Discovery, validation and characterization of Erbb4 and Nrg1 haplotypes using data from three genome-wide association studies of schizophrenia.

Schizophrenia is one of the most common and complex neuropsychiatric disorders, which is contributed both by genetic and environmental exposures. Recently, it is shown that NRG1-mediated ErbB4 signalling regulates many important cellular and molecular processes such as cellular growth, differentiation and death, particularly in myelin-producing cells, glia and neurons. Recent association studies have revealed genomic regions of NRG1 and ERBB4, which are significantly associated with risk of developing schizophrenia; however, inconsistencies exist in terms of validation of findings between distinct populations. In this study, we aim to validate the previously identified regions and to discover novel haplotypes of NRG1 and ERBB4 using logistic regression models and Haploview analyses in three independent datasets from GWAS conducted on European subjects, namely, CATIE, GAIN and nonGAIN. We identified a significant 6-kb block in ERBB4 between chromosome locations 212,156,823 and 212,162,848 in CATIE and GAIN datasets (p = 0.0206 and 0.0095, respectively). In NRG1, a significant 25-kb block, between 32,291,552 and 32,317,192, was associated with risk of schizophrenia in all CATIE, GAIN, and nonGAIN datasets (p = 0.0005, 0.0589, and 0.0143, respectively). Fine mapping and FastSNP analysis of genetic variation located within significantly associated regions proved the presence of binding sites for several transcription factors such as SRY, SOX5, CEPB, and ETS1. In this study, we have discovered and validated haplotypes of ERBB4 and NRG1 in three independent European populations. These findings suggest that these haplotypes play an important role in the development of schizophrenia by affecting transcription factor binding affinity.

Bioinformatic analyses identifies novel protein-coding pharmacogenomic markers associated with paclitaxel sensitivity in NCI60 cancer cell lines.

BACKGROUND: Paclitaxel is a microtubule-stabilizing drug that has been commonly used in treating cancer. Due to genetic heterogeneity within patient populations, therapeutic response rates often vary. Here we used the NCI60 panel to identify SNPs associated with paclitaxel sensitivity. Using the panel's GI50 response data available from Developmental Therapeutics Program, cell lines were categorized as either sensitive or resistant. PLINK software was used to perform a genome-wide association analysis of the cellular response to paclitaxel with the panel's SNP-genotype data on the Affymetrix 125 k SNP array. FastSNP software helped predict each SNP's potential impact on their gene product. mRNA expression differences between sensitive and resistant cell lines was examined using data from BioGPS. Using Haploview software, we investigated for haplotypes that were more strongly associated with the cellular response to paclitaxel. Ingenuity Pathway Analysis software helped us understand how our identified genes may alter the cellular response to paclitaxel. RESULTS: 43 SNPs were found significantly associated (FDR<0.005) with paclitaxel response, with 10 belonging to protein-coding genes (CFTR, ROBO1, PTPRD, BTBD12, DCT, SNTG1, SGCD, LPHN2, GRIK1, ZNF607). SNPs in GRIK1, DCT, SGCD and CFTR were predicted to be intronic enhancers, altering gene expression, while SNPs in ZNF607 and BTBD12 cause conservative missense mutations. mRNA expression analysis supported these findings as GRIK1, DCT, SNTG1, SGCD and CFTR showed significantly (p<0.05) increased expression among sensitive cell lines. Haplotypes found in GRIK1, SGCD, ROBO1, LPHN2, and PTPRD were more strongly associated with response than their individual SNPs. CONCLUSIONS: Our study has taken advantage of available genotypic data and its integration with drug response data obtained from the NCI60 panel. We identified 10 SNPs located within protein-coding genes that were not previously shown to be associated with paclitaxel response. As only five genes showed differential mRNA expression, the remainder would not have been detected solely based on expression data. The identified haplotypes highlight the role of utilizing SNP combinations within genomic loci of interest to improve the risk determination associated with drug response. These genetic variants represent promising biomarkers for predicting paclitaxel response and may play a significant role in the cellular response to paclitaxel.