Genome-Wide Association Studies for Growth Curves in Meat Rabbits Through the Single-Step Nonlinear Mixed Model

Abstract

Growth is a complex trait with moderate to high heritability in livestock and must be described by the longitudinal data measured over multiple time points. Therefore, the used phenotype in genome-wide association studies (GWAS) of growth traits could be either the measures at the preselected time point or the fitted parameters of whole growth trajectory. A promising alternative approach was recently proposed that combined the fitting of growth curves and estimation of single-nucleotide polymorphism (SNP) effects into single-step nonlinear mixed model (NMM). In this study, we collected the body weights at 35, 42, 49, 56, 63, 70, and 84 days of age for 401 animals in a crossbred population of meat rabbits and compared five fitting models of growth curves (Logistic, Gompertz, Brody, Von Bertalanffy, and Richards). The logistic model was preferably selected and subjected to GWAS using the approach of single-step NMM, which was based on 87,704 genome-wide SNPs. A total of 45 significant SNPs distributed on five chromosomes were found to simultaneously affect the two growth parameters of mature weight (A) and maturity rate (K). However, no SNP was found to be independently associated with either A or K. Seven positional genes, including KCNIP4, GBA3, PPARGC1A, LDB2, SHISA3, GNA13, and FGF10, were suggested to be candidates affecting growth performances in meat rabbits. To the best of our knowledge, this is the first report of GWAS based on single-step NMM for longitudinal traits in rabbits, which also revealed the genetic architecture of growth traits that are helpful in implementing genome selection.

Weighted Single-Step Genomic Best Linear Unbiased Prediction Method Application for Assessing Pigs on Meat Productivity and Reproduction Traits

“…It should be noted that the genomic assessment procedure (based on the ssGBLUP and wssGBLUP methods) can significantly improve the preliminary prediction accuracy of the individual genetic values within a particular population. An increase in the reference group size provides more stable individual marker weight values, and the proportion of genetic variance explained by each of them, especially when the reference group is closely related to the assessed population [23]. Since the strength of the LD between SNP markers and QTL may decay over time due to the impact of genetic forces, genomic predictions calculated based on the reference group may lose accuracy.…”

“…This is due to several objective and subjective reasons: trait polygenicity, low marker effect values, the insufficient information content of traditional phenotypic measurements, the significant influence of paratype factors on the phenotype, which cannot always be taken into account, and the utilization of different models and approaches [13][14][15][16][17][18][19][20][21][22]. At the same time, other studies have identified the same candidate genes associated with trait variability in different animal species [23]. The idea of preliminary identification and selection of significant SNP markers for inclusion in calculating genomic estimates served as the basis for developing the weighted ssGBLUP procedure [11,24].…”

Genome-wide association studies and mapping of ROH identifies candidate genes for growth traits in hybrid rabbits

“…Based on the original data, we carried out systematic quality control of the original genotype data by referring to Liao's method [27], and remove unquali ed samples. Use GATK v 4.2[6] to set the parameter "QD < 2.0 || FS > 60.0 || MQ < 40.0" to lter SNP.…”

Genome‐wide association study of multiyear dynamic growth traits in hybrid Liriodendron identifies robust genetic loci associated with growth trajectories

“…In Norway spruce, Baison et al (2019) used the least absolute shrinkage and selection operator-based association mapping method to identify QTLs related to B-spline regression parameters and indirectly identified candidate genes related to wood formation. Therefore, this method allows the loci affecting these characteristic parameters to be mined and the underlying features of the displayed growth trajectory to be captured by performing a GWAS of model parameters after choosing an appropriate model to fit the growth curves (Liao et al, 2021). Additionally, by allowing good consideration of their own heteroskedasticity and correlation, random regression models provide a robust framework for modeling growth trajectories and performing genetic analyses simultaneously (Campbell et al, 2019).…”