Flexible Software Architecture for Genetic Data Processing in Alpaca Breeding Programs

Gama-Zapata A.; Barra-Quipse F.; Vidal E. · International Journal of Advanced Computer Science and Applications (2025) · DOI: 10.14569/ijacsa.2025.0160410

Resumen

Improving alpaca fiber quality is an important objective in the textile industry. There are different kinds of techniques aimed to enhance breeding outcomes. This study proposes and validates a flexible software architecture for managing genetic information in alpaca breeding, integrating genomic selection methods. The proposed architecture consists of three components: 1) Input—capturing data from individual records, pedigree, phenotypic traits, fiber characteristics, genomic, and non-genomic information; 2) Processing—implementing statistical methods such as BLUP, GBLUP, and SSGBLUP, alongside inbreeding coefficient calculation and machine learning techniques; and 3) Output—generating reports for mating list proposals, estimated breeding values, and genetic evaluations. Designing a software architecture for genetic improvement in alpaca breeding programs could help software developers with maintainability, extensibility, and adaptability, considering different kinds of data sources for future advancements in alpaca breeding. This work shows the implementation and validation of software for an alpaca breeding program based on the proposed architecture. © (2025), (Science and Information Organization). All Rights Reserved.

Instituciones: Universidad Nacional de San Agustín de Arequipa