Explore the Features of Brain-Derived Neurotrophic Factor in Mood Disorders

<div><p>Objectives</p><p>Brain-derived neurotrophic factor (BDNF) plays important roles in neuronal survival and differentiation; however, the effects of BDNF on mood disorders remain unclear. We investigated <i>BDNF</i> from the perspective of various aspects of systems biology, including its molecular evolution, genomic studies, protein functions, and pathway analysis.</p><p>Methods</p><p>We conducted analyses examining sequences, multiple alignments, phylogenetic trees and positive selection across 12 species and several human populations. We summarized the results of previous genomic and functional studies of pro-BDNF and mature-BDNF (m-BDNF) found in a literature review. We identified proteins that interact with <i>BDNF</i> and performed pathway-based analysis using large genome-wide association (GWA) datasets obtained for mood disorders.</p><p>Results</p><p>BDNF is encoded by a highly conserved gene. The chordate <i>BDNF</i> genes exhibit an average of 75% identity with the human gene, while vertebrate orthologues are 85.9%-100% identical to human <i>BDNF</i>. No signs of recent positive selection were found. Associations between <i>BDNF</i> and mood disorders were not significant in most of the genomic studies (e.g., linkage, association, gene expression, GWA), while relationships between serum/plasma BDNF level and mood disorders were consistently reported. Pro-BDNF is important in the response to stress; the literature review suggests the necessity of studying both pro- and m-BDNF with regard to mood disorders. In addition to conventional pathway analysis, we further considered proteins that interact with BDNF (I-Genes) and identified several biological pathways involved with BDNF or I-Genes to be significantly associated with mood disorders.</p><p>Conclusions</p><p>Systematically examining the features and biological pathways of BDNF may provide opportunities to deepen our understanding of the mechanisms underlying mood disorders.</p></div

<i>BDNF</i> genes in 12 species.

<p>^a NCBI Entrez Gene database;</p><p>^b Ensembl database; CDS: coding DNA sequence</p><p>Chimpanzee: Pan troglodytes; Macaque: Macaca mulatta; Pig: Sus scrofa; Dog: Canis lupus familiaris; Cattle: Bos Taurus; Mouse: Mus musculus; Rat: Rattus norvegicus; Finch: Taeniopygia guttata; Turkey: Meleagris gallopavo; Chicken: <u>Gallus gallus</u>; Fish: <u>Danio rerio</u></p><p><i>BDNF</i> genes in 12 species.</p

A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease

<div><p>The rapid advances in sequencing technologies and the resulting next-generation sequencing data provide the opportunity to detect disease-associated variants with a better solution, in particular for low-frequency variants. Although both common and rare variants might exert their independent effects on the risk for the trait of interest, previous methods to detect the association effects rarely consider them simultaneously. We proposed a class of test statistics, the generalized weighted-sum statistic (GWSS), to detect disease associations in the presence of common and rare variants with a case-control study design. Information of rare variants was aggregated using a weighted sum method, while signal directions and strength of the variants were considered at the same time. Permutations were performed to obtain the empirical <i>p</i>-values of the test statistics. Our simulation showed that, compared to the existing methods, the GWSS method had better performance in most of the scenarios. The GWSS (in particular VDWSS-<i>t</i>) method is particularly robust for opposite association directions, association strength, and varying distributions of minor-allele frequencies. It is therefore promising for detecting disease-associated loci. For empirical data application, we also applied our GWSS method to the Genetic Analysis Workshop 17 data, and the results were consistent with the simulation, suggesting good performance of our method. As re-sequencing studies become more popular to identify putative disease loci, we recommend the use of this newly developed GWSS to detect associations with both common and rare variants.</p></div

Detection power for identical MAF distributions of signal and noise rare variants (only list methods with better performance).

<p>Detection power for identical MAF distributions of signal and noise rare variants (only list methods with better performance).</p

Effects of disease liability using the Genetic Analysis Workshop 17 data (only list methods with better performance).

<p>Effects of disease liability using the Genetic Analysis Workshop 17 data (only list methods with better performance).</p

Pathway Analysis Using Information from Allele-Specific Gene Methylation in Genome-Wide Association Studies for Bipolar Disorder

<div><p>Bipolar disorder (BPD) is a complex psychiatric trait with high heritability. Despite efforts through conducting genome-wide association (GWA) studies, the success of identifying susceptibility loci for BPD has been limited, which is partially attributed to the complex nature of its pathogenesis. Pathway-based analytic strategy is a powerful tool to explore joint effects of gene sets within specific biological pathways. Additionally, to incorporate other aspects of genomic data into pathway analysis may further enhance our understanding for the underlying mechanisms for BPD. Patterns of DNA methylation play important roles in regulating gene expression and function. A commonly observed phenomenon, allele-specific methylation (ASM) describes the associations between genetic variants and DNA methylation patterns. The present study aimed to identify biological pathways that are involve in the pathogenesis of BPD while incorporating brain specific ASM information in pathway analysis using two large-scale GWA datasets in Caucasian populations. A weighting scheme was adopted to take ASM information into consideration for each pathway. After multiple testing corrections, we identified 88 and 15 enriched pathways for their biological relevance for BPD in the Genetic Association Information Network (GAIN) and the Wellcome Trust Case Control Consortium dataset, respectively. Many of these pathways were significant only when applying the weighting scheme. Three ion channel related pathways were consistently identified in both datasets. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053092#s3">Results</a> in the GAIN dataset also suggest for the roles of extracellular matrix in brain for BPD. Findings from Gene Ontology (GO) analysis exhibited functional enrichment among genes of non-GO pathways in activity of gated channel, transporter, and neurotransmitter receptor. We demonstrated that integrating different data sources with pathway analysis provides an avenue to identify promising and novel biological pathways for exploring the underlying molecular mechanisms for bipolar disorder. Further basic research can be conducted to target the biological mechanisms for the identified genes and pathways.</p> </div

Over-representing genes in enriched pathways in the two GWAS datasets of the GAIN and the WTCCC.

<p><b>ASM</b>: Gene set of allele-specific methylation; <b>Non-ASM:</b> Gene set of other than ASM in pathway analysis.</p>#<p><b>:</b> significant level: p-value less than 0.05.</p

Robustness of all methods in situations of identical/different MAF distributions of signal and noise rare variants (only list methods with better performance).

<p>Robustness of all methods in situations of identical/different MAF distributions of signal and noise rare variants (only list methods with better performance).</p

Concordant enriched pathways among GWA datasets of the GAIN and the WTCCC by different pathway-based methods.

<p><b>ASM</b>: Gene set of allele-specific methylation; <b>GSEA</b>: Gene Set Enrichment Analysis; <b>SUMST</b>: sum-statistic; <b>SUMSQ</b>: sum-square-statistic.</p>#<p><b>:</b> The p-value after correction by the Benjamini and Hochberg (BH) multiple comparison procedure.</p>a<p><b>:</b> Empirical p-values of non-weighting method is less than weighting;</p>b<p>: Empirical p-values of weighting method is less than non-weighting;</p>c<p>: Empirical p-values of non-weighting and weighting are equivalent.</p

The summary description of present pathway-based method.

<p>*Gene with CpG site that is regulated by SNPs in the ASM list.</p