Research: Johns Hopkins MedicineJohns Hopkins Medicine · Department of Psychiatry and Behavioral SciencesUSA · Baltimore
Research: University of Southern MississippiUniversity of Southern Mississippi · Department of Biological SciencesUSA · Hattiesburg
Answer added in Human Genetics5 How to convert plink files to VCF (or other) easily parseable format?By Julian Gough · University of BristolMehdi Pirooznia · Johns Hopkins MedicineUse plink/seq (http://atgu.mgh.harvard.edu/plinkseq/) Note: you need the reference allele file to have plink to specify reference allele #!/bin/sh # ... [more]Use plink/seq (http://atgu.mgh.harvard.edu/plinkseq/) Note: you need the reference allele file to have plink to specify reference allele #!/bin/sh # 1. have plink binary to specify reference allele plink --noweb --bfile $plink_file --reference-allele $ref_Allele_file --make-bed --out $plink_file_modified # 2. create plinkseq project pseq $pseq_project new-project #3. load plink file into plink/seq pseq $pseq_project load-plink --file $plink_file_modified --id $plink_file_modified #4. write out vcf file pseq $pseq_project write-vcf | gzip > $plink_file_modified.vcf.gz hope it helps!Following
Mehdi Pirooznia, Tao Wang, Dimitrios Avramopoulos, David Valle, Gareth Thomas, Richard L Huganir, Fernando S Goes, James B Potash, Peter P Zandi[show abstract] [hide abstract]
ABSTRACT: MOTIVATION: The synapse is integral to the function of the brain and may be an important source of dysfunction underlying many neuropsychiatric disorders. Consequently, it is an excellent candidate for large-scale genomic and proteomic study. However, while the tools and databases available for the annotation of high-throughput DNA and protein are generally robust, a comprehensive resource dedicated to the integration of information about the synapse is lacking. RESULTS: We present an integrated database, called SynaptomeDB, to retrieve and annotate genes comprising the synaptome. These genes encode components of the synapse including neurotransmitters and their receptors, adhesion/cytoskeletal proteins, scaffold proteins, membrane transporters. SynaptomeDB integrates various and complex data sources for synaptic genes and proteins.Bioinformatics 01/2012; 28(6):897-9. · 5.47 Impact Factor
F S Goes, M L Hamshere, F Seifuddin, M Pirooznia, P Belmonte-Mahon, R Breuer, T Schulze, M Nöthen, S Cichon, M Rietschel, P Holmans, P P Zandi, N Craddock, J B Potash[show abstract] [hide abstract]
ABSTRACT: Mood-incongruent psychotic features (MICP) are familial symptoms of bipolar disorder (BP) that also occur in schizophrenia (SZ), and may represent manifestations of shared etiology between the major psychoses. In this study we have analyzed three large samples of BP with imputed genome-wide association data and have performed a meta-analysis of 2196 cases with MICP and 8148 controls. We found several regions with suggestive evidence of association (P<10(-6)), although no marker met genome-wide significance criteria. The top associations were on chromosomes: 6q14.2 within the PRSS35/SNAP91 gene complex (rs1171113, P=9.67 × 10(-8)); 3p22.2 downstream of TRANK/LBA1 (rs9834970, P=9.71 × 10(-8)); and 14q24.2 in an intron of NUMB (rs2333194, P=7.03 × 10(-7)). These associations were present in all three samples, and both rs1171113 and rs2333194 were found to be overrepresented in an analysis of MICP cases compared with all other BP cases. To test the relationship of MICP with SZ, we performed polygenic analysis using the Psychiatric GWAS Consortium SZ results and found evidence of association between SZ polygenes and the presence of MICP in BP cases (meta-analysis P=0.003). In summary, our analysis of the MICP phenotype in BP has provided suggestive evidence for association of common variants in several genes expressed in the nervous system. The results of our polygenic analysis provides support for a modest degree of genetic overlap between BP with MICP and SZ, highlighting that phenotypic correlations across syndromes may be due to the influence of polygenic risk factors.Translational psychiatry. 01/2012; 2:e180.
