Genome-wide association study reveals genetic architecture and evolution of human retinal pigmentation.

Pigmentation varies widely across humans and is shaped by melanin quantity, type, and spatial distribution. Retinal pigmentation protects against light-induced damage, yet its genetic and evolutionary bases remain unclear. We developed a deep learning framework (DeepGRP) to quantify retinal pigmentation from high-resolution fundus images and conducted a genome-wide association study (GWAS), identifying 42 signals, including 26 previously unidentified loci, with single-nucleotide polymorphism-based heritability of 21.4%. Single-nucleus assay for transposase-accessible chromatin by sequencing and RNA sequencing of human fetal retinal tissues revealed key cellular contributors, including retinal pigment epithelium and photoreceptor cells. Among candidate genes, ARHGAP18 emerged as a previously unrecognized regulator of melanogenesis. Evidence of polygenic adaptation in Europeans suggests selection driven by snow-reflected light at high latitudes. A polygenic risk score for retinal pigmentation correlated with a 4.8-fold higher risk of myopia and a 1.5-fold lower risk of skin cancer. These findings demonstrate the power of deep learning for large-scale ocular phenotyping and reveal insights into the genetic and evolutionary architecture of retinal pigmentation.