Author
Mara Parellada
Other affiliations: Complutense University of Madrid
Bio: Mara Parellada is an academic researcher from Hospital General Universitario Gregorio Marañón. The author has contributed to research in topics: Psychosis & First episode. The author has an hindex of 47, co-authored 165 publications receiving 8419 citations. Previous affiliations of Mara Parellada include Complutense University of Madrid.
Topics: Psychosis, First episode, Medicine, Autism, Psychology
Papers published on a yearly basis
Papers
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Icahn School of Medicine at Mount Sinai1, Carnegie Mellon University2, Harvard University3, University of Toronto4, Wellcome Trust Sanger Institute5, University of Pittsburgh6, Nagoya University7, University of Freiburg8, King's College London9, Vanderbilt University10, King Abdulaziz University11, University of Santiago de Compostela12, University of Utah13, Duke University14, Memorial University of Newfoundland15, Trinity College, Dublin16, University of Pennsylvania17, University of Illinois at Chicago18, Boston Children's Hospital19, Columbia University20, German Cancer Research Center21, University College London22, Kaiser Permanente23, Broad Institute24, Cardiff University25, Complutense University of Madrid26, Newcastle University27, Baylor College of Medicine28, University of California, San Francisco29, RWTH Aachen University30, National Health Service31, McMaster University32, Saarland University33, Karolinska Institutet34, National Institutes of Health35, University of Helsinki36, Emory University37
TL;DR: Using exome sequencing, it is shown that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate of < 0.05, plus a set of 107 genes strongly enriched for those likely to affect risk (FDR < 0.30).
Abstract: The genetic architecture of autism spectrum disorder involves the interplay of common and rare variants and their impact on hundreds of genes. Using exome sequencing, here we show that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, plus a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects. Many of the genes implicated encode proteins for synaptic formation, transcriptional regulation and chromatin-remodelling pathways. These include voltage-gated ion channels regulating the propagation of action potentials, pacemaking and excitability-transcription coupling, as well as histone-modifying enzymes and chromatin remodellers-most prominently those that mediate post-translational lysine methylation/demethylation modifications of histones.
2,228 citations
F. Kyle Satterstrom1, F. Kyle Satterstrom2, Jack A. Kosmicki, Jiebiao Wang3 +198 more•Institutions (53)
TL;DR: The largest exome sequencing study of autism spectrum disorder (ASD) to date, using an enhanced analytical framework to integrate de novo and case-control rare variation, identifies 102 risk genes at a false discovery rate of 0.1 or less, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD.
1,169 citations
TL;DR: In this article , a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals was conducted, and the authors reported common variant associations at 287 distinct genomic loci.
Abstract: Schizophrenia has a heritability of 60–80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies. A genome-wide association study including over 76,000 individuals with schizophrenia and over 243,000 control individuals identifies common variant associations at 287 genomic loci, and further fine-mapping analyses highlight the importance of genes involved in synaptic processes.
558 citations
Mental Health Foundation1, King's College London2, South London and Maudsley NHS Foundation Trust3, University of Bath4, University of Cambridge5, University College London6, University of Palermo7, University of Bologna8, French Institute of Health and Medical Research9, Hospital General Universitario Gregorio Marañón10, University of Amsterdam11, University of Barcelona12, University of São Paulo13, Maastricht University Medical Centre14, University of Hong Kong15, University Medical Center Utrecht16
TL;DR: Differences in frequency of daily cannabis use and in use of high-potency cannabis contributed to the striking variation in the incidence of psychotic disorder across the 11 studied sites, giving important implications for public health.
496 citations
F. Kyle Satterstrom1, Jack A. Kosmicki1, Jiebiao Wang2, Michael S. Breen3 +150 more•Institutions (45)
TL;DR: Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, 102 risk genes are identified at a false discovery rate of ≤ 0.1, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.
Abstract: We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n=35,584 total samples, 11,986 with ASD). Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate ≤ 0.1. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained for severe neurodevelopmental delay, while 53 show higher frequencies in individuals ascertained for ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most of the risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In human cortex single-cell gene expression data, expression of risk genes is enriched in both excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.
461 citations
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9,362 citations
TL;DR: The remarkable range of discoveriesGWASs has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics are reviewed.
Abstract: Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.
2,669 citations
TL;DR: It is proposed that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways.
2,257 citations
TL;DR: A fully-automated segmentation method that uses manually labelled image data to provide anatomical training information and is assessed both quantitatively, using Leave-One-Out testing on the 336 training images, and qualitatively,Using an independent clinical dataset involving Alzheimer's disease.
2,047 citations
TL;DR: A meta-analysis of studies that have attempted to longitudinally predict a specific STB-related outcome suggests the need for a shift in focus from risk factors to machine learning-based risk algorithms.
Abstract: Suicidal thoughts and behaviors (STBs) are major public health problems that have not declined appreciably in several decades. One of the first steps to improving the prevention and treatment of STBs is to establish risk factors (i.e., longitudinal predictors). To provide a summary of current knowledge about risk factors, we conducted a meta-analysis of studies that have attempted to longitudinally predict a specific STB-related outcome. This included 365 studies (3,428 total risk factor effect sizes) from the past 50 years. The present random-effects meta-analysis produced several unexpected findings: across odds ratio, hazard ratio, and diagnostic accuracy analyses, prediction was only slightly better than chance for all outcomes; no broad category or subcategory accurately predicted far above chance levels; predictive ability has not improved across 50 years of research; studies rarely examined the combined effect of multiple risk factors; risk factors have been homogenous over time, with 5 broad categories accounting for nearly 80% of all risk factor tests; and the average study was nearly 10 years long, but longer studies did not produce better prediction. The homogeneity of existing research means that the present meta-analysis could only speak to STB risk factor associations within very narrow methodological limits-limits that have not allowed for tests that approximate most STB theories. The present meta-analysis accordingly highlights several fundamental changes needed in future studies. In particular, these findings suggest the need for a shift in focus from risk factors to machine learning-based risk algorithms. (PsycINFO Database Record
2,013 citations