Introduction Sugar kelp ( Saccharina latissima ) has a biphasic life cycle, allowing selection on both thediploid sporophytes (SPs) and haploid gametophytes (GPs). Methods We trained a genomic selection (GS) model from farm-tested SP phenotypic data and used a mixed-ploidy additive relationship matrix to predict GP breeding values. Topranked GPs were used to make crosses for further farm evaluation. The relationship matrix included 866 individuals: a) founder SPs sampled from the wild; b) progeny GPs from founders; c) Farm-tested SPs crossed from b); and d) progeny GPs from farm-tested SPs. The complete pedigree-based relationship matrix was estimated for all individuals. A subset of founder SPs ( n = 58) and GPs ( n = 276) were genotyped with Diversity Array Technology and whole genome sequencing, respectively. We evaluated GS prediction accuracy via cross validation for SPs tested on farm in 2019 and 2020 using a basic GBLUP model. We also estimated the general combining ability (GCA) and specific combining ability (SCA) variances of parental GPs. A total of 11 yield-related and morphology traits were evaluated. Results The cross validation accuracies for dry weight per meter ( r ranged from 0.16 to 0.35) and wet weight per meter ( r ranged 0.19 to 0.35) were comparable to GS accuracy for yield traits in terrestrial crops. For morphology traits, cross validation accuracy exceeded 0.18 in all scenarios except for blade thickness in the second year. Accuracy in a third validation year (2021) was 0.31 for dry weight per meter over a confirmation set of 87 individuals. Discussion Our findings indicate that progress can be made in sugar kelp breeding by using genomic selection.

Saccharina latissima (sugar kelp) is one of the most widely cultivated brown marine macroalgae species in the North Atlantic and the eastern North Pacific Oceans. To meet the expanding demands of the sugar kelp mariculture industry, selecting and breeding sugar kelp that is best suited to offshore farm environments is becoming necessary. To that end, a multi-year, multi-institutional breeding program was established by the U.S. Department of Energy's (DOE) Advanced Research Projects Agency-Energy (ARPA-E) Macroalgae Research Inspiring Novel Energy Resources (MARINER) program. Hybrid sporophytes were generated using 203 unique gametophyte cultures derived from wild-collected Saccharina spp. for two seasons of farm trials (2019–2020 and 2020–2021). The wild sporophytes were collected from 10 different locations within the Gulf of Maine (USA) region, including both sugar kelp (Saccharina latissima) and the skinny kelp species (Saccharina angustissima). We harvested 232 common farm plots during these two seasons with available data. We found that farmed kelp plots with skinny kelp as parents had an average increased yield over the mean (wet weight 2.48 ± 0.90 kg m−1 and dry weight 0.32 ± 0.10 kg m−1) in both growing seasons. We also found that blade length positively correlated with biomass in skinny kelp x sugar kelp crosses or pure sugar kelp crosses. The skinny x sugar progenies had significantly longer and narrower blades than the pure sugar kelp progenies in both seasons. Overall, these findings suggest that sugar x skinny kelp crosses provide improved yield compared to pure sugar kelp crosses.

Though Saccharina japonica cultivation has been established for many decades in East Asian countries, the domestication process of sugar kelp (Saccharina latissima) in the Northeast United States is still at its infancy. In this study, by using data from our breeding experience, we will demonstrate how obstacles for accelerated genetic gain can be assessed using simulation approaches that inform resource allocation decisions. Thus far, we have used 140 wild sporophytes that were sampled in 2018 from the northern Gulf of Maine to southern New England. From these sporophytes, we sampled gametophytes and made and evaluated over 600 progeny sporophytes from crosses among the gametophytes in 2019 and 2020. The biphasic life cycle of kelp gives a great advantage in selective breeding as we can potentially select both on the sporophytes and gametophytes. However, several obstacles exist, such as the amount of time it takes to complete a breeding cycle, the number of gametophytes that can be maintained in the laboratory, and whether positive selection can be conducted on farm-tested sporophytes. Using the Gulf of Maine population characteristics for heritability and effective population size, we simulated a founder population of 1,000 individuals and evaluated the impact of overcoming these obstacles on rate of genetic gain. Our results showed that key factors to improve current genetic gain rely mainly on our ability to induce reproduction of the best farm-tested sporophytes, and to accelerate the clonal vegetative growth of released gametophytes so that enough gametophyte biomass is ready for making crosses by the next growing season. Overcoming these challenges could improve rates of genetic gain more than 2-fold. Future research should focus on conditions favorable for inducing spring reproduction, and on increasing the amount of gametophyte tissue available in time to make fall crosses in the same year.

