A Systems Biology Approach for Hypothesizing the Effect of Genetic Variants on Neuroimaging Features in Alzheimer's Disease

Abstract

Background: Neuroimaging markers provide quantitative insight into brain structure and function in neurodegenerative diseases, such as Alzheimer's disease, where we lack mechanistic insights to explain pathophysiology. These mechanisms are often mediated by genes and genetic variations and are often studied through the lens of genome-wide association studies. Linking these two disparate layers (i.e., imaging and genetic variation) through causal relationships between biological entities involved in the disease's etiology would pave the way to large-scale mechanistic reasoning and interpretation.

Objective: We explore how genetic variants may lead to functional alterations of intermediate molecular traits, which can further impact neuroimaging hallmarks over a series of biological processes across multiple scales.

Methods: We present an approach in which knowledge pertaining to single nucleotide polymorphisms and imaging readouts is extracted from the literature, encoded in Biological Expression Language, and used in a novel workflow to assist in the functional interpretation of SNPs in a clinical context.

Results: We demonstrate our approach in a case scenario which proposes KANSL1 as a candidate gene that accounts for the clinically reported correlation between the incidence of the genetic variants and hippocampal atrophy. We find that the workflow prioritizes multiple mechanisms reported in the literature through which KANSL1 may have an impact on hippocampal atrophy such as through the dysregulation of cell proliferation, synaptic plasticity, and metabolic processes.

Conclusion: We have presented an approach that enables pinpointing relevant genetic variants as well as investigating their functional role in biological processes spanning across several, diverse biological scales.

Keywords: Alzheimer’s disease; genetic variants; knowledge graph; neuroimaging; systems biology.

Fig. 1

The two workflows developed for (A) gene prioritization and for (B) generating the mechanistic knowledge assembly around the effect of genetic variants on neuroimaging features in AD. In workflow A, the first step involves the selection of a corpus of relevant scientific literature. Next, the SNPs extracted from this corpus were subjected to LD block analysis and the subsequently obtained SNPs were mapped to their corresponding or associated genes. KANSL1, a novel AD gene, was selected from this pool of mapped genes for further investigation. In workflow B, corpus for the selected gene is extracted and translated into BEL to generate a knowledge assembly model for hypothesis generation.

Fig. 2

This figure shows the results obtained from the LD block analysis and gene mapping. The generation of the SNP-Neuroimaging corpus yielded 745 SNPs. Following LD block analysis, 6,070 SNPs that occur with the SNPs extracted from the literature were identified and located on 136 unique AD associated genes. These genes were then classified according to the number of evidences which are available in the scientific literature. The first group, incorporating 78 AD associated genes, comprises well-known genes characterized by a high number of publications in the AD context. The second group, that includes 58 AD associated genes, comprises emerging genes in the context of AD. From the latter group, KANSL1 was selected.

Fig. 3

The putative role of KANSL1 in hippocampal atrophy. A) KANSL1 role in hippocampal neurogenesis. B) KANSL1 function in hippocampal metabolic processes. C) KANSL1 role in hippocampal synaptic plasticity. [https://nbviewer.jupyter.org/github/sepehrgolriz/GeVa_NeIF/blob/master/Semi_automatic_developed_pipeline/Exploring% 20KANSL1% 20putative% 20role% 20graph% 20in% 20hippocampal% 20atrophy.ipynb].