Novel PHEX gene locus-specific database: Comprehensive characterization of vast number of variants associated with X-linked hypophosphatemia (XLH).

X-linked hypophosphatemia (XLH), the most common form of hereditary hypophosphatemia, is caused by disrupting variants in the PHEX gene, located on the X chromosome. XLH is inherited in an X-linked pattern with complete penetrance observed for both males and females. Patients experience lifelong symptoms resulting from chronic hypophosphatemia, including impaired bone mineralization, skeletal deformities, growth retardation, and diminished quality of life. This chronic condition requires life-long management with disease-specific therapies, which can improve patient outcomes especially when initiated early in life. To centralize and disseminate PHEX variant information, we have established a new PHEX gene locus-specific database, PHEX LSDB. As of April 30, 2021, 870 unique PHEX variants, compiled from an older database of PHEX variants, a comprehensive literature search, a sponsored genetic testing program, and XLH clinical trials, are represented in the PHEX LSDB. This resource is publicly available on an interactive, searchable website (https://www.rarediseasegenes.com/), which includes a table of variants and associated data, graphical/tabular outputs of genotype-phenotype analyses, and an online submission form for reporting new PHEX variants. The database will be updated regularly with new variants submitted on the website, identified in the published literature, or shared from genetic testing programs.

Spectrum of DDC variants causing aromatic l-amino acid decarboxylase (AADC) deficiency and pathogenicity interpretation using ACMG-AMP/ACGS recommendations.

Pathogenic variants in dopa decarboxylase (DDC), the gene encoding the aromatic l-amino acid decarboxylase (AADC) enzyme, lead to a severe deficiency of neurotransmitters, resulting in neurological, neuromuscular, and behavioral manifestations clinically characterized by developmental delays, oculogyric crises, dystonia, and severe neurologic dysfunction in infancy. Historically, therapy has been aimed at compensating for neurotransmitter abnormalities, but response to pharmacologic therapy varies, and in most cases, the therapy shows little or no benefit. A novel human DDC gene therapy was recently approved in the European Union that targets the underlying genetic cause of the disorder, providing a new treatment option for patients with AADC deficiency. However, the applicability of human DDC gene therapy depends on the ability of laboratories and clinicians to interpret the results of genetic testing accurately enough to diagnose the patient. An accurate interpretation of genetic variants depends in turn on expert-guided curation of locus-specific databases. The purpose of this research was to identify previously uncharacterized DDC variants that are of pathologic significance in AADC deficiency as well as characterize and curate variants of unknown significance (VUSs) to further advance the diagnostic accuracy of genetic testing for this condition. DDC variants were identified using existing databases and the literature. The pathogenicity of the variants was classified using modified American College of Medical Genetics and Genomics/Association for Molecular Pathology/Association for Clinical Genomic Science (ACMG-AMP/ACGS) criteria. To improve the current variant interpretation recommendations, in silico variant interpretation tools were combined with structural 3D modeling of protein variants and applied comparative analysis to predict the impact of the variant on protein function. A total of 422 variants were identified (http://biopku.org/home/pnddb.asp). Variants were identified on nearly all introns and exons of the DDC gene, as well as the 3' and 5' untranslated regions. The largest percentage of the identified variants (48%) were classified as missense variants. The molecular effects of these missense variants were then predicted, and the pathogenicity of each was classified using a number of variant effect predictors. Using ACMG-AMP/ACGS criteria, 7% of variants were classified as pathogenic, 32% as likely pathogenic, 58% as VUSs of varying subclassifications, 1% as likely benign, and 1% as benign. For 101 out of 108 reported genotypes, at least one allele was classified as pathogenic or likely pathogenic. In silico variant pathogenicity interpretation tools, combined with structural 3D modeling of variant proteins and applied comparative analysis, have improved the current DDC variant interpretation recommendations, particularly of VUSs.

Allelic phenotype values: a model for genotype-based phenotype prediction in phenylketonuria.

