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• W4313252032 abstract "Nearly 14% of disease-causing germline variants result from the disruption of mRNA splicing. Most (67%) DNA variants predicted in silico to disrupt splicing are classified as variants of uncertain significance. An analytic workflow—splice effect event resolver (SPEER)—was developed and validated to use mRNA sequencing to reveal significant deviations in splicing, pinpoint the DNA variants potentially involved, and measure the deleterious effects of the altered splicing on mRNA transcripts, providing evidence for assessing the pathogenicity of the variant. SPEER was used to analyze leukocyte RNA encoding 63 hereditary cancer syndrome–related genes in 20,317 patients. Among 3563 patients (17.5%) with at least one DNA variant predicted to affect splicing, 971 (4.8%) had altered splicing with a deleterious effect on the transcript, and 40 had altered splicing due to a DNA variant located outside of the reportable range of the test. Integrating SPEER results into the interpretation of variants allowed variants of uncertain significance to be reclassified as pathogenic or likely pathogenic in 0.4%, and as benign or likely benign in 5.9%, of the 20,317 patients. SPEER-based evidence was associated with a significantly greater effect on classifications of pathogenic or likely pathogenic and benign or likely benign in nonwhite versus non-Hispanic white patients, illustrating that evidence derived from mRNA splicing analysis may help to reduce ethnic/ancestral disparities in genetic testing. Nearly 14% of disease-causing germline variants result from the disruption of mRNA splicing. Most (67%) DNA variants predicted in silico to disrupt splicing are classified as variants of uncertain significance. An analytic workflow—splice effect event resolver (SPEER)—was developed and validated to use mRNA sequencing to reveal significant deviations in splicing, pinpoint the DNA variants potentially involved, and measure the deleterious effects of the altered splicing on mRNA transcripts, providing evidence for assessing the pathogenicity of the variant. SPEER was used to analyze leukocyte RNA encoding 63 hereditary cancer syndrome–related genes in 20,317 patients. Among 3563 patients (17.5%) with at least one DNA variant predicted to affect splicing, 971 (4.8%) had altered splicing with a deleterious effect on the transcript, and 40 had altered splicing due to a DNA variant located outside of the reportable range of the test. Integrating SPEER results into the interpretation of variants allowed variants of uncertain significance to be reclassified as pathogenic or likely pathogenic in 0.4%, and as benign or likely benign in 5.9%, of the 20,317 patients. SPEER-based evidence was associated with a significantly greater effect on classifications of pathogenic or likely pathogenic and benign or likely benign in nonwhite versus non-Hispanic white patients, illustrating that evidence derived from mRNA splicing analysis may help to reduce ethnic/ancestral disparities in genetic testing. The disruption of normal mRNA splicing is a common cause of genetic disease. Approximately 14% of germline pathogenic or likely pathogenic (P/LP) variants cause disease through mis-splicing,1Truty R. Ouyang K. Rojahn S. Garcia S. Colavin A. Hamlington B. Freivogel M. Nussbaum R.L. Nykamp K. Aradhya S. Spectrum of splicing variants in disease genes and the ability of RNA analysis to reduce uncertainty in clinical interpretation.Am J Hum Genet. 2021; 108: 696-708Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar with the vast majority (>90%) of these P/LP splicing variants disrupting the canonical donor and acceptor splice sites (first 1 to 2 bp flanking an exon). In contrast, many DNA variants other than in the canonical donor and acceptor dinucleotide splice sites are predicted by commonly used algorithms to affect splicing, but most (69%) of these potential splicing variants (PSpVs) are classified as variants of uncertain significance (VUSs),1Truty R. Ouyang K. Rojahn S. Garcia S. Colavin A. Hamlington B. Freivogel M. Nussbaum R.L. Nykamp K. Aradhya S. Spectrum of splicing variants in disease genes and the ability of RNA analysis to reduce uncertainty in clinical interpretation.Am J Hum Genet. 