Open AccessIntegrating Genomics with Nutrition Models to Improve the Prediction of Cattle Performance and Carcass Composition under Feedlot Conditions
Author(s) -
L. O. Tedeschi
Publication year - 2015
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0143483
Subject(s) - feedlot , crossbreed , dry matter , snp , zoology , biology , single nucleotide polymorphism , microbiology and biotechnology , genetics , gene , genotype
Cattle body composition is difficult to model because several factors affect the composition of the average daily gain ( ADG ) of growing animals. The objective of this study was to identify commercial single nucleotide polymorphism ( SNP ) panels that could improve the predictability of days on feed ( DOF ) to reach a target United States Department of Agriculture ( USDA ) grade given animal, diet, and environmental information under feedyard conditions. The data for this study was comprised of crossbred heifers (n = 681) and steers (n = 836) from commercial feedyards. Eleven molecular breeding value ( MBV ) scores derived from SNP panels of candidate gene polymorphisms and two-leptin gene SNP (UASMS2 and E2FB) were evaluated. The empty body fat ( EBF ) and the shrunk body weight ( SBW ) at 28% EBF ( AFSBW ) were computed by the Cattle Value Discovery System ( CVDS ) model using hip height (EBF HH and AFSBW HH ) or carcass traits (EBF CT and AFSBW CT ) of the animals. The DOF HH was calculated when AFSBW HH and ADG HH were used and DOF CT was calculated when AFSBW CT and ADG CT were used. The CVDS estimates dry matter required ( DMR ) by individuals fed in groups when observed ADG and AFSBW are provided. The AFSBW CT was assumed more accurate than the AFSBW HH because it was computed using carcass traits. The difference between AFSBW CT and AFSBW HH , DOF CT and DOF HH , and DMR and dry matter intake ( DMI ) were regressed on the MBV scores and leptin gene SNP to explain the variation. Our results indicate quite a large range of correlations among MBV scores and model input and output variables, but MBV ribeye area was the most strongly correlated with the differences in DOF, AFSBW, and DMI by explaining 8, 13.2 and 6.5%, respectively, of the variation. This suggests that specific MBV scores might explain additional variation of input and output variables used by nutritional models in predicting individual animal performance.