Applying data mining algorithms to identify key variants and candidate genes for adaptation to hypoxia in Iranian indigenous chickens

This is the first report about identifying key adaptive variants in hypoxia conditions using data mining algorithms in Iranian native chickens. Adaptation to hypoxia, low oxygen condition, is a complex process that includes complex biological pathways. Thereby, understanding the genetics factors underlying adaptation to high-altitude conditions in domestic animals can provide new source of science for finding the adaptation process. We resequenced and analyzed the whole genome of highland (Altitude:2087m) and lowland native chickens (Altitude: 54m) in Iran for identifying differential variants between the two chicken ecotypes. Our results indicated that there were 216 differential SNVs (single nucleotide variations), which can change the amino acid sequences in protein structure. Five attribute weighting algorithms including Uncertainty, Information gain, Gini Index and Relief were run on 216 SNVs by Rapidminer software (7) for discovering the key differential variants between highland and lowland chickens. Our results showed that 23 common variants among the results of attribute weighting analysis have the highest weighting coefficient. The linking variants to protein structure analysis illustrate that key variants have main role in DNA repair process (SLF1, RIF1). A possible explanation for this, might be that high-dose UV radiation in high-altitude condition can lead to DNA damage. Therefore, candidate genes which are involved in DNA repair can be considered. Additionally, our results showed that reproduction (GAS8) and organ developments (NPNT) may contribute in adaptation to hypoxia. High-altitude condition has an extensive effect on the genomic variation, thus our reveals new insight to the adaptive pathways to hypoxia conditions.

Keywords:

Hypoxia , Data mining , differential variants , native chickens.