2022/03/17 by Xiaolin Wu, Kristen L. Parker Gaddis, Wu, Xiao-Lin +13
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · #Animal Nutrition and Physiology #Applications (stat.AP) #FOS: Computer and information sciences #Genetic and phenotypic traits in livestock #Reproductive Physiology in Livestock #Ruminant Nutrition and Digestive Physiology
paper · pdf · doi:10.48550/arxiv.2203.09609
openalex publication_date 2022/03/17 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
There has been an increasing interest in residual feed intake (RFI) as a\nmeasure of net feed efficiency in dairy cattle. RFI phenotypes are obtained as\nresiduals from linear regression encompassing relevant factors (i.e., energy\nsinks) to account for body tissue mobilization. However, fitting energy sink\nphenotypes as regression variables in standard linear regression was criticized\nbecause phenotypes are subject to measurement errors. Multiple-trait models\nhave been proposed which derive RFI by follow-up partial regression. By\nre-arranging the single-trait linear regression, we showed a causal RFI\ninterpretation underlying the linear regression for RFI. It postulates\nrecursive effects in energy allocation from energy sinks on dry matter intake,\nbut the feedback or simultaneous effects are assumed to be nonexistent. A\nBayesian recursive structural equation model was proposed for directly\npredicting RFI and energy sinks and estimating relevant genetic parameters\nsimultaneously. A simplified Markov chain Monte Carlo algorithm that\nimplemented the Bayesian recursive model was described. The recursive model is\nasymptotically equivalent to one-step linear regression for RFI, yet extends\nthe analytical capacity to multiple-trait analysis. It is equivalent to\nBayesian-implemented, multiple-trait model reparameterized based on Cholesky\ndecomposition of phenotypic (co)variance matrix for evaluating RFI, but varied\nin assumptions about relationships between energy sinks.\n