Fusarium head blight (FHB) is a disease in wheat (Triticum aestivum L.) caused by the fungal pathogen Fusarium graminearum Schwabe. Fusarium head blight poses potential economic losses and health risks due to the accumulation of the mycotoxin deoxynivalenol (DON) on infected seed heads. The objectives of this study were to identify novel FHB resistance loci using a genome‐wide association study (GWAS) approach and to evaluate two genomic selection (GS) approaches to improve prediction accuracies for FHB traits in a population of 354 soft red winter wheat (SRWW) genotypes. The GS approaches included GS+GWAS, where markers associated with a trait were used as fixed effects, and multivariate GS (MVGS), where correlated traits were used as covariates. The population was evaluated in FHB nurseries in Fayetteville and Newport, AR, and Winnsboro, LA, from 2014 to 2017. Genotypes were phenotyped for DON, Fusarium‐damaged kernels (FDK), incidence (INC), and severity (SEV). Forty‐two single nucleotide polymorphism (SNP) markers were significantly (false discovery rate, q [FDRq] ≤ .10) associated with resistance traits across 17 chromosomes. Ten significant SNPs were identified for DON, notably on chromosomes 2BL and 3BL. Eleven were identified for FDK, notably on chromosomes 4BL, 3AL, 1BL, 5BL, and 5DL. Nine were identified for INC, notably on chromosomes 2BS, 2BL, 7BL, 5DL, 6AS, and 5DS. Twelve were identified for SEV, notably on chromosomes 3BL, 4AL, and 4BL. The naïve GS models significantly outperformed the GS+GWAS model for all traits, whereas MVGS models significantly outperformed the naïve GS models for all traits. Results from this study will facilitate the development of SRWW cultivars with improved FHB resistance.

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Many studies have evaluated the effectiveness of genomic selection (GS) using cross-validation within training populations; however, few have looked at its performance for forward prediction within a breeding program. The objectives for this study were to compare the performance of naïve GS (NGS) models without covariates and multi-trait GS (MTGS) models by predicting two years of F4:7 advanced breeding lines for three Fusarium head blight (FHB) resistance traits, deoxynivalenol (DON) accumulation, Fusarium damaged kernels (FDK), and severity (SEV) in soft red winter wheat and comparing predictions with phenotypic performance over two years of selection based on selection accuracy and response to selection. On average, for DON, the NGS model correctly selected 69.2% of elite genotypes, while the MTGS model correctly selected 70.1% of elite genotypes compared with 33.0% based on phenotypic selection from the advanced generation. During the 2018 breeding cycle, GS models had the greatest response to selection for DON, FDK, and SEV compared with phenotypic selection. The MTGS model performed better than NGS during the 2019 breeding cycle for all three traits, whereas NGS outperformed MTGS during the 2018 breeding cycle for all traits except for SEV. Overall, GS models were comparable, if not better than phenotypic selection for FHB resistance traits. This is particularly helpful when adverse environmental conditions prohibit accurate phenotyping. This study also shows that MTGS models can be effective for forward prediction when there are strong correlations between traits of interest and covariates in both training and validation populations.

Siblings of hospitalized newborns in neonatal intensive care units (NICU) experience unique thoughts and feelings in response to this situational crisis. Providing an opportunity for siblings and their parents to address both of their concerns can improve sibling adjustment to the NICU, and is also consistent with a family-centered care philosophy. This article traces the development and evolution of sibling policy and program changes at Helen DeVos Children's Hospital (HDVCH) NICU, and describes the current comprehensive model for inclusion of siblings. Particular emphasis will be given to the cornerstone program "Celebrating Siblings Pizza Party." Infection control considerations and the importance of an interdisciplinary team approach to enhance an array of sibling services are also highlighted.