English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As the amount and complexity of genetic information increases it is necessarythat we explore some efficient ways of handling these data. This study takesthe "divide and conquer" approach for analyzing high dimensional genomic data.Our aims include reducing the dimensionality of the problem that has to bedealt one at a time, improving the performance and interpretability of themodels. We propose using the inherent structures in the genome; to divide thebigger problem into manageable parts. In plant and animal breeding studies adistinction is made between the commercial value (additive + epistatic geneticeffects) and the breeding value (additive genetic effects) of an individualsince it is expected that some of the epistatic genetic effects will be lostdue to recombination. In this paper, we argue that the breeder can takeadvantage of some of the epistatic marker effects in regions of lowrecombination. The models introduced here aim to estimate local epistatic lineheritability by using the genetic map information and combine the localadditive and epistatic effects. To this end, we have used semi-parametric mixedmodels with multiple local genomic relationship matrices with hierarchicaltesting designs and lasso post-processing for sparsity in the final model andspeed. Our models produce good predictive performance along with geneticassociation information.

Locally epistatic genomic relationship matrices for genomic association, prediction and selection