Genomic Prediction Using Imputed Whole-Genome Sequence Data in Australian Angus Cattle.

Whole-genome sequence (WGS) data was used to estimate genomic breeding values for growth and carcass traits in Australian Angus cattle. The study aimed to compare the accuracy and bias of genomic predictions with three marker densities, including 50K, high-density (HD) and WGS. The dataset used in this study consisted of animals born between 2013 and 2022.

The Impact of Genetic and Non-Genetic Factors on Lamb Loin Shear Force.

Shear force is commonly used to evaluate tenderness, one of the most crucial eating quality aspects of sheep meat. The effect size of various factors on tenderness is still unknown. Studies have suggested that both genetic and environmental factors contribute to the variation in meat tenderness, and there are possible interactions between these factors.

Genotyping both live and dead animals to improve post-weaning survival of pigs in breeding programs.

Background: In this study, we tested whether genotyping both live and dead animals (GSD) realises more genetic gain for post-weaning survival (PWS) in pigs compared to genotyping only live animals (GOS).

Methods: Stochastic simulation was used to estimate the rate of genetic gain realised by GSD and GOS at a 0.01 rate of pedigree-based inbreeding in three breeding schemes, which differed in PWS (95%, 90% and 50%) and litter size (6 and 10).

Segregation GWAS to linearize a non-additive locus with incomplete penetrance: an example of horn status in sheep.

Background: The objective of this study was to introduce a genome-wide association study (GWAS) in conjunction with segregation analysis on monogenic categorical traits. Genotype probabilities calculated from phenotypes, mode of inheritance and pedigree information, are expressed as the expected allele count (EAC) (range 0 to 2), and are inherited additively, by definition, unlike the original phenotypes, which are non-additive and could be of incomplete penetrance. The EAC are regressed on the single nucleotide polymorphism (SNP) genotypes, similar to an additive GWAS.

Towards a Learning Health System for Symptom Management in Hospice Care.

The reuse of real-world symptom monitoring data is essential in improving the quality of hospice care. A framework for achieving this is a Learning Health System, in which the development of a well-defined dataset is essential. This paper discusses the challenges in the design of a comprehensive dataset, focusing on variations in two electronic health record systems and divergent care processes.