Accurate and consistent interpretation of sequence variants is integral to the delivery of safe and reliable diagnostic genetic services. To standardize the interpretation process, in 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published a joint guideline based on a set of shared standards for the classification of variants in Mendelian diseases. The generality of these standards and their subjective interpretation between laboratories has prompted efforts to reduce discordance of variant classifications, with a focus on the expert specification of the ACMG/AMP guidelines for individual genes or diseases. Herein, we describe our experience as a ClinGen Variant Curation Expert Panel to adapt the ACMG/AMP criteria for the classification of variants in three globin genes (HBB, HBA2, and HBA1) related to recessively inherited hemoglobinopathies, including five evidence categories, as use cases demonstrating the process of specification and the underlying rationale.

BackgroundThe Malaysian Node of the Human Variome Project (MyHVP) is one of the eighteen official Human Variome Project (HVP) country-specific nodes. Since its inception in 9th October 2010, MyHVP has attracted the significant number of Malaysian clinicians and researchers to participate and contribute their data to this project. MyHVP also act as the center of coordination for genotypic and phenotypic variation studies of the Malaysian population. A specialized database was developed to store and manage the data based on genetic variations which also associated with health and disease of Malaysian ethnic groups. This ethnic-specific database is called the Malaysian Node of the Human Variome Project database (MyHVPDb).FindingsCurrently, MyHVPDb provides only information about the genetic variations and mutations found in the Malays. In the near future, it will expand for the other Malaysian ethnics as well. The data sets are specified based on diseases or genetic mutation types which have three main subcategories: Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV) followed by the mutations which code for the common diseases among Malaysians. MyHVPDb has been open to the local researchers, academicians and students through the registration at the portal of MyHVP (http://hvpmalaysia.kk.usm.my/mhgvc/index.php?id=register).ConclusionsThis database would be useful for clinicians and researchers who are interested in doing a study on genomics population and genetic diseases in order to obtain up-to-date and accurate information regarding the population-specific variations and also useful for those in countries with similar ethnic background.

Haemoglobinopathies are the commonest monogenic diseases worldwide and are caused by variants in the globin gene clusters. With over 2400 variants detected to date, their interpretation using the ACMG/AMP guidelines is challenging and computational evidence can provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for different genes and diseases. In this study, we evaluate 31 in silico predictors using a dataset of 1627 variants in HBA1, HBA2, and HBB. By varying the decision threshold for each tool, we analyse their performance (a) as binary classifiers of pathogenicity, and (b) by using different non-overlapping pathogenic and benign thresholds for their optimal use in the ACMG/AMP framework. Our results show that CADD, Eigen-PC, and REVEL are the overall top performers, with the former reaching moderate strength level for pathogenic prediction. Eigen-PC and REVEL achieve the highest accuracies for missense variants, while CADD is also a reliable predictor of non-missense variants. Moreover, SpliceAI is the top performing splicing predictor, reaching strong level of evidence, while GERP++ and phyloP are the most accurate conservation tools. This study provides evidence about the optimal use of computational tools in globin gene clusters under the ACMG/AMP framework.

BackgroundDatabases for gene variants are very useful for sharing genetic data and to facilitate the understanding of the genetic basis of diseases. This report summarises the issues surrounding the development of the Malaysian Human Variome Project Country Node. The focus is on human germline variants. Somatic variants, mitochondrial variants and other types of genetic variation have corresponding databases which are not covered here, as they have specific issues that do not necessarily apply to germline variations.ResultsThe ethical, legal, social issues, intellectual property, ownership of the data, information technology implementation, and efforts to improve the standards and systems used in data sharing are discussed.ConclusionAn overarching framework such as provided by the Human Variome Project to co-ordinate activities is invaluable. Country Nodes, such as MyHVP, enable human gene variation associated with human diseases to be collected, stored and shared by all disciplines (clinicians, molecular biologists, pathologists, bioinformaticians) for a consistent interpretation of genetic variants locally and across the world.

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Introduction: Haemoglobinopathies are the commonest monogenic diseases worldwide and are caused by variants in the globin gene clusters. With over 2400 variants detected to date, their interpretation using the ACMG/AMP guidelines is challenging, with computational evidence able to provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for different genes and diseases. Materials and Methods: We evaluate 31 in silico predictors using a dataset of 1627 variants in HBA1, HBA2, and HBB. Through varying the decision threshold for each tool, we analyse their performance (a) as binary classifiers of pathogenicity, and (b) using different non-overlapping pathogenic and benign thresholds for their optimal use in the ACMG/AMP framework. Results: CADD, Eigen-PC, and REVEL are the overall top performers, with the former reaching moderate strength level for pathogenic prediction. Eigen-PC and REVEL achieve the highest accuracies for missense variants, while CADD is also a reliable predictor of non-missense variants. Moreover, SpliceAI is the top performing splicing predictor, reaching strong level of evidence, while GERP++ and phyloP are the most accurate conservation tools. Discussion: This study provides evidence about the optimal use of computational evidence in globin gene clusters under the ACMG/AMP framework.

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