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 (k) 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 k 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.