DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning.

MOTIVATION: Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in different ways from single nucleotide variants. Interpreting the phenotypic consequences of structural variants relies on information about gene functions, haploinsufficiency or triplosensitivity and other genomic features. Phenotype-based methods to identifying variants that are involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been applied successfully to single nucleotide variants as well as short insertions and deletions, the complexity of structural variants makes it more challenging to link them to phenotypes. Furthermore, structural variants can affect a large number of coding regions, and phenotype information may not be available for all of them. RESULTS: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. We incorporate phenotypes linked to genes, functions of gene products, gene expression in individual cell types and anatomical sites of expression, and systematically relate them to their phenotypic consequences through ontologies and machine learning. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families. AVAILABILITY AND IMPLEMENTATION: https://github.com/bio-ontology-research-group/DeepSVP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The rate of secondary genomic findings in the Saudi population.

Secondary findings (SF) are defined as genetic conditions discovered unintentionally during an evaluation of raw data for another disease. We aimed to identify the rate of secondary genetic findings in the Saudi population in the 59 genes of the American College of Medical Genetics and Genomics (ACMG) list. In our study, the raw data of 1254 individuals, generated from exome sequencing for clinical purposes, were studied. Variants detected in the 59 genes on the ACMG list of secondary findings were investigated. Pathogenicity classifications were assigned to those variants based on the ACMG scoring system. We identified 2409 variants in the 59 gene list, 45 variants were classified as pathogenic/likely pathogenic variants according to the ACMG classification. The LDLR gene had the greatest number of pathogenic/likely pathogenic variants 12%. Cardiovascular genetic diseases had the highest frequency of disorders detected as secondary findings. In this study, the overall rate of positive cases identified with secondary findings in the Saudi population was 8%. The different in our current study and the previous studies in Saudi Arabia can be explained by the differences between the sequencing method, the criteria used for variant classification, the availability of newer evidence at the time of the publication, and the fact that we identified Saudi novel variants never reported in other populations.

MEFV c.2230G>T p.(Ala744Ser) rs61732874 previously misclassified as pathogenic variant due to lack of a population specific database.

BACKGROUND: Familial Mediterranean fever is a hereditary inflammatory disorder caused by variants in MEFV. c.2230G>T p.(Ala744Ser) rs61732874 is considered to be an established pathogenic variant in MEFV, but in this study we provide a complete evaluation that suggests this variant is likely benign. METHODS: Using an in-house exome database from 924 individuals, we extracted all individuals harboring this variant for clinical, laboratory, and familial evaluation. RESULTS: We identified the variant in 58 individuals from 39 families. The allele frequency of this variant in our database is 4.2%. None of the identified individuals match the diagnosis of Familial Mediterranean Fever. Using the American College of Medical Genetics and Genomics guidelines for variant classification, this variant is classified as likely benign and not pathogenic. CONCLUSION: Conflicting evidence about variants creates challenges for testing laboratories and impacts patient care. Sharing information drawn mainly from underrepresented populations and clinical phenotyping are important tools for precise curation of genetic variants.

Genomic testing and counseling: The contribution of next-generation sequencing to epilepsy genetics.

INTRODUCTION: Currently, next-generation sequencing (NGS) technology is more accessible and available to detect the genetic causation of diseases. Though NGS technology benefited some clinical phenotypes, for some clinical diagnoses such as seizures and epileptic disorders, adaptation occurred slowly. The genetic diagnosis was mainly based on epilepsy gene panels and not on whole exome and/or genome sequencing. METHOD: We retrospectively analyzed 420 index cases, referred for NGS over a period of 18 months, to investigate the challenges in diagnosing epilepsy. RESULT: Of the 420 cases, 65 (15%) were referred due to epilepsy with one third having a positive family history. The result of the NGS was 14 positive cases (21.5%), 16 inconclusive cases (24%), and 35 (53%) negative cases. No gene has been detected twice in the inconclusive and positive groups. Comparative genomic hybridization has been performed for all 30 NGS negative cases and four cases with pathogenic variants (deletion in 15q11.213.1, deletion of 2p16.3, deletion in Xq22.1, and deletion in 17p13.3) were identified. CONCLUSION: These findings have implications for our understanding of the approach to genetic testing and counseling of patients affected with seizures and epilepsy disorders. The overall diagnostic yield of exome/genome sequencing in our cohort was 23%. The main characteristic is genetic heterogeneity, supporting NGS technology as a suitable testing approach for seizures and epilepsy disorders. Genetic counseling for newly identified disease-causing variants depends on the pedigree interpretation, within the context of disease penetrance and variable expressivity.

