The SORL1 gene and convergent neural risk for Alzheimer's disease across the human lifespan

Abstract

Prior to intervention trials in individuals genetically at-risk for late-onset Alzheimer's disease, critical first steps are identifying where (neuroanatomic effects), when (timepoint in the lifespan) and how (gene expression and neuropathology) Alzheimer's risk genes impact the brain. We hypothesized that variants in the sortilin-like receptor (SORL1) gene would affect multiple Alzheimer's phenotypes before the clinical onset of symptoms. Four independent samples were analyzed to determine effects of SORL1 genetic risk variants across the lifespan at multiple phenotypic levels: (1) microstructural integrity of white matter using diffusion tensor imaging in two healthy control samples (n=118, age 18-86; n=68, age 8-40); (2) gene expression using the Braincloud postmortem healthy control sample (n=269, age 0-92) and (3) Alzheimer's neuropathology (amyloid plaques and tau tangles) using a postmortem sample of healthy, mild cognitive impairment (MCI) and Alzheimer's individuals (n=710, age 66-108). SORL1 risk variants predicted lower white matter fractional anisotropy in an age-independent manner in fronto-temporal white matter tracts in both samples at 5% family-wise error-corrected thresholds. SORL1 risk variants also predicted decreased SORL1 mRNA expression, most prominently during childhood and adolescence, and significantly predicted increases in amyloid pathology in postmortem brain. Importantly, the effects of SORL1 variation on both white matter microstructure and gene expression were observed during neurodevelopmental phases of the human lifespan. Further, the neuropathological mechanism of risk appears to primarily involve amyloidogenic pathways. Interventions targeted toward the SORL1 amyloid risk pathway may be of greatest value during early phases of the lifespan.

Figure 1

1A. Results of TBSS white matter analysis for CAMH (A) and Zucker Hillside (B) imaging-genetics datasets. The average white matter FA skeletons for each sample have been overlaid on the MNI152 1mm T1-weighted brain standard and significant voxels are indicated by yellow-red colouring, corrected for multiple comparisons using TFCE at p<0.05. Only voxels within the mean FA skeleton (Green) were analyzed, surrounding voxels have been colo ured for emphasis. UNF = uncinate fasciculus; IFOF = inferior fronto-occipital fasciculus; CB = cingulum bundle; CC = corpus callosum; IC = internal capsule; ARC/SLF = arcuate fasciculus/superior longitudinal fasciculus; (R) = right; (L) = left. 1B. Regression model residuals of white matter fractional anisotropy at select peak voxels (as determined using TBSS) plotted against age, according to SORL1 rs689021 genotypic group ([A] allele homozygotes vs. [G] allele-carriers) in both the CAMH and Zucker Hillside samples. Models co-varied for sex and APOE ε4 status.

Figure 1

1A. Results of TBSS white matter analysis for CAMH (A) and Zucker Hillside (B) imaging-genetics datasets. The average white matter FA skeletons for each sample have been overlaid on the MNI152 1mm T1-weighted brain standard and significant voxels are indicated by yellow-red colouring, corrected for multiple comparisons using TFCE at p<0.05. Only voxels within the mean FA skeleton (Green) were analyzed, surrounding voxels have been colo ured for emphasis. UNF = uncinate fasciculus; IFOF = inferior fronto-occipital fasciculus; CB = cingulum bundle; CC = corpus callosum; IC = internal capsule; ARC/SLF = arcuate fasciculus/superior longitudinal fasciculus; (R) = right; (L) = left. 1B. Regression model residuals of white matter fractional anisotropy at select peak voxels (as determined using TBSS) plotted against age, according to SORL1 rs689021 genotypic group ([A] allele homozygotes vs. [G] allele-carriers) in both the CAMH and Zucker Hillside samples. Models co-varied for sex and APOE ε4 status.

Figure 2

SORL1 mRNA expression in the prefrontal cortex plotted against age, according to SNP 9 (rs689021) genotype in the BrainCloud postmortem sample. Raw expression data are shown fit with loess smoothing curves for each genotype. Ordinary least squares regression model shows a non-linear genotype by age interaction (interaction effect: F_11,78=7.03, two-tailed p=0.0003).