A genome-wide association study for eumelanin pigmentation in chicken plumage using a computer vision approach.

Chicken plumage colouration is an important trait related to productivity in poultry industry. Therefore, the genetic basis for pigmentation in chicken plumage is an area of great interest. However, the colour trait is generally regarded as a qualitative trait and representing colour variations is difficult. In this study, we developed a method to quantify and classify colour using an F2 population crossed from two pure lines: White Leghorn and the Korean indigenous breed Yeonsan Ogye. Using red, green, and blue values in the cropped body region, we identified significant genomic regions on chromosomes 33:3 160 480-7 447 197 and Z:78 748 287-79 173 793. Furthermore, we identified two potential candidate genes (PMEL and MTAP) that might have significant effects on melanin-based plumage pigmentation. Our study presents a new phenotyping method using a computer vision approach and provides new insights into the genetic basis of melanin-based feather colouration in chickens.

Genome-wide association study for the free amino acid and nucleotide components of breast meat in an F2 crossbred chicken population.

Flavor is an important sensory trait of chicken meat. The free amino acid (FAA) and nucleotide (NT) components of meat are major factors affecting meat flavor during the cooking process. As a genetic approach to improve meat flavor, we performed a genome-wide association study (GWAS) to identify the potential candidate genes related to the FAA and NT components of chicken breast meat. Measurements of FAA and NT components were recorded at the age of 10 weeks from 764 and 767 birds, respectively, using a White leghorn and Yeonsan ogye crossbred F2 chicken population. For genotyping, we used 60K Illumina single-nucleotide polymorphism (SNP) chips. We found a total of nine significant SNPs for five FAA traits (arginine, glycine, lysine, threonine content, and the essential FAAs and one NT trait (inosine content), and six significant genomic regions were identified, including three regions shared among the essential FAAs, arginine, and inosine content traits. A list of potential candidate genes in significant genomic regions was detected, including the KCNRG, KCNIP4, HOXA3, THSD7B, and MMUT genes. The essential FAAs had significant gene regions the same as arginine. The genes related to arginine content were involved in nitric oxide metabolism, while the inosine content was possibly affected by insulin activity. Moreover, the threonine content could be related to methylmalonyl-CoA mutase. The genes and SNPs identified in this study might be useful markers in chicken selection and breeding for chicken meat flavor.

Application of genomic big data to analyze the genetic diversity and population structure of Korean domestic chickens.

Genetic diversity analysis is crucial for maintaining and managing genetic resources. Several studies have examined the genetic diversity of Korean domestic chicken (KDC) populations using microsatellite markers, but it is difficult to capture the characteristics of the whole genome in this manner. Hence, this study analyzed the genetic diversity of several KDC populations using high-density single nucleotide polymorphism (SNP) genotype data. We examined 935 birds from 21 KDC populations, including indigenous and adapted Korean native chicken (KNC), Hyunin and Jeju KDC, and Hanhyup commercial KDC populations. A total of 212,420 SNPs of 21 KDC populations were used for calculating genetic distances and fixation index, and for ADMIXTURE analysis. As a result of the analysis, the indigenous KNC groups were genetically closer and more fixed than the other groups. Furthermore, Hyunin and Jeju KDC were similar to the indigenous KNC. In comparison, adapted KNC and Hanhyup KDC populations derived from the same original species were genetically close to each other, but had different genetic structures from the others. In conclusion, this study suggests that continuous evaluation and management are required to prevent a loss of genetic diversity in each group. Basic genetic information is provided that can be used to improve breeds quickly by utilizing the various characteristics of native chickens.

A genome-wide association study for the fatty acid composition of breast meat in an F2 crossbred chicken population.

The composition of fatty acids determines the flavor and quality of meat. Flavor compounds are generated during the cooking process by the decomposition of volatile fatty acids via lipid oxidation. A number of research on candidate genes related to fatty acid content in livestock species have been published. The majority of these studies focused on pigs and cattle; the association between fatty acid composition and meat quality in chickens has rarely been reported. Therefore, this study investigated candidate genes associated with fatty acid composition in chickens. A genome-wide association study (GWAS) was performed on 767 individuals from an F2 crossbred population of Yeonsan Ogye and White Leghorn chickens. The Illumina chicken 60K significant single-nucleotide polymorphism (SNP) genotype data and 30 fatty acids (%) in the breast meat of animals slaughtered at 10 weeks of age were analyzed. SNPs were shown to be significant in 15 traits: C10:0, C14:0, C18:0, C18:1n-7, C18:1n-9, C18:2n-6, C20:0, C20:2, C20:3n-6, C20:4n-6, C20:5n-3, C24:0, C24:1n-9, monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA). These SNPs were mostly located on chromosome 10 and around the following genes: ACSS3, BTG1, MCEE, PPARGC1A, ACSL4, ELOVL4, CYB5R4, ME1, and TRPM1. Both oleic acid and arachidonic acid contained the candidate genes: MCEE and TRPM1. These two fatty acids are antagonistic to each other and have been identified as traits that contribute to the production of volatile fatty acids. The results of this study improve our understanding of the genetic mechanisms through which fatty acids in chicken affect the meat flavor.

Genome-wide association study revealed the genomic regions associated with skin pigmentation in an Ogye x White Leghorn F2 chicken population.

Skin color in chickens is an economically important trait that determines the first impression of a consumer toward a broiler and can ultimately affect consumer choice in the market. Therefore, identification of genomic regions associated with skin color is crucial for increasing the sales value of chickens. Although previous studies have attempted to reveal the genetic markers associated with the skin coloration in chickens, most were limited to investigations of candidate genes, such as melanin-related genes, and focused on case/control studies based on a single or small population. In this study, we performed a genome-wide association study (GWAS) on 770 F2 intercrosses produced by an experimental population of 2 chicken breeds, namely Ogye and White Leghorns, with different skin colors. The GWAS demonstrated that the L* value among the 3 skin color traits is highly heritable, and the genomic regions located on 2 chromosomes (20 and Z) were detected to harbor SNPs significantly associated with the skin color trait, accounting for most of the total genetic variance. Particular genomic regions spanning a ∼2.94 Mb region on GGA Z and a ∼3.58 Mb region on GGA 20 were significantly associated with skin color traits, and in these regions, certain candidate genes, including MTAP, FEM1C, GNAS, and EDN3, were found. Our findings could help elucidate the genetic mechanisms underlying chicken skin pigmentation. Furthermore, the candidate genes can be used to provide a valuable breeding strategy for the selection of specific chicken breeds with ideal skin coloration.

Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach.

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.