A statistical framework for cross-tissue transcriptome-wide association analysis

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.

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Fig. 1: UTMOST workflow.
Fig. 2: Improvement in gene expression imputation accuracy.
Fig. 3: Cross-tissue analysis improves statistical power.
Fig. 4: UTMOST identified more associations in biologically relevant tissues for 50 complex traits.
Fig. 5: Multi-tissue analysis identifies more associations for LDL-C.
Fig. 6: Manhattan plot for LOAD meta-analysis.

Data availability

All data used in the manuscript are publicly available (see URLs). GTEx and GERA data can be accessed by application to dbGaP. CommonMind data are available through formal application to NIMH. ADGC phase 2 summary statistics used for validation are available through the NIAGADS portal under accession number NG00076.

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Acknowledgements

This study was supported in part by NIH grants R01 GM59507 and 3P30AG021342-16S2 (Y.H., M.L., Q.L., and H.Z.), CTSA UL1TR000427 (Q.L.), R01 AG042437 and U01 AG006781 (P.K.C. and S. Mukherjee); the Yale World Scholars Program sponsored by the China Scholarship Council (J.W., and Z.Y.); Neil Shen’s SJTU Medical Research Fund, the SJTU-Yale Collaborative Research Seed Fund; and NSFC 31728012 (J.G., H.L., and H.Z.), and the National Key R&D Program of China 2018YFC0910500 (J.G., and H.L). We thank C. Brown for assistance in matching GTEx tissues to Roadmap cell types. This study makes use of summary statistics from many GWAS consortia. We thank the investigators in these GWAS consortia for generously sharing their data. We thank the IGAP for providing summary results data for these analyses. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in the analysis or writing of this report. IGAP was made possible by the generous participation of the subjects and their families. The i-Select chips were funded by the French National Foundation on Alzheimer’s disease and related disorders. EADI was supported by the LABEX (Laboratory of Excellence Program Investment for the Future) DISTALZ grant, Inserm, Institut Pasteur de Lille, Université de Lille 2, and the Lille University Hospital. The Genetic and Environmental Risk in AD consortium (GERAD) was supported by the Medical Research Council (grant no. 503480), Alzheimer’s Research UK (grant no. 503176), the Wellcome Trust (grant no. 082604/2/07/Z), and the German Federal Ministry of Education and Research (BMBF): Competence Network Dementia (CND) grant nos. 01GI0102, 01GI0711, and 01GI0420. The Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE) was partly supported by NIH/NIA grant no. R01 AG033193, NIA grant no. AG081220, AGES contract N01–AG–12100, NHLBI grant no. R01 HL105756, the Icelandic Heart Association, and the Erasmus Medical Center and Erasmus University. ADGC was supported by NIH/NIA grants nos. U01 AG032984, U24 AG021886, and U01 AG016976, and the Alzheimer’s Association grant no. ADGC–10–196728. We thank the contributors who collected the samples used in this study, as well as the patients and their families, whose help and participation made this work possible; data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer’s Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689-01). We are also grateful for all the consortia and investigators that provided publicly accessible GWAS summary statistics.

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Y.H., M.L., Q.L., H.L., and H.Z. conceived the study and developed the statistical model. Y.H., M.L., Q.L., H.W., J.W., S.M.Z., B.L., Y.S., S. Muchnik, and J.G. performed the statistical analyses. S.M.Z. and P.N. assisted in LDL analysis. Y.H., M.L., Z.Y., and Q.L. implemented the software. B.W.K. prepared ADGC summary statistics. A.N., A.K., and Y.Z. assisted in data preparation. S. Mukherjee and P.K.C. assisted in Alzheimer’s disease data application, curation, and interpretation. Y.H., M.L., Q.L., H.L., and H.Z. wrote the manuscript. H.Z. advised on statistical and genetic issues. All authors contributed to manuscript editing and approved the manuscript.

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Correspondence to Hongyu Zhao.

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Hu, Y., Li, M., Lu, Q. et al. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat Genet 51, 568–576 (2019). https://doi.org/10.1038/s41588-019-0345-7

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