Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated.
View Article and Find Full Text PDFObjective: To identify current maternal and infant predictors of infant mortality, including maternal sociodemographic and economic status, maternal perinatal smoking and obesity, mode of delivery, and infant birthweight and gestational age.
Methods: This retrospective study analyzed data from the linked birth and infant death files (birth cohort) and live births from the Birth Statistical Master files (BSMF) in California compiled by the California Department of Public Health for 2007-2015. The birth cohort study comprised 4,503,197 singleton births including 19,301 infant deaths during the nine-year study period.
Objective: To determine recent trends in maternal prepregnancy body mass index (BMI) and to quantify its association with birth and maternal outcomes.
Methods: A population-based retrospective cohort study included resident women with singleton births in the California Birth Statistical Master Files (BSMF) database from 2007 to 2016. There were 4,621,082 women included out of 5,054,968 women registered in the database.
Objective: This study aimed to determine associations between maternal cigarette smoking and adverse birth and maternal outcomes.
Study Design: This is a 10-year population-based retrospective cohort study including 4,971,896 resident births in California. Pregnancy outcomes of maternal smokers were compared with those of nonsmokers.