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

Nature Genetics volume 51, pages 568–576 (2019)Cite this article

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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.

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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.

Author information

Author notes

  1. These authors contributed equally: Yiming Hu, Mo Li, Qiongshi Lu.

  2. A list of members and affiliations appears in the Supplementary Note.

Authors and Affiliations

  1. Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA

    Yiming Hu, Mo Li, Boyang Li, Yu Shi & Hongyu Zhao

  2. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA

    Qiongshi Lu

  3. Division of Biostatistics, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, NT, Hong Kong

    Haoyi Weng

  4. Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA

    Jiawei Wang, Zhaolong Yu & Hongyu Zhao

  5. Yale School of Medicine, New Haven, CT, USA

    Seyedeh M. Zekavat

  6. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    Seyedeh M. Zekavat & Pradeep Natarajan

  7. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Seyedeh M. Zekavat & Pradeep Natarajan

  8. SJTU–Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, China

    Jianlei Gu, Hui Lu & Hongyu Zhao

  9. Department of Genetics, Yale School of Medicine, New Haven, CT, USA

    Sydney Muchnik & Hongyu Zhao

  10. John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA

    Brian W. Kunkle

  11. Department of Medicine, University of Washington, Seattle, WA, USA

    Shubhabrata Mukherjee & Paul K. Crane

  12. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Pradeep Natarajan

  13. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Pradeep Natarajan

  14. Center for Clinical Epidemiology and Biostatistic, and the Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Adam Naj

  15. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Adam Naj, Amanda Kuzma & Yi Zhao

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  1. Yiming Hu
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  2. Mo Li
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  17. Yi Zhao
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  19. Hui Lu
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  20. Hongyu Zhao
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Consortia

Alzheimer’s Disease Genetics Consortium,

Contributions

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.

Corresponding author

Correspondence to Hongyu Zhao.

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The authors declare no competing interests.

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Supplementary information

Supplementary Text and Figures

Supplementary Note, Supplementary Tables 1–13, 21–23 and 26–29, and Supplementary Figures 1–13

Reporting Summary

Supplementary Tables

Supplementary Tables 14–20, 24 and 25

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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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