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/

Zendy Plus

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

Zendy Tools

Zendy Open

Focusing on polygenic signal detection in high dimensional geneticassociation studies of complex traits, we develop an adaptive test forgeneralized linear models to accommodate different alternatives. To facilitatevalid post-selection inference for high dimensional data, our study hereadheres to the original sampling-splitting principle but does so, repeatedly,to increase stability of the inference. We show the asymptotic nulldistributions of the proposed test for both fixed and diverging number ofvariants. We also show the asymptotic properties of the proposed test underlocal alternatives, providing insights on why power gain attributed to variableselection and weighting can compensate for efficiency loss due to samplesplitting. We support our analytical findings through extensive simulationstudies and two applications. The proposed procedure is computationallyefficient and has been implemented as the R package DoubleCauchy.

A stable and adaptive polygenic signal detection method based on repeated sample splitting