Dataset

NG00076 - ADGC case-control summary statistics on 7050 samples not included in the IGAP-2013 discovery stage

Overview

Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression levels from genotypes using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. To address these challenges, UTMOST (Unified Test for MOlecular SignaTures) was developed, a principled method to perform cross-tissue expression imputation and gene-level association analysis, which first uses a multi-task learning method to jointly impute gene expression in 44 human tissues and then applies summary statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene-trait association. Compared with single-tissue methods, this approach achieved an average 39% improvement in imputation accuracy and generated effective imputation models for an average 120% more genes. UTMOST was applied to multiple genome wide association results and demonstrate its advantages over single-tissue strategies.

Multi-stage association study for late-onset Alzheimer’s disease (LOAD) was performed. This GWAS summary statistics was used as a replication dataset. The analysis was conducted on the samples in the Alzheimer’s Disease Genetics Consortium (ADGC) that were not used in the discovery stage of IGAP analysis (N = 7,050). The results were generated by first analyzing individual datasets using logistic regression adjusting for age, sex and the first three principal components in the program SNPTest v2. Meta-analysis of the individual dataset results was then performed using the inverse-variance weighted approach in METAL.

The p-value data is generally available to all users using the link below; however, gaining access to the complete dataset requires a formal data request.

Summary Statistics p-values only

Molecular Data Type

Disease

AD
Submission date: 
1/3/2019