Maximum-likelihood estimation of admixture proportions from genetic data.

Area of Science:

• Population Genetics
• Genomics
• Evolutionary Biology

Background:

• Estimating genetic contributions from parental populations is crucial for understanding admixed populations.
• Existing methods for admixture proportion estimation have limitations in accuracy and scope.

Purpose of the Study:

• To develop a novel likelihood method for estimating admixture proportions and genetic drift.
• To jointly infer population split times, hybridization events, and subsequent genetic drift.

Main Methods:

• A likelihood-based statistical framework was developed.
• Extensive simulations were conducted with varying parameter values.
• The method was applied to human and wolflike canids genetic data.

Main Results:

• The proposed likelihood method provides more accurate and precise admixture proportion estimates than previous methods.
• The method also yields reliable estimates of genetic drift, enabling effective population size (N(e)) inference.
• It demonstrates flexibility with different admixture models, marker types, and handles missing data.

Conclusions:

• The new likelihood method offers a significant advancement in analyzing admixed populations.
• It provides a robust tool for inferring demographic history and genetic contributions.
• The method's efficiency and flexibility make it broadly applicable in population genetics studies.