Introduction

Niemann–Pick disease type C (NPC) is a rare, autosomal recessive neurodegenerative condition which leads to a variety of progressive neurological and non-neurological manifestations. NPC is estimated to affect 1 in 100,000–120,000 live births1. It is caused by mutations of the NPC1 gene in 95% of cases while mutations of NPC2 account for the other 5%. The spectrum of clinical presentation is wide with different onsets which have been classified as: perinatal (shortly before and after birth, 3–12% of cases), early infantile (3 months to < 2 years, 3–37% of cases), late infantile (2 to < 6 years, 21–39% of cases), juvenile (6 to < 15 years, 21–54% of cases), and adult (15 years and greater, 5–27% of cases)2,3. In the majority of cases, patients first develop liver, spleen or lung involvement with a high variability of disease severity. These are then followed by progressive neurological symptoms classically presenting with cerebellar ataxia, vertical supranuclear gaze palsy, gelastic cataplexy, seizures and eventually dementia4. While studies of NPC2 are easily interpretable, NPC1 is highly polymorphic where variants of unknown significance (VUS) are common, with one third of published variants being classified as VUS on ClinVar3,5.

NPC is one of the few degenerative ataxias for which a treatment is currently available. Miglustat (Zavesca) has been approved in several countries for treating progressive neurological complications of NPC6. The drug has been shown to slow progression of neurological manifestations in children without severe neurological symptoms when initiating therapy, as shown by nonsignificant improvement in horizontal saccadic eye movement velocity in a preliminary open-label randomized controlled trial7. Miglustat was also associated with improved swallowing function and decreased aspiration risk in observational studies8,9.

Despite most cases being diagnosed at a young age, the adult-onset form can be more insidious and often manifests with neuropsychiatric disturbances. The diagnosis is based on clinical evaluation and history with biomarker screening. Several blood-based biomarkers can be used to assist with diagnosis including oxysterols, lysosphingolipids and bile acid metabolites. Consensus guidelines still recommend completing the workup of suspected cases with two additional diagnostic methods: filipin staining of unesterified fibroblasts or molecular testing6,10. The former requires a skin biopsy with a specialized laboratory, but can be inconclusive in 15% of cases without molecular testing11. Molecular testing is more practical but can also be inconclusive in up to 15% of cases mainly due to VUS and the absence of allele segregation studies2. The assessment of these variants will require additional data input from various laboratories to allow for more specific classification.

CARTaGENE (CaG) is a cohort of healthy individuals living in Quebec12. The cohort contains a total of 43,000 individuals, including 55% of women, ranging between 40 and 69 years of age. The recruitment for this cohort started in 2010 and participants have been followed up to this date. Genetic data is available for some of these individuals in the form of RNA as well as exome sequencing. This data can thus be used to screen for potential variants in a healthy pool of the population.

The study aim was to identify potential pathogenic variants in NPC1 and NPC2 in healthy individuals from the CaG cohort and classify their risk of pathogenicity using the American College of Medical Genetics and Genomics (ACMG) guidelines revised version of 201513 to help assist with the future interpretation of variants by providing useful additional information derived from large databases.

Materials and methods

Initial data

This study was based on a random sample from the CaG cohort, which was representative of the regional distribution of the Quebec population. Data acquisition was made from 911 individuals for the RNA-sequencing (RNA-seq) and 198 individuals for exome-sequencing (exome-seq). 93 of these individuals were in both RNA-seq and exome-seq. A bio-informatic pipeline was therefore used to analyse a total of 1016 individuals. Baseline characteristics as well as screening medical questionnaires were obtained for each participant from the CaG database. Informed consent was previously obtained by CaG researchers for all study participants. The Sample and Data Access Committee (SDAC) of CaG approved the use of the genetic and baseline characteristics for our study. All genetic and bioinformatic analysis were carried out in accordance with relevant guidelines and regulations. Our protocol was approved by our institution’s research ethics board (CR-CHUM REB, Project 18.116).

Bio-informatic pipeline

The FASTQ files were aligned to the reference genome (Hg19) using BWA for exome sequencing and STAR for RNA-sequencing14,15. In both cases, variant calling was performed with GATK and annotated using ANNOVAR and custom scripts16,17. The bioinformatic pipeline in place allows the detection of single nucleotide variants (SNV) either non-synonymous, splice junction or synonymous, multi-nucleotide variants (MNV) and indels.