2021; 108: 696-708Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar demonstrating the challenge of classifying non–canonical donor and acceptor splice site PSpVs in the absence of confirmatory RNA testing. Knowing which of these PSpVs have a clinically significant effect on splicing could inform the interpretation of variants, resulting in fewer VUSs and increased actionability in genetic testing.2Smirnov D. Schlieben L.D. Peymani F. Berutti R. Prokisch H. Guidelines for clinical interpretation of variant pathogenicity using RNA phenotypes.Hum Mutat. 2022; 43: 1056-1070Crossref PubMed Scopus (4) Google Scholar Here, a novel method is described: the splice effect event resolver (SPEER), which uses RNA sequencing to determine whether variants found during DNA sequencing cause aberrant mRNA splicing and have a deleterious impact on the structure and function of transcripts. SPEER accomplishes this task in three steps: i) determining whether splicing is abnormal compared to that in controls, ii) quantifying the reduction in normal splicing at a crucial splice junction, and iii) examining the consequences of abnormal-splicing events on mRNA structure and function. SPEER begins with short-read sequencing of cDNA and several overlapping RNA-based analytical methods,3Li Y.I. Knowles D.A. Humphrey J. Barbeira A.N. Dickinson S.P. Im H.K. Pritchard J.K. Annotation-free quantification of RNA splicing using LeafCutter.Nat Genet. 2018; 50: 151-158Crossref PubMed Scopus (283) Google Scholar, 4Frésard L. Smail C. Ferraro N.M. Teran N.A. Li X. Smith K.S. Bonner D. Kernohan K.D. Marwaha S. Zappala Z. Balliu B. Davis J.R. Liu B. Prybol C.J. Kohler J.N. Zastrow D.B. Reuter C.M. Fisk D.G. Grove M.E. Davidson J.M. Hartley T. Joshi R. Strober B.J. Utiramerur S. Lind L. Ingelsson E. Battle A. Bejerano G. Bernstein J.A. Ashley E.A. Boycott K.M. Merker J.D. Wheeler M.T. Montgomery S.B. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.Nat Med. 2019; 25: 911-919Crossref PubMed Scopus (157) Google Scholar, 5Mertes C. Scheller I.F. Yépez V.A. Çelik M.H. Liang Y. Kremer L.S. Gusic M. Prokisch H. Gagneur J. Detection of aberrant splicing events in RNA-seq data using FRASER.Nat Commun. 2021; 12: 529Crossref PubMed Scopus (49) Google Scholar, 6Jenkinson G. Li Y.I. Basu S. Cousin M.A. Oliver G.R. Klee E.W. LeafCutterMD: an algorithm for outlier splicing detection in rare diseases.Bioinformatics. 2020; 36: 4609-4615Crossref PubMed Scopus (22) Google Scholar, 7Vaquero-Garcia J. Barrera A. Gazzara M.R. González-Vallinas J. Lahens N.F. Hogenesch J.B. Lynch K.W. Barash Y. A new view of transcriptome complexity and regulation through the lens of local splicing variations.Elife. 2016; 5: e11752Crossref PubMed Scopus (221) Google Scholar to analyze exon–exon junctions and to determine whether the alterations observed in the mRNA-splicing pattern of a gene containing a PSpV are statistically significant relative to those in a panel of controls. In this way, SPEER distinguishes variant-specific alterations in splicing from the naturally occurring alternative splicing that generates protein diversity and function specificity across tissue types.8Sibley C.R. Blazquez L. Ule J. Lessons from non-canonical splicing.Nat Rev Genet. 2016; 17: 407-421Crossref PubMed Scopus (160) Google Scholar, 9Baralle F.E. Giudice J. Alternative splicing as a regulator of development and tissue identity.Nat Rev Mol Cell Biol. 2017; 18: 437-451Crossref PubMed Scopus (672) Google Scholar, 10Ule J. Blencowe B.J. Alternative splicing regulatory networks: functions, mechanisms, and evolution.Mol Cell. 2019; 76: 329-345Abstract Full Text Full Text PDF PubMed Scopus (287) Google Scholar Next, it provides a metric for quantifying the extent to which normal splicing is lost in a patient sample compared to controls. Finally, SPEER collects all overlapping junction changes into one or more groups of abnormal-splicing events, which can be evaluated for reading frame shifts and potential nonsense-mediated mRNA decay (NMD). With the assessment of the deleterious impact of a DNA variant on transcript structure and function, the evidence generated by SPEER can be used for the interpretation of variants within a system, such as Sherloc,11Nykamp K. Anderson M. Powers M. Garcia J. Herrera B. Ho Y.