Estimation of genetic variability and identification of regions under selection based on runs of homozygosity in Beijing-You Chickens

The genetic composition of populations is the result of a long-term process of selection and adaptation to specific environments and ecosystems. Runs of homozygosity (ROHs) are homozygous segments of the genome where the 2 haplotypes inherited from the parents are identical. The detection of ROH can be used to describe the genetic variability and quantify the level of inbreeding in an individual. Here, we investigated the occurrence and distribution of ROHs in 40 Beijing-You Chickens from the random breeding population (BJY_C) and 40 Beijing-You Chickens from the intramuscular fat (IMF) selection population (BJY_S). Principal component analysis (PCA) and maximum likelihood (ML) analyses showed that BJY_C was completely separated from the BJY_S. The nucleotide diversity of BJY_C was higher than that of BJY_S, and the decay rate of LD of BJY_C was faster. The ROHs were identified for a total of 7,101 in BJY_C and 9,273 in BJY_S, respectively. The ROH-based inbreeding estimate (F(ROH)) of BJY_C was 0.079, which was significantly lower than that of BJY_S (F(ROH) = 0.114). The results were the same as the estimates of the inbreeding coefficients calculated based on homozygosity (F(HOM)), the correlation between uniting gametes (F(UNI)), and the genomic relationship matrix (F(GRM)). Additionally, the distribution and number of ROH islands in chromosomes of BJY_C and BJY_S were significantly different. The ROH islands of BJY_S that included genes associated with lipid metabolism and fat deposition, such as CIDEA and S1PR1, were absent in BJY_C. However, GPR161 was detected in both populations, which is a candidate gene for the formation of the unique five-finger trait in Beijing-You chickens. Our findings contributed to the understanding of the genetic diversity of random or artificially selected populations, and allowed the accurate monitoring of population inbreeding using genomic information, as well as the detection of genomic regions that affect traits under selection.