Since NPC1 (NM_000271) and NPC2 (NM_006432) genes are located on chromosome 18 or 14 respectively, we only extracted variants on those chromosomes for each sample. We also added information from the NP-C database (NPC-db2) made by the University of Tübingen, using a custom Python script. The database was last searched in July 2019. When a variant was found in the NPC-db2, its pathogenicity classification based on their criteria was added to the resulting file.

Analysis

As we were identifying rare variants, common variants (defined as > 1%) found in dbSNP, 1000 Genomes, Exome Variant Server, GnomAD and internal databases were filtered out. Non-synonymous, putative splicing variants and coding indels were prioritized. More specifically, we set a threshold for a CADD score higher than 15, a Polyphen2 score higher than 0.75 with a score of one being very likely pathogenic, a SIFT score, that was reversed to match the Polyphen2 score, with the same criteria as Polyphen2 to filter the variants18,19,20,21,22. For the conservation scores, we identified the variants with a score higher than 500 for Phast cons and higher than 5 for GERP. Phast cons ranges between 0 and 1000 and the GERP score between − 12.3 and 6.17.

Classification

The ACMG 2015 revised guidelines were used to classify the different variants. The classification uses five distinct categories: benign, likely benign, uncertain significance, likely pathogenic and pathogenic. Each variant was analyzed using the ACMG criteria except the ones requiring segregation and laboratory data which were not available for our dataset. We extracted the NPC-db2 classification for each variant. Thereafter, the variants were searched on ClinVar for previous classification by other groups, using the ACMG criteria. Classifications based on other sets of criteria were not included in our tables. Finally, we searched the largest published study on NPC variants by Wassif et al. for identical variants already identified in their results. Previously unreported variants will be submitted to the ClinVar public database.

Results

Baseline characteristics

The total sample size for both RNA-seq and exome-seq was 1016 patients and 2032 chromosomes. Clinical data was available for 1004 patients (Table 1). Females represented 51% of our population. The highest represented ethnicity was white from European descent (91.5%). The majority of these individuals were employed (64.3%). Age ranged from 44 to 69, with 42% of patients in the 40–49 range. This sample has a similar distribution as the general CaG cohort (Table 1). Additionally, the sample is also representative of the Quebec population based on the most recent epidemiological data23.

Table 1 Baseline characteristics.
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RNA-seq

Our study identified 32 unique rare variants from the 911 RNA samples that were run in the bio-informatic pipeline (Table 2, Fig. 1). Each variant was only present in one chromosome, for an allele frequency of 0.05% in our population. None of the study participants were heterozygous for two rare variants. Twenty of these variants were non-synonymous SNVs while the others were frameshift deletions. Among these variants, two were classified as pathogenic. Indeed, the p.Ile1061Thr is a known protein change that leads to a change from isoleucine to threonine. This variant has been described as causative of NPC in 15–20% of disease alleles in the United States and Europe. Additionally, biological studies have shown that this missense change affects proper protein localization and causes proteasomal degradation in cell culture. Another pathological variant, p.Pro543Leu, has been identified in 1 homozygous and 4 compound heterozygous individuals with symptomatic disease24. It has previously been reported that this mutation leads to early-infantile form of NPC25. Both participants were heterozygous for these mutations and were asymptomatic according to the baseline medical screening obtained from CaG. The remaining twelve variants were indels and all were classified as VUS. Given the limited coverage, these could represent artifacts and their exact significance is difficult to interpret.

Table 2 Rare variants in CARTaGENE sample, RNA-seq.
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Figure 1
figure 1

Venn diagram of included variants from RNA-seq for each bioinformatic filter.

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Comparison with other databases

The NPC-db2 database was searched for identified variants which were also present in our population. Twelve out of the 32 variants identified were also present in their sample. Our classification based on the ACMG criteria was overall very similar to their classification for shared variants. Notably, the p.Pro543Leu protein change was marked as potentially pathogenic in NPC-db2, while we were able to classify it as pathogenic based on previous publications and computational/predictive data. Additionally, we identified 13 variants that were filtered out by our bioinformatic pipeline, but that were present both in our population and in the NPC-db2 database (Table 3). These were all previously classified as benign. The main reason for their exclusion in the pipeline was a high allelic frequency (> 1%).

Table 3 Variants in the CARTaGENE RNA-seq sample excluded from the pipeline but identified in NPC-db2.
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The variants in the study by Wassif et al. were also compared with variants identified in our study (Table 2)26. Despite not specifically using the ACMG criteria, we were able to compare their five-level scale of classification to our data. Twelve out of the 32 variants were also classified in their study. One notable difference in classification in the variant p.Ile1061Thr was probably due to a mistake in their table as they present it as benign, while describing it as on of the most common pathogenic variant in their text26. Moreover, five variants that were in both our databases were excluded by our pipeline. Once again, these were classified as benign and the main reason was a high allelic frequency (> 1%).

