Prevalence of Mendelian Kidney Disease Among Patients With High-Risk APOL1 Genotypes Undergoing Commercial Genetic Testing in the United States.

Introduction: Among individuals with high-risk APOL1 genotypes, the lifetime risk of developing kidney failure is ∼15%, indicating that other genetic variants or nongenetic modifiers likely contribute substantially to an individual patient's risk of progressive kidney disease. Here, we estimate the prevalence and distribution of Mendelian kidney diseases among patients with high-risk APOL1 genotypes undergoing commercial genetic testing in the United States. Methods: We analyzed clinical exome sequencing data from 15,181 individuals undergoing commercial genetic testing for Mendelian kidney disease in the United States from 2020 to 2021. We identified patients with high-risk APOL1 genotypes by the presence of G1/G1, G1/G2, or G2/G2 alleles. Patients carrying single risk APOL1 alleles were identified as G1/G0, G2/G0; the remainder of patients were G0/G0. We estimated the prevalence and distribution of Mendelian kidney disease stratified by APOL1 genotype and genetically predicted ancestry. Results: Of 15,181 patients, 3119 had genetic testing results consistent with a molecular diagnosis of Mendelian kidney disease (20.5%). Of 15,181 patients, 1035 (6.8%) had high-risk APOL1 genotypes. Among patients with recent genomic African ancestry, the prevalence of Mendelian kidney diseases was lower in those with high-risk APOL1 genotypes (9.6%; n = 91/944) compared with single risk APOL1 allele carriers (13.6%; n = 198/1453) and those with G0/G0 APOL1 genotypes (16.6%; n = 213/1281). Among patients with Mendelian kidney disease and recent genomic African ancestry, we observed differences in the prevalence of pathogenic/likely pathogenic variants in PKD1 (19.8% in high-risk vs. 30.2% in low-risk genotypes), and COL4A4 (24.2% in high-risk vs. 10.5% in low-risk genotypes). Conclusion: In this selected population of patients undergoing clinical genetic testing, we found evidence of Mendelian kidney disease in ∼10% patients with high-risk APOL1 genotypes.

Heterozygous loss of function variants in IFT140 are associated with polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) affects 1 in 1000 adults. Most cases result from causative PKD1 or PKD2 variants. HNF1B, GANAB and ALG9 variants are also associated with ADPKD. Recent evidence indicates that monoallelic loss-of-function (LoF) IFT140 variants are a cause for non-syndromic ADPKD. We describe 368 patients with IFT140 LoF variants and a spectrum of phenotypic findings that support the association of IFT140 with PKD. We reviewed patients with an unknown cause for their cystic disease and those with heterozygous LoF IFT140 variants classified as pathogenic or likely pathogenic from a cohort that received genetic testing using a panel of 385 renal disease-associated genes. IFT140 LoF variants were significantly enriched in patients with cystic disease when compared with those without cystic disease. A cystic phenotype was reported in 223 of the 368 (60.6%) individuals harboring an IFT140 LoF variant, 98% of which had no other identified cause for their cystic disease. Of 122 unique LoF IFT140 variants identified, 56 (46%) were frameshift, 38 (31%) nonsense, 22 (18%) splice site and 6 (5%) exon-level deletions. Only six IFT140 individuals were reported with end-stage kidney disease, consistent with observed milder clinical presentations in IFT140-related PKD. This study offers further evidence for the involvement of LoF IFT140 variants in PKD, particularly when no additional molecular etiology has been identified.

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.

APPLICATION OF THE ACMG/AMP FRAMEWORK TO CAPTURE EVIDENCE RELEVANT TO PREDICTED AND OBSERVED IMPACT ON SPLICING: RECOMMENDATIONS FROM THE CLINGEN SVI SPLICING SUBGROUP.

