Dataset Information

Polysubstance abuse-vulnerability genes: genome scans for association, using 1,004 subjects and 1,494 single-nucleotide polymorphisms.

ABSTRACT: Strong genetic contributions to drug abuse vulnerability are well documented, but few chromosomal locations for human drug-abuse vulnerability alleles have been confirmed. We now identify chromosomal markers whose alleles distinguish drug abusers from control individuals in each of two samples, on the basis of pooled-sample microarray and association analyses. Reproducibly positive chromosomal regions defined by these markers in conjunction with previous results were especially unlikely to have been identified by chance. Positive markers identify the alcohol dehydrogenase (ADH) locus, flank the brain-derived neurotropic factor (BDNF) locus, and mark seven other regions previously linked to vulnerability to nicotine or alcohol abuse. These data support polygenic contributions of common allelic variants to polysubstance abuse vulnerability.

SUBMITTER: Uhl GR

PROVIDER: S-EPMC1235541 | biostudies-literature | 2001 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Polysubstance abuse-vulnerability genes: genome scans for association, using 1,004 subjects and 1,494 single-nucleotide polymorphisms.

Uhl G R GR Liu Q R QR Walther D D Hess J J Naiman D D

American journal of human genetics 20011106 6

Strong genetic contributions to drug abuse vulnerability are well documented, but few chromosomal locations for human drug-abuse vulnerability alleles have been confirmed. We now identify chromosomal markers whose alleles distinguish drug abusers from control individuals in each of two samples, on the basis of pooled-sample microarray and association analyses. Reproducibly positive chromosomal regions defined by these markers in conjunction with previous results were especially unlikely to have ...[more]

PMID: 11704927

Similar Datasets

Project description:ObjectiveThe purpose of the present study was to evaluate the associations between seven tagging single nucleotide polymorphisms (tSNPs) and risk of breast cancer assessed by tumor pathological characteristics and body mass index (BMI).MethodsSeven tSNPs of four breast cancer susceptibility genes were analyzed in 734 Chinese women with breast cancer and 672 age-matched healthy controls; then, the association with clinicopathological characteristics, BMI, molecular subtype, TNM (T, tumor; N, lymph node; M, metastasis) staging and lymph node status was determined by unconditional logistic regression.ResultsRs12951053 in TP53 and rs16945628 in BRIP1, displayed increased risk of breast cancer in the BMI ≧ 25 kg/m2 group (OR=1.50, 95% CI: 1.02-2.21, P=0.041 and OR=1.92, 95% CI: 1.13-3.26, P=0.015, respectively). The other five tSNPs (rs1805812, rs2735385 and rs6999227 in NBS1, rs7220719 in BRIP1 and rs2299941 in PTEN) displayed a decreased risk of breast cancer in the 18.5≤BMI<25 kg/m2 group. Rs12951053 in TP53 and rs7220719 in BRIP1 exhibited an increased risk of triple-negative breast cancer (OR=1.50, 95% CI: 1.05-2.15, P=0.026 and OR=2.13, 95% CI: 1.05-4.29, P=0.032, respectively), but three tSNPs in NBS1 (rs1805812, rs2735385 and rs6999227) all displayed a negative association with both luminal B and triple-negative breast cancer. The tSNP rs2299941 in PTEN also exhibited a negative association with each molecular subtype, except triple-negative breast cancer. The majority of tSNPs displayed a negative association with stage II or III breast cancer. Most tSNPs showed a negative association with breast cancer that was lymph node negative or with 1-3 positive nodes. Only rs12951053 in TP53 displayed a positive association with lymph node-negative breast cancer (OR=1.43, 95% CI: 1.08-1.91, P=0.013).ConclusionThe majority of tSNPs displayed a negative association with breast cancer and only a few tSNPs (rs12951053 in TP53, rs16945628 and rs7220719 in BRIP1) showed an increased risk of breast cancer as defined by clinicopathological characteristics.

