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Introduction: In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where "desired gain selection indices" come in handy, which can yield optimal gains in each trait based on the breeder's prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit., Methods: Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits., Results: We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat ( Triticum aestivum ) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction., Discussion: Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from Grains Research and Development Corporation. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication., (Copyright © 2024 Joukhadar, Li, Thistlethwaite, Forrest, Tibbits, Trethowan and Hayden.)

Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders' main interest - response to selection - was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 He, Jiang, Thistlethwaite, Hayden, Trethowan and Daetwyler.)

Key Message: Genomic selection enabled accurate prediction for the concentration of 13 nutritional element traits in wheat. Wheat biofortification is one of the most sustainable strategies to alleviate mineral deficiency in human diets. Here, we investigated the potential of genomic selection using BayesR and Bayesian ridge regression (BRR) models to predict grain yield (YLD) and the concentration of 13 nutritional elements in grains (B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P and Zn) using a population of 1470 spring wheat lines. The lines were grown in replicated field trials with two times of sowing (TOS) at 3 locations (Narrabri-NSW, all lines; Merredin-WA and Horsham-VIC, 200 core lines). Narrow-sense heritability across environments (locations/TOS) ranged from 0.09 to 0.45. Co, K, Na and Ca showed low to negative genetic correlations with other traits including YLD, while the remaining traits were negatively correlated with YLD. When all environments were included in the reference population, medium to high prediction accuracy was observed for the different traits across environments. BayesR had higher average prediction accuracy for mineral concentrations (r = 0.55) compared to BRR (r = 0.48) across all traits and environments but both methods had comparable accuracies for YLD. We also investigated the utility of one or two locations (reference locations) to predict the remaining location(s), as well as the ability of one TOS to predict the other. Under these scenarios, BayesR and BRR showed comparable performance but with lower prediction accuracy compared to the scenario of predicting reference environments for new lines. Our study demonstrates the potential of genomic selection for enriching wheat grain with nutritional elements in biofortification breeding., (© 2021. Crown.)

Key Message: Several stable QTL were detected using metaGWAS analysis for different agronomic and quality traits under 26 normal and heat stressed environments. Heat stress, exacerbated by global warming, has a negative influence on wheat production worldwide and climate resilient cultivars can help mitigate these impacts. Selection decisions should therefore depend on multi-environment experiments representing a range of temperatures at critical stages of development. Here, we applied a meta-genome wide association analysis (metaGWAS) approach to detect stable QTL with significant effects across multiple environments. The metaGWAS was applied to 11 traits scored in 26 trials that were sown at optimal or late times of sowing (TOS1 and TOS2, respectively) at five locations. A total of 2571 unique wheat genotypes (13,959 genotypes across all environments) were included and the analysis conducted on TOS1, TOS2 and both times of sowing combined (TOS1&2). The germplasm was genotyped using a 90 k Infinium chip and imputed to exome sequence level, resulting in 341,195 single nucleotide polymorphisms (SNPs). The average accuracy across all imputed SNPs was high (92.4%). The three metaGWAS analyses revealed 107 QTL for the 11 traits, of which 16 were detected in all three analyses and 23 were detected in TOS1&2 only. The remaining QTL were detected in either TOS1 or TOS2 with or without TOS1&2, reflecting the complex interactions between the environments and the detected QTL. Eight QTL were associated with grain yield and seven with multiple traits. The identified QTL provide an important resource for gene enrichment and fine mapping to further understand the mechanisms of gene × environment interaction under both heat stressed and unstressed conditions.

Key Message: A multi-environment genomic prediction model incorporating environmental covariates increased the prediction accuracy of wheat grain protein content. The advantage of the haplotype-based model was dependent upon the trait of interest. The inclusion of environment covariates (EC) in genomic prediction models has the potential to precisely model environmental effects and genotype-by-environment interactions. Together with EC, a haplotype-based genomic prediction approach, which is capable of accommodating the interaction between local epistasis and environment, may increase the prediction accuracy. The main objectives of our study were to evaluate the potential of EC to portray the relationship between environments and the relevance of local epistasis modelled by haplotype-based approaches in multi-environment prediction. The results showed that among five traits: grain yield (GY), plant height, protein content, screenings percentage (SP) and thousand kernel weight, protein content exhibited a 2.1% increase in prediction accuracy when EC was used to model the environmental relationship compared to treatment of the environment as a regular random effect without a variance-covariance structure. The approach used a Gaussian kernel to characterise the relationship among environments that displayed no advantage in contrast to the use of a genomic relationship matrix. The prediction accuracies of haplotype-based approaches for SP were consistently higher than the genotype-based model when the numbers of single-nucleotide polymorphisms (SNP) in a haplotype were from three to ten. In contrast, for GY, haplotype-based models outperformed genotype-based methods when two to four SNPs were used to construct the haplotype.

