Table 1.

Examples of artificial intelligence methods to potentially address current challenges in the study of dementia genetics and omics.

Challenge	Use of AI/ML/DL
Multi-scale or non-linear epistatic interactions are overlooked when investigating genetic variants individually through GWAS	• ML accurately predicts multiple outcomes at a time • Tree-based methods can be used to capture complex non-linear epistatic interactions and select interacting genetic variants
GWAS are limited by genetic detection of genome-wide hits	• DL models can deal with non-linear associations between the phenotype and non-genetic covariates to improve GWAS hits detection
GWAS are limited by European ancestry based research	• ML models in some cases are better to incorporate trans-ethnic variation and implement transfer learning
Cell-type effects and specific pathologies are difficult to reproducibly categorize	• DL can predict cell-type-specific regulatory effects using multi-omics data integration substantially reducing the false positive rate • DL and computer vision can be used for generating harmonized digital pathology datasets
PRS are limited by predictive accuracy and hampered by heritability	• Novel DL-based model that does not only rely on the addictive effect of risk SNPs, may outperform more traditional PRS models across a variety of disease phenotypes
Causal inferences are often underpowered and limited in scope	• DeepMR [41] approaches integrate ML with MR by using multi-task DL models to learn the relationship between different sets of genomic marks associated with a pathway or phenotype of interest and then uses MR to examine causal relationships between them.

AI - artificial intelligence; ML - machine learning; DL - deep learning; GWAS - Genome-wide association studies; PRS - polygenic risk score; MR - Mendelian Randomization; DeepMR - Deep Mendelian Randomization.