scholarly journals NOTCH3 variants are common in the general population and associated with stroke and vascular dementia: an analysis of 200,000 participants

2020 ◽  
Author(s):  
Bernard PH Cho ◽  
Stefania Nannoni ◽  
Eric L Harshfield ◽  
Daniel J Tozer ◽  
Stefan Gräf ◽  
...  
Keyword(s):  
White Matter ◽  
General Population ◽  
Vascular Dementia ◽  
Exome Sequencing ◽  
Visual Analysis ◽  
White Matter Hyperintensity ◽  
Sequencing Data ◽  
Exome Sequencing Data ◽  
Increased Risk ◽  
Mri Changes

ABSTRACTBackgroundCysteine-altering NOTCH3 variants identical to those causing the rare monogenic form of stroke, CADASIL, have been reported more common than expected in the general population, but their clinical significance and contribution to stroke and dementia risk in the community remains unclear.MethodsCysteine-altering NOTCH3 variants were identified in UK Biobank whole-exome sequencing data (N=200,632). Frequency of stroke, dementia and other clinical features of CADASIL, and MRI white matter hyperintensity volume were compared between variant carriers and non-carriers. MRIs from those with variants were visually rated, each matched with three controls.ResultsOf 200,632 participants with exome sequencing data available, 443 (∼1 in 450) carried 67 different cysteine-altering NOTCH3 variants. After adjusting for age, sex, and ancestry principal components, NOTCH3 variant carriers had increased risk of stroke (OR: 2.33, p=0.0003), and vascular dementia (OR: 5.03, p=0.007), and increased WMH volume (standardised difference: 0.52, p<0.001), and white matter ultrastructural damage on DTI-PSMD (standardised difference: 0.71, p<0.001). On visual analysis of MRIs from 47 carriers and 148 matched controls, variants were associated with presence of lacunes (OR: 4.83, p<0.001) and cerebral microbleeds (OR: 3.61, p<0.001). WMH prevalence was most increased in the anterior temporal lobes (OR: 6.92, p<0.001) and external capsule (OR: 12.44, p<0.001).ConclusionsCysteine-changing NOTCH3 variants are common in the general population and are risk factors for apparently “sporadic” stroke and vascular dementia. They are associated with MRI changes of SVD, in a distribution similar to that seen in CADASIL.

scholarly journals NOTCH3 variants are more common than expected in the general population and associated with stroke and vascular dementia: an analysis of 200 000 participants

2021 ◽  
pp. jnnp-2020-325838
Author(s):  
Bernard P H Cho ◽  
Stefania Nannoni ◽  
Eric L Harshfield ◽  
Daniel Tozer ◽  
Stefan Gräf ◽  
...  
Keyword(s):  
White Matter ◽  
General Population ◽  
Vascular Dementia ◽  
Exome Sequencing ◽  
Visual Analysis ◽  
Small Vessel Disease ◽  
White Matter Hyperintensity ◽  
Sequencing Data ◽  
Exome Sequencing Data ◽  
Increased Risk

BackgroundCysteine-altering NOTCH3 variants identical to those causing the rare monogenic form of stroke, CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy), have been reported more common than expected in the general population, but their clinical significance and contribution to stroke and dementia risk in the community remain unclear.MethodsCysteine-altering NOTCH3 variants were identified in UK Biobank whole-exome sequencing data (N=200 632). Frequency of stroke, vascular dementia and other clinical features of CADASIL, and MRI white matter hyperintensity volume were compared between variant carriers and non-carriers. MRIs from those with variants were visually rated, each matched with three controls.ResultsOf 200 632 participants with exome sequencing data available, 443 (~1 in 450) carried 67 different cysteine-altering NOTCH3 variants. After adjustment for various covariates, NOTCH3 variant carriers had increased risk of stroke (OR: 2.33, p=0.0004) and vascular dementia (OR: 5.00, p=0.007), and increased white matter hyperintensity volume (standardised difference: 0.52, p<0.001) and white matter ultrastructural damage on diffusion MRI (standardised difference: 0.72, p<0.001). On visual analysis of MRIs from 47 carriers and 148 matched controls, variants were associated with presence of lacunes (OR: 5.97, p<0.001) and cerebral microbleeds (OR: 4.38, p<0.001). White matter hyperintensity prevalence was most increased in the anterior temporal lobes (OR: 7.65, p<0.001) and external capsule (OR: 13.32, p<0.001).ConclusionsCysteine-changing NOTCH3 variants are more common in the general population than expected from CADASIL prevalence and are risk factors for apparently ‘sporadic’ stroke and vascular dementia. They are associated with MRI changes of small vessel disease, in a distribution similar to that seen in CADASIL.

