Abstract
Transcriptome-wide association studies (TWASs) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. TWASs have become increasingly popular. They have been used to analyze many complex traits with expression profiles from different tissues, successfully enhancing the discovery of genetic risk loci for complex traits. Though conceptually straightforward, some steps are required to perform the TWAS properly. Here we provide a step-by-step guide to integrate eQTL data with both GWAS individual-level data and GWAS summary statistics from complex traits.
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Acknowledgements
This work was supported by grant R-913-200-098-263 from the Duke-NUS Medical School, and AcRF Tier 2 (MOE2016-T2-2-029, MOE2018-T2-1-046 and MOE2018-T2-2-006) from the Ministry of Education, Singapore, and grant No. 71501089 from the National Natural Science Foundation of China.
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Shi, X., Yang, C., Liu, J. (2021). Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies. In: Wong, KC. (eds) Epistasis. Methods in Molecular Biology, vol 2212. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0947-7_7
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DOI: https://doi.org/10.1007/978-1-0716-0947-7_7
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