Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders.

Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders.

DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data.

Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all data to the correct racial group in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS.

Molecular network analysis enhances understanding of the biology of mental disorders.

We provide an introduction to network theory, evidence to support a connection between molecular network structure and neuropsychiatric disease, and examples of how network approaches can expand our knowledge of the molecular bases of these diseases. Without systematic methods to derive their biological meanings and inter-relatedness, the many molecular changes associated with neuropsychiatric disease, including genetic variants, gene expression changes, and protein differences, present an impenetrably complex set of findings. Network approaches can potentially help integrate and reconcile these findings, as well as provide new insights into the molecular architecture of neuropsychiatric diseases. Network approaches to neuropsychiatric disease are still in their infancy, and we discuss what might be done to improve their prospects.

The impact of common variants on gene expression in the human brain: from RNA to protein to schizophrenia risk.

Background: The impact of genetic variants on gene expression has been intensely studied at the transcription level, yielding in valuable insights into the association between genes and the risk of complex disorders, such as schizophrenia (SCZ). However, the downstream impact of these variants and the molecular mechanisms connecting transcription variation to disease risk are not well understood. Results: We quantitated ribosome occupancy in prefrontal cortex samples of the BrainGVEX cohort. Together with transcriptomics and proteomics data from the same cohort, we performed cis-Quantitative Trait Locus (QTL) mapping and identified 3,253 expression QTLs (eQTLs), 1,344 ribosome occupancy QTLs (rQTLs), and 657 protein QTLs (pQTLs) out of 7,458 genes quantitated in all three omics types from 185 samples. Of the eQTLs identified, only 34% have their effects propagated to the protein level. Further analysis on the effect size of prefrontal cortex eQTLs identified from an independent dataset showed clear post-transcriptional attenuation of eQTL effects. To investigate the biological relevance of the attenuated eQTLs, we identified 70 expression-specific QTLs (esQTLs), 51 ribosome-occupancy-specific QTLs (rsQTLs), and 107 protein-specific QTLs (psQTLs). Five of these omics-specific QTLs showed strong colocalization with SCZ GWAS signals, three of them are esQTLs. The limited number of GWAS colocalization discoveries from omics-specific QTLs and the apparent prevalence of eQTL attenuation prompted us to take a complementary approach to investigate the functional relevance of attenuated eQTLs. Using S-PrediXcan we identified 74 SCZ risk genes, 34% of which were novel, and 67% of these risk genes were replicated in a MR-Egger test. Notably, 52 out of 74 risk genes were identified using eQTL data and 70% of these SCZ-risk-gene-driving eQTLs show little to no evidence of driving corresponding variations at the protein level. Conclusion: The effect of eQTLs on gene expression in the prefrontal cortex is commonly attenuated post-transcriptionally. Many of the attenuated eQTLs still correlate with SCZ GWAS signal. Further investigation is needed to elucidate a mechanistic link between attenuated eQTLs and SCZ disease risk.

The DGCR5 long noncoding RNA may regulate expression of several schizophrenia-related genes.

A number of studies indicate that rare copy number variations (CNVs) contribute to the risk of schizophrenia (SCZ). Most of these studies have focused on protein-coding genes residing in the CNVs. Here, we investigated long noncoding RNAs (lncRNAs) within 10 SCZ risk-associated CNV deletion regions (CNV-lncRNAs) and examined their potential contribution to SCZ risk. We used RNA sequencing transcriptome data derived from postmortem brain tissue from control individuals without psychiatric disease as part of the PsychENCODE BrainGVEX and Developmental Capstone projects. We carried out weighted gene coexpression network analysis to identify protein-coding genes coexpressed with CNV-lncRNAs in the human brain. We identified one neuronal function-related coexpression module shared by both datasets. This module contained a lncRNA called DGCR5 within the 22q11.2 CNV region, which was identified as a hub gene. Protein-coding genes associated with SCZ genome-wide association study signals, de novo mutations, or differential expression were also contained in this neuronal module. Using DGCR5 knockdown and overexpression experiments in human neural progenitor cells derived from human induced pluripotent stem cells, we identified a potential role for DGCR5 in regulating certain SCZ-related genes.

Genetic and genomic analyses as a basis for new diagnostic nosologies.

For schizophrenia, bipolar disorder, and autism, clinical descriptions are precise and reliable, but there is great overlap among diagnoses in associated genetic polymorphisms and rare variants, treatment response, and other phenomenological findings such as brain imaging. It is widely hoped that new diagnostic categories can be developed which are more precise and predictive of important features of illness, particularly response to pharmacological agents. It is the intent of this paper to describe the diagnostic implications of some current genetic findings, and to describe how the genetic associations with diagnosis may be teased apart into new associations with biologically coherent diagnostic entities and scales, based on the various functional aspects of the associated genes and functional genomic data.

