Genomic approaches and bioinformatics tools to identify genomic regions for economic traits and their applications in chicken breeding programs | ||||
| Annals of Agricultural Science, Moshtohor | ||||
| Article 4, Volume 56, 4th ICBAA - Serial Number 1, 2018, Page 23-30 PDF (305.95 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/assjm.2018.57252 | ||||
 View on SCiNiTO | ||||
| Author | ||||
MOSTAFA K. NASSAR 
| ||||
| Department of Animal Production, Faculty of Agriculture, Cairo University, 12613 Giza, Egypt | ||||
| Abstract | ||||
| Most of the economic traits considered in genetic improvement programs are of quantitative nature. They are genetically determined by many genes. To apply major genes or linked markers in gene- or marker-assisted selection program, they must first be identified in the genome. Mapping genomic regions for the economic traits is the first step to identify genes influencing traits of interest. This report gives the most comprehensive information on discovering quantitative traits loci (QTL) and their underlying genes for economic traits in chicken, in particular on chromosome 4 (GGA4), using up-to-date genomic approaches and bioinformatics tools. This work is based on several publications (Goraga et al. 2010, 2012; Nassar and Brockmann 2011, 2013; Nassar et al. 2012, 2013, 2015; Lyu et al. 2016, 2017) and other published QTL results in chicken QTL database (Chicken QTLdb). I had done this work in collaboration with Humboldt-Universität zu Berlin, Germany, during the year 2010 to 2017. In brief, we mapped several genomic regions on 22 chromosomes affecting 24 traits. The majority of identified loci showed additive effects on several growth and body composition traits. The biggest effect on analysed traits was detected on the distal region of GGA4. The confidence interval of the QTL region on GGA4 harbours hundreds of genes. The final identification of genes and mutations will contribute to our understanding of the complex inheritance pattern of growth regulation, muscle development and fat deposition in chicken. Such information would support breeders in using this information for genetic improvement in breeding programs. | ||||
| Keywords | ||||
| Growth; muscle mass; fat deposition; candidate gene; QTL mapping; Bioinformatics; whole genome sequencing; Microsatellite; SNP arrays; linkage and association analyses; molecular breeding | ||||
|
Statistics Article View: 198 PDF Download: 269 |
||||
