A Hidden Markov Model Approach for Simultaneously Estimating Local Ancestry and Admixture Time Using Next Generation...

Area of Science:

• Evolutionary Biology
• Population Genetics
• Genomics

Background:

• Admixture, the mixing of divergent genomes, is crucial for evolution.
• Existing local ancestry inference methods have limitations: requiring genotype data, assuming specific ancestry tract lengths, and often limited to diploid samples.

Purpose of the Study:

• To develop a novel hidden Markov model for local ancestry inference.
• To overcome limitations of existing methods by using read pileup data, supporting arbitrary ploidy, and estimating admixture time.

Main Methods:

• Developed a hidden Markov model (HMM) for local ancestry inference using read pileup data.
• Generalized the HMM for arbitrary ploidy and integrated admixture time estimation.
• Applied the method to pooled sequencing data from Drosophila melanogaster populations.

Main Results:

• The HMM accurately estimates local ancestry and admixture time, performing well on high-ploidy samples.
• Negative correlation found between local recombination rates and African ancestry proportion in Drosophila melanogaster.
• Clinal outlier loci are enriched for gene regulatory functions, suggesting a role in adaptation.

Conclusions:

• The novel HMM offers a versatile tool for local ancestry inference across diverse populations and ploidies.
• Admixture patterns in Drosophila melanogaster reveal selection dynamics influenced by recombination rates.
• Regulatory evolution plays a key role in the ecological adaptation of admixed populations.