Parameters of a dynamic mechanistic model of cattle growth retain enough biological interpretation for genotype-to-phenotype mapping.

This study aimed to investigate the predictability of a phenotype when using a dynamic model of cattle growth. Genotypic and phenotypic information on Nellore (Bos indicus) cattle were used in a genome-wide association analysis designed to contrast the biological interpretation of core parameters [conversion efficiency of metabolizable energy to net energy for gain (kg) and adjusted final shrunk body weight (AFSBW)] to their associated genomic regions and nearby quantitative trait loci (QTLs). Single nucleotide polymorphisms (SNPs) were used to develop prediction equations for kg and AFSBW, which enter the model for simulative prediction purposes. QTLs and genes, one related to mature body weight and another to growth efficiency, are consistent with the model equations. Significantly associated SNPs were used to compute parameters, which yielded reasonable model outcomes when compared with regular parameter computations. Our results provide evidence of the biological validity of using such parameters as component traits of higher phenotypes and the possibility of using genomic data for genotype-to-parameter mapping.

Systems genetics and genome-wide association approaches for analysis of feed intake, feed efficiency, and performance in beef cattle.

Feed intake, feed efficiency, and weight gain are important economic traits of beef cattle in feedlots. In the present study, we investigated the physiological processes underlying such traits from the point of view of systems genetics. Firstly, using data from 1334 Nellore (Bos indicus) cattle and 943,577 single nucleotide polymorphisms (SNPs), a genome-wide association analysis was performed for dry matter intake, average daily weight gain, feed conversion ratio, and residual feed intake with a Bayesian Lasso procedure. Genes within 50-kb SNPs, most relevant for explaining the genomic variance, were annotated and the biological processes underlying the traits were inferred from Database for Annotation, Visualization and Integrated Discovery (DAVID) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Our results indicated several putative genomic regions associated with the target phenotypes and showed that almost all genomic variances were in the SNPs located in the intergenic and intronic regions. We further identified five main metabolic pathways related to ion transport, body composition, and feed intake control, which influenced the four phenotypes simultaneously. The systems genetics approach used in this study revealed novel pathways related to feed efficiency traits in beef cattle.