Gene-Set Enrichment Analysis for Identifying Genes and Biological Activities Associated with Growth Traits in Dromedaries.
Morteza Bitaraf SaniZahra RoudbariOmid KarimiMohammad Hossein BanabaziSaeid EsmaeilkhanianNader AsadzadehJavad Zare HarofteAli Shafei NaderiPamela Anna BurgerPublished in: Animals : an open access journal from MDPI (2022)
Growth is an important heritable economic trait for dromedaries and necessary for planning a successful breeding program. Until now, genome-wide association studies (GWAS) and QTL-mapping have identified significant single nucleotide polymorphisms (SNPs) associated with growth in domestic animals, but in dromedaries, the number of studies is very low. This project aimed to find biological themes affecting growth in dromedaries. In the first step, 99 candidate SNPs were chosen from a previously established set of SNPs associated with body weight, gain, and birth weight in Iranian dromedaries. Next, 0.5 kb upstream and downstream of each candidate SNP were selected from NCBI (assembly accession: GCA_000803125.3). The annotation of fragments with candidate SNPs regarding the reference genome was retrieved using the Blast2GO tool. Candidate SNPs associated with growth were mapped to 22 genes, and 25 significant biological themes were identified to be related to growth in dromedaries. The main biological functions included calcium ion binding, protein binding, DNA-binding transcription factor activity, protein kinase activity, tropomyosin binding, myosin complex, actin-binding, ATP binding, receptor signaling pathway via JAK-STAT , and cytokine activity. EFCAB5 , MTIF2 , MYO3A , TBX15 , IFNL3 , PREX1 , and TMOD3 genes are candidates for improving growth in camel breeding programs.
Keyphrases
- genome wide
- dna binding
- binding protein
- weight gain
- transcription factor
- birth weight
- genome wide association
- dna methylation
- signaling pathway
- body mass index
- copy number
- epithelial mesenchymal transition
- protein kinase
- public health
- weight loss
- oxidative stress
- quality improvement
- bioinformatics analysis
- induced apoptosis