This study was designed to discover molecular marker associated with the interferon INF-γ and avian influenza (AI) antibody titer traits in Jinghai Yellow chicken (Gallus gallus). Serum samples were taken from 400 female chickens and the INF-γ concentrations and AI antibody titer levels were measured. A genome-wide association study was carried out using specific-locus amplified fragment (SLAF) sequencing. Bioinformatics analysis was applied to detect single-nucleotide polymorphisms (SNPs) associated with the two traits. After sequencing and quality control, 103,680 SLAFs and 90,961 SNPs were obtained. The 400 samples were divided into 10 subgroups to reduce the effects of group stratification. The Bonferroni adjusted P-value of genome-wide significance was set at 1.87E−06 according to the number of independent SNP markers and linkage disequilibrium blocks. A SNP that was significantly associated with INF-γ concentration was detected in the myomesin 1 (MYOM1) gene on chromosome 2, and another SNPthat was significantly associated with the AI antibody titer level was detected in an RNA methyltransferase gene (Nsun7), which was found to have an important biological function. We propose that MYOM1 and Nsun7 are valuable candidate genes that influence the disease resistance characters of chicken. However, in-depth investigations are needed to determine the essential roles of these genes in poultry disease resistance and their possible application in breeding disease resistant poultry.
Keywords:
Avian influenza; disease resistance breeding; INF-γ; SLAF-seq
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Note: The distribution of all the SLAFs in the genomes of 400 samples was determined by calculating the number of SNPs per every 100kb in the genome. Horizontal lines represent chromosomes, with chromosomal length on the X axis. The scale in the top right corner indicates the number of SLAFs, with black indicating more than 50 SLAFs.

Note: We assumed that the 400-samples’ subgroup number (Q value) was 1-15 for the cluster analysis and ensured the number of subgroups by the peak ΔQ value positions. The subgroups with a minimum ΔQ peak value were the best. The rsults indicated that a Q value of 10 is the lowest peak value.
