Gene Testing Strategies for Neurological Genetic Diseases

Cite this article: Luo Wei, Chen Si. Gene testing strategies for neurological genetic diseases. Journal of Chongqing Medical University, 2021, 7(46):798-803.

Abstract In recent years, various gene testing technologies, including single-gene sequencing, gene panels, whole exome sequencing, and whole genome sequencing, have been widely applied in the diagnosis of neurological genetic diseases. The rapid development of gene testing technologies enables clinicians to explore more complex phenotypes and greater genetic heterogeneity diseases, helping them make fast and accurate diagnoses. This article aims to assist clinicians in selecting appropriate methods among the diverse gene testing technologies to aid in the diagnosis of neurological genetic diseases.

Keywords: Neurological genetic diseases; Gene testing; Methods

Neurological genetic diseases are diverse, and the genotype-phenotype relationship is complex. One gene often relates to multiple phenotypes, such as the PMP22 gene, where a deletion leads to hereditary pressure-sensitive peripheral neuropathy, while a duplication causes Charcot-Marie-Tooth disease. On the other hand, a phenotype can be associated with multiple pathogenic genes, such as dopamine-responsive dystonia, which has pathogenic genes like GCH1, SRP, and TH, which act at various stages of dopamine synthesis. In recent years, various emerging gene testing technologies have helped clinicians discover and identify numerous pathogenic genes and phenotypes related to neurological genetic diseases, enhancing awareness of genetic diseases and making clinical diagnosis more efficient and precise. Therefore, the rational application of gene testing in neurological genetic diseases is especially important. It not only aids in clarifying clinical diagnoses but also brings hope for subsequent treatments and guides family planning. This article provides a brief overview of the applicability of various gene testing methods, the influence of phenotypes on gene testing, the interpretation of gene testing reports, and existing problems in gene testing.

1 Different Gene Testing Technologies for Different Variants

1.1Single Gene Sequencing

Single gene sequencing (single gene testing) has various testing methods, mainly including Sanger sequencing, multiplex ligation-dependent probe amplification (MLPA), repeat-primed polymerase chain reaction (RP-PCR), long-range PCR, and Southern blotting. Sanger sequencing can be used to detect missense mutations, synonymous mutations, nonsense mutations, small insertions/deletions, and splicing mutations occurring in a single gene coding sequence. MLPA is used to detect copy number variations (CNVs) on genes. RP-PCR can detect dynamic mutations by amplifying unstable repeat sequences on DNA. When a patient has the following characteristics, clinicians may prefer single gene sequencing: ① clear family history; ② clinical phenotype and other test results point to a specific type of disease; ③ only one pathogenic gene or the most common pathogenic mutation on a certain gene currently exists; ④ patient’s economic conditions are limited.

Sanger sequencing is suitable for most gene sequencing to discover point mutations on target segments (<1 kb) and has been considered the “gold standard” in clinical gene testing for the past decade. For example, Wilson’s disease, an autosomal recessive copper metabolism disorder, has ATP7B as its only pathogenic gene involved in copper transport, with 78% of patients having mutations p.R778L, p.P992L, and p.T935M. Therefore, selecting Sanger sequencing to directly test the ATP7B gene for clinically suspected Wilson’s disease patients is a fast, sensitive, and cost-effective gene testing method. Another example is paroxysmal kinesigenic dyskinesia (PKD), characterized by brief unilateral or bilateral involuntary movements triggered by voluntary movements starting in childhood, including dystonia, chorea, or athetosis. Studies have found that about 1/3 of PKD patients carry mutations on the autosomal dominant pathogenic gene PRRT2, with the mutation c.649dupC (p.Arg217Profs*8) accounting for as much as 76.47%. Therefore, direct Sanger sequencing of the PRRT2 gene can be performed for clinically suspected PKD patients. Additionally, Sanger sequencing is often used for co-segregation verification in family members of probands with known pathogenic mutations. However, negative Sanger sequencing results can only rule out specific mutations in the target region, and are not suitable for detecting large insertions, copy number variations, or mutations in non-coding regions. Furthermore, for genes with many exons, this method can be time-consuming and labor-intensive.