The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) framework for classifying variants uses six evidence categories related to the splicing potential of variants: PVS1 (null variant in a gene where loss-of-function is the mechanism of disease), PS3 (functional assays show damaging effect on splicing), PP3 (computational evidence supports a splicing effect), BS3 (functional assays show no damaging effect on splicing), BP4 (computational evidence suggests no splicing impact), and BP7 (silent change with no predicted impact on splicing). 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 (SVI) Splicing Subgroup was established to refine recommendations for applying ACMG/AMP codes relating to splicing data and computational predictions. Our study utilised 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 bioinformatic splice prediction tools. We propose repurposing of 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 impact on splicing for both intronic and synonymous variants, and for missense variants if protein functional impact has been excluded. Furthermore, we propose that the PS3 and BS3 codes are applied only for well-established assays that measure functional impact that is 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 to a known Pathogenic variant. The recommendations and approaches for consideration and evaluation of RNA assay evidence described aim to help standardise variant pathogenicity classification processes and result in greater consistency when interpreting splicing-based evidence.

Effect of a Neoprene Knee Sleeve on Performance and Muscle Activity in Men and Women During High-Intensity, High-Volume Resistance Training.

ABSTRACT: Hatfield, DL, Stranieri, AM, Vincent, LM, and Earp, JE. Effect of a neoprene knee sleeve on performance and muscle activity in men and women during high-intensity, high-volume resistance training. J Strength Cond Res 35(12): 3300-3307, 2021-The purpose of this study was to assess the effects of a commercially available neoprene knee sleeve (KS) on exercise performance and muscle activity during an exhaustive leg press exercise. Twenty resistance-trained individuals, 11 men {21.0 ± 2.2 years; 77.7 ± 8.7 kg; 1 repetition maximum (1RM/body mass [BM]): 0.30 ± 0.04} and 9 women (22.0 ± 3.5 years; 66.1 ± 9.1 kg; 1RM/BM: 0.30 ± 0.04), all subjects (21.5 ± 2.8 years; 72.5 ± 10.5 kg; 1RM/BM: 0.30 ± 0.04), participated in 3 testing sessions. The second and third sessions were performed using a counterbalanced and randomized design in which subjects exercised with (WS) or without (NS) KSs and performed 6 sets of leg press exercise at 80% of 1RM until failure with a 3-minute rest between sets. Number of repetitions, blood lactate (BL), heart rate (HR), rating of perceived exertion (RPE), and peak and average power were recorded after each set. Surface electromyography (EMG) of the right and left vastus lateralis muscles was also recorded to compare muscle activity between conditions. Significance was set at p ≤ 0.05, and values are presented as mean ± SD. No significant differences were observed in the total number of repetitions for all sets (p = 0.3; WS 75.3 ± 33.7, NS 79.8 ± 34.3) and the number of repetitions per set between conditions (p ≤ 0.05) or between men and women. Similarly, no significance differences (p ≤ 0.05) were observed for BL, HR, RPE, or EMG per set between conditions or between men and women. These results suggest that wearing compressive neoprene KSs has no effect on improving performance and associated variables during high-load, high-volume lower-body resistance training.

Next-generation sequencing for constitutional variants in the clinical laboratory, 2021 revision: a technical standard of the American College of Medical Genetics and Genomics (ACMG).

Next-generation sequencing (NGS) technologies are now established in clinical laboratories as a primary testing modality in genomic medicine. These technologies have reduced the cost of large-scale sequencing by several orders of magnitude. It is now cost-effective to analyze an individual with disease-targeted gene panels, exome sequencing, or genome sequencing to assist in the diagnosis of a wide array of clinical scenarios. While clinical validation and use of NGS in many settings is established, there are continuing challenges as technologies and the associated informatics evolve. To assist clinical laboratories with the validation of NGS methods and platforms, the ongoing monitoring of NGS testing to ensure quality results, and the interpretation and reporting of variants found using these technologies, the American College of Medical Genetics and Genomics (ACMG) has developed the following technical standards.

Correction: Adapting ACMG/AMP sequence variant classification guidelines for single-gene copy-number variants.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Adapting ACMG/AMP sequence variant classification guidelines for single-gene copy number variants.

