Minimal-assumption inference from population-genomic data.

Area of Science:

• Population Genetics
• Genomics
• Evolutionary Biology

Background:

• Complete genome sequences offer insights into population evolutionary history, particularly through polymorphism associations.
• Existing methods often rely on explicit models of coalescence and recombination, limiting their applicability.

Purpose of the Study:

• To introduce a novel method, Minimal-Assumption Genomic Inference of Coalescence (MAGIC), for reconstructing evolutionary history.
• To develop a method that integrates genomic information across scales without explicit evolutionary models.
• To analyze large genomic samples efficiently and without prior assumptions about population structure or evolutionary processes.

Main Methods:

• MAGIC integrates information across genomic length scales.
• The method does not use an explicit model of coalescence or recombination.
• It analyzes arbitrarily large samples without phasing and makes no assumptions about ancestral structure, linked selection, or gene conversion.

Main Results:

• MAGIC's performance is comparable to existing methods like PSMC on simulated data.
• The method effectively analyzes single diploid samples generated with standard models.
• Application to human genomes suggests non-demographic factors influence coalescence.

Conclusions:

• MAGIC provides a powerful, flexible tool for inferring population evolutionary history from genomic data.
• The method's minimal assumptions allow for broader applicability and discovery of novel evolutionary insights.
• Evidence from human genomes indicates the significant role of non-demographic factors in shaping evolutionary trajectories.