Pharmacogenomic testing interrogates germline sequence variants implicated in interindividual drug response variability to infer a drug response phenotype and to guide medication management for certain drugs. Specifically, discrete aspects of pharmacokinetics, such as drug metabolism, and pharmacodynamics, as well as drug sensitivity, can be predicted by genes that code for proteins involved in these pathways. Pharmacogenomics is unique and differs from inherited disease genetics because the drug response phenotype can be drug-dependent and is often unrecognized until an unexpected drug reaction occurs or a patient fails to respond to a medication. Genes and variants with sufficiently high levels of evidence and consensus may be included in a clinical pharmacogenomic test; however, result interpretation and phenotype prediction can be challenging for some genes and medications. This document provides a resource for laboratories to develop and implement clinical pharmacogenomic testing by summarizing publicly available resources and detailing best practices for pharmacogenomic nomenclature, testing, result interpretation, and reporting.

The Association for Molecular Pathology Variant Interpretation Testing Among Laboratories (VITAL) Working Group convened to evaluate the Standards and Guidelines for the Interpretation of Sequence Variants implementation into clinical practice, identify problematic classification rules, and define implementation challenges. Variants and associated clinical information were provided to volunteer respondents. Participant variant classifications were compared with intended consensus-derived classifications of the Working Group. The 24 variant challenges received 1379 responses; 1119 agreed with the intended response (81%; 95% CI, 79% to 83%). Agreement ranged from 44% to 100%, with 16 challenges (67%; 47% to 82%) reaching consensus (≥80% agreement). Participant classifications were also compared to a calculated interpretation of the ACMG Guidelines using the participant-reported criteria as input. The 24 variant challenges had 1368 responses with specific evidence provided and 1121 (82%; 80% to 84%) agreed with the calculated interpretation. Agreement for challenges ranged from 63% to 98%; 15 (63%; 43% to 79%) reaching consensus. Among 81 individual participants, 32 (40%; 30% to 50%) reached agreement with at least 80% of the intended classifications and 42 (52%; 41% to 62%) with the calculated classifications. This study demonstrated that although variant classification remains challenging, published guidelines are being utilized and adapted to improve variant calling consensus. This study identified situations where clarifications are warranted and provides a model for competency assessment.

The Association for Molecular Pathology Variant Interpretation Testing Among Laboratories (VITAL) Working Group convened to evaluate the Standards and Guidelines for the Interpretation of Sequence Variants implementation into clinical practice, identify problematic classification rules, and define implementation challenges. Variants and associated clinical information were provided to volunteer respondents. Participant variant classifications were compared with intended consensus-derived classifications of the Working Group. The 24 variant challenges received 1379 responses; 1119 agreed with the intended response (81%; 95% CI, 79% to 83%). Agreement ranged from 44% to 100%, with 16 challenges (67%; 47% to 82%) reaching consensus (≥80% agreement). Participant classifications were also compared to a calculated interpretation of the ACMG Guidelines using the participant-reported criteria as input. The 24 variant challenges had 1368 responses with specific evidence provided and 1121 (82%; 80% to 84%) agreed with the calculated interpretation. Agreement for challenges ranged from 63% to 98%; 15 (63%; 43% to 79%) reaching consensus. Among 81 individual participants, 32 (40%; 30% to 50%) reached agreement with at least 80% of the intended classifications and 42 (52%; 41% to 62%) with the calculated classifications. This study demonstrated that although variant classification remains challenging, published guidelines are being utilized and adapted to improve variant calling consensus. This study identified situations where clarifications are warranted and provides a model for competency assessment.

Molecular genetic testing of the FMR1 gene is commonly performed in clinical laboratories. Pathogenic variants in the FMR1 gene are associated with fragile X syndrome, fragile X-associated tremor ataxia syndrome (FXTAS), and fragile X-associated primary ovarian insufficiency (FXPOI). This document provides updated information regarding FMR1 pathogenic variants, including prevalence, genotype-phenotype correlations, and variant nomenclature. Methodological considerations are provided for Southern blot analysis and polymerase chain reaction (PCR) amplification of FMR1, including triplet repeat-primed and methylation-specific PCR.The American College of Medical Genetics and Genomics (ACMG) Laboratory Quality Assurance Committee has the mission of maintaining high technical standards for the performance and interpretation of genetic tests. In part, this is accomplished by the publication of the document ACMG Technical Standards for Clinical Genetics Laboratories, which is now maintained online ( http://www.acmg.net ). This subcommittee also reviews the outcome of national proficiency testing in the genetics area and may choose to focus on specific diseases or methodologies in response to those results. Accordingly, the subcommittee selected fragile X syndrome to be the first topic in a series of supplemental sections, recognizing that it is one of the most frequently ordered genetic tests and that it has many alternative methods with different strengths and weaknesses. This document is the fourth update to the original standards and guidelines for fragile X testing that were published in 2001, with revisions in 2005 and 2013, respectively.This versionClarifies the clinical features associated with different FMRI variants (Section 2.3)Discusses important reporting considerations (Section 3.3.1.3)Provides updates on technology (Section 4.1).

