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The HGVS nomenclature guidelines are used worldwide for genetic variant interpretation but can seem complicated and difficult to understand and apply. That is why we have created this beginner’s guide to mutation nomenclature using the HGVS recommendations, with clear visual examples that break down the process into bitesize pieces.
1. What is HGVS nomenclature?
2. How to read mutation nomenclature: Breaking down the variant description
2.1 Reference sequence e.g., NM
2.2 Description of variant e.g., c.4375C>T
2.3 Predicted consequence e.g., p.(Arg1459*)
3. The 3 prime rule for mutation
4. Final thoughts and helpful tool
The Human Genome Variation Society (HGVS) nomenclature standard was developed to prevent the misinterpretation of variants in DNA, RNA, and protein sequences. The HGVS nomenclature standard is used worldwide, especially in clinical diagnostics, and is authorized by the Human Genome Organisation (HUGO).1,2
HGVS General Terminology Recommendations1
| X Do not use | ✔️ Recommended terminology |
| Mutation or polymorphism | Variant, change, allelic variant Can be used for cancer tissue: Mutation load and tumor mutation burden |
| Pathogenic | Affects function, disease-associated, phenotype-associated |
HGVS follow recognized standards for the nomenclature of DNA and RNA nucleotides, the genetic code, amino acid descriptions, and cytogenetic band position in chromosomes.3
The HGVS recommendations for mutation nomenclature state that the format of a complete variant description should first include the reference sequence, followed by the variant description, and then the predicted consequence in parentheses. For example, NM-004006.2:c.4375C>T p.(Arg1459*) (Figure 1).
The HGVS nomenclature recommendations for sequence variants state that a complete variant description should begin with the reference sequence.1 The reference sequence accession number begins with a two-letter abbreviation (explained in Table 1), followed by a multi-digit number, and finally a version number.
Table 1. Meaning of the two-letter abbreviation at the beginning of a reference sequence accession number.
| Abbreviation | Reference sequence based on a: |
| NC | Chromosome |
| NG | Gene or genomic region |
| LRG | Locus Reference Genomic sequence: Gene or genomic region, used in a diagnostic setting |
| NM | Protein-coding RNA (mRNA) |
| NR | Non-protein-coding RNA |
| NP | Protein (amino acid) sequence |
The variant description begins by depicting the type of reference sequence used (c = coding DNA sequence, g = genomic reference sequence). When a protein-coding reference sequence is used (c), the nucleotide numbering begins with a 1, which represents the first position in the protein-coding region (the A of the translation-initiating ATG), and ends at the last position of the stop codon. Thus, if you divide the position number by 3, you can identify the affected amino acid in the protein sequence e.g., using the same example as above, 4375/3 = 1459, indicating that the predicted consequence affects amino acid 1459, which is an arginine. Different variants are indicated using different notations (explained in Table 2).
Table 2. HGVS notation and examples for the most common types of mutations2
| Notation | Example | Explanation |
| > | c.4375C>T | Substitution of the C nucleotide at position c.4375 with a T |
| del | c.4375_4379del or c.4375_4379delCGATT | Nucleotides from position c.4375 to c.4379 deleted |
| dup | c.4375_4385dup or c.4375_4385dupCGATTATTCCA | Nucleotides from position c.4375 to c.4385 duplicated |
| ins | c.4375_4376insACCT | ACCT inserted between positions c.4375 and c.4376 |
| delins | c.4375_4376delinsACTT or c.4375_4376delCGinsAGTT | Nucleotides from position c.4375 to c.4376 (CG) are deleted and replaced by ACTT |
When only DNA has been analyzed, the RNA- and protein-level consequences of the variant can only be predicted, and should thus be reported in parentheses e.g., p.(Arg1459*) is the predicted effect at protein level (p) for the example described above.
For all variant descriptions using HGVS nomenclature, the nucleotide at the most 3’ position of the variation in the reference sequence is arbitrarily assigned to have changed (see how to apply this rule in Figure 2).4 The exception is for deletions/duplications around exon junctions for which shifting the variant 3’ would place it in the next exon.5
Although the HGVS recommendations can be difficult to understand and might take a bit of getting used to, if you break them down and refer to the examples in this guide, you are on the road to success!
