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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Using a system of differential equations that models cattle growth to uncover the genetic basis of complex traits

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Freua, Mateus Castelani ; de Almeida Santana, Miguel Henrique ; Ventura, Ricardo Vieira ; Tedeschi, Luis Orlindo ; Sterman Ferraz, Jose Bento
Total Authors: 5
Document type: Journal article
Source: JOURNAL OF APPLIED GENETICS; v. 58, n. 3, p. 393-400, AUG 2017.
Web of Science Citations: 1

The interplay between dynamic models of biological systems and genomics is based on the assumption that genetic variation of the complex trait (i.e., outcome of model behavior) arises from component traits (i.e., model parameters) in lower hierarchical levels. In order to provide a proof of concept of this statement for a cattle growth model, we ask whether model parameters map genomic regions that harbor quantitative trait loci (QTLs) already described for the complex trait. We conducted a genome-wide association study (GWAS) with a Bayesian hierarchical LASSO method in two parameters of the Davis Growth Model, a system of three ordinary differential equations describing DNA accretion, protein synthesis and degradation, and fat synthesis. Phenotypic and genotypic data were available for 893 Nellore (Bos indicus) cattle. Computed values for parameter k(1) (DNA accretion rate) ranged from 0.005 +/- 0.003 and for alpha (constant for energy for maintenance requirement) 0.134 +/- 0.024. The expected biological interpretation of the parameters is confirmed by QTLs mapped for k(1) and alpha. QTLs within genomic regions mapped for k(1) are expected to be correlated with the DNA pool: body size and weight. Single nucleotide polymorphisms (SNPs) which were significant for alpha mapped QTLs that had already been associated with residual feed intake, feed conversion ratio, average daily gain (ADG), body weight, and also dry matter intake. SNPs identified for k(1) were able to additionally explain 2.2% of the phenotypic variability of the complex ADG, even when SNPs for k(1) did not match the genomic regions associated with ADG. Although improvements are needed, our findings suggest that genomic analysis on component traits may help to uncover the genetic basis of more complex traits, particularly when lower biological hierarchies are mechanistically described by mathematical simulation models. (AU)

FAPESP's process: 13/26902-0 - Use of genetic variance in dynamic mechanistic models of growth to predict cattle performance and carcass composition under feedlot conditions
Grantee:Mateus Castelani Freua
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 13/20571-2 - Functional genomics of feed intake and efficiency in Nellore cattle
Grantee:Miguel Henrique de Almeida Santana
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 14/07566-2 - Genomics applied to ruminant production
Grantee:José Bento Sterman Ferraz
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 12/02039-9 - Genomic study of feed intake and efficiency in Nellore cattle
Grantee:José Bento Sterman Ferraz
Support Opportunities: Regular Research Grants