Reversible jumps for the joint modeling of the dimension of pseudo-functional models in chromosomal windows in genome-wide selection.
BACKGROUND With the advance of large-scale genotyping and the high availability of single nucleotide polymorphism (SNP) markers, genomic selection (GS) has become fundamental for plant and animal breeding. However, this technique faces three classical problems: multicollinearity, high marker dimensionality, and high computational cost. To address these problems, mixed models or Bayesian inference methods are generally used. More recently, bin-based genomic window approaches have been widely used, as they can group redundant markers, reducing dimensionality and mitigating the effects of multicollinearity. However, the performance of these methods depends, among other factors, on the number, size, and location of the bins. In general, existing approaches group markers based on linkage disequilibrium (LD) patterns or use fixed-size genome partitions. RESULTS A transdimensional method was proposed to jointly infer the dimension, location, and composition of genomic compartments, as well as the parameters associated with the model, through reversible jump Markov chain Monte Carlo (RJ-MCMC) sampling. The method was evaluated in simulated F2 and F10 populations under oligogenic and polygenic architectures, as well as in human SNP data from the HapMap project with simulated phenotypes and real eucalyptus data. In the simulated datasets, the method achieved the best results under oligogenic architectures (F2 and F10). In more complex polygenic scenarios and real data, performance was satisfactory and comparable to that of rr-BLUP, Bayes B, and Hu, considering mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2). CONCLUSIONS The proposed method incorporates model complexity as an inferential component, allowing the automatic estimation of the number, location, and composition of genomic compartments jointly with the model effects. The method demonstrated robust predictive performance, in addition to improving the ability to identify causal regions, which may represent a promising methodological alternative for both genomic selection and QTL mapping.