Model-based genotype and ancestry estimation for potential hybrids with mixed-ploidy.

Area of Science:

• Population genetics
• Genomics
• Evolutionary biology

Background:

• Non-random mating leads to population stratification and genetic structure.
• Understanding genetic relatedness is crucial for trait mapping and evolutionary studies.
• Existing ancestry estimation models are limited for polyploid and mixed-ploidy organisms.

Purpose of the Study:

• To extend and test a hierarchical Bayesian model (entropy) for estimating genotype and ancestry parameters.
• To enable analysis in autopolyploid and mixed-ploidy individuals using low-depth sequence data.
• To address limitations in current models for complex ploidy levels.

Main Methods:

• Developed and tested a hierarchical Bayesian model named entropy.
• Utilized low-depth sequence data for genotype and ancestry estimation.
• Analyzed simulated data and admixture in diploid and tetraploid Arabidopsis arenosa populations.

Main Results:

• The entropy model accurately estimates genotype and ancestry in polyploid and mixed-ploidy individuals.
• Low-depth sequencing across a larger genome fraction showed lower error than high-depth sequencing on a smaller fraction.
• The model demonstrated high accuracy and sensitivity in simulations and real data.

Conclusions:

• The entropy model provides a robust method for ancestry estimation in complex ploidy populations.
• Low-depth sequencing is a viable strategy for genome-wide ancestry analysis in polyploids.
• This work advances the genetic analysis of non-random mating and population structure.