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Efficient detection of copy-number variations using exome data: Batch- and sex-based analyses

Uchiyama Y, Yamaguchi D, Iwama K, Miyatake S, Hamanaka K, Tsuchida N, et al.

Hum Mutat, 2021 01;42(1):50-65.
PMID: 33131168 DOI: 10.1002/humu.24129

Many algorithms to detect copy number variations (CNVs) using exome sequencing (ES) data have been reported and evaluated on their sensitivity and specificity, reproducibility, and precision. However, operational optimization of such algorithms for a better performance has not been fully addressed. ES of 1199 samples including 763 patients with different disease profiles was performed. ES data were analyzed to detect CNVs by both the eXome Hidden Markov Model (XHMM) and modified Nord's method. To efficiently detect rare CNVs, we aimed to decrease sequencing biases by analyzing, at the same time, the data of all unrelated samples sequenced in the same flow cell as a batch, and to eliminate sex effects of X-linked CNVs by analyzing female and male sequences separately. We also applied several filtering steps for more efficient CNV selection. The average number of CNVs detected in one sample was <5. This optimization together with targeted CNV analysis by Nord's method identified pathogenic/likely pathogenic CNVs in 34 patients (4.5%, 34/763). In particular, among 142 patients with epilepsy, the current protocol detected clinically relevant CNVs in 19 (13.4%) patients, whereas the previous protocol identified them in only 14 (9.9%) patients. Thus, this batch-based XHMM analysis efficiently selected rare pathogenic CNVs in genetic diseases.
Genetic and clinical landscape of childhood cerebellar hypoplasia and atrophy

Sakamoto M, Iwama K, Sasaki M, Ishiyama A, Komaki H, Saito T, et al.

Genet Med, 2022 Dec;24(12):2453-2463.
PMID: 36305856 DOI: 10.1016/j.gim.2022.08.007

PURPOSE: Cerebellar hypoplasia and atrophy (CBHA) in children is an extremely heterogeneous group of disorders, but few comprehensive genetic studies have been reported. Comprehensive genetic analysis of CBHA patients may help differentiating atrophy and hypoplasia and potentially improve their prognostic aspects.
METHODS: Patients with CBHA in 176 families were genetically examined using exome sequencing. Patients with disease-causing variants were clinically evaluated.
RESULTS: Disease-causing variants were identified in 96 of the 176 families (54.5%). After excluding 6 families, 48 patients from 42 families were categorized as having syndromic associations with CBHA, whereas the remaining 51 patients from 48 families had isolated CBHA. In 51 patients, 26 aberrant genes were identified, of which, 20 (76.9%) caused disease in 1 family each. The most prevalent genes were CACNA1A, ITPR1, and KIF1A. Of the 26 aberrant genes, 21 and 1 were functionally annotated to atrophy and hypoplasia, respectively. CBHA+S was more clinically severe than CBHA-S. Notably, ARG1 and FOLR1 variants were identified in 2 families, leading to medical treatments.
CONCLUSION: A wide genetic and clinical diversity of CBHA was revealed through exome sequencing in this cohort, which highlights the importance of comprehensive genetic analyses. Furthermore, molecular-based treatment was available for 2 families.
Brain monoamine vesicular transport disease caused by homozygous SLC18A2 variants: A study in 42 affected individuals

Saida K, Maroofian R, Sengoku T, Mitani T, Pagnamenta AT, Marafi D, et al.

Genet Med, 2023 Jan;25(1):90-102.
PMID: 36318270 DOI: 10.1016/j.gim.2022.09.010

PURPOSE: Brain monoamine vesicular transport disease is an infantile-onset movement disorder that mimics cerebral palsy. In 2013, the homozygous SLC18A2 variant, p.Pro387Leu, was first reported as a cause of this rare disorder, and dopamine agonists were efficient for treating affected individuals from a single large family. To date, only 6 variants have been reported. In this study, we evaluated genotype-phenotype correlations in individuals with biallelic SLC18A2 variants.
METHODS: A total of 42 affected individuals with homozygous SLC18A2 variant alleles were identified. We evaluated genotype-phenotype correlations and the missense variants in the affected individuals based on the structural modeling of rat VMAT2 encoded by Slc18a2, with cytoplasm- and lumen-facing conformations. A Caenorhabditis elegans model was created for functional studies.
RESULTS: A total of 19 homozygous SLC18A2 variants, including 3 recurrent variants, were identified using exome sequencing. The affected individuals typically showed global developmental delay, hypotonia, dystonia, oculogyric crisis, and autonomic nervous system involvement (temperature dysregulation/sweating, hypersalivation, and gastrointestinal dysmotility). Among the 58 affected individuals described to date, 16 (28%) died before the age of 13 years. Of the 17 patients with p.Pro237His, 9 died, whereas all 14 patients with p.Pro387Leu survived. Although a dopamine agonist mildly improved the disease symptoms in 18 of 21 patients (86%), some affected individuals with p.Ile43Phe and p.Pro387Leu showed milder phenotypes and presented prolonged survival even without treatment. The C. elegans model showed behavioral abnormalities.
CONCLUSION: These data expand the phenotypic and genotypic spectra of SLC18A2-related disorders.
Fulltext GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

Lesmann H, Hustinx A, Moosa S, Klinkhammer H, Marchi E, Caro P, et al.

Res Sq, 2024 Jun 10.
PMID: 38903062 DOI: 10.21203/rs.3.rs-4438861/v1

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.
Fulltext GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

Lesmann H, Hustinx A, Moosa S, Klinkhammer H, Marchi E, Caro P, et al.

medRxiv, 2024 May 21.
PMID: 37503210 DOI: 10.1101/2023.06.06.23290887

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.