AUTHOR=Homann Jan , Osburg Tim , Ohlei Olena , Dobricic Valerija , Deecke Laura , Bos Isabelle , Vandenberghe Rik , Gabel Silvy , Scheltens Philip , Teunissen Charlotte E. , Engelborghs Sebastiaan , Frisoni Giovanni , Blin Olivier , Richardson Jill C. , Bordet Regis , Lleó Alberto , Alcolea Daniel , Popp Julius , Clark Christopher , Peyratout Gwendoline , Martinez-Lage Pablo , Tainta Mikel , Dobson Richard J. B. , Legido-Quigley Cristina , Sleegers Kristel , Van Broeckhoven Christine , Wittig Michael , Franke Andre , Lill Christina M. , Blennow Kaj , Zetterberg Henrik , Lovestone Simon , Streffer Johannes , ten Kate Mara , Vos Stephanie J. B. , Barkhof Frederik , Visser Pieter Jelle , Bertram Lars TITLE=Genome-Wide Association Study of Alzheimer’s Disease Brain Imaging Biomarkers and Neuropsychological Phenotypes in the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery Dataset JOURNAL=Frontiers in Aging Neuroscience VOLUME=14 YEAR=2022 URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2022.840651 DOI=10.3389/fnagi.2022.840651 ISSN=1663-4365 ABSTRACT=

Alzheimer’s disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging 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 as well as SMOC2 (SPARC related modular calcium binding 2; 6p27) with immediate 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.