MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer

Xiaoyu Song, Jiayi Ji, Joseph H. Rothstein, Stacey E. Alexeeff, Lori C. Sakoda, Adriana Sistig, Ninah Achacoso, Eric Jorgenson, Alice S. Whittemore, Robert J. Klein, Laurel A. Habel, Pei Wang, Weiva Sieh

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.

Original languageEnglish
Article number377
JournalNature Communications
Volume14
Issue number1
DOIs
StatePublished - Dec 2023

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