MLPA is mainly used for diseases primarily caused by copy number variations in pathogenic genes, such as Charcot-Marie-Tooth disease (CMT), one of the most common hereditary peripheral neuropathies with an incidence of about 1/2500. The classic phenotype of CMT typically manifests in the ages of 10-20, with clinical features mainly including distal limb weakness, sensory loss, and foot deformities. Currently, over 100 pathogenic genes related to CMT have been reported, with the most common pathogenic mutation being a large segment duplication in the 17p11.2 region, which contains the gene PMP22. Reports indicate that mutations in the PMP22 gene account for 70% of CMT1 cases, and up to 50% of all CMT cases. Studies using MLPA combined with Sanger sequencing on 465 Chinese CMT families found that PMP22 gene duplications accounted for 29.5%, while point mutations only accounted for 2.2%. This indicates that for clinically suspected CMT patients, MLPA should be prioritized for testing the PMP22 gene. If whole exome sequencing is chosen first, while it may increase the detection rate of other pathogenic genes related to CMT, it is prone to missing copy number variations on the PMP22 gene due to technical limitations. Furthermore, Duchenne muscular dystrophy (DMD), an X-linked recessive disorder, often presents in early childhood with proximal muscle weakness, calf hypertrophy, and significantly elevated creatine kinase. Its pathogenic gene is DMD, with cases caused by large deletions on this gene accounting for 65%-70%, frequently occurring in the hotspot regions of exons 45-53. Therefore, MLPA is also commonly prioritized in clinical practice. For spinal muscular atrophy (SMA), caused by the pathogenic gene SMN1, about 96% of mutations are homozygous deletions of exon 7. The most common pathogenic gene for hereditary spastic paraplegia (HSP) is SPG4, with 20%-25% of mutations being copy number variations. Thus, for pathogenic gene testing primarily involving copy number variations, MLPA is often chosen for its convenience and lower cost.

RP-PCR, long-range PCR, Southern blotting, and long-read sequencing (LRS) technologies can be used to detect repeat expansions. Since the discovery in 1991 that the CGG repeat expansion in the 5’ untranslated region of the FMR-1 gene can lead to fragile X syndrome (FXS), various diseases associated with repeat mutations have been identified. Repeat expansions can occur in coding regions, such as spinal and bulbar muscular atrophy (SBMA) caused by CAG repeat expansion in exon 1 of the AR gene, and Huntington’s disease (HD) caused by CAG repeat expansion in the coding region of the HTT gene. They can also occur in UTR regions or introns, such as myotonic dystrophy (DM), which is divided into DM1 and DM2 types, with pathogenic mutations being CTG repeat in the 3’ UTR region of the DMPK gene and CCTG repeat in the intron 1 of the CNBP gene. In recent years, repeat expansions have become a research hotspot both domestically and internationally. In 2018, familial cortical myoclonic tremor with epilepsy (FCMTE) type 1 was found to be caused by pentanucleotide repeat (TTTCA) expansion in the intron region of the SAMD12 gene. Subsequent research teams reported various genes (STARD7, MARCH6, YEATS2, TNRC6A, and RAPGEF2) with TTTCA repeat mutations in their introns causing different FCMTE subtypes. In 2019, a UK team identified the pathogenic mutation in cerebellar ataxia, neuropathy, vestibular areflexia syndrome (CANVAS) as AAGGG repeat expansion in the intron region of the RFC1 gene, a rare late-onset ataxia. Recently, Tian Y et al. confirmed that the GGC repeat in the 5’ UTR region of the NOTCH2NLC gene is the pathogenic gene for neuronal intranuclear inclusion disease (NIID), and it has recently been found that GGC repeats are associated with Parkinson’s disease, Alzheimer’s disease, amyotrophic lateral sclerosis (ALS), multiple system atrophy (MSA), and essential tremor (ET). In 2020, Deng J et al. discovered another pathogenic gene for oculopharyngodistal myopathy (OPDM) as GGC repeat in the 5’ UTR region of the GIPC1 gene. With the development of gene testing technologies, especially the application of long-read sequencing, research on repeat expansion mutations in neurological genetic diseases has made rapid progress.