Clinical genetic variant classification science is a growing subspecialty of clinical genetics and genomics. The field's continued improvement is essential for the success of precision medicine in both germline (hereditary) and somatic (oncology) contexts. This review focuses on variant classification for DNA next-generation sequencing tests. We first summarize current limitations in variant discovery and definition, and then describe the current five- and four-tier classification systems outlined in dominant standards and guideline publications for germline and somatic tests, respectively. We then discuss measures of variant classification discordance and the field's bias for positive results, as well as considerations for panel size and population screening in the context of estimates of positive predictive value thatincorporate estimated variant classification imperfections. Finally, we share opinions on the current state of variant classification from some of the authors of the most widely used standards and guideline publications and from other domain experts.

A comment to this article is available online at https://doi.org/10.1038/s41436-021-01141-w.

PURPOSE:Clinical genome sequencing (cGS) followed by orthogonal confirmatory testing is standard practice. While orthogonal testing significantly improves specificity, it also results in increased turnaround time and cost of testing. The purpose of this study is to evaluate machine learning models trained to identify false positive variants in cGS data to reduce the need for orthogonal testing.METHODS:We sequenced five reference human genome samples characterized by the Genome in a Bottle Consortium (GIAB) and compared the results with an established set of variants for each genome referred to as a truth set. We then trained machine learning models to identify variants that were labeled as false positives.RESULTS:After training, the models identified 99.5% of the false positive heterozygous single-nucleotide variants (SNVs) and heterozygous insertions/deletions variants (indels) while reducing confirmatory testing of nonactionable, nonprimary SNVs by 85% and indels by 75%. Employing the algorithm in clinical practice reduced overall orthogonal testing using dideoxynucleotide (Sanger) sequencing by 71%.CONCLUSION:Our results indicate that a low false positive call rate can be maintained while significantly reducing the need for confirmatory testing. The framework that generated our models and results is publicly available at https://github.com/HudsonAlpha/STEVE .

The coronavirus disease 2019 (COVID-19) emerged in early 2020 and since, has brought about tremendous cost to economies and healthcare systems universally. Reports of pediatric patients with inherited conditions and COVID-19 infections are emerging. Specific risks for morbidity and mortality that this pandemic carries for different categories of genetic disorders are still mostly unknown. Thus, there are no specific recommendations for the diagnosis, management, and treatment of patients with genetic disorders during the COVID-19 or other pandemics. Emerging publications, from Upper-Middle Income countries (UMIC), discuss the recent experiences of genetic centers in the continuity of care for patients with genetic disorders in the context of this pandemic. Many measures to facilitate the plan to continuous genetic care in a well-developed health system, may not be applicable in Low and Middle Income countries (LMIC). With poorly structured health systems and with the lack of established genetic services, the COVID-19 pandemic will easily exacerbate the access to care for patients with genetic disease in these countries. This article focuses on the unique challenges of providing genetic healthcare services during emergency situations in LMIC countries and provides practical preparations for this and other pandemic situations.

Genetic characterization of CYP2D6 post-mortem may help explain drug involvement in cause of death. Here we describe methods for DNA extraction, CYP2D6 genotyping and copy number variation (CNV) testing using dried blood archived at autopsy with FTA® cards. Bloodstained cards (n=75) were obtained from the Utah Office of the Medical Examiner. DNA was extracted from 3mm punches; DNA yield was 9-100 ng/μL; the 260/280 ratio was 1.2-2.0. CYP2D6 alleles detected using the iPLEX® genotyping assay and MassARRAY (Agena Bioscience) include (n=) *2A (20), *3 (2), *4 (26), *5(3), *6 (2), *10 (1), *29 (1), *35 (9) and*41 (10). CYP2D6 genotype could not be determined in one sample that failed to amplify. More than two copies of CYP2D6 were detected in 11 samples. CNV could not be determined in six samples. The commercially available methods described here were successful for CYP2D6 testing of post-mortem blood samples archived with FTA® cards.

