ORCID Profile
0000-0001-5879-3486
Current Organisations
UCL Institute of Neurology, University College London
,
Van Andel Research Institute
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Publisher: Springer Science and Business Media LLC
Date: 11-12-2013
DOI: 10.1038/NATURE12825
Publisher: Springer Science and Business Media LLC
Date: 17-03-2015
DOI: 10.1038/MP.2015.23
Publisher: Springer Science and Business Media LLC
Date: 29-01-2020
DOI: 10.1186/S40478-020-0879-Z
Abstract: Dementia with Lewy bodies (DLB) is a clinically heterogeneous disorder with a substantial burden on healthcare. Despite this, the genetic basis of the disorder is not well defined and its boundaries with other neurodegenerative diseases are unclear. Here, we performed whole exome sequencing of a cohort of 1118 Caucasian DLB patients, and focused on genes causative of monogenic neurodegenerative diseases. We analyzed variants in 60 genes implicated in DLB, Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia, and atypical parkinsonian or dementia disorders, in order to determine their frequency in DLB. We focused on variants that have previously been reported as pathogenic, and also describe variants reported as pathogenic which remain of unknown clinical significance, as well as variants associated with strong risk. Rare missense variants of unknown significance were found in APP, CHCHD2, DCTN1, GRN, MAPT, NOTCH3, SQSTM1, TBK1 and TIA1 . Additionally, we identified a pathogenic GRN p.Arg493* mutation, potentially adding to the ersity of phenotypes associated with this mutation. The rarity of previously reported pathogenic mutations in this cohort suggests that the genetic overlap of other neurodegenerative diseases with DLB is not substantial. Since it is now clear that genetics plays a role in DLB, these data suggest that other genetic loci play a role in this disease.
Publisher: Public Library of Science (PLoS)
Date: 16-06-2015
Publisher: Frontiers Media SA
Date: 30-01-2018
Publisher: Elsevier BV
Date: 02-2016
Publisher: Elsevier BV
Date: 07-2019
Publisher: Cold Spring Harbor Laboratory
Date: 26-10-2018
DOI: 10.1101/454249
Abstract: Recent large-scale genetic studies have allowed for the first glimpse of the effects of common genetic variability in dementia with Lewy bodies (DLB), identifying risk variants with appreciable effect sizes. However, it is currently well established that a substantial portion of the genetic heritable component of complex traits is not captured by genome-wide significant SNPs. To overcome this issue, we have estimated the proportion of phenotypic variance explained by genetic variability (SNP heritability) in DLB using a method that is unbiased by allele frequency or linkage disequilibrium properties of the underlying variants. This shows that the heritability of DLB is nearly twice as high as previous estimates based on common variants only (31% vs 59.9%). We also determine the amount of phenotypic variance in DLB that can be explained by recent polygenic risk scores from either Parkinson’s disease (PD) or Alzheimer’s disease (AD), and show that, despite being highly significant, they explain a low amount of variance. Additionally, to identify pleiotropic events that might improve our understanding of the disease, we performed genetic correlation analyses of DLB with over 200 diseases and biomedically relevant traits. Our data shows that DLB has a positive correlation with education phenotypes, which is opposite to what occurs in AD. Overall, our data suggests that novel genetic risk factors for DLB should be identified by larger GWAS and these are likely to be independent from known AD and PD risk variants.