-Y. Kobayashi Y. Patil N. Thusberg J. Westbrook M. Topper S. Invitae Clinical Genomics GroupSherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria.Genet Med. 2017; 19: 1105-1117Abstract Full Text Full Text PDF PubMed Scopus (404) Google Scholar that combines many other types of evidence to ultimately assess the pathogenicity of a variant. This study reports on the validation of the capacity of SPEER to detect aberrant-splicing events and its application in a cohort of >20,000 patients undergoing testing for hereditary cancer syndrome–related genes to identify which PSpVs detected during DNA sequencing significantly altered splicing and had a deleterious effect on transcript structure and function. SPEER also detected abnormal-splicing changes in a small number of patients with DNA variants located outside of the reportable range of a next-generation sequencing (NGS) DNA test. When SPEER-based evidence was used in Sherloc for the interpretation of variants, a significant fraction of PSpVs were interpreted definitively as P/LP or as benign or likely benign (B/LB); most notably, SPEER allowed for an even greater rate of definitive classification of PSpVs among patients who self-reported as nonwhite compared to those who self-reported as non-Hispanic white. In stage 1 of this two-stage study, a retrospective cohort of 532 research participants whose data were to be used for SPEER validation was ascertained. Of these, 342 had undergone prior genetic testing for hereditary cancer syndromes, performed at Invitae Corporation (San Francisco, CA), and had a germline DNA variant that was known or predicted to alter splicing (PSpV), and 190 did not have a PSpV, but had a personal or family history strongly suggestive of a particular hereditary cancer syndrome, such as familial adenomatous polyposis (Online Mendelian Inheritance in Man no. 175100). Informed written consent was obtained under a protocol approved by the WCG Institutional Review Board (protocol approval number 20190811). As a reference panel, 273 de-identified samples from male and female blood donors presumed to be healthy were used (BioIVT, Westbury, NY); self-reported ethnicities/ancestries underrepresented in genetic studies, including African American (51.2%) and Hispanic (30.4%), were oversampled. This sampling strategy was designed to guard against mistaken inference of aberrant splicing when comparing splicing in nonwhite versus white individuals by failing to take into account potential variation in splicing of germline transcripts among individuals of different ethnicities/ancestries, as has been seen in tumor samples in The Cancer Genome Atlas.12Al Abo M. Hyslop T. Qin X. Owzar K. George D.J. Patierno S.R. Freedman J.A. Differential alternative RNA splicing and transcription events between tumors from African American and white patients in The Cancer Genome Atlas.Genomics. 2021; 113: 1234-1246Crossref PubMed Scopus (5) Google Scholar In stage 2, a prospective cohort of 20,317 patients referred to Invitae for hereditary cancer–related gene testing underwent paired DNA and RNA sequencing between July 2021 and May 2022. SPEER was used to analyze the leukocyte RNA of 63 hereditary cancer–related genes in these patients. The use of de-identified samples and data was approved by an independent Institutional Review Board (approval number 20161796; WCG). A total of 85 hereditary cancer–related genes were initially considered for RNA analysis (Supplemental Table S1). Twenty-two of these were ultimately excluded from the final assay because: i) in leukocyte transcripts they are expressed at a level too low to allow a statistically significant demonstration of a splicing change, ii) they are genes in which a loss of function—the usual effect of abnormal splicing—is not a known mechanism of an increased risk for cancer, or iii) they have only a single known loss-of-function missense variant associated with an increased risk for cancer. NGS of gene panels was performed as previously described,1Truty R. Ouyang K. Rojahn S. Garcia S. Colavin A. Hamlington B. Freivogel M. Nussbaum R.L. Nykamp K. Aradhya S. Spectrum of splicing variants in disease genes and the ability of RNA analysis to reduce uncertainty in clinical interpretation.Am J Hum Genet. 2021; 108: 696-708Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar,13Truty R. Paul J. Kennemer M. Lincoln S.E. Olivares E. Nussbaum R.L. Aradhya S. Prevalence and properties of intragenic copy-number variation in mendelian disease genes.Genet Med. 2019; 21: 114-123Abstract Full Text Full Text PDF PubMed Scopus (98) Google Scholar,14Kurian A.W. Hare E.E. Mills M.A. Kingham K.E. McPherson L. Whittemore A.S. McGuire V. Ladabaum U. Kobayashi Y. Lincoln S.E. Cargill M. Ford J.M. Clinical evaluation of a multiple-gene sequencing panel for hereditary cancer risk assessment.J Clin Oncol. 