Dataset Information
JEPEGMIX: gene-level joint analysis of functional SNPs in cosmopolitan cohorts.
Ontology highlight
ABSTRACT: Motivation
To increase detection power, gene level analysis methods are used to aggregate weak signals. To greatly increase computational efficiency, most methods use as input summary statistics from genome-wide association studies (GWAS). Subsequently, gene statistics are constructed using linkage disequilibrium (LD) patterns from a relevant reference panel. However, all methods, including our own Joint Effect on Phenotype of eQTL/functional single nucleotide polymorphisms (SNPs) associated with a Gene (JEPEG), assume homogeneous panels, e.g. European. However, this renders these tools unsuitable for the analysis of large cosmopolitan cohorts.Results
We propose a JEPEG extension, JEPEGMIX, which similar to one of our software tools, Direct Imputation of summary STatistics of unmeasured SNPs from MIXed ethnicity cohorts, is capable of estimating accurate LD patterns for cosmopolitan cohorts. JEPEGMIX uses this accurate LD estimates to (i) impute the summary statistics at unmeasured functional variants and (ii) test for the joint effect of all measured and imputed functional variants which are associated with a gene. We illustrate the performance of our tool by analyzing the GWAS meta-analysis summary statistics from the multi-ethnic Psychiatric Genomics Consortium Schizophrenia stage 2 cohort. This practical application supports the immune system being one of the main drivers of the process leading to schizophrenia.Availability and implementation
Software, annotation database and examples are available at http://dleelab.github.io/jepegmix/.Contact
donghyung.lee@vcuhealth.orgSupplementary information
Supplementary material is available at Bioinformatics online.
SUBMITTER: Lee D
PROVIDER: S-EPMC4708106 | biostudies-literature | 2016 Jan
REPOSITORIES: biostudies-literature
Publications
JEPEGMIX: gene-level joint analysis of functional SNPs in cosmopolitan cohorts.
Bioinformatics (Oxford, England) 20151001 2
<h4>Motivation</h4>To increase detection power, gene level analysis methods are used to aggregate weak signals. To greatly increase computational efficiency, most methods use as input summary statistics from genome-wide association studies (GWAS). Subsequently, gene statistics are constructed using linkage disequilibrium (LD) patterns from a relevant reference panel. However, all methods, including our own Joint Effect on Phenotype of eQTL/functional single nucleotide polymorphisms (SNPs) associ ...[more]
