Brain MRI Phenotypes Associated with Polymorphisms at or Near the SLC39A8 (ZIP8) Metal Transporter Gene. (29th May 2020)

• Record Type: Journal Article
• Authors: Hermann, Evan; Chambers, Emily; Davis, Danielle; Montgomery, Mckale; Chowanadisai, Winyoo
• Is Part Of: Current developments in nutrition. Volume 4(2020)Supplement 2
• Page Start: 1808
• Page End: 1808
• Publication Date: 2020-05-29
• Subjects: Nutrition -- Periodicals; Nutritional Physiological Phenomena; Nutrition; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 612.3
• DOI: 10.1093/cdn/nzaa067_035
• Languages: English
• ISSNs: 2475-2991

Abstract

Abstract: Objectives: The SLC39A8 gene encodes a divalent metal transporter, ZIP8. ZIP8 polymorphisms are associated with pleiotropic effects including altered risks for schizophrenia. Our objective is to determine the different brain MRI phenotypes associated at or near the SLC39A8 (ZIP8) genetic locus using a phenome-wide association (PheWAS) approach followed by joint and conditional association analysis. Methods: Using the summary statistics database containing brain MRI genome-wide association study (GWAS) data, we systematically selected all brain MRI phenotypes which were associated with single-nucleotide polymorphisms (SNPs) within 1 million bp of the SLC39A8 genetic locus, as defined as reaching a P -value significance cutoff of P < 5.0 × 10–8. For all brain MRI phenotypes reaching significance, we used GCTA-COJO to determine the number of independent association signals using settings of P < 1.0 × 10–5 and a window of 10 million base pairs. Using SNPclip and the European 1000 Genomes linkage panel with a linkage disequilibrium cutoff of r2 > 0.8, we identified SNP candidates at each index SNP. Linkage equilibrium for brain phenotypes with multiple independent signals was confirmed by LDpair. Results: We identified 25 brain MRI phenotypes that vary due to MRI type and brain region that all contain a SNP associated with the SLC39A8 locus. All of these datasets have at least 1 index SNP directly labeling or in high linkage disequilibrium with rs13107325, which encodes a missense mutation in the SLC39A8 (ZIP8). Among the 25 datasets, an additional 4 association signals were identified by GCTA-COJO and confirmed to be in linkage equilibrium with rs13107325 using LDpair. For these additional association signals, probable causative SNPs were identified from the index SNP using SNPclip. Conclusions: From the 25 brain MRI phenotypes, we identified new probable causative SNPs in addition to a previously reported missense SNP (rs13107325) associated with schizophrenia. This study provides leads into how SNPs in genes involved in trace metal transport influence brain structures and affect risks for schizophrenia. Funding Sources: This work was funded by grants from the Oklahoma Center for the Advancement of Science and Technology and the Oklahoma Agricultural Experiment Station.