F4290_F1739_F1626_F4227_F7150_F6705_F2021Affymetrix array: 6.0
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Danielle Moreira
PROVIDER: E-MTAB-2821 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Suicide and suicide attempts are complex behaviors that result from the interaction of different factors, including genetic variants that increase the predisposition to suicidal behaviors. Copy number variations (CNVs) are deletions or duplications of a segment of DNA usually larger than one kilobase. These structural genetic changes, although quite rare, have been associated with genetic liability to mental disorders, such as autism, schizophrenia, and bipolar disorder. No genome-wide level studies have been published investigating the potential role of CNVs in suicidal behaviors. Based on single-nucleotide polymorphism array data, we followed the Penn-CNV standards to detect CNVs in 1,608 subjects, comprising 475 suicide and suicide attempt cases and 1,133 controls. Although the initial algorithms determined the presence of CNVs on chromosomes 6 and 12 in seven and eight cases, respectively, compared with none of the controls, visual inspection of the raw data did not support this finding. Furthermore we were unable to validate these findings by CNV-specific real-time polymerase chain reaction. Additionally, rare CNV burden analysis did not find an association between the frequency or length of rare CNVs and suicidal behavior in our sample population. Although our findings suggest CNVs do not play an important role in the etiology of suicidal behaviors, they are not inconsistent with the strong evidence from the literature suggesting that other genetic variants account for a portion of the total phenotypic variability in suicidal behavior.
2015-04-17 | E-MTAB-3519 | biostudies-arrayexpress
Project description:Using data from high-density genomic profiling arrays, we describe the profiles of somatic copy-number aberrations (SCNAs) in 486 adenocarcinomas across all three major digestive organs, including 296 gastric and esophageal cancers. This analysis revealed that although patterns of broad, chromosome arm-level alterations are similar across the three types of adenocarcinoma, focal genomic amplifications are substantially more prevalent in gastric/esophageal adenocarcinoma. A statistical analysis identified 64 regions of significantly recurrent amplification and deletion, including those shared across these tumors and those uniquely significant in adenocarcinomas from a single organ. Among significantly amplified genes are those encoding therapeutically targetable kinases such as ERBB2, FGFR1, FGFR2, EGFR, and MET, events noted in 14% of colorectal adenocarcinomas and 37% of gastric/esophageal tumors suggesting that analysis of genomic amplification will be a critical source of biomarkers to guide therapies in upper gastrointestinal adenocarcinomas. While many of the other significant loci of amplifications implicate genes recognized to play roles in gastrointestinal and other cancers, other loci point to regions that may harbor novel genes contributing to these cancers. One such event is a recurrent focal deletion present in 15% of esophageal adenocarcinomas, which we narrow to a single likely target, the Runt transcription factor subunit RUNX1. Indeed, reintroduction of RUNX1 into a cell model with this deletion inhibited anchorage-independent growth. Overall, these results demonstrate genomic features common to these tumors and identify key differences that reflect distinctive biology and potential opportunities for therapeutic intervention. Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from 271 cancer DNAs derived from primary tissues, as well as from DNA obtained from 60 normal DNA samples. Signal intensities were normalized to raw copy number estimates using the tangent normalization method, as described in Beroukhim et al., In Press and Mermel et al., In preparation. The SNP 6.0 data from this submission were segmented using CBS. These segmented data were then combined with segmented Affymetrix 250K Sty data for 128 colon, 13 gastric and 74 esophageal adenocarcinomas using common markers to anchor the segments. Data analysis across samples was performed using this GISTIC 2.0 algorithm (Mermel C et al, Genome Biology 2011).
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2012-11-20 | E-GEOD-35394 | biostudies-arrayexpress
Project description:We have explored the potential role of genetics in the development of osteonecrosis of the jaw (ONJ) in multiple myeloma (MM) patients under bisphosphonate therapy. A genome wide association study was performed using 500.568 single nucleotide polymorphisms (SNPs) in two series of homogeneously treated MM patients: one with ONJ (22 MM cases) and another without ONJ (65 matched MM controls). Four SNPs (rs1934951, rs1934980, rs1341162 and rs17110453) mapped within the Cytochrome P450-2C gene (CYP2C8) showed a different distribution between cases and controls with statistically significant differences (p=1.07x10-6, p=4.231x10-6, p=6.22x10-6 and p=2.15x10-5, respectively). SNP rs1934951 was significantly associated with a higher risk of ONJ development even after Bonferroni correction (P corrected value=0.02). Genotyping results displayed an overrepresentation of the T allele in cases as compared with controls (48% vs. 12%). Thus, individuals homozygous for the T allele had an increased likelihood of developing ONJ (Odds ratio 12.75, 95% confidence interval 3.7 to 43.5). We studied 22 cases (MM with ONJ) and 65 controls (MM without ONJ), matched for age, gender and ethnicity (note: all of the cases and controls are Caucasian). All patients were enrolled in the GEM-00 protocol, which consists on polychemotherapy and autologous transplantation. All received BPs therapy, either Pamidronate (16 cases, 57 controls) or Zoledronic Acid (6 cases, 8 controls) planned for 2 years (median 22 months, range 9-24 months). Clinical characteristics were similar between controls and cases. Study protocols were approved by the ethics committee and written informed consent was obtained from all participants. Each patient was genotyped using the Affymetrix GeneChip Mapping 500K set of microarrays (Affymetrix, Santa Clara, CA) according to the manufacturer’s recommendations. Genotypes were determined using the BRLMM algorithm with cases and controls undergoing joint cluster analysis, after ensuring a robust association test through quality filtering tests. Based on stringent quality control criteria a total of 339.972 SNPs were selected for subsequent analyses. Criteria for exclusion were: 1) call rate<90%, 2) minor allele frequency <5% and 3) deviations from Hardy-Weinberg equilibrium with a p<0.00001. Sexual chromosomes were excluded for analysis. To test for allelic associations between SNPs and ONJ, we constructed 2x2 contingency tables and compared using the two-sided Fisher’s exact or Chisquare tests through SPSS software (SPSS 14.0, Inc. Chicago, IL, USA). P-values were corrected (Pc) using the Bonferroni correction. The strength of association was estimated by the odds ratio (OR), and their 95% confidence intervals (CI) were calculated by Cornfield methods. Linkage disequilibrium between SNPs was analyzed using the Arlequin Software (http://anthro.unige.ch/arlequin).
2008-09-25 | E-GEOD-11948 | biostudies-arrayexpress
Project description:We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.megasoftware.net free of charge.
| S-EPMC3840312 | biostudies-literature