Biology subjects

Botia, J. A.

Publications and source records attributed to Botia, J. A..

30 records · Page 2Linked to original sources

bioRxiv2020

Large-scale pathway-specific polygenic risk, transcriptomic community networks and functional inferences in Parkinson disease

Polygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological pathways underlying PD using the largest currently available cohorts of genetic data and gene expression data from International Parkinsons Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership - Parkinsons disease initiative (AMP-PD), among other sources. We placed these insights into a cellular context. We applied large-scale pathway-specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk in a cohort of 457,110 individuals by focusing on a compilation of 2,199 publicly annotated gene sets representative of curated pathways, of which we nominate 46 pathways associated with PD risk. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data, including 4,331 individuals. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for adult dopaminergic neurons, serotonergic neurons, and radial glia. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of 1,612 PD patients, which revealed 54 connecting networks associated with PD. Our analyses highlight several promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.

genetics↗

bioRxiv2020

Human-lineage-specific genomic elements: relevance to neurodegenerative disease and APOE transcript usage

Knowledge of genomic features specific to the human lineage may provide insights into brain-related diseases. We leverage high-depth whole genome sequencing data to generate a combined annotation identifying regions simultaneously depleted for genetic variation (constrained regions) and poorly conserved across primates. We propose that these constrained, non-conserved regions (CNCRs) have been subject to human-specific purifying selection and are enriched for brain-specific elements. We find that CNCRs are depleted from protein-coding genes but enriched within lncRNAs. We demonstrate that per-SNP heritability of a range of brain-relevant phenotypes are enriched within CNCRs. We find that genes implicated in neurological diseases have high CNCR density, including APOE, highlighting an unannotated intron-3 retention event. Using human brain RNA-sequencing data, we show the intron-3-retaining transcript/s to be more abundant in Alzheimers disease with more severe tau and amyloid pathological burden. Thus, we demonstrate the importance of human-lineage-specific sequences in brain development and neurological disease. We release our annotation through vizER (https://snca.atica.um.es/browser/app/vizER).

neuroscience↗

bioRxiv2020

Transcriptomic analysis of dystonia-associated genes reveals functional convergence within specific cell types and shared neurobiology with psychiatric disorders

Dystonia is a neurological disorder characterized by sustained or intermittent muscle contractions causing abnormal movements and postures, often occurring in absence of any structural brain abnormality. Psychiatric comorbidities, including anxiety, depression, obsessive-compulsive disorder and schizophrenia, are frequent in dystonia patients. While mutations in a fast-growing number of genes have been linked to Mendelian forms of dystonia, the cellular, anatomical, and molecular basis remains unknown for most genetic forms of dystonia, as does its genetic and biological relationship to neuropsychiatric disorders. Here we applied an unbiased systems-biology approach to explore the cellular specificity of all currently known dystonia-associated genes, predict their functional relationships, and test whether dystonia and neuropsychiatric disorders share a genetic relationship. To determine the cellular specificity of dystonia-associated genes in the brain, single-nuclear transcriptomic data derived from mouse brain was used together with expression-weighted cell-type enrichment. To identify functional relationships amongst dystonia-associated genes, we determined the enrichment of these genes in co-expression networks constructed from ten human brain regions. Stratified linkage-disequilibrium score regression was used to test whether co-expression modules enriched for dystonia-associated genes significantly contribute to the heritability of anxiety, major depressive disorder, obsessive-compulsive disorder, schizophrenia, and Parkinsons disease. Dystonia-associated genes were significantly enriched in adult nigral dopaminergic neurons and striatal medium spiny neurons. Furthermore, four of the 220 gene co-expression modules tested were significantly enriched for the dystonia-associated genes. The identified modules were derived from the substantia nigra, putamen, frontal cortex, and white matter, and were all significantly enriched for genes associated with synaptic function. Finally, we demonstrated significant enrichments of the heritability of depression, obsessive-compulsive disorder and schizophrenia, but not anxiety and Parkinsons disease, within the putamen and white matter modules. In conclusion, multiple dystonia-associated genes interact and contribute to pathogenesis likely through dysregulation of synaptic signalling in striatal medium spiny neurons, adult nigral dopaminergic neurons and frontal cortical neurons. Furthermore, the enrichment of the heritability of psychiatric disorders in the co-expression modules enriched for dystonia-associated genes indicates that psychiatric symptoms associated with dystonia are likely to be intrinsic to its pathophysiology.

