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.
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.
Alterations to molecular network structure in the brain are associated with multiple neuropsychiatric diseases. We briefly introduce networks, then discuss how characterizing networks incorporating molecular variation, co‐expression, and interaction data, as well as environmental and other non‐molecular effects, can expand our understanding of the biological bases of mental disorders and therapies.
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.
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.
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
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
knockdown and overexpression experiments in human neural progenitor cells derived from human induced pluripotent stem cells, we identified a potential role for
in regulating certain SCZ-related genes.
Timothy Syndrome (TS) is caused by very rare exonic mutations of the CACNA1C gene that produce delayed inactivation of Cav1.2 voltage-gated calcium channels during cellular action potentials, with ...greatly increased influx of calcium into the activated cells. The major clinical feature of this syndrome is a long QT interval that results in cardiac arrhythmias. However, TS also includes cognitive impairment, autism and major developmental delays in many of the patients. We observed the appearance of bipolar disorder (BD) in a patient with a previously reported case of TS, who is one of the very few patients to survive childhood. This is most interesting because the common single-nucleotide polymorphism (SNP) most highly associated with BD is rs1006737, which we show here is a cis-expression quantitative trait locus for CACNA1C in human cerebellum, and the risk allele (A) is associated with decreased expression. To combine the CACNA1C perturbations in the presence of BD in this patient and in patients with the common CACNA1C SNP risk allele, we would propose that either increase or decrease in calcium influx in excitable cells can be associated with BD. In treatment of BD with calcium channel blocking drugs, we would predict better response in patients without the risk allele, because they have increased CACNA1C expression.
Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable psychiatric disorders. Associated genetic and gene expression changes have been identified, but many have not been replicated and ...have unknown functions. We identified groups of genes whose expressions varied together, that is co-expression modules, then tested them for association with SCZ. Using weighted gene co-expression network analysis, we show that two modules were differentially expressed in patients versus controls. One, upregulated in cerebral cortex, was enriched with neuron differentiation and neuron development genes, as well as disease genome-wide association study genetic signals; the second, altered in cerebral cortex and cerebellum, was enriched with genes involved in neuron protection functions. The findings were preserved in five expression data sets, including sets from three brain regions, from a different microarray platform, and from BD patients. From those observations, we propose neuron differentiation and development pathways may be involved in etiologies of both SCZ and BD, and neuron protection function participates in pathological process of the diseases.
Cellular heterogeneity in the human brain obscures the identification of robust cellular regulatory networks, which is necessary to understand the function of non-coding elements and the impact of ...non-coding genetic variation. Here we integrate genome-wide chromosome conformation data from purified neurons and glia with transcriptomic and enhancer profiles, to characterize the gene regulatory landscape of two major cell classes in the human brain. We then leverage cell-type-specific regulatory landscapes to gain insight into the cellular etiology of several brain disorders. We find that Alzheimer's disease (AD)-associated epigenetic dysregulation is linked to neurons and oligodendrocytes, whereas genetic risk factors for AD highlighted microglia, suggesting that different cell types may contribute to disease risk, via different mechanisms. Moreover, integration of glutamatergic and GABAergic regulatory maps with genetic risk factors for schizophrenia (SCZ) and bipolar disorder (BD) identifies shared (parvalbumin-expressing interneurons) and distinct cellular etiologies (upper layer neurons for BD, and deeper layer projection neurons for SCZ). Collectively, these findings shed new light on cell-type-specific gene regulatory networks in brain disorders.
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