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Gestational age in sheep can be closely predicted through ultrasonographic measurement of fetal bones when correlated to standardized fetal growth curves. However, these standardized curves do not account for factors that are known modulators of fetal growth, such as maternal nutrition or health status. Despite being seasonal breeders, and studies reporting an effect of season on birth weight, the influence of season on fetal growth has not been well characterized. In this study, we hypothesized that season of conception will affect fetal growth curves during mid-gestation and that pre-conceptional nutrition would have no effect. We investigated this by provisioning treatments of low, control, and high planes of nutrition during the lactation and flushing pre-conceptional periods to multiparous Dorset x Polypay and Dorset ewes over two seasons (the optimal breeding season [n = 97] and the suboptimal breeding season [n = 104]). Females were mated naturally with mating dates recorded, fetal biparietal diameter measured via ultrasound between gestational days 35-71, and newborn weights recorded at lambing. Pre-conceptional nutritional treatments did not affect fetal biparietal diameter. However, low vs. high nutrition in the pre-conceptional lactation (but not flushing) period resulted in reduced lamb birth weights (P < 0.001). Early fetal growth tended to be faster in the suboptimal breeding season than in the optimal breeding season (P < 0.061) with lambs being heavier at birth in the optimal breeding season (P < 0.001). There was no effect of fetal sex or litter size on fetal biparietal diameter during the first half of pregnancy, however both sex and litter size influenced lamb birth weight (P < 0.001) with males being heavier than females and singletons being heavier than twins and triplets. Mating date within the flushing period had a significant effect on lamb birth weight regardless of season and independent of treatment, with ewes that conceived later in the flushing period having heavier lambs at birth (P = 0.007). These findings suggest that pre-conceptional under- or overnutrition resulting in substantial changes in body condition does not affect fetal growth during the first half of pregnancy. However, the reduction in lamb birth weight indicates that pre-conceptional maternal nutrition during the previous lactation period may affect fetal growth later in pregnancy.

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SUMMARY: This article presents multi-omic integration with sparse value decomposition (MOSS), a free and open-source R package for integration and feature selection in multiple large omics datasets. This package is computationally efficient and offers biological insight through capabilities, such as cluster analysis and identification of informative omic features. AVAILABILITY AND IMPLEMENTATION: https://CRAN.R-project.org/package=MOSS. SUPPLEMENTARY INFORMATION: Supplementary information can be found at https://github.com/agugonrey/GonzalezReymundez2021.

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The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype-environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.

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Genome-wide association studies (GWAS) is one of the most popular methods of studying the genetic control of traits. This methodology has been intensely performed on inbred genotypes to identify causal variants. Nonetheless, the lack of covariance between the phenotype of inbred lines and their offspring in cross-pollinated species (such as maize) raises questions on the applicability of these findings in a hybrid breeding context. To address this topic, we incorporated previously reported parental lines GWAS information into the prediction of a low heritability trait in hybrids. This was done by marker-assisted selection based on significant markers identified in the lines and by genomic prediction having these markers as fixed effects. Additive-dominance GWAS of hybrids, a non-conventional procedure, was also performed for comparison purposes. Our results suggest that incorporating information from parental inbred lines GWAS led to decreases in the predictive ability of hybrids. Correspondingly, inbred lines and hybrids-based GWAS yielded different results. These findings do not invalidate GWAS on inbred lines for selection purposes, but mean that it may not be directly useful for hybrid breeding.

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Several studies have shown differences in the abilities of maize genotypes to facilitate or impede Azospirillum brasilense colonization and to receive benefits from this association. Hence, our aim was to study the genetic control, heterosis effect and the prediction accuracy of the shoot and root traits of maize in response to A. brasilense. For that, we evaluated 118 hybrids under two contrasting scenarios: i) N stress (control) and ii) N stress plus A. brasilense inoculation. The diallel analyses were performed using mixed model equations, and the genomic prediction models accounted for the general and specific combining ability (GCA and SCA, respectively) and the presence or not of G×E effects. In addition, the genomic models were fitted considering parametric (G-BLUP) and semi-parametric (RKHS) kernels. The genotypes showed significant inoculation effect for five root traits, and the GCA and SCA were significant for both. The GCA in the inoculated treatment presented a greater magnitude than the control, whereas the opposite was observed for SCA. Heterosis was weakly influenced by the inoculation, and the heterozygosity and N status in the plant can have a role in the benefits that can be obtained from this Plant Growth-Promoting Bacteria (PGPB). Prediction accuracies for N stress plus A. brasilense ranged from 0.42 to 0.78, depending on the scenario and trait, and were higher, in most cases, than the non-inoculated treatment. Finally, our findings provide an understanding of the quantitative variation of maize responsiveness to A. brasilense and important insights to be applied in maize breeding aiming the development of superior hybrids for this association.

