Genetic testing in motor neurone disease

A minority (10%–15%) of cases of amyotrophic lateral sclerosis (ALS), the most common form of motor neurone disease (MND), are currently attributable to pathological variants in a single identifiable gene. With the emergence of new therapies targeting specific genetic subtypes of ALS, there is an increasing role for routine genetic testing for all those with a definite diagnosis. However, potential harm to both affected individuals and particularly to asymptomatic relatives can arise from the indiscriminate use of genetic screening, not least because of uncertainties around incomplete penetrance and variants of unknown significance. The most common hereditary cause of ALS, an intronic hexanucleotide repeat expansion in C9ORF72, may be associated with frontotemporal dementia independently within the same pedigree. The boundary of what constitutes a possible family history of MND has therefore extended to include dementia and associated psychiatric presentations. Notwithstanding the important role of clinical genetics specialists, all neurologists need a basic understanding of the current place of genetic testing in MND, which holds lessons for other neurological disorders.

Determinants of trafficking, conduction, and disease within a K+ channel revealed through multiparametric deep mutational scanning

A longstanding goal in protein science and clinical genetics is to develop quantitative models of sequence, structure, and function relationships and delineate the mechanisms by which mutations cause disease. Deep Mutational Scanning (DMS) is a promising strategy to map how amino acids contribute to protein structure and function and to advance clinical variant interpretation. Here, we introduce 7,429 single residue missense mutation into the Inward Rectifier K+ channel Kir2.1 and determine how this affects folding, assembly, and trafficking, as well as regulation by allosteric ligands and ion conduction. Our data provide high-resolution information on a cotranslationally-folded biogenic unit, trafficking and quality control signals, and segregated roles of different structural elements in fold-stability and function. We show that Kir2.1 trafficking mutants are underrepresented in variant effect databases, which has implications for clinical practice. By comparing fitness scores with expert-reviewed variant effects, we can predict the pathogenicity of variants of unknown significance and disease mechanisms of know pathogenic mutations. Our study in Kir2.1 provides a blueprint for how multiparametric DMS can help us understand the mechanistic basis of genetic disorders and the structure-function relationships of proteins.

Clinical Genetics of Inherited Arrhythmogenic Disease in the Pediatric Population

Sudden death is a rare event in the pediatric population but with a social shock due to its presentation as the first symptom in previously healthy children. Comprehensive autopsy in pediatric cases identify an inconclusive cause in 40–50% of cases. In such cases, a diagnosis of sudden arrhythmic death syndrome is suggested as the main potential cause of death. Molecular autopsy identifies nearly 30% of cases under 16 years of age carrying a pathogenic/potentially pathogenic alteration in genes associated with any inherited arrhythmogenic disease. In the last few years, despite the increasing rate of post-mortem genetic diagnosis, many families still remain without a conclusive genetic cause of the unexpected death. Current challenges in genetic diagnosis are the establishment of a correct genotype–phenotype association between genes and inherited arrhythmogenic disease, as well as the classification of variants of uncertain significance. In this review, we provide an update on the state of the art in the genetic diagnosis of inherited arrhythmogenic disease in the pediatric population. We focus on emerging publications on gene curation for genotype–phenotype associations, cases of genetic overlap and advances in the classification of variants of uncertain significance. Our goal is to facilitate the translation of genetic diagnosis to the clinical area, helping risk stratification, treatment and the genetic counselling of families.

The Birth Prevalence of Spinal Muscular Atrophy: A Population Specific Approach in Estonia

Background: Rare diseases are an important population health issue and many promising therapies have been developed in recent years. In light of novel genetic treatments expected to significantly improve spinal muscular atrophy (SMA) patients’ quality of life and the urgent need for SMA newborn screening (NBS), new epidemiological data were needed to implement SMA NBS in Estonia.Objective: We aimed to describe the birth prevalence of SMA in the years 1996–2020 and to compare the results with previously published data.Methods: We retrospectively analyzed clinical and laboratory data of SMA patients referred to the Department of Clinical Genetics of Tartu University Hospital and its branch in Tallinn.Results: Fifty-seven patients were molecularly diagnosed with SMA. SMA birth prevalence was 1 per 8,286 (95% CI 1 per 6,130–11,494) in Estonia. Patients were classified as SMA type 0 (1.8%), SMA I (43.9%), SMA II (22.8%), SMA III (29.8%), and SMA IV (1.8%). Two patients were compound heterozygotes with an SMN1 deletion in trans with a novel single nucleotide variant NM_000344.3:c.410dup, p.(Asn137Lysfs*11). SMN2 copy number was assessed in 51 patients.Conclusion: In Estonia, the birth prevalence of SMA is similar to the median birth prevalence in Europe. This study gathered valuable information on the current epidemiology of SMA, which can guide the implementation of spinal muscular atrophy to the newborn screening program in Estonia.