PURPOSE: The ability of a single technology, next-generation sequencing, to provide both sequence and copy number variant (CNV) results has driven the merger of clinical cytogenetics and molecular genetics. Consequently, the distinction between the definition of a sequence variant and a CNV is blurry. As the 2015 American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) standards and guidelines for interpretation of sequence variants address CNV classification only sparingly, this study focused on adapting ACMG/AMP criteria for single-gene CNV interpretation. METHODS: CNV-specific modifications of the 2015 ACMG/AMP criteria were developed and their utility was independently tested by three diagnostic laboratories. Each laboratory team interpreted the same 12 single-gene CNVs using three systems: (1) without ACMG/AMP guidance, (2) with ACMG/AMP criteria, and (3) with new modifications. A replication study of 12 different CNVs validated the modified criteria. RESULTS: The adapted criteria system presented here showed improved concordance and usability for single-gene CNVs compared with using the ACMG/AMP interpretation guidelines focused on sequence variants. CONCLUSION: These single-gene CNV criteria modifications could be used as a supplement to the ACMG/AMP guidelines for sequence variants, allowing for a streamlined workflow and a step toward a uniform classification system for both sequence and copy number alterations.

Activating Mutations of RRAS2 Are a Rare Cause of Noonan Syndrome.

Aberrant signaling through pathways controlling cell response to extracellular stimuli constitutes a central theme in disorders affecting development. Signaling through RAS and the MAPK cascade controls a variety of cell decisions in response to cytokines, hormones, and growth factors, and its upregulation causes Noonan syndrome (NS), a developmental disorder whose major features include a distinctive facies, a wide spectrum of cardiac defects, short stature, variable cognitive impairment, and predisposition to malignancies. NS is genetically heterogeneous, and mutations in more than ten genes have been reported to underlie this disorder. Despite the large number of genes implicated, about 10%-20% of affected individuals with a clinical diagnosis of NS do not have mutations in known RASopathy-associated genes, indicating that additional unidentified genes contribute to the disease, when mutated. By using a mixed strategy of functional candidacy and exome sequencing, we identify RRAS2 as a gene implicated in NS in six unrelated subjects/families. We show that the NS-causing RRAS2 variants affect highly conserved residues localized around the nucleotide binding pocket of the GTPase and are predicted to variably affect diverse aspects of RRAS2 biochemical behavior, including nucleotide binding, GTP hydrolysis, and interaction with effectors. Additionally, all pathogenic variants increase activation of the MAPK cascade and variably impact cell morphology and cytoskeletal rearrangement. Finally, we provide a characterization of the clinical phenotype associated with RRAS2 mutations.

Predictors and consequences of sudden gains in transdiagnostic cognitive-behavioural therapy for anxiety disorders.

Sudden gains-substantial stable symptom improvements between consecutive therapy sessions-are a common phenomenon. As condensed points of change, examination of sudden gains can provide insight into mechanisms of therapeutic change. This study investigated the association between sudden gains and cognitive change, therapeutic alliance, and/or client engagement in transdiagnostic group cognitive-behavioural therapy for anxiety disorders. Of 58 treatment initiators, 21% (n = 12) exhibited a sudden gain. Consistent with previous research, sudden gainers demonstrated significantly greater pre- to post-treatment symptom improvement than non-sudden gainers. Observational coding of therapy sessions found that sudden gains were associated with elevated levels of cognitive change and client engagement in the pre-gain session, and elevated levels of cognitive change and therapeutic alliance in the post-gain session. However, these results varied by use of within- and between-subject control comparisons, highlighting the value using a dual control methodology. In context of previous research, the results on cognitive change replicate previous findings in depressive populations, and clarify mixed findings in anxiety populations. The results on therapeutic alliance replicate previous findings for the first time in an anxiety sample, although the between-subject control comparisons revealed complexity previously undetected. This study was also the first to investigate and thus establish the relation between client engagement and sudden gains.

National Scouting Combine Scores as Performance Predictors in the National Football League.

