FARID RAJABLI (United States of America)
University of Miami
John P. Hussman Institute for Human Genetics
Dr. Rajabli is an Associate Scientist at the John P. Hussman Institute for Human Genomics (HIHG) at the University of Miami Miller School of Medicine. He has 10 years of experience in machine learning and computational biology, with a primary focus on the neurological disorders of Alzheimer’s disease. During his doctoral and postdoctoral training, he has mastered a variety of techniques including genome-wide association studies, family-based association studies, next-generation sequence analysis methods, population genetics, and whole-exome sequencing.

Presenter of 1 Presentation

 Conference Calendar

ADMIXTURE MAPPING IDENTIFIES NOVEL ALZHEIMER DISEASE RISK REGIONS IN AFRICAN AMERICANS

Session Name
1720 - GENETICS OF NEURODEGENERATION 4 (ID 208)
Session Type
SYMPOSIUM
Date
Sat, 19.03.2022
Session Time
05:15 PM - 06:45 PM
Room
ONSITE: 133-134
Lecture Time
06:00 PM - 06:15 PM
Presenter

Abstract

Aims

African American (AA) population with the admixed genetic ancestry provides a unique opportunity to identify novel genetic factors associated with Alzheimer's disease (AD) related to genetic ancestry. Admixture mapping provides a complementary and sometimes more powerful approach to SNP-based genome-wide association studies in admixed populations. This study used admixture mapping to prioritize the genetic regions associated with AD in AA individuals, followed by ancestry-aware regression analysis to fine-map the prioritized regions.

Methods

We analyzed 10,271 individuals from 17 different AA datasets. We performed admixture mapping and meta-analyzed the results. We then used regression analysis, adjusting for local ancestry main effects and interactions with genotype, to refine the regions identified from admixture mapping. Finally, we leveraged in silico annotation and differential gene expression data to prioritize AD-related variants and genes.

Results

Admixture mapping identified two genome-wide significant loci on chromosomes 17p13.2 (p=2.2 x10-5) and 18q21.33 (p=1.2x10-5). The ancestry-aware regression model identified a significant interaction term on chromosome 17p13.2 (p-value=7.93x10-5; parametric bootstrap FDR < 0.05). Our ancestry-aware regression approach showed that AA individuals have a lower risk of AD if they inherited 17p13.2 locus from their African ancestors.

Conclusions

We identified two novel AD risk genetic loci in AAs; however, the 17p13.2 region was identified as genome-wide significant in three previous meta-analysis studies in non-Hispanic White populations. Our fine-mapping of the chromosome 17p13.2 and 18q21.33 regions revealed several interesting genes, some of which have been implicated in AD risk, such as the MINK1, KIF1C, and BCL2.

Hide