Hu, Y;
Li, M;
Lu, Q;
Weng, H;
Wang, J;
Zekavat, SM;
Yu, Z;
... Yu, L; + view all
(2019)
A statistical framework for cross-tissue transcriptome-wide association analysis.
Nature Genetics
, 51
(3)
pp. 568-576.
10.1038/s41588-019-0345-7.
Preview |
Text
Hu_NG_2019.pdf - Published Version Download (4MB) | Preview |
Abstract
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 by 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. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average of 39% improvement in imputation accuracy and generated effective imputation models for an average of 120% more genes. We describe a summary-statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene–trait association. We applied our method, called UTMOST (unified test for molecular signatures), to multiple genome-wide-association results and demonstrate its advantages over single-tissue strategies.
| Type: | Article |
|---|---|
| Title: | A statistical framework for cross-tissue transcriptome-wide association analysis |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41588-019-0345-7 |
| Publisher version: | https://doi.org/10.1038/s41588-019-0345-7 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Genetics & Heredity, GENE-EXPRESSION, IDENTIFIES VARIANTS, INTEGRATIVE ANALYSIS, SUSCEPTIBILITY LOCI, COMMON VARIANTS, RISK PREDICTION, ALZHEIMERS, GWAS, METAANALYSIS, DISEASE |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10084678 |
Archive Staff Only
 |
View Item |