EMC10 homozygous variant identified in a family with global developmental delay, mild intellectual disability, and speech delay.

In recent years, several genes have been implicated in the variable disease presentation of global developmental delay (GDD) and intellectual disability (ID). The endoplasmic reticulum membrane protein complex (EMC) family is known to be involved in GDD and ID. Homozygous variants of EMC1 are associated with GDD, scoliosis, and cerebellar atrophy, indicating the relevance of this pathway for neurogenetic disorders. EMC10 is a bone marrow-derived angiogenic growth factor that plays an important role in infarct vascularization and promoting tissue repair. However, this gene has not been previously associated with human disease. Herein, we describe a Saudi family with two individuals segregating a recessive neurodevelopmental disorder. Both of the affected individuals showed mild ID, speech delay, and GDD. Whole-exome sequencing (WES) and Sanger sequencing were performed to identify candidate genes. Further, to elucidate the functional effects of the variant, quantitative real-time PCR (RT-qPCR)-based expression analysis was performed. WES revealed a homozygous splice acceptor site variant (c.679-1G>A) in EMC10 (chromosome 19q13.33) that segregated perfectly within the family. RT-qPCR showed a substantial decrease in the relative EMC10 gene expression in the patients, indicating the pathogenicity of the identified variant. For the first time in the literature, the EMC10 gene variant was associated with mild ID, speech delay, and GDD. Thus, this gene plays a key role in developmental milestones, with the potential to cause neurodevelopmental disorders in humans.

Whole-genome sequencing offers additional but limited clinical utility compared with reanalysis of whole-exome sequencing.

PURPOSE: Whole-exome sequencing (WES) and whole-genome sequencing (WGS) are used to diagnose genetic and inherited disorders. However, few studies comparing the detection rates of WES and WGS in clinical settings have been performed. METHODS: Variant call format files were generated and raw data analysis was performed in cases in which the final molecular results showed discrepancies. We classified the possible explanations for the discrepancies into three categories: the time interval between the two tests, the technical limitations of WES, and the impact of the sequencing system type. RESULTS: This cohort comprised 108 patients with negative array comparative genomic hybridization and negative or inconclusive WES results before WGS was performed. Ten (9%) patients had positive WGS results. However, after reanalysis the WGS hit rate decreased to 7% (7 cases). In four cases the variants were identified by WES but missed for different reasons. Only 3 cases (3%) were positive by WGS but completely unidentified by WES. CONCLUSION: In this study, we showed that 30% of the positive cases identified by WGS could be identified by reanalyzing the WES raw data, and WGS achieved an only 7% higher detection rate. Therefore, until the cost of WGS approximates that of WES, reanalyzing WES raw data is recommended before performing WGS.

Reclassifying variations of unknown significance in diseases affecting Saudi Arabia's population reveal new associations.

Introduction: Physicians face diagnostic dilemmas upon reports indicating disease variants of unknown significance (VUS). The most puzzling cases are patients with rare diseases, where finding another matched genotype and phenotype to associate their results is challenging. This study aims to prove the value of updating patient files with new classifications, potentially leading to better assessment and prevention. Methodology: We recruited retrospective phenotypic and genotypic data from King Saud Medical City, Riyadh, Kingdom of Saudi Arabia. Between September 2020 and December 2021, 1,080 patients' genetic profiles were tested in a College of American Pathologists accredited laboratory. We excluded all confirmed pathogenic variants, likely pathogenic variants and copy number variations. Finally, we further reclassified 194 VUS using different local and global databases, employing in silico prediction to justify the phenotype-genotype association. Results: Of the 194 VUS, 90 remained VUS, and the other 104 were reclassified as follows: 16 pathogenic, 49 likely pathogenic, nine benign, and 30 likely benign. Moreover, most of these variants had never been observed in other local or international databases. Conclusion: Reclassifying the VUS adds value to understanding the causality of the phenotype if it has been reported in another family or population. The healthcare system should establish guidelines for re-evaluating VUS, and upgrading VUS should reflect on individual/family risks and management strategies.

