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.
Highlights
Is reflected by the stroma of the iris, and the eye colour is perceived as grey to blue through Tyndall scattering[3]
Several gene-mapping studies on eye colour were previously conducted by using manually defined phenotype categories[6,7,8,9,10,11,12,13,14], inevitably oversimplifying the continuous nature of human eye colour variation. This incomplete use of the underlying basics of eye colour variation reduces the thoroughness of such studies, several eye colour genes were previously identified with this simplified phenotyping approach[5,6,7,8,9,10,11,12,13]. It provides an accurate prediction from DNA with reasonably high accuracies for at least the extreme categories of blue and brown demonstrated via the IrisPlex system[15], a system consisting of only six single nucleotide polymorphisms (SNPs) from six genes
By using genotypes of 12 SNPs previously involved in human eye colour variation that we generated in the same individuals, we demonstrate the impact of this novel pigmentation phenotyping approach on genetic association, epistasis, and prediction
Summary
Detection and segmentation of the iris in digital imagery. Prior to colour assessment, the iris needed to be segmented from the pupil and sclera. In the majority of imagery available to us, (i) the pupil was always centred in the middle (Fig. 1a), (ii) the iris was fully visible (those images where it was not were excluded from the analyses), and iii) eye lashes rarely overlapped with the iris Because of these features, we followed a previously proposed[30] two-step procedure, which we implemented in Matlab (R2007a). We considered the proportion of the clustered pixels relative to the segmented iris as our quantitative eye colour phenotype, reflecting the equivalent amounts of non-pigmented, pheomelanin, and eumelanin areas per iris (see Fig. 1c), the sum of which equals to one. The manually graded eye colour categories blue and brown were best differentiated by the non-pigmentation/eumelanin-space generated by our new method (Fig. 2c, HD = 0.923), followed by the non-pigmentation/pheomelanin space from our method (Fig. 2b, HD = 0.911).
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