  The concept of individual admixture (IA) assumes that the genome of
individuals 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 study
laws of large numbers and central limit theorems describing effects of
finiteness of both, $M$ and $N$, on the estimate of $q$. We recall results for
the effect of finite $M$, and provide a central limit theorem for the effect of
finite $N$, introduce a new way to express the uncertainty in estimates in
standard barplots, give simulation results, and discuss applications in
forensic genetics.
