Joint estimation of immigration and mating system parameters in gymnosperms using the EM algorithm

Area of Science:

• Population Genetics
• Plant Breeding
• Evolutionary Biology

Background:

• Mixed-mating systems are common in gymnosperms, influencing genetic diversity and adaptation.
• Accurate estimation of mating system parameters (selfing, outcrossing, immigration) is crucial for understanding population dynamics.
• Existing methods may have limitations in handling complex genetic data or specific population structures.

Purpose of the Study:

• To present an Expectation-Maximization (EM) algorithm for the joint maximum-likelihood estimation of immigration and mating system parameters in gymnosperm mixed-mating models.
• To develop a robust method that accommodates multiallelic and multilocus genetic data from both mature populations and pollen pools.
• To ensure estimates are biologically realistic and approach global maximum-likelihood values.

Main Methods:

• Developed an EM algorithm procedure for joint estimation.
• Incorporated handling of multiallelic and multilocus data.
• Ensured estimates are insensitive to foreign population allelic frequency changes.

Main Results:

• The EM algorithm provides maximum-likelihood estimates for selfing (Ŝ), outcrossing (Ô), and immigration (Î) rates.
• Estimates are bounded within the natural biological range (0 ≤ Ô + Î ≤ 1; Ŝ + Ô + Î = 1).
• The procedure approaches global maximum-likelihood estimates with iterations, irrespective of starting values.

Conclusions:

• The presented EM algorithm offers a robust and accurate method for estimating key parameters in gymnosperm mating systems.
• This approach enhances our ability to study genetic diversity, gene flow, and evolutionary processes in these important plant groups.
• The method's insensitivity to foreign allele frequencies and guaranteed biological bounds make it a valuable tool for population geneticists.