Publisher: Elsevier BV
Date: 05-2021
DOI: 10.1016/J.NEUROBIOLAGING.2020.11.009
Abstract: Synapse loss is an early event in late-onset Alzheimer's disease (LOAD). In this study, we have assessed the capacity of a polygenic risk score (PRS) restricted to synapse-encoding loci to predict LOAD. We used summary statistics from the International Genetics of Alzheimer's Project genome-wide association meta-analysis of 74,046 patients for model construction and tested the "synaptic PRS" in 2 independent data sets of controls and pathologically confirmed LOAD. The mean synaptic PRS was 2.3-fold higher in LOAD than that in controls (p < 0.0001) with a predictive accuracy of 72% in the target data set (n = 439) and 73% in the validation data set (n = 136), a 5%-6% improvement compared with the APOE locus (p < 0.00001). The model comprises 8 variants from 4 previously identified (BIN1, PTK2B, PICALM, APOE) and 2 novel (DLG2, MINK1) LOAD loci involved in glutamate signaling (p = 0.01) or APP catabolism or tau binding (p = 0.005). As the simplest PRS model with good predictive accuracy to predict LOAD, we conclude that synapse-encoding genes are enriched for LOAD risk-modifying loci. The synaptic PRS could be used to identify in iduals at risk of LOAD before symptom onset.
Publisher: Elsevier BV
Date: 05-2015
Publisher: Springer Science and Business Media LLC
Date: 17-04-2019
DOI: 10.1038/S41531-019-0076-6
Abstract: Parkinson’s disease (PD), with its characteristic loss of nigrostriatal dopaminergic neurons and deposition of α-synuclein in neurons, is often considered a neuronal disorder. However, in recent years substantial evidence has emerged to implicate glial cell types, such as astrocytes and microglia. In this study, we used stratified LD score regression and expression-weighted cell-type enrichment together with several brain-related and cell-type-specific genomic annotations to connect human genomic PD findings to specific brain cell types. We found that PD heritability attributable to common variation does not enrich in global and regional brain annotations or brain-related cell-type-specific annotations. Likewise, we found no enrichment of PD susceptibility genes in brain-related cell types. In contrast, we demonstrated a significant enrichment of PD heritability in a curated lysosomal gene set highly expressed in astrocytic, microglial, and oligodendrocyte subtypes, and in LoF-intolerant genes, which were found highly expressed in almost all tested cellular subtypes. Our results suggest that PD risk loci do not lie in specific cell types or in idual brain regions, but rather in global cellular processes detectable across several cell types.
Publisher: Wiley
Date: 21-11-2017
DOI: 10.1002/ANA.25084
Abstract: Differential diagnosis of autosomal recessive cerebellar ataxias can be challenging. A ranking algorithm named RADIAL that predicts the molecular diagnosis based on the clinical phenotype of a patient has been developed to guide genetic testing and to align genetic findings with the clinical context. An algorithm that follows clinical practice, including patient history, clinical, magnetic resonance imaging, electromyography, and biomarker features, was developed following a review of the literature on 67 autosomal recessive cerebellar ataxias and personal clinical experience. Frequency and specificity of each feature were defined for each autosomal recessive cerebellar ataxia, and corresponding prediction scores were assigned. Clinical and paraclinical features of patients are entered into the algorithm, and a patient's total score for each autosomal recessive cerebellar ataxia is calculated, producing a ranking of possible diagnoses. Sensitivity and specificity of the algorithm were assessed by blinded analysis of a multinational cohort of 834 patients with molecularly confirmed autosomal recessive cerebellar ataxia. The performance of the algorithm was assessed versus a blinded panel of autosomal recessive cerebellar ataxia experts. The correct diagnosis was ranked within the top 3 highest-scoring diagnoses at a sensitivity and specificity of >90% for 84% and 91% of the evaluated genes, respectively. Mean sensitivity and specificity of the top 3 highest-scoring diagnoses were 92% and 95%, respectively. The algorithm outperformed the panel of ataxia experts (p = 0.001). Our algorithm is highly sensitive and specific, accurately predicting the underlying molecular diagnoses of autosomal recessive cerebellar ataxias, thereby guiding targeted sequencing or facilitating interpretation of next-generation sequencing data. Ann Neurol 2017 :892-899.