genetics↗

bioRxiv2019

An integrated genomic approach to dissect the genetic landscape regulating the cell-to-cell transfer of a-synuclein

Neuropathological and experimental evidence suggests that the cell-to-cell transfer of a-synuclein has an important role in the pathogenesis of Parkinsons disease (PD). However, the mechanism underlying this phenomenon is not fully understood. We undertook an siRNA, genome-wide high throughput screen to identify genes regulating the cell-to-cell transfer of a-synuclein. We transiently transfected HEK cells stably overexpressing a-synuclein with a construct encoding GFP-2a-aSynuclein-RFP. The cells expressing a-synuclein-RFP through transfection were double positive for GFP and RFP fluorescence, whereas the cells receiving it through transfer were positive only for RFP fluorescence. The amount of a-synuclein transfer was quantified by high content microscopy. A series of unbiased screens confirmed the involvement of 38 genes in the regulation of a-synuclein-RFP transfer. One of those hits was ITGA8, a candidate gene recently identified through a large PD genome wide association study (GWAS). Weighted gene co-expression network analysis (WGCNA) and weighted protein-protein network interaction analysis (WPPNIA) showed that the hits clustered in networks that included known PD Mendelian and GWAS risk genes more frequently than expected than random chance. Given the genetic overlap between a-synuclein transfer and PD, those findings provide supporting evidence for the importance of the cell-to-cell transfer of a-synuclein in the pathogenesis of PD, and expand our understanding of the mechanism of a-synuclein spread.

neuroscience↗

bioRxiv2019

Integration of eQTL and Parkinson’s disease GWAS data implicates 11 disease genes

Substantial genome-wide association study (GWAS) work in Parkinsons disease (PD) has led to an increasing number of loci shown reliably and robustly to be associated with the increased risk of the disease. Prioritising causative genes and pathways from these studies has proven problematic. Here, we present a comprehensive analysis of PD GWAS data with expression and methylation quantitative trait loci (eQTL/mQTL) using Colocalisation analysis (Coloc) and transcriptome-wide association analysis (TWAS) to uncover putative gene expression and splicing mechanisms driving PD GWAS signals. Candidate genes were further characterised by determining cell-type specificity, weighted gene co-expression (WGNCA) and protein-protein interaction (PPI) networks.\n\nGene-level analysis of expression revealed 5 genes (WDR6, CD38, GPNMB, RAB29, TMEM163) that replicated using both Coloc and TWAS analyses in both GTEx and Braineac expression datasets. A further 6 genes (ZRANB3, PCGF3, NEK1, NUPL2, GALC, CTSB) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell-types compared to neurons. The WGNCA analysis showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes related with protein ubiquitination (protein ubiquitination (p=7.47e-10) and ubiquitin-dependent protein catabolic process (p = 2.57e-17) in nucleus accumbens, caudate and putamen, while TMEM163 and ZRANB3 were both important in modules indicating regulation of signalling (p=1.33e-65] and cell communication (p=7.55e-35) in the frontal cortex and caudate respectively. PPI analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known Mendelian PD and parkinsonism proteins than would be expected by chance. The proteins core proteins this network were enriched for regulation of the ERBB receptor tyrosine protein kinase signalling pathways.\n\nTogether, these results point to a number of candidate genes and pathways that are driving the associations observed in PD GWAS studies.

