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Assessing the Causal Effects of Human Serum Metabolites on 5 Major Psychiatric Disorders.

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TLDR
This study provided novel insights into integrating metabolomics with genomics in order to understand the mechanisms underlying the pathogenesis of human diseases.
Abstract
Psychiatric disorders are the leading cause of disability worldwide while the pathogenesis remains unclear. Genome-wide association studies (GWASs) have made great achievements in detecting disease-related genetic variants. However, functional information on the underlying biological processes is often lacking. Current reports propose the use of metabolic traits as functional intermediate phenotypes (the so-called genetically determined metabotypes or GDMs) to reveal the biological mechanisms of genetics in human diseases. Here we conducted a two-sample Mendelian randomization analysis that uses GDMs to assess the causal effects of 486 human serum metabolites on 5 major psychiatric disorders, which respectively were schizophrenia (SCZ), major depression (MDD), bipolar disorder (BIP), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD). Using genetic variants as proxies, our study has identified 137 metabolites linked to the risk of psychiatric disorders, including 2-methoxyacetaminophen sulfate, which affects SCZ (P = 1.7 × 10-5) and 1-docosahexaenoylglycerophosphocholine, which affects ADHD (P = 5.6 × 10-5). Fourteen significant metabolic pathways involved in the 5 psychiatric disorders assessed were also detected, such as glycine, serine, and threonine metabolism for SCZ (P = .0238), Aminoacyl-tRNA biosynthesis for both MDD (P = .0144) and ADHD (P = .0029). Our study provided novel insights into integrating metabolomics with genomics in order to understand the mechanisms underlying the pathogenesis of human diseases.

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Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: Quantifying the epidemiological transition

Christopher J L Murray, +611 more
TL;DR: The Global Burden of Disease Study 2013 (GBD 2013) aims to bring together all available epidemiological data using a coherent measurement framework, standardised estimation methods, and transparent data sources to enable comparisons of health loss over time and across causes, age-sex groups, and countries as discussed by the authors.
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Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction.

TL;DR: A broad survey of the single-omics studies of schizophrenia can be found in this paper, where the advantages and challenges of different omics technologies are summarized and then the studies in which multiple omics data are integrated to unravel the complex pathophysiology of schizophrenia.
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Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations.

TL;DR: A metabolome-wide association study (MWAS) was presented in this article, which uses genetic and metabolomic data to impute metabolites into large samples with genomewide association summary statistics.
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Assessing the causal association between human blood metabolites and the risk of epilepsy

TL;DR: In this paper , the authors conducted two-sample Mendelian randomization (MR) analysis and identified six metabolites with causal effects on epilepsy from the International League Against Epilepsy (ILAE) consortium.
References
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Journal ArticleDOI

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Journal ArticleDOI

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

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Journal ArticleDOI

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TL;DR: A novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate is presented, which is consistent even when up to 50% of the information comes from invalid instrumental variables.
Journal ArticleDOI

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.

TL;DR: The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions.
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