scholarly journals Variable Phenotypes of Epilepsy, Intellectual Disability, and Schizophrenia Caused by 12p13.33–p13.32 Terminal Microdeletion in a Korean Family: A Case Report and Literature Review

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1001
Author(s):  
Jiyoon Han ◽  
Joonhong Park
Keyword(s):  
Intellectual Disability ◽  
Exome Sequencing ◽  
Environmental Influence ◽  
Copy Number Variations ◽  
Genetic Modifiers ◽  
Sequencing Data ◽  
Exome Sequencing Data ◽  
Korean Family ◽  
Coverage Analysis ◽  
Patient Will

A simultaneous analysis of nucleotide changes and copy number variations (CNVs) based on exome sequencing data was demonstrated as a potential new first-tier diagnosis strategy for rare neuropsychiatric disorders. In this report, using depth-of-coverage analysis from exome sequencing data, we described variable phenotypes of epilepsy, intellectual disability (ID), and schizophrenia caused by 12p13.33–p13.32 terminal microdeletion in a Korean family. We hypothesized that CACNA1C and KDM5A genes of the six candidate genes located in this region were the best candidates for explaining epilepsy, ID, and schizophrenia and may be responsible for clinical features reported in cases with monosomy of the 12p13.33 subtelomeric region. On the background of microdeletion syndrome, which was described in clinical cases with mild, moderate, and severe neurodevelopmental manifestations as well as impairments, the clinician may determine whether the patient will end up with a more severe or milder end‐phenotype, which in turn determines disease prognosis. In our case, the 12p13.33–p13.32 terminal microdeletion may explain the variable expressivity in the same family. However, further comprehensive studies with larger cohorts focusing on careful phenotyping across the lifespan are required to clearly elucidate the possible contribution of genetic modifiers and the environmental influence on the expressivity of 12p13.33 microdeletion and associated characteristics.

FC 011KIDNEYNETWORK: USING KIDNEY DERIVED GENE EXPRESSION DATA TO PREDICT AND PRIORITIZE NOVEL GENES INVOLVED IN KIDNEY DISEASE

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Floranne Boulogne ◽  
Laura Claus ◽  
Henry Wiersma ◽  
Roy Oelen ◽  
Floor Schukking ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Candidate Gene ◽  
Exome Sequencing ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Genetic Diagnosis ◽  
Specific Gene ◽  
Sequencing Data ◽  
Exome Sequencing Data