If you want to accelerate your variant annotation and interpretation, Alamut™ Visual Plus is a comprehensive, full genome browser for efficient and user-friendly variant interpretation. The software accelerates the complex and time-consuming assessment of variants thanks to its user-friendly interface and integrated features for variant annotation and analysis.
Find out how Alamut™ Visual Plus applies the HGVS nomenclature recommendations to ensure that variant annotation follows the universally applied standards for variant analysis, interpretation, and reporting in our dedicated Technical Note.
Alamut™️ Visual Plus is for Research Use Only. Not for use in diagnostic procedures.
References
Dr. Mohamed Z Alimohamed kindly summarized his upcoming peer-reviewed publication in Gene:
“Current splice prediction algorithms have limited sensitivity and specificity, therefore many potential splice variants are classified as variants of uncertain significance (VUSs).
However, functional assessment of VUSs to test splicing is labor-intensive and time-consuming. We have developed a decision tree, SEPT-GD, by setting thresholds for the splice prediction programs implemented in Alamut™️ to prioritize potential splice variants associated with cardiomyopathies for functional studies, and functionally verified the outcome of the decision tree.
SEPT-GD outperforms the tools commonly used for RNA splicing prediction and improves prioritization of variants in cardiomyopathy genes for functional splicing analysis.”
Click here to read the full publication.
Alimohamed MZ, Boven LG, van Dijk KK, Vos YJ, Hoedemaekers YM, van der Zwaag PA, Sijmons RH, Jongbloed JDH, Sikkema-Raddatz B, Westers H. SEPT-GD: A decision tree to prioritise potential RNA splice variants in cardiomyopathy genes for functional splicing assays in diagnostics. Gene. 2023 Jan 30;851:146984.
Alamut™️ is for Research Use Only. Not for use in diagnostic procedures.
Discover what makes SOPHiA DDM™ effective at calling mtDNA variants from exome sequencing data, and see for yourself how in a single workflow, the SOPHiA DDM™ Platform complemented by Alamut™ Visual Plus can be used to identify and interpret variants in mtDNA alongside SNVs, Indels, and CNVs in nuclear DNA.
Our Technical Note outlines the guidelines and standards behind the nomenclature convention deployed in Alamut™ Visual Plus. We also explain how Alamut™ Visual Plus applies them to ensure that variant annotation follows the universally applied standards for variant analysis.
At the American Society of Human Genetics (ASHG) Annual Meeting this year, our esteemed speakers shared the ins and outs of how the SOPHiA DDM™ Platform, in combination with Alamut™ Visual Plus, adapted to their laboratories’ needs to provide sample-to-report workflows that streamlined the identification and interpretation of nuclear and mitochondrial variants associated with rare and inherited diseases, including hereditary cancers.
All SOPHiA GENETICS™️ products discussed in this article are for Research Use Only – not for use in diagnostic procedures. SOPHiA GENETICS™ does not facilitate and does not accept any liability for any validation of SOPHiA GENETICS™ products for clinical use by a third party.
Exome sequencing with combined mitochondrial genome sequencing for the detection of nuclear and mitochondrial DNA variants
Jessica Van Ziffle, PhD, FACMG Associate Clinical Professor, Pathology at University of California, San Francisco, California, United States
In the Pathology laboratory at the University of California, the proportion of positive/probably positive variants detected in pediatric and prenatal cases analyzed using the SOPHiA DDM™ Custom Whole Exome Solution (optimized for the Illumina NovaSeq 6000) was consistent with the literature (see charts). Approximately 70% of the positive pediatric cases were associated with autosomal dominant inheritance. Most reported variants were missense single nucleotide variants (SNVs), with the positive cases fairly equally split between frameshift, nonsense, and missense variants.