Integrating Whole-Genome Sequencing in Clinical Genetics: A Novel Disruptive Structural Rearrangement Identified in the Dystrophin Gene (DMD)

While in most patients the identification of genetic alterations causing dystrophinopathies is a relatively straightforward task, a significant number require genomic and transcriptomic approaches that go beyond a routine diagnostic set-up. In this work, we present a Becker Muscular Dystrophy patient with elevated creatinine kinase levels, progressive muscle weakness, mild intellectual disability and a muscle biopsy showing dystrophic features and irregular dystrophin labelling. Routine molecular techniques (Southern-blot analysis, multiplex PCR, MLPA and genomic DNA sequencing) failed to detect a defect in the DMD gene. Muscle DMD transcript analysis (RT-PCR and cDNA-MLPA) showed the absence of exons 75 to 79, seen to be present at the genomic level. These results prompted the application of low-coverage linked-read whole-genome sequencing (WGS), revealing a possible rearrangement involving DMD intron 74 and a region located upstream of the PRDX4 gene. Breakpoint PCR and Sanger sequencing confirmed the presence of a ~8 Mb genomic inversion. Aberrant DMD transcripts were subsequently identified, some of which contained segments from the region upstream of PRDX4. Besides expanding the mutational spectrum of the disorder, this study reinforces the importance of transcript analysis in the diagnosis of dystrophinopathies and shows how WGS has a legitimate role in clinical laboratory genetics.

seGMM: a new tool to infer sex from massively parallel sequencing data

Inspecting concordance between self-reported sex and genotype-inferred sex from genomic data is a significant quality control measure in clinical genetic testing. Numerous tools have been developed to infer sex for genotyping array, whole-exome sequencing, and whole-genome sequencing data. However, improvements in sex inference from targeted gene sequencing panels are warranted. Here, we propose a new tool, seGMM, which applies unsupervised clustering (Gaussian Mixture Model) to determine the gender of a sample from the called genotype data integrated aligned reads. seGMM consistently demonstrated >99% sex inference accuracy in publicly available (1000 Genomes) and our in-house panel dataset, which achieved obviously better sex classification than existing popular tools. Compared to including features only in the X chromosome, our results show that adding additional features from Y chromosomes (e.g. reads mapped to the Y chromosome) can increase sex classification accuracy. Notably, for WES and WGS data, seGMM also has an extremely high degree of accuracy. Finally, we proved the ability of seGMM to infer sex in single patient or trio samples by combining with reference data and pinpointing potential sex chromosome abnormality samples. In general, seGMM provides a reproducible framework to infer sex from massively parallel sequencing data and has great promise in clinical genetics.

Clinical genetics laboratories use divergent demographic frameworks across countries: comparing data structures for race, ethnicity, and ancestry on test requisition forms

Purpose. The goal of this study is to investigate how population groups are represented on requisition forms for clinical genetic testing in different laboratories. Methods. Clinical laboratory test requisition forms (RFs) were obtained from 70 laboratories in the US, Canada, Europe, and Australia. Details about the laboratories and how RFs represent patient demographics were extracted and analyzed for trends between forms in the U.S. (N=213) and other countries (N=203). Results. Clinical genetics laboratories included in the analysis vary widely regarding the format of demographic data collected on test requisition forms. US-based laboratory RFs are more likely than those from other countries to include race or ethnicity. These are most often represented as categorical data, with multiple-choice options. RFs from laboratories in other countries do not include race, and those that include ethnicity most often provide a blank space for open-ended responses. Conclusions. These results are consistent with existing research on heterogeneity in the nomenclature and number of categories used to describe patient populations across clinical genetics laboratories in the US. It also suggests systemic differences in the way measures of diversity are conceptualized in the US compared to other countries.

Mosaicism in clinical genetics

Genetic mosaicism is the state in which there are two or more different sets of cells in a single individual because of one or more postzygotic mutations, and its importance in clinical genetics has long been recognized (Hall, Am J Hum Genet43: 355 [1988]). In this Perspective, a paper in this special issue on mosaicism from Cook et al. (Cold Spring Harb Mol Case Studies7: a006125 [2021]) is discussed.

Isolation and Enrichment of Circulating Fetal Cells for NIPD: An Overview

Prenatal diagnosis plays a crucial role in clinical genetics. Non-invasive prenatal diagnosis using fetal cells circulating in maternal peripheral blood has become the goal of prenatal diagnosis, to obtain complete fetal genetic information and avoid risks to mother and fetus. The development of high-efficiency separation technologies is necessary to obtain the scarce fetal cells from the maternal circulation. Over the years, multiple approaches have been applied, including choice of the ideal cell targets, different cell recovering technologies, and refined cell isolation yield procedures. In order to provide a useful tool and to give insights about limitations and advantages of the technologies available today, we review the genetic research on the creation and validation of non-invasive prenatal diagnostic testing protocols based on the rare and labile circulating fetal cells during pregnancy.

A Complete Pedigree-Based Graph Workflow for Rare Candidate Variant Analysis

Methods that use a linear genome reference for genome sequencing data analysis are reference biased. In the field of clinical genetics for rare diseases, a resulting reduction in genotyping accuracy in some regions has likely prevented the resolution of some cases. Pangenome graphs embed population variation into a reference structure. While pangenome graphs have helped to reduce reference mapping bias, further performance improvements are possible. We introduce VG-Pedigree, a pedigree-aware workflow based on the pangenome-mapping tool of Giraffe (Sirén et al. 2021) and the variant-calling tool DeepTrio (Kolesnikov et al. 2021) using a specially-trained model for Giraffe-based alignments. We demonstrate mapping and variant calling improvements in both single-nucleotide variants (SNVs) and insertion and deletion (INDEL) variants over those produced by alignments created using BWA MEM to a linear-reference and Giraffe mapping to a pangenome graph containing data from the 1000 Genomes Project. We have also adapted and upgraded the deleterious-variant (DV) detecting methods and programs of Gu et al. into a streamlined workflow (Gu et al. 2019). We used these workflows in combination to detect small lists of candidate DVs among 15 family quartets and quintets of the Undiagnosed Diseases Program (UDP). All candidate DVs that were previously diagnosed using the mendelian models covered by the previously published Gu et al. methods were recapitulated by these workflows. The results of these experiments indicate a slightly greater absolute count of DVs are detected in the proband population than in their matched unaffected siblings.