Expansion of Schizophrenia Gene Network Knowledge Using Machine Learning Selected Signals From Dorsolateral Prefrontal Cortex and Amygdala RNA-seq Data.

It is widely accepted, given the complex nature of schizophrenia (SCZ) gene networks, that a few or a small number of genes are unlikely to represent the underlying functional pathways responsible for SCZ pathogenesis. Several studies from large cohorts have been performed to search for key SCZ network genes using different analytical approaches, such as differential expression tests, genome-wide association study (GWAS), copy number variations, and differential methylations, or from the analysis of mutations residing in the coding regions of the genome. However, only a small portion (<10%) of candidate genes identified in these studies were considered SCZ disease-associated genes in SCZ pathways. RNA sequencing (RNA-seq) has been a powerful method to detect functional signals. In this study, we used RNA-seq data from the dorsolateral prefrontal cortex (DLPFC) from 254 individuals and RNA-seq data from the amygdala region from 46 individuals. Analysis was performed using machine learning methods, including random forest and factor analysis, to prioritize the numbers of genes from previous SCZ studies. For genes most differentially expressed between SCZ and healthy controls, 18 were added to known SCZ-associated pathways. These include three genes (GNB2, ITPR1, and PLCB2) for the glutamatergic synapse pathway, six genes (P2RX6, EDNRB, GHR, GRID2, TSPO, and S1PR1) for neuroactive ligand-receptor interaction, eight genes (CAMK2G, MAP2K1, RAF1, PDE3A, RRAS2, VAV1, ATP1B2, and GLI3) for the cAMP signaling pathway, and four genes (GNB2, CAMK2G, ITPR1, and PLCB2) for the dopaminergic synapse pathway. Besides the previously established pathways, 103 additional gene interactions were expanded to SCZ-associated networks, which were shared among both the DLPFC and amygdala regions. The novel knowledge of molecular targets gained from this study brings opportunities for a more complete picture of the SCZ pathogenesis. A noticeable fact is that hub genes, in the expanded networks, are not necessary differentially expressed or containing hotspots from GWAS studies, indicating that individual methods, such as differential expression tests, are not enough to identify the underlying SCZ pathways and that more integrative analysis is required to unfold the pathobiology of SCZ.

Heritability and genome-wide association study of benign prostatic hyperplasia (BPH) in the eMERGE network.

Benign prostatic hyperplasia (BPH) results in a significant public health burden due to the morbidity caused by the disease and many of the available remedies. As much as 70% of men over 70 will develop BPH. Few studies have been conducted to discover the genetic determinants of BPH risk. Understanding the biological basis for this condition may provide necessary insight for development of novel pharmaceutical therapies or risk prediction. We have evaluated SNP-based heritability of BPH in two cohorts and conducted a genome-wide association study (GWAS) of BPH risk using 2,656 cases and 7,763 controls identified from the Electronic Medical Records and Genomics (eMERGE) network. SNP-based heritability estimates suggest that roughly 60% of the phenotypic variation in BPH is accounted for by genetic factors. We used logistic regression to model BPH risk as a function of principal components of ancestry, age, and imputed genotype data, with meta-analysis performed using METAL. The top result was on chromosome 22 in SYN3 at rs2710383 (p-value = 4.6 × 10-7; Odds Ratio = 0.69, 95% confidence interval = 0.55-0.83). Other suggestive signals were near genes GLGC, UNCA13, SORCS1 and between BTBD3 and SPTLC3. We also evaluated genetically-predicted gene expression in prostate tissue. The most significant result was with increasing predicted expression of ETV4 (chr17; p-value = 0.0015). Overexpression of this gene has been associated with poor prognosis in prostate cancer. In conclusion, although there were no genome-wide significant variants identified for BPH susceptibility, we present evidence supporting the heritability of this phenotype, have identified suggestive signals, and evaluated the association between BPH and genetically-predicted gene expression in prostate.

Non-coding RNA dysregulation in the amygdala region of schizophrenia patients contributes to the pathogenesis of the disease.

Schizophrenia (SCZ) is a neuropsychiatric disorder with a complex genetic etiology. The redundancy of the gene networks underlying SCZ indicates that many gene combinations have the potential to cause a system dysfunction that can manifest as SCZ or a related neurodevelopmental disorder. Recent studies show that small non-coding microRNA (miRNA) and long non-coding RNA (lncRNA) are important factors in shaping these networks and are dynamically regulated by neuronal activation. We investigated the genome-wide transcription profiles of 46 human amygdala samples obtained from 22 SCZ patients and 24 healthy controls. Using RNA sequencing (RNA-seq), we determined lncRNA expression levels in all samples and generated miRNA profiles for 27 individuals (13 cases and 14 controls). Previous studies have identified differentially expressed miRNAs in SCZ, including miR-132, miR-212, and miR-34a/miR-34c. Here we report differential expression of a novel miRNA, miR1307, in SCZ. Notably, miR1307 maps to a locus previously associated with SCZ through GWAS. Additionally, one lncRNA that was overexpressed in SCZ, AC005009.2, also maps to a region previously associated with SCZ based on GWAS and overlapped SCZ-related genes. The results were replicated in a large independent data set of 254 dorsolateral prefrontal cortex samples from the CommonMind consortium. Taken together, these results suggest that miRNA and lncRNAs are important contributors to the pathogenesis of SCZ.