Being one of the most important staple dietary constituents globally, genetic enhancement of cultivated rice for yield, agronomically important traits is of substantial importance. Even though the climatic factors and crop management practices impact complex traits like yield immensely, the contribution of variation by underlying genetic factors surpasses them all. Previous studies have highlighted the importance of utilizing exotic germplasm, landraces in enhancing the diversity of gene pool, leading to better selections and thus superior cultivars. Thus, to fully exploit the potential of progenitor of Asian cultivated rice for productivity related traits, genome wide association study (GWAS) for seven agronomically important traits was conducted on a panel of 346 O. rufipogon accessions using a set of 15,083 high-quality single nucleotide polymorphic markers. The phenotypic data analysis indicated large continuous variation for all the traits under study, with a significant negative correlation observed between grain parameters and agronomic parameters like plant height, culm thickness. The presence of 74.28% admixtures in the panel as revealed by investigating population structure indicated the panel to be very poorly genetically differentiated, with rapid LD decay. The genome-wide association analyses revealed a total of 47 strong MTAs with 19 SNPs located in/close to previously reported QTL/genic regions providing a positive analytic proof for our studies. The allelic differences of significant MTAs were found to be statistically significant at 34 genomic regions. A total of 51 O. rufipogon accessions harboured combination of superior alleles and thus serve as potential candidates for accelerating rice breeding programs. The present study identified 27 novel SNPs to be significantly associated with different traits. Allelic differences between cultivated and wild rice at significant MTAs determined superior alleles to be absent at 12 positions implying substantial scope of improvement by their targeted introgression into cultivars. Introgression of novel significant genomic regions into breeder's pool would broaden the genetic base of cultivated rice, thus making the crop more resilient.

Our team has initiated a selective breeding program for regional strains of sugar kelp, Saccharina latissima, to improve the competitiveness of kelp farming in the United States. Within our breeding program, we also include an endemic putative species, Saccharina angustissima, locally referred to as skinny kelp. We crossed uniclonal gametophyte cultures derived from 37 wild-collected blades representing five sugar kelp strains and one skinny kelp strain to produce 104 unique crosses. Each cross was outplanted on a near-shore research farm located in the Gulf of Maine (GOM). After the first farming season, our results indicated that sugar kelp and skinny kelp were interfertile, and produced mature and reproductively viable sporophytes. Morphological traits of individual blades varied depending on the parental contribution (sugar vs. skinny), with significant differences found in progeny blade length, width, thickness, and in stipe length and diameter. Despite these differences, wet weight and blade density per plot showed no statistical differences regardless of the cross. Given their published genetic similarity and their interfertility shown here, S. angustissima and S. latissima may not be different species, and may each contribute genetic diversity to breeding programs aimed at meeting ocean farming and market needs.