Proportion of positive/probably positive findings for pediatric and prenatal exome cases
Figure sourced from Jessica Van Ziffle’s presentation
With the goal of increasing the proportion of positive findings, Jessica and the team at UCSF explored what additional variants could be identified by exome sequencing to potentially solve the ∼10% of inconclusive cases and ∼65% of negative cases. First, the team investigated the impact of calling copy number variants (CNVs), specifically contiguous gene changes, whole gene changes, and exon-level changes. UCSF worked closely with SOPHiA GENETICS™ to optimize their exome sequencing to ensure high and even coverage for accurate CNV detection, and indeed doubled their sequencing depth to 80M reads to ensure the sensitive detection of CNVs 1-2 exons in size.
Next, UCSF wanted to be able to simultaneously call variants in mitochondrial DNA, which is especially relevant for metabolic diseases. Different cell types have different numbers of mitochondria, and each mitochondrion has its own genome that can have different variants in it (heteroplasmy). The UCSF team, therefore, wanted to assess the lower limit of detection for mitochondrial heteroplasmy through a mixing study, which concluded that exome testing could detect variants down to 5% variant allele fraction with high sensitivity.
Streamlining clinical implementation of hereditary cancer analysis and reporting with a custom application
Hong Wang, PhD, FCCMG, FACMG, DABMGG Laboratory Geneticist at North York General Hospital, Toronto, Ontario, Canada
Andrea Vaags, PhD, FCCMG Discipline Co-Lead and Laboratory Geneticist at Trillium Health Partners – Credit Valley Hospital, Mississauga, Ontario, Canada
Drs Wang and Vaags provided a step-by-step overview of how they developed a brand new hereditary cancer panel to meet the Ontario Health - Cancer Care Ontario criteria for hereditary cancer testing.
Laboratory and clinical working groups were established to evaluate evidence and identify key genes and non-coding variants to include in a cutting-edge custom hereditary cancer panel. Furthermore, genetic testing eligibility criteria were co-developed with the Hereditary Cancer Clinical Eligibility Working Group. The laboratory working group used an evidence-based framework to design a standardized 76-gene panel, organized into 13 larger disease site-linked panels, and 25 single/small gene panels. After designing the panel, the Ontario group worked with SOPHiA GENETICS™ to expeditiously develop and implement the custom SOPHiA DDMTM Hereditary Cancer application in academic community hospitals (see timeline).
Timeline of SOPHiA DDM™ Hereditary Cancer panel implementation in Ontario hospitals
Figure sourced from the Ontario hospital responsible for validating this product
Thanks to the streamlined end-to-end SOPHiA GENETICS™ workflow, hereditary cancer testing approximately doubled, according to Dr Vaags.
The workflow for each batch of 70 samples (plus one control) in the Ontario group laboratories, consists of DNA preparation, automated 3-day library preparation using the SOPHiA GENETICS program on the Hamilton STARlet, and sequencing on a NextSeq® 550 using mid-output. Sequencing data are automatically uploaded to the SOPHiA DDM™ cloud for processing ahead of analysis. For additional time savings, genes requiring special consideration due to the presence of pseudogenes are flagged with a warning in the SOPHiA DDM™ Platform and the SOPHiA GENETICS™ support team is on hand to answer queries on unusual findings. The cloud-based software for data management enables the laboratories to streamline data access, storage, and archiving back-up. Dr Wang shared that the time saved through this workflow has been instrumental in maintaining turnaround times, especially with significant understaffing during challenging periods.
By applying Virtual Panels and custom filters, the teams can analyze from as little as a single variant to as many as 76 genes using a single workflow. The high analytical sensitivity and specificity enable the laboratories to pick up unusual findings, such as Alu insertions, Boland inversions, and low-level mosaicism of copy number changes. And finally, the one-step secondary and tertiary analysis for concurrent detection of SNVs and CNVs allows the teams to significantly speed up their analysis, and the pseudogene pipeline enables the laboratories to minimize reflex testing. In summary, the custom SOPHiA DDM™ Hereditary Cancer application provides the Ontario laboratories with a one-size-fits-all solution.
