  Background: Although Alzheimer's disease (AD) is a central nervous system
disease and type 2 diabetes mellitus (T2DM) is a metabolic disorder, an
increasing number of genetic epidemiological studies show clear link between AD
and T2DM. The current approach to uncovering the shared pathways between AD and
T2DM involves association analysis; however, such analyses lack power to
discover the mechanisms of the diseases. Methods: We develop novel statistical
methods to shift the current paradigm of genetic analysis from association
analysis to deep causal inference for uncovering the shared mechanisms between
AD and T2DM, and develop pipelines to infer multilevel omics causal networks
which lead to shifting the current paradigm of genetic analysis from genetic
analysis alone to integrated causal genomic, epigenomic, transcriptional and
phenotypic data analysis. To discover common causal paths from genetic variants
to AD and T2DM, we also develop algorithms that can automatically search the
causal paths from genetic variants to diseases and Results: The proposed
methods and algorithms are applied to ROSMAP dataset with 432 individuals who
simultaneously had genotype, RNA-seq, DNA methylation and some phenotypes. We
construct multi-omics causal networks and identify 13 shared causal genes, 16
shared causal pathways between AD and T2DM, and 754 gene expression and 101
gene methylation nodes that were connected to both AD and T2DM in multi-omics
causal networks. Conclusions: The results of application of the proposed
pipelines for identifying causal paths to real data analysis of AD and T2DM
provided strong evidence to support the link between AD and T2DM and unraveled
causal mechanism to explain this link.
