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Revealing the range of maximum likelihood estimates in the admixture model
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Abstract
Many ancestry inference tools, including STRUCTURE and ADMIXTURE, rely on the admixture model to infer both, allele frequencies
p
and individual admixture proportions
q
for a collection of individuals relative to a set of hypothetical ancestral populations. We show that under realistic conditions the likelihood in the admixture model is typically flat in some direction around a maximum likelihood estimate
. In particular, the maximum likelihood estimator is non-unique and there is a complete spectrum of possible estimates. Common inference tools typically identify only a few points within this spectrum.
We provide an algorithm which computes the set of equally likely
, when starting from
. It is analytic for
K
= 2 ancestral populations and numeric for
K >
2. We apply our algorithm to data from the 1000 genomes project, and show that inter-European estimators of
q
can come with a large set of equally likely possibilities. In general, markers with large allele frequency differences between populations in combination with individuals with concentrated admixture proportions lead to small areas with a flat likelihood.
Our findings imply that care must be taken when interpreting results from STRUCTURE and ADMIXTURE if populations are not separated well enough.
Title: Revealing the range of maximum likelihood estimates in the admixture model
Description:
Abstract
Many ancestry inference tools, including STRUCTURE and ADMIXTURE, rely on the admixture model to infer both, allele frequencies
p
and individual admixture proportions
q
for a collection of individuals relative to a set of hypothetical ancestral populations.
We show that under realistic conditions the likelihood in the admixture model is typically flat in some direction around a maximum likelihood estimate
.
In particular, the maximum likelihood estimator is non-unique and there is a complete spectrum of possible estimates.
Common inference tools typically identify only a few points within this spectrum.
We provide an algorithm which computes the set of equally likely
, when starting from
.
It is analytic for
K
= 2 ancestral populations and numeric for
K >
2.
We apply our algorithm to data from the 1000 genomes project, and show that inter-European estimators of
q
can come with a large set of equally likely possibilities.
In general, markers with large allele frequency differences between populations in combination with individuals with concentrated admixture proportions lead to small areas with a flat likelihood.
Our findings imply that care must be taken when interpreting results from STRUCTURE and ADMIXTURE if populations are not separated well enough.
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