A 10-month-old Caucasian male infant was referred to the genetics clinic for tetralogy of Fallot and macrothrombocytopenia.He also displayed symmetric growth restriction with microcephaly, developmental delay, cryptorchidism, and facial dysmorphisms.Microarray-based comparative genomic hybridization and myosin heavy chain 9 (MYH9) 3associated thrombocytopenia testing were within the reference interval.Platelet function study was consistent with a platelet secretion disorder, and a bone marrow biopsy was planned.Whole-genome sequencing via next-generation sequencing revealed a pathogenic, de novo missense variant in cell division cycle 42 (CDC42; p.Tyr64Cys). QUESTIONS1. What is the child's unifying diagnosis?2. How might the sequencing result impact the child's medical management? 3. What factors are important to consider in the classification of this genetic variant as pathogenic?

Newborn screening is an incredibly useful tool for the early identification of many metabolic disorders, including fatty acid oxidation (FAO) disorders. In many cases, molecular tests are necessary to reach a final diagnosis, highlighting the need for a thorough evaluation of genes implicated in FAO disorders. Using the ClinGen (Clinical Genome Resource) clinical validity framework, thirty genes were analyzed for the strength of evidence supporting their association with FAO disorders. Evidence was gathered from the literature by biocurators and presented to disease experts for review in order to assign a clinical validity classification of Definitive, Strong, Moderate, Limited, Disputed, Refuted, or No Reported Evidence. Of the gene-disease relationships evaluated, 22/30 were classified as Definitive, three as Moderate, one as Limited, three as No Reported Evidence and one as Disputed. Gene-disease relationships with a Limited, Disputed, and No Reported Evidence were found on two, six, and up to four panels out of 30 FAO disorder-specific panels, respectively, in the National Institute of Health Genetic Testing Registry, while over 70% of the genes on panels are definitively associated with an FAO disorder. These results highlight the need to systematically assess the clinical relevance of genes implicated in fatty acid oxidation disorders in order to improve the interpretation of genetic testing results and diagnosis of patients with these disorders.

On the Cover: This cover image is based on the Special Article Unique aspects of sequence variant interpretation for inborn errors of metabolism (IEM): The ClinGen IEM Working Group and the Phenylalanine Hydroxylase Gene by Diane B. Zastrow et al., Pages 1569–1580. DOI: 10.1002/humu.23649.

The American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) Interpretation of Sequence Work Group (ISV WG) of 2015 appreciates the opportunity to respond to the recommendations from the ClinGen Sequence Variant Interpretation Working Group.1.Biesecker LG & Harrison SM. The ACMG/AMP reputable source criteriafor the interpretation of sequence variants. Genet Med 2018;20:XXX–XXX.Google Scholar We commend this expert working group for their thoughtful and sustained efforts to refine the original criteria set forth in the ISV recommendations of 2015.2.Richards S. Aziz N. Bale E. et al.Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.10.1038/gim.2015.30Genet Med. 2015; 17: 405-424Google Scholar We also appreciate and value the many publications that have suggested modifications to the original publication.3.Kleinberger J. Maloney K.A. Pollin T.I. Jeng L.J. An openly available online tool for implementing the ACMG/AMP standards and guidelines for the interpretation of sequence variants.1:CAS:528:DC%2BC28XhvVehtr7P10.1038/gim.2016.13Genet Med. 2016; 18: 1165Google Scholar,4.Nykamp K. Anderson M. Powers M. et al.Sherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria.10.1038/gim.2017.37Genet Med. 2017; 19: 1105-1117Google Scholar,5.Kelly M.A. Caleshu C. Morales A. et al.Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen’s Inherited Cardiomyopathy Expert Panel.10.1038/gim.2017.218Genet Med. 2018; 20: 351-359Google Scholar,6.Jarvik G.P. Browning B.L. Consideration of cosegregation in the pathogenicity classification of genomic variants.1:CAS:528:DC%2BC28XptVSlsrw%3D10.1016/j.ajhg.2016.04.003Am J Hum Genet. 2016; 98: 1077-1081Google Scholar The ISV WG has not had the opportunity to evaluate thoroughly the consequences of any of these recommended changes on the overall scoring system, including this current one suggesting removal of the supporting-level reputable source criteria (PP5 and BP6), and therefore refrains from making any recommendations at this time. The ACMG/AMP ISV WG agrees that primary data are preferable to expert opinion. Laboratories may choose to consult ClinVar or other databases to determine how their colleagues have classified a variant, although that in itself would not determine classification. While 81% of variants reported in ClinVar have evidence provided, there are a significant number of variants (19%) that do not, and thus could be taken to meet these criteria. Before our committee can evaluate removing a subset of criteria we must determine how removal might impact the current scoring system that utilizes a unified set of criteria for evidence. Thus, it would be important to provide data that show that the use of these two evidence criteria actually leads to errors in variant classification. We assume that removal of the PP5 and BP6 criteria as only supporting evidence is not likely to impact classification (i.e., in the absence of much stronger evidence), although data to support this would be helpful. Generally, for all ACMG publications, document review is performed every 5 years, unless there is an urgent issue that must be resolved quickly. In that case, a revision to the original document, approved by the participating organizations’ Board of Directors (ACMG and AMP in this case), could move forward more quickly to address needed changes. This document was intended to be a complete and comprehensive process for interpretation of genome-level sequence variants for genes published in the literature to be causative of diseases. Therefore, the ISV WG is reluctant to revise individual criteria of the guideline, without careful consideration of the impact on the whole system. Because the field of genetic testing is growing and evolving rapidly, it may be necessary for ACMG to reconvene the ISV WG, which could collaborate directly with the ClinGen Sequence Variant Interpretation Working Group, along with other experts in the field to develop an addendum to, or even revision of, the current guidelines within the next few years. In the meantime, the ISV WG will work with ACMG and AMP to provide additional educational sessions at the annual meetings and perhaps through other venues to update clinical laboratory members on specific areas of the guideline that may require a different approach to variant interpretation. Finally, our work group wholeheartedly agrees with the viewpoint stated in the ClinGen letter that the interpretation of variants is the responsibility of the clinical laboratory director. Therefore, we encourage laboratory directors to seriously consider and evaluate these discussions. All work group members are clinical service providers. The following work group members have a commercial conflict of interest: E.S. (Fabric Genomics, consultant); D.B. (Envision Genomics, stock; Genomic England, scientific advisor); K.V. (Pierian Dx, scientific advisor); E.L. (Complete Genomics, advisory board; Roche, advisory board; Genome Canada, reviewer). The other authors declare no conflict of interest.