PURPOSE: The nature of phenylalanine hydroxylase (PAH) variants determines residual enzyme activity, which modifies the clinical phenotype in phenylketonuria (PKU). We exploited the statistical power of a large genotype database to determine the relationship between genotype and phenotype in PKU. METHODS: A total of 9336 PKU patients with 2589 different genotypes, carrying 588 variants, were investigated using an allelic phenotype value (APV) algorithm. RESULTS: We identified 251 0-variants encoding inactive PAH, and assigned APVs (0 = classic PKU; 5 = mild PKU; 10 = mild hyperphenylalaninaemia) to 88 variants in PAH-functional hemizygous patients. The genotypic phenotype values (GPVs) were set equal to the higher-APV allele, which was assumed to be dominant over the lower-APV allele and to determine the metabolic phenotype. GPVs for 8872 patients resulted in cut-off ranges of 0.0-2.7 for classic PKU, 2.8-6.6 for mild PKU and 6.7-10.0 for mild hyperphenylalaninaemia. Genotype-based phenotype prediction was 99.2% for classic PKU, 46.2% for mild PKU and 89.5% for mild hyperphenylalaninaemia. The relationships between known pretreatment blood phenylalanine levels and GPVs (n = 4217), as well as tetrahydrobiopterin responsiveness and GPVs (n = 3488), were significant (both P < 0.001). CONCLUSIONS: APV and GPV are powerful tools to investigate genotype-phenotype associations, and can be used for genetic counselling of PKU families.

Uncovering a multitude of human glucocorticoid receptor variants: an expansive survey of a single gene.

BACKGROUND: Glucocorticoids are commonly used in the clinical setting for their potent anti-inflammatory effects; however, significant variations in response to treatment have been demonstrated. Although the underlying mechanisms have yet to be fully understood, this variable response may be a result of alterations in human glucocorticoid receptor (hGR) expression and function. In addition to hGRα, the biologically active isoform, a screening of current databases and publications revealed five alternative splice isoforms and hundreds of variants that have been reported to date. Many of these changes in the hGR-coding gene, NR3C1, have been linked to pathophysiology. However, many studies focus on evaluating hGR expression in vitro or detecting previously reported variants. RESULTS: In this study, blood from healthy volunteers, burn and asthma patients, as well as from peripheral blood mononuclear cells isolated from leukoreduced donor whole blood, were screened for NR3C1 isoforms. We identified more than 1500 variants, including an additional 21 unique splice isoforms which contain 15 new cryptic exons. A dynamic database, named the Universal hGR (UhGR), was created to annotate and visualize the variants. CONCLUSION: This identification of naturally occurring and stress-induced hGR isoforms, as well as the establishment of an hGR-specific database, may reveal new patterns or suggest areas of interest that will lead to the improved understanding of the human stress response system.

LAMA2 gene mutation update: Toward a more comprehensive picture of the laminin-α2 variome and its related phenotypes.

Congenital muscular dystrophy type 1A (MDC1A) is one of the main subtypes of early-onset muscle disease, caused by disease-associated variants in the laminin-α2 (LAMA2) gene. MDC1A usually presents as a severe neonatal hypotonia and failure to thrive. Muscle weakness compromises normal motor development, leading to the inability to sit unsupported or to walk independently. The phenotype associated with LAMA2 defects has been expanded to include milder and atypical cases, being now collectively known as LAMA2-related muscular dystrophies (LAMA2-MD). Through an international multicenter collaborative effort, 61 new LAMA2 disease-associated variants were identified in 86 patients, representing the largest number of patients and new disease-causing variants in a single report. The collaborative variant collection was supported by the LOVD-powered LAMA2 gene variant database (https://www.LOVD.nl/LAMA2), updated as part of this work. As of December 2017, the database contains 486 unique LAMA2 variants (309 disease-associated), obtained from direct submissions and literature reports. Database content was systematically reviewed and further insights concerning LAMA2-MD are presented. We focus on the impact of missense changes, especially the c.2461A > C (p.Thr821Pro) variant and its association with late-onset LAMA2-MD. Finally, we report diagnostically challenging cases, highlighting the relevance of modern genetic analysis in the characterization of clinically heterogeneous muscle diseases.

Monogenic diabetes syndromes: Locus-specific databases for Alström, Wolfram, and Thiamine-responsive megaloblastic anemia.