2014; 32: 2001-2009Crossref PubMed Scopus (385) Google Scholar using oligonucleotide baits (Twist Bioscience, South San Francisco, CA; Integrated DNA Technologies, Coralville, IA) to capture coding exon sequences ±20 bp of flanking intronic sequences, and certain noncoding regions of clinical interest, defined as the reportable range. Targeted regions were sequenced to a minimum depth of 50× and a mean depth of 350× read coverage at each nucleotide position within the reportable range. Full gene sequencing, deletion/duplication analysis, and the interpretation of variants were performed at Invitae, as previously described.11Nykamp K. Anderson M. Powers M. Garcia J. Herrera B. Ho Y.-Y. Kobayashi Y. Patil N. Thusberg J. Westbrook M. Topper S. Invitae Clinical Genomics GroupSherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria.Genet Med. 2017; 19: 1105-1117Abstract Full Text Full Text PDF PubMed Scopus (404) Google Scholar,14Kurian A.W. Hare E.E. Mills M.A. Kingham K.E. McPherson L. Whittemore A.S. McGuire V. Ladabaum U. Kobayashi Y. Lincoln S.E. Cargill M. Ford J.M. Clinical evaluation of a multiple-gene sequencing panel for hereditary cancer risk assessment.J Clin Oncol. 2014; 32: 2001-2009Crossref PubMed Scopus (385) Google Scholar PSpVs were predicted initially using MaxEntScan,15Yeo G. Burge C.B. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals.J Comput Biol. 2004; 11: 377-394Crossref PubMed Scopus (1393) Google Scholar SpliceSiteFinder-Like,16Shapiro M.B. Senapathy P. RNA splice junctions of different classes of eukaryotes: sequence statistics and functional implications in gene expression.Nucleic Acids Res. 1987; 15: 7155-7174Crossref PubMed Scopus (1988) Google Scholar and the Alamut Splicing Module, all available within Alamut HT software version 1.4.4_2016.02.03 (Interactive Biosoftware, Rouen, France). Later in the prospective study, PSpVs were predicted using SpliceAI software version 1.4.0.17Jaganathan K. Kyriazopoulou Panagiotopoulou S. McRae J.F. Darbandi S.F. Knowles D. Li Y.I. Kosmicki J.A. Arbelaez J. Cui W. Schwartz G.B. Chow E.D. Kanterakis E. Gao H. Kia A. Batzoglou S. Sanders S.J. Farh K.K.-H. Predicting splicing from primary sequence with deep learning.Cell. 2019; 176: 535-548.e24Abstract Full Text Full Text PDF PubMed Scopus (835) Google Scholar RNA was extracted from whole blood in PaxGene RNA tubes (catalog number 762165; Becton, Dickinson, and Company, Franklin Lakes, NJ) containing additives that inhibit RNA degradation, including NMD, using RNAdvance Blood (catalog number A35604; Beckman Coulter, Indianapolis, IN), and quantified by fluorometry. Residual DNA contamination was removed by DNaseI treatment (catalog number M0303L; New England BioLabs, Ipswich, MA). Indexed cDNA libraries were prepared using the KAPA hyper RNA kit (catalog number KK8541; F. Hoffmann-La Roche, Basel, Switzerland), substituting proprietary adapters and indexing primers. Transcripts of interest were enriched via hybridization with custom-designed biotinylated oligonucleotide baits (Integrated DNA Technologies) followed by streptavidin bead capture. Illumina-compatible P5 and P7 sequences were added during post-capture amplification, and libraries were submitted for 2 × 150 bp paired-end NGS (Illumina, San Diego, CA), targeting 15 million clusters per sample. Adapters were trimmed from sequencing reads and unique molecular identifiers were extracted. Trimmed reads were aligned on the GRCh37 human genome assembly with STAR software version 2.7.5a,18Dobin A. Davis C.A. Schlesinger F. Drenkow J. Zaleski C. Jha S. Batut P. Chaisson M. Gingeras T.R. STAR: ultrafast universal RNA-seq aligner.Bioinformatics. 2012; 29: 15-21Crossref PubMed Scopus (21979) Google Scholar using a RefSeq-based transcript annotation. Duplicate reads were removed using unique molecular identifier sequences (UMI-Tools software version 1.0.119Smith T. Heger A. Sudbery I. UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy.Genome Res. 2017; 27: 491-499Crossref PubMed Scopus (724) Google Scholar). Raw split-read counts were extracted from the deduplicated BAM files using the sam2bed.pl and bed2junc.pl scripts in LeafCutter software version 0.2.7.3Li Y.I. Knowles D.A. Humphrey J. Barbeira A.N. Dickinson S.P. Im H.K. Pritchard J.K. Annotation-free quantification of RNA splicing using LeafCutter.Nat Genet. 