13% sensitivity and 99% specificity to detect spliceogenic variants. Overall performance as measured by area under the receiving operator curve was 0.986, better than SpliceAI and SQUIRLS (0.965 and 0.766) for the same data set. SPiP lends itself to a unique suite for comprehensive prediction of spliceogenicity in the genomic medicine era. SPiP is available at: https://sourceforge.net/projects/splicing-prediction-pipeline/.

Clinical, splicing, and functional analysis to classify BRCA2 exon 3 variants: Application of a points-based ACMG/AMP approach.

Skipping of BRCA2 exon 3 (∆E3) is a naturally occurring splicing event, complicating clinical classification of variants that may alter ∆E3 expression. This study used multiple evidence types to assess pathogenicity of 85 variants in/near BRCA2 exon 3. Bioinformatically predicted spliceogenic variants underwent mRNA splicing analysis using minigenes and/or patient samples. ∆E3 was measured using quantitative analysis. A mouse embryonic stem cell (mESC) based assay was used to determine the impact of 18 variants on mRNA splicing and protein function. For each variant, population frequency, bioinformatic predictions, clinical data, and existing mRNA splicing and functional results were collated. Variant class was assigned using a gene-specific adaptation of ACMG/AMP guidelines, following a recently proposed points-based system. mRNA and mESC analysis combined identified six variants with transcript and/or functional profiles interpreted as loss of function. Cryptic splice site use for acceptor site variants generated a transcript encoding a shorter protein that retains activity. Overall, 69/85 (81%) variants were classified using the points-based approach. Our analysis shows the value of applying gene-specific ACMG/AMP guidelines using a points-based approach and highlights the consideration of cryptic splice site usage to appropriately assign PVS1 code strength.

Re-analysis of genomic data: An overview of the mechanisms and complexities of clinical adoption.

Re-analyzing genomic information from a patient suspected of having an underlying genetic condition can improve the diagnostic yield of sequencing tests, potentially providing significant benefits to the patient and to the health care system. Although a significant number of studies have shown the clinical potential of re-analysis, less work has been performed to characterize the mechanisms responsible for driving the increases in diagnostic yield. Complexities surrounding re-analysis have also emerged. The terminology itself represents a challenge because "re-analysis" can refer to a range of different concepts. Other challenges include the increased workload that re-analysis demands of curators, adequate reimbursement pathways for clinical and diagnostic services, and the development of systems to handle large volumes of data. Re-analysis also raises ethical implications for patients and families, most notably when re-classification of a variant alters diagnosis, treatment, and prognosis. This review highlights the possibilities and complexities associated with the re-analysis of existing clinical genomic data. We propose a terminology that builds on the foundation presented in a recent statement from the American College of Medical Genetics and Genomics and describes each re-analysis process. We identify mechanisms for increasing diagnostic yield and provide perspectives on the range of challenges that must be addressed by health care systems and individual patients.