Disentangling genetic feature selection and aggregation in transcriptome-wide association studies

Abstract 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 simultan...

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Published in:Genetics (Austin) 2022-02, Vol.220 (2)
Main Authors: Cao, Chen, Kossinna, Pathum, Kwok, Devin, Li, Qing, He, Jingni, Su, Liya, Guo, Xingyi, Zhang, Qingrun, Long, Quan
Format: Article
Language:English
Subjects:
Adaptability
Agglomeration
Data analysis
Feature selection
Genetics
Genotypes
Investigation
Kernel functions
Machine learning
Phenotypes
Prediction models
Single-nucleotide polymorphism
Statistical power
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Summary:Abstract 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.
ISSN:1943-2631
0016-6731
1943-2631
DOI:10.1093/genetics/iyab216