2018; 50: 151-158Crossref PubMed Scopus (283) Google Scholar SPEER used targeted sequencing of RNA isolated from blood samples to determine splice-junction counts across 63 genes of interest (Supplemental Table S1). To assess the statistical significance of splice-junction usage in a patient sample, SPEER (Figure 1, A and B ) measured the usage of known and novel splice junctions using percent spliced in (PSI),20Katz Y. Wang E.T. Airoldi E.M. Burge C.B. Analysis and design of RNA sequencing experiments for identifying isoform regulation.Nat Methods. 2010; 7: 1009-1015Crossref PubMed Scopus (925) Google Scholar,21Schafer S. Miao K. Benson C.C. Heinig M. Cook S.A. Hubner N. Alternative splicing signatures in RNA-seq data: percent spliced in (PSI).Curr Protoc Hum Genet. 2015; 87: 11.16.1-11.16.14Crossref PubMed Scopus (83) Google Scholar defined as the ratio of the number of reads supporting the junction divided by the total number of reads overlapping the junction (ie, supporting reads plus nonsupporting reads). SPEER assessed the statistical significance of each splicing change in a patient sample by comparing it to the splice-junction usage from the 273-sample reference panel using a β-binomial test described in -Binomial Model Description and Parameter Estimatio. PSI was considered significantly different from that of the control panel at P ≤ 0.001. SPEER uses these significant junctions as seeds to recursively find other altered (P ≤ 0.05) junctions that share breakpoints in order to link all impacted junctions into a group of abnormal-splicing events, for downstream interpretation. For every junction observed in at least one control, a PSI normal model was calculated using the β-binomial distribution. In this model, the number of reads that overlapped the junction was considered as the number of trials, and the number of reads that supported the junction was considered the number of successes. For each junction, the parameters ν (approximately the mean PSI) and ρ (a measure of the increased variance relative to the binomial distribution) were fit to their maximum-likelihood values and stored as a panel of normal splicing (PON) bed file. The control parameters of each junction were then compared to the corresponding PSI in each patient sample to generate a P value using a β-binomial likelihood ratio test. This statistical approach was inspired by the ShearwaterML algorithm for detecting low-frequency somatic DNA variants from NGS-based data.22Martincorena I. Roshan A. Gerstung M. Ellis P. Van Loo P. McLaren S. Wedge D.C. Fullam A. Alexandrov L.B. Tubio J.M. Stebbings L. Menzies A. Widaa S. Stratton M.R. Jones P.H. Campbell P.J. Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin.Science. 2015; 348: 880-886Crossref PubMed Scopus (1088) Google Scholar Each abnormal splicing–event group observed in a patient sample included a reduction in the PSI of one or more canonical splice junctions, and an increase in PSI of one or more newly generated or noncanonical splice junctions (Figure 1A). To measure the reduction in normal splicing associated with a PSpV, a ratio fold-change in PSI (PSI-X) was calculated, as follows: (PSI of the canonical junction determined to be directly impacted by the PSpV)/(PSI of that same junction in the control panel). The PSI-X was expected to correlate with the loss of the normal or most commonly expressed transcript. Moreover, a significant reduction in PSI-X was expected to correlate with pathogenicity for PSpVs in genes known to cause disease with an autosomal dominant, loss-of-function mechanism. Directly impacted canonical junctions were defined as follows: for intronic PSpVs, the canonical junction that was normally generated by splicing out the intron that contained the PSpV was chosen; for exonic PSpVs, the canonical junction with the highest PSI in the control panel that also shared a breakpoint with a newly generated, noncanonical junction was chosen. If the highest PSI value of a canonical junction was less than twofold that of other canonical junctions with shared breakpoints, the mean PSI across all directly impacted canonical junctions was calculated. Abnormal splicing–event groups with both a reduction in PSI-X and at least one breakpoint within 50 bp of a PSpV were flagged for review by trained variant interpretation scientists (K.N.). This step was essential for assessing