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Gurung RL, Burdon KP, McComish BJ. A Guide to Genome-Wide Association Study Design for Diabetic Retinopathy. Methods Mol Biol 2023; 2678:49-89. [PMID: 37326705 DOI: 10.1007/978-1-0716-3255-0_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Diabetic retinopathy (DR) is the most common microvascular complication related to diabetes. There is evidence that genetics play an important role in DR pathogenesis, but the complexity of the disease makes genetic studies a challenge. This chapter is a practical overview of the basic steps for genome-wide association studies with respect to DR and its associated traits. Also described are approaches that can be adopted in future DR studies. This is intended to serve as a guide for beginners and to provide a framework for further in-depth analysis.
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Affiliation(s)
- Rajya L Gurung
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.
| | - Kathryn P Burdon
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.
| | - Bennet J McComish
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
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Wojcik GL, Murphy J, Edelson JL, Gignoux CR, Ioannidis AG, Manning A, Rivas MA, Buyske S, Hendricks AE. Opportunities and challenges for the use of common controls in sequencing studies. Nat Rev Genet 2022; 23:665-679. [PMID: 35581355 PMCID: PMC9765323 DOI: 10.1038/s41576-022-00487-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2022] [Indexed: 01/02/2023]
Abstract
Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.
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Affiliation(s)
- Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jessica Murphy
- Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
- Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA
| | - Jacob L Edelson
- Department of Biomedical Data Science, Stanford Medical School, Stanford, CA, USA
| | - Christopher R Gignoux
- Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
- Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Alexander G Ioannidis
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Alisa Manning
- Metabolism Program, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Manuel A Rivas
- Department of Biomedical Data Science, Stanford Medical School, Stanford, CA, USA
| | - Steven Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
| | - Audrey E Hendricks
- Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA.
- Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA.
- Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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