neuroscience↗

bioRxiv2019

ERASE: Extended Randomization for assessment of annotation enrichment in ASE datasets

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with various human phenotypes and many of these loci are thought to act at a molecular level by regulating gene expression. Detection of allele specific expression (ASE), namely preferential usage of an allele at a transcribed locus, is an increasingly important means of studying the genetic regulation of gene expression. However, there are currently a paucity of tools available to link ASE sites with GWAS risk loci. Existing integration methods first use ASE sites to infer cis-acting expression quantitative trait loci (eQTL) and then apply eQTL-based approaches. ERASE is a method that assesses the enrichment of risk loci amongst ASE sites directly. Furthermore, ERASE enables additional biological insights to be made through the addition of other SNP level annotations. ERASE is based on a randomization approach and controls for read depth, a significant confounder in ASE analyses. In this paper, we demonstrate that ERASE can efficiently detect the enrichment of eQTLs and risk loci within ASE data and that it remains sensitive even when used with underpowered GWAS datasets. Finally, using ERASE in combination with GWAS data for Parkinsons disease and data on the splicing potential of individual SNPs, we provide evidence to suggest that risk loci for Parkinsons disease are enriched amongst ASEs likely to affect splicing. Thus, we show that ERASE is an important new tool for the integration of ASE and GWAS data, capable of providing novel insights into the pathophysiology of complex diseases.

bioinformatics↗

bioRxiv2019

Regulatory sites for known and novel splicing in human basal ganglia are enriched for disease-relevant information

Genome-wide association studies have generated an increasing number of common genetic variants that affect neurological and psychiatric disease risk. Given that many causal variants are likely to operate by regulating gene expression, an improved understanding of the genetic control of gene expression in human brain is vital. However, the difficulties of sampling human brain, and its complexity, has meant that brain-related expression quantitative trait loci (eQTL) and allele specific expression (ASE) signals have been more limited in their explanatory power than might otherwise be expected. To address this, we use paired genomic and transcriptomic data from putamen and substantia nigra dissected from 117 brains, combined with a comprehensive set of analyses, to interrogate regulation at different stages of RNA processing and uncover novel transcripts. We identify disease-relevant regulatory loci and reveal the types of analyses and regulatory positions yielding the most disease-specific information. We find that splicing eQTLs are enriched for neuron-specific regulatory information; that ASE analyses provide highly cell-specific regulatory information; and that incomplete annotation of the brain transcriptome limits the interpretation of risk loci for neuropsychiatric disease. We release this rich resource of regulatory data through a searchable webserver, http://braineacv2.inf.um.es/.

bioinformatics↗

bioRxiv2018

Genetic variation within genes associated with mitochondrial function is significantly associated with later age at onset of Parkinson disease and contributes to disease risk

Mitochondrial dysfunction has been implicated in the aetiology of monogenic Parkinsons disease (PD). Yet the role that mitochondrial processes play in the most common form of the disease; sporadic PD, is yet to be fully established. Here we comprehensively assessed the role of mitochondrial function associated genes in sporadic PD by leveraging improvements in the scale and analysis of PD GWAS data with recent advances in our understanding of the genetics of mitochondrial disease. First, we identified that a proportion of the \"missing heritability\" of the PD can be explained by common variation within genes implicated in mitochondrial disease (primary gene list) and mitochondrial function (secondary gene list). Next we calculated a mitochondrial-specific polygenic risk score (PRS) and showed that cumulative small effect variants within both our primary and secondary gene lists are significantly associated with increased PD risk. Most significantly we further report that the PRS of the secondary mitochondrial gene list was significantly associated with later age at onset. Finally, to identify possible functional genomic associations we implemented Mendelian randomisation, which showed that 14 of these mitochondrial function associated genes showed functional consequence associated with PD risk. Further analysis suggested that the 14 identified genes are not only involved in mitophagy but implicate new mitochondrial processes. Our data suggests that therapeutics targeting mitochondrial bioenergetics and proteostasis pathways distinct from mitophagy could be beneficial to treating the early stage of PD.

