Coccidiosis caused by the Eimeria species is a highly problematic disease in the chicken industry. Here, we used RNA sequencing to observe the time-dependent host responses of Eimeria-infected chickens to examine the genes and biological functions associated with immunity to the parasite. Transcriptome analysis was performed at three time points: 4, 7, and 21 days post-infection (dpi). Based on the changes in gene expression patterns, we defined three groups of genes that showed differential expression. This enabled us to capture evidence of endoplasmic reticulum stress at the initial stage of Eimeria infection. Furthermore, we found that innate immune responses against the parasite were activated at the first exposure; they then showed gradual normalization. Although the cytokine-cytokine receptor interaction pathway was significantly operative at 4 dpi, its downregulation led to an anti-inflammatory effect. Additionally, the construction of gene co-expression networks enabled identification of immunoregulation hub genes and critical pattern recognition receptors after Eimeria infection. Our results provide a detailed understanding of the host-pathogen interaction between chicken and Eimeria. The clusters of genes defined in this study can be utilized to improve chickens for coccidiosis control.

Genome-wide association study for the free amino acid and nucleotide components of breast meat in an F2 crossbred chicken population Running Title (within 10 words)GWAS for free amino acid and nucleotide of chicken meat

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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.

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