Screening for genetic variants in hereditary cancer syndromes using the end-to-end SOPHiA DDM™ workflow
Mark Williams, FHGSA – Chief Scientist at Genomic Diagnostics, Heidelberg, Victoria, Australia
Speaker Mark Williams began his talk by highlighting that a key goal of the Genomic Diagnostics lab is to facilitate equal access to hereditary cancer testing. To do this, the lab set multiple criteria that were highly important to them when developing a new hereditary cancer application. Employing the complete SOPHiA GENETICS™ workflow for Hereditary Cancer allowed the team at Genomic Diagnostics to successfully meet these testing criteria.
In collaboration with Genomic Diagnostics, the custom SOPHiA DDM™ Hereditary Cancer application was designed to include genes that align with current practice guidelines. It was important to the lab that the pipeline could detect SNVs, Indels, and copy number variations (CNVs) in a single workflow. Mark confirmed that the resultant application effectively detects CNVs and is scalable, with turnaround times that meet their needs, even with testing volumes increasing year-on-year. In addition, the solution provides high-quality, consistent results, a full record of curation, visualization of BAM files, and is easily accessible and usable by all laboratory staff.
Like numerous other SOPHiA GENETICS™️ customers, Mark concluded that the SOPHiA DDM™ Platform offers a robust, automated, and secure bioinformatics pipeline that meets Australia’s privacy regulations. In addition, the software is extremely user-friendly, from its visual interface to the detailed QC metrics, annotation information, and links to databases. All laboratory personnel can effectively use the end-to-end solution, even without prior bioinformatics expertise.
The integrated workflow and affordable price allowed Genomic Diagnostics to expand access to the custom SOPHiA DDM™ Hereditary Cancer application (see chart), meeting Genomic Diagnostics’ goal of facilitating equal access to hereditary cancer testing.
Increasing access to hereditary cancer genomic testing over time
Figure sourced from Mark Williams’ presentation
We thank all our speakers for sharing their research stories at our ASHG symposium this year. We’re delighted to hear how their integrated SOPHiA DDM™ workflows are reducing workloads, expanding access, and continuing to discover new variants associated with rare diseases and hereditary cancers.
Familial Hypercholesterolemia (FH), is the most common monogenic autosomal dominant disorder where affected individuals present with significantly elevated low-density lipoprotein (LDL) cholesterol in the blood, tendinous xanthomas, corneal arcus, and coronary artery disease (CAD)1. This affects about 1 in every 200-250 people globally, but most people are unaware they have it2, 3.
FH is known to be caused by inherited mutations in genes used to regulate and remove cholesterol in the blood. Among the individuals with a clinical diagnosis of FH, pathogenic variants can be identified in one of the four genes - LDLR, APOB, LDLRAP1, and PCSK9 in about 60-80% of adult and 60-95% of pediatric FH patients1, 2, 4. Other known genes implicated in FH are – ABCG8, ABCG5, APOE, and LIPA5, 6.
There are two types of FH – Heterozygous FH (HeFH) and Homozygous FH (HoFH). HeFH is caused by a single inherited variant from one parent. In rare cases, an individual can have HoFH, which is caused by having two causal variants inherited from each parent. Individuals with HoFH typically have a more severe form of the disease. LDL receptors usually remove LDL-C from the blood to the liver by carrying the lipoproteins that fix LDL-C for transport. Genetic mutations in the LDLR gene can cause a decrease in the number of LDL receptors or interfere with its normal functions. This causes the high LDL cholesterol levels seen in FH patients.
FH remains underdiagnosed and undertreated globally. Additionally, patients with HoFH are diagnosed much later and are at higher premature Atherosclerotic Cardiovascular Disease (ASCVD) risk.
FH can be diagnosed using targeted molecular testing or next-generation sequencing (NGS) gene panel strategies; however, the latter is still not widely used6, 7. In 20-30% of individuals that meet clinical criteria for FH, standard clinical genetic testing may be negative due to either technical limitations (e.g., clinical sensitivity of current technology) or causal genes that are not yet discovered. FH is diagnosed in children with LDL‐C persistently over 160 mg/dL (4.1 mmol/L) and adults with LDL‐C greater than 190 mg/dL (4.9 mmol/L), especially if there is a family history of early‐onset CAD, and in all patients with early CAD.