1.2 Gene Panels

For many neurological genetic diseases, the clinical phenotype is relatively complex, and there are often multiple pathogenic genes causing the disease. In this case, a gene panel that can test multiple genes is required to improve the positive rate of results. Gene panels based on next-generation sequencing (NGS) platforms are suitable in the following situations: ① diseases with high genetic heterogeneity, such as HSP, CMT, PD, primary torsion dystonia (PTD), etc.; ② a group of diseases with similar clinical phenotypes, such as myopathies; ③ different diseases with similar clinical phenotypes, such as hereditary leukoencephalopathies, including adrenoleukodystrophy, hereditary diffuse leukoencephalopathy with neuroaxonal spheroids (HDLS), autosomal dominant adult-onset demyelinating leukodystrophy (ADLD), and cerebral arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL).

Gene panels have higher coverage, sensitivity, and specificity, resulting in a higher positive rate compared to whole exome sequencing or whole genome sequencing. Different gene testing companies may have slightly different gene panels for the same group of diseases. Pursuing comprehensiveness by including many genes that are far from the characteristic disease or have low penetrance may lead to the detection of many variants of uncertain significance (VUS), making the interpretation of gene testing reports more complicated. Therefore, the number of genes included in testing is not necessarily “the more, the better.” For instance, in childhood epilepsy, a diagnostic rate of about 30% was observed with a panel containing 172 related genes, of which 156 genes did not reveal any meaningful pathogenic mutations, indicating that the diagnostic rate does not linearly increase with the number of genes included. Some gene testing companies are more conservative, only selecting genes with strong evidence related to specific diseases, which may lead to missing other related genes. Therefore, when choosing gene panel sequencing, clinicians need to carefully verify whether the gene package includes a category of genes related to the phenotype of interest.

1.3 Whole Exome Sequencing and Whole Genome Sequencing

Whole exome sequencing (WES) and whole genome sequencing (WGS) have traditionally been used for patients with negative gene panels or complex phenotypes, as they can assess all known genes related to the disease and provide opportunities for future re-analysis as new genes are discovered and databases are updated. With the promotion and price reduction of next-generation sequencing technology, WES and WGS will gradually replace gene panels and single-gene sequencing, exerting more powerful detection potential and re-analysis advantages, thus increasing the positive detection rate. Reportedly, the diagnostic rate of clinical WES sequencing for suspected Mendelian diseases can reach about 25%, and some patients may have false-negative results due to the following reasons: ① errors in any step of aligning raw data with reference sequences, data filtering, and variant annotation can lead to missed pathogenic mutations; ② if the testing laboratory cannot obtain key elements of the disease phenotype, the likelihood of prioritizing pathogenic mutations decreases; ③ some databases have incomplete genotype-phenotype and variant-phenotype data, requiring specific searches through search engines; ④ approximately 250 new genes and 9,200 variants are confirmed to be associated with diseases each year, and databases are continuously updated. Therefore, to maximize the diagnostic rate of WES, clinicians should provide detailed and comprehensive descriptions of clinical phenotypes for testing companies to filter target genes. Additionally, they should have a certain level of literature knowledge and information retrieval skills regarding the disease, and require companies to regularly re-analyze cases with negative testing results.

WGS can detect almost all types of genomic variations (such as point mutations, dynamic mutations, copy number variations, and deep intronic variations), simplifying the genetic evaluation process. There is increasing evidence that it has broad application prospects in clinical diagnosis and gene discovery. Reportedly, the diagnostic rate of WGS in pediatric genetic diseases can reach 41%, significantly higher than that of traditional gene testing methods like chromosomal microarray analysis and gene panels (24%; P=0.01). Compared to targeted gene testing technologies, WGS can even serve as a first-line testing method due to its higher diagnostic rate. Since most CNV breakpoints are located in intronic regions, the accuracy of WES in detecting CNVs is limited because exon deletions identified by WES may include large segments of intronic sequences. However, WGS, through bioinformatics analysis, is more likely to detect this form of CNV. For instance, WGS discovered a partial duplication of the TENM3 gene on chromosome 4 in a patient with intellectual disability, but the CNV occurring in this gene was insufficient to explain the patient’s clinical phenotype. Further bioinformatics analysis confirmed that the duplicated TENM3 gene was translocated and inserted into the X chromosome’s IQSEC2 gene, which explained the cause of the patient’s intellectual disability. Other studies have shown that among 50 patients with developmental delays, the detection rate of CNVs by WGS can reach 18%, identifying three exon deletions missed by chromosomal microarray and MLPA. Currently, non-amplified WGS (PCR-free WGS) is less affected by regions with high GC content in the genome, providing better overall coverage and uniformity. Although the average sequencing depth is lower, it has broader exon coverage and is expected to be widely used clinically as sequencing prices decrease.

