Abstract
Genome association analyses have been successful in identifying quantitative trait loci (QTLs) for pig body weights measured at a single age. However, when considering the whole weight trajectories over time in the context of genome association analyses, it is important to look at the markers that affect growth curve parameters. The easiest way to consider them is via the two-step method, in which the growth curve parameters and marker effects are estimated separately, thereby resulting in a reduction of the statistical power and the precision of estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint modeling of the individual growth curves and marker effects. Our aim was to propose a genome association analysis for growth curves in pigs based on NMM as well as to compare it with the traditional two-step method. In addition, we also aimed to identify the nearest candidate genes related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all significant SNPs from the two-step method were also reported in the NMM analysis. The ontology of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs (simultaneously affecting A and K) allows us to make inferences with regards to their contribution to the pig growth process in the population studied.
Highlights
Differences in individual growth curves reflect partly genetic influences, with multiple genes contributing at different levels to the overall growth trajectory
1291.42 and BIC = 1309.11), logistic (AIC = 1282.18 and BIC = 1301.01), von Bertalanffy (AIC = 1293.42 and BIC = 1310.00), and Richards (AIC = 1284.56 and BIC = 1304.88). These results revealed the superiority of the logistic model, which was chosen to describe the pig growth curves in the subsequent analyses
The nonlinear mixed models (NMM) provided significant SNPs for parameter A, which were located on SSC1 and SSC7, whereas for parameter K the SNPs were located on SSC1, SSC4, SSC7, SSC8, and SSC17 (Table 1)
Summary
Differences in individual growth curves reflect partly genetic influences, with multiple genes contributing at different levels to the overall growth trajectory. In the current post-genomic era, the understanding of the genetic architecture of pig growth cannot be limited to the detection of QTLs for body weights at a specific age (Ai et al, 2012; Yoo et al, 2014) It can be extended for a more general purpose by considering whole growth trajectories over time as phenotypes. One efficient solution is to adopt nonlinear mixed models, which enables a joint modeling of the individual growth curves and genetic effects This class of models has already been adopted for the traditional (Varona et al, 1999; Blasco et al, 2003) and genomic (Ibañez-Escriche and Blasco, 2011) prediction of breeding values, there are no reports about their use in genome association analyses
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