Integrating Genomics with Nutrition Models to Improve the Prediction of Cattle Performance and Carcass Composition under Feedlot Conditions.

Luis O Tedeschi,Marinus F.W Te Pas

doi:10.1371/journal.pone.0143483

Abstract

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 (EBFHH and AFSBWHH) or carcass traits (EBFCT and AFSBWCT) of the animals. The DOFHH was calculated when AFSBWHH and ADGHH were used and DOFCT was calculated when AFSBWCT and ADGCT were used. The CVDS estimates dry matter required (DMR) by individuals fed in groups when observed ADG and AFSBW are provided. The AFSBWCT was assumed more accurate than the AFSBWHH because it was computed using carcass traits. The difference between AFSBWCT and AFSBWHH, DOFCT and DOFHH, 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.

Highlights

A recent survey of U.S consumers regarding meat quality indicated a greater satisfaction and preference for United States Department of Agriculture (USDA) Choice or Canadian AAA graded meat versus lower quality grade meat (i.e., USDA Selected and Canada AA) [1], but typically about only 60% of the cattle in the United States grade USDA Choice or higher [2]
Nutrition and growth models can assist in the management of feedlot cattle by grouping animals that are likely to reach a common carcass composition when fed a diet for a given period
An evaluation of four growth models indicated that they differ in the predictions of average daily gain (ADG), dry matter intake (DMI), and body and carcass composition likely because the assumptions and definitions used during their development were different among them [10]

Summary

Introduction

Nutrition and growth models can assist in the management of feedlot cattle by grouping animals that are likely to reach a common carcass composition when fed a diet for a given period. This management strategy has the potential to decrease the heterogeneity of the carcasses among animals within a pen and to assist in the decision making process of when to slaughter animals, which typically leads to increased profitability. Despite these high correlations under controlled conditions, several assumptions and inputs can affect the prediction outcome, including mature size [17]

Objectives

Methods

Results