Malting quality and preharvest sprouting traits are genetically correlated in spring malting barley.

KEY MESSAGE: Malt for craft "all-malt" brewing can have high quality, PHS resistance, and malted in normal timeframes. Canadian style adjunct malt is associated with PHS susceptibility. Expansion of malting barley production into non-traditional growing regions and erratic weather has increased the demand for preharvest sprouting (PHS) resistant, high quality malting barley cultivars. This is hindered by the relatively unknown relationships between PHS resistance and malting quality. Here we present a three-year study of malting quality and germination at different after-ripening durations post physiological maturity. Malting quality traits alpha amylase (AA) and free amino nitrogen (FAN) and germination rate at six days post PM shared a common association with a SNP in HvMKK3 on chromosome 5H in the Seed Dormancy 2 (SD2) region responsible for PHS susceptibility. Soluble protein (SP) and soluble over total protein (S/T) both shared a common association with a marker in the SD2 region. Significant genetic correlations between PHS resistance and the malting quality traits AA, FAN, SP, S/T were detected across and within HvMKK3 allele groups. High adjunct malt quality was related to PHS susceptibility. Selection for PHS resistance led to a correlated response in malting quality traits. Results strongly suggest pleiotropy of HvMKK3 on malting quality traits and that the classic "Canadian-style" malt is caused by a PHS susceptible allele of HvMKK3. PHS susceptibility appears to benefit the production of malt intended for adjunct brewing, while PHS resistance is compatible with all-malt brewing specifications. Here we present our analysis on the effect of combining complexly inherited and correlated traits with contrasting goals to inform breeding practice in malting barley, the general principles of which can be extended to other breeding programs.

QTL x environment modeling of malting barley preharvest sprouting.

KEY MESSAGE: HvMKK3 alleles are temperature sensitive and are major contributors to environmental stability of preharvest sprouting in barley. Preharvest sprouting (PHS) can severely damage barley (Hordeum vulgare L.) malting quality, but PHS resistance is often negatively correlated with malting quality. Seed dormancy is closely related to PHS. Increased temperature during grain fill can decrease seed dormancy in barley, but genetic components of seed dormancy temperature sensitivity are poorly understood. Six years of PHS data were used to fit quantitative trait locus (QTL) x environment mixed models incorporating marker data from seed dormancy genes HvAlaAT1, HvGA20ox1, and HvMKK3 and weather covariates in spring and winter two-row malting barley. Variation in winter barley PHS was best modeled by average temperature range during grain fill and spring barley PHS by total precipitation during grain fill. Average high temperature during grain fill also accurately modeled PHS for both datasets. A highly non-dormant HvMKK3 allele determined baseline PHS susceptibility and HvAlaAT1 interactions with multiple HvMKK3 alleles conferred environmental sensitivity. Polygenic variation for PHS within haplotype was detected. Residual genotype and QTL by environment interaction variance indicated additional environmental and genetic factors involved in PHS. These models provide insight into genotype and environmental regulation of barley seed dormancy, a method for PHS forecasting, and a tool for breeders to improve PHS resistance.

Remote sensing for estimating genetic parameters of biomass accumulation and modeling stability of growth curves in alfalfa.

Multi-spectral imaging by unoccupied aerial vehicles provides a non-destructive, high throughput approach to measuring biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests. Information from estimated growth curves can be used to infer harvest biomass and to gain insights into the relationship between growth dynamics and forage biomass stability across cuttings and years. In this study, multi-spectral imaging and several common vegetation indices were used to estimate genetic parameters and model growth of alfalfa cultivars to determine the longitudinal relationship between vegetation indices and forage biomass. Results showed moderate heritability for vegetation indices, with median plot level heritability ranging from 0.11-0.64, across multiple cuttings in three trials planted in Ithaca, NY, and Las Cruces, NM. Genetic correlations between the normalized difference vegetation index and forage biomass were moderate to high across trials, cuttings, and the timing of multi-spectral image capture. To evaluate the relationship between growth parameters and forage biomass stability across cuttings and environmental conditions, random regression modeling approaches were used to estimate the growth parameters of cultivars for each cutting and the variance in growth was compared to the variance in genetic estimates of forage biomass yield across cuttings. These analyses revealed high correspondence between stability in growth parameters and stability of forage yield. The results of this study indicate that vegetation indices are effective at modeling genetic components of biomass accumulation, presenting opportunities for more efficient screening of cultivars and new longitudinal modeling approaches that can provide insights into temporal factors influencing cultivar stability.

Genome-wide marker effect heterogeneity is associated with a large effect dormancy locus in winter malting barley.

Prediction of trait values in plant breeding populations typically relies on assumptions about marker effect homogeneity across populations. Evidence is presented for winter malting barley (Hordeum vulgare L.) germination traits that a single, causative, large-effect gene in the Seed dormancy 1 region on Chromosome 5H, HvAlaAT1 (Qsd1), leads to heterogeneous estimated marker effects genome wide between groups of otherwise related individuals carrying different Qsd1 alleles. This led to reduced prediction accuracy across alleles when a model was trained either on individuals carrying both alleles or one allele. Several genomic prediction models were tested to increase prediction accuracy within the Qsd1 allele groups. Small gains (5-12%) in prediction accuracy were realized using structured genomic best linear unbiased predictor models when information about the Qsd1 allele was used to stratify the population. We concluded that a single large-effect locus can lead to heterogeneous marker effects in the same breeding family. Variance partitioning based on large-effect loci can be used to inform best practices in designing genomic prediction models; however, there are likely few cases for which it may be practical to do this. For malting barley, if germination traits are highly associated with malting quality traits, then similar steps should be considered for malting quality trait prediction.