The results of population-based studies of genetic screening for FH have demonstrated that there is no fixed LDL-C threshold for making the diagnosis of HoFH8. Although an untreated LDL-C of over 400 mg/dL (10 mmol/L) should lead to a consideration of the diagnosis of HoFH, LDL-C levels of less than 400 mg/dL have also been documented in patients with genetically confirmed HoFH. These high cholesterol levels can lead to a variety of symptoms. Cholesterol build-up can deposit in different parts of the body which can lead to deposits around the elbows and knees, Achilles tendon pain, and or a white or grey ring around the iris of the eye. FH is an inherited disease present from birth, but symptoms may not manifest until adulthood, which makes more early testing necessary.
Universal screening for FH is currently not feasible or cost-effective. The most effective means to identify new cases of FH is by cascade screening family members of a known index case8. Cascade genetic testing is beneficial in developing early intervention strategies for affected family members. Screening of relatives can be done by measuring LDL-C, genetic analysis, or both, and FH has been designated as a tier 1 genomics application for family screening by the US Centers for Disease Control and Prevention Office of Public Health Genomics. Universal pediatric screening coupled with reverse cascade screening has been proposed as another strategy to identify new FH cases as the discrimination power between FH and non-FH cases based on LDL-C levels is better during childhood8.
Despite the lack of screening, FH disorders are easily actionable. Patients with FH can have an excellent prognosis once the disorder is diagnosed, and a treatment plan is put in place. Dietary and lifestyle modifications are the starting points for LDL‐C lowering in patients with FH, but multidrug treatment is often required to achieve adequate LDL‐C levels. The risk of developing CAD is increased up to 13-fold in untreated FH subjects, and 22-fold when an FH mutation is present.
Statins are potent competitive inhibitors of 3-hydroxy-3-methylglutaryl coenzyme-A reductase and have proven useful in the treatment of FH. It is recommended to treat FH patients with high doses of high‐intensity statins, which are capable of lowering LDL‐C by 50% to 60%8. If high‐dose, high‐intensity statins are not tolerated, the maximally tolerated statin dose is prescribed. PCSK9 inhibitors such as inclisiran (small interfering RNA) and evolocumab, (human monoclonal antibody), can also be used to lower lipid levels. Lipoprotein apheresis is indicated in HoFH or severe heterozygous FH patients with inadequate response to cholesterol-lowering therapies. Lastly, significant global disparities exist in treatment regimens, control of LDL cholesterol levels, and cardiovascular event-free survival, which demands a critical re-evaluation of global health policy to reduce inequalities and improve outcomes for all patients with HoFH.
Studies show that as few as 10% of individuals living with FH could be aware of their diagnosis7. Consequently, FH is most often diagnosed in adulthood after a cardiac event. The widespread access to genetic testing could be part of the solution, but concerted efforts are still necessary to raise awareness of FH and identify barriers to comprehensive screening, early diagnostics, and treatment.
Whole exome sequencing (WES) is one of the latest advancements in next-generation sequencing (NGS) technology.This method sequences about 233,785 exons, or protein coding regions of the genome. This region is about 20,000 genes which makes up only about 1 percent of the human genome. Mutations in these regions can alter the respective proteins leading to various phenotypic implications. Analyzing the whole exome can unravel causative variants for diseases ranging from Mendelian to complex phenotypes.
When to use whole exome sequencing versus targeted or whole-genome sequencing
Targeted or panel sequencing only sequences genes that are known to be associated with a disease. It is often used, if the given disease has a clinical testing panel available and if the focus is solving the case with a short turnaround time.
However, if disease mechanisms are poorly understood, it can take several gene panels to identify the putative variant. Thus, WES allows for all protein-coding genes to be analyzed at once, which increases the probability of identifying the causative variant in a single sequencing assay. Since WES is not restricted to evaluating genes that have previously been associated with a specific disease, it gives a more comprehensive overview of the exome. Additionally, WES can help identify novel disease-gene associations in a research setting.
