Identify Alzheimer’s disease subtypes and markers from multi‐omic data of human brain and blood with a subspace merging algorithm

Abstract

AbstractBackgroundAlzheimer’s disease (AD) is a highly heterogeneous disease with different disease trajectory, brain pathological changes, and risk factors. Identifying subtypes of AD can have greatly impact AD diagnosis, treatment, and disease management. Our goal is to identify subtypes of AD by integrating multiple types of omics data from AD patients’ brain samples and blood samples.MethodWe applied a subspace merging algorithm on AD patient brain samples with matched transcriptomic, DNA methylation, and proteomics data collected from the ROSMAP study. This algorithm merges patient graphs generated from these three datasets on a Grassmann manifold followed by spectral clustering. Differential gene expression and pathway analysis were applied to establish the gene signatures between the subtypes (patient clusters) and to the transcriptomic data of the peripheral blood monocyte (PBMC) from the matching patients to identify possible blood markers.ResultOur analysis identified two patient clusters between which the cognitive test scores or the degree of AD pathology are significantly different. Differentially expressed genes between the two groups were identified from both brain and blood samples. Gene set enrichment analysis identified a group of genes significantly enriched with synaptic functions in the brain samples. The Ingenuity Pathway Analysis was used to examine any connections between the significantly differentially expressed genes in the brain samples with the ones identified from the PBMC samples. The low density lipoprotein receptor (LDLR) gene, which was an APOE receptor observed from the blood sample is connected with 12 genes from the brain sample. The results suggested that LDLR could be a novel blood biomarker for AD subtyping.ConclusionThe subspace merging algorithm integrated multimodal omics data from the patients and successfully identified two clusters with distinct clinical progression. Differential expression analysis identified 388 significant genes from the brain samples and 18 from the PBMC samples. The pathway analysis indicated that LDLR could be a novel blood biomarker for AD subtyping. Future work will focus on validating LDLR as a candidate blood marker for AD subtyping using other public datasets with similar study design. The clinical relevance and molecular mechanism basis for the findings will also be investigated.