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miqoGraph: fitting admixture graphs using mixed-integer quadratic optimization.

Julia Yan1, Nick Patterson2, Vagheesh M Narasimhan2,3,4

  • 1Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Bioinformatics (Oxford, England)
|November 28, 2020
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Summary
This summary is machine-generated.

This study introduces miqoGraph, a Julia package for analyzing population genetics. It automates admixture graph fitting using mixed-integer quadratic optimization, significantly speeding up the process.

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Area of Science:

  • Population genetics
  • Computational biology
  • Bioinformatics

Background:

  • Admixture graphs model complex genetic relationships between populations, including divergence and gene flow.
  • Inferring these graphs typically involves time-consuming manual processes.

Purpose of the Study:

  • To present miqoGraph, a novel Julia package for admixture graph inference.
  • To automate and accelerate the fitting of graph topology, drift lengths, and admixture proportions.

Main Methods:

  • Utilizes mixed-integer quadratic optimization (MIQO) for simultaneous parameter estimation.
  • Applies the method to both simulated and real population genetic datasets.

Main Results:

  • miqoGraph successfully fits admixture graph parameters, including topology, drift, and admixture proportions.
  • The MIQO approach significantly reduces the computational time and manual effort required for graph inference.

Conclusions:

  • Integer optimization provides an efficient and automated solution for admixture graph analysis.
  • miqoGraph offers a powerful tool for advancing population genetic studies.