# Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

## TL;DR

This paper introduces a new algorithm for identifying causal eQTLs, improving accuracy in heart-related disease research.

## Contribution

A novel algorithm, CausalEQTL, combining L0 + L1 penalized regression with an ensemble approach for better eQTL detection.

## Key findings

- CausalEQTL outperforms traditional models like LASSO and Elastic Net in power and performance.
- The algorithm provides deeper insights into heart-related tissue eQTL detection using GTEx data.

## Abstract

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L0 +L1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

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