Advancing genomics through the AMD Genomics Consortium

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Age-related macular degeneration (AMD) is one of the leading causes of blindness in the elderly. It has a significant impact on the independence, quality of life, and healthcare costs for those afflicted and the additional social cost on caregivers and family members is incalculable. There is substantial variability in the AMD phenotype and the primary treatment, to repeatedly inject anti-VEGF antibodies into the eye of those severely affected, is effective in only a subset of individuals. Thus a better understanding of the underlying causes of AMD is needed to help guide development of more universal and effective treatments and potential preventive measures for AMD. AMD is strongly influenced by genomic variation. Through the initial funding period of this proposal, we supported the development of the International AMD Genomics Consortium (IAMDGC), which brought together 26 research groups from around the world. The IAMDGC has increased the known genomic loci from 12 to 52 and successfully performed a new and larger genotyping study focused on rarer variation using a high-density genome-wide SNP chip with exome content. This collaborative effort has also spawned numerous additional interesting avenues of research that we now need to explore in more detail through the renewal of this highly successful project. While the current genotypic dataset of over 50,000 samples has much left to be mined, expansion of the available samples, with a particular focus on families and minority samples, is necessary if we are to achieve our stated goal of completely defining the genetic architecture of AMD. To address these unresolved issues we propose four specific aims: 1) Expand the IAMDGC resource with additional datasets and expansion of current datasets, with a focus on family data and diverse genetic ancestry; 2) Expand the range of clinical diagnostic measures (e.g. fundus photos, OCT measures), biomarker, comorbidity, and covariate data associated with the samples; 3) Use an analytical hub infrastructure to perform detailed analyses of these data; and 4) Support the logistics and administration of the IAMDGC.