Rajarshi Ghosh

Performance of ACMG-AMP Variant-Interpretation Guidelines among Nine Laboratories in the Clinical Sequencing Exploratory Research Consortium

Evaluating the pathogenicity of a variant is challenging given the plethora of types of genetic evidence that laboratories consider. Deciding how to weigh each type of evidence is difficult, and standards have been needed. In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for the assessment of variants in genes associated with Mendelian diseases. Nine molecular diagnostic laboratories involved in the Clinical Sequencing Exploratory Research (CSER) consortium piloted these guidelines on 99 variants spanning all categories (pathogenic, likely pathogenic, uncertain significance, likely benign, and benign). Nine variants were distributed to all laboratories, and the remaining 90 were evaluated by three laboratories. The laboratories classified each variant by using both the laboratorys own method and the ACMG-AMP criteria. The agreement between the two methods used within laboratories was high (K-alpha = 0.91) with 79% concordance. However, there was only 34% concordance for either classification system across laboratories. After consensus discussions and detailed review of the ACMG-AMP criteria, concordance increased to 71%. Causes of initial discordance in ACMG-AMP classifications were identified, and recommendations on clarification and increased specification of the ACMG-AMP criteria were made. In summary, although an initial pilot of the ACMG-AMP guidelines did not lead to increased concordance in variant interpretation, comparing variant interpretations to identify differences and having a common framework to facilitate resolution of those differences were beneficial for improving agreement, allowing iterative movement toward increased reporting consistency for variants in genes associated with monogenic disease.

Erratum: Performance of ACMG-AMP Variant-Interpretation Guidelines among Nine Laboratories in the Clinical Sequencing Exploratory Research Consortium (American Journal of Human Genetics (2016) 98(6) (1067–1076) (S0002929716300593) (10.1016/j.ajhg.2016.03.024))

Laura M. Amendola,1,16 Gail P. Jarvik,1,16,* Michael C. Leo,2 Heather M. McLaughlin,3 Yassmine Akkari,4 Michelle D. Amaral,5 Jonathan S. Berg,6 Sawona Biswas,7 Kevin M. Bowling,5 Laura K. Conlin,7 Greg M. Cooper,5 Michael O. Dorschner,8 Matthew C. Dulik,9 Arezou A. Ghazani,10 Rajarshi Ghosh,11 Robert C. Green,3,12,15 Ragan Hart,1 Carrie Horton,13 Jennifer J. Johnston,14 Matthew S. Lebo,3,12 Aleksandar Milosavljevic,11 Jeffrey Ou,1 Christine M. Pak,4 Ronak Y. Patel,11 Sumit Punj,4 Carolyn Sue Richards,4 Joseph Salama,1 Natasha T. Strande,6 Yaping Yang,11 Sharon E. Plon,11 Leslie G. Biesecker,14 and Heidi L. Rehm3,12,15,*

Caenorhabditis elegans Recognizes a Bacterial Quorum-sensing Signal Molecule through the AWCON Neuron

Background: The nematode Caenorhabditis elegans consumes bacteria as its sole food source. Results: The bacterium Vibrio cholerae produces a quorum-sensing signal molecule called CAI-1, which C. elegans detects through the AWCON chemosensory neuron. Conclusion: C. elegans uses bacterial-produced molecules as cues, and these molecules are also physiologically significant to bacteria. Significance: The V. cholerae molecule CAI-1 enables cross-kingdom chemical interaction. In a process known as quorum sensing, bacteria use chemicals called autoinducers for cell-cell communication. Population-wide detection of autoinducers enables bacteria to orchestrate collective behaviors. In the animal kingdom detection of chemicals is vital for success in locating food, finding hosts, and avoiding predators. This behavior, termed chemotaxis, is especially well studied in the nematode Caenorhabditis elegans. Here we demonstrate that the Vibrio cholerae autoinducer (S)-3-hydroxytridecan-4-one, termed CAI-1, influences chemotaxis in C. elegans. C. elegans prefers V. cholerae that produces CAI-1 over a V. cholerae mutant defective for CAI-1 production. The position of the CAI-1 ketone moiety is the key feature driving CAI-1-directed nematode behavior. CAI-1 is detected by the C. elegans amphid sensory neuron AWCON. Laser ablation of the AWCON cell, but not other amphid sensory neurons, abolished chemoattraction to CAI-1. These analyses define the structural features of a bacterial-produced signal and the nematode chemosensory neuron that permit cross-kingdom interaction.