INTRODUCTION:The rapid development and dramatic decrease in cost of sequencing techniques have ushered the implementation of genomic testing in patient care. Next generation DNA sequencing (NGS) techniques have been used increasingly in clinical laboratories to scan the whole or part of the human genome in order to facilitate diagnosis and/or prognostics of genetic disease. Despite many hurdles and debates, pharmacogenomics (PGx) is believed to be an area of genomic medicine where precision medicine could have immediate impact in the near future. Areas covered: This review focuses on lessons learned through early attempts of clinically implementing PGx testing; the challenges and opportunities that PGx testing brings to precision medicine in the era of NGS. Expert commentary: Replacing targeted analysis approach with NGS for PGx testing is neither technically feasible nor necessary currently due to several technical limitations and uncertainty involved in interpreting variants of uncertain significance for PGx variants. However, reporting PGx variants out of clinical whole exome or whole genome sequencing (WES/WGS) might represent additional benefits for patients who are tested by WES/WGS.

The ClinGen Inborn Errors of Metabolism Working Group was tasked with creating a comprehensive, standardized knowledge base of genes and variants for metabolic diseases. Phenylalanine hydroxylase (PAH) deficiency was chosen to pilot development of the Working Group's standards and guidelines. A PAH variant curation expert panel (VCEP) was created to facilitate this process. Following ACMG-AMP variant interpretation guidelines, we present the development of these standards in the context of PAH variant curation and interpretation. Existing ACMG-AMP rules were adjusted based on disease (6) or strength (5) or both (2). Disease adjustments include allele frequency thresholds, functional assay thresholds, and phenotype-specific guidelines. Our validation of PAH-specific variant interpretation guidelines is presented using 85 variants. The PAH VCEP interpretations were concordant with existing interpretations in ClinVar for 69 variants (81%). Development of biocurator tools and standards are also described. Using the PAH-specific ACMG-AMP guidelines, 714 PAH variants have been curated and will be submitted to ClinVar. We also discuss strategies and challenges in applying ACMG-AMP guidelines to autosomal recessive metabolic disease, and the curation of variants in these genes.

The rapid evolution of genomic testing gives new meaning to the term “high-complexity testing.” Variant classification is clearly challenging, but with the aid of American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP) professional guidelines for combining evidence used in conjunction with national efforts, laboratories may be better able to standardize and establish quality metrics. Additionally, guidelines for evaluating types of evidence and interpreting sequence variations have been developed. This session will review these guidelines to address expectations of data quality, as well as elements of reporting identified variants and their interpretations. Efforts by the molecular community to address consistency in using these guidelines will also be shown. The objectives of this presentation are to list technical guidelines for genomic sequencing to ensure quality data, to describe evidence used to classify variants, and to identify variability in how evidences are used.