AI Article Synopsis
- The text discusses a new optimization algorithm for the STRUCTURE model, which is important in population genetics for classifying individuals into discrete ancestry components.
- The algorithm reportedly achieves higher likelihood solutions in the same amount of computational time compared to existing methods and can handle data from Next Generation Sequencing (NGS), accounting for uncertainties in genotype calling.
- The new methods, including a technique for estimating population trees using Gaussian approximations, are implemented in a software package called Ohana, which is accessible online along with user guides and workflows.
Article Abstract
Motivation: Structure methods are highly used population genetic methods for classifying individuals in a sample fractionally into discrete ancestry components.
Contribution: We introduce a new optimization algorithm for the classical STRUCTURE model in a maximum likelihood framework. Using analyses of real data we show that the new method finds solutions with higher likelihoods than the state-of-the-art method in the same computational time. The optimization algorithm is also applicable to models based on genotype likelihoods, that can account for the uncertainty in genotype-calling associated with Next Generation Sequencing (NGS) data. We also present a new method for estimating population trees from ancestry components using a Gaussian approximation. Using coalescence simulations of diverging populations, we explore the adequacy of the STRUCTURE-style models and the Gaussian assumption for identifying ancestry components correctly and for inferring the correct tree. In most cases, ancestry components are inferred correctly, although sample sizes and times since admixture can influence the results. We show that the popular Gaussian approximation tends to perform poorly under extreme divergence scenarios e.g. with very long branch lengths, but the topologies of the population trees are accurately inferred in all scenarios explored. The new methods are implemented together with appropriate visualization tools in the software package Ohana.
Availability And Implementation: Ohana is publicly available at https://github.com/jade-cheng/ohana . In addition to source code and installation instructions, we also provide example work-flows in the project wiki site.
Contact: jade.cheng@birc.au.dk.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543773 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btx098 | DOI Listing |
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