Prospect of genetic disorders in Saudi Arabia.

Introduction: Rare diseases (RDs) create a massive burden for governments and families because sufferers of these diseases are required to undergo long-term treatment or rehabilitation to maintain a normal life. In Saudi Arabia (SA), the prevalence of RDs is high as a result of cultural and socio-economic factors. This study, however, aims to shed light on the genetic component of the prevalence of RDs in SA. Methodology: A retrospective study was conducted between September 2020 and December 2021 at King Saud Medical City, a tertiary hospital of the Ministry of Health (MOH), SA. A total of 1080 individuals with 544 potentially relevant variants were included. The index was 738, and the samples were tested in a commercialized laboratory using different molecular techniques, including next-generation sequencing. Result: A total of 867 molecular genetics tests were conducted on 738 probands. These tests included 610 exome sequencing (ES) tests, four genome sequencing (GS) tests, 82 molecular panels, 106 single nucleotide polymorphism (SNP) array, four methylation studies, 58 single-gene studies and three mitochondrial genome sequencing tests. The diagnostic yield among molecular genetics studies was 41.8% in ES, 24% in panels, 12% in SNP array and 24% in single gene studies. The majority of the identified potential variants (68%) were single nucleotide variants (SNV). Other ascertained variants included frameshift (11%), deletion (10%), duplication (5%), splicing (9%), in-frame deletion (3%) and indels (1%). The rate of positive consanguinity was 56%, and the autosomal recessive accounted for 54%. We found a significant correlation between the ES detection rate and positive consanguinity. We illustrated the presence of rare treatable conditions in DNAJC12, SLC19A3, and ALDH7A1, and the presence of the founder effect variant in SKIC2. Neurodevelopmental disorders were the main phenotype for which genetics studies were required (35.7%). Conclusion: This is the sixth-largest local study reporting next-generation sequencing. The results indicate the influence of consanguineous marriages on genetic disease and the burden it causes for the Kingdom of SA. This study highlights the need to enrich our society's knowledge of genetic disorders. We recommend utilising ES as a first-tier test to establish genetic diagnosis in a highly consanguineous population.

The variant artificial intelligence easy scoring (VARIES) system.

PURPOSE: Medical artificial intelligence (MAI) is artificial intelligence (AI) applied to the healthcare field. AI can be applied to many different aspects of genetics, such as variant classification. With little or no prior experience in AI coding, we share our experience with variant classification using the Variant Artificial Intelligence Easy Scoring (VARIES), an open-access platform, and the Automatic Machine Learning (AutoML) of the Google Cloud Platform. METHODS: We investigated exome sequencing data from a sample of 1410 individuals. The majority (80%) were used for training and 20% for testing. The user-friendly Google Cloud Platform was used to create the VARIES model, and the TRIPOD checklist to develop and validate the prediction model for the development of the VARIES system. RESULTS: The learning rate of the training dataset reached optimal results at an early stage of iteration, with a loss value near zero in approximately 4 min. For the testing dataset, the results for F1 (micro average) was 0.64, F1 (macro average) 0.34, micro-average area under the curve AUC (one-over-rest) 0.81 and the macro-average AUC (one-over-rest) 0.73. The overall performance characteristics of the VARIES model suggest the classifier has a high predictive ability. CONCLUSION: We present a systematic guideline to create a genomic AI prediction tool with high predictive power, using a graphical user interface provided by Google Cloud Platform, with no prior experience in creating the software programs required.

Common disease-associated gene variants in a Saudi Arabian population.