genetics↗

bioRxiv2018

A systems-level analysis highlights microglial activation as a modifying factor in common forms of human epilepsy

The common human epilepsies are associated with distinct patterns of reduced cortical thickness, detectable on neuroimaging, with important clinical consequences. To explore underlying mechanisms, we layered MRI-based cortical structural maps from a large-scale epilepsy neuroimaging study onto highly spatially-resolved human brain gene expression data, identifying >2,500 genes overexpressed in regions of reduced cortical thickness, compared to relatively-protected regions. The resulting set of differentially-expressed genes shows enrichment for microglial markers, and in particular, activated microglial states. Parallel analyses of cell-specific eQTLs show enrichment in human genetic signatures of epilepsy severity, but not epilepsy causation. Post mortem brain tissue from humans with epilepsy shows excess activated microglia. In an experimental model, depletion of activated microglia prevents cortical thinning, but not the development of chronic seizures. These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.

neuroscience↗

bioRxiv2018

Moving beyond neurons: the role of cell type-specific gene regulation in Parkinson’s disease heritability

Parkinsons disease (PD), with its characteristic loss of nigrostriatal dopaminergic neurons and deposition of -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 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 specifically expressed in astrocytic and microglial subtypes. Our results suggest that PD risk loci do not lie in specific cell types or individual brain regions, but rather in global cellular processes to which cell types may have varying vulnerability.

genomics↗

bioRxiv2018

Parkinson’s disease genetics: identifying novel risk loci, providing causal insights and improving estimates of heritable risk

We performed the largest genome-wide association study of PD to date, involving the analysis of 7.8M SNPs in 37.7K cases, 18.6K UK Biobank proxy-cases, and 1.4M controls. We identified 90 independent genome-wide significant signals across 78 loci, including 38 independent risk signals in 37 novel loci. These variants explained 26-36% of the heritable risk of PD. Tests of causality within a Mendelian randomization framework identified putatively causal genes for 70 risk signals. Tissue expression enrichment analysis suggested that signatures of PD loci were heavily brain-enriched, consistent with specific neuronal cell types being implicated from single cell expression data. We found significant genetic correlations with brain volumes, smoking status, and educational attainment. In sum, these data provide the most comprehensive understanding of the genetic architecture of PD to date by revealing many additional PD risk loci, providing a biological context for these risk factors, and demonstrating that a considerable genetic component of this disease remains unidentified.

genetics↗

bioRxiv2018

G2P: Using machine learning to understand and predict genes causing rare neurological disorders

To facilitate precision medicine and neuroscience research, we developed a machine-learning technique that scores the likelihood that a gene, when mutated, will cause a neurological phenotype. We analysed 1126 genes relating to 25 subtypes of Mendelian neurological disease defined by Genomics England (March 2017) together with 154 gene-specific features capturing genetic variation, gene structure and tissue-specific expression and co-expression. We randomly re-sampled genes with no known disease association to develop bootstrapped decision-tree models, which were integrated to generate a decision tree-based ensemble for each disease subtype. Genes generating larger numbers of distinct transcripts and with higher probability of having missense mutations in normal individuals were significantly more likely to cause neurological diseases. Using mouse-mutant phenotypic data we tested the accuracy of gene-phenotype predictions and found that for 88% of all disease subtypes there was a significant enrichment of relevant phenotypic abnormalities when predicted genes were mutated in mice and in many cases mutations produced specific and matching phenotypes. Furthermore, using only newly identified genes included in the Genomics England November 2017 release, we assessed our gene-phenotype predictions and showed an 8.3 fold enrichment relative to chance for correct predictions. Thus, we demonstrate both the explanatory and predictive power of machine-learning-based models in neurological disease.

genetics↗