Toward reporting standards for the pathogenicity of variant combinations involved in multilocus/oligogenic diseases

Genetics of inherited peripheral neuropathies and the next frontier: looking backwards to progress forwards

Inherited peripheral neuropathies (IPNs) encompass a clinically and genetically heterogeneous group of disorders causing length-dependent degeneration of peripheral autonomic, motor and/or sensory nerves. Despite gold-standard diagnostic testing for pathogenic variants in over 100 known associated genes, many patients with IPN remain genetically unsolved. Providing patients with a diagnosis is critical for reducing their 'diagnostic odyssey', improving clinical care, and for informed genetic counselling. The last decade of massively parallel sequencing technologies has seen a rapid increase in the number of newly described IPN-associated gene variants contributing to IPN pathogenesis. However, the scarcity of additional families and functional data supporting variants in potential novel genes is prolonging patient diagnostic uncertainty and contributing to the missing heritability of IPNs. We review the last decade of IPN disease gene discovery to highlight novel genes, structural variation and short tandem repeat expansions contributing to IPN pathogenesis. From the lessons learnt, we provide our vision for IPN research as we anticipate the future, providing examples of emerging technologies, resources and tools that we propose that will expedite the genetic diagnosis of unsolved IPN families.

Exploring the Genotype-Phenotype Correlations in a Child with Inherited Seizure and Thrombocytopenia by Digenic Network Analysis

Understanding the correlation between genotype and phenotype remains challenging for modern genetics. Digenic network analysis may provide useful models for understanding complex phenotypes that traditional Mendelian monogenic models cannot explain. Clinical data, whole exome sequencing data, in silico, and machine learning analysis were combined to construct a digenic network that may help unveil the complex genotype-phenotype correlations in a child presenting with inherited seizures and thrombocytopenia. The proband inherited a maternal heterozygous missense variant in SCN1A (NM_001165963.4:c.2722G>A) and a paternal heterozygous missense variant in MYH9 (NM_002473.6:c.3323A>C). In silico analysis showed that these two variants may be pathogenic for inherited seizures and thrombocytopenia in the proband. Moreover, focusing on 230 epilepsy-associated genes and 35 thrombopoiesis genes, variant call format data of the proband were analyzed using machine learning tools (VarCoPP 2.0) and Digenic Effect predictor. A digenic network was constructed, and SCN1A and MYH9 were found to be core genes in the network. Further analysis showed that MYH9 might be a modifier of SCN1A, and the variant in MYH9 might not only influence the severity of SCN1A-related seizure but also lead to thrombocytopenia in the bone marrow. In addition, another eight variants might also be co-factors that account for the proband's complex phenotypes. Our data show that as a supplement to the traditional Mendelian monogenic model, digenic network analysis may provide reasonable models for the explanation of complex genotype-phenotype correlations.

Prioritization of oligogenic variant combinations in whole exomes

MOTIVATION

Whole exome sequencing (WES) has emerged as a powerful tool for genetic research, enabling the collection of a tremendous amount of data about human genetic variation. However, properly identifying which variants are causative of a genetic disease remains an important challenge, often due to the number of variants that need to be screened. Expanding the screening to combinations of variants in two or more genes, as would be required under the oligogenic inheritance model, simply blows this problem out of proportion.

RESULTS

We present here the High-throughput oligogenic prioritizer (Hop), a novel prioritization method that uses direct oligogenic information at the variant, gene and gene pair level to detect digenic variant combinations in WES data. This method leverages information from a knowledge graph, together with specialized pathogenicity predictions in order to effectively rank variant combinations based on how likely they are to explain the patient's phenotype. The performance of Hop is evaluated in cross-validation on 36 120 synthetic exomes for training and 14 280 additional synthetic exomes for independent testing. Whereas the known pathogenic variant combinations are found in the top 20 in approximately 60% of the cross-validation exomes, 71% are found in the same ranking range when considering the independent set. These results provide a significant improvement over alternative approaches that depend simply on a monogenic assessment of pathogenicity, including early attempts for digenic ranking using monogenic pathogenicity scores.

AVAILABILITY AND IMPLEMENTATION

Hop is available at https://github.com/oligogenic/HOP.

Additional mutation in PROKR2 and phenotypic differences in a Kallmann syndrome/normosmic congenital hypogonadotropic hypogonadism family carrying FGFR1 missense mutation

Congenital hypogonadotropic hypogonadism (CHH) is a genetically and clinically diverse disorder encompassing Kallmann syndrome (KS) and normosmic CHH (nCHH). Although mutations in numerous genes account for nearly 50% of CHH cases, a significant portion remains genetically uncharacterized. While most mutations follow the traditional Mendelian inheritance patterns, evidence suggests oligogenic interactions between CHH genes, acting as modifier genes to explain variable expressivity and incomplete penetrance associated with certain mutations.In this study, the proband presented with nCHH, while his son exhibited KS. We employed whole-exome sequencing (WES) to investigate the genetic differences between the two, and Sanger sequencing was used to validate the results obtained from WES.Genetic analysis revealed that both the proband and his son harboured a mutation in FGFR1 gene. Notably, an additional rare mutation in PROKR2 gene was exclusively identified in the son, which suggests the cause of the phenotypic difference between KS and nCHH.

Co-Inheritance of Pathogenic Variants in PKD1 and PKD2 Genes Determined by Parental Segregation and De Novo Origin: A Case Report

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary renal disease, and it is typically caused by PKD1 and PKD2 heterozygous variants. Nonetheless, the extensive phenotypic variability observed among affected individuals, even within the same family, suggests a more complex pattern of inheritance. We describe an ADPKD family in which the proband presented with an earlier and more severe renal phenotype (clinical diagnosis at the age of 14 and end-stage renal disease aged 24), compared to the other affected family members. Next-generation sequencing (NGS)-based analysis of polycystic kidney disease (PKD)-associated genes in the proband revealed the presence of a pathogenic PKD2 variant and a likely pathogenic variant in PKD1, according to the American College of Medical Genetics and Genomics (ACMG) criteria. The PKD2 nonsense p.Arg872Ter variant was segregated from the proband's father, with a mild phenotype. A similar mild disease presentation was found in the proband's aunts and uncle (the father's siblings). The frameshift p.Asp3832ProfsTer128 novel variant within PKD1 carried by the proband in addition to the pathogenic PKD2 variant was not found in either parent. This report highlights that the co-inheritance of two or more PKD genes or alleles may explain the extensive phenotypic variability among affected family members, thus emphasizing the importance of NGS-based techniques in the definition of the prognostic course.

Faster and more accurate pathogenic combination predictions with VarCoPP2.0

BACKGROUND

The prediction of potentially pathogenic variant combinations in patients remains a key task in the field of medical genetics for the understanding and detection of oligogenic/multilocus diseases. Models tailored towards such cases can help shorten the gap of missing diagnoses and can aid researchers in dealing with the high complexity of the derived data. The predictor VarCoPP (Variant Combinations Pathogenicity Predictor) that was published in 2019 and identified potentially pathogenic variant combinations in gene pairs (bilocus variant combinations), was the first important step in this direction. Despite its usefulness and applicability, several issues still remained that hindered a better performance, such as its False Positive (FP) rate, the quality of its training set and its complex architecture.

RESULTS

We present VarCoPP2.0: the successor of VarCoPP that is a simplified, faster and more accurate predictive model identifying potentially pathogenic bilocus variant combinations. Results from cross-validation and on independent data sets reveal that VarCoPP2.0 has improved in terms of both sensitivity (95% in cross-validation and 98% during testing) and specificity (5% FP rate). At the same time, its running time shows a significant 150-fold decrease due to the selection of a simpler Balanced Random Forest model. Its positive training set now consists of variant combinations that are more confidently linked with evidence of pathogenicity, based on the confidence scores present in OLIDA, the Oligogenic Diseases Database ( https://olida.ibsquare.be ). The improvement of its performance is also attributed to a more careful selection of up-to-date features identified via an original wrapper method. We show that the combination of different variant and gene pair features together is important for predictions, highlighting the usefulness of integrating biological information at different levels.

CONCLUSIONS

Through its improved performance and faster execution time, VarCoPP2.0 enables a more accurate analysis of larger data sets linked to oligogenic diseases. Users can access the ORVAL platform ( https://orval.ibsquare.be ) to apply VarCoPP2.0 on their data.