Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves
Keywords: SNP markers, body weight, longitudinal data
AbstractGenome 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.
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How to Cite
Silva, F., Zambrano, M., Varona, L., Glória, L., Lopes, P., Silva, M., Arbex, W., Lázaro, S., Resende, M., & Guimarães, S. (2017). Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves. Scientia Agricola, 74(1), 1-7. https://doi.org/10.1590/1678-992x-2016-0023
Biometry, Modeling and Statistics
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