Vincent, LM, Blissmer, BJ, and Hatfield, DL. National Scouting Combine scores as performance predictors in the National Football League. J Strength Cond Res 33(1): 104-111, 2019-The National Football League (NFL) hosts an annual scouting combine to evaluate the approximately 300 elite college football players who are most likely to be selected in the upcoming NFL draft. Given the public interest, player obligations, coaching staff commitments, and business aspects of the combine, several questions have arose in recent years concerning the applicability of combine scores to eventual draft NFL performance. The primary purpose of this study is to investigate the relationship between specified National Scouting Combine (NSC) scores and measures of performance by player position. A secondary aim was to determine whether correlated variables could predict player performance at the quarterback (QB), running back (RB), wide receiver (WR), defensive end (DE), defensive tackle (DT), and linebacker (LB) positions. Subjects in this study were combine participants between the years 2005-2010 who subsequently played in the NFL. The positional groups investigated were QBs (N = 44), RBs (N = 82), WRs (N = 116), LBs (N = 139), DEs (N = 59), and DTs (N = 72). Combine raw scores for 40-yd dash time, countermovement vertical jump (CMVJ) height, standing long jump (SLJ) distance, and pro-agility time were recorded. Measures of horizontal and vertical power were calculated for the 40-yd dash and CMVJ. Combine scores and on-field positional statistics for the first 4 years for QBs and 3 years of all other players' careers were analyzed to investigate relationships. Significant correlations were shown between at least one combine measure and on-field success at every position. Hierarchal regression showed combine measures could predict between 4% and 62% of the variance for individual on-field variables. Quarterback rushing yards were significantly correlated with 40T, CMVJ, vertical jump power (VJP), vertical jump relative power (VJRP), and horizontal power (HP), and those factors accounted for 62.2% of the total variance. Horizontal power and VJP were predictive of QB rushing attempts (r = 0.370). At RB, 40T and SLJ combined were predictive of total rushing yards (r = 0.200), rushing attempts (r = 0.195), and yards per game (r = 0.197). Power variables were predictive of total tackles for DEs' 40HP (r = 0.096) and VJP (r = 0.018), accounting for a total of 21% of the variance. The current study suggests that combine tests are modest predictors of future performance. Should the NFL change the current NSC testing battery, the addition of horizontal and vertical power measurements, as well as position-specific skill tests are recommended.

Scaling resolution of variant classification differences in ClinVar between 41 clinical laboratories through an outlier approach.

ClinVar provides open access to variant classifications shared from many clinical laboratories. Although most classifications are consistent across laboratories, classification differences exist. To facilitate resolution of classification differences on a large scale, clinical laboratories were encouraged to reassess outlier classifications of variants with medically significant differences (MSDs). Outliers were identified by first comparing ClinVar submissions from 41 clinical laboratories to detect variants with MSDs between the laboratories (650 variants). Next, MSDs were filtered for variants with ≥3 classifications (244 variants), of which 87.6% (213 variants) had a majority consensus in ClinVar, thus allowing for identification of outlier classifications in need of reassessment. Laboratories with outlier classifications were sent a custom report and encouraged to reassess variants. Results were returned for 204 (96%) variants, of which 62.3% (127) were resolved. Of those 127, 64.6% (82) were resolved due to reassessment prompted by this study and 35.4% (45) resolved by a previously completed reassessment. This study demonstrates a scalable approach to classification resolution and capitalizes on the value of data sharing within ClinVar. These activities will help the community move toward more consistent variant classifications, which will improve the care of patients with, or at risk for, genetic disorders.

Assessing the gene-disease association of 19 genes with the RASopathies using the ClinGen gene curation framework.

The RASopathies are a complex group of conditions regarding phenotype and genetic etiology. The ClinGen RASopathy Expert Panel (RAS EP) assessed published and other publicly available evidence supporting the association of 19 genes with RASopathy conditions. Using the semiquantitative literature curation method developed by the ClinGen Gene Curation Working Group, evidence for each gene was curated and scored for Noonan syndrome (NS), Costello syndrome, cardiofaciocutaneous syndrome, NS with multiple lentigines, and Noonan-like syndrome with loose anagen hair. The curated evidence supporting each gene-disease relationship was then discussed and approved by the ClinGen RASopathy Expert Panel. Each association's strength was classified as definitive, strong, moderate, limited, disputed, or no evidence. Eleven genes were classified as definitively associated with at least one RASopathy condition. Two genes classified as strong for association with at least one RASopathy condition while one gene was moderate and three were limited. The RAS EP also disputed the association of two genes for all RASopathy conditions. Overall, our results provide a greater understanding of the different gene-disease relationships within the RASopathies and can help in guiding and directing clinicians, patients, and researchers who are identifying variants in individuals with a suspected RASopathy.