KEY MESSAGE:Genomic selection using data from an on-going breeding program can improve gain from selection, relative to phenotypic selection, by significantly increasing the number of lines that can be evaluated. The early stages of phenotyping involve few observations and can be quite inaccurate. Genomic selection (GS) could improve selection accuracy and alter resource allocation. Our objectives were (1) to compare the prediction accuracy of GS and phenotyping in stage-1 and stage-2 field evaluations and (2) to assess the value of stage-1 phenotyping for advancing lines to stage-2 testing. We built training populations from 1769 wheat breeding lines that were genotyped and phenotyped for yield, test weight, Fusarium head blight resistance, heading date, and height. The lines were in cohorts, and analyses were done by cohort. Phenotypes or GS estimated breeding values were used to determine the trait value of stage-1 lines, and these values were correlated with their phenotypes from stage-2 trials. This was repeated for stage-2 to stage-3 trials. The prediction accuracy of GS and phenotypes was similar to each other regardless of the amount (0, 50, 100%) of stage-1 data incorporated in the GS model. Ranking of stage-1 lines by GS predictions that used no stage-1 phenotypic data had marginally lower correspondence to stage-2 phenotypic rankings than rankings of stage-1 lines based on phenotypes. Stage-1 lines ranked high by GS had slightly inferior phenotypes in stage-2 trials than lines ranked high by phenotypes. Cost analysis indicated that replacing stage-1 phenotyping with GS would allow nearly three times more stage-1 candidates to be assessed and provide 0.84-2.23 times greater gain from selection. We conclude that GS can complement or replace phenotyping in early stages of phenotyping.

An assessment of genetic diversity of marine populations is critical not only for the understanding and preserving natural biodiversity but also for its commercial potential. As commercial demand rises for marine resources, it is critical to generate baseline information for monitoring wild populations. Furthermore, anthropogenic stressors on the coastal environment, such as warming sea temperatures and overharvesting of wild populations, are leading to the destruction of keystone marine species such as kelps. In this study, we conducted a fine-scale genetic analysis using genome-wide high-density markers on Northwest Atlantic sugar kelp. The population structure for a total of 149 samples from the Gulf of Maine (GOM) and Southern New England (SNE) was investigated using AMOVA, FST, admixture, and PCoA. Genome-wide association analyses were conducted for six morphological traits, and the extended Lewontin and Krakauer (FLK) test was used to detect selection signatures. Our results indicate that the GOM region is more heterogeneous than SNE. These two regions have large genetic difference (between-location FST ranged from 0.21 to 0.32) and were separated by Cape Cod, which is known to be the biogeographic barrier for other taxa. We detected one significant SNP (P = 2.03 × 10–7) associated with stipe length, and 248 SNPs with higher-than-neutral differentiation. The findings of this study provide baseline knowledge on sugar kelp population genetics for future monitoring, managing and potentially restoring wild populations, as well as assisting in selective breeding to improve desirable traits for future commercialization opportunities.

ABSTRACT An assessment of genetic diversity of marine populations is critical not only for the understanding and preservation of natural biodiversity but also for its economic potential. As commercial demand rises for marine resources, it is critical to generate baseline information for monitoring wild populations. Furthermore, anthropogenic stressors on the coastal environment, such as warming sea surface temperatures and overharvesting of wild populations, are leading to the destruction of keystone marine species such as kelps. In this study, we conducted a fine-scale genetic analysis using genome-wide high-density markers on Northwest Atlantic sugar kelp species, Saccharina latissima and putative species, Saccharina angustissima . The population structure for a total of 149 samples from the Gulf of Maine (GOM) and Southern New England (SNE) was investigated using AMOVA, Fst, admixture, and PCoA. Genome-wide association analyses were conducted for six morphological traits, and the extended Lewontin and Krakauer (FLK) test was used to detect selection signatures. Our results indicate that the GOM region is moderately more heterogeneous than SNE. While admixture was observed between regions, these results confirm that Cape Cod acts as a biogeographic barrier for sugar kelp gene flow. We detected one significant SNP (P-value=2.03×10 −7 ) associated with stipe length, and 243 SNPs with higher-than-neutral differentiation. The findings of this study provide fundamental knowledge on sugar kelp population genetics for future monitoring, managing and potentially restoring wild populations, as well as assisting in selective breeding to improve desirable traits for cultivation and bioenergy production.

