Genome sequencing in persistently unsolved white matter disorders
Open accessAbstract
Genetic white matter disorders have heterogeneous etiologies and overlapping clinical presentations. We performed a study of the diagnostic efficacy of genome sequencing in 41 unsolved cases with prior exome sequencing, resolving an additional 14 from an historical cohort (n = 191). Reanalysis in the context of novel disease‐associated genes and improved variant curation and annotation resolved 64% of cases. The remaining diagnoses were directly attributable to genome sequencing, including cases with small and large copy number variants (CNVs) and variants in deep intronic and technically difficult regions. Genome sequencing, in combination with other methodologies, achieved a diagnostic yield of 85% in this retrospective cohort.
AI snapshot
Written from the paper’s full text.
TL;DR
Genome sequencing in 41 families with persistently unsolved genetic white matter disorders after exome sequencing resolved 14 additional cases (34%). Nine of those 14 diagnoses (64%) came from reanalysis with newly described disease genes or improved variant curation, while five were directly attributable to genome sequencing findings such as CNVs, deep intronic variants, and a GC-rich region.
Key findings
- Genome sequencing established a molecular diagnosis in 14 of 41 persistently unsolved families (34%) after prior exome sequencing.
- Of the 14 resolved cases, 9 (64%) were resolved through reanalysis in the context of novel disease-associated genes or improved variant curation/annotation; 5 were directly attributable to genome sequencing, including 3 CNVs, 1 deep intronic variant, and 1 variant in a technically difficult region.
- Across the original testable cohort, exome or genome sequencing solved 44/71 cases (62%), and total resolution was 145/172 (84%) in the discussion; the abstract reports an overall diagnostic yield of 85% for genome sequencing combined with other methodologies.
- No recurring diagnoses were made in the 41-family genome sequencing cohort, and only a minority across all NGS tiers (20%) were associated with canonical leukodystrophy genes.
Methods
- Retrospective study of 41 families from the Myelin Disorders Bioregistry Project who remained unsolved after prior exome sequencing; all had abnormal white matter on neuroimaging suggestive of leukodystrophy, with symptom onset from birth to 19 years.
- Genome sequencing was performed at Illumina on an Illumina 2000 with 2 × 125 nucleotide paired-end reads; dual analyses used BWA/GATK/SnpEff and Illumina Secondary Analysis Software v5.11.0, with variants prioritized by allele frequency, conservation, genotype, inheritance, disease association, consequence, and predicted pathogenicity.
- Putatively causal variants were assessed per ACMG guidelines; candidate variants in potentially novel disease genes were submitted to GeneMatcher, and candidate splice-altering variants were validated with a minigene splicing assay.
Limitations
- The paper does not include a dedicated limitations section; it notes that some variant types detected by genome sequencing may become detectable by exome sequencing as assays and pipelines improve, while others will remain difficult due to assay limitations such as GC-rich regions or accurate detection of large genomic alterations in single cases.
- The paper states that one case was clinically resolved but not included in the definitively resolved count because the variant remained a VUS per ACMG criteria.
- Evident from the text: the cohort is small (n = 41), retrospective, and drawn from a single referral registry; the reported overall diagnostic yield differs between the abstract (85%) and discussion (145/172, 84%), and the genome sequencing-specific yield is partly confounded by reanalysis and newly described gene–disease relationships rather than sequencing modality alone.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
15 of 28 cited sources checked · 6 supported · 0 flagged · accuracy not yet measured
- Findingcitation
The GJC2 duplication was previously undetected by exome sequencing due to a lack of coverage as it lies in the middle of a 110 nucleotide block of a ~95% GC-rich region.
“The duplication was previously undetected by exome sequencing due to a lack of coverage as it lies in the middle of a 110 nucleotide block of a ~ 95% GC‐rich region (Fig. 1B).1”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [1] Vanderver A, Simons C, Helman G, et al. Whole exome sequencing in patients with white matter abnormalities. Ann Neurol 2016; 79: 1031– 1037. 27159321 PMC5354169 source
The Methods (Cohort description; page unavailable) describe exome-sequencing coverage in the cohort, but not coverage at a GJC2 duplication. Searches for GJC2, the duplication, and the 110-nucleotide, approximately 95% GC-rich region found no passage addressing why this variant was missed. (LLM, conf=0.75)
Experimental — accuracy not yet measured
- Findingown data
LD_0315 had biallelic variants in DARS2, associated with leukoencephalopathy with brainstem and spinal cord involvement and lactate elevation (LBSL).
“LD_0315 had biallelic variants in DARS2 (OMIM:610956), associated with leukoencephalopathy with brainstem and spinal cord involvement and lactate elevation (LBSL [OMIM:611105]).”
Quote in the paperSubstantial claim - Findingown data
Of the original 191 families examined in this cohort, 101 received a definitive diagnosis using enzymatic, biochemical, or single gene molecular approaches (53%).
“Of the original 191 families examined in this cohort, 101 received a definitive diagnosis using enzymatic, biochemical, or single gene molecular approaches (53%).”
Quote in the paperSubstantial claim - Findingown data
Genome sequencing CNV analysis provided a diagnosis in three affected individuals.
“Genome sequencing CNV analysis provided a diagnosis in three affected individuals (Table 1).”
Quote in the paperSubstantial claim - Findingown data
Reanalysis in the context of novel disease-associated genes and improved variant curation and annotation resolved 64% of the cases.
“Reanalysis in the context of novel disease‐associated genes and improved variant curation and annotation resolved 64% of cases.”
Quote in the paperSubstantial claim
- Findingown data
Genome sequencing resolved an additional 14 families from a historical cohort of 191 families with prior exome sequencing.
“We performed a study of the diagnostic efficacy of genome sequencing in 41 unsolved cases with prior exome sequencing, resolving an additional 14 from an historical cohort (n = 191).”
Quote in the paperSubstantial claim - Findingreasoning
The LD_0587 case was not included in overall numbers of definitively resolved cases as this variant remains a variant of uncertain significance per the ACMG criteria.
“This case was not included in overall numbers of definitively resolved cases as this variant remains a variant of uncertain significance per the ACMG criteria.”
Quote in the paperNot a substantial claim - Findingown data
Nine diagnoses were achieved through improvements in variant curation or novel disease-associated genes described since the exome sequencing analysis.
“Nine diagnoses were achieved through improvements in variant curation or novel disease‐associated genes described since the exome sequencing analysis.”
Quote in the paperSubstantial claim - Findingown data
LD_0671 was found to harbor a 21kb de novo deletion encompassing a region in chromosome 2 covering SATB2, previously associated with Glass syndrome.
