Gene effects and heterosis for grain iron and zinc density in pearl millet (Pennisetum glaucum (L.) R. Br)

Pearl millet [Pennisetum glaucum (L.) R. Br.] is a major warm-season cereal, grown primarily for grain production in the arid and semi-arid tropical regions of Asia and Africa. Iron (Fe) and zinc (Zn) deficiencies have been reported to be a food-related primary health problem affecting nearly two billion people worldwide. Improving Fe and Zn densities of staple crops by breeding offers a cost-effective and sustainable solution to reducing micronutrient malnutrition in resource poor communities. An understanding of the genetics of these micronutrients can help to accelerate the breeding process, but little is known about the genetics and heterosis pattern of Fe and Zn densities in pearl millet. In the present study, ten inbred lines and their full diallel crosses were used to study the nature of gene action and heterosis for these micronutrients. The general combining ability (GCA) effects of parents and specific combining ability (SCA) effects of hybrids showed significant differences for both of the micronutrients. However, the predictability ratio (2σ2gca/(2σ2gca + σ2sca)) was around unity both for Fe and Zn densities, implying preponderance of additive gene action. Further, highly significant positive correlation between mid-parent values and hybrid performance, and no correlation between mid-parent values and mid-parent heterosis confirmed again the predominant role of additive gene action for these micronutrients. Barring a few exceptions with one parent, hybrids did not outperform the parents having high Fe and Zn levels. This showed that there would be little opportunity, if any, to exploit heterosis for these mineral micronutrients in pearl millet. In general, high Fe and Zn levels in both of the parental lines would be required to increase the probability of breeding high Fe and Zn hybrids

Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat

Key message: Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Abstract: Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1–9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder’s advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs

First molecular identification of canine Parvovirus type 2 (CPV2) in Chile reveals high occurrence of CPV2c antigenic variant

Canine parvovirus type 2 (CPV2) is one of the most important intestinal pathogens in dogs and puppies. CPV2 has been evolved into three genetic and antigenic variants (2a, 2b, and 2c), which are distributed worldwide. We reported the first study of genetic diversity of CPV2 in Chile. Sixty-five samples were collected from puppies presenting with severe gastroenteritis and different vaccination statuses. PCR, restriction fragment length polymorphism (RFLP), and partial sequencing of the coding region of the structural viral protein VP2 was performed. Thirty of a total of 65 samples tested positive by PCR out of which 19 were further classified as CPV2c and one as CPV2a using RFLP and Sanger sequencing. The phylogeny was in concordance with the RFLP analysis. This is the first report of the genetic characterization of CPV2 in Chile and reveals a high occurrence of CPV2c

Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones

Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines’ performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha−1 across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario

Bayesian multitrait kernel methods improve multienvironment genome-based prediction

When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2–17.45% (datasets 1–3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel

Provitamin A Carotenoids in Grain Reduce Aflatoxin Contamination of Maize While Combating Vitamin A Deficiency

Aflatoxin contamination of maize grain and products causes serious health problems for consumers worldwide, and especially in low- and middle-income countries where monitoring and safety standards are inconsistently implemented. Vitamin A deficiency (VAD) also compromises the health of millions of maize consumers in several regions of the world including large parts of sub-Saharan Africa. We investigated whether provitamin A (proVA) enriched maize can simultaneously contribute to alleviate both of these health concerns. We studied aflatoxin accumulation in grain of 120 maize hybrids formed by crossing 3 Aspergillus flavus resistant and three susceptible lines with 20 orange maize lines with low to high carotenoids concentrations. The hybrids were grown in replicated, artificially-inoculated field trials at five environments. Grain of hybrids with larger concentrations of beta-carotene (BC), beta-cryptoxanthin (BCX) and total proVA had significantly less aflatoxin contamination than hybrids with lower carotenoids concentrations. Aflatoxin contamination had negative genetic correlation with BCX (-0.28, p < 0.01), BC (-0.18, p < 0.05), and proVA (-0.23, p < 0.05). The relative ease of breeding for increased proVA carotenoid concentrations as compared to breeding for aflatoxin resistance in maize suggests using the former as a component of strategies to combat aflatoxin contamination problems for maize. Our findings indicate that proVA enriched maize can be particularly beneficial where the health burdens of exposure to aflatoxin and prevalence of VAD converge with high rates of maize consumption

Why did I not prepare for this? The politics of negotiating fieldwork access, identity, and methodology in researching microfinance institutions

It has been increasingly recognized that undertaking qualitative research can pose many challenges for researchers. However, scanty literature focuses directly on the experiences of doctoral research students from developing countries studying in Western Europe and other similar geographic regions, and the challenges of doing fieldwork when they return “back home”. In this article, I use my experiences in the process of undertaking PhD fieldwork on two donor-funded microfinance institutions located in Zambia to demonstrate that doctoral students from specific regions (Africa in particular) undertaking research in their native countries can struggle to manage and make sense of the challenges and identity issues raised in their “familiar” environments. I also present a detailed discussion of how various gatekeepers and participants facilitated access, identity alteration, and the impact of insider–outsider positionality on collected data. It is concluded that organizational “politics” and local context can have significant bearing on power relationships, identities of researchers, and methodological preferences