Accounting for population structure in deep learning models for genomic analysis.

Background: Deep learning methods are becoming increasingly popular for genotype analyses in recent years. In conventional genomic analyses, it is important to account for confounders to avoid biasing the results. Genetic relatedness is one of the most common confounders in conventional genomic analyses and there is a general consensus that it should be considered in the analysis to prevent distant levels of common ancestry from affecting the identification of causal variants. In contrast, genetic relatedness is not considered or ignored in many of the recently published deep learning models.

Objective: This study investigates whether the omission of genetic relatedness in deep learning models, common in recent literature, introduces confounding effects similar to those observed in conventional genomic analyses, particularly due to ancestry-related variants.

Methods: We developed and used a deep learning model to perform classifications based on single nucleotide polymorphism data from simulated and real-world datasets to examine whether population structure is confounding the model and potentially causing shortcut learning.

Results: The results of this study suggest that population structure may not significantly affect the performance of the deep learning model. However, explainable AI revealed notable differences in the focus between the confounded and unconfounded models when examining SNP feature importance.

Conclusion: While population structure may not heavily affect model performance, it is important to reduce the models' capabilities of shortcut learning when designing deep learning models for analyzing genomic datasets, by using ancestry-related variants over potentially relevant biomarkers of the disease or disorder in question. The code used to perform these analyses can be found at: https://github.com/notTrivial/populationStructure.