Publisher: Wiley
Date: 23-08-2014
Publisher: Springer Science and Business Media LLC
Date: 03-07-2202
DOI: 10.1038/NG.803
Publisher: Elsevier BV
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 18-10-2011
DOI: 10.1038/MP.2011.125
Publisher: Springer Science and Business Media LLC
Date: 17-07-2017
DOI: 10.1038/NG.3916
Publisher: Massachusetts Medical Society
Date: 10-01-2013
Publisher: Elsevier BV
Date: 03-2019
Publisher: Springer Science and Business Media LLC
Date: 06-09-2009
DOI: 10.1038/NG.440
Publisher: Oxford University Press (OUP)
Date: 27-06-2014
DOI: 10.1093/HMG/DDU334
Publisher: Springer Science and Business Media LLC
Date: 09-2014
DOI: 10.1038/NG.3043
Publisher: Oxford University Press (OUP)
Date: 11-11-2012
DOI: 10.1093/HMG/DDS476
Publisher: Public Library of Science (PLoS)
Date: 15-11-2010
Publisher: American Association for the Advancement of Science (AAAS)
Date: 22-06-2018
Abstract: Consistent classification of neuropsychiatric diseases is problematic because it can lead to misunderstanding of etiology. The Brainstorm Consortium examined multiple genome-wide association studies drawn from more than 200,000 patients for 25 brain-associated disorders and 17 phenotypes. Broadly, it appears that psychiatric and neurologic disorders share relatively little common genetic risk. However, different and independent pathways can result in similar clinical manifestations (e.g., psychosis, which occurs in both schizophrenia and Alzheimer's disease). Schizophrenia correlated with many psychiatric disorders, whereas the immunopathological affliction Crohn's disease did not, and posttraumatic stress syndrome was also largely independent of underlying traits. Essentially, the earlier the onset of a disorder, the more inheritable it appeared to be. Science , this issue p. eaap8757
Publisher: Public Library of Science (PLoS)
Date: 11-02-2011
DOI: 10.1371/ANNOTATION/A0BB886D-D345-4A20-A82E-ADCE9B047798
Publisher: Springer Science and Business Media LLC
Date: 07-10-2014
DOI: 10.1038/MP.2014.107
Publisher: BMJ
Date: 29-11-2019
DOI: 10.1136/JMEDGENET-2019-106283
Abstract: Classical randomisation of clinical trial patients creates a source of genetic variance that may be contributing to the high failure rate seen in neurodegenerative disease trials. Our objective was to quantify genetic difference between randomised trial arms and determine how imbalance can affect trial outcomes. 5851 patients with Parkinson’s disease of European ancestry data and two simulated virtual cohorts based on public data were used. Data were res led at different sizes for 1000 iterations and randomly assigned to the two arms of a simulated trial. False-negative and false-positive rates were estimated using simulated clinical trials, and per cent difference in genetic risk score (GRS) and allele frequency was calculated to quantify variance between arms. 5851 patients with Parkinson’s disease (mean (SD) age, 61.02 (12.61) years 2095 women (35.81%)) as well as simulated patients from virtually created cohorts were used in the study. Approximately 90% of the iterations had at least one statistically significant difference in in idual risk SNPs between each trial arm. Approximately 5%–6% of iterations had a statistically significant difference between trial arms in mean GRS. For significant iterations, the average per cent difference for mean GRS between trial arms was 130.87%, 95% CI 120.89 to 140.85 (n=200). Glucocerebrocidase (GBA) gene-only simulations see an average 18.86%, 95% CI 18.01 to 19.71 difference in GRS scores between trial arms (n=50). When adding a drug effect of −0.5 points in MDS-UPDRS per year at n=50, 33.9% of trials resulted in false negatives. Our data support the hypothesis that within genetically unmatched clinical trials, genetic heterogeneity could confound true therapeutic effects as expected. Clinical trials should undergo pretrial genetic adjustment or, at the minimum, post-trial adjustment and analysis for failed trials.
Location: No location found
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Rita Guerreiro.