Genome-wide association and pathway analysis of feed efficiency in pigs reveal candidate genes and pathways for residual feed intake

Residual feed intake (RFI) is a complex trait that is economically important for livestock production; however, the genetic and biological mechanisms regulating RFI are largely unknown in pigs. Therefore, the study aimed to identify single nucleotide polymorphisms (SNPs), candidate genes and biological pathways involved in regulating RFI using Genome-wide association (GWA) and pathway analyses. A total of 596 Yorkshire boars with phenotypes for two different measures of RFI (RFI1 and 2) and 60k genotypic data was used. Genome-wide association analysis was performed using a univariate mixed model and 12 and 7 SNPs were found to be significantly associated with RFI1 and RFI2, respectively. Several genes such as XIRP2, TTC29, SOGA1, MAS1, GRK5, PROX1, GPR155 and ZFYVE26 were identified as putative candidates for RFI based on their genomic location in the vicinity of these SNPs. Genes located within 50 kilo base pairs of SNPs significantly associated with RFI and RFI2 (q-value ≤ 0.2) were subsequently used for pathway analyses. These analyses were performed by assigning genes to biological pathways and then testing the association of individual pathways with RFI using a Fisher’s exact test. Metabolic pathway was significantly associated with both RFIs. Other biological pathways regulating phagosome, tight junctions, olfactory transduction, and insulin secretion were significantly associated with both RFI traits when relaxed threshold for cut-off p-value was used (p ≤ 0.05). These results implied porcine RFI is regulated by multiple biological mechanisms, although the metabolic processes might be the most important. Olfactory transduction pathway controlling the perception of feed via smell, insulin pathway controlling food intake might be important pathways for RFI. Furthermore, our study revealed key genes and genetic variants that control feed efficiency that could potentially be useful for genetic selection of more feed efficient pigs

A new mathematical model for combining growth and energy intake in animals:the case of the growing pig

International audienceA simultaneous model for analysis of net energy intake and growth curves is presented, viewing the animal's responses as a two-dimensional outcome. The model is derived from four assumptions: 1) the intake is a quadratic function of metabolic weight; 2) the rate of body energy accretion represents the difference between intake and maintenance; 3) the relationship between body weight and body energy is allometric and 4) animal intrinsic variability affects the outcomes so the intake and growth trajectories are realizations of a stochastic process. Data on cumulated net energy intake and body weight measurements registered from weaning to maturity were available for 13 pigs. The model was fitted separately to 13 datasets. Furthermore, slaughter data obtained from 170 littermates was available for validation of the model. The parameters of the model were estimated by maximum likelihood within a stochastic state space model framework where a transform-both-sides approach was adopted to obtain constant variance. A suitable autocorrelation structure was generated by the stochastic process formulation. The pigs' capacity for intake and growth were quantified by eight parameters: body weight at maximum rate of intake (149–281kg); maximum rate of intake (25.7 – 35.7 MJ/day); metabolic body size exponent (fixed: 0.75); the daily maintenance requirement per kg metabolic body size (0.232 – 0.303 MJ/(day×kg)); reciprocal scaled energy density (0.192 – 0.641kg/MJ); a dimensional exponent, θ (0.730 – 0.867); coefficient for animal intrinsic variability in intake (0.120 – 0.248 MJ) and coefficient for animal intrinsic variability in growth (0.029 – 0.065 kg). Model parameter values for maintenance requirements and body energy gains were in good agreement with those obtained from slaughter data. In conclusion, the model provides biologically relevant parameter values, which cannot be derived by traditional analysis of growth and energy intake data