ClinGen Variant Curation Expert Panel experiences and standardized processes for disease and gene-level specification of the ACMG/AMP guidelines for sequence variant interpretation.

Genome-scale sequencing creates vast amounts of genomic data, increasing the challenge of clinical sequence variant interpretation. The demand for high-quality interpretation requires multiple specialties to join forces to accelerate the interpretation of sequence variant pathogenicity. With over 600 international members including clinicians, researchers, and laboratory diagnosticians, the Clinical Genome Resource (ClinGen), funded by the National Institutes of Health, is forming expert groups to systematically evaluate variants in clinically relevant genes. Here, we describe the first ClinGen variant curation expert panels (VCEPs), development of consistent and streamlined processes for establishing new VCEPs, and creation of standard operating procedures for VCEPs to define application of the ACMG/AMP guidelines for sequence variant interpretation in specific genes or diseases. Additionally, ClinGen has created user interfaces to enhance reliability of curation and a Sequence Variant Interpretation Working Group (SVI WG) to harmonize guideline specifications and ensure consistency between groups. The expansion of VCEPs represents the primary mechanism by which curation of a substantial fraction of genomic variants can be accelerated and ultimately undertaken systematically and comprehensively. We welcome groups to utilize our resources and become involved in our effort to create a publicly accessible, centralized resource for clinically relevant genes and variants.

Reclassification of the BRAF p.Ile208Val variant by case-level data sharing.

The ClinVar database is a useful tool for patients and physicians to view variant interpretations submitted by clinical and nonclinical labs. However, variants of uncertain significance (VUS) in ClinVar can pose a significant burden on patients. If possible, it is important to resolve discrepancies and uncertainties surrounding interpreted variants. Here we highlight a case of a family who received a report of a variant (c.622A>G, p.Ile208Val) in BRAF following prenatal RASopathy testing. The variant had been previously classified by our laboratory as a VUS, so the mother contacted our laboratory via ClinVar for further information, which prompted reevaluation of the variant. Multiple sources of case-level data as well as the presence of the variant in the general population yielded sufficient evidence to reclassify the variant as likely benign. This reclassification alleviated significant concern for the family, and the child was born healthy with no clinical manifestations of Noonan syndrome or a RASopathy.

ClinGen's RASopathy Expert Panel consensus methods for variant interpretation.

PURPOSE: Standardized and accurate variant assessment is essential for effective medical care. To that end, Clinical Genome (ClinGen) Resource clinical domain working groups (CDWGs) are systematically reviewing disease-associated genes for sufficient evidence to support disease causality and creating disease-specific specifications of American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines for consistent and accurate variant classification. METHODS: The ClinGen RASopathy CDWG established an expert panel to curate gene information and generate gene- and disease-specific specifications to ACMG-AMP variant classification framework. These specifications were tested by classifying 37 exemplar pathogenic variants plus an additional 66 variants in ClinVar distributed across nine RASopathy genes. RESULTS: RASopathy-related specifications were applied to 16 ACMG-AMP criteria, with 5 also having adjustable strength with availability of additional evidence. Another 5 criteria were deemed not applicable. Key adjustments to minor allele frequency thresholds, multiple de novo occurrence events and/or segregation, and strength adjustments impacted 60% of variant classifications. Unpublished case-level data from participating laboratories impacted 45% of classifications supporting the need for data sharing. CONCLUSION: RAS-specific ACMG-AMP specifications optimized the utility of available clinical evidence and Ras/MAPK pathway-specific characteristics to consistently classify RASopathy-associated variants. These specifications highlight how grouping genes by shared features promotes rapid multigenic variant assessment without sacrificing specificity and accuracy.

Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen's Inherited Cardiomyopathy Expert Panel.

