Early prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding material.

KEY MESSAGE: Hyperspectral data is a promising complement to genomic data to predict biomass under scenarios of low genetic relatedness. Sufficient environmental connectivity between data used for model training and validation is required. The demand for sustainable sources of biomass is increasing worldwide. The early prediction of biomass via indirect selection of dry matter yield (DMY) based on hyperspectral and/or genomic prediction is crucial to affordably untap the potential of winter rye (Secale cereale L.) as a dual-purpose crop. However, this estimation involves multiple genetic backgrounds and genetic relatedness is a crucial factor in genomic selection (GS). To assess the prospect of prediction using reflectance data as a suitable complement to GS for biomass breeding, the influence of trait heritability ([Formula: see text]) and genetic relatedness were compared. Models were based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices to predict DMY and other biomass-related traits such as dry matter content (DMC) and fresh matter yield (FMY). For this, 270 elite rye lines from nine interconnected bi-parental families were genotyped using a 10 k-SNP array and phenotyped as testcrosses at four locations in two years (eight environments). From 400 discrete narrow bands (410 nm-993 nm) collected by an uncrewed aerial vehicle (UAV) on two dates in each environment, 32 hyperspectral bands previously selected by Lasso were incorporated into a prediction model. HBLUP showed higher prediction abilities (0.41 - 0.61) than GBLUP (0.14 - 0.28) under a decreased genetic relationship, especially for mid-heritable traits (FMY and DMY), suggesting that HBLUP is much less affected by relatedness and [Formula: see text]. However, the predictive power of both models was largely affected by environmental variances. Prediction abilities for DMY were further enhanced (up to 20%) by integrating both matrices and plant height into a bivariate model. Thus, data derived from high-throughput phenotyping emerges as a suitable strategy to efficiently leverage selection gains in biomass rye breeding; however, sufficient environmental connectivity is needed.

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

KEY MESSAGE: 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 across the spectrum. Correlations between DMY and single bands were generally significant (p < 0.05) but low (≤ 0.29). Across environments and training set (TRN) sizes, the bivariate model showed the highest prediction abilities (0.56-0.75), followed by the multi-kernel (0.45-0.71) and single-kernel (0.33-0.61) models. With reduced TRN, HBLUP performed better than GBLUP. The HBLUP model fitted with a set of selected bands was preferred. Within and across environments, prediction abilities increased with larger TRN. Our results suggest that in the era of digital breeding, the integration of high-throughput phenotyping and genomic selection is a promising strategy to achieve superior selection gains in hybrid rye.

Mapping of common bunt resistance gene Bt9 in wheat.

KEY MESSAGE: The Bt9 resistance locus was mapped and shown to be distinct from the Bt10 locus. New markers linked to Bt9 have been identified and may be used to breed for resistance towards the seed-borne disease. Increasing organic wheat production in Denmark, and in other wheat-producing areas, in conjunction with legal requirements for organic seed production, may potentially lead to a rise in common bunt occurrence. As systemic pesticides are not used in organic farming, organic wheat production systems may benefit from genetic resistances. However, little is known about the underlying genetic mechanisms and locations of the resistance factors for common bunt resistance in wheat. A double haploid (DH) population segregating for common bunt resistance was used to identify the chromosomal location of common bunt resistance gene Bt9. DH lines were phenotyped in three environments and genotyped with DArTseq and SSR markers. The total length of the resulting linkage map was 2882 cM distributed across all 21 wheat chromosomes. Bt9 was mapped to the distal end of chromosome 6DL. Since wheat common bunt resistance gene Bt10 is also located on chromosome 6D, the possibility of their co-location was investigated. A comparison of marker sequences linked to Bt9 and Bt10 on physical maps of chromosome 6D confirmed that Bt9 and Bt10 are two distinct resistance factors located at the distal (6DL) and proximal (6DS) end, respectively, of chromosome 6D. Five new SSR markers Xgpw4005-1, Xgpw7433, Xwmc773, Xgpw7303 and Xgpw362 and many SNP and PAV markers flanking the Bt9 resistance locus were identified and they may be used in the future for marker-assisted selection.