Another type of NGS is whole-genome sequencing (WGS), which evaluates the entire genome. This method can also be valuable for diseases with complex phenotypes or for cases where large structural variations are the primary cause of the disease. WGS can identify large structural variations and splicing variants in deep intronic regions. However, the large volumes of sequencing data pose some unique challenges for data analysis and storage. Although genomic sequencing continues to become cheaper and more accessible, it can be cost restrictive for some institutions.
Hence, WES can present a Goldilocks option between targeted gene panels and WGS. It generates a more comprehensive genomic view than targeted panels, but creates a data volume that is more manageable to analyze than that generated by WGS. In some specific cases, it also gives the researcher an opportunity to analyze copy number variations (CNV). Lastly, WES has a higher throughput and faster turnaround time compared to WGS.
Specific patient types who could benefit from whole exome sequencing
WES is an effective tool that can be used in a multitude of situations, but there are a few specific situations in which it presents significant advantages. One such situation is when an individual has overlapping phenotypes across multiple rare diseases. Using WES and taking a genotype-first approach can identify causal disease-gene associations and help diagnose overlapping conditions. This more comprehensive view can also shorten turnaround time for diagnosis compared with running a series of targeted panels.
The shorter turnaround time and higher diagnostic yield with WES also presents significant advantages for neonatal patients. Phenotypes can be difficult to assess in neonatal patients and some symptoms might not yet manifest. Comprehensive genetic testing such as WES can help to reach an early diagnosis, which not only allows for the implementation of an optimized care plan but can also provide information on any preventative measures and/or other potential complications that could arise as a result of the disorder.
Rare disease diagnosis is often an exhaustive journey for patients and family members seeking answers for their accurate diagnosis and disease management through several specialists, while undergoing multitude of tests and procedures, in pursuit of receiving the most effective treatment. WES can be a good follow-up to those initial tests, providing information that could uncover relevant variants in genes that were not previously implicated in a given disease (diagnosis).
Thus, WES cannot only be used for identifying causative variants related to the patient’s disorder, but the comprehensive approach can provide information about additional diseases or any genetic predispositions to other inherited disorders. This information is sometimes known as secondary or incidental findings and allows for patients to better manage their condition and understand the associated health risks. In some cases, this information may not be reported back to the patient, however, having the sequenced exomes allows for easier reanalysis of the data and future germline assessment of the same patient.
Considerations for implementing whole exome sequencing
A major logistical consideration for implementing WES beyond (the cost of) validating a new test is choosing which bioinformatics platform will be used to analyze the data. As touched on previously, WES produces much more data than sequencing using a targeted panel. Most whole-exome solutions sequence around 20,000 genes and a targeted panel that may sequence less than 100 genes. This larger volume of data is responsible for the advantages of WES, by creating a more comprehensive view of the genome which can increase diagnostic yield. Institutions that are interested in bringing on WES need to consider the large volume of data being generated and how this bioinformatic workflow can be challenging for finding relevant variants.
A bioinformatics platform for WES analysis should accurately and reliably identify multiple variant types in a single workflow and have a fast turnaround time and multiple filtering options to help streamline variant interpretation. The SOPHiA DDM™ Platform delivers advanced analytical performance, can complete WES analysis overnight, and has dedicated filtering features and a rich knowledgebase to help identify variants of interest associated with rare diseases for research purposes.
Dr Alessandra Terracciano is part of the large team at the Medical Genetics Laboratory in the Bambino Gesù Children’s Hospital, Rome, Italy. She analyzes next-generation sequencing (NGS) data, with a focus on rare pediatric conditions. Her laboratory primarily works with samples from the Bambino Gesù Children’s Hospital across multiple specialties, but it also acts as an Italian reference center for some conditions, such as kidney diseases.