Project description:IntroductionSchizophrenia is a chronic heterogenic neurodevelopment disorder. Many genes interfere in the development of SCZ. All four genes, NrCAM, PRODH, ANK3, and ANKK1, which were evaluated in this study, were previously reported to be associated with Schizophrenia. The NrCAM contributes to creating cognitive deficiencies through the CAM's signaling pathway. PRODH plays a vital role in creating SCZ negative symptoms through the signaling pathway of glutamatergic and NMDA receptors. ANK3 affects ion channel and molecular adhesion in Ranvier and initial segments of axons, leading to mental retardation, sleep disorder, and SCZ. ANKK1 encodes a protein kinase and was reported to be associated with alcohol addiction, Attention Deficit Hyperactivity Disorder (ADHD), and SCZ.MethodsThe subjects were selected from Schizophrenic patients referring to the Psychiatric Ward of Imam-Hussein Hospital and Schizophrenic Patients Support Institution (AHEBBA). 95 (30 Schizoaffective patients, 57 Paranoid patients, and 8 disorganized) patients were recruited as the subjects in the present case-control association study. 120 healthy subjects were recruited from the Tehran Medical Genetics Laboratory staff and a group of students from the Islamic Azad University of Science and Research in Tehran. The genotypes were determined with molecular genotyping techniques of PCR-RFLP, ARMS-PCR, and Cycle sequencing. Results were analyzed by the Chi-Square test using SPSS V. 24 and R, SNP STATE Package to investigate significant differences between cases and controls.ResultsThe incidence of schizophrenia was 68% and 32% among men and women, respectively. The evaluation of the allelic association between schizophrenia and all the candidate SNPs showed a significant association between NrCAM's SNP rs10235968 and SCZ (P=0.001). Haplotype T, T, C in rs10235968, rs6967368, rs3763463, respectively, within the NrCAM gene, showed significant association with schizophrenia disorder (P=0.0001).ConclusionNo association was found between other candidate SNPs and SCZ among the subjects.

Project description:The advent of high throughput sequencing technology has enabled the 1000 Genomes Project Pilot 3 to generate complete sequence data for more than 906 genes and 8,140 exons representing 697 subjects. The 1000 Genomes database provides a critical opportunity for further interpreting disease associations with single nucleotide polymorphisms (SNPs) discovered from genetic association studies. Currently, direct sequencing of candidate genes or regions on a large number of subjects remains both cost- and time-prohibitive.To accelerate the translation from discovery to functional studies, we propose an in silico gene sequencing method (ISS), which predicts phased sequences of intragenic regions, using SNPs. The key underlying idea of our method is to infer diploid sequences (a pair of phased sequences/alleles) at every functional locus utilizing the deep sequencing data from the 1000 Genomes Project and SNP data from the HapMap Project, and to build prediction models using flanking SNPs. Using this method, we have developed a database of prediction models for 611 known genes. Sequence prediction accuracy for these genes is 96.26% on average (ranges 79%-100%). This database of prediction models can be enhanced and scaled up to include new genes as the 1000 Genomes Project sequences additional genes on additional individuals. Applying our predictive model for the KCNJ11 gene to the Wellcome Trust Case Control Consortium (WTCCC) Type 2 diabetes cohort, we demonstrate how the prediction of phased sequences inferred from GWAS SNP genotype data can be used to facilitate interpretation and identify a probable functional mechanism such as protein changes.Prior to the general availability of routine sequencing of all subjects, the ISS method proposed here provides a time- and cost-effective approach to broadening the characterization of disease associated SNPs and regions, and facilitating the prioritization of candidate genes for more detailed functional and mechanistic studies.

Dataset Information

Polysubstance abuse-vulnerability genes: genome scans for association, using 1,004 subjects and 1,494 single-nucleotide polymorphisms.

Publications

Polysubstance abuse-vulnerability genes: genome scans for association, using 1,004 subjects and 1,494 single-nucleotide polymorphisms.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets