Biology subjects

Gaspar, H. A.

Publications and source records attributed to Gaspar, H. A..

6 recordsLinked to original sources

bioRxiv2018

Navigome: Navigating the Human Phenome

We now have access to a sufficient number of genome-wide association studies (GWAS) to cluster phenotypes into genetic-informed categories and to navigate the \"phenome\" space of human traits. Using a collection of 465 GWAS, we generated genetic correlations, pathways, gene-wise and tissue-wise associations using MAGMA and S-PrediXcan for 465 human traits. Testing 7267 biological pathways, we found that only 898 were significantly associated with any trait. Similarly, out of ~20,000 tested protein-coding genes, 12,311 genes exhibited an association. Based on the genetic correlations between all traits, we constructed a phenome map using t-distributed stochastic neighbor embedding (t-SNE), where each of the 465 traits can be visualized as an individual point. This map reveals well-defined clusters of traits such as education/high longevity, lower longevity, height, body composition, and depression/anxiety/neuroticism. These clusters are enriched in specific groups of pathways, such as lipid pathways in the lower longevity cluster, and neuronal pathways for body composition or education clusters. The map and all other analyses are available in the Navigome web interface (https://phenviz.navigome.com).

bioinformatics↗

bioRxiv2018

Probabilistic ancestry maps: a method to assess and visualize population substructures in genetics

Principal component analysis (PCA) is a standard method to correct for population stratification in ancestry-specific genome-wide association studies (GWASs) and is used to cluster individuals by ancestry. Using the 1000 genomes project data, we examine how non-linear dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE) or generative topographic mapping (GTM) can be used to provide improved ancestry maps by accounting for a higher percentage of explained variance in ancestry, and how they can help to estimate the number of principal components necessary to account for population stratification. GTM also generates posterior probabilities of class membership which can be used to assess the probability of an individual to belong to a given population - as opposed to t-SNE, GTM can be used for both clustering and classification. This paper is a first application of GTM for ancestry classification models. Our maps and software are available online.\n\nAuthor summaryWith this paper, we seek to encourage researchers working in genetics to use other methods than PCA to visualize ancestry and identify substructures in populations. We propose to use methods which do not only allow visualization of ancestry, but also the estimation of probabilities of belonging to different ancestry groups.

bioinformatics↗

bioRxiv2018

Assessing 42 inflammatory markers in 321 control subjects and 887 major depressive disorder cases: BMI and other confounders and overall predictive ability for current depression

Inflammatory markers such as cytokines represent potential biomarkers for major depressive disorder (MDD). Many, generally small studies have examined the role of single markers and found significant associations. We assessed 42 inflammatory markers, namely cytokines, in the blood of 321 control subjects and 887 MDD cases. We tested whether individual inflammatory marker levels were significantly affected by MDD case/control status, current episode, or current depression severity, co-varying for age, sex, body mass index (BMI), smoking, current antidepressant use, ethnicity, assay batch and study effects. We further used machine learning algorithms to investigate if we could use our data to blindly discriminate MDD patients, or those in a current episode. We found broad and powerful influences of confounding factors on log-protein levels. Notably, IL-6 levels were very strongly influenced by BMI (p = 1.37 x 10-43, variance explained = 18%), while Interleukin-16 was the most significant predictor of current depressive episode (p = 0.003, variance explained = 0.9%, q < 0.1). No single inflammatory marker predicted MDD case/control status when a subject was not in a depressed episode, nor did any predict depression severity. Machine learning results revealed that using inflammatory marker data with clinical confounder information significantly increased precision for differentiating MDD patients who were in an episode. To conclude, a wide panel of inflammatory markers alongside clinical information may aid in predicting the onset of symptoms, but no single inflammatory protein is likely to represent a clinically useful biomarker for MDD diagnosis or prognosis. We note that the potential influence of physical health related and population stratification related confounders on inflammatory biomarker studies in psychiatry is considerable.

neuroscience↗

bioRxiv2018

Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder

The major depressive disorder (MDD) working group of the Psychiatric Genomics Consortium (PGC) has published a genome-wide association study (GWAS) for MDD in 130,664 cases, identifying 44 risk variants. We used these results to investigate potential drug targets and repurposing opportunities. We built easily interpretable bipartite drug-target networks integrating interactions between drugs and their targets, genome-wide association statistics and genetically predicted expression levels in different tissues, using our online tool Drug Targetor (drugtargetor.com). We also investigated drug-target relationships and drug effects on gene expression that could be impacting MDD. MAGMA was used to perform pathway analyses and S-PrediXcan to investigate the directionality of tissue-specific expression levels in patients vs. controls. Outside the major histocompatibility complex (MHC) region, 25 druggable genes were significantly associated with MDD after multiple testing correction, and 19 were suggestively significant. Several drug classes were significantly enriched, including monoamine reuptake inhibitors, sex hormones, antipsychotics and antihistamines, indicating an effect on MDD and potential repurposing opportunities. These findings require validation in model systems and clinical examination, but also show that GWAS may become a rich source of new therapeutic hypotheses for MDD and other psychiatric disorders that need new - and better - treatment options.

genomics↗

bioRxiv2017

Functional consequences of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals

Variance in IQ is associated with a wide range of health outcomes, and 1% of the population are affected by intellectual disability. Despite a century of research, the fundamental neural underpinnings of intelligence remain unclear. We integrate results from genome-wide association studies (GWAS) of intelligence with brain tissue and single cell gene expression data to identify tissues and cell types associated with intelligence. GWAS data for IQ (N = 78,308) were meta-analyzed with an extreme-trait cohort of 1,247 individuals with mean IQ [~]170 and 8,185 controls. Genes associated with intelligence implicate pyramidal neurons of the somatosensory cortex and CA1 region of the hippocampus, and midbrain embryonic GABAergic neurons. Tissue-specific analyses find the most significant enrichment for frontal cortex brain expressed genes. These results suggest specific neuronal cell types and genes may be involved in intelligence and provide new hypotheses for neuroscience experiments using model systems.

genomics↗

bioRxiv2016

Pathways analyses of schizophrenia GWAS focusing on known and novel drug targets

Using successful genome-wide association results in psychiatry for drug repurposing is an ongoing challenge. Databases collecting drug targets and gene annotations are growing and can be harnessed to shed a new light on psychiatric disorders. We used GWAS summary statistics from the Psychiatric Genetics Consortium (PGC) Schizophrenia working group and a repositioning model for schizophrenia by testing the enrichment of antipsychotics. As sample size increases, schizophrenia GWAS results show increasing enrichment for known antipsychotic drugs, selective calcium channel blockers, and antiepileptics. Each of these therapeutical classes targets different gene subnetworks. We identify 162 Bonferroni-significant druggable genes, and 128 FDR-significant biological pathways related to neurons, synapses, genic intolerance, membrane transport, epilepsy, and mental disorders. These results suggest that, in schizophrenia, current well-powered GWAS results can reliably detect known schizophrenia drugs and thus may hold considerable potential for the identification of new therapeutic leads. Moreover, antiepileptics and calcium channel blockers may provide repurposing opportunities. This study also reveals significant pathways in schizophrenia that were not identified previously, and provides a workflow for pathway analysis and drug repurposing using GWAS results.

genomics↗