“LD_0671 was found to harbor a 21kb de novo deletion encompassing a region in chromosome 2 covering SATB2 (OMIM:608148), previously associated with Glass syndrome (OMIM:612313).23”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Zarate YA, Smith‐Hicks CL, Greene C, et al. Natural history and genotype‐phenotype correlations in 72 individuals with SATB2‐associated syndrome. Am J Med Genet A 2018; 176: 925– 935. 29436146 source
- Findingown data
Genome sequencing, in combination with other methodologies, achieved a diagnostic yield of 85% in this retrospective cohort.
“Genome sequencing, in combination with other methodologies, achieved a diagnostic yield of 85% in this retrospective cohort.”
Quote in the paperSubstantial claim - Findingcitation
An intronic variant, c.228-21delTinsCC, p.(Arg76Serfs*5) that has been shown to affect mRNA splicing of DARS2 with skipping of exon 3 was found in trans with a previously unreported missense variant, c.294G>T, p.(Glu98Asp).
“An intronic variant, c.228‐21delTinsCC, p.(Arg76Serfs*5) that has been shown to affect mRNA splicing of DARS2 with skipping of exon 319 was found in trans with a previously unreported missense variant, c.294G>T, p.(Glu98Asp).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [19] van Berge L, Hamilton EM, Linnankivi T, et al. Leukoencephalopathy with brainstem and spinal cord involvement and lactate elevation: clinical and genetic characterization and target for therapy. Brain 2014; 137(Pt 4): 1019– 1029. 24566671 source
- Findingown data
A minigene splicing reporter assay demonstrated that each of the intronic and exonic DARS2 variants results in the skipping of exon 3.
“To confirm the predicted splicing change of intronic and exonic variants based on SpliceAI annotations,20 we generated a minigene splicing reporter assay (Appendix S1), demonstrating each variant results in the skipping of exon 3 (Appendix S1).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [20] Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, et al. Predicting splicing from primary sequence with deep learning. Cell 2019; 176: 535– 548.e24. 30661751 source
- Findingown data
The remaining five cases were resolved due to the identification of variants not typically identifiable by exome sequencing.
“The remaining five cases were resolved due to the identification of variants not typically identifiable by exome sequencing.”
Quote in the paperSubstantial claim - Findingown data
In family LD_0393, genome sequencing revealed a homozygous intronic variant, c.1969+115_1969+116delAG in CSF1R.
“In family LD_0393, genome sequencing revealed a homozygous intronic variant, c.1969+115_1969+116delAG in CSF1R (OMIM:164770).”
Quote in the paperSubstantial claim - Findingown data
In two individuals, genome sequencing revealed variants not identified on exome sequencing analysis due to lack of variant annotation or stringent filtering.
“In two individuals, genome sequencing revealed variants not identified on exome sequencing analysis due to lack of variant annotation or stringent filtering.”
Quote in the paperSubstantial claim - Findingcitation
These individuals were found to have a novel syndrome due to biallelic variants in CSF1R characterized by brain malformation and skeletal dysplasia, reported in Guo et al. (2019).
“These individuals were found to have a novel syndrome due to biallelic variants in CSF1R characterized by brain malformation and skeletal dysplasia, reported in Guo et al. (2019).21”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [21] Guo L, Bertola DR, Takanohashi A, et al. Bi‐allelic CSF1R mutations cause skeletal dysplasia of dysosteosclerosis‐pyle disease spectrum and degenerative encephalopathy with brain malformation. Am J Hum Genet 2019; 104: 925– 935. 30982609 PMC6507048 source
- Findingown data
LD_0594 was found to have an X-linked 396kb duplication covering ARX, previously associated with a broad spectrum of neuroimaging abnormalities and early infantile epileptic encephalopathy.
“LD_0594 was found to have an X‐linked 396kb duplication covering ARX (OMIM: 300382), previously associated with a broad spectrum of neuroimaging abnormalities and early infantile epileptic encephalopathy (OMIM:308350).”
Quote in the paperSubstantial claim - Findingown data
Reanalysis in the context of recently published literature (and associated annotation pipelines) allowed resolution of an additional eight cases.
“Reanalysis in the context of recently published literature (and associated annotation pipelines) allowed resolution of an additional eight cases.”
Quote in the paperSubstantial claim - Findingown data
For LD_0725, genome sequencing revealed a mitochondrial DNA variant, m.3243A>G in MT-TL1, carried on 42% of reads in the affected individual and 15% of the maternal reads.
“For LD_0725, genome sequencing revealed a mitochondrial DNA variant, m.3243A>G in MT‐TL1 (OMIM:590050), carried on 42% of reads in the affected individual and 15% of the maternal reads.”
Quote in the paperSubstantial claim - Findingown data
The remaining diagnoses were directly attributable to genome sequencing, including cases with small and large copy number variants (CNVs) and variants in deep intronic and technically difficult regions.
“The remaining diagnoses were directly attributable to genome sequencing, including cases with small and large copy number variants (CNVs) and variants in deep intronic and technically difficult regions.”
Quote in the paperSubstantial claim - Findingown data
Five diagnoses were achieved through identification of CNVs (three cases), deep intronic variants (one case), and variants in technically difficult regions (one case).
“Five diagnoses were achieved through identification of CNVs (three cases), deep intronic variants (one case), and variants in technically difficult regions (one case).”
Quote in the paperSubstantial claim - Findingown data
Genome sequencing findings improved the overall diagnostic yield in this cohort by ~11% (5/44 cases solved by NGS), through the detection of variants in GC-rich regions, deep intronic variants, and CNVs.
“Genome sequencing findings improved the overall diagnostic yield in this cohort by ~11% (5/44 cases solved by NGS), through the detection of variants in GC‐rich regions, deep intronic variants, and CNVs.”
Quote in the paperSubstantial claim - Findingnone given
Improvements in variant annotation and filtering detected variants in mitochondrial DNA and intronic regions proximal to captured exons.
“Improvements in variant annotation and filtering detected variants in mitochondrial DNA and intronic regions proximal to captured exons.”
Quote in the paperSubstantial claim - Interpretationreasoning
It is expected that these variants would have been identified on exome sequencing if performed with current testing modalities.
“It is expected that these would have been identified on exome sequencing if performed with current testing modalities.”
Quote in the paperSubstantial claim - Interpretationcitation
Our study lends support to a previous study that suggested that currently up to 80% of white matter disorders may be able to be solved.
“Our study lends support to a previous study that suggested that currently up to 80% of white matter disorders may be able to be solved.2”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [2] Kevelam SH, Steenweg ME, Srivastava S, et al. Update on leukodystrophies: a historical perspective and adapted definition. Neuropediatrics 2016; 47: 349– 354. 27564080 source
- Interpretationown data
Our data support this approach, favoring genome sequencing over exome sequencing.