Rising global temperatures cause substantial yield losses in many wheat growing environments. Emmer wheat ( Triticum dicoccon Schrank), one of the first wheat species domesticated, carries significant variation for tolerance to abiotic stresses. This study identified new genetic variability for high-temperature tolerance in hexaploid progeny derived from crosses with emmer wheat. Eight hexaploid and 11 tetraploid parents were recombined in 43 backcross combinations using the hexaploid as the recurrent parent. A total of 537 emmer-based hexaploid lines were developed by producing approximately 10 doubled haploids on hexaploid like BC 1 F 1 progeny and subsequent selection for hexaploid morphology. These materials and 17 commercial cultivars and hexaploid recurrent parents were evaluated under two times of sowing in the field, in 2014-2016. The materials were genotyped using a 90K SNP platform and these data were used to estimate the contribution of emmer wheat to the progeny. Significant phenotypic and genetic variation for key agronomical traits including grain yield, TKW and screenings was observed. Many of the emmer derived lines showed improved performance under heat stress (delayed sowing) compared with parents and commercial cultivars. Emmer derived lines were the highest yielding material in both sowing dates. The emmer wheat parent contributed between 1 and 44% of the genome of the derived lines. Emmer derived lines with superior kernel weight and yield generally had a greater genetic contribution from the emmer parent compared to those with lower trait values. The study showed that new genetic variation for key traits such as yield, kernel weight and screenings can be introduced to hexaploid wheat from emmer wheat. These genetic resources should be explored more systematically to stabilize grain yield and quality in a changing climate.

Background: The safety and immune tolerance potential of donor marrow infusion with cadaveric source renal transplants was evaluated in a series of non-randomized multicenter pilot trials by the NIH Cooperative Clinical Trials in Transplantation (CCTT) Group., Patients and Methods: Three strategies were tested: (1) immunosuppression with cyclosporin, azathioprine and prednisone with a single post-transplant day 1 infusion of 5 x 107 viable cells/kg, (2) OKT3 induction with triple drug therapy and marrow transfusion on day 1, or (3) same therapy as (2) but with an additional marrow transfusion on day 10-12., Results: Thirty-eight marrow recipients and 35 contemporaneous controls were entered with a mean follow-up of over 5 yr. Graft survival was initially better in the marrow recipients than the controls but was similar after 5 yr. Microchimerism rates were similar for the marrow infusion and control groups throughout the follow-up period, regardless of the immunosuppression strategies., Discussion: Bone marrow infusions were well tolerated by a group of cadaver renal allograft recipients. There were no complications from the infusion(s), no episodes of graft-vs.-host disease (GVHD) and no increase in infections or other complications. There was a trend toward early improved graft survival in marrow recipients. Decreased rejection rates were observed in black recipients.

Objective: Cholesteryl ester transfer (CET) is accelerated in patients with IDDM treated with conventional (subcutaneous) insulin therapy (CIT) and a number of other disorders associated with premature cardiovascular disease. We have shown that in IDDM this disturbance is closely linked to iatrogenic hyperinsulinemia (HI), because it was reversed when insulin was administered by the intraportal (i.p.) route. In this study, we sought to determine whether HI after successful pancreas-kidney transplantation (PKT) has the same adverse effect on CET., Research Design and Methods: CET was measured by both mass and isotopic assays and compared in two groups of euglycemic non-insulin-requiring IDDM PKT patients with either systemically draining allografts and persistent HI or grafts with portal vein anastomoses that were normoinsulinemic (PK-P). A third group of eight nondiabetic kidney transplant (KT) patients receiving the same immunosuppressive drugs served as control subjects., Results: CET in pancreas-kidney transplantation subjects with systemic venous drainage (PK-S) was increased (P < 0.001) to the same level we have reported previously in IDDM patients receiving CIT and was significantly higher (P < 0.001) than in those subjects with PK-P. CET in the PK-P group did not differ from that of the KT control patients., Conclusions: CET is affected by variations in systemic insulin levels in pancreas transplant patients with allografts that have differing venous drainage. Because high systemic insulin levels are linked to the activation of (ET, euglycemic HI IDDM pancreas allograft recipients may continue to be at high risk for macrovascular complications.

Contrast-enhanced perfusion patterns of newly transplanted kidneys were determined in 10 patients. Albumin-stabilized sonicated microspheres were injected into the iliac-renal artery of the transplanted kidney while continuous two-dimensional ultrasound images were recorded. Doppler derived resistance index (RI) of the transplanted kidney's blood flow before injection of contrast (0.68 +/- 0.8) did not differ significantly from RI measured immediately after injection (0.72 +/- 0.13) or RI 24 h after surgery (0.69 +/- 0.11). Heart rate, mean arterial pressure, central venous pressure, and electrocardiogram (ECG) signs for ischemia did not change during contrast injections. Renal scintigraphy and renal biopsy revealed acute tubular necrosis and/or rejection in two patients at 24-48 h. Videodensitometry was used to assess the ratio of inner to outer peak pixel intensity from the recorded tomographic images in six patients. In both patients with acute rejection, the inner to outer cortex peak pixel intensity was greater than 1, whereas it was less than 1 in the remaining four patients with normal postoperative renal function. Visual scores (0-3) of contrast enhancement for three doses of Albunex were evaluated (0.5 mL, 1.0 mL, 2.0 mL). Two milliliters always enabled perfusion assessment. In seven patients the identical dose of Albunex was injected immediately before and 30 s after 2 mg of verapamil was injected directly into the renal artery at the time of surgery. The contrast enhancement score before verapamil (1.4 +/- 0.6) was significantly less than the enhancement score after (2.1 +/- 0.6), implying greater renal blood flow after verapamil.(ABSTRACT TRUNCATED AT 250 WORDS)