We developed a variant database for diabetes syndrome genes, using the Leiden Open Variation Database platform, containing observed phenotypes matched to the genetic variations. We populated it with 628 published disease-associated variants (December 2016) for: WFS1 (n = 309), CISD2 (n = 3), ALMS1 (n = 268), and SLC19A2 (n = 48) for Wolfram type 1, Wolfram type 2, Alström, and Thiamine-responsive megaloblastic anemia syndromes, respectively; and included 23 previously unpublished novel germline variants in WFS1 and 17 variants in ALMS1. We then investigated genotype-phenotype relations for the WFS1 gene. The presence of biallelic loss-of-function variants predicted Wolfram syndrome defined by insulin-dependent diabetes and optic atrophy, with a sensitivity of 79% (95% CI 75%-83%) and specificity of 92% (83%-97%). The presence of minor loss-of-function variants in WFS1 predicted isolated diabetes, isolated deafness, or isolated congenital cataracts without development of the full syndrome (sensitivity 100% [93%-100%]; specificity 78% [73%-82%]). The ability to provide a prognostic prediction based on genotype will lead to improvements in patient care and counseling. The development of the database as a repository for monogenic diabetes gene variants will allow prognostic predictions for other diabetes syndromes as next-generation sequencing expands the repertoire of genotypes and phenotypes. The database is publicly available online at https://lovd.euro-wabb.org.

RettBASE: Rett syndrome database update.

Rett syndrome (RTT) is an X-linked progressive neurodevelopmental disorder that primarily affects females. Mutations in the MECP2 gene have been attributed as the major genetic cause of RTT. Recently, mutations in CDKL5 and FOXG1 genes have also been suggested to give rise to RTT, although subsequent more extensive studies suggest that diseases resulting from mutations in these two genes should be considered as distinct clinical entities. While the genetic basis for the RTT has been recognized, so far there is no effective cure for the disease and the treatments available are mainly aimed at ameliorating clinical problems associated with the disorder. The swift identification of the mutations in children is crucial for pursuing the best therapeutic care. RettBASE was created in 2002 as a MECP2 variant database and has grown to become a comprehensive variant database for RTT and related clinical phenotypes, containing a curated collection of variants for MECP2, CDKL5, and FOXG1 genes. Here, we describe the development and growth of RettBASE after its inception in 2001. Currently, RettBASE holds a total of 4,668 variants in MECP2, 498 variants in CDKL5, and 64 variants in FOXG1.

Mendelian Disorders of Cornification Caused by Defects in Intracellular Calcium Pumps: Mutation Update and Database for Variants in ATP2A2 and ATP2C1 Associated with Darier Disease and Hailey-Hailey Disease.

The two disorders of cornification associated with mutations in genes coding for intracellular calcium pumps are Darier disease (DD) and Hailey-Hailey disease (HHD). DD is caused by mutations in the ATP2A2 gene, whereas the ATP2C1 gene is associated with HHD. Both are inherited as autosomal-dominant traits. DD is mainly defined by warty papules in seborrheic and flexural areas, whereas the major symptoms of HHD are vesicles and erosions in flexural skin. Both phenotypes are highly variable. In 12%-40% of DD patients and 12%-55% of HHD patients, no mutations in ATP2A2 or ATP2C1 are found. We provide a comprehensive review of clinical variability in DD and HHD and a review of all reported mutations in ATP2A2 and ATP2C1. Having the entire spectrum of ATP2A2 and ATP2C1 variants allows us to address the question of a genotype-phenotype correlation, which has not been settled unequivocally in DD and HHD. We created a database for all mutations in ATP2A2 and ATP2C1 using the Leiden Open Variation Database (LOVD v3.0), for variants reported in the literature and future inclusions. This data may be of use as a reference tool in further research on treatment of DD and HHD.

TP53 Variations in Human Cancers: New Lessons from the IARC TP53 Database and Genomics Data.

TP53 gene mutations are one of the most frequent somatic events in cancer. The IARC TP53 Database (http://p53.iarc.fr) is a popular resource that compiles occurrence and phenotype data on TP53 germline and somatic variations linked to human cancer. The deluge of data coming from cancer genomic studies generates new data on TP53 variations and attracts a growing number of database users for the interpretation of TP53 variants. Here, we present the current contents and functionalities of the IARC TP53 Database and perform a systematic analysis of TP53 somatic mutation data extracted from this database and from genomic data repositories. This analysis showed that IARC has more TP53 somatic mutation data than genomic repositories (29,000 vs. 4,000). However, the more complete screening achieved by genomic studies highlighted some overlooked facts about TP53 mutations, such as the presence of a significant number of mutations occurring outside the DNA-binding domain in specific cancer types. We also provide an update on TP53 inherited variants including the ones that should be considered as neutral frequent variations. We thus provide an update of current knowledge on TP53 variations in human cancer as well as inform users on the efficient use of the IARC TP53 Database.

