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Background: Non‐Hispanic White APOE4 carriers have a higher risk of developing AD compared to African American APOE4 carriers. The local ancestry (LA) surrounding the APOE region was previously shown to be the primary factor in this risk difference. APOE4 carriers of European LA (ELA) have been found to have higher APOE4 expression and chromatin accessibility compared to African LA (ALA). We sought to investigate whether the LA around APOE3 has the same effect between ancestries. Differences between alleles could provide insight into ancestry‐specific regulatory areas controlling the APOE4 expression. Method: We identified AD autopsy samples by GSA, all homozygous for APOE3 and LA from 4 ADRC brain banks: Miami, Emory, Duke, and Rush. We performed single nuclei RNA sequencing (snRNA‐seq) on eight frozen frontal cortex (B9 area) samples using 10x Genomics. Result: We performed snRNA‐seq in a total of 51,462 nuclei from eight brains (4 ELA and 4 ALA). We identified 35 distinct cell clusters at a resolution of 0.6. The proportion of cells per cluster between ELA and ALA samples was similar for all clusters, except for cluster 32 (Excitatory Neurons) which had a greater than 2‐fold difference in ALA. Our data shows that APOE3 carriers with ELA have a significantly higher APOE expression in Excitatory Neurons (cluster 7) than those of ALA and contrary to our previous observations in APOE4 carriers (Griswold, A. et al, (2021)), APOE3 carriers of ALA express higher APOE in astrocytes (cluster 2) and Microglia (cluster 6). However, overall, comparison of APOE3 vs APOE4 carriers in this study demonstrated that APOE3 alleles have significantly lower gene expression than APOE4 carriers of the same LA. Conclusion: Our preliminary data suggest that the LA surrounding APOE3 is associated with different effects on APOE expression compared to APOE4 allele. Further, within the same LA, we observed that, overall, the different cell types express less APOE in APOE3 carriers compared to APOE4 carriers. Altogether, this may provide additional insight into the regulatory mechanisms affecting APOE4 expression. [ABSTRACT FROM AUTHOR]

Background: The effect size of APOE4 varies across genetic ancestries with African (AFR) local ancestry conferring a lower risk when compared to other ancestries. Recently, we identified a strong effect of the A allele of rs10423769 (with a minor allele frequency of 0.12 in AFR and 0.003 in Europeans), which lowers the risk of AD by 71% in APOE4 carriers. rs10423769 is 2 MB from APOEe4 in a region of segmental duplications (SD) containing a cluster of pregnancy specific beta‐1 glycoprotein genes (PSGs) and a lncRNA. To gain insights into the mechanism of protection, we characterized the haplotype harboring the variant and investigated differences in methylation and structural variation (SV) between haplotypes. Method: We used the Alzheimer's Disease Sequencing Project (ADSP) short read sequencing database to fine map and extend the protective haplotype using Haploview 4.2. In addition, long read sequencing (LRS) with the Oxford Nanopore PromethION was performed on 16 samples of individuals carrying one or two copies of rs10423769_A. LRS was used for SV calling with Sniffles2 and differential methylation analysis using Nanomethphase. Result: The minimum shared haplotype harboring rs10423769_A allele was ∼21kbp (chr19:43099521‐ 43120243). Though the region is rich in SDs, high quality LRS reads mapped uniquely to the region around the A allele confirming that the haplotype is unique and not duplicated elsewhere in the genome of the sequenced individuals. Moreover, the rs10423769_A haplotype harbors 23 differential CpG sites (fdr<0.05) compared to rs10423769_G, although no differential methylated region was identified. LRS revealed a 140bp insertion of repetitive MEF2 binding motifs. Initial examination demonstrated that the insertion was present in 80% of the A allele carriers. Conclusion: We have identified an AF‐specific haplotype protective against AD risk conferred by APOE ε4. Considering the distance from APOE and the complexity of the highly SD region where the variant is located, we have identified the minimum shared haplotype from AFR origin and are investigating the potential effects of SVs and differential CpG sites. Ongoing analyses include 3D chromatin structure and LRS at higher coverage to assemble and resolve the complex genomic context of the allele. [ABSTRACT FROM AUTHOR]

Background: In the US, African Americans (admixed with African and European) followed by Hispanics (admixed with Amerindian, African, and European) are the most affected groups compared to non‐Hispanic Whites (NHW). While genetic diversity and admixture play crucial roles in disease risk, the ancestry‐specific mechanisms remain poorly understood with most AD‐related studies focusing on NHW. Despite the recent field efforts to include genetically admixed populations, there continues to be a lack of functional studies in AD across the different cell types in these populations. Given the importance of Microglia in AD, we here characterize the genetic regulatory architecture (GRA) on iPSC‐derived Microglia (MGL) in African and Amerindian genomes. Method: iPSC lines derived from controls and AD patients with >90% genomic content from different ancestries (Amerindian, African, and European) were differentiated into MGL. We performed bulk RNA‐seq and ATAC‐seq, followed by differential expression and accessibility analyses to study the GRA of these admixed populations and its contributions to AD. Result: We identified 1,103 differentially expressed genes (DEGs) and 267 differentially accessible genes (DAGs) across ancestries. We observed the most differences on both chromatin accessibility and gene expression levels between AI and AF. On the chromatin level and in the context of AD, we observed 2 DAGs (PRDM7 and SCIMP) between AI and AF, and 1 DAG between AI and EU (PRDM7). In addition, we identified 10 AD‐risk modifying genes that are differentially expressed between AI and AF ancestries (ABI3, CTSB, JAZF1, MS4A6A, PILRA, PLEKHA1, RASGEF1C, SORL1, TREM2, and TREML2) and 3 DEGs between AI and EU (JAZF1, MS4A6A, and SORL1). We identified several DEGs to be involved in lipid metabolism, cholesterol biosynthesis and metabolism, lysosomal activity, and immune response ‐ all highly relevant processes in AD pathology. Conclusion: We provide new insights into ancestry‐specific genetic risk factors in AD pathophysiology. Here, we report novel transcriptomic and chromatin accessibility data in microglia of AI and AF ancestries that potentially contribute to a differential genetic risk in AD in the different ancestries. Interestingly, those ancestries with greatest migratory differences revealed the largest DEG. [ABSTRACT FROM AUTHOR]