Approximate Genome-Based Kernel Models for Large Data Sets Including Main Effects and Interactions

Frontiers in Genetics
Jaime CuevasJosé Crossa

Abstract

The rapid development of molecular markers and sequencing technologies has made it possible to use genomic prediction (GP) and selection (GS) in animal and plant breeding. However, when the number of observations (n) is large (thousands or millions), computational difficulties when handling these large genomic kernel relationship matrices (inverting and decomposing) increase exponentially. This problem increases when genomic × environment interaction and multi-trait kernels are included in the model. In this research we propose selecting a small number of lines m(m < n) for constructing an approximate kernel of lower rank than the original and thus exponentially decreasing the required computing time. First, we describe the full genomic method for single environment (FGSE) with a covariance matrix (kernel) including all n lines. Second, we select m lines and approximate the original kernel for the single environment model (APSE). Similarly, but including main effects and G × E, we explain a full genomic method with genotype × environment model (FGGE), and including m lines, we approximated the kernel method with G × E (APGE). We applied the proposed method to two different wheat data sets of different sizes (n) using the standa...Continue Reading

References

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Citations

Mar 3, 2021·Heredity·Abelardo Montesinos-LópezJosé Crossa
May 21, 2021·TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·Pallavi SinhaRajeev K Varshney

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Methods Mentioned

BETA
genome-based prediction

Software Mentioned

PP
APSE
APGE
BGLR R package
Ridge Regression best prediction ( rrBLUP )
R
Ridge Regression
Genomic interaction ( BGGE )
FGGE
rrBLUP

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