whether the transcript generated by noncanonical junctions may have compensated for the reduction in normally spliced mRNA. For example, a PSpV that abolishes a well-annotated acceptor splice may have resulted in a substantially decreased PSI-X while also activating a cryptic acceptor splice site 3 nucleotides upstream, resulting in a novel protein with a single amino acid insertion. Whether this novel protein will compensate for the loss of the normally expressed protein is uncertain, thus warranting a VUS classification of the PSpV. The first step in this review was to classify each splice junction in an event group, as follows: i) canonical (a junction formed by adjacent exons in the full-length canonical transcript); ii) exon skipping (nonadjacent junctions with one more exons being skipped); iii) partial exon exclusion (a portion of a canonical exon was not included in the transcript); iv) partial intron inclusion (a portion of an intron was included in a transcript); and v) cryptic exon (two noncanonical junctions flanked an intronic sequence). Event groups with a single noncanonical junction causing a frameshift and/or premature termination codon upstream of the last coding exon were classified as NMD+. Those expected to result in in-frame insertions or deletions anywhere in the transcript, or premature termination codons downstream of the last exon junction, were classified as NMD−. Event groups with multiple noncanonical junctions, and with different predicted effects on protein translation, were classified as complex. Rare variants located outside of the reportable range (all coding exons plus 20 bp flanking each exon) of the test are missed by standard panel-based NGS. If SPEER identified an abnormal splicing–event group at the variant discovery–significance threshold (ie, P ≤ 0.0001), and if one or more abnormal junctions exhibited a PSI-X of ≥10,000, the primary NGS–based data were manually examined for a DNA variant that may have explained the abnormal-splicing event. If none was found in the primary data, long-read DNA sequencing (PacBio, Menlo Park, CA) was performed throughout the region surrounding the abnormal-splicing event. If a PSpV was identified in the region surrounding the abnormal-splicing event, it was flagged for interpretation by the variant-interpretation scientist team. If a PSpV could not be identified, the abnormal-splicing event was presumed to be a technical false positive or the result of a natural variation in splicing, and was not included in the clinical report. To standardize the application of SPEER-based evidence for variant classification, a new category of evidence was generated within the semiquantitative variant-classification framework, Sherloc.11Nykamp K. Anderson M. Powers M. Garcia J. Herrera B. Ho Y.-Y. Kobayashi Y. Patil N. Thusberg J. Westbrook M. Topper S. Invitae Clinical Genomics GroupSherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria.Genet Med. 2017; 19: 1105-1117Abstract Full Text Full Text PDF PubMed Scopus (404) Google Scholar This evidence category was referred to as observed RNA effects (Supplemental Table S2). Each criterion within this evidence category was assigned pathogenic (P) or benign (B) point scores. Point scores were assigned and calibrated by applying the criteria on observed RNA effects to >300 PSpVs observed in the retrospective cohort of 532 research participants in stage 1. Final classifications in stage 2 of the study were assigned based on the sum of all available evidence, including the criteria for observed RNA effects, when applicable. Rates of PSpV upgrades (VUS to LP/P) and downgrades (VUS to LB/B) observed during stage 2 of the study were calculated by removing the criteria for observed RNA effects and asking whether the final classification (and Sherloc score) was changed from P/LP or B/LB to VUS. Differences in these classification rates among self-reported ethnicity/ancestry groups were tested for significance using the one-sided Fisher exact test, by comparing the percentage of patients with downgrades and upgrades in the most commonly self-reported ethnic/ancestral group (non-Hispanic white) to those in each other ethnic/ancestral group (black/African American, Hispanic, Asian, and other). SPEER was validated using 50 positive-control samples containing 46 unique DNA variants, as follows: 10 samples with 7 unique variants well documented to alter splicing according