Genomic-assisted prediction of genetic value with semiparametric procedures

Abstract

Semiparametric procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are presented. The methods focus on the treatment of massive information provided by, e.g., single-nucleotide polymorphisms. It is argued that standard parametric methods for quantitative genetic analysis cannot handle the multiplicity of potential interactions arising in models with, e.g., hundreds of thousands of markers, and that most of the assumptions required for an orthogonal decomposition of variance are violated in artificial and natural populations. This makes nonparametric procedures attractive. Kernel regression and reproducing kernel Hilbert spaces regression procedures are embedded into standard mixed-effects linear models, retaining additive genetic effects under multivariate normality for operational reasons. Inferential procedures are presented, and some extensions are suggested. An example is presented, illustrating the potential of the methodology. Implementations can be carried out after modification of standard software developed by animal breeders for likelihood-based or Bayesian analysis.

Figure 1.—

Impact of window width on the regression function of log-income on age (Chu and Marron 1991). (A) Scatter plot and (B) smooths for earning power data. Kernel is N(0, 1). Window widths are represented by curves: solid curves, h = 3; dotted curves, h =1; dashed curves, h = 9.

Figure 2.—

LOESS curves of protein yield deviations (EBLUP) in Jersey cows against their inbreeding coefficients (F, %). The thick curve is the fitted regression (second-degree local polynomial, spanning parameter = 0.9); the other curves are 100 bootstrap replicates.