HarvestPlus

Anemia with and without iron deficiency during pregnancy and association with adverse birth outcomes: BRINDA Project

Objectives: To investigate association between anemia status along with the changes during pregnancy and birth outcomes (including preterm birth, small-for-gestational age [SGA], and low birth weight [LBW]). Methods: Data from the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) Project included 7 datasets (RCT) across 6 countries (n=7,176). Pregnant women were categorized as having IDA (anemia per WHO cutoffs and iron deficiency [inflammation-adjusted serum ferritin < 15 μg/L or soluble transferrin receptor >8.3 mg/L]), non-IDA, or no anemia based on the first study visit. In addition, for women with two or more visits (n=6163), biomarkers from the first and last visits were used to define any change in anemia status (e.g., from IDA to non-IDA), resulting in 9 combinations of anemia status change. Associations between first-visit anemia status and the changes during pregnancy with adverse birth outcomes were assessed using modified Poisson regression to obtain relative risk estimate (RR). Models adjusted for age, inflammation, dataset, socioeconomic status, study intervention, and malaria. Results: At the first visit (median 13 weeks gestation), 6.4% had IDA and 15.2% had non-IDA. IDA vs. no anemia was associated with a higher risk of preterm birth (RR=1.55, p< 0.01), while non-IDA was not associated with any adverse birth outcomes. Compared with women without anemia throughout pregnancy, having no-anemia (first visit) to IDA (last visit) was associated with a reduced risk of SGA (RR=0.86, p=0.03) and LBW (0.73, p< 0.01), but having no anemia to non-IDA was associated with an increased risk of SGA (1.12, p=0.04) and LBW (1.27, p< 0.01). Having non-IDA to no anemia had a lower risk of SGA (0.77, p=0.04) vs. those stayed with non-IDA. Conclusions: IDA early in pregnancy is associated with an increased risk of preterm birth. Changes in anemia status across pregnancy show mixed effects on birth outcomes and better understanding the underlying mechanisms may be useful to reduce adverse outcomes.

Biofortification: Conventional plant breeding to increase the nutrient density of commonly consumed staple foods

Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis

Recurrent selection is an important breeding method for population improvement and selecting elite inbreds or fixed lines from the improved germplasm. Recently, a computer simulation tool called QuMARS has been developed, which allows the simulation and optimization of various recurrent selection strategies. Our major objective in this study was to use the QuMARS tool to compare phenotypic recurrent, marker-assisted recurrent, and genomic selections (abbreviated respectively as PS, MARS and GS) for both short- and long- term breeding procedures. For MARS, two marker selection models were considered, i.e., stepwise (Rstep) and forward regressions (Forward). For GS, three prediction models were considered, i.e., genomic best linear unbiased predictors (GBLUP), ridge regression (Ridge), and regression by Moore-Penrose general inverse (InverseMP). To generate genotypes and phenotypes for a given individual during simulation, one additive and two epistasis genetic models were considered with three levels of heritability. Results demonstrated that selection responses from GBLUP-based GS and MARS (Forward) were consistently greater than those from PS under the additive model, particularly in early selection cycles. In contrast, selection response from PS was consistently superior over MARS and GS under epistatic models. For the two epistasis models, total genetic variance and the additive variance component were increased in some cases after selection. Through simulation, we concluded that GS and PS were effective recurrent selection methods for improved breeding of targeted traits controlled by additive and epistatic quantitative trait loci (QTL). QuMARS provides an opportunity for breeders to compare, optimize and integrate new technology into their conventional breeding programs.