to the published literature; 13 samples with 12 unique variants in the essential GT or AG dinucleotides, but without published functional evidence; and 27 samples with 27 unique variants located within 50 nucleotides of a significant abnormal-splicing event (P ≤ 0.001). A false positive was defined as any significant (P ≤ 0.001) abnormal-splicing event detected throughout the 63-gene panel for a canonical junction not within 50 bp of a known PSpV. True negatives (TNs) were derived from two sets of samples. The first set was the same 50 samples that had served as true positives in one junction–variant pair, repurposed as a source of TNs by analysis of all other splice junctions lacking a PSpV within 50 bp. The second set of TN samples consisted of 16 samples with a PSpV that nonetheless yielded a normal splicing pattern based on a manual review of RNA sequence alignments to establish the absence of a splicing change. The combination of the two sets yielded 66 TN samples suitable for calculating sensitivity, specificity, accuracy, and reproducibility of SPEER-based findings between replicates of the same sample analyzed in parallel and in the same sample over time (Figure 1, C and D, and Supplemental Table S3). SPEER detected a significant splicing change in 45 of 46 positive-control DNA variants in the 50 positive-control samples (97.8% sensitivity) (Figure 1C), while maintaining a specificity of 99.7% and accuracy of 99.7%. Specificity was calculated as 100 ∗ (TN)/(TN + false positives), and accuracy was calculated as 100 ∗ (true positives + TN)/(total number of calls). Accuracy was similar to specificity because the vast majority of loci were negative for splice variants. Two of the remaining 4 positive-control samples, along with 8 of the negative-control samples, were used to perform reproducibility and repeatability testing in triplicate across all 10 samples. All 30 of the reproducibility samples (100%) and 29 of 30 repeatability samples (96.7%) yielded the expected results (Figure 1C). The single replicate that failed the repeatability component showed a false-positive splice aberration at a normally spliced locus. True-positive and TN calls demonstrated strong separation at the P threshold used for detecting splicing effects (Figure 1D), and the same variants found in multiple samples demonstrated consistent PSI values (data not shown). SPEER also enabled the detection of variants in APC (c.730-494C>T, c.933_829A>G), ATM (c.1899-123A>G), and MSH2 (c.2459-954A>G) outside of the reportable ranges in four patients from the stage 1 research cohort (Supplemental Table S4), all of whom had presented with a personal and/or family history suggestive of high risk for a hereditary cancer syndrome. SPEER detected abnormal-splicing events associated with PSpVs with very high accuracy. The next step was to calculate PSI-X as a measure of the loss of normal splicing at the canonical junction most relevant to the PSpV compared to controls. The distribution of PSI-X values was plotted for variants previously classified as P/LP due to a clinicall" @default.
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• W4313252032 title "A Systematic Method for Detecting Abnormal mRNA Splicing and Assessing Its Clinical Impact in Individuals Undergoing Genetic Testing for Hereditary Cancer Syndromes" @default.
• W4313252032 cites W1956871134 @default.
• W4313252032 cites W2034691490 @default.
• W4313252032 cites W2095787833 @default.
• W4313252032 cites W2099013108 @default.
• W4313252032 cites W2115149771 @default.
• W4313252032 cites W2159730169 @default.
• W4313252032 cites W2169456326 @default.
• W4313252032 cites W2255131131 @default.
• W4313252032 cites W2415174329 @default.
• W4313252032 cites W2612091911 @default.
• W4313252032 cites W2612976808 @default.
• W4313252032 cites W2774883395 @default.
• W4313252032 cites W2808661329 @default.
• W4313252032 cites W2909194804 @default.
• W4313252032 cites W2948477377 @default.
• W4313252032 cites W2952109315 @default.
• W4313252032 cites W2980949872 @default.
• W4313252032 cites W3017021858 @default.
• W4313252032 cites W3125383934 @default.
• W4313252032 cites W3134586971 @default.
• W4313252032 cites W3137526291 @default.
• W4313252032 cites W4281615186 @default.
• W4313252032 cites W4293052134 @default.
• W4313252032 doi "https://doi.org/10.1016/j.jmoldx.2022.12.002" @default.
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