Identifying genetic markers enriched by brain imaging endophenotypes in Alzheimer's disease

Abstract

Background: Alzheimer's disease (AD) is a complex neurodegenerative disorder and the most common type of dementia. AD is characterized by a decline of cognitive function and brain atrophy, and is highly heritable with estimated heritability ranging from 60 to 80[Formula: see text]. The most straightforward and widely used strategy to identify AD genetic basis is to perform genome-wide association study (GWAS) of the case-control diagnostic status. These GWAS studies have identified over 50 AD related susceptibility loci. Recently, imaging genetics has emerged as a new field where brain imaging measures are studied as quantitative traits to detect genetic factors. Given that many imaging genetics studies did not involve the diagnostic outcome in the analysis, the identified imaging or genetic markers may not be related or specific to the disease outcome.

Results: We propose a novel method to identify disease-related genetic variants enriched by imaging endophenotypes, which are the imaging traits associated with both genetic factors and disease status. Our analysis consists of three steps: (1) map the effects of a genetic variant (e.g., single nucleotide polymorphism or SNP) onto imaging traits across the brain using a linear regression model, (2) map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model, and (3) detect SNP-diagnosis association via correlating the SNP effects with the diagnostic effects on the brain-wide imaging traits. We demonstrate the promise of our approach by applying it to the Alzheimer's Disease Neuroimaging Initiative database. Among 54 AD related susceptibility loci reported in prior large-scale AD GWAS, our approach identifies 41 of those from a much smaller study cohort while the standard association approaches identify only two of those. Clearly, the proposed imaging endophenotype enriched approach can reveal promising AD genetic variants undetectable using the traditional method.

Conclusion: We have proposed a novel method to identify AD genetic variants enriched by brain-wide imaging endophenotypes. This approach can not only boost detection power, but also reveal interesting biological pathways from genetic determinants to intermediate brain traits and to phenotypic AD outcomes.

Keywords: Brain imaging genetics; Genome-wide association study; Imaging-diagnosis map; Imaging-genetics map.

Fig. 1

The significance heat map for imaging-diagnosis analysis. The effect of the diagnosis outcome on AV-45 imaging QT data is estimated at each ROI. For each ROI-diagnosis pair, $-lo{g}_{10}(p)$ is color-coded and shown in the heat map

Fig. 2

The distribution of the number of SNPs identified from applying the proposed pipeline to randomly selected 54 SNPs (across 10,000 runs). a–c show the histograms of the number of SNPs identified for three different diagnostic comparisons (i.e., CN vs. AD, CN vs. LMCI, and CN vs. EMCI) respectively. The red dashed line indicates the number of SNPs identified from the pipeline using 54 AD susceptibility loci

Fig. 3

The significance heat map for imaging-genetics analysis. Sub-figures (a), (b), (c) show the p-value significance of imaging-genetics analysis for all SNP-ROI pairs on three data sets (i.e., CN vs. AD, CN vs. LMCI, and CN vs. EMCI), respectively. For each ROI-SNP pair, $-lo{g}_{10}(p)$ is color-coded and shown in the heat map