Shalanee Weerasinghe
University of New England (Australia)
6 Papers
6 Citations
Shalanee Weerasinghe is an academic researcher from University of New England (Australia). The author has contributed to research in topics: Livestock & Sample size determination. The author has an hindex of 3, co-authored 6 publications. Previous affiliations of Shalanee Weerasinghe include University of New England (United States).
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Papers
A Single-Step Hybrid Marker Effects Model Using Random Regression for Stayability in Hereford Cattle
Bruce Golden,Shalanee Weerasinghe,Brad Crook,Stacy Sanders,Dorian J. Garrick +4 more
- 01 Jan 2018
TL;DR: A random regression single step hybrid marker effects model implemented using a Bayes C sampling approach on large datasets was feasible and effective for national cattle evaluation and should be particularly useful in improving the accuracy of prediction of stayability for young genotyped selection candidates.
Genetic diversity of the indigenous cattle of Kenya, Uganda, Ethiopia and Tanzania using high-density SNP data
Shalanee Weerasinghe,John P. Gibson,Cedric Gondro,Ally Okeyo Mwai,Julie M.K. Ojango,Elizaphan J.O. Rao,Tadelle Dessie,Denis Mujibi,J.E.O. Rege +8 more
- 01 Jan 2018
TL;DR: The genetic structure and admixture levels of several East African indigenous cattle breeds in Ethiopia and Tanzania, including Ethiopian indigenous breeds, have been identified and are useful for genetic conservation and genetic improvement programs.
Genotype-environment interaction on human cognitive function conditioned on the status of breastfeeding and maternal smoking around birth.
TL;DR: G × E has important implications for future studies on the genetic architecture, genome-wide association studies and genomic predictions, and genomic prediction accuracies were significantly higher when using the target and discovery sample from the same environmental group than when using those from the different environmental groups.
Using information of relatives in genomic prediction to apply effective stratified medicine.
Sang Hong Lee,Shalanee Weerasinghe,Naomi R. Wray,Michael E. Goddard,Julius H. J. van der Werf +4 more
TL;DR: A theoretical framework is presented to demonstrate that prediction accuracy can be improved by targeting more informative individuals in the data set used to generate the predictors to include those with genetically close relationships with the subjects put forward for risk prediction.