A new Bayesian method to identify the environmental factors that influence recent migration.

Area of Science:

• Population Genetics
• Human Evolutionary Biology
• Bioinformatics

Background:

• Understanding recent immigration rates and the environmental drivers of gene flow is crucial for population genetics.
• Accurate estimation of population-specific genetic parameters like inbreeding coefficients and allele frequencies is essential for evolutionary studies.

Purpose of the Study:

• To develop and validate a novel multilocus genotype method for inferring recent immigration rates and identifying environmental factors influencing gene flow.
• To estimate population-specific inbreeding coefficients, allele frequencies, and local F(ST) values, and perform individual assignments.
• To assess the reliability of the method across different parameter spaces using synthetic data.

Main Methods:

• Development of a new multilocus genotype analysis method.
• Generation of synthetic datasets to evaluate method performance and parameter space limitations.
• Application of the method to Pakistani human population data.

Main Results:

• The simulation study demonstrated reliable estimates when global genetic differentiation (F(ST)) exceeded 1%, with 10 loci and 50 individuals per population.
• Application to Pakistani data indicated that altitude is a stronger explanatory factor for genetic patterns than geographic distance.
• Southern, low-altitude populations exhibited higher migration rates compared to northern, high-altitude populations.

Conclusions:

• The developed multilocus genotype method provides accurate insights into recent immigration and gene flow drivers.
• Altitude plays a significant role in shaping human genetic variation in Pakistan.
• Environmental factors like altitude are critical for understanding population structure and migration dynamics.