“Our data support this approach, favoring genome sequencing over exome sequencing.”
Quote in the paperSubstantial claim - Interpretationown data
These data indicate that only a fraction of affected individuals would achieve a diagnosis using targeted testing approaches, including panels of classically defined leukodystrophy genes now in widespread use.
“These data indicate that only a fraction of affected individuals would achieve a diagnosis using targeted testing approaches, including panels of classically defined leukodystrophy genes now in widespread use.”
Quote in the paperSubstantial claim
- May combine two claims. 19
- Classified as probably not a substantial claim. 7
May combine two claims.
PremisecitationIn a previously reported cohort of 191 families with suspected leukodystrophy, 71 were persistently unsolved despite targeted molecular and enzymatic testing and 19 were lost to follow-up.
“Previously, we reported on a cohort of 191 families with a suspected leukodystrophy, of which 71 were persistently unsolved despite targeted molecular and enzymatic testing and 19 were lost to follow‐up.1”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [1] Vanderver A, Simons C, Helman G, et al. Whole exome sequencing in patients with white matter abnormalities. Ann Neurol 2016; 79: 1031– 1037. 27159321 PMC5354169 source
The Methods (Cohort description; page not provided) report an initial 191 cases, of which 101 received diagnoses and 90 remained unsolved; 71 of the unsolved families had suitable samples for exome sequencing. The other 19 were excluded from that testing—not reported as lost to follow-up: nine obtained sequencing elsewhere, and ten lacked suitable samples despite collection attempts. (LLM, conf=0.90)
Experimental — accuracy not yet measured
May combine two claims.
Premiseown dataTrio-or-greater exome sequencing analysis resolved 42% (30/71) of unsolved cases, which, in combination with standard of care approaches, yielded a 77% overall diagnostic rate from 172 families available for testing.
“Trio‐or‐greater exome sequencing analysis resolved 42% (30/71) of unsolved cases, which, in combination with standard of care approaches, yielded a 77% overall diagnostic rate from 172 families available for testing.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [1] Vanderver A, Simons C, Helman G, et al. Whole exome sequencing in patients with white matter abnormalities. Ann Neurol 2016; 79: 1031– 1037. 27159321 PMC5354169 source
The Abstract (page unavailable) reports a 42% clinical diagnostic yield in 71 trio-or-greater WES cases, comprising 25 cases with diagnostic variants and five with potentially pathogenic variants. The Methods describe 101 diagnoses by standard approaches, but the Discussion reports an overall rate of approximately 72% across the initial 191 families—not 77% across 172 families. The claim also treats all 30 WES findings as resolved without preserving the distinction between diagnostic and potentially pathogenic variants. (LLM, conf=0.90)
Experimental — accuracy not yet measured
May combine two claims.
FindingcitationLD_0587 has a clinical presentation with intellectual disability, epilepsy, aggressive behavior, and macrocephaly, all consistent features of KDM5C-related disorder, and is classified as clinically resolved but the identified variant lacks definitive proof to be classified as likely pathogenic or pathogenic.
“LD_0587 has a clinical presentation with intellectual disability, epilepsy, aggressive behavior, and macrocephaly, all consistent features of KDM5C‐related disorder and is classified as clinically resolved but the identified variant lacks definitive proof to be classified as likely pathogenic or pathogenic.17”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [17] Vallianatos CN, Farrehi C, Friez MJ, et al. Altered gene‐regulatory function of KDM5C by a novel mutation associated with autism and intellectual disability. Front Mol Neurosci 2018; 11: 104. 29670509 PMC5893713 source
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
May combine two claims.
FindingcitationStringent variant filtering performed during the prior exome sequencing analysis excluded this intronic variant and precluded a diagnosis.
“Stringent variant filtering performed during the prior exome sequencing analysis excluded this intronic variant and precluded a diagnosis.1”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [1] Vanderver A, Simons C, Helman G, et al. Whole exome sequencing in patients with white matter abnormalities. Ann Neurol 2016; 79: 1031– 1037. 27159321 PMC5354169 source
The Methods (Cohort description; page unavailable) describe variant filtering, but do not say that it excluded an intronic variant or prevented a diagnosis. A search for “intronic” found no passage in our copy establishing that event. (LLM, conf=0.73)
Experimental — accuracy not yet measured
May combine two claims.
Findingown dataAffected individuals in families LD_0579 and LD_0587 had variants in genes previously implicated in neurologic syndromes where improvements to the analysis pipeline or improved phenotypic understanding permitted a diagnosis or prioritized a high confidence candidate, in L1CAM and KDM5C, respectively.
“Affected individuals in families LD_0579 and LD_0587 had variants in genes previously implicated in neurologic syndromes where improvements to our analysis pipeline or improved phenotypic understand permitted a diagnosis or prioritized a high confidence candidate, in L1CAM (OMIM:308840)16 and KDM5C (OMIM:314690),17 respectively.”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [16] Tsurusaki Y, Okamoto N, Suzuki Y, et al. Exome sequencing of two patients in a family with atypical X‐linked leukodystrophy. Clin Genet 2011; 80: 161– 166. 21644943 source
- cites [17] Vallianatos CN, Farrehi C, Friez MJ, et al. Altered gene‐regulatory function of KDM5C by a novel mutation associated with autism and intellectual disability. Front Mol Neurosci 2018; 11: 104. 29670509 PMC5893713 source
Split into 2 atoms — 1 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
May combine two claims.
Findingown dataReanalysis by genome sequencing also led to the identification of variants in recently described disease genes AIFM1, HIKESHI, H3F3B, NKX6-2, and SPATA5, and genes missed in the previous analysis (L1CAM).
“Reanalysis by genome sequencing also led to the identification of variants in recently described disease genes AIFM1,12 HIKESHI, 13 H3F3B, 11 NKX6‐2, 14 and SPATA5, 15 and genes missed in the previous analysis (L1CAM).”
