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 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 c

SUBMITTER: Cao C 

PROVIDER: S-EPMC9208638 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature