@article {Zou763656, author = {Angela Zou and Kerry Nadeau and Pauline W. Wang and Jee Yeon Lee and David S. Guttman and Shayan Sharif and Doug Korver and John H. Brumell and John Parkinson}, title = {Accumulation of genetic variants associated with immunity in the selective breeding of broilers}, elocation-id = {763656}, year = {2019}, doi = {10.1101/763656}, publisher = {Cold Spring Harbor Laboratory}, abstract = {To satisfy an increasing demand for dietary protein, the poultry industry has employed genetic selection to increase the growth rate of broilers by over 400\% in the past 50 years. Although modern broilers reach a marketable weight of \~{}2 kg in a short span of 35 days, a speed twice as fast as a broiler 50 years ago, the expedited growth has been associated with several negative detrimental consequences. Aside from heart and musculoskeletal problems, which are direct consequences of additional weight, the immune response is also thought to be altered in modern broilers. Given that identifying the underlying genetic basis responsible for a less sensitive innate immune response would be economically beneficial for poultry breeding, we decided to compare the genomes of two unselected meat control strains that are representative of broilers from 1957 and 1978, and a current commercial broiler line. Through analysis of genetic variants, we developed a custom prioritization strategy to identify genes and pathways that have accumulated genetic changes and are biologically relevant to immune response and growth performance. Our results highlight two genes, TLR3 and PLIN3, with genetic variants that are predicted to enhance growth performance at the expense of immune function. Placing these new genomes in the context of other chicken lines, reveal genetic changes that have specifically arisen in selective breeding programs that were implemented in the last 50 years.}, URL = {https://www.biorxiv.org/content/early/2019/09/12/763656}, eprint = {https://www.biorxiv.org/content/early/2019/09/12/763656.full.pdf}, journal = {bioRxiv} }