In this article, we review studies detailing the correspondence between peripheral blood and brain tissue across various domains of high-throughput -omic analysis in order to provide a context for evaluating blood-based biomarker studies. Specifically, we reviewed seven studies comparing patterns of DNA methylation (i.e., an aspect of the epigenome), eight articles comparing patterns of gene expression (i.e., the transcriptome), and three articles comparing patterns of protein expression (i.e., the proteome). Our review of the epigenomic literature suggests that CpG-island methylation levels are generally highly correlated (r ¼ 0.90) between blood and brain. Our review of transcriptomic studies suggests that between 35% and 80% of known transcripts are present in both brain and blood tissue samples; estimates of cross-tissue correlation in expression levels were found to range from 0.25 to 0.64, with stronger correlations observed among particular subsets of genes. Relative to the epigenome and transcriptome, the proteome has not been as fully compared between brain and blood samples, highlighting an important area for future work as whole-proteome profiling methods mature. Beyond reviewing the relevant studies, we discuss some of the assumptions, methodological issues, and gaps in knowledge that should be addressed in order to better understand how the multiple ''-omes'' of the brain are reflected in the peripheral blood. A better understanding of these relationships is a critical precursor to the validation of biomarkers for brain disorders. Ó 2013 Wiley Periodicals, Inc.Key words: blood; brain; epigenome; gene expression; genome; methylation; neuropsychiatry; proteome; transcriptome INTRODUCTIONTechnological advances in molecular biology over the last two decades have fundamentally altered our approach to studying brain disorders. The ''throughput'' of many molecular-profiling techniques, which had heretofore been a major bottleneck to discovery, has increased exponentially. This, in turn, has initiated a notable trend in the field away from hypothesis-testing of candidate genes, transcripts, and proteins, and toward hypothesis-generation through the simultaneous evaluation of all members of a particular molecular species (i.e., an ''-ome''). As a consequence, the number of -omes entering the scientific vernacular has also increased rapidly to include the genome (all DNA sequence variations), epigenome (all chemical modifications to the DNA and histone proteins), transcriptome (all expressed RNA transcripts), and proteome (all expressed proteins), among others.Given the demonstrable heritability of many neuropsychiatric disorders [Glatt et al., 2010], much research has been dedicated to identifying the genomic variations underlying their susceptibility. These genomic studies have been predicated on the fundamental and largely true assumption that the genome is equivalent in sequence and structure in all cells and tissues of the same organism. This tenet allows for the standard practice of examining DNA sequen...

Aim This study aimed to evaluate the relationship between a human GRIK4 gene polymorphism (rs1954787) and responsiveness to antidepressant treatment in depressed patients. Methods A meta-analysis was carried out on five studies. Pooled odds ratios (ORs), 95% CIs and a χ2 test measuring heterogeneity were calculated. A test of publication bias was also conducted. Results Alleles and genotypes from a total of 2169 depressed patients were analyzed. The results showed that the C allele appeared more frequently than the T allele in responders to treatment (OR: 1.22; 95% CI: 1.035–1.445; z = 2.36; p = 0.018). Similarly, CC homozygotes were more likely than TT homozygotes to respond to treatment (OR: 1.45; 95% CI: 1.107–1.913; z = 2.69; p = 0.007). No evidence of publication bias was detected. Conclusion Subjects possessing the C allele or CC genotype of the GRIK4 polymorphism rs1954787 are more likely to respond to antidepressant treatment relative to subjects harboring the T allele and TT genotype. Additional replication of this result is required before this association can be considered definitive, after which it may become possible to employ this marker in conjunction with other known predictors in order to anticipate the outcomes of treatment with antidepressant medications.

In 2009, the U.S. National Institute of Mental Health (NIMH) proposed an approach toward the deconstruction of psychiatric nosology under the research domain criteria (RDoC) framework. The overarching goal of RDoC is to identify robust, objective measures of behavior, emotion, cognition, and other domains that are more closely related to neurobiology than are diagnoses. A preliminary framework has been constructed, which has connected molecules, genes, brain circuits, behaviors, and other elements to dimensional psychiatric constructs. Although the RDoC framework has salience in emerging studies, foundational literature that pre-dated this framework requires synthesis and translation to the evolving objectives and nomenclature of RDoC. Toward this end, we review the candidate-gene association, linkage, and genome-wide studies that have implicated a variety of loci and genetic polymorphisms in selected Positive Valence Systems (PVS) constructs. Our goal is to review supporting evidence to currently listed genes implicated in this domain and novel candidates. We systematically searched and reviewed literature based on keywords listed under the June, 2011, edition of the PVS matrix on the RDoC website (http://www.nimh.nih.gov/research-priorities/rdoc/positive-valence-systems-workshop-proceedings.shtml), which were supplemented with de novo keywords pertinent to the scope of our review. Several candidate genes linked to the PVS framework were identified from candidate-gene association studies. We also identified novel candidates with loose association to PVS traits from genome-wide studies. There is strong evidence suggesting that PVS constructs, as currently conceptualized under the RDoC initiative, index genetically influenced traits; however, future research, including genetic epidemiological, and psychometric analyses, must be performed.