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BrightFocus Research Grants Funding
Grant Funding for Alzheimer's Research
Grant Funding for Macular Degeneration Research
Grant Funding for Glaucoma Research


Macular Degeneration Research
Completed Award

Dr. Jonathan Haines

Jonathan L. Haines, Ph.D.

Vanderbilt University Medical Center
Nashville, TN, United States

Title: The Genetics of AMD in African-Americans
Non-Technical Title: The Genetics of Age-Related Macular Degeneration in African-Americans

Anita Agarwal, M.D.
Vanderbilt University

Duration: July 1, 2011 - June 30, 2013
Award Type: Standard
Award Amount: $100,000


The primary goal of this project is ascertain at least 100 African-American individuals with age-related macular degeneration and 100 African-American unrelated controls. We will use multiple modalities to identify and enroll these individuals. We will then test this dataset for known genetic risk factors to determine if these effects generalize in the African-American population.


Many of the risk genes for age‐related macular degeneration (AMD) were discovered by looking at DNA samples from Caucasian (white with European ancestry) subjects. Drs. Jonathan Haines, Anita Agarwal, and collaborators will look for new risk genes by comparing the “spelling” of the DNA of African‐Americans with AMD to the DNA from unrelated African‐Americans without AMD. If this group discovers new risk genes, then it may imply that AMD starts and/or progresses in African‐Americans in a different way than it does for other ethnicities. However, the identity of the genes from this study may give ideas for new types of preventions and treatments for everyone diagnosed with AMD.

Progress Updates:

The goal of this study is to better understand the genetic risk factors for AMD in African-Americans. The first step in this inquiry is to develop a dataset of African-American AMD cases and controls. This is difficult because AMD is much less common in African Americans than in European Caucasians. Dr. Haines’ and Agarwal’s team is using many methods to identify these individuals, including recruiting from multiple clinics and directly from the community. The team has spent substantial energy in developing contacts and giving presentations to various lay groups (e.g. churches, senior centers). They have now enrolled 44 African-American individuals (16 with some level of AMD, 28 controls). Once they have enrolled enough individuals, the team will also compare for the effects of the known genetic risk factors already identified in European Caucasians. Preliminary data suggests that the known genetic risk factors may act differently in African-Americans. If true, this has very significant implications for developing and applying predictive risk models. In addition, these data may help clarify the underlying disease processes, which will improve the ability to develop more effective preventions or treatments for all individuals with AMD.

Investigator Biography:

Dr. Haines is a genetic epidemiologist, Louise B. McGavock Professor of Human Genetics, director of the Center for Human Genetics Research (CHGR), Chief of the Division of Human Genomics, and Professor of Molecular Physiology & Biophysics, Neurology, and Ophthalmology & Visual Sciences at Vanderbilt University. Haines has been active in illuminating the underlying genetic architecture of common and complex genetic diseases since his graduate work at the University of Minnesota in the early 1980's and his post-doctoral work at Indiana University in the mid-1980's. In 1987 he joined the faculty of Harvard Medical School where he started working on understanding the genetic causes of complex diseases and started his research on age-related macular degeneration. He joined the faculty of Vanderbilt University in 1997 where he founded the Center for Human Genetics Research. He has published more than 425 peer-reviewed papers and 60 reviews and book chapters. His current research focuses on the dissection of the genetic architecture of common diseases, including age-related macular degeneration, and on developing and applying computational methods to analyze massively large genomic datasets.