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

Conducting genome-wide association studies (GWAS) in copy number variation(CNV) level is a field where few people involves and little statisticalprogresses have been achieved, traditional methods suffer from many problemssuch as batch effects, heterogeneity across genome, leading to low power orhigh false discovery rate. We develop a new robust method to finddisease-risking regions related to CNV's disproportionately distributed betweencase and control samples, even if there are batch effects between them, ourtest formula is robust to such effects. We propose a new empirical Bayes ruleto deal with overfitting when estimating parameters during testing, this rulecan be extended to the field of model selection, it can be more efficientcompared with traditional methods when there are too much potential models tobe specified. We also give solid theoretical guarantees for our proposedmethod, and demonstrate the effectiveness by simulation and realdata analysis.

A robust statistical method for Genome-wide association analysis of human copy number variation