Improved locus-specific database for OPA1 mutations allows inclusion of advanced clinical data.

Autosomal-dominant optic atrophy (ADOA) is the most common inherited optic neuropathy, due to mutations in the optic atrophy 1 gene (OPA1) in about 60%-80% of cases. At present, the clinical heterogeneity of patients carrying OPA1 variants renders genotype-phenotype correlations difficulty. Since 2005, when we published the first locus-specific database (LSDB) dedicated to OPA1, a large amount of new clinical and genetic knowledge has emerged, prompting us to update this database. We have used the Leiden Open-Source Variation Database to develop a clinico-biological database, aiming to add clinical phenotypes related to OPA1 variants. As a first step, we validated this new database by registering several patients previously reported in the literature, as well as new patients from our own institution. Contributors may now make online submissions of clinical and molecular descriptions of phenotypes due to OPA1 variants, including detailed ophthalmological and neurological data, with due respect to patient anonymity. The updated OPA1 LSDB (http://opa1.mitodyn.org/) should prove useful for molecular diagnoses, large-scale variant statistics, and genotype-phenotype correlations in ADOA studies.

VitiVar: A locus specific database of vitiligo associated genes and variations.

Vitiligo is the most common skin pigmentation disorder which affects around 1% of the population worldwide. The disease has complex pathogenesis and is of multifactorial etiology, that finally culminates in patchy depigmentation of skin. Genetic contribution to the disease is well studied, however the information about multiple associated genes and contributing variations are scattered across the literature. To address this complex disorder affecting the skin, we systematically cataloged the genes and variations by creating a Locus Specific Database for vitiligo called, "VitiVar". This comprehensive resource houses manually curated 322 genes and 254 variations, from 202 articles indexed in PubMed. We applied an integrative approach to stratify genes and variations to facilitate dissection of vitiligo pathogenesis by layering it with expression status in specific constituent cell types of skin and in-house vitiligo expression data. Finally, we were able to demonstrate the utility of VitiVar by generating a vitiligo interactome using GeneMANIA and overlaying the vitiligo and cell type specific information. This interaction network yielded 20 new genes (apart from 322 VitiVar genes) of which we were able to prioritize IFI27 and IFI6 for further validation. This, thereby makes VitiVar a comprehensive integrative platform in unravelling disease biology by providing meaningful leads for functional interrogation. VitiVar is freely accessible to the research community for prioritizing and validating the candidate genes and variations (http://vitivar.igib.res.in/).

VitiVar: A locus specific database of vitiligo associated genes and variations.

Vitiligo is the most common skin pigmentation disorder which affects around 1% of the population worldwide. The disease has complex pathogenesis and is of multifactorial etiology, that finally culminates in patchy depigmentation of skin. Genetic contribution to the disease is well studied, however the information about multiple associated genes and contributing variations are scattered across the literature. To address this complex disorder affecting the skin, we systematically cataloged the genes and variations by creating a Locus Specific Database for vitiligo called, "VitiVar". This comprehensive resource houses manually curated 322 genes and 254 variations, from 202 articles indexed in PubMed. We applied an integrative approach to stratify genes and variations to facilitate dissection of vitiligo pathogenesis by layering it with expression status in specific constituent cell types of skin and in-house vitiligo expression data. Finally, we were able to demonstrate the utility of VitiVar by generating a vitiligo interactome using GeneMANIA and overlaying the vitiligo and cell type specific information. This interaction network yielded 20 new genes (apart from 322 VitiVar genes) of which we were able to prioritize IFI27 and IFI6 for further validation. This, thereby makes VitiVar a comprehensive integrative platform in unravelling disease biology by providing meaningful leads for functional interrogation. VitiVar is freely accessible to the research community for prioritizing and validating the candidate genes and variations (http://vitivar.igib.res.in/).

Towards precision medicine: advances in computational approaches for the analysis of human variants.

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.