Format

Send to

Choose Destination
Invest Ophthalmol Vis Sci. 2006 Nov;47(11):4756-61.

Heritability of refractive error and ocular biometrics: the Genes in Myopia (GEM) twin study.

Author information

1
Centre for Eye Research Australia, University of Melbourne, 32 Gisborne Street, East Melbourne 3002, Australia. m.dirani@pgrad.unimelb.edu.au

Abstract

PURPOSE:

A classic twin study was undertaken to assess the contribution of genes and environment to the development of refractive errors and ocular biometrics in a twin population.

METHODS:

A total of 1224 twins (345 monozygotic [MZ] and 267 dizygotic [DZ] twin pairs) aged between 18 and 88 years were examined. All twins completed a questionnaire consisting of a medical history, education, and zygosity. Objective refraction was measured in all twins, and biometric measurements were obtained using partial coherence interferometry.

RESULTS:

Intrapair correlations for spherical equivalent and ocular biometrics were significantly higher in the MZ than in the DZ twin pairs (P < 0.05), when refraction was considered as a continuous variable. A significant gender difference in the variation of spherical equivalent and ocular biometrics was found (P < 0.05). A genetic model specifying an additive, dominant, and unique environmental factor that was sex limited was the best fit for all measured variables. Heritability of spherical equivalents of 88% and 75% were found in the men and women, respectively, whereas, that of axial length was 94% and 92%, respectively. Additive genetic effects accounted for a greater proportion of the variance in spherical equivalent, whereas the variance in ocular biometrics, particularly axial length was explained mostly by dominant genetic effects.

CONCLUSIONS:

Genetic factors, both additive and dominant, play a significant role in refractive error (myopia and hypermetropia) as well as in ocular biometrics, particularly axial length. The sex limitation ADE model (additive genetic, nonadditive genetic, and environmental components) provided the best-fit genetic model for all parameters.

PMID:
17065484
DOI:
10.1167/iovs.06-0270
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center