Abstract Background and Aims Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes that are not known to be involved in kidney disease, which makes it difficult to prioritize and interpret the relevance of these variants. As such, there is a clear need for methods that predict the phenotypic consequences of gene expression in a way that is as unbiased as possible. To help identify candidate genes we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions. Method We combined gene co-expression in 878 publicly available kidney RNA-sequencing samples with the co-expression of a multi-tissue RNA-sequencing dataset of 31,499 samples to build KidneyNetwork. The expression patterns were used to predict which genes have a kidney-related function, and which (disease) phenotypes might be caused when these genes are mutated. By integrating the information from the HPO database, in which known phenotypic consequences of disease genes are annotated, with the gene co-expression network we obtained prediction scores for each gene per HPO term. As proof of principle, we applied KidneyNetwork to prioritize variants in exome-sequencing data from 13 kidney disease patients without a genetic diagnosis. Results We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, a multi-tissue co-expression network we previously developed. In KidneyNetwork, we observe a significantly improved prediction accuracy of kidney-related HPO-terms, as well as an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). To examine its clinical utility, we applied KidneyNetwork to 13 patients with a suspected hereditary kidney disease without a genetic diagnosis. Based on the HPO terms “Renal cyst” and “Hepatic cysts”, combined with a list of potentially damaging variants in one of the undiagnosed patients with mild ADPKD/PCLD, we identified ALG6 as a new candidate gene. ALG6 bears a high resemblance to other genes implicated in this phenotype in recent years. Through the 100,000 Genomes Project and collaborators we identified three additional patients with kidney and/or liver cysts carrying a suspected deleterious variant in ALG6. Conclusion We present KidneyNetwork, a kidney specific co-expression network that accurately predicts what genes have kidney-specific functions and may result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted by KidneyNetwork. We show the added value of KidneyNetwork by applying it to exome sequencing data of kidney disease patients without a molecular diagnosis and consequently we propose ALG6 as a promising candidate gene. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes to better understand kidney physiology and pathophysiology. Acknowledgments This research was made possible through access to the data and findings generated by the 100,000 Genomes Project; http://www.genomicsengland.co.uk.

scholarly journals EthSEQ: ethnicity annotation from whole exome sequencing data

2017 ◽  
Vol 33 (15) ◽  
pp. 2402-2404 ◽  
Author(s):  
Alessandro Romanel ◽  
Tuo Zhang ◽  
Olivier Elemento ◽  
Francesca Demichelis
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Sequencing Data ◽  
Exome Sequencing Data ◽  
Whole Exome ◽  
Whole Exome Sequencing Data

scholarly journals Finding the lost treasures in exome sequencing data

2013 ◽  
Vol 29 (10) ◽  
pp. 593-599 ◽  
Author(s):  
David C. Samuels ◽  
Leng Han ◽  
Jiang Li ◽  
Sheng Quanghu ◽  
Travis A. Clark ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Sequencing Data ◽  
Exome Sequencing Data

scholarly journals White matter hyperintensities are common in midlife and already associated with cognitive decline

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Tracy d’Arbeloff ◽  
Maxwell L Elliott ◽  
Annchen R Knodt ◽  
Tracy R Melzer ◽  
Ross Keenan ◽  
...  
Keyword(s):  
White Matter ◽  
Cognitive Decline ◽  
White Matter Hyperintensities ◽  
Clinical Symptoms ◽  
White Matter Hyperintensity ◽  
Dementia Prevention ◽  
Increased Risk ◽  
Weighted Magnetic Resonance Imaging ◽  
Intervention Trials ◽  
The Brain

Abstract White matter hyperintensities proliferate as the brain ages and are associated with increased risk for cognitive decline as well as Alzheimer’s disease and related dementias. As such, white matter hyperintensities have been targeted as a surrogate biomarker in intervention trials with older adults. However, it is unclear at what stage of aging white matter hyperintensities begin to relate to cognition and if they may be a viable target for early prevention. In the Dunedin Study, a population-representative cohort followed since birth, we measured white matter hyperintensities in 843 45-year-old participants using T2-weighted magnetic resonance imaging and we assessed cognitive decline from childhood to midlife. We found that white matter hyperintensities were common at age 45 and that white matter hyperintensity volume was modestly associated with both lower childhood (ß = −0.08, P = 0.013) and adult IQ (ß=−0.15, P &lt; 0.001). Moreover, white matter hyperintensity volume was associated with greater cognitive decline from childhood to midlife (ß=−0.09, P &lt; 0.001). Our results demonstrate that a link between white matter hyperintensities and early signs of cognitive decline is detectable decades before clinical symptoms of dementia emerge. Thus, white matter hyperintensities may be a useful surrogate biomarker for identifying individuals in midlife at risk for future accelerated cognitive decline and selecting participants for dementia prevention trials.

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