Although grain yield is the most important trait for growers, milling and baking industries demand high‐quality and scab‐free grains for various end products. To accelerate breeding of wheat (Triticum aestivum L.) cultivars with high grain quality, genetic dissection of quality traits is necessary. Genome‐wide association studies (GWAS) were conducted to identify genomic regions responsible for milling and baking quality traits in soft red winter wheat (SRWW). Seven quality traits were evaluated in two locations and 2 yr for 270 elite SRWW lines. These traits include flour yield, softness equivalent, flour protein, and four solvent (lactose, sodium carbonate, sucrose, and water) retention capacity measurements. In this study, 27,449 single nucleotide polymorphism (SNP) markers were developed by using both genotyping‐by‐sequencing and 90K SNP array technologies. A linear mixed model in GWAS, accounting for population structure and kinship, was fitted to identify 18 [−log(P) ≥ 4.0] genomic regions on 12 different chromosomes associated with the quality traits. Only one of these associations seems to be a previously identified quantitative trait locus, whereas others are novel associations. The most significant associations for quality traits were identified on chromosomes 1B, 2A, 2B, 4B, 5A, 7A, and 7D. Candidate gene searches, facilitated by the wheat genome assembly, led us to the identification of putative genes related to SRWW quality traits including fasciclin‐like arabinogalactan. The results of this study can be used in developing high‐quality SRWW varieties for the eastern region of the United States.

Rice blast (RB), caused by the fungal pathogen Magnaporthe oryzae, is a major disease in rice (Oryzae sativa L.) with resistance controlled by major and minor genes. Genomic selection (GS) is a breeding technology applicable for selecting traits controlled by many genes. Our objective was to assess the utility of GS in improving RB resistance. A population of 161 accessions from Africa and another population of 162 accessions from the USA were evaluated for resistance to six and eight RB isolates, respectively. Each rice population was genotyped with single nucleotide polymorphism (SNP) markers. The accuracy of GS was determined using seven models: genomic best linear unbiased prediction (gBLUP), gBLUP with some markers as fixed effects (fgBLUP), gBLUP model with population structure as a covariate (sgBLUP), multitrait gBLUP (mgBLUP), Bayesian (BayesA and BayesC) models, and a multiple linear regression model using significant markers (MLR). Each set of population had accessions with good resistance to multiple isolates. Using cross-validation, the accuracy of gBLUP ranged from 0.15 to 0.72; the gBLUP, sgBLUP, mgBLUP, and Bayesian methods had similar accuracy, while fgBLUP gave the greatest accuracy. Without cross-validation, gBLUP, sgBLUP, fgBLUP, and Bayesian methods were similar and were superior to mgBLUP and MLR. In general, a GS model built on data from one isolate was able to predict the phenotypes generated from other isolates, suggesting common genes controlling resistance across isolates. Our results demonstrate that GS may be a very useful method to improve RB resistance. The fgBLUP model could be used to effectively select for both durable and resistance traits conferred by major genes.

Climate change will have severe effects on wheat production, but crop phenology can be an important component of wheat adaptation. In this study, elite soft winter wheat and hard spring wheat (HSW) populations were phenotyped for heading date (HD) in North America and Tanzania (HSW only). All lines were genotyped with common single nucleotide polymorphism markers to compare the genetics and prediction accuracy of genomic selection (GS) for HD in winter and spring wheat. Lines were tested under diverse environments and the HSW germplasm was assessed for their early maturity performance in Africa. Two clusters of environments were formed for each population. One cluster consisted of southern environments and the other consisted of northern environments. The latter produced a more narrow range of HD than the southern cluster. Thirteen highly significant (p < 0.0005) quantitative trait loci (QTLs) for HD were detected in two populations. Within each population, the QTL effects were consistent between clusters of environments. Within each population, GS model developed using data from one cluster of environments could predict HD in the other cluster. The prediction accuracy of GS between two populations was minimal. Similarly, only a few minor effects QTL were in common between the two populations. Additionally, we identified 15 spring wheat genotypes with HD earlier than commercial Tanzanian wheat varieties. These genotypes could be used as a resource for creating early HD wheat varieties for Tanzania.

