Evaluating the Role of Race, Ethnicity, and Genetic Ancestry in Alzheimer Disease-associated Genes
The strongest risk gene identified for Alzheimer disease (AD) is APOE. However, this gene does not increase the risk for AD in every ethnic population. For example, individuals with an African ethnic background do not seem to be very affected by this variation. This is due to the fact that individuals from different races/ethnicities harbor genetic differences at the site of the APOE gene. This is why it is important to study populations separately and to take into account their genetic background, also called local ancestry, when analyzing the genetic effect on the disease. We propose to explore the relationship between local ancestry of African-American and Caribbean-Hispanic people and AD risk genes. We will facilitate the discovery of ethnic-specific genes and genetic changes increasing the risk for AD. This approach will allow us to move a further step toward personalized and precision medicine.
Our goal in this study is to evaluate the role of race/ethnicity and ancestry-specific genetic factors in known Alzheimer disease (AD) genes in our multi-ethnic dataset, and to identify novel risk loci correlated with genetic ancestry. In the experimental design, we will perform a series of ancestry-aware statistical tests to characterize the influence of ancestral background located in or near the genomic region of known genes in AD. Furthermore, to identify novel genes specifically through under-studied ancestries, such as African and Amerindian ancestral backgrounds, we will perform genome-wide analysis by testing the genetic factors in the context of genetic ancestry. This study will be the first study to systematically assess the role of genetic ancestry on AD risk in a diverse set of multi-ethnic samples, potentially leading to a set of genes and genomic regions that modify existing AD risk factors. This gives us the opportunity to identify novel factors influencing AD that may contribute to health disparities. New test approach will provide insights about the risk factors correlated with ancestral backgrounds and will enhance the power and extend studies in under-studied populations. In particular, identification of population-specific variations that influence disease could inform precision medicine initiatives, and lead to development of ancestry-specific AD treatments. This would improve treatments, and help reduce health disparities.
About the Researcher
I am a postdoctoral fellow with two years’ experience researching neurodegenerative diseases using machine learning approaches. Following completion of my undergraduate degree in electrical-electronics engineering at the Bilkent University, Turkey, I went on to study bioinformatics at Ankara University, Turkey. My PhD research focused on developing a novel approach to overcome the sample size problem in family-based genome-wide association studies. For the past two years, I have been working as a postdoctoral fellow in the John P. Hussman Institute for Human Genomics at the University of Miami, Miller School of Medicine, Miami, FL. My research uses computational, and genetic techniques to evaluate the role of ethnic-specific genetic factors in multi-ethnic datasets in Alzheimer disease. I am interested in developing the ancestry-aware approaches to test the influence of the ethnic-specific genetic factors in Alzheimer disease.
I have always been interested in developing and applying novel algorithms and solving challenging problems in basic sciences. While studying in high school, I participated in the national and international chemistry Olympiads competitions and was awarded gold and silver medals. Early on I understood the importance of strong mathematical skills, and that understanding led me to a deep interest in analytical and computational approaches, statistics, mathematics, algorithm analysis, data mining, and machine learning. During my PhD research, I began applying these methods to genetics and used them to develop a method for prediction of non-stationary signals in the gene expression microarrays data. I have chosen to pursue a career in computational biology because this area, through the use of these methods to model biological data and systems, allows me to extract useful knowledge to understand disease modifying factors that may lead to new interventional therapeutic strategies. This desire led me to develop a novel approach to test the association of genetic markers with disease in family-based study as part of my doctoral thesis. While I was working in this field I began studying neurobiological data and became interested in neuroscience. Human cognitive architecture and neurodegenerative disorders, such as Alzheimer and Parkinson disease, are fascinating, and I wanted to apply analytical methods to understand the underlying genetic or environmental reasons.
I am now applying these concepts to Alzheimer disease and population statistics in my ongoing post-doctoral fellowship in the John P. Hussman Institute for Human Genomics (HIHG) at the University of Miami, Miller School of Medicine. My training in statistics, engineering, and computational approaches has lead me to be in a unique position to develop and test new models to identify ancestry-specific risk/protective factors related to AD. I am currently in the ideal environment to execute my future career plan. I believe that the BrightFocus Award will help me to achieve my career goals, and I would like to say thanks to the BrightFocus Foundation for providing me with this opportunity.
First published on: September 25, 2018
Last modified on: June 22, 2020