Quote in the paperSubstantial claim2 of 3 sources checked- cites [12] Miyake N, Wolf NI, Cayami FK, et al. X‐linked hypomyelination with spondylometaphyseal dysplasia (H‐SMD) associated with mutations in AIFM1. Neurogenetics 2017; 18: 185– 194. 28842795 PMC5705759 source
- cites [13] Edvardson S, Kose S, Jalas C, et al. Leukoencephalopathy and early death associated with an Ashkenazi‐Jewish founder mutation in the Hikeshi gene. J Med Genet 2016; 53: 132– 137. 26545878 source
- cites [11] Farwell Hagman KD, Shinde DN, Mroske C, et al. Candidate‐gene criteria for clinical reporting: diagnostic exome sequencing identifies altered candidate genes among 8% of patients with undiagnosed diseases. Genet Med 2017; 19: 224– 235. 27513193 PMC5303763 source
The Materials and Methods section describes whole-exome sequencing, not the claimed genome-sequencing reanalysis. Searches for AIFM1, HIKESHI, H3F3B, NKX6-2, SPATA5, L1CAM, and the reanalysis wording found no passage in our copy reporting those findings. (LLM, conf=0.76)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationGenome sequencing has the potential to detect a wide variety of variant types, including single-nucleotide variants and indels, copy number variants, repeat expansions, and pathogenic nonprotein-coding variation that is missed by other NGS approaches.
“Genome sequencing has the potential to detect a wide variety of variant types, including single‐nucleotide variants and indels (out‐performing exome sequencing in protein‐coding regions), copy number variants (CNV),4 repeat expansions,5 and detection of pathogenic nonprotein‐coding variation that is missed by other NGS approaches.”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [4] Roller E, Ivakhno S, Lee S, et al. Canvas: versatile and scalable detection of copy number variants. Bioinformatics 2016; 32: 2375– 2377. 27153601 source
- cites [5] Dolzhenko E, van Vugt J, Shaw RJ, et al. Detection of long repeat expansions from PCR‐free whole‐genome sequence data. Genome Res 2017; 27: 1895– 1903. 28887402 PMC5668946 source
May combine two claims.
Findingown dataThirty cases were solved by exome sequencing, and in this study, genome sequencing established a diagnosis in an additional 14 families yielding a 20% improvement, and an overall diagnostic efficacy of NGS of genome or exome sequencing increased to 44/71 (62%) and in total resolution of 145/172 (84%) of the original testable cohort.
“Thirty cases were solved by exome sequencing,1 and in this study, genome sequencing established a diagnosis in an additional 14 families yielding a 20% improvement, and an overall diagnostic efficacy of NGS of genome or exome sequencing increased to 44/71 (62%) and in total resolution of 145/172 (84%) of the original testable cohort.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [1] Vanderver A, Simons C, Helman G, et al. Whole exome sequencing in patients with white matter abnormalities. Ann Neurol 2016; 79: 1031– 1037. 27159321 PMC5354169 source
- Rests on an assumption: The 19 families lost to follow-up do not differ systematically in diagnostic outcome from the 172 families available for testing.
May combine two claims.
FindingcitationCausal nonprotein-coding variants were identified in SNORD118, confirming a clinical diagnosis of leukoencephalopathy with calcifications and cysts in LD_0807.
“Finally, causal nonprotein‐coding variants were identified in SNORD118 (OMIM:616663), confirming a clinical diagnosis of leukoencephalopathy with calcifications and cysts (OMIM:614561) in LD_0807.18”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [18] Jenkinson EM, Rodero MP, Kasher PR, et al. Mutations in SNORD118 cause the cerebral microangiopathy leukoencephalopathy with calcifications and cysts. Nat Genet 2016; 48: 1185– 1192. 27571260 PMC5045717 source
The Results (page unavailable) identify rare SNORD118 variants in patients with leukoencephalopathy with calcifications and cysts, and the Discussion (page unavailable) concludes that these variants cause the disease. Our copy does not identify LD_0807 or report variants confirming that individual's diagnosis (searched: LD_0807 and LD0807). (LLM, conf=0.87)
Experimental — accuracy not yet measured
May combine two claims.
FindingcitationSNORD118 variants were found as part of a targeted cohort study and concomitantly found in our genome sequencing cohort.
“SNORD118 variants were found as part of a targeted cohort study and concomitantly found in our genome sequencing cohort.18”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [18] Jenkinson EM, Rodero MP, Kasher PR, et al. Mutations in SNORD118 cause the cerebral microangiopathy leukoencephalopathy with calcifications and cysts. Nat Genet 2016; 48: 1185– 1192. 27571260 PMC5045717 source
May combine two claims.
FindingcitationReanalysis identified a de novo missense variant in H3F3B, a hemizygous synonymous variant in AIFM1, and biallelic variants in HIKESHI, NKX6-2, and SPATA5, all previously associated with prominent white matter disease in multiple affected individuals.
“These included a de novo missense variant in H3F3B (OMIM:601058) (LD_0246), 11 a hemizygous, synonymous variant in AIFM1 (OMIM:300169) (LD_0500),12 and biallelic variants in HIKESHI (OMIM:614908) (LD_0162),13 NKX6‐2 (OMIM:605955) (LD_0527), 14 and SPATA5 (OMIM:613940) (LD_0808),15 all previously associated with prominent white matter disease in multiple affected individuals.”
Quote in the paperSubstantial claim2 of 3 sources checked- cites [11] Farwell Hagman KD, Shinde DN, Mroske C, et al. Candidate‐gene criteria for clinical reporting: diagnostic exome sequencing identifies altered candidate genes among 8% of patients with undiagnosed diseases. Genet Med 2017; 19: 224– 235. 27513193 PMC5303763 source
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured - cites [12] Miyake N, Wolf NI, Cayami FK, et al. X‐linked hypomyelination with spondylometaphyseal dysplasia (H‐SMD) associated with mutations in AIFM1. Neurogenetics 2017; 18: 185– 194. 28842795 PMC5705759 source
- cites [13] Edvardson S, Kose S, Jalas C, et al. Leukoencephalopathy and early death associated with an Ashkenazi‐Jewish founder mutation in the Hikeshi gene. J Med Genet 2016; 53: 132– 137. 26545878 source
Classified as probably not a substantial claim.
Study design and conductcitationPutatively causal variants were assessed as per the American College of Medical Genetics (ACMG) guidelines.
“Putatively causal variants were assessed as per the American College of Medical Genetics (AGMC) guidelines.9”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [9] Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015; 17: 405– 424. 25741868 PMC4544753 source
May combine two claims.
FindingcitationLD_0498 was found to have impaired peroxisomal β-oxidation of C26:0 and genome sequencing revealed a homozygous, 7.5kb deletion in ACBD5 causing loss of exons 7 and 8, confirmed by Sanger sequencing.
“LD_0498 was found to have impaired peroxisomal β‐oxidation of C26:0 and genome sequencing revealed a homozygous, 7.5kb deletion in ACBD5 (OMIM:616618) causing loss of exons 7 and 8, confirmed by Sanger sequencing.22”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [22] Ferdinandusse S, Falkenberg KD, Koster J, et al. ACBD5 deficiency causes a defect in peroxisomal very long‐chain fatty acid metabolism. J Med Genet 2016; 54: 330– 337. 27799409 source
May combine two claims.
