Project description:Genome-wide association study (GWAS) was performed in 120 patient-parents trio samples from Japanese schizophrenia pedigrees ABSTRACT: Schizophrenia is a devastating neuropsychiatric disorder with genetically complex traits. Genetic variants should explain a considerable portion of the risk for schizophrenia, and genome-wide association study (GWAS) is a potentially powerful tool for identifying the risk variants that underlie the disease. Here, we report the results of a three-stage analysis of three independent cohorts consisting of a total of 2,535 samples from Japanese and Chinese populations for searching schizophrenia susceptibility genes using a GWAS approach. Firstly, we examined 115,770 single nucleotide polymorphisms (SNPs) in 120 patient-parents trio samples from Japanese schizophrenia pedigrees. In stage II, we evaluated 1,632 SNPs (1,159 SNPs of p < 0.01 and 473 SNPs of p < 0.05 that located in previously reported linkage regions). The second sample consisted of 1,012 case-control samples of Japanese origin. The most significant p value was obtained for the SNP in the ELAVL2 [(embryonic lethal, abnormal vision, Drosophila)-like 2] gene located on 9p21.3 (p = 0.00087). In stage III, we scrutinized the ELAVL2 gene by genotyping gene-centric tagSNPs in the third sample set of 293 family samples (1,163 individuals) of Chinese descent and the SNP in the gene showed a nominal association with schizophrenia in Chinese population (p = 0.026). The current data in Asian population would be helpful for deciphering ethnic diversity of schizophrenia etiology. Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from peripheral blood samples. SUPPLEMENTARY FILES: CEL files were processed by manufacture's protocol. Genotype data were analyzed with the GeneSpring GT (Varia) 2.0 software package developed by Agilent Technologies (Santa Clara, CA). Matrix tables for the Genetic programs were produced (Linkage format: http://bioinformatics.med.utah.edu/~alun/software/docs/linkage.html) File 1: Matrix file_SNP_Map.txt File 2: Matrix file_Family_Information.txt File 3: Matrix file_Pedigree_Format.txt (Genotyping data of Linkage format) Transmission disequilibrium test was performed using the R program (http://www.r-project.org). File 4: Matrix file_results.txt

Project description:Goal: To identify copy number variation in normal individuals using high density, non-polymorphic oligonucleotide probes Background DNA sequence diversity within the human genome may be more greatly affected by copy number variations (CNVs) than single nucleotide polymorphisms (SNPs). Although the importance of CNVs in genome wide association studies (GWAS) is becoming widely accepted, the optimal methods for identifying these variants are still under evaluation. We have previously reported a comprehensive view of CNVs in the HapMap DNA collection using high density 500K EA (Early Access) SNP genotyping arrays which revealed greater than 1,000 CNVs ranging in size from 1kb to over 3Mb. Although the arrays used most commonly for GWAS predominantly interrogate SNPs, CNV identification and detection does not necessarily require the use of DNA probes centered on polymorphic nucleotides and may even be hindered by the dependence on a successful SNP genotyping assay. Results In this study, we have designed and evaluated a high density array predicated on the use of non-polymorphic oligonucleotide probes for CNV detection. This approach effectively uncouples copy number detection from SNP genotyping and thus has the potential to significantly improve probe coverage for genome-wide CNV identification. This array, in conjunction with PCR-based, complexity-reduced DNA target, queries over 1.3M independent NspI restriction enzyme fragments in the 200bp to 1100bp size range, which is a several fold increase in marker density as compared to the 500K EA array. In addition, a novel algorithm was developed and validated to extract CNV regions and boundaries. Conclusions Using a well-characterized pair of DNA samples, close to 200 CNVs were identified, of which nearly 50% appear novel yet were independently validated using quantitative PCR. The results indicate that non-polymorphic probes provide a robust approach for CNV identification, and the increasing precision of CNV boundary delineation should allow a more complete analysis of their genomic organization. A set of five genomic DNA samples containing different numbers of X chromosomes (1X to 5X sample set, including NA15510 and NA10851) were hybridized to Nsp copy number (CN) arrays in triplicate to evaluate detection of copy number variation using high density, non-polymorphic oligonucleotide probes. 6 Hapmap samples were hybridized to Nsp CN arrays to evaluate Mendelian inheritance of CNVs.

Project description:Goal: To identify copy number variation in normal individuals using high density, non-polymorphic oligonucleotide probes Background DNA sequence diversity within the human genome may be more greatly affected by copy number variations (CNVs) than single nucleotide polymorphisms (SNPs). Although the importance of CNVs in genome wide association studies (GWAS) is becoming widely accepted, the optimal methods for identifying these variants are still under evaluation. We have previously reported a comprehensive view of CNVs in the HapMap DNA collection using high density 500K EA (Early Access) SNP genotyping arrays which revealed greater than 1,000 CNVs ranging in size from 1kb to over 3Mb. Although the arrays used most commonly for GWAS predominantly interrogate SNPs, CNV identification and detection does not necessarily require the use of DNA probes centered on polymorphic nucleotides and may even be hindered by the dependence on a successful SNP genotyping assay. Results In this study, we have designed and evaluated a high density array predicated on the use of non-polymorphic oligonucleotide probes for CNV detection. This approach effectively uncouples copy number detection from SNP genotyping and thus has the potential to significantly improve probe coverage for genome-wide CNV identification. This array, in conjunction with PCR-based, complexity-reduced DNA target, queries over 1.3M independent NspI restriction enzyme fragments in the 200bp to 1100bp size range, which is a several fold increase in marker density as compared to the 500K EA array. In addition, a novel algorithm was developed and validated to extract CNV regions and boundaries. Conclusions Using a well-characterized pair of DNA samples, close to 200 CNVs were identified, of which nearly 50% appear novel yet were independently validated using quantitative PCR. The results indicate that non-polymorphic probes provide a robust approach for CNV identification, and the increasing precision of CNV boundary delineation should allow a more complete analysis of their genomic organization. Keywords: Copy number variation (CNV) detection

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