BACKGROUND: Screening programs for the most prevalent conditions occurring in a country is an evidence-based prevention strategy. The burden of autosomal recessive disease variations in Saudi Arabia is high because of the highly consanguineous population. The optimal solution for estimating the carrier frequency of the most prevalent diseases is carrier screening. OBJECTIVES: Identify the most influential recessive alleles associated with disease in the Saudi population. DESIGN: We used clinical whole-exome sequencing data from an in-house familial database to evaluate the most prevalent genetic variations associated with disease in a Saudi population. SETTINGS: King Abdullah International Medical Research Center (KAIMRC) and King Abdulaziz Medical City. METHODS: Whole exome sequencing data obtained from clinical studies of family members, a cohort of 1314 affected and unaffected individuals, were filtered using the in-house pipeline to extract the most prevalent variant in the dataset. MAIN OUTCOME MEASURES: Most prevalent genetic variations associated with disease in the Saudi population. SAMPLE SIZE: 1314 affected and unaffected individuals. RESULTS: We identified 37 autosomal recessive variants and two heterozygous X-linked variants in 35 genes associated with the most prevalent disorders, which included hematologic (32%), endocrine (21%), metabolic (11%) and immunological (10%) diseases. CONCLUSION: This study provides an update of the most frequently occurring alleles, which support future carrier screening programs. LIMITATIONS: Single center that might represent the different regions but may be biased. In addition, most of the families included in the database are part of the proband's genetic identification for specific phenotypes. CONFLICT OF INTEREST: None.

Biallelic variants in COPB1 cause a novel, severe intellectual disability syndrome with cataracts and variable microcephaly.

BACKGROUND: Coat protein complex 1 (COPI) is integral in the sorting and retrograde trafficking of proteins and lipids from the Golgi apparatus to the endoplasmic reticulum (ER). In recent years, coat proteins have been implicated in human diseases known collectively as "coatopathies". METHODS: Whole exome or genome sequencing of two families with a neuro-developmental syndrome, variable microcephaly and cataracts revealed biallelic variants in COPB1, which encodes the beta-subunit of COPI (ß-COP). To investigate Family 1's splice donor site variant, we undertook patient blood RNA studies and CRISPR/Cas9 modelling of this variant in a homologous region of the Xenopus tropicalis genome. To investigate Family 2's missense variant, we studied cellular phenotypes of human retinal epithelium and embryonic kidney cell lines transfected with a COPB1 expression vector into which we had introduced Family 2's mutation. RESULTS: We present a new recessive coatopathy typified by severe developmental delay and cataracts and variable microcephaly. A homozygous splice donor site variant in Family 1 results in two aberrant transcripts, one of which causes skipping of exon 8 in COPB1 pre-mRNA, and a 36 amino acid in-frame deletion, resulting in the loss of a motif at a small interaction interface between ß-COP and ß'-COP. Xenopus tropicalis animals with a homologous mutation, introduced by CRISPR/Cas9 genome editing, recapitulate features of the human syndrome including microcephaly and cataracts. In vitro modelling of the COPB1 c.1651T>G p.Phe551Val variant in Family 2 identifies defective Golgi to ER recycling of this mutant ß-COP, with the mutant protein being retarded in the Golgi. CONCLUSIONS: This adds to the growing body of evidence that COPI subunits are essential in brain development and human health and underlines the utility of exome and genome sequencing coupled with Xenopus tropicalis CRISPR/Cas modelling for the identification and characterisation of novel rare disease genes.

What is the right sequencing approach? Solo VS extended family analysis in consanguineous populations.

BACKGROUND: Testing strategies is crucial for genetics clinics and testing laboratories. In this study, we tried to compare the hit rate between solo and trio and trio plus testing and between trio and sibship testing. Finally, we studied the impact of extended family analysis, mainly in complex and unsolved cases. METHODS: Three cohorts were used for this analysis: one cohort to assess the hit rate between solo, trio and trio plus testing, another cohort to examine the impact of the testing strategy of sibship genome vs trio-based analysis, and a third cohort to test the impact of an extended family analysis of up to eight family members to lower the number of candidate variants. RESULTS: The hit rates in solo, trio and trio plus testing were 39, 40, and 41%, respectively. The total number of candidate variants in the sibship testing strategy was 117 variants compared to 59 variants in the trio-based analysis. We noticed that the average number of coding candidate variants in trio-based analysis was 1192 variants and 26,454 noncoding variants, and this number was lowered by 50-75% after adding additional family members, with up to two coding and 66 noncoding homozygous variants only, in families with eight family members. CONCLUSION: There was no difference in the hit rate between solo and extended family members. Trio-based analysis was a better approach than sibship testing, even in a consanguineous population. Finally, each additional family member helped to narrow down the number of variants by 50-75%. Our findings could help clinicians, researchers and testing laboratories select the most cost-effective and appropriate sequencing approach for their patients. Furthermore, using extended family analysis is a very useful tool for complex cases with novel genes.