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. 2017 Feb 27;7:43359.
doi: 10.1038/srep43359.

Novel Quantitative Pigmentation Phenotyping Enhances Genetic Association, Epistasis, and Prediction of Human Eye Colour

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Free PMC article

Novel Quantitative Pigmentation Phenotyping Enhances Genetic Association, Epistasis, and Prediction of Human Eye Colour

Andreas Wollstein et al. Sci Rep. .
Free PMC article

Abstract

Success of genetic association and the prediction of phenotypic traits from DNA are known to depend on the accuracy of phenotype characterization, amongst other parameters. To overcome limitations in the characterization of human iris pigmentation, we introduce a fully automated approach that specifies the areal proportions proposed to represent differing pigmentation types, such as pheomelanin, eumelanin, and non-pigmented areas within the iris. We demonstrate the utility of this approach using high-resolution digital eye imagery and genotype data from 12 selected SNPs from over 3000 European samples of seven populations that are part of the EUREYE study. In comparison to previous quantification approaches, (1) we achieved an overall improvement in eye colour phenotyping, which provides a better separation of manually defined eye colour categories. (2) Single nucleotide polymorphisms (SNPs) known to be involved in human eye colour variation showed stronger associations with our approach. (3) We found new and confirmed previously noted SNP-SNP interactions. (4) We increased SNP-based prediction accuracy of quantitative eye colour. Our findings exemplify that precise quantification using the perceived biological basis of pigmentation leads to enhanced genetic association and prediction of eye colour. We expect our approach to deliver new pigmentation genes when applied to genome-wide association testing.

Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. Example of fully automated iris segmentation and eye colour quantification using our new approach.
Panel (a) shows the iris picture taken by the Topcon camera system used under normalized conditions. Panel (b) depicts the iris as automatically extracted with our iris segmentation approach. Panel (c) exemplifies the assignment of each pixel of the iris image into one of three types of clusters: non-pigmented areas (blue), pheomelanin (yellow), and eumelanin (red) with our new approach.
Figure 2
Figure 2. Manually categorized irises from the entire study population (N = 3087) into 3 eye colour categories blue (depicted in blue colour), intermediate (depicted in green colour), and brown (depicted in red colour) as arranged in the colour space of different quantification approaches for eye colour.
Each data point depicts one individual iris categorized in one of the eye colour categories in the respective continuous colour space. Panels (a–h) depict the separation of the manually graded eye colours in two-dimensional continuous colour spaces, e.g. Pheomelanin vs. Eumelanin. Panels (i,j) depict the separation of manually graded eye colours on one-dimensional colour spaces, i.e PIE score and T-index respectively.
Figure 3
Figure 3
Example of three eyes that were manually categorized as blue (panel a), intermediate (panel b) or brown (panel c). The most left image panel represents the eye images taken with the Topcon camera system. The second from left image panel represents the segmented irises using our approach with the result of the supervised clustering into pheomelanin, eumelanin, and non-pigmented areas. The histograms on the most right show the observed (blue bars) and DNA-predicted (red bars) proportions of the respective types of eye pigment, i.e. eumelanin, pheomelanin and no pigmentation.

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References

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