Exome-seq

Exome sequencing was performed for 198 individuals, composed of 93 individuals for whom we also had the RNA-seq and 105 new individuals for whom we only had exome-seq. Overall, 19 unique variants were identified in the samples, 4 of which were also present in the RNA-seq (Fig. 2). In participants for whom both RNA-seq and exome-seq data were available, the exact same sequence variants were found using both methods. After filtering by the bioinformatic pipeline, 10 variants were identified. Four of those were already found in our RNA-seq, including one classified as pathogenic (Table 4). The six other variants were found in individuals for which we only had exome data. These included two variants in splicing regions, one of which was causative of disease in previous publications24,27. The participant was heterozygous for this variant and was asymptomatic according to baseline medical screening obtained from CaG. The RNA-seq for these individuals were not available to confirm the presence of abnormal splicing.

Figure 2
figure 2

Venn diagram of included variants from exome-seq for each bioinformatic filter.

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Table 4 Rare variants in CARTaGENE sample, exome-seq.
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Discussion

Our study evaluated rare variants in NPC1 and NPC2 genes in a sample from the Quebec population in Canada. This population is unique given the important founder effect from French colonisation in the early seventeenth century28. We had 911 RNA samples and 198 exome samples, with an overlap of 93 individuals. This study presents new variants that have not been previously described in the literature. In addition, known variants were reclassified based on the most recent literature. In fact, we classified each individual variant based on the ACMG 2015 guidelines and compared them to the NPC-db2 database and previously published studies on such variants. The majority of identified variants were of uncertain significance, likely benign or benign. However, we have also identified some likely pathogenic and pathogenic variants in heterozygous individuals.

To select rare variants in our population, we used a pre-specified bioinformatic pipeline. The variants were filtered based on their allelic frequency, with a coverage of at least 15% and where the alternative base was supported at least twice. This ensured that the classification would be applied to the most pertinent variants in our sample. We then focused our analysis on indels and non-synonymous variants, which were more likely to lead to pathogenic mutations. Additional variants were manually identified by comparing all variants present in our sample to those in the NPC-db2 database. These were filtered out from our pipeline according to the aforementioned criteria but were still classified by our laboratory because they were coincidentally present in another study population. The main reason for exclusion was allelic frequency > 1%.

With the increased use of genetic testing and the identification of more variants, it has become essential to apply rigorous classification in clinical genetic testing29. The set of criteria must be evidence-based, standardized and objective30. The ACMG 2015 guidelines, used in our study, have been largely used and therefore allow for easier comparison with previous publications. Individual laboratories also share their own classification in large databases (including ClinVar), but it is difficult to compare with their conclusions as the set of criteria is different. Thus, we have only compared our classification with published literature using the same set of criteria.

The CARTaGENE database encompasses a large sample of genomic data, but also baseline information based on detailed questionnaires. Answers included demographics, socioeconomic status, education and medical surveys. Given the possible adult-onset of NPC, we searched for potential symptoms in the questionnaires of patients with pathogenic or likely pathogenic variants. None of the identified individuals presented symptoms suggestive of the disease, as we had expected given the heterozygosity of the alleles. These pathogenic variants will allow us to estimate the carrier frequency in the Quebec population.

Our study has several potential limitations. First, our dataset is based on a relatively small sample size of 1016 individuals. However, given the important founder effect in our Quebec population pool, genetic variation is relatively lower when compared to other populations31. Second, we did not perform any functional biology experiments which limits our ability to classify some of these mutations based on functional criteria. Third, no segregation data was available in the database, which can often provide strong evidence for a benign variant in a new mutation.

In brief, this study analyzed variants in the NPC1 and NPC2 genes from a representative sample of the Quebec population. The results described novel variants that were not previously described in the literature. In addition, known variants were reclassified using the ACMG guidelines. Despite identifying pathogenic or likely pathogenic variants, the individuals were heterozygous and asymptomatic based on baseline questionnaires. Classifying these variants is arduous given the scarcity of available literature, even so in a population of healthy individuals, leading to a large proportion of variants of uncertain significance. Using this data, we were able to identify three pathogenic variants within our population and several new rare variants in NPC1 and NPC2 which had not previously been identified. This additional information should help clinicians interpret the pathogenicity of variants identified in these two genes moving forward.