Findingown dataThe c.916_928dupGCCTCCGCCCCCG GJC2 variant called by genome sequencing was supported by six of 19 genome sequencing reads covering the position, and was confirmed by Sanger sequencing.
“The c.916_928dupGCCTCCGCCCCCG GJC2 variant called by genome sequencing was supported by six of 19 genome sequencing reads covering the position (Fig. 1C), and was confirmed by Sanger sequencing (Fig. 1D).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataNineteen families were subsequently lost to follow-up, leaving 71 families able to be tested by NGS.
“Nineteen families were subsequently lost to follow‐up, leaving 71 families able to be tested by NGS.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenOne case was considered clinically resolved following multidisciplinary review.
“One case was considered clinically resolved following multidisciplinary review.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe biallelic CSF1R variant affects a cryptic splice site and results in the inclusion of a novel 99nt pseudo-exon, with an in-frame stop codon p.(Pro658Serfs*24) resulting in nonsense-mediated decay of the truncated protein.
“This biallelic variant affects a cryptic splice site and results in the inclusion of a novel 99nt pseudo‐exon, with an in‐frame stop codon p.(Pro658Serfs*24) resulting in nonsense‐mediated decay of the truncated protein.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataBiallelic variants were identified in LD_0617 in GJC2, causative of Pelizaeus-Merzbacher-like disease (PMLD), a maternally inherited missense variant c.203A>G, p.(Tyr68Cys), and a 13 base paternally inherited duplication c.916_928dupGCCTCCGCCCCCG, p.(Ala310fs).
“Biallelic variants were identified in LD_0617 in GJC2 (OMIM:608803), causative of Pelizaeus‐Merzbacher‐like disease (PMLD [OMIM:608804]), a maternally inherited missense variant c.203A>G, p.(Tyr68Cys), and a 13 base paternally inherited duplication c.916_928dupGCCTCCGCCCCCG, p.(Ala310fs).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataNo recurring diagnoses were made in the current cohort of n=41, and only a minority across all tiers of NGS testing (20%) were associated with canonical leukodystrophy genes.
“Notably, no recurring diagnoses were made in the current cohort of n = 41, and only a minority across all tiers of NGS testing (20%) were associated with canonical leukodystrophy genes.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe study had approval from the institutional review boards at collaborating institutions.
“The study had approval from the institutional review boards at collaborating institutions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWhite matter abnormalities are a rare association with L1CAM-related disorder.
“White matter abnormalities are a rare association with L1CAM‐related disorder.16”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [16] Tsurusaki Y, Okamoto N, Suzuki Y, et al. Exome sequencing of two patients in a family with atypical X‐linked leukodystrophy. Clin Genet 2011; 80: 161– 166. 21644943 source
May combine two claims.
Study design and conductown dataThe mean read depth in probands was 34X and on average, 91% of the genome had coverage depth greater than 20X.
“The mean read depth in probands was 34X and on average, 91% of the genome had coverage depth greater than 20X (Appendix S1).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductcitationCandidate splice-altering variants were validated using a minigene splicing assay.
“Candidate splice‐altering variants were validated using a minigene splicing assay (Appendix S1).8”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Gaildrat P, Killian A, Martins A, et al. Use of splicing reporter minigene assay to evaluate the effect on splicing of unclassified genetic variants. Methods Mol Biol 2010; 653: 249– 257. 20721748 source
Classified as probably not a substantial claim.
Study design and conductnone givenCases with variants in known disease genes meeting ACMG criteria for pathogenic or likely pathogenic, and whose clinical features were concordant with the established gene-disease relationship (including MRI patterns), were classified as resolved.
“Cases with variants in known disease genes meeting the ACMG criteria for pathogenic or likely pathogenic, and whose clinical features were concordant with the established gene–disease relationship (including magnetic resonance imaging (MRI) patterns) were classified as resolved.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThis is consistent with multiple recent studies that have shown increases of up to 10% in diagnostic yield with reanalysis within 24-36 months.
“This is consistent with multiple recent studies that have shown increases of up to 10% in diagnostic yield with reanalysis within 24–36 months.10”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [10] Costain G, Jobling R, Walker S, et al. Periodic reanalysis of whole‐genome sequencing data enhances the diagnostic advantage over standard clinical genetic testing. Eur J Hum Genet 2018; 26: 740– 744. 29453418 PMC5945683 source
May combine two claims.
InterpretationreasoningIn our exome analysis, these variants were not found due to limitations of either sequencing or analysis, some of which will be difficult to overcome due to limitations of the assay (e.g. GC-rich regions) or the ability of exome to accurately detect large genomic alterations in single cases.
“In our exome analysis, however, these variants were not found due to limitations of either sequencing or analysis, some of which will be difficult to overcome due to limitations of the assay (e.g. GC‐rich regions) or the ability of exome to accurately detect large genomic alterations in single cases.”
Quote in the paperSubstantial claim
The paper relies on this without support.
The 19 families lost to follow-up do not differ systematically in diagnostic outcome from the 172 families available for testing.
“Nineteen families were subsequently lost to follow‐up, leaving 71 families able to be tested by NGS.”
The paper relies on this without support.
The m.3243A>G variant in MT-TL1 is associated with MELAS.
“This variant has been previously associated with mitochondrial encephalomyopathy, lactic acidosis, and stroke‐like episodes (MELAS [OMIM:540000]).”
The paper relies on this without support.
Heterozygous CSF1R variants are associated with hereditary diffuse leukoencephalopathy with spheroids.
“Heterozygous CSF1R variants are associated with another leukodystrophy, hereditary diffuse leukoencephalopathy with spheroids (OMIM:221820).”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Earlier overall diagnostic rate does not match the reported case counts
MinorThe paper reports 101 standard-of-care diagnoses and 30 exome diagnoses among 172 families available for testing. That is 131/172, or 76.2%, rather than the stated 77%.
“Trio‐or‐greater exome sequencing analysis resolved 42% (30/71) of unsolved cases, which, in combination with standard of care approaches, yielded a 77% overall diagnostic rate from 172 families available for testing.”
