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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Genetic variation in human populations is influenced by geographic ancestrydue to spatial locality in historical mating and migration patterns. Spatialpopulation structure in genetic datasets has been traditionally analyzed usingeither model-free algorithms, such as principal components analysis (PCA) andmultidimensional scaling, or using explicit spatial probabilistic models ofallele frequency evolution. We develop a general probabilistic model and anassociated inference algorithm that unify the model-based and data-drivenapproaches to visualizing and inferring population structure. Our algorithm,Geographic Ancestry Positioning (GAP), relates local genetic distances betweensamples to their spatial distances, and can be used for visually discerningpopulation structure as well as accurately inferring the spatial origin ofindividuals on a two-dimensional continuum. On both simulated and several realdatasets from diverse human populations, GAP exhibits substantially lower errorin reconstructing spatial ancestry coordinates compared to PCA.  Our spatial inference algorithm can also be effectively applied to theproblem of population stratification in genome-wide association studies (GWAS),where hidden population structure can create fictitious associations whenpopulation ancestry is correlated with both the genotype and the trait. Wedevelop an association test that uses the ancestry coordinates inferred by GAPto accurately account for ancestry-induced correlations in GWAS. Based onsimulations and analysis of a dataset of 10 metabolic traits measured in aNorthern Finland cohort, which is known to exhibit significant populationstructure, we find that our method has superior power to current approaches.

Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies