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Integrating predicted transcriptome from multiple tissues improves association detection

Author

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  • Alvaro N Barbeira
  • Milton Pividori
  • Jiamao Zheng
  • Heather E Wheeler
  • Dan L Nicolae
  • Hae Kyung Im

Abstract

Integration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restrict our ability to detect associations. Here we propose an efficient statistical method (MultiXcan) that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes. MultiXcan integrates evidence across multiple panels using multivariate regression, which naturally takes into account the correlation structure. We apply our method to simulated and real traits from the UK Biobank and show that, in realistic settings, we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed a summary result-based extension called S-MultiXcan, which we show yields highly concordant results with the individual level version when LD is well matched. Our multivariate model-based approach allowed us to use the individual level results as a gold standard to calibrate and develop a robust implementation of the summary-based extension. Results from our analysis as well as software and necessary resources to apply our method are publicly available.Author summary: We develop a new method, MultiXcan, to test the mediating role of gene expression variation on complex traits, integrating information available across multiple tissue studies. We show this approach has higher power than traditional single-tissue methods. We extend this method to use only summary-statistics from public GWAS. We apply these methods to 222 complex traits available in the UK Biobank cohort, and 109 complex traits from public GWAS and discuss the findings.

Suggested Citation

  • Alvaro N Barbeira & Milton Pividori & Jiamao Zheng & Heather E Wheeler & Dan L Nicolae & Hae Kyung Im, 2019. "Integrating predicted transcriptome from multiple tissues improves association detection," PLOS Genetics, Public Library of Science, vol. 15(1), pages 1-20, January.
  • Handle: RePEc:plo:pgen00:1007889
    DOI: 10.1371/journal.pgen.1007889
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    Cited by:

    1. Benjamin J. Schmiedel & Job Rocha & Cristian Gonzalez-Colin & Sourya Bhattacharyya & Ariel Madrigal & Christian H. Ottensmeier & Ferhat Ay & Vivek Chandra & Pandurangan Vijayanand, 2021. "COVID-19 genetic risk variants are associated with expression of multiple genes in diverse immune cell types," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Helian Feng & Nicholas Mancuso & Alexander Gusev & Arunabha Majumdar & Megan Major & Bogdan Pasaniuc & Peter Kraft, 2021. "Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies," PLOS Genetics, Public Library of Science, vol. 17(4), pages 1-21, April.
    3. Milton Pividori & Sumei Lu & Binglan Li & Chun Su & Matthew E. Johnson & Wei-Qi Wei & Qiping Feng & Bahram Namjou & Krzysztof Kiryluk & Iftikhar J. Kullo & Yuan Luo & Blair D. Sullivan & Benjamin F. V, 2023. "Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    4. Mike Thompson & Mary Grace Gordon & Andrew Lu & Anchit Tandon & Eran Halperin & Alexander Gusev & Chun Jimmie Ye & Brunilda Balliu & Noah Zaitlen, 2022. "Multi-context genetic modeling of transcriptional regulation resolves novel disease loci," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    5. Linda Ottensmann & Rubina Tabassum & Sanni E. Ruotsalainen & Mathias J. Gerl & Christian Klose & Elisabeth Widén & Kai Simons & Samuli Ripatti & Matti Pirinen, 2023. "Genome-wide association analysis of plasma lipidome identifies 495 genetic associations," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    6. Angela Andaleon & Lauren S Mogil & Heather E Wheeler, 2019. "Genetically regulated gene expression underlies lipid traits in Hispanic cohorts," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-21, August.
    7. Ashley Budu-Aggrey & Anna Kilanowski & Maria K. Sobczyk & Suyash S. Shringarpure & Ruth Mitchell & Kadri Reis & Anu Reigo & Reedik Mägi & Mari Nelis & Nao Tanaka & Ben M. Brumpton & Laurent F. Thomas , 2023. "European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    8. Mengge Liu & Lu Wang & Yujie Zhang & Haoyang Dong & Caihong Wang & Yayuan Chen & Qian Qian & Nannan Zhang & Shaoying Wang & Guoshu Zhao & Zhihui Zhang & Minghuan Lei & Sijia Wang & Qiyu Zhao & Feng Li, 2024. "Investigating the shared genetic architecture between depression and subcortical volumes," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    9. Diptavo Dutta & Yuan He & Ashis Saha & Marios Arvanitis & Alexis Battle & Nilanjan Chatterjee, 2022. "Aggregative trans-eQTL analysis detects trait-specific target gene sets in whole blood," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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