“Of the original 191 families examined in this cohort, 101 received a definitive diagnosis using enzymatic, biochemical, or single gene molecular approaches (53%). Nineteen families were subsequently lost to follow‐up, leaving 71 families able to be tested by NGS. Thirty cases were solved by exome sequencing,1”
Cites
Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W. W., Hegde, M., Lyon, E., Spector, E., et al. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.30 · Cited by 32,594 · see citing works
Cingolani, P., Platts, A., Wang, L. L., Coon, M., Nguyen, T., Wang, L., Land, S. J., Lu, X., & Ruden, D. M. (2012) · Fly · doi:10.4161/fly.19695 · Cited by 11,816 · see citing works
Van der Auwera, G. A., Carneiro, M. O., Hartl, C., Poplin, R., del Angel, G., Levy‐Moonshine, A., Jordan, T., Shakir, K., Roazen, D., Thibault, J., et al. (2013) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1110s43 · Cited by 6,687 · see citing works
Predicting splicing from primary sequence with deep learning
Jaganathan, K., Kyriazopoulou Panagiotopoulou, S., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., Kosmicki, J. A., Arbelaez, J., Cui, W., Schwartz, G. B., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.015 · Cited by 2,853 · see citing works
van der Knaap, M. S., & Bugiani, M. (2017) · Acta Neuropathologica · doi:10.1007/s00401-017-1739-1 · Cited by 403 · see citing works
Detection of long repeat expansions from PCR-free whole-genome sequence data
Dolzhenko, E., van Vugt, J. J. F. A., Shaw, R. J., Bekritsky, M. A., van Blitterswijk, M., Narzisi, G., Ajay, S. S., Rajan, V., Lajoie, B. R., Johnson, N. H., et al. (2017) · Genome Research · doi:10.1101/gr.225672.117 · Cited by 388 · see citing works
Self-citationCanvas: versatile and scalable detection of copy number variants
Roller, E., Ivakhno, S., Lee, S., Royce, T., & Tanner, S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw163 · Cited by 206 · see citing works
Parikh, S., Bernard, G., Leventer, R. J., van der Knaap, M. S., Hove, J. V., Pizzino, A. M., McNeill, N. H., Helman, G., Simons, C., Schmidt, J., et al. (2014) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2014.12.434 · Cited by 198 · see citing works
Self-citationGaildrat, P., Killian, A., Martins, A., Tournier, I., Frébourg, T., & Tosi, M. (2010) · Methods in Molecular Biology · doi:10.1007/978-1-60761-759-4_15 · Cited by 198 · see citing works
Tanaka, A., Cho, M. T., Millan, F., Juusola, J., Retterer, K., Joshi, C., Niyazov, D. M., Garnica, A. D., Gratz, E. S., Deardorff, M. A., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.07.014 · Cited by 152 · see citing works
Jenkinson, E. M., Rodero, M. P., Kasher, P. R., Uggenti, C., Oojageer, A., Goosey, L. C., Rose, Y., Kershaw, C. J., Urquhart, J. E., Williams, S. G., et al. (2016) · Nature Genetics · doi:10.1038/ng.3661 · Cited by 148 · see citing works
Self-citationWhole exome sequencing in patients with white matter abnormalities
Vanderver, A., Simons, C., Helman, G., Crawford, J., Wolf, N. I., Bernard, G., Pizzino, A., Schmidt, J. L., Takanohashi, A., Miller, D., et al. (2016) · Annals of Neurology · doi:10.1002/ana.24650 · Cited by 145 · see citing works
Self-citationCostain, G., Jobling, R., Walker, S., Reuter, M. S., Snell, M., Bowdin, S., Cohn, R. D., Dupuis, L., Hewson, S., Mercimek-Andrews, S., et al. (2018) · European Journal of Human Genetics · doi:10.1038/s41431-018-0114-6 · Cited by 138 · see citing works
Guo, L., Bertola, D. R., Takanohashi, A., Saito, A., Segawa, Y., Yokota, T., Ishibashi, S., Nishida, Y., Yamamoto, G. L., Franco, J. F. D. S., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.03.004 · Cited by 133 · see citing works
Self-citationACBD5 deficiency causes a defect in peroxisomal very long-chain fatty acid metabolism
Ferdinandusse, S., Falkenberg, K. D., Koster, J., Mooyer, P. A., Jones, R., van Roermund, C. W. T., Pizzino, A., Schrader, M., Wanders, R. J. A., Vanderver, A., et al. (2017) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2016-104132 · Cited by 120 · see citing works
Self-citationUpdate on leukodystrophies: A historical perspective and adapted definition
Kevelam, S., Steenweg, M., Srivastava, S., Helman, G., Naidu, S., Schiffmann, R., Blaser, S., Vanderver, A., Wolf, N., & van der Knaap, M. (2016) · Neuropediatrics · doi:10.1055/s-0036-1588020 · Cited by 112 · see citing works
Self-citationvan Berge, L., Hamilton, E. M., Linnankivi, T., Uziel, G., Steenweg, M. E., Isohanni, P., Wolf, N. I., Krageloh-Mann, I., Brautaset, N. J., Andrews, P. I., et al. (2014) · Brain · doi:10.1093/brain/awu026 · Cited by 109 · see citing works
Self-citationZarate, Y. A., Smith‐Hicks, C. L., Greene, C., Abbott, M., Siu, V. M., Calhoun, A. R. U. L., Pandya, A., Li, C., Sellars, E. A., Kaylor, J., et al. (2018) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.38630 · Cited by 71 · see citing works
Vallianatos, C. N., Farrehi, C., Friez, M. J., Burmeister, M., Keegan, C. E., & Iwase, S. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00104 · Cited by 71 · see citing works
Farwell Hagman, K. D., Shinde, D. N., Mroske, C., Smith, E., Radtke, K., Shahmirzadi, L., El-Khechen, D., Powis, Z., Chao, E. C., Alcaraz, W. A., et al. (2017) · Genetics in Medicine · doi:10.1038/gim.2016.95 · Cited by 54 · see citing works
Miyake, N., Wolf, N. I., Cayami, F. K., Crawford, J., Bley, A., Bulas, D., Conant, A., Bent, S. J., Gripp, K. W., Hahn, A., et al. (2017) · neurogenetics · doi:10.1007/s10048-017-0520-x · Cited by 48 · see citing works
Self-citationEdvardson, S., Kose, S., Jalas, C., Fattal-Valevski, A., Watanabe, A., Ogawa, Y., Mamada, H., Fedick, A. M., Ben-Shachar, S., Treff, N. R., et al. (2016) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2015-103232 · Cited by 33 · see citing works
Biallelic mutations in the homeodomain of NKX6-2 underlie a severe hypomyelinating leukodystrophy
Dorboz, I., Aiello, C., Simons, C., Stone, R. T., Niceta, M., Elmaleh, M., Abuawad, M., Doummar, D., Bruselles, A., Wolf, N. I., et al. (2017) · Brain · doi:10.1093/brain/awx207 · Cited by 24 · see citing works
Self-citationExome sequencing of two patients in a family with atypical X‐linked leukodystrophy
Tsurusaki, Y., Okamoto, N., Suzuki, Y., Doi, H., Saitsu, H., Miyake, N., & Matsumoto, N. (2011) · Clinical Genetics · doi:10.1111/j.1399-0004.2011.01721.x · Cited by 9 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Hypomyelinating leukodystrophies — unravelling myelin biology
Wolf, N. I., ffrench‐Constant, C., & van der Knaap, M. S. (2020) · Nature Reviews Neurology · doi:10.1038/s41582-020-00432-1 · Cited by 164 · see citing works
Self-citationWhole exome and genome sequencing in mendelian disorders: a diagnostic and health economic analysis
Ewans, L., Minoche, A. E., SCHOFIELD, D. J., Shrestha, R. N., Puttick, C., Zhu, Y., Drew, A. P., Gayevskiy, V., Elakis, G., Walsh, C., et al. (2022) · European Journal of Human Genetics · doi:10.1038/s41431-022-01162-2 · Cited by 123 · see citing works
Schobers, G., Schieving, J., Yntema, H. G., Pennings, M., Pfundt, R., Derks, R. C., Hofste, T. G. J., de Wijs, I. J., Wieskamp, N., van den Heuvel, S., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01069-z · Cited by 66 · see citing works
Modeling CSF‐1 receptor deficiency diseases – how close are we?
