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

Taking advantages of high-throughput genotyping technology of singlenucleotide polymorphism (SNP), large genome-wide association studies (GWASs)have been considered as the promise to unravel the complex relationshipsbetween genotypes and phenotypes, in particularly common diseases. However,current multi-locus-based methods are insufficient, in terms of computationalcost and discrimination power, to detect statistically significant interactionsand they are lacking in the ability of finding diverse genetic effects onmultifarious diseases. Especially, multiple statistic tests for high-orderepistasis ($ \geq $ 2 SNPs) will raise huge analytical challenges because thecomputational cost increases exponentially as the growth of the cardinality ofSNPs in an epistatic module. In this paper, we develop a simple, fast andpowerful method, named JS-MA, using the Jensen-Shannon divergence and ahigh-dimensional $ k $-mean clustering algorithm for mapping the genome-widemulti-locus epistatic interactions on multiple diseases. Compared with somestate-of-the-art association mapping tools, our method is demonstrated to bemore powerful and efficient from the experimental results on the systematicalsimulations. We also applied JS-MA to the GWAS datasets from WTCCC for twocommon diseases, i.e. Rheumatoid Arthritis and Type 1 Diabetes. JS-MA not onlyconfirms some recently reported biologically meaningful associations but alsoidentifies some novel findings. Therefore, we believe that our method issuitable and efficient for the full-scale analysis of multi-disease-relatedinteractions in the large GWASs.

JS-MA: A Jensen-Shannon Divergence Based Method for Mapping Genome-wide Associations on Multiple Diseases