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

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Rasoarahona, R (Author) Other; Wattanadilokchatkun, P (Author) Other; Panthum, T (Author) Other; Thong, T (Author) Other; Singchat, W (Author) Other; Ahmad, SF (Author) Other; Chaiyes, A (Author) Other; Han, KYD (Author) Other; Kraichak, E (Author) Other; Muangmai, N (Author) Other; Koga, A (Author) Other; Duengkae, P (Author) Other; Agostinho Antunes (Author) FCUP; Srikulnath, K (Author) Other

Journal

Title: BIOLOGY-BASEL

Vol. 12

Final page: 1280

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Z-3EG

DOI: 10.3390/biology12101280

Abstract (EN): Simple Summary Microsatellite markers are widely used molecular markers for genetic studies, but choosing the right set involves a challenging trade-off between effectiveness and cost. The research aims to enhance the widely used ant colony optimization algorithm by integrating marker effectiveness indicators. By considering the genetic properties of the markers such as the polymorphic information content, the study seeks to determine the suitable way to select a reduced set of microsatellites. The approach addresses the accuracy-cost trade-off, aiding genetic assessments, breeding, and conservation efforts with cost-effective solutions. This research provides valuable insights into real-world genetic studies, including breeding programs and conservation initiatives.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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16