Purchase this article with an account.
Christopher J Hammond; Big Data -Omics: Approaches for Genetics and Biological Research. Invest. Ophthalmol. Vis. Sci. 2019;60(9):15.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Presentation Description :
Modern technology has enabled huge datasets to be created for hypothesis-free research, including genome-wide association studies, next-generation sequencing, metabolomics, epigenetics and metagenomics (microbiome). Coupled with regional and national biobanks, such as the UK Biobank cohort, these -Omics studies allow powerful research into complex, age-related diseases. However, there are challenges in analysis of these datasets, which include the amount of storage space and computer processing needed, and the difficulties of multiple testing to determine truly significant results for rare genetic variants. Machine learning and other methods are now being applied, and herald a new era of biological discovery of biological mechanisms of disease, the ability to predict risk of disease with the ultimate aim of personalised medicine. This presentation will give examples, from the UK Biobank and TwinsUK cohorts, of the size and scale and complexity of these datasets, and how Big Data can identify new genes and pathways in eye disease.
This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.
This PDF is available to Subscribers Only