PurposeIntegrating genomic sequencing in clinical care requires standardization of variant interpretation practices. The Clinical Genome Resource has established expert panels to adapt the American College of Medical Genetics and Genomics/Association for Molecular Pathology classification framework for specific genes and diseases. The Cardiomyopathy Expert Panel selected MYH7, a key contributor to inherited cardiomyopathies, as a pilot gene to develop a broadly applicable approach.MethodsExpert revisions were tested with 60 variants using a structured double review by pairs of clinical and diagnostic laboratory experts. Final consensus rules were established via iterative discussions.ResultsAdjustments represented disease-/gene-informed specifications (12) or strength adjustments of existing rules (5). Nine rules were deemed not applicable. Key specifications included quantitative frameworks for minor allele frequency thresholds, the use of segregation data, and a semiquantitative approach to counting multiple independent variant occurrences where fully controlled case-control studies are lacking. Initial inter-expert classification concordance was 93%. Internal data from participating diagnostic laboratories changed the classification of 20% of the variants (n = 12), highlighting the critical importance of data sharing.ConclusionThese adapted rules provide increased specificity for use in MYH7-associated disorders in combination with expert review and clinical judgment and serve as a stepping stone for genes and disorders with similar genetic and clinical characteristics.

Noonan syndrome in diverse populations.

Noonan syndrome (NS) is a common genetic syndrome associated with gain of function variants in genes in the Ras/MAPK pathway. The phenotype of NS has been well characterized in populations of European descent with less attention given to other groups. In this study, individuals from diverse populations with NS were evaluated clinically and by facial analysis technology. Clinical data and images from 125 individuals with NS were obtained from 20 countries with an average age of 8 years and female composition of 46%. Individuals were grouped into categories of African descent (African), Asian, Latin American, and additional/other. Across these different population groups, NS was phenotypically similar with only 2 of 21 clinical elements showing a statistically significant difference. The most common clinical characteristics found in all population groups included widely spaced eyes and low-set ears in 80% or greater of participants, short stature in more than 70%, and pulmonary stenosis in roughly half of study individuals. Using facial analysis technology, we compared 161 Caucasian, African, Asian, and Latin American individuals with NS with 161 gender and age matched controls and found that sensitivity was equal to or greater than 94% for all groups, and specificity was equal to or greater than 90%. In summary, we present consistent clinical findings from global populations with NS and additionally demonstrate how facial analysis technology can support clinicians in making accurate NS diagnoses. This work will assist in earlier detection and in increasing recognition of NS throughout the world.

Clinical laboratories collaborate to resolve differences in variant interpretations submitted to ClinVar.

PURPOSE: Data sharing through ClinVar offers a unique opportunity to identify interpretation differences between laboratories. As part of a ClinGen initiative, four clinical laboratories (Ambry, GeneDx, Partners Healthcare Laboratory for Molecular Medicine, and University of Chicago Genetic Services Laboratory) collaborated to identify the basis of interpretation differences and to investigate if data sharing and reassessment resolve interpretation differences by analyzing a subset of variants. METHODS: ClinVar variants with submissions from at least two of the four participating laboratories were compared. For a subset of identified differences, laboratories documented the basis for discordance, shared internal data, independently reassessed with the American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines, and then compared interpretations. RESULTS: At least two of the participating laboratories interpreted 6,169 variants in ClinVar, of which 88.3% were initially concordant. Laboratories reassessed 242/724 initially discordant variants, of which 87.2% (211) were resolved by reassessment with current criteria and/or internal data sharing; 12.8% (31) of reassessed variants remained discordant owing to differences in the application of the ACMG-AMP guidelines. CONCLUSION: Participating laboratories increased their overall concordance from 88.3 to 91.7%, indicating that sharing variant interpretations in ClinVar-thereby allowing identification of differences and motivation to resolve those differences-is critical to moving toward more consistent variant interpretations.Genet Med advance online publication 09 March 2017.

Reassessment of Genomic Sequence Variation to Harmonize Interpretation for Personalized Medicine.

Accurate interpretation of DNA sequence variation is a prerequisite for implementing personalized medicine. Discrepancies in interpretation between testing laboratories impede the effective use of genetic test results in clinical medicine. To better understand the underpinnings of these discrepancies, we quantified differences in variant classification internally over time and those between our diagnostic laboratory and other laboratories and resources. We assessed the factors that contribute to these discrepancies and those that facilitate their resolution. Our process resolved 72% of nearly 300 discrepancies between pairs of laboratories to within a one-step classification difference and identified key sources of data that facilitate changes in variant interpretation. The identification and harmonization of variant discrepancies will maximize the clinical use of genetic information; these processes will be fostered by the accumulation of additional population data as well as the sharing of data between diagnostic laboratories.