Dr Terracciano kicked off our symposium at the European Society of Human Genetics (ESHG) Conference 2022 by describing the laboratory workflows, tailored sequencing analytics, and multidisciplinary team meetings that enable the Bambino Gesù Children's Hospital to successfully solve challenging pediatric cases. Afterwards, she kindly sat down with us to answer a few questions on her experience working with SOPHiA GENETICS and what she sees for our collaboration in the future.
Tell us a little bit about your collaborations with SOPHiA GENETICS
My experience with SOPHiA GENETICS started when developing the SOPHiA DDM™ Platform Nephropathies Solution. We collaborated to develop an enrichment kit and analytical pipeline that successfully discriminates the PKD1 gene from its pseudogene. Although it was a tricky request, by the end of the validation process, the performance of the application was very, very good! For more details about this collaboration, read the case study with Dr Antonio Novelli
I also worked with SOPHiA GENETICS on a validation program for the analysis of genes associated with metabolic diseases. Again, SOPHiA DDM™ Platform was very helpful, as the solution is very robust and able to detect point mutations and copy number variations (CNVs) at the same time.
SOPHiA DDM™ Platform is very user-friendly - the visualization makes it easy to analyze CNVs.
For whole-exome sequencing (WES), our lab performs a double-analysis. We use both our sequencer’s pipeline, and SOPHiA DDM™ Platform, which is particularly useful for tricky cases such as those involving CNVs.
What are your favorite SOPHiA DDM™ Platform features?
SOPHiA DDM™ Platform is very robust. I trust it, believe the data, and feel safe when using it. In addition, the platform is very user-friendly, with the visualization making it easy to analyze CNVs. It is also helpful that I can easily export data from the platform, for further analysis using other tools if necessary. The platform is very comprehensive. If I find, for example, a point mutation, I have access to all the relevant information about it, such as the ClinVar annotation, the frequency, and the experience of other users. I can tag variants myself and can see the tags made by any of my colleagues.
The platform is very comprehensive – I have access to all of the relevant information about a point mutation, for example
In your opinion, what role does SOPHiA GENETICS play in improving rare and inherited disease research in the future?
SOPHiA GENETICS have a very powerful tool for people to share their experience. Sharing information on NGS analysis for rare pediatric conditions is very important, especially for the interpretation of variants of uncertain significance (VUS).
SOPHiA DDM™ Platform is a very powerful tool for people to share their experience
We’d like to thank Dr Terracciano for her time and all her work. We look forward to continuing our successful collaboration with the Bambino Gesù Children’s Hospital.
This year, ESHG was pleased to reunite face-to-face from June 11-14 in Vienna, Austria for the 55th Annual European Human Genetics Conference. ESHG provided a hybrid platform for the dissemination of the most exciting advancements in the field of human genetics, with an engaging program covering emerging concepts, mechanisms, and technologies in human genetics.
Read our summary of three presentations that explored the role of genetic testing in the diagnosis and care of sick children across the world. Specifically, we discover how next-generation sequencing of specific genes, whole exomes, and even whole genomes can play a role in advancing research and improving the healthcare and outcomes of pediatric patients.
Incidence of pediatric cancer predisposition in Czechia
Lucie Slamova, Michal Zapotocky, Lucie Šrámková, Martina Bittoova, Leona Cerna, Monika Koudová
Genetic predisposition plays an important role in the onset of cancers, especially those not related to environmental factors. However, pediatric cancer predisposition is not fully understood. This study by Slamova et al. used targeted next-generation sequencing to examine predisposing genetic variants in a pediatric cancer population in the Czech Republic.
The investigators adapted targeted panels covering 226 genes to include an additional 64 genes specific to the pediatric cohort. These panels were used to evaluate genetic variants in 217 children with cancer. Pathogenic variants related to cancer predisposition were detected in 31/217 patients (14%) in the TP53, NF1, RB1, PALB2, EXT2, BRCA1, WNR, ABRAXAS1, STK11, HOXB13, NBN, MUTYH, ATM, CHEK2, FANCA, SBDS, FANCG, and FANCI genes. Variants in MUTYH, CHEK2, and WRN were particularly prevalent in the neuro-oncology subgroup. Likely pathogenic variants were detected in 14/217 patients (6%), in the ALK, BRIP1, ERCC3, FANCD2, FANCL, LIG4, MSH2, SMARCE1, SMARCA4, RECQL, and RECQL4 genes. Therefore, pathogenic or likely pathogenic variants related to cancer predisposition were identified in 20.7% of children with cancer in this Czech cohort, with variants of uncertain significance (VUS) discovered in an additional 55 patients (25%).
