A genome-wide association study of morphometric traits in dromedaries.

BACKGROUND: Investigating genomic regions associated with morphometric traits in camels is valuable, because it allows a better understanding of adaptive and productive features to implement a sustainable management and a customised breeding program for dromedaries. OBJECTIVES: With a genome-wide association study (GWAS) including 96 Iranian dromedaries phenotyped for 12 morphometric traits and genotyped-by-sequencing (GBS) with 14,522 SNPs, we aimed at identifying associated candidate genes. METHODS: The association between SNPs and morphometric traits was investigated using a linear mixed model with principal component analysis (PCA) and kinship matrix. RESULTS: With this approach, we detected 59 SNPs located in 37 candidate genes potentially associated to morphometric traits in dromedaries. The top associated SNPs were related to pin width, whither to pin length, height at whither, muzzle girth, and tail length. Interestingly, the results highlight the association between whither height, muzzle circumference, tail length, whither to pin length. The identified candidate genes were associated with growth, body size, and immune system in other species. CONCLUSIONS: We identified three key hub genes in the gene network analysis including ACTB, SOCS1 and ARFGEF1. In the central position of gene network, ACTB was detected as the most important gene related to muscle function. With this initial GWAS using GBS on dromedary camels for morphometric traits, we show that this SNP panel can be effective for genetic evaluation of growth in dromedaries. However, we suggest a higher-density SNP array may greatly improve the reliability of the results.

Identification of Candidate Genes for Pigmentation in Camels Using Genotyping-by-Sequencing.

The coat color of dromedary is usually uniform and varies from black to white, although dark- to light-brown colors are the most common phenotypes. This project was designed to gain knowledge on novel color-related variants using genotyping-by-sequencing (GBS). The association between the SNPs and coat color was tested using MLM (mixed linear models) with kinship matrix. Three GWAS models including white color vs. non-white color, black vs. non-black color, and light-brown vs. dark-brown color were performed. There were no distinct genetic clusters detected based on the color phenotypes. However, admixture occurred among all individuals of the four different coat color groups. We identified nine significant SNPs associated with white color after Bonferroni correction, located close to ANKRD26, GNB1, TSPYL4, TEKT5, DEXI, CIITA, TVP23B, CLEC16A, TMPRSS13, FXYD6, MPZL3, ANKRD26, HFM1, CDC7, TGFBR3, and HACE1 genes in neighboring flanking regions. The 13 significant SNPs associated with black color and the candidate genes were: CAPN7, CHRM4, CIITA, CLEC16A, COL4A4, COL6A6, CREB3L1, DEXI, DGKZ, DGKZ, EAF1, HDLBP, INPP5F, MCMBP, MDK, SEC23IP, SNAI1, TBX15, TEKT5, TMEM189, trpS, TSPYL4, TVP23B, and UBE2V1. The SNAI1 gene interacted with MCIR, ASIP and KIT genes. These genes play a key role in the melanin biosynthetic and pigmentation biological process and melanogenesis biological pathway. Further research using a larger sample size and pedigree data will allow confirmation of associated SNPs and the identified candidate genes.