Bhattacharya Arjun, Hirbo Jibril B, Zhou Dan, Zhou Wei, Zheng Jie, Kanai Masahiro, Pasaniuc Bogdan, Gamazon Eric R, Cox Nancy J
Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Institute of Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Cell Genom. 2022 Oct 12;2(10). doi: 10.1016/j.xgen.2022.100180.
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.
全球生物样本库荟萃分析倡议组织（GBMI）因其多样性，为研究全人群和特定血统的基因关联提供了宝贵机会。然而，由于各生物样本库采用了多种确定策略和多血统研究人群，GBMI在实施统计遗传学方法方面面临独特挑战。全转录组关联研究（TWAS）通过将全基因组关联研究（GWAS）中的基因变异与性状关联和基因表达预测模型相结合，提高了对基因关联的检测能力，并为其提供生物学背景。TWAS除了面临GWAS所具有的独特挑战外，在多生物样本库的荟萃分析环境中尤其如此。在此，我们介绍GBMI的TWAS流程，概述在框架的每个步骤中有关血统和组织特异性、荟萃分析策略以及开放性挑战的实际考虑因素。我们建议使用特定血统的表达模型进行血统分层的TWAS，并使用逆方差加权对结果进行荟萃分析，以显示最小的检验统计量膨胀。我们的工作为在与生物样本库相关的GWAS中添加转录组背景奠定了基础，从而实现基于血统的发现，加速基因组医学的发展。