Chiţu, V., Gökhan, Ş., & Stanley, E. R. (2021) · FEBS Journal · doi:10.1111/febs.16085 · Cited by 47 · see citing works
Mahdieh, N., Soveizi, M., Tavasoli, A. R., Rabbani, A., Ashrafi, M. R., Kohlschütter, A., & Rabbani, B. (2021) · Scientific Reports · doi:10.1038/s41598-021-82778-0 · Cited by 41 · see citing works
Dutta, D., Briere, L. C., Kanca, O., Marcogliese, P. C., Walker, M., High, F. A., Vanderver, A. L., Krier, J. B., Carmichael, N. E., Callahan, C. J., et al. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa081 · Cited by 39 · see citing works
Self-citationAdult-onset leukodystrophies: a practical guide, recent treatment updates, and future directions
Muthusamy, K., Sivadasan, A., Dixon, L. V. M., Sudhakar, S. V., Thomas, M. M., Danda, S., Wszołek, Z. K., Wierenga, K. J., Dhamija, R., & Gavrilova, R. H. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1219324 · Cited by 30 · see citing works
Dulski, J., Souza, J., Santos, M. L. S. F., & Wszołek, Z. K. (2023) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-023-02772-9 · Cited by 26 · see citing works
Schlüter, A., Rodríguez‐Palmero, A., Verdura, E., Vélez-Santamaría, V., Ruíz, M., Fourcade, S., Planas‐Serra, L., Martínez, J. J., Guilera, C., Girós, M., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000013278 · Cited by 24 · see citing works
The evolution of comprehensive genetic analysis in neurology: Implications for precision medicine
Papadopoulou, E., Pepe, G., Konitsiotis, S., Chondrogiorgi, M., Grigoriadis, N., Kimiskidis, V. Κ., Tsivgoulis, G. K., Mitsikostas, D. D., Chroni, E., Domouzoglou, E. M., et al. (2023) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2023.120609 · Cited by 22 · see citing works
Genetic testing in adults with neurologic disorders: indications, approach, and clinical impacts
Dratch, L., Azage, M., Baldwin, A., Johnson, K., Paul, R. A., Bardakjian, T., Michon, S.-C., Amado, D. A., Baer, M., Deik, A., et al. (2023) · Journal of Neurology · doi:10.1007/s00415-023-12058-6 · Cited by 22 · see citing works
Bartlett, M., Nasiri, N., Pressman, R., Bademci, G., & Forghani, I. (2021) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.62073 · Cited by 20 · see citing works
Fission Impossible (?)—New Insights into Disorders of Peroxisome Dynamics
Carmichael, R. E., Islinger, M., & Schrader, M. (2022) · Cells · doi:10.3390/cells11121922 · Cited by 19 · see citing works
Genetic disorders with central nervous system white matter abnormalities: An update
Shukla, A., Kaur, P., Narayanan, D. L., do Rosario, M. C., Kadavigere, R., & Girisha, K. M. (2020) · Clinical Genetics · doi:10.1111/cge.13863 · Cited by 18 · see citing works
Kaur, P., do Rosario, M. C., Hebbar, M., Sharma, S., Kausthubham, N., Nair, K. V., Aroor, S., Ramesh Bhat, Y., Lewis, L. E. S., Nampoothiri, S., et al. (2021) · Clinical Genetics · doi:10.1111/cge.14037 · Cited by 17 · see citing works
Recent Advancements in the Diagnosis and Treatment of Leukodystrophies
Bradbury, A. M., & Ream, M. A. (2021) · Seminars in Pediatric Neurology · doi:10.1016/j.spen.2021.100876 · Cited by 17 · see citing works
Knuutinen, O. A., Oikarainen, J. H., Suo-Palosaari, M. H., Kangas, S. M., Rahikkala, E., Pokka, T. M.-L., Moilanen, J. S., Hinttala, R., Vieira, P., & Uusimaa, J. (2021) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.14884 · Cited by 14 · see citing works
Albuquerque, A. L. B., dos Santos, G. G., Sadok, S. H., Antonello, B. B., de Jesus, L. M., Carvalho, M. O., Mutarelli², A. M., & Ribeiro, P. V. Z. (2025) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.64146 · Cited by 13 · see citing works
Kors, S., Schuster, M., Maddison, D. C., Kilaru, S., Schrader, T. A., Costello, J. L., Islinger, M., Smith, G. A., & Schrader, M. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2024.119843 · Cited by 12 · see citing works
Vargas, C., Rojas, J. A., Aivasovsky-Trotta, I., Vergara-Cardenas, S., Castellanos, M., Prieto, C., & Celis, L. G. (2020) · Genes · doi:10.3390/genes11121437 · Cited by 9 · see citing works
Helman, G., Zerem, A., Almad, A., Hacker, J. L., Woidill, S., Sase, S., LeFevre, A. N., Ekstein, J., Johansson, M. M., Stutterd, C., et al. (2021) · Pediatric Neurology · doi:10.1016/j.pediatrneurol.2021.04.014 · Cited by 9 · see citing works
Self-citationBeerepoot, S., Verbeke, J. I. M. L., Plantinga, M., Nierkens, S., Pouwels, P. J. W., Wolf, N. I., Simons, C., & van der Knaap, M. S. (2024) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.63800 · Cited by 7 · see citing works
Self-citationStutterd, C., Vanderver, A. L., Lockhart, P. J., Helman, G., Pope, K., Uebergang, E., Love, C., Delatycki, M. B., Thorburn, D. R., Mackay, M. T., et al. (2022) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2022.104551 · Cited by 6 · see citing works
Self-citationPerrier, S., Guerrero, K., Tran, L. T., Michell‐Robinson, M. A., Legault, G., Brais, B., Sylvain, M., Dorman, J., Demos, M. K., Köhler, W., et al. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1148377 · Cited by 6 · see citing works
Self-citationMuirhead, K. J., Clause, A. R., Schlachetzki, Z., Dubbs, H. A., Perry, D., Hagelstrom, R. T., Taft, R. J., & Vanderver, A. L. (2021) · Molecular Case Studies · doi:10.1101/mcs.a006143 · Cited by 6 · see citing works
