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Brueggeman, L.

Publications and source records attributed to Brueggeman, L..

5 recordsLinked to original sources

Genetic Identification of Cell Types Underlying Brain Complex Traits Yields Novel Insights Into the Etiology of Parkinson's Disease

Genome-wide association studies (GWAS) have discovered hundreds of loci associated with complex brain disorders, and provide the best current insights into the etiology of these idiopathic traits. However, it remains unclear in which cell types these variants may be active, which is essential for understanding disease etiology and for disease modelling. Here we integrate GWAS results with single-cell transcriptomic data from the entire nervous system to systematically identify cell types underlying psychiatric disorders, neurological conditions, and other brain complex traits. We show that psychiatric disorders are predominantly associated with excitatory neurons from the cortex/hippocampus, medium spiny neurons from the striatum, diverse sets of midbrain neurons, and inhibitory neurons from the cortex/hippocampus. Cognitive traits were generally associated with similar cell types but their associations were driven by different genes. Neurological disorders were associated with different cell types, consistent with other lines of evidence. Notably, we found that Parkinsons disease is not only genetically associated with dopaminergic neurons but also with serotonergic neurons and cells from the oligodendrocyte lineage. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Altogether, our study provides a solid framework for understanding the cellular basis of complex brain disorders and reveals a new unexpected role of oligodendrocytes in Parkinsons disease.

genomics↗

Exome sequencing of 457 autism families recruited online provides evidence for novel ASD genes

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. Additionally, we identified variants that are possibly associated with autism in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.2 provides statistical support for 34 ASD risk genes with at least one damaging variant identified in SPARK. Nine of these genes (BRSK2, DPP6, EGR3, FEZF2, ITSN1, KDM1B, NR4A2, PAX5 and RALGAPB) are newly emerging genes in autism, of which BRSK2 has the strongest statistical support as a risk gene for autism (TADA q-value = 0.0015). Future studies leveraging the thousands of individuals with ASD that have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate autism research that incorporates genetic etiology.

genetics↗

Impact of genetic variations on the human brain: when and where?

Genetic studies have found candidates in most of neurological and psychiatric conditions. However, it remains challenging to assess, in which brain region, and when in the brain development, a mutation is more likely to have an impact, especially for intergenic loci. Here we present, BRAVA, a publicly available resource for estimating genetic mutations spatio-temporal context on the human brain. Different contexts were observed for schizophrenia associated loci, suggesting both neurodevelopmental and late onset components.

bioinformatics↗

Forecasting autism gene discovery with machine learning and genome-scale data

BackgroundGenes are one of the most powerful windows into the biology of autism, and it has been estimated that perhaps a thousand or more genes may confer risk. However, less than 100 genes are currently viewed as having robust enough evidence to be considered true \"autism genes\". Massive genetic studies are underway to produce data to implicate additional genes, but this approach, although necessary, is costly and slow-moving.\n\nMethodsWe approach autism gene discovery as a machine learning problem, rather than a genetic association problem, and use genome-scale data as predictors for identifying further genes that have similar properties in the feature space compared to established autism risk genes. This approach, which we call forecASD, integrates spatiotemporal gene expression, heterogeneous network data, and previous gene-level predictors of autism association into an ensemble classifier that yields a single score that indexes each genes evidence for being involved in the etiology of autism.\n\nResultsWe demonstrate that forecASD has substantially increased sensitivity and specificity compared to previous gene-level predictors of autism association, including genetic measures such as TADA. On an independent test set, consisting of newly-released pilot data from the SPARK Genomics Consortium, we show that forecASD best predicts which genes will have an excess of likely gene disrupting (LGD) de novo mutations. We further use independent data from a recent post mortem study of case/control gene expression to show that forecASD is also a significant predictor of genes implicated in ASD through differential expression. Using forecASD results, we show which molecular pathways are currently under-represented in the autism literature and likely represent under-appreciated biological mechanisms of autism. Finally, forecASD correctly predicted 12 of 16 genes implicated at FDR=0.2 by the latest ASD gene discovery study, while also identifying the most likely false positives among the candidate genes.\n\nConclusionsThese results demonstrate that forecASD bridges the gap between genetic- and expression-based ASD gene discovery, and provides a data-driven replacement to much of the manual filtering and curation that is a critical step in ensuring the robustness of gene discovery studies.

bioinformatics↗

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data was integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

bioinformatics↗