For the first time, this study determines the incidence of cancer predisposition in the Czech pediatric cancer population, demonstrating the value and utility of genetic testing in childhood cancers.
Mainstreaming genomic testing for children with undiagnosed inborn errors of immunity
Tatiane Yanes, Anna Sullivan, Pasquale Barbaro, Kristian Brion, Jane Peake, Peter McNaughton
A genetic diagnosis in patients with pediatric inborn errors of immunity (IEI) can influence management decisions and clinical outcomes by opening the door to targeted and curative treatments. However, in Queensland, Australia, children with undiagnosed IEI have historically been referred to a state-wide clinical genetic service, delaying genomic testing and increasing the burden on the genetics clinic. In this study, Yanes et al. examined the feasibility and efficacy of a mainstream model of care that they developed in Brisbane, Australia to allow timely access to genomic testing for pediatric IEI.
21 custom virtual gene lists were developed for whole-exome sequencing (WES). In addition, a genetic councelor was embedded within the pediatric immunology service, and fortnightly multidisciplinary team meetings, and variant prioritization meetings were held.
Overall, the program ran between Nov 2020 and Sep 2021, with 9/43 children (21%) receiving a genetic diagnosis. All diagnosed children underwent changes in their management, such as curative hematopoietic stem cell transplantation (n = 4) and IVIg infusions (n = 2). An additional five children were referred for further investigation of a variant of uncertain significance (VUS). On average, 14 healthcare providers attended the multidisciplinary team meetings, demonstrating engagement with the program.
This novel program demonstrated that WES can be successfully mainstreamed for pediatric IEI in Queensland, Australia. The program improved access to genomic testing and facilitated treatment decision-making, including access to curative therapies.
NAGENpediatrics: Rapid whole genome sequencing in neonatal/pediatric intensive care in Navarra, Spain
Monica Arasanz Armengol, Sara Ciria Abad, Leslie Matalonga, Gemma Bullich Vilanova, Ida Paramonov, Edurne Urrutia Lafuente, Oscar Teijido Hermida, Alberto Maillo, Maria Miranda Perez, Iranzu González Borja, Gonzalo Etayo Nagore, Mercè Artigas López, Juan Jose Beloqui Lizaso, Angel Alonso Sanchez, Nerea Gorria-Redondo, Josune Hualde Olascoaga
Approximately 2-3% of newborns have a congenital anomaly, with at least 50% having a genetic cause. Rapid genomic testing (with a 2-3-week turnaround) has been found to facilitate the diagnosis of ∼21-26% of critically ill children, influencing their clinical outcomes. This pioneering study by Armengol et al. aimed to evaluate the diagnostic and therapeutic utility of rapid whole-genome sequencing (WGS) for acutely sick children in Navarra, Spain.
The ongoing study involved 34 trios in the first year, supported by a multidisciplinary team. Extensive clinical and phenotypic data were collected and rapid WGS was performed on germline DNA to identify potential causative variants. Each family received a detailed genetic counselling consultation. Pathogenic variants associated with clinical manifestations were identified in 14 families, for a diagnostic yield of 41%. The genetic results were delivered in an average of 2-3 weeks, significantly reducing the diagnostic odyssey for these patients, and influencing clinical decisions.
This study identified new genetic variants associated with rare diseases which would not have been found using other diagnostic methods in rapid turnaround times. The results demonstrated that rapid WGS was successful in enabling the delivery of precision medicine to children in Navarra, Spain, with potential extensive benefits for patients, clinicians, and the Regional Health System.
SOPHiA GENETICS products are for Research Use Only and not for use in diagnostic procedures unless specified otherwise.
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