Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative

Arjun Bhattacharya; Jibril B Hirbo; Dan Zhou; Wei Zhou; Jie Zheng; Masahiro Kanai; Global Biobank Meta-analysis Initiative; Bogdan Pasaniuc; Eric R Gamazon; Nancy J Cox

doi:10.1016/j.xgen.2022.100180

Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative

Cell Genom. 2022 Oct 12;2(10):100180. doi: 10.1016/j.xgen.2022.100180.

Authors

Arjun Bhattacharya^{1

2

3

4}, Jibril B Hirbo^{5

6

3}, Dan Zhou^{5

6}, Wei Zhou^{7

8

9}, Jie Zheng¹⁰, Masahiro Kanai^{7

8

9

11

12}; Global Biobank Meta-analysis Initiative; Bogdan Pasaniuc^{1

13

14

3}, Eric R Gamazon^{5

6

15

3}, Nancy J Cox^{5

6

3}

Affiliations

¹ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
² Institute of Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
³ These authors contributed equally.
⁴ Lead contact.
⁵ Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.
⁶ Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
⁷ Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
⁸ Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁹ Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
¹⁰ MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.
¹¹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
¹² Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
¹³ Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁴ Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁵ MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.

Abstract

The Global Biobank Meta-analysis Initiative (GBMI), through its diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ancestry study populations across biobanks, GBMI presents unique challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWASs) boost detection power for and provide biological context to genetic associations by integrating genetic variant-to-trait associations from genome-wide association studies (GWASs) with predictive models of gene expression. TWASs present unique challenges beyond GWASs, especially in a multi-biobank, meta-analytic setting. Here, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity, meta-analytic strategies, and open challenges at every step of the framework. We advise conducting ancestry-stratified TWASs using ancestry-specific expression models and meta-analyzing results using inverse-variance weighting, showing the least test statistic inflation. Our work provides a foundation for adding transcriptomic context to biobank-linked GWASs, allowing for ancestry-aware discovery to accelerate genomic medicine.

Abstract

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