Genome-wide association study of Alzheimer’s disease brain imaging biomarkers and neuropsychological phenotypes in the EMIF-AD Multimodal Biomarker Discovery dataset

Abstract

Alzheimer's disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, ageing populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e. case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n=931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses. As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1 (EH domain binding protein 1; on chromosome 2p15) and CEP112 (centrosomal protein 112; 17q24.1) with delayed recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 (interleukin 1 receptor accessory protein like 1; Xp21.3). While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline.

X chromosome-wide association study of quantitative biomarkers from the Alzheimer’s Disease Neuroimaging Initiative study

“…Christopher et al also conducted an XWAS and found that the SNP rs11094635 located upstream of the MTM1 gene is associated with beta-amyloid accumulation in the ADNI database (Christopher et al, 2018), but ignored the studies of other QBs in the ADNI database. Note that so far, we are not aware of any XWAS for different types of QBs in the ADNI database, although there have been some previous GWAS based on autosomal loci (Li et al, 2017;Kong et al, 2018;Zhou et al, 2018;Wang et al, 2021;Homann et al, 2022;Oatman 10.3389/fnagi.2023.1277731 Frontiers in Aging Neuroscience 03 frontiersin.org et al, 2023). Therefore, there is an urgent need to perform XWAS on the QBs of the ADNI.…”

Retinal Alterations Predict Early Prodromal Signs of Neurodegenerative Disease

“…A small fraction of individuals, approximately 1-5% between the ages of 30 and 60, with Alzheimer's disease develop an early-onset form of the disease. These early-onset forms are often attributed to rare mutations in three genes encoding APP and presenilin 1 and 2 (PSEN1/PSEN2) [148,149]. On the environmental front, modifiable risk factors for AD include hypertension, smoking, diabetes, depression, head injury, physical inactivity, depression, low educational attainment, and obesity [150].…”

Clinical effects of novel susceptibility genes for beta-amyloid: a gene-based association study in the Korean population

“…Recent studies have focused on AD endophenotypes, including Aβ and neurodegeneration markers. This approach related to endophenotypes may help identify additional AD-related genetic variants (Potkin et al, 2009;Ramanan et al, 2015;Maxwell et al, 2018;Elsheikh et al, 2020;Homann et al, 2022).…”