ClinGen Pathogenicity Calculator: a configurable system for assessing pathogenicity of genetic variants

BackgroundThe success of the clinical use of sequencing based tests (from single gene to genomes) depends on the accuracy and consistency of variant interpretation. Aiming to improve the interpretation process through practice guidelines, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) have published standards and guidelines for the interpretation of sequence variants. However, manual application of the guidelines is tedious and prone to human error. Web-based tools and software systems may not only address this problem but also document reasoning and supporting evidence, thus enabling transparency of evidence-based reasoning and resolution of discordant interpretations.ResultsIn this report, we describe the design, implementation, and initial testing of the Clinical Genome Resource (ClinGen) Pathogenicity Calculator, a configurable system and web service for the assessment of pathogenicity of Mendelian germline sequence variants. The system allows users to enter the applicable ACMG/AMP-style evidence tags for a specific allele with links to supporting data for each tag and generate guideline-based pathogenicity assessment for the allele. Through automation and comprehensive documentation of evidence codes, the system facilitates more accurate application of the ACMG/AMP guidelines, improves standardization in variant classification, and facilitates collaborative resolution of discordances. The rules of reasoning are configurable with gene-specific or disease-specific guideline variations (e.g. cardiomyopathy-specific frequency thresholds and functional assays). The software is modular, equipped with robust application program interfaces (APIs), and available under a free open source license and as a cloud-hosted web service, thus facilitating both stand-alone use and integration with existing variant curation and interpretation systems. The Pathogenicity Calculator is accessible at http://calculator.clinicalgenome.org.ConclusionsBy enabling evidence-based reasoning about the pathogenicity of genetic variants and by documenting supporting evidence, the Calculator contributes toward the creation of a knowledge commons and more accurate interpretation of sequence variants in research and clinical care.

Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines

BackgroundThe American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. The ACMG/AMP guidelines recommend complete concordance of predictions among all in silico algorithms used without specifying the number or types of algorithms. The subjective nature of this recommendation contributes to discordance of variant classification among clinical laboratories and prevents definitive classification of variants.ResultsUsing 14,819 benign or pathogenic missense variants from the ClinVar database, we compared performance of 25 algorithms across datasets differing in distinct biological and technical variables. There was wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants. We also identify a previously unreported source of error in variant interpretation (false concordance) where concordant in silico predictions are opposite to the evidence provided by other sources. We identified recently developed algorithms with high predictive power and robust to variables such as disease mechanism, gene constraint, and mode of inheritance, although poorer performing algorithms are more frequently used based on review of the clinical genetics literature (2011–2017).ConclusionsOur analyses identify algorithms with high performance characteristics independent of underlying disease mechanisms. We describe combinations of algorithms with increased concordance that should improve in silico algorithm usage during assessment of clinically relevant variants using the ACMG/AMP guidelines.

Updated recommendation for the benign stand-alone ACMG/AMP criterion

The Clinical Genome Resource (ClinGen) Sequence Variant Interpretation Working Group set out to refine the American College of Medical Genetics and Genomics and the Association of Molecular Pathologists (ACMG/AMP) variant pathogenicity recommendations for stand‐alone rule BA1 (a variant with minor allele frequency [MAF] > 0.05 is benign), by clarifying how it should be used and specifying a set of variants that should be exempted from this rule. We cross‐referenced ClinVar and Exome Aggregation Consortium data to identify variants for which there was a plausible argument for pathogenicity and the variant exists in one or more population data sets at MAF > 0.05. We identified nine such variants that were present in these data sets that may not be benign. The ACMG/AMP criteria were applied to these variants that resulted in four pathogenic and five variants of uncertain significance. We have refined benign rule BA1 by clarifying terms used to describe its use, which databases we recommend using, and assumptions made about this rule. We also recognized an initial list of nine variants for which there was some evidence of pathogenicity even though the MAF was high for these variants. We specify processes whereby individuals can petition ClinGen for amendments to our variant‐specific assertions and the criteria experts should use when setting a numerically lower threshold for BA1 for specific genes.

