Optimizing Microsatellite Marker Panels for Genetic Diversity and Population Genetic Studies: An Ant Colony Algorithm Approach with Polymorphic Information Content

Abstract

Microsatellites are polymorphic and cost-effective. Optimizing reduced microsatellite panels using heuristic algorithms eases budget constraints in genetic diversity and population genetic assessments. Microsatellite marker efficiency is strongly associated with its polymorphism and is quantified as the polymorphic information content (PIC). Nevertheless, marker selection cannot rely solely on PIC. In this study, the ant colony optimization (ACO) algorithm, a widely recognized optimization method, was adopted to create an enhanced selection scheme for refining microsatellite marker panels, called the PIC–ACO selection scheme. The algorithm was fine-tuned and validated using extensive datasets of chicken (Gallus gallus) and Chinese gorals (Naemorhedus griseus) from our previous studies. In contrast to basic optimization algorithms that stochastically initialize potential outputs, our selection algorithm utilizes the PIC values of markers to prime the ACO process. This increases the global solution discovery speed while reducing the likelihood of becoming trapped in local solutions. This process facilitated the acquisition of a cost-efficient and optimized microsatellite marker panel for studying genetic diversity and population genetic datasets. The established microsatellite efficiency metrics such as PIC, allele richness, and heterozygosity were correlated with the actual effectiveness of the microsatellite marker panel. This approach could substantially reduce budgetary barriers to population genetic assessments, breeding, and conservation programs.

Assessment of Global Forest Coverage through Machine Learning Algorithms

“…world population percentage, and other related variables from the World Bank's Open Data Portal. Authors have used Python and various data visualization tools such as [22] Matplotlib and Seaborn to analyse and visualize the dataset. Initially 214 rows and 40 columns were there and after pre-processing 214 rows and 12 columns were left altogether.…”