Genomic selection (GS) uses training population (TP) data to estimate the value of lines in a selection population. In breeding, the TP and selection population are often grown in different environments, which can cause low prediction accuracy when the correlation of genetic effects between the environments is low. Subsets of TP data may be more predictive than using all TP data. Our objectives were (i) to evaluate the effect of using subsets of TP data on GS accuracy between environments, and (ii) to assess the accuracy of models incorporating marker × environment interaction (MEI). Two wheat (Triticum aestivum L.) populations were phenotyped for 11 traits in independent environments and genotyped with single‐nucleotide polymorphism markers. Within each population–trait combination, environments were clustered. Data from one cluster were used as the TP to predict the value of the same lines in the other cluster(s) of environments. Models were built using all TP data or subsets of markers selected for their effect and stability. The GS accuracy using all TP data was >0.25 for 9 of 11 traits. The between‐environment accuracy was generally greatest using a subset of stable and significant markers; accuracy increased up to 48% relative to using all TP data. We also assessed accuracy using each population as the TP and the other as the selection population. Using subsets of TP data or the MEI models did not improve accuracy between populations. Using optimized subsets of markers within a population can improve GS accuracy by reducing noise in the prediction data set.

Genomic selection (GS) is a breeding tool that estimates breeding values (GEBVs) of individuals based solely on marker data by using a model built using phenotypic and marker data from a training population (TP). The effectiveness of GS increases as the correlation of GEBVs and phenotypes (accuracy) increases. Using phenotypic and genotypic data from a TP of 470 soft winter wheat lines, we assessed the accuracy of GS for grain yield, Fusarium Head Blight (FHB) resistance, softness equivalence (SE), and flour yield (FY). Four TP data sampling schemes were tested: (1) use all TP data, (2) use subsets of TP lines with low genotype-by-environment interaction, (3) use subsets of markers significantly associated with quantitative trait loci (QTL), and (4) a combination of 2 and 3. We also correlated the phenotypes of relatives of the TP to their GEBVs calculated from TP data. The GS accuracy within the TP using all TP data ranged from 0.35 (FHB) to 0.62 (FY). On average, the accuracy of GS from using subsets of data increased by 54% relative to using all TP data. Using subsets of markers selected for significant association with the target trait had the greatest impact on GS accuracy. Between-environment prediction accuracy was also increased by using data subsets. The accuracy of GS when predicting the phenotypes of TP relatives ranged from 0.00 to 0.85. These results suggest that GS could be useful for these traits and GS accuracy can be greatly improved by using subsets of TP data.

Based on the estimates of accuracy, genomic selection would be useful for selecting for improved trait values and trait stability for agronomic and quality traits in wheat. Trait values and trait stability estimated by two methods were generally independent indicating a breeder could select for both simultaneously.

Food-grade soybeans [Glycine max (L.) Merr] used to produce tofu have specific seed composition, shape, size and colour requirements. Seed qualities, such as protein content, have been correlated with tofu texture. The objective of this study was to determine the relationships between textural qualities of tofu and seed characters, including shape, size, density, weight, protein content and oil content. Tofu was produced from the seed of 48 high-protein or food-grade soybean lines grown at two locations in 2010. Four tofu textural traits were assessed including work to failure, deformability, stiffness and hardness. Seed protein and oil content were significantly correlated with stiffness and hardness, and protein was additionally correlated with work to failure. Pearson's correlation coefficients ranged from 0.45 to 0.60 (adjusted P-values<0.002) for protein and -0.49 to -0.35 for oil (adjusted P-values<0.016). No significant correlations between tofu texture and seed volume or shape were detected. Seed protein content can be assessed for indirect selection for tofu firmness, whereas this study reveals no evidence that seed size affects tofu texture.