2 Clinical Phenotypes Play a Critical Role in Gene Testing Technologies

Choosing the appropriate gene testing technology requires understanding not only the variant types of pathogenic genes in different diseases but also conducting a complete and thorough clinical examination to determine the clinical phenotype. For example, our research team encountered a 35-year-old male patient diagnosed as “Wilson’s disease” in another hospital due to “trembling hands and instability while walking for 10 years, and slurred speech for the past six months.” Through WES analysis, a heterozygous point mutation c.A2785G (p.I929V) was found in the ATP7B gene, but its significance was unclear. After a clinical re-evaluation, it was found that the proband also had a history of epilepsy, and the “trembling hands” were actually myoclonic manifestations. The proband’s sister also had a similar disease history, but the parents were normal. Fundoscopic examination did not reveal K-F rings, and blood biochemistry showed no abnormalities; thus, the diagnosis was revised to progressive myoclonic epilepsy (PME). After re-analysis of the WES data, a homozygous mutation c.A544G (p.S182G) was found in the pathogenic gene for sialidosis (type I). Co-segregation verification with the proband’s parents and sister showed that both parents were heterozygous for the mutation, while the sister and proband were both homozygous. This indicates that the clinical phenotype determines the direction of gene testing, helping to improve the positive rate of gene testing, thereby reducing costs and shortening diagnosis time.

3 Interpretation of Gene Testing Results

Research shows that each individual’s genome contains hundreds of loss-of-function variants (LOF) and thousands of variants of uncertain significance (VUS). Therefore, each WES still has about 150~500 non-synonymous mutations or splicing mutations after data filtering, and healthy individuals may carry 40~110 variants deemed pathogenic in the human gene mutation database (HGMD), making the boundary between actual pathogenic mutations and rare benign variants difficult to discern. While WES or WGS increases the positive diagnostic rate, they also produce more VUS compared to gene panels, presenting significant challenges for laboratories in prioritizing variants. Currently, when assessing the pathogenicity of variants found in gene testing results, gene testing laboratories primarily follow the variant interpretation guidelines established by the American College of Medical Genetics (ACMG).

Sometimes, rare variants are found on a pathogenic gene in patients with Mendelian diseases, but they may not explain the clinical phenotype of the patient. Literature has reported a case of an infant with intellectual disability, developmental delay, microcephaly, and brain fissure malformation, whose parents were consanguineous, and the sister exhibited a similar phenotype without brain fissure malformation. WES identified a homozygous frameshift mutation c.421_422insA (p.Q141fs) on the MCPH1 gene, but this locus could not explain the differences in phenotype between the two affected children. Subsequent re-analysis revealed that the proband also had one homozygous mutation each in the ALG8 and CLN5 genes, which are associated with abnormal brain structure development, thus better explaining the proband’s clinical phenotype. Other rare variant loci in the genetic background also play a crucial role in the patient’s clinical phenotype. During co-segregation within the family, individuals carrying the same mutation as the proband may not exhibit clinical symptoms. Research indicates that besides incomplete penetrance of the gene, it may also be due to a higher mutational burden caused by the proband carrying more rare variants on other genes. Furthermore, our research team encountered a case of CADASIL, where the WES report indicated the presence of compound heterozygous variants c.2656C>T (p.R886C) and c.6202G>A (p.G2068R) on the NOTCH3 gene. Literature indicates that only mutations affecting the number of cysteine residues in the epidermal growth factor-like region (i.e., c.2656C>T) have high pathogenicity. Therefore, mutations on disease-related genes still require further clarification of their pathogenicity through literature review, co-segregation verification, or biological experiments. Consequently, clinicians must not solely rely on laboratory analyses for interpreting gene testing results; they need a certain genetic background to analyze and discern the testing reports, providing patients with more accurate genetic counseling.

