diagnostics Article Integrated Genomic, Transcriptomic and Proteomic Analysis for Identifying Markers of Alzheimer’s Disease Laura Madrid , Sandra C. Labrador, Antonio González-Pérez , María E. Sáez * and The Alzheimer’s Disease Neuroimaging Initiative (ADNI) †   Citation: Madrid, L.; Labrador, S.C.; González-Pérez, A.; Sáez, M.E.; The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Integrated Genomic, Transcriptomic and Proteomic Analysis for Identifying Markers of Alzheimer’s Disease. Diagnostics 2021, 11, 2303. https:// doi.org/10.3390/diagnostics11122303 Academic Editors: Giulia Bivona and Panteleimon Giannakopoulos Received: 17 September 2021 Accepted: 5 December 2021 Published: 8 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). CAEBi Bioinformática, Rio de la Plata 2, 41013 Sevilla, Spain; lmadrid@caebi.es (L.M.); sandra091999@gmail.com (S.C.L.); agonzalez@caebi.es (A.G.-P.) * Correspondence: mesaez@caebi.es † Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Abstract: There is an urgent need to identify biomarkers for Alzheimer’s disease (AD), but the identification of reliable blood-based biomarkers has proven to be much more difficult than initially expected. The current availability of high-throughput multi-omics data opens new possibilities in this titanic task. Candidate Single Nucleotide Polymorphisms (SNPs) from large, genome-wide association studies (GWAS), meta-analyses exploring AD (case-control design), and quantitative measures for cortical structure and general cognitive performance were selected. The Genotype- Tissue Expression (GTEx) database was used for identifying expression quantitative trait loci (eQTls) among candidate SNPs. Genes significantly regulated by candidate SNPs were investigated for differential expression in AD cases versus controls in the brain and plasma, both at the mRNA and protein level. This approach allowed us to identify candidate susceptibility factors and biomarkers of AD, facing experimental validation with more evidence than with genetics alone. Keywords: eQTLs; differential expression; integrative analysis; Alzheimer’s disease 1. Introduction Alzheimer’s disease (AD) is the leading cause of dementia worldwide, affecting 36 million people nowadays, and it is expected to triple its prevalence by mid-century. Familial forms of AD are caused by mutations on the amyloid-related genes PSEN1, PSEN2, and APP, while diverse candidate genes and pathways have been reported for sporadic AD, mainly provided by genome-wide association studies (GWAS) [1–7]. The largest risk factor for AD identified so far is the apolipoprotein E (APOE)E4 allele, conferring up to 16-fold increased risk in the homozygous state, but despite its relevance, APOE pathogenic role in AD has not been fully elucidated yet. The clinical major hallmarks of AD are amyloid deposits and neurofibrillary tangles. Consistently with this observation, amyloid, tau protein (T-tau), and tau phosphorylated at position threonine 181 (P-tau) have been found to be present at low levels in the cere- brospinal fluid (CSF) of AD patients when compared to controls, being the only AD biomarkers currently employed in the clinical setting [8–10]. Considerable efforts have been put into identifying biomarkers of the disease, especially in the prodromal stage, when early intervention is expected to reduce the burden of the disease. Diverse CSF biomarkers have been proposed, including the neurofilament light protein (NFL) [11], neurogranin (Ng) [12], the neuron-specific enolase (NSE) [13], the visinin-like protein 1 (VLP-1) [14], the monocyte chemotactic protein 1 (MCP-1) [15] or the glial fibrillary acidic protein (GFAP) [16]. Diagnostics 2021, 11, 2303. https://doi.org/10.3390/diagnostics11122303 https://www.mdpi.com/journal/diagnostics