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

Area of Science:

• Genetics
• Ophthalmology
• Computational Biology

Background:

• Traditional eye color assessment relies on subjective categorization, limiting detailed genetic analysis.
• Objective, quantitative methods are needed to correlate genetic variations with nuanced eye color phenotypes.

Purpose of the Study:

• To introduce a novel, objective method for quantifying eye color using digital image analysis.
• To establish a continuous scale for eye color measurement, facilitating genetic association studies.
• To validate the accuracy of the developed method against human perception and existing genetic prediction models.

Main Methods:

• Development of the Digital Iris Analysis Tool (DIAT) software for automated iris segmentation and analysis.
• Calculation of a Pixel Index of the Eye (PIE-score) based on blue and brown pixel distribution, ranging from -1 (brown) to 1 (blue).
• Correlation analysis of PIE-scores with six established IrisPlex SNPs (e.g., HERC2 rs12913832) in 570 individuals.

Main Results:

• DIAT successfully extracted iris regions and calculated PIE-scores for 94% of analyzed images.
• A high correlation was observed between PIE-scores and human perception of eye color.
• Significant associations were found between PIE-scores and key eye color SNPs, including HERC2 rs12913832 and SNPs with minor effects like OCA2 rs1800407.
• The PIE-score method demonstrated better handling of HERC2 rs12913832 genetic variations compared to existing prediction models.

Conclusions:

• The DIAT software and PIE-score provide a robust, objective, and quantitative measure of eye color.
• This method enhances the ability to perform large-scale genetic association studies for identifying novel loci influencing eye color.
• The PIE-score offers a valuable tool for researchers in genetics, ophthalmology, and anthropology for standardized eye color phenotyping.