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BACKGROUND: The selection of hybrids is an essential step in maize breeding. However, evaluating a large number of hybrids in field trials can be extremely costly. However, genomic models can be used to predict the expected performance of un-tested genotypes. Bayesian models offer a very flexible framework for hybrid prediction. The Bayesian methodology can be used with parametric and semi-parametric assumptions for additive and non-additive effects. Furthermore, samples from the posterior distribution of Bayesian models can be used to estimate the variance due to general and specific combining abilities even in cases where additive and non-additive effects are not mutually orthogonal. Also, the use of Bayesian models for analysis and prediction of hybrid performance has remained fairly limited. RESULTS: We provided an overview of Bayesian parametric and semi-parametric genomic models for prediction of agronomic traits in maize hybrids and discussed how these models can be used to decompose the genotypic variance into components due to general and specific combining ability. We applied the methodology to data from 906 single cross tropical maize hybrids derived from a convergent population. Our results show that: (1) non-additive effects make a sizable contribution to the genetic variance of grain yield; however, the relative importance of non-additive effects was much smaller for ear and plant height; (2) genomic prediction can achieve relatively high accuracy in predicting phenotypes of un-tested hybrids and in pre-screening. CONCLUSIONS: Genomic prediction can be a useful tool in pre-screening of hybrids and could contribute to the improvement of the efficiency and efficacy of maize hybrids breeding programs. The Bayesian framework offers a great deal of flexibility in modeling hybrid performance. The methodology can be used to estimate important genetic parameters and render predictions of the expected hybrid performance as well measures of uncertainty about such predictions.

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KEY MESSAGE: Our study indicates that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids. Moreover, predicting hybrid phenotypes by combining additive-dominance effects with copy variants has the potential to be a viable predictive model. Non-additive effects resulting from the actions of multiple loci may influence trait variation in single-cross hybrids. In addition, complementation of allelic variation could be a valuable contributor to hybrid genetic variation, especially when crossing inbred lines with higher contents of copy gains. With this in mind, we aimed (1) to study the association between copy number variation (CNV) and hybrid phenotype, and (2) to compare the predictive ability (PA) of additive and additive-dominance genomic best linear unbiased prediction model when combined with the effects of CNV in two datasets of maize hybrids (USP and HELIX). In the USP dataset, we observed a significant negative phenotypic correlation of low magnitude between copy number loss and plant height, revealing a tendency that more copy losses lead to lower plants. In the same set, when CNV was combined with the additive plus dominance effects, the PA significantly increased only for plant height under low nitrogen. In this case, CNV effects explicitly capture relatedness between individuals and add extra information to the model. In the HELIX dataset, we observed a pronounced difference in PA between additive (0.50) and additive-dominance (0.71) models for predicting grain yield, suggesting a significant contribution of dominance. We conclude that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids, although the inclusion of CNVs into datasets does not return significant gains concerning PA.

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The objectives of this study were to verify the resistance of common bean lines derived from recurrent selection for white mold resistance and to identify those more stable to different isolates; to compare the aggressiveness of different Sclerotinia sclerotiorum isolates; and to verify isolates x lines interaction. Fifteen common bean lines were evaluated, twelve derived from recurrent selection for white mold resistance, one non-adapted source of resistance (Cornell 605), one moderately resistant and adapted (VC-16) and one susceptible to white mold (Corujinha). Ten isolates were used to inoculate the common bean lines through the straw test. A total of ten experiments were performed, one for each isolate. The randomized complete block design with three replications was used in each experiment. Each plot had five plants inoculated in two main branches, therefore the plot data was the average of the ten evaluations through a scale of nine grades. Diallel analysis were used to estimate the general reaction capacity (lines) and general aggressiveness capacity (isolates) to measure the resistance to white mold and the aggressiveness of the isolates, respectively. The GGE biplot analysis was used to group the common bean lines based on their resistance alleles and identify those more instable to the isolates. The resistance of the lines P4 and P10 was similar to Cornell 605, and they had stable reaction to different isolates and "Carioca" grain type. The lines of the advanced cycles of recurrent selection accumulated more favorable alleles than those of the first cycles, confirming the efficiency of the recurrent selection to increase white mold resistance in common bean. In addition, it was identified more aggressive isolates, UFLA 109 and UFLA 116, and a small magnitude of isolates x lines interaction, indicating a predominance of the horizontal resistance of the lines.

Os objetivos deste estudo foram verificar a resistência de linhagens de feijoeiro derivadas de diferentes ciclos de seleção recorrente para resistência ao mofo branco e identificar aquelas mais estáveis quando inoculadas com diferentes isolados; comparar a agressividade de diferentes isolados de Sclerotinia sclerotiorum e verificar se há interação isolados x linhagens. Quinze linhagens de feijoeiro comum foram avaliadas, doze derivadas de seleção recorrente para mofo branco, uma fonte de resistência não adaptada (Cornell 605), uma moderadamente resistente e adaptada (VC-16) e uma suscetível ao mofo branco (Corujinha). Dez isolados foram utilizados para inocular as linhagens de feijoeiro através do straw test. Foram realizados dez experimentos, um para cada isolado. O delineamento experimental utilizado foi o de blocos casualizados, com três repetições. Em cada parcela, cinco plantas foram inoculadas em dois ramos principais, portanto, os dados da parcela foram a média de dez avaliações utilizando uma escala de nove notas. A análise dialélica foi utilizada para estimar a capacidade geral de reação (linhagens) e capacidade geral de agressividade (isolados) para medir, respectivamente, a resistência das linhagens e a agressividade dos isolados. A análise GGE biplot foi utilizada para agrupar as linhagens baseado em seus alelos de resistência e identificar as mais estáveis aos isolados. A resistência das linhagens P4 e P10 foi semelhante à Cornell 605, com reação estável e grãos tipo "Carioca". Como esperado, as linhagens dos ciclos mais avançados de seleção recorrente acumularam mais alelos favoráveis que aquelas dos primeiros ciclos confirmando a eficiência da seleção. Além disso, foram identificados isolados mais agressivos, UFLA109 e UFLA 116 e interação isolados x linhagens de pequena magnitude, indicando um predomínio da resistência horizontal.

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