Cogburn Laboratory
The goal of our functional genomics consortium project was to examine global patterns of gene expression in tissue (i.e., liver, muscle, fat and hypothalamus/pituitary gland) of broiler chickens divergently selected for either growth rate (fast-growing versus slow-growing) or body composition (fatness versus leanness). We have constructed and normalized five broiler chicken cDNA libraries [liver, fat, skeletal muscle/bone growth plate, pituitary/hypothalamus/pineal, and reproductive tract (testes/ovary/oviduct)]. After high throughput DNA sequencing of more than 31,000 cDNA clones, we have used the CAP3 sequence assembly program to build contigs (in silico cDNAs) from our expressed sequence tags (ESTs) and those in public databases. From our annotated EST clone collection, we have produced four custom microarrays: UD_Liver_3.2K
(GEO Accession # GPL1742), UD 7.4K Metabolic/Somatic Systems (GEO Accession # GPL1737), Chicken Neuroendocrine System 5K (GEO Accession # GPL1744), and the DEL-MAR 14K Integrated Systems (GEO Accession # GPL1731). The DEL-MAR 14K Integrated Systems microarray was used for transcriptional profiling in five tissues from the divergently-selected broiler chickens. Global gene expression data was used for gene cluster analyses and identification of key metabolic pathways that control expression of important production traits (growth rate and body composition). We have created two novel F2 resource populations from an intercross of the divergent genetic lines (FL x LL and FGL x SGL). A total of 14 phenotypic traits were measured on 695 individuals from each of the two F2 populations (FL x LL and HG x LG). Two pan-genomic QTL scans were completed using 135 informative markers. The QTL scan in the HG x LG F2 population revealed significant QTL for growth, breast meat yield, abdominal fat, shank length and width on GGA1, 2, 3, 4 and 5. Several QTL for breast meat yield were identified on GGA1, 2, 3 and 5. QTL analysis of the FL x LL intercross revealed six QTL for abdominal fat on GGA1, 3, 5 and 7. We also found a single QTL for breast muscle weight (% BW at 9 wk) on GGA1 in the FL x LL F2 population. Our current efforts are focused on merging the transcriptional and QTL maps and identification of polymorphic candidate genes for development of molecular markers.