|Title||Automated discovery and quantification of image-based complex phenotypes: a twin study of drusen phenotypes in age-related macular degeneration.|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Quellec, Gwénolé, Russell Stephen R., Seddon Johanna M., Reynolds Robyn, Scheetz Todd, Mahajan Vinit B., Stone Edwin M., and Abràmoff Michael D.|
|Journal||Invest Ophthalmol Vis Sci|
|Date Published||2011 Nov 25|
|Keywords||Aged, 80 and over, Diseases in Twins, Female, Genetic Predisposition to Disease, Genotype, Humans, Macular Degeneration, Male, Phenotype, Registries, Retinal Drusen, Twins, Dizygotic, Twins, Monozygotic|
PURPOSE: 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).
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
|Alternate Journal||Invest. Ophthalmol. Vis. Sci.|
|PubMed Central ID||PMC3302481|
|Grant List||R01 EY016822 / EY / NEI NIH HHS / United States |
R01 EY011309 / EY / NEI NIH HHS / United States
R01 EY017066 / EY / NEI NIH HHS / United States
R01 EY16822 / EY / NEI NIH HHS / United States
R01 EY11309 / EY / NEI NIH HHS / United States