Gene-specific criteria for PTEN variant curation: Recommendations from the ClinGen PTEN Expert Panel

The ClinGen PTEN Expert Panel was organized by the ClinGen Hereditary Cancer Clinical Domain Working Group to assemble clinicians, researchers, and molecular diagnosticians with PTEN expertise to develop specifications to the 2015 ACMG/AMP Sequence Variant Interpretation Guidelines for PTEN variant interpretation. We describe finalized PTEN‐specific variant classification criteria and outcomes from pilot testing of 42 variants with benign/likely benign (BEN/LBEN), pathogenic/likely pathogenic (PATH/LPATH), uncertain significance (VUS), and conflicting (CONF) ClinVar assertions. Utilizing these rules, classifications concordant with ClinVar assertions were achieved for 14/15 (93.3%) BEN/LBEN and 16/16 (100%) PATH/LPATH ClinVar consensus variants for an overall concordance of 96.8% (30/31). The variant where agreement was not reached was a synonymous variant near a splice donor with noncanonical sequence for which in silico models cannot predict the native site. Applying these rules to six VUS and five CONF variants, adding shared internal laboratory data enabled one VUS to be classified as LBEN and two CONF variants to be as classified as PATH and LPATH. This study highlights the benefit of gene‐specific criteria and the value of sharing internal laboratory data for variant interpretation. Our PTEN‐specific criteria and expertly reviewed assertions should prove helpful for laboratories and others curating PTEN variants.

Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline CDH1 sequence variants

The variant curation guidelines published in 2015 by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) provided the genetics community with a framework to assess variant pathogenicity; however, these rules are not gene specific. Germline pathogenic variants in the CDH1 gene cause hereditary diffuse gastric cancer and lobular breast cancer, a clinically challenging cancer predisposition syndrome that often requires a multidisciplinary team of experts to be properly managed. Given this challenge, the Clinical Genome Resource (ClinGen) Hereditary Cancer Domain prioritized the development of the CDH1 variant curation expert panel (VCEP) to develop and implement rules for CDH1 variant classifications. Here, we describe the CDH1 specifications of the ACMG/AMP guidelines, which were developed and validated after a systematic evaluation of variants obtained from a cohort of clinical laboratory data encompassing ∼827,000 CDH1 sequenced alleles. Comparing previously reported germline variants that were classified using the 2015 ACMG/AMP guidelines to the CDH1 VCEP recommendations resulted in reduced variants of uncertain significance and facilitated resolution of variants with conflicted assertions in ClinVar. The ClinGen CDH1 VCEP recommends the use of these CDH1‐specific guidelines for the assessment and classification of variants identified in this clinically actionable gene.

Integrating somatic variant data and biomarkers for germline variant classification in cancer predisposition genes

In its landmark paper about Standards and Guidelines for the Interpretation of Sequence Variants, the American College of Medical Genetics and Genomics (ACMG), and Association for Molecular Pathology (AMP) did not address how to use tumor data when assessing the pathogenicity of germline variants. The Clinical Genome Resource (ClinGen) established a multidisciplinary working group, the Germline/Somatic Variant Subcommittee (GSVS) with this focus. The GSVS implemented a survey to determine current practices of integrating somatic data when classifying germline variants in cancer predisposition genes. The GSVS then reviewed and analyzed available resources of relevant somatic data, and performed integrative germline variant curation exercises. The committee determined that somatic hotspots could be systematically integrated into moderate evidence of pathogenicity (PM1). Tumor RNA sequencing data showing altered splicing may be considered as strong evidence in support of germline pathogenicity (PVS1) and tumor phenotypic features such as mutational signatures be considered supporting evidence of pathogenicity (PP4). However, at present, somatic data such as focal loss of heterozygosity and mutations occurring on the alternative allele are not recommended to be systematically integrated, instead, incorporation of this type of data should take place under the advisement of multidisciplinary cancer center tumor‐normal sequencing boards.

Genetics of Intraspecies Variation in Avoidance Behavior Induced by a Thermal Stimulus in Caenorhabditis elegans

Individuals within a species vary in their responses to a wide range of stimuli, partly as a result of differences in their genetic makeup. Relatively little is known about the genetic and neuronal mechanisms contributing to diversity of behavior in natural populations. By studying intraspecies variation in innate avoidance behavior to thermal stimuli in the nematode Caenorhabditis elegans, we uncovered genetic principles of how different components of a behavioral response can be altered in nature to generate behavioral diversity. Using a thermal pulse assay, we uncovered heritable variation in responses to a transient temperature increase. Quantitative trait locus mapping revealed that separate components of this response were controlled by distinct genomic loci. The loci we identified contributed to variation in components of thermal pulse avoidance behavior in an additive fashion. Our results show that the escape behavior induced by thermal stimuli is composed of simpler behavioral components that are influenced by at least six distinct genetic loci. The loci that decouple components of the escape behavior reveal a genetic system that allows independent modification of behavioral parameters. Our work sets the foundation for future studies of evolution of innate behaviors at the molecular and neuronal level.