4 Discussion

With the advent of high-throughput sequencing, clinicians’ diagnostic needs for neurological genetic diseases are continuously increasing. Choosing appropriate gene testing technologies helps clarify diagnoses, reducing unnecessary and painful medical processes and various costly clinical examinations for patients. After a clear diagnosis, clinicians can assist patients in formulating personalized treatment plans, intervene early for relatives yet to manifest the disease, guide employment, and plan for life. For young patients with reproductive requirements, gene testing results can serve as the basis for prenatal diagnosis, preventing the disease from being passed down through generations.

Currently, the widespread application of next-generation sequencing technology has rapidly advanced the understanding of neurological genetic diseases, leading to the identification of numerous pathogenic genes and further refinement of clinical phenotypes. However, current gene testing still faces many challenges. Firstly, the coverage of gene sequences varies significantly across different laboratory sequencing platforms, and data analysis lags behind the development of sequencing technologies, lacking consistent consensus: ① not all target sequences have high coverage; if the sequencing chip has low coverage on a certain pathogenic gene, it will affect the effective detection of pathogenic mutations; ② variant pathogenicity prediction software (e.g., Mutation Taster, PolyPhen-2) is not 100% accurate and may sometimes classify pathogenic mutations as benign or polymorphic; ③ some pathogenic mutations are more frequent in certain populations, and filtering based solely on default reference mutation frequencies may lead to missed diagnoses (such as the pathogenic gene HFE, with a pathogenic mutation p.Cys282Tyr having a frequency of up to 11% in the North American population; if mutations with frequencies >5% are filtered out, this will lead to misdiagnosis); thus, gene testing technologies may miss true pathogenic genes due to the imperfections of sequencing platforms and data analysis. Secondly, genetic heterogeneity complicates genotype-phenotype correlation studies: ① individuals carrying pathogenic genes may not exhibit corresponding disease phenotypes, i.e., incomplete penetrance, which is more common in dominant inheritance patterns. Therefore, some dominant inheritance diseases may show skipped generations (the next generation carrying pathogenic genes may not display clinical symptoms, i.e., asymptomatic carriers), making it challenging to clarify the pattern of inheritance in families (especially when the family size is small, the phenomenon of skipped generations is more likely to occur). ② Patients carrying the same pathogenic gene can exhibit varying degrees of clinical phenotypes, mainly related to the number of repeats in dynamic mutations and different mutation types on the same gene; thus, functional studies of mutations on genes are crucial for genotype-phenotype correlation analysis. ③ A single gene can simultaneously affect two or more different phenotypic traits (i.e., gene pleiotropy); for example, the protein encoded by the VCP gene is involved in various cellular activities and is associated with multiple system diseases such as inclusion body myopathy, Paget’s disease, and frontotemporal dementia. Therefore, gene pleiotropy makes prioritizing genes related to clinical phenotypes particularly challenging. Finally, there are numerous issues in analyzing and interpreting gene testing results, such as whether it is necessary to inform patients about known pathogenic mutations or expected mutations that may lead to diseases discovered incidentally during gene testing.

Moreover, it is essential to understand that gene testing is an auxiliary tool to assist clinicians in clarifying clinical diagnoses. Overuse will not only fail to improve diagnostic rates but also impose unnecessary psychological and financial burdens on patients.

Second Affiliated Hospital of Zhejiang University School of Medicine

Luo Wei

Chief physician, doctoral supervisor, currently serves as the deputy director of the neurology department at the Second Affiliated Hospital of Zhejiang University School of Medicine. He has visited the University of Chicago Medical Center, University College London, Grenoble University Hospital, and Northwestern University in the USA. He serves as an editorial board member for the SCI-indexed journal Am J Med Genet B Neuropsychiatr Genet and a communication editorial board member for the Chinese Journal of Medical Genetics. His team has long focused on the clinical diagnosis and basic research of movement disorders such as Parkinson’s disease and neurological genetic diseases, and has identified a new pathogenic gene in primary familial brain calcification and familial cortical myoclonic tremor epilepsy in recent years. In the past five years, he has published over 20 SCI-indexed papers as a corresponding author, including 2 in Brain and 7 in Movement Disorders.Research direction: Clinical diagnosis and treatment of movement disorders and neurological genetic diseases.

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