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Discovery of SLC16A9 and SLC22A1 as regulators of acylcarnitines associated with Alzheimer’s disease
Author(s) -
HorgusluogluMoloch Emrin,
Arnold Matthias,
KaddurahDaouk Rima F.,
Zhang Bin
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.043411
Subject(s) - genome wide association study , cohort , single nucleotide polymorphism , alzheimer's disease neuroimaging initiative , psychology , disease , genetic association , medicine , alzheimer's disease , oncology , genetics , biology , gene , genotype
Background Propionylcarnitine (C3) as short‐chain acylcarnitine is highly associated with brain volume changes and cognitive impairment in symptomatic stages of Alzheimer’s Disease (AD), and the lower concentration of C3 level is associated with cerebrospinal fluid amyloid‐beta level in AD (Varma, Vijay et al. 2018). However, the genetic mechanism and key biological pathways underlying changes in propionylcarnitine in AD remain unclear. To better understand the key drivers and biological pathways underlying changes in propionylcarnitine in AD, we performed a genome‐wide association study (GWAS) of propionylcarnitine using Alzheimer’s Disease Neuroimaging Initiative (ADNI) Cohort. Method GWAS for propionylcarnitine from Non‐Hispanic Caucasian participants in the ADNI cohort (328 controls, 84 significant memory concern, 712 mild cognitive impairment, and 276 AD) was performed using an additive model with a linear association analysis for quantitative traits in Plink v1.90. Age, gender, education, body mass index (BMI), medication history, cohort, and diagnosis status were used as a covariate. A summary p‐value for each gene (including ± 5kb) was calculated from GWAS results using GATES (Li, Gui et al. 2011). For the pathway analysis of GWAS results, the Molecular Signatures Database was used to identify biological pathways representing the enrichment of association in the GWAS. False discovery rate (FDR) P < 0.05 was considered to identify significant pathways. Result We identified nine SNPs resides in SLC16A9 (a carnitine efflux transporter) gene, and four SNPs reside in SLC22A1 (related to acylcarnitine efflux) gene (Table 1; Figure 1). Analysis of the genotyping level of the most significant SNP (rs1171616) shows that the participants carrying at least one copy of the minor allele (N = 560; GT/GG genotype) have less C3 metabolite level in the blood compared to those without minor allele (N = 828; TT genotype) (Figure 2A). Participants with two minor alleles have worse episodic memory scores (Figure 2B and 2C). Enrichment pathway analysis with C3 associated genes (nominal p‐value < 0.05) revealed that the immune system, Alzheimer’s Disease, mitochondrion, neurogenesis, metabolic process, and enzyme binding are among the pathways enriched for the genes from the gene‐based association analysis (Table 2). Conclusion SLC16A9 and SLC22A1 are potential regulators of acylcarnitine levels associated with AD.