Article: SVAw - a web-based application tool for automated surrogate variable analysis of gene expression studies.[show abstract] [hide abstract]
ABSTRACT: BACKGROUND: Surrogate variable analysis (SVA) is a powerful method to identify, estimate, and utilize the components of gene expression heterogeneity due to unknown and/or unmeasured technical, genetic, environmental, or demographic factors. These sources of heterogeneity are common in gene expression studies, and failing to incorporate them into the analysis can obscure results. Using SVA increases the biological accuracy and reproducibility of gene expression studies by identifying these sources of heterogeneity and correctly accounting for them in the analysis. RESULTS: Here we have developed a web application called SVAw (Surrogate variable analysis Web app) that provides a user friendly interface for SVA analyses of genome-wide expression studies. The software has been developed based on open source bioconductor SVA package. In our software, we have extended the SVA program functionality in three aspects: (i) the SVAw performs a fully automated and user friendly analysis workflow; (ii) It calculates probe/gene Statistics for both pre and post SVA analysis and provides a table of results for the regression of gene expression on the primary variable of interest before and after correcting for surrogate variables; and (iii) it generates a comprehensive report file, including graphical comparison of the outcome for the user. CONCLUSIONS: SVAw is a web server freely accessible solution for the surrogate variant analysis of high-throughput datasets and facilitates removing all unwanted and unknown sources of variation. It is freely available for use at http://psychiatry.igm.jhmi.edu/sva. The executable packages for both web and standalone application and the instruction for installation can be downloaded from our web site.Source Code for Biology and Medicine 03/2013; 8(1):8.
Yun-Ching Chen, Hannah Carter, Jennifer Parla, Melissa Kramer, Fernando S Goes, Mehdi Pirooznia, Peter P Zandi, W Richard McCombie, James B Potash, Rachel Karchin[show abstract] [hide abstract]
ABSTRACT: In the past few years, case-control studies of common diseases have shifted their focus from single genes to whole exomes. New sequencing technologies now routinely detect hundreds of thousands of sequence variants in a single study, many of which are rare or even novel. The limitation of classical single-marker association analysis for rare variants has been a challenge in such studies. A new generation of statistical methods for case-control association studies has been developed to meet this challenge. A common approach to association analysis of rare variants is the burden-style collapsing methods to combine rare variant data within individuals across or within genes. Here, we propose a new hybrid likelihood model that combines a burden test with a test of the position distribution of variants. In extensive simulations and on empirical data from the Dallas Heart Study, the new model demonstrates consistently good power, in particular when applied to a gene set (e.g., multiple candidate genes with shared biological function or pathway), when rare variants cluster in key functional regions of a gene, and when protective variants are present. When applied to data from an ongoing sequencing study of bipolar disorder (191 cases, 107 controls), the model identifies seven gene sets with nominal p-values[Formula: see text]0.05, of which one MAPK signaling pathway (KEGG) reaches trend-level significance after correcting for multiple testing.PLoS Genetics 01/2013; 9(1):e1003224. · 8.69 Impact Factor
V L Willour, F Seifuddin, P B Mahon, D Jancic, M Pirooznia, J Steele, B Schweizer, F S Goes, F M Mondimore, D F Mackinnon, [......], S Cichon, H Gurling, S Purcell, J W Smoller, N Craddock, J R DePaulo, T G Schulze, F J McMahon, P P Zandi, J B Potash[show abstract] [hide abstract]
ABSTRACT: The heritable component to attempted and completed suicide is partly related to psychiatric disorders and also partly independent of them. Although attempted suicide linkage regions have been identified on 2p11-12 and 6q25-26, there are likely many more such loci, the discovery of which will require a much higher resolution approach, such as the genome-wide association study (GWAS). With this in mind, we conducted an attempted suicide GWAS that compared the single-nucleotide polymorphism (SNP) genotypes of 1201 bipolar (BP) subjects with a history of suicide attempts to the genotypes of 1497 BP subjects without a history of suicide attempts. In all, 2507 SNPs with evidence for association at P<0.001 were identified. These associated SNPs were subsequently tested for association in a large and independent BP sample set. None of these SNPs were significantly associated in the replication sample after correcting for multiple testing, but the combined analysis of the two sample sets produced an association signal on 2p25 (rs300774) at the threshold of genome-wide significance (P=5.07 × 10(-8)). The associated SNPs on 2p25 fall in a large linkage disequilibrium block containing the ACP1 (acid phosphatase 1) gene, a gene whose expression is significantly elevated in BP subjects who have completed suicide. Furthermore, the ACP1 protein is a tyrosine phosphatase that influences Wnt signaling, a pathway regulated by lithium, making ACP1 a functional candidate for involvement in the phenotype. Larger GWAS sample sets will be required to confirm the signal on 2p25 and to identify additional genetic risk factors increasing susceptibility for attempted suicide.Molecular psychiatry 03/2011; 17(4):433-44. · 15.05 Impact Factor