# ASTWAS: modeling alternative polyadenylation and SNP effects in kernel-driven TWAS reveal novel genetic associations for complex traits

## TL;DR

This paper introduces ASTWAS, a new method that improves genetic association studies by modeling alternative polyadenylation and SNP effects, uncovering novel genes linked to complex diseases like type 1 diabetes and rheumatoid arthritis.

## Contribution

ASTWAS introduces a kernel-driven framework that models both linear and nonlinear SNP effects on APA, outperforming traditional TWAS methods in detecting genetic associations.

## Key findings

- ASTWAS shows higher statistical power than linear APA-TWAS in simulations with various genetic architectures.
- ASTWAS identifies novel candidate genes like GABBR1 and RGL2 in immune-related pathways for complex diseases.
- The method is implemented in Python and available on GitHub.

## Abstract

Alternative polyadenylation (APA) of \documentclass[12pt]{minimal}
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$3^{\prime}$\end{document}untranslated regions (\documentclass[12pt]{minimal}
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$3^{\prime}$\end{document}UTRs) is a pervasive mechanism that regulates mRNA stability, localization, and translational efficiency by generating isoforms with distinct \documentclass[12pt]{minimal}
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$3^{\prime}$\end{document}UTR lengths and regulatory element composition. Despite its critical role in fine-tuning gene expression, APA has been largely overlooked in transcriptome-wide association studies (TWAS), which traditionally rely on linear models of SNP effects. To bridge this gap, we developed ASTWAS, a two-stage framework that first trains APA usage prediction models (BLUP, Elastic Net, LASSO, and TOP1) to quantify SNP impacts on distal poly(A) site choice via the percentage of distal poly(A) site usage index, and then aggregates weighted SNP effects within a kernel method to capture both linear and nonlinear genetic interactions. In extensive simulations spanning additive, epistatic, heterogeneous, compensatory, and single-variant architectures under both pleiotropy and causality scenarios, ASTWAS shows higher statistical power than linear APA-TWAS (\documentclass[12pt]{minimal}
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$3^{\prime}$\end{document}aTWAS), especially at low heritability and in the presence of SNP interactions. Applied to WTCCC type 1 diabetes and rheumatoid arthritis cohorts, ASTWAS not only rediscovers known susceptibility genes but also suggests novel candidates (e.g. GABBR1, RGL2) that form coherent interaction modules and enrich immune-related pathways, underscoring the biological significance of our algorithm in complex trait genetics. ASTWAS is implemented in Python and freely available at https://github.com/wl-Simplecss/ASTWAS.