Association analysis of rare variants near the APOE region with CSF and neuroimaging biomarkers of Alzheimer’s disease

Abstract

BackgroundThe APOE ε4 allele is the most significant common genetic risk factor for late-onset Alzheimer’s disease (LOAD). The region surrounding APOE on chromosome 19 has also shown consistent association with LOAD. However, no common variants in the region remain significant after adjusting for APOE genotype. We report a rare variant association analysis of genes in the vicinity of APOE with cerebrospinal fluid (CSF) and neuroimaging biomarkers of LOAD.MethodsWhole genome sequencing (WGS) was performed on 817 blood DNA samples from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Sequence data from 757 non-Hispanic Caucasian participants was used in the present analysis. We extracted all rare variants (MAF (minor allele frequency) < 0.05) within a 312 kb window in APOE’s vicinity encompassing 12 genes. We assessed CSF and neuroimaging (MRI and PET) biomarkers as LOAD-related quantitative endophenotypes. Gene-based analyses of rare variants were performed using the optimal Sequence Kernel Association Test (SKAT-O).ResultsA total of 3,334 rare variants (MAF < 0.05) were found within the APOE region. Among them, 72 rare non-synonymous variants were observed. Eight genes spanning the APOE region were significantly associated with CSF Aβ1-42 (p < 1.0 × 10−3). After controlling for APOE genotype and adjusting for multiple comparisons, 4 genes (CBLC, BCAM, APOE, and RELB) remained significant. Whole-brain surface-based analysis identified highly significant clusters associated with rare variants of CBLC in the temporal lobe region including the entorhinal cortex, as well as frontal lobe regions. Whole-brain voxel-wise analysis of amyloid PET identified significant clusters in the bilateral frontal and parietal lobes showing associations of rare variants of RELB with cortical amyloid burden.ConclusionsRare variants within genes spanning the APOE region are significantly associated with LOAD-related CSF Aβ1-42 and neuroimaging biomarkers after adjusting for APOE genotype. These findings warrant further investigation and illustrate the role of next generation sequencing and quantitative endophenotypes in assessing rare variants which may help explain missing heritability in AD and other complex diseases.

CRL4-Cereblon complex in Thalidomide Embryopathy: a translational investigation

“…In accordance with previous findings 10 , individuals with TE presented a high number of rare variants (MAF < 0.01). Rare variants have been widely studied in complex traits including diabetes 30 , Alzheimer 31 and cancer 32 , reflecting a shift in human genetic research of multifactorial diseases 33 .Comparison with a European sample of the 1000Genomes project demonstrated individuals with TE present a summation effect of identified variants. The results were similar when evaluating the total number of variants in TE individuals and comparing scores determined in the heatmap.…”

Association of Uncommon, Noncoding Variants in the APOE Region With Risk of Alzheimer Disease in Adults of European Ancestry

“…Associations between many other single-nucleotide variants (SNVs) at the APOE locus with AD risk, age at onset, and/or biomarkers have been reported. 7 , 8 …”

“…Many of these SNVs are in linkage disequilibrium (LD) with rs429358 in European ancestry samples, and most are noncoding changes that could affect gene expression. 7 , 8 The APOE locus includes a long cluster of genes transcribed in the same direction, suggesting that they may be coregulated by cis regulatory elements. These genes have also been implicated in shared biological pathways, including lipid metabolism, the immune system, and mitochondrial function, 5 , 7 which suggests that changes in either quality or quantity of the products of these genes may also be associated with AD.…”

Rare variants in the splicing regulatory elements of EXOC3L4 are associated with brain glucose metabolism in Alzheimer’s disease

“…Recently, the genetics of AD has been investigated in the context of imaging data. By combining information from genetic architecture, functional neuroimaging, and multi-omics data, genetic variation associated with AD-related imaging biomarkers have been identified and thus the potential influence of genetic variation on brain structure and function related to AD pathophysiology [ 10 , 11 ]. Furthermore, imaging endophenotypes can substantially increase statistical detection power of genetic association analysis through the use of quantitative traits as phenotypes [ 12 ].…”