期刊
GENETICS
卷 220, 期 2, 页码 -出版社
GENETICS SOCIETY AMERICA
DOI: 10.1093/genetics/iyab216
关键词
statistical genetics; transcriptome-wide association studies; feature selection; kernel machine; statistical power
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN2017-04860]
- Canada Foundation for Innovation JELF grant [36605]
- New Frontiers in Research Fund [NFRFE-2018-00748]
- HBI Pilot grant
- NSERC [RGPIN-2018-05147]
- University of Calgary VPR Catalyst grant
- Alberta Children's Hospital Research Institute (ACHRI) scholarship
- NSERC USRA award
This study investigates the core component of genetically regulated expression (GReX) in transcriptome-wide association studies (TWAS). The research shows that separating feature selection and feature aggregation steps can improve the predictive accuracy of TWAS compared to standard protocols relying on GReX. Furthermore, the study highlights the importance of methodological research focusing on optimal combinations of feature selection and aggregation approaches to enhance TWAS protocols.
The success of transcriptome-wide association studies (TWAS) has led to substantial research toward improving the predictive accuracy of its core component of genetically regulated expression (GReX). GReX links expression information with genotype and phenotype by playing two roles simultaneously: it acts as both the outcome of the genotype-based predictive models (for predicting expressions) and the linear combination of genotypes (as the predicted expressions) for association tests. From the perspective of machine learning (considering SNPs as features), these are actually two separable steps-feature selection and feature aggregation-which can be independently conducted. In this study, we show that the single approach of GReX limits the adaptability of TWAS methodology and practice. By conducting simulations and real data analysis, we demonstrate that disentangled protocols adapting straightforward approaches for feature selection (e.g., simple marker test) and aggregation (e.g., kernel machines) outperform the standard TWAS protocols that rely on GReX. Our development provides more powerful novel tools for conducting TWAS. More importantly, our characterization of the exact nature of TWAS suggests that, instead of questionably binding two distinct steps into the same statistical form (GReX), methodological research focusing on optimal combinations of feature selection and aggregation approaches will bring higher power to TWAS protocols.
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