Las descripciones clínicas para la esquizofrenia, el trastorno bipolar y el autismo son precisas y confiables, pero hay una gran sobreposición entre estos diagnósticos encuanto a polimorfismos genéticos asociados y variantes raras, respuesta a tratamiento y otros hallazgos como las imágenes cerebrales. Hay grandes expectativas de que se puedan desarrollar nuevas categorías diagnósticas, que sean más precisas y predictoras de las características importantes de la enfermedad, en especial de la respuesta a los fármacos. La intención de este artículo es describir las implicancias diagnósticas de algunos hallazgos genéticos actuales y también describir cómo las asociaciones genéticas con los diagnósticos pueden dar origen a nuevas asociaciones con entidades diagnósticas y escalas biológicamente coherentes, basadas en varios aspectos funcionales de los genes asociados y datos genómicos funcionales.

Pour la schizophrénie, les troubles bipolaires et l'autisme, les descriptions cliniques sont précises et fiables, mais il existe un chevauchement important entre ces diagnostics pour les variants rares et les polymorphismes génétiques associés, la réponse au traitement et d'autres symptômes et signes comme l'imagerie cérébrale. De nouvelles catégories diagnostiques plus précises et prédictives des caractéristiques importantes de la maladie, en particulier la réponse aux produits pharmacologiques, sont fortement espérées. Cet article a pour but de décrire les implications diagnostiques de certains résultats génétiques actuels et de décrire comment les associations génétiques observées avec ces catégories diagnostiques peuvent être séparées en de nouvelles associations avec des dimensions et des entités diagnostiques biologiquement cohérentes, à partir des nombreux aspects fonctionnels des gènes associés et des données de génomique fonctionnelle.

Ethical and public policy challenges for pharmacogenomics.

It is timely to consider the ethical and social questions raised by progress in pharmacogenomics, based on the current importance of pharmacogenomics for avoidance of predictable side effects of drugs, and for correct choice of medications in certain cancers. It has been proposed that the entire population be genotyped for drug-metabolizing enzyme polymorphisms, as a measure that would prevent many untoward and dangerous drug reactions. Pharmacologic treatment targeting based on genomics of disease can be expected to increase greatly in the coming years. Policy and ethical issues exist on consent for large-scale genomic pharmacogenomic data collection, public vs corporate ownership of genomic research results, testing efficacy and safety of drugs used for rare genomic indications, and accessibility of treatments based on costly research that is applicable to relatively few patients. In major psychiatric disorders and intellectual deficiency, rare and de novo deletion or duplication of chromosomal segments (copy number variation), in the aggregate, are common causes of increased risk. This implies that the policy problems of pharmacogenomics will be particularly important for the psychiatric disorders.

Es oportuno tener en cuenta las preguntas éticas y sociales que surgen del progreso en la farmacogenómíca, dada la importancía actual de ésta para evitar los efectos secundarios predecíbles de los fármacos y para la elección correcta de medícamentos en ciertos tipos de cáncer. Se ha propuesto que se le realice el genotipo para el polimorfismo de las enzimas metabolizadoras de fármacos a toda la población, como una medida que podría prevenir muchas reacciones adversas y peligrosas a fármacos. Se puede esperar que el tratamiento farmacológico focalizado basado en la genómica de la enfermedad aumente de manera importante en los próximos años. Existen aspectos políticos y éticos en el consentimiento de la obtención a gran escala de datos genómicos/farmacogenómicos, en la propiedad pública versus privada de los resultados de la investigación genómica, en las pruebas de eficacia y seguridad de fármacos utilizados en indicaciones genómicas raras, y en la accesibilidad a tratamientos basados en costosa investigación que sea aplicable a relativamente pocos pacientes. La deleción rara y de novo o la duplicación de segmentos cromosómicos (variación en el número de copias) son en general causas comunes de aumento del riesgo de los principales trastornos psiquiátricos y del déficit intelectual. Esto implica que los problemas políticos de la farmacogenómica serán especialmente importantes para los trastornos psiquiátricos.

Les progrès de la pharmacogénomique pour éviter les effets indésirables prévisibles des médicaments et pour choisir correctement les traitements dans certains cancers, ont soulevé des questions éthiques et sociales qu'il est temps d'examiner. Le génotypage de la population entière pour les polymorphismes enzymatiques métabolisant les médicaments a été proposé afin de prévenir des effets indésirables nocifs et dangereux. Le ciblage des traitements pharmacologiques fondé sur la génomique va vraisemblablement augmenter considérablement durant les années à venir. Des questions éthiques et politiques se posent au sujet de l'autorisation du recueil de grande envergure des données génomiques et pharmacogénomiques, de la propriété publique vs privée des données de recherche génomique, des essais d'efficacité et de sécurité d'emploi des médicaments utilisés pour des indications génomiques rares, et de l'accessibilité des traitements basés sur une recherche coûteuse bénéficiant à relativement peu de patients. Dans les troubles psychiatriques majeurs et la déficience intellectuelle, la délétion rare et de novo de segments chromosomiques ou leur duplication (variation du nombre de copies), sont dans l'ensemble des causes courantes d'augmentation du risque. Ceci signifie que les questions de politiques publiques par rapport à la pharmacogénomique seront particulièrement importantes avec les troubles psychiatriques.

Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods.

The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by "batch effects," the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.