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利用一种新颖的客观方法对人类眼睛颜色进行基因分析,以实现眼睛颜色分类。

Genetic analyses of the human eye colours using a novel objective method for eye colour classification.

机构信息

Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark.

出版信息

Forensic Sci Int Genet. 2013 Sep;7(5):508-15. doi: 10.1016/j.fsigen.2013.05.003. Epub 2013 Jun 28.

Abstract

In this study, we present a new objective method for measuring the eye colour on a continuous scale that allows researchers to associate genetic markers with different shades of eye colour. With the use of the custom designed software Digital Iris Analysis Tool (DIAT), the iris was automatically identified and extracted from high resolution digital images. DIAT was made user friendly with a graphical user interface. The software counted the number of blue and brown pixels in the iris image and calculated a Pixel Index of the Eye (PIE-score) that described the eye colour quantitatively. The PIE-score ranged from -1 to 1 (brown to blue). The software eliminated the need for user based interpretation and qualitative eye colour categories. In 94% (570) of 605 analyzed eye images, the iris region was successfully extracted and a PIE-score was calculated. A very high correlation between the PIE-score and the human perception of eye colour was observed. The correlations between the PIE-scores and the six IrisPlex SNPs (HERC2 rs12913832, OCA2 rs1800407, SLC24A4 rs12896399, TYR rs1393350, SLC45A2 rs16891982 and IRF4 rs12203592) were analyzed in 570 individuals. Significant differences (p<10(-6)) in the PIE-scores of the individuals typed as HERC2 rs12913832 G (PIE=0.99) and rs12913832 GA (PIE=-0.71) or A (PIE=-0.87) were observed. We adjusted for the effect of HERC2 rs12913832 and showed that the quantitative PIE-scores were significantly associated with SNPs with minor effects (OCA2 rs1800407, SLC24A4 rs12896399 and TYR rs1393350) on the eye colour. We evaluated the two published prediction models for eye colour (IrisPlex [1] and Snipper[2]) and compared the predictions with the PIE-scores. We found good concordance with the prediction from individuals typed as HERC2 rs12913832 G. However, both methods had difficulties in categorizing individuals typed as HERC2 rs12913832 GA because of the large variation in eye colour in HERC2 rs12913832 GA individuals. With the use of the DIAT software and the PIE-score, it will be possible to automatically compare the iris colour of large numbers of iris images obtained by different studies and to perform large meta-studies that may reveal loci with small effects on the eye colour.

摘要

在这项研究中,我们提出了一种新的客观方法来连续测量眼睛颜色,使研究人员能够将遗传标记与不同色调的眼睛颜色联系起来。该方法使用定制的软件 Digital Iris Analysis Tool(DIAT),自动识别和提取高分辨率数字图像中的虹膜。DIAT 通过图形用户界面变得易于使用。该软件统计虹膜图像中的蓝色和棕色像素数量,并计算出描述眼睛颜色的定量像素索引(PIE 分数)。PIE 分数范围从-1 到 1(棕色到蓝色)。该软件消除了对用户解释和定性眼睛颜色类别的需求。在分析的 605 个眼睛图像中,有 94%(570 个)成功提取了虹膜区域并计算了 PIE 分数。观察到 PIE 分数与人类对眼睛颜色的感知之间存在非常高的相关性。在 570 个人中分析了 PIE 分数与六个 IrisPlex SNPs(HERC2 rs12913832、OCA2 rs1800407、SLC24A4 rs12896399、TYR rs1393350、SLC45A2 rs16891982 和 IRF4 rs12203592)之间的相关性。在 HERC2 rs12913832 G(PIE=0.99)和 rs12913832 GA(PIE=-0.71)或 A(PIE=-0.87)基因型个体中观察到 PIE 分数存在显著差异(p<10(-6))。我们调整了 HERC2 rs12913832 的影响,并表明定量 PIE 分数与眼睛颜色的微小效应 SNP(OCA2 rs1800407、SLC24A4 rs12896399 和 TYR rs1393350)显著相关。我们评估了两种已发表的眼睛颜色预测模型(IrisPlex [1] 和 Snipper[2]),并将预测结果与 PIE 分数进行了比较。我们发现与 HERC2 rs12913832 G 基因型个体的预测结果具有很好的一致性。然而,这两种方法在对 HERC2 rs12913832 GA 基因型个体进行分类时都存在困难,因为 HERC2 rs12913832 GA 个体的眼睛颜色差异很大。使用 DIAT 软件和 PIE 分数,将有可能自动比较不同研究获得的大量虹膜图像的虹膜颜色,并进行可能揭示眼睛颜色微小效应的大型荟萃研究。

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