Integration of genotypic, hyperspectral, and phenotypic data to improve biomass yield prediction in hybrid rye.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Rodrigo José GalánThomas Miedaner

Abstract

Hyperspectral and genomic data are effective predictors of biomass yield in winter rye. Variable selection procedures can improve the informativeness of reflectance data. Integrating cutting-edge technologies is imperative to sustainably breed crops for a growing global population. To predict dry matter yield (DMY) in winter rye (Secale cereale L.), we tested single-kernel models based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices, a multi-kernel model combining both matrices and a bivariate model fitted with plant height as a secondary trait. In total, 274 elite rye lines were genotyped using a 10 k-SNP array and phenotyped as testcrosses for DMY and plant height at four locations in Germany in two years (eight environments). Spectral data consisted of 400 discrete narrow bands ranging between 410 and 993 nm collected by an unmanned aerial vehicle (UAV) on two dates on each environment. To reduce data dimensionality, variable selection of bands was performed, resulting in the least absolute shrinkage and selection operator (Lasso) as the best method in terms of predictive abilities. The mean heritability of reflectance data was moderate ([Formula: see text] = 0.72) and highly variable a...Continue Reading

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Citations

Feb 26, 2021·TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·Rodrigo José GalánThomas Miedaner
Apr 24, 2021·Plant Phenomics : a Science Partner Journal·Meiyan ShuYuntao Ma

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Methods Mentioned

BETA
imaging technique
chip

Software Mentioned

HBLUP
HySpex PostProcessor
ASReml
GBLUP
environment
Linkimpute
GS
GlmNet R package
HySpex Mjolnir 1024
R package adegenet

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