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

The concept of individual admixture (IA) assumes that the genome ofindividuals is composed of alleles inherited from $K$ ancestral populations.Each copy of each allele has the same chance $q_k$ to originate from population$k$, and together with the allele frequencies $p$ in all populations at all $M$markers, comprises the admixture model. Here, we assume a supervised scheme,i.e.\ allele frequencies $p$ are given through a reference database of size$N$, and $q$ is estimated via maximum likelihood for a single sample. We studylaws of large numbers and central limit theorems describing effects offiniteness of both, $M$ and $N$, on the estimate of $q$. We recall results forthe effect of finite $M$, and provide a central limit theorem for the effect offinite $N$, introduce a new way to express the uncertainty in estimates instandard barplots, give simulation results, and discuss applications inforensic genetics.

A central limit theorem concerning uncertainty in estimates of individual admixture