Genome-Wide Association Studies Reveal Candidate Genes Associated with Pigmentation Patterns of Single Feathers of Tianfu Nonghua Ducks

In modern advanced genetics and breeding programs, the study of genes related to pigmentation in ducks is gaining much attention and popularity. Genes and DNA mutation cause variations in the plumage color traits of ducks. Therefore, discovering related genes responsible for different color traits and pigment patterns on each side of the single feathers in Chinese ducks is important for genetic studies. In this study, we collected feather images from 340 ducks and transported them into Image Pro Plus (IPP) 6.0 software to quantify the melanin content in the feathers. Thereafter, a genome-wide association study was conducted to reveal the genes responsible for variations in the feather color trait. The results from this study revealed that the pigmented region was larger in the male ducks as compared to the female ducks. In addition, the pigmented region was larger on the right side of the feather vane than on the left side in both dorsal and ventral feathers, and a positive correlation was observed among the feather color traits. Further, among the annotated genes, WNT3A, DOCK1, RAB1A, and ALDH1A3 were identified to play important roles in the variation in pigmented regions of the various feathers. This study also revealed that five candidate genes, including DPP8, HACD3, INTS14, SLC24A1, and DENND4A, were associated with the color pigment on the dorsal feathers of the ducks. Genes such as PRKG1, SETD6, RALYL, and ZNF704 reportedly play important roles in ventral feather color traits. This study revealed that genes such as WNT3A, DOCK1, RAB1A, and ALDH1A3 were associated with different pigmentation patterns, thereby providing new insights into the genetic mechanisms of single-feather pigmentation patterns in ducks.

Generation of skin tone and pigmented region-modified images using a pigment discrimination model trained with an optical approach

BACKGROUND

Skin tone and pigmented regions, associated with melanin and hemoglobin, are critical indicators of skin condition. While most prior research focuses on pigment analysis, the capability to simulate diverse pigmentation conditions could greatly broaden the range of applications. However, current methodologies have limitations in terms of numerical control and versatility.

METHODS

We introduce a hybrid technique that integrates optical methods with deep learning to produce skin tone and pigmented region-modified images with numerical control. The pigment discrimination model produces melanin, hemoglobin, and shading maps from skin images. The outputs are reconstructed into skin images using a forward problem-solving approach, with model training aimed at minimizing the discrepancy between the reconstructed and input images. By adjusting the melanin and hemoglobin maps, we create pigment-modified images, allowing precise control over changes in melanin and hemoglobin levels. Changes in pigmentation are quantified using the individual typology angle (ITA) for skin tone and melanin and erythema indices for pigmented regions, validating the intended modifications.

RESULTS

The pigment discrimination model achieved correlation coefficients with clinical equipment of 0.915 for melanin and 0.931 for hemoglobin. The alterations in the melanin and hemoglobin maps exhibit a proportional correlation with the ITA and pigment indices in both quantitative and qualitative assessments. Additionally, regions overlaying melanin and hemoglobin are demonstrated to verify independent adjustments.

CONCLUSION

The proposed method offers an approach to generate modified images of skin tone and pigmented regions. Potential applications include visualizing alterations for clinical assessments, simulating the effects of skincare products, and generating datasets for deep learning.