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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In genome-wide association studies, new schemes are needed to incorporate multiple-locus information. In this article, we proposed a two-stage sliding-window approach to detect associations between a disease and multiple genetic polymorphisms. In the proposed approach, we measured the genetic association between a disease and a single-nucleotide polymorphism window by the newly developed likelihood ratio test-principal components statistic, and performed a sliding-window technique to detect disease susceptibility windows. We split the whole sample into two sub-samples, each of which contained a portion of cases and controls. In the first stage, we selected the top R windows by the statistics based on the first sub-sample, and in the second stage, we claimed significant windows by false-discovery rate correction on the p-values of the statistics based on the second sub-sample. By applying the new approach to the Genetic Analysis Workshop 16 Problem 1 data set, we detected 212 out of 531,601 windows to be responsible for rheumatoid arthritis. Except for chromosomes 4 and 18, each of the other 20 autosomes was found to harbor risk windows. Our results supported the findings of some rheumatoid arthritis susceptibility genes identified in the literature. In addition, we identified several new single-nucleotide polymorphism windows for follow-up studies.

Incorporating multiple-marker information to detect risk loci for rheumatoid arthritis