Improving genetic variant identification for quantitative traits using ensemble learning-based approaches

Genome-wide association studies (GWAS) are rapidly advancing due to the improved resolution and completeness provided by Telomere-to-Telomere (T2T) and pangenome assemblies. While recent advancements in GWAS methods have primarily focused on identifying genetic variants associated with discrete phenotypes, approaches for quantitative traits (QTs) remain underdeveloped. This has often led to significant variants being overlooked due to biases from genotype multicollinearity and strict p-value thresholds.

We propose an enhanced ensemble learning approach for QT analysis that integrates regularized variant selection with machine learning-based association methods, validated through comprehensive biological enrichment analysis. We benchmarked four widely recognized single nucleotide polymorphism (SNP) feature selection methods-least absolute shrinkage and selection operator, ridge regression, elastic-net, and mutual information-alongside four association methods: linear regression, random forest, support vector regression (SVR), and XGBoost. Our approach is evaluated on simulated datasets and validated using a subset of the PennCATH real dataset, including imputed versions, focusing on low-density lipoprotein (LDL)-cholesterol levels as a QT. The combination of elastic-net with SVR outperformed other methods across all datasets. Functional annotation of top 100 SNPs identified through this superior ensemble method revealed their expression in tissues involved in LDL cholesterol regulation. We also confirmed the involvement of six known genes (APOB, TRAPPC9, RAB2A, CCL24, FCHO2, and EEPD1) in cholesterol-related pathways and identified potential drug targets, including APOB, PTK2B, and PTPN12.

In conclusion, our ensemble learning approach effectively identifies variants associated with QTs, and we expect its performance to improve further with the integration of T2T and pangenome references in future GWAS.