Signatures of Introgression across the Allele Frequency Spectrum.

Area of Science:

• Genomics
• Evolutionary Biology
• Population Genetics

Background:

• Genomic data analysis is revolutionizing our understanding of species evolution and adaptive variation.
• The ABBA-BABA test (D-statistic) is a common method for detecting introgression by measuring allele sharing between related species.
• The standard D-statistic offers simplicity but limited insights into introgression timing and direction.

Purpose of the Study:

• To introduce a novel extension of the D-statistic, termed D frequency spectrum (DFS).
• To demonstrate how DFS partitions introgression signals based on derived allele frequencies.
• To enhance the informativeness of introgression detection, particularly regarding the timing and directionality of gene flow.

Main Methods:

• Development of the D frequency spectrum (DFS) method.
• Extensive simulations across a wide parameter space to evaluate DFS performance.
• Reanalysis of published empirical genomic data from diverse animal and plant taxa.

Main Results:

• DFS effectively partitions introgression signals according to derived allele frequencies.
• Recent introgression events characteristically produce a peak in DFS at low-frequency derived alleles.
• Model assumption violations can be identified by a lack of signal in low-frequency DFS, distinguishing them from true introgression signals.

Conclusions:

• DFS provides novel insights into introgression dynamics, complementing existing methods.
• The method reliably indicates recent introgression and helps diagnose potential violations of population genetic model assumptions.
• DFS is a valuable descriptive tool for introgression analysis and has potential for future integration into probabilistic inference frameworks.