Self-citationTsai, C., Hsu, J. S., Chen, P., & Wu, C. (2024) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2024.2396866 · Cited by 5 · see citing works
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Gnanaolivu, R. D., Oliver, G. R., Jenkinson, G., Blake, E., Chen, W., Chia, N. V. C., Klee, E. W., & Wang, C. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06096-2 · Cited by 3 · see citing works
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Saitsu, H., & Miyamoto, S. (2025) · Brain and Development · doi:10.1016/j.braindev.2025.104378 · Cited by 2 · see citing works
Calame, D. G., Fatih, J. M., Herman, I., Coban‐Akdemir, Z., Du, H., Mitani, T., Jhangiani, S. N., Marafi, D., Gibbs, R. A., Posey, J. E., et al. (2021) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.51454 · Cited by 2 · see citing works
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Sevagamoorthy, A., Vanderver, A. L., Fraser, J. L., & Orthmann‐Murphy, J. L. (2024) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041457 · Cited by 2 · see citing works
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Twigg, S. R. F., Greene, N. D. E., Henderson, D. J., Mill, P., & Liu, K. J. (2025) · Mammalian Genome · doi:10.1007/s00335-025-10114-2 · Cited by 1 · see citing works
Shurrab, S., Cordeiro, D., Mercimek‐Andrews, S., & Shuen, A. Y. (2023) · Brain Disorders · doi:10.1016/j.dscb.2023.100069 · Cited by 1 · see citing works
Approaches to diagnosis for individuals with a suspected inherited white matter disorder
Helman, G., Orthmann‐Murphy, J. L., & Vanderver, A. L. (2024) · Handbook of clinical neurology · doi:10.1016/b978-0-323-99209-1.00009-0 · Cited by 1 · see citing works
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Bergant, G., Maver, A., & Peterlin, B. (2024) · Research Square · doi:10.21203/rs.3.rs-4465187/v1
Engelen, M., & Wolf, N. I. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-19041-4.00074-1
Self-citationSchobers, G., Schieving, J. H., Yntema, H. G., Pennings, M., Pfundt, R., Derks, R. C., Hofste, T. G. J., de Wijs, I. J., Wieskamp, N., van den Heuvel, S., et al. (2022) · Figshare · doi:10.6084/m9.figshare.20088152
- Authors
- Helman, G., Lajoie, B. R., Crawford, J., Takanohashi, A., Walkiewicz, M., Dolzhenko, E., Gross, A. M., Gainullin, V. G., Bent, S. J., Jenkinson, E. M., Ferdinandusse, S., Waterham, H. R., Dorboz, I., Bertini, E., Miyake, N., Wolf, N. I., Abbink, T. E. M., Kirwin, S. M., Tan, C. M., Hobson, G. M., Guo, L., Ikegawa, S., Pizzino, A., Schmidt, J. L., Bernard, G., Schiffmann, R., van der Knaap, M. S., Simons, C., Taft, R. J., & Vanderver, A.
- Affiliations
- Royal Children's Hospital (AU)
- Illumina (United States) (US)
- The University of Queensland (AU)
- Children's Hospital of Philadelphia (US)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- University of Manchester (GB)
- Amsterdam University Medical Centers (NL)
- Inserm (FR)
- Bambino Gesù Children's Hospital (IT)
- Yokohama City University (JP)
- Emma Kinderziekenhuis (NL)
- Alfred I. duPont Hospital for Children (US)
- RIKEN Center for Integrative Medical Sciences (JP)
- Departments of Neurology and Neurosurgery, Pediatrics, and Human Genetics McGill University Montreal Canada
- Baylor Scott & White Research Institute (US)
- Department of Child Neurology Emma Children’s Hospital Amsterdam University Medical Centers Vrije Universiteit Amsterdam and Amsterdam Neuroscience Amsterdam The Netherlands
- Corresponding author
- Simons, Cas
- Taft, Ryan J.
- Vanderver, Adeline
- ORCID
- Helman, Guy
- Lajoie, Bryan R.
- Crawford, Joanna
- Takanohashi, Asako
- Dolzhenko, Egor
- Gross, Andrew M.
- Gainullin, Vladimir G.
- Bent, Stephen J.
- Jenkinson, Emma M.
- Ferdinandusse, Sacha
- Waterham, Hans R.
- Dorboz, Imen
- Bertini, Enrico
- Miyake, Noriko
- Wolf, Nicole I.
- Abbink, Truus E. M.
- Kirwin, Susan M.
- Hobson, Grace M.
- Guo, Long
- Ikegawa, Shiro
- Pizzino, Amy
- Schmidt, Johanna L.
- Schiffmann, Raphael
- Simons, Cas
- Taft, Ryan J.
- Vanderver, Adeline
- Venue
- Annals of Clinical and Translational Neurology
- Publisher
- Wiley
- Published
- 2020-01
- Volume
- 7
- Issue
- 1
- Pages
- 144-152
- ISSN
- 2328-9503 (ISSN-L)
- PMC
- PMC6952322
- OpenAlex
- W3000498499
- Semantic Scholar
- 6d54028268d4835ae67037510bbdf2956b56410d
- Cited by
- 39
- References
- 24
- Citations by year
- 2025: 4 · 2024: 8 · 2023: 7 · 2022: 7 · 2021: 9 · 2020: 4
- FWCI
- 3.53
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-12-05)
- Open access
- gold
- doi.org
- Licence
- Locations
- Annals of Clinical and Translational Neurology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- VU Research Portal (submittedVersion, other-oa)
- Funders
- Canadian Institutes of Health Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Hereditary Neurological Disorders
- Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
- Keywords
- white matter disease, genetic diagnosis, genome sequencing, diagnostic yield, copy number variation, deep intronic variants, variant curation, variant annotation
- MeSH terms
- Whole Genome Sequencing, Adolescent, Child, Child, Preschool, Female, Humans, Male, Pedigree, Registries, Leukoencephalopathies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-20.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-20.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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