Automated discovery and quantification of image-based complex phenotypes: a twin study of drusen phenotypes in age-related macular degeneration

Gwenole Quellec; Stephen R Russell; Johanna M Seddon; Robyn Reynolds; Todd Scheetz; Vinit B Mahajan; Edwin M Stone; Michael D Abràmoff

doi:10.1167/iovs.10-6793

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Automated discovery and quantification of image-based complex phenotypes: a twin study of drusen phenotypes in age-related macular degeneration

Journal article

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Peer reviewed

Automated discovery and quantification of image-based complex phenotypes: a twin study of drusen phenotypes in age-related macular degeneration

Gwenole Quellec, Stephen R Russell, Johanna M Seddon, Robyn Reynolds, Todd Scheetz, Vinit B Mahajan, Edwin M Stone and Michael D Abràmoff

Investigative ophthalmology & visual science, Vol.52(12), pp.9195-9206

11/25/2011

DOI: 10.1167/iovs.10-6793

PMCID: PMC3302481

PMID: 22039249

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Abstract

Determining the relationships between phenotype and genotype of many disorders can improve clinical diagnoses, identify disease mechanisms, and enhance therapy. Most genetic disorders result from interaction of many genes that obscure the discovery of such relationships. The hypothesis for this study was that image analysis has the potential to enable formalized discovery of new visible phenotypes. It was tested in twins affected with age-related macular degeneration (AMD). Fundus images from 43 monozygotic (MZ) and 32 dizygotic (DZ) twin pairs with AMD were examined. First, soft and hard drusen were segmented. Then newly defined phenotypes were identified by using drusen distribution statistics that significantly separate MZ from DZ twins. The ACE model was used to identify the contributions of additive genetic (A), common environmental (C), and nonshared environmental (E) effects on drusen distribution phenotypes. Four drusen distribution characteristics significantly separated MZ from DZ twin pairs. One encoded the quantity, and the remaining three encoded the spatial distribution of drusen, achieving a zygosity prediction accuracy of 76%, 74%, 68%, and 68%. Three of the four phenotypes had a 55% to 77% genetic effect in an AE model, and the fourth phenotype showed a nonshared environmental effect (E model). Computational discovery of genetically determined features can reveal quantifiable AMD phenotypes that are genetically determined without explicitly linking them to specific genes. In addition, it can identify phenotypes that appear to result predominantly from environmental exposure. The approach is rapid and unbiased, suitable for large datasets, and can be used to reveal unknown phenotype-genotype relationships.

Phenotype

Registries

Genetic Predisposition to Disease

Retinal Drusen - genetics

Diseases in Twins - genetics

Twins, Monozygotic - genetics

Humans

Genotype

Male

Macular Degeneration - diagnosis

Twins, Dizygotic - genetics

Retinal Drusen - diagnosis

Macular Degeneration - genetics

Aged, 80 and over

Female

Details

Title: Subtitle: Automated discovery and quantification of image-based complex phenotypes: a twin study of drusen phenotypes in age-related macular degeneration
Creators: Gwenole Quellec - Institute for Vision Research, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA 52242, USA
Stephen R Russell
Johanna M Seddon
Robyn Reynolds
Todd Scheetz
Vinit B Mahajan
Edwin M Stone
Michael D Abràmoff
Resource Type: Journal article
Publication Details: Investigative ophthalmology & visual science, Vol.52(12), pp.9195-9206
DOI: 10.1167/iovs.10-6793
PMID: 22039249
PMCID: PMC3302481
NLM abbreviation: Invest Ophthalmol Vis Sci
ISSN: 0146-0404
eISSN: 1552-5783
Publisher: United States
Grant note: R01 EY017066 / NEI NIH HHS R01 EY016822 / NEI NIH HHS R01 EY011309 / NEI NIH HHS R01 EY16822 / NEI NIH HHS R01 EY11309 / NEI NIH HHS
Language: English
Date published: 11/25/2011
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Stead Family Department of Pediatrics; Iowa Neuroscience Institute; Ophthalmology and Visual Sciences
Record Identifier: 9983806250002771

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