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

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.

A holistic approach to maximise diagnostic output in trio exome sequencing

Introduction

Rare genetic diseases are a major cause for severe illness in children. Whole exome sequencing (WES) is a powerful tool for identifying genetic causes of rare diseases. For a better and faster assessment of the vast number of variants that are identified in the index patient in WES, parental sequencing can be applied ("trio WES").

Methods

We assessed the diagnostic rate of routine trio WES including analysis of copy number variants in 224 pediatric patients during an evaluation period of three years.

Results

Trio WES provided a diagnosis in 67 (30%) of all 224 analysed children. The turnaround time of trio WES analysis has been reduced significantly from 41 days in 2019 to 23 days in 2021. Copy number variants could be identified to be causative in 10 cases (4.5%), underlying the importance of copy number variant analysis. Variants in three genes which were previously not associated with a clinical condition (GAD1, TMEM222 and ZNFX1) were identified using the matching tool GeneMatcher and were part of the first description of a new syndrome.

Discussion

Trio WES has proven to have a high diagnostic yield and to shorten the process of identifying the correct diagnosis in paediatric patients. Re-evaluation of all 224 trio WES 1-3 years after initial analysis did not establish new diagnoses. Initiating (trio) WES as a first-tier diagnostics including copy number variant detection should be considered as early as possible, especially for children treated in ICU, if a monogenetic disease is suspected.

Resources and tools for rare disease variant interpretation

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

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.

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.

Predicting candidate genes from phenotypes, functions and anatomical site of expression

Motivation

Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease–gene prioritization task. These methods generally compute the similarity between a patient’s phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms, such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine-learning models.

Results

We developed a novel graph-based machine-learning method for biomedical ontologies, which is able to exploit axioms in ontologies and other graph-structured data. Using our machine-learning method, we embed genes based on their associated phenotypes, functions of the gene products and anatomical location of gene expression. We then develop a machine-learning model to predict gene–disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state-of-the-art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes, which are associated with phenotypes, functions or site of expression.

Availability and implementation

Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec.

Supplementary information

Supplementary data are available at Bioinformatics online.

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 (β-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 β-COP and β'-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 β-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.

Phenotypic and Genomic Analysis of Cystic Hygroma in Pigs

Cystic hygroma is a malformation of the lymphatic and vascular system and is recognized as a benign congenital tumor that affects humans and animals in the perinatal period. This congeni-tal disorder is rarely described in animals, and until today, cystic hygroma in pigs has not been described in the literature. In a purebred Piètrain litter with twelve live-born piglets, cystic hy-groma was noticed on the rump of two male pigs within the first week of life. In addition, a third case of a crossbred weaner (Large White × Landrace) was detected during a herd examina-tion. To rule out common differential diagnoses, e.g., abscess or hematoma, further clinical and pathological investigations were conducted. During clinical examination, a painless and soft mass, which was compressible, was detected on the rump of all affected animals. The ultra-sonographic examination revealed a fluid-filled and cavernous subcutaneous structure. In addi-tion, a puncture of the cyst was conducted, revealing a serosanguinous fluid with negative bacte-riological culture. In all cases, a necropsy was performed, showing that the animals had fluid-filled cysts lined by well-differentiated lymphatic endothelium. Based on the clinicopathological examination, cystic hygroma was diagnosed. Furthermore, SNP array genotyping and whole-genome sequencing was performed and provided no evidence for a chromosomal disorder. In the Piètrain family, several genome regions were homozygous in both affected piglets. None-theless, a dominant acting de novo germline variant could not be ruled out, and therefore differ-ent filtering strategies were used to find pathogenic variants. The herein presented lists of pri-vate variants after filtering against hundreds of control genomes provide no plausible candidate and no shared variants among the two sequenced cases. Therefore, further studies are needed to evaluate possible genetic etiology. In general, systematic surveillance is needed to identify ge-netic defects as early as possible and to avoid the occurrence of losses in the pig population.