## 4 Combined Approaches in Assessing Genetic Diversity in Pig Breeds  
The integration of different techniques, such as microsatellite markers, SNP genotyping, and pedigree analysis, provides a comprehensive overview of genetic variability, population structure, and breeding strategies. Recent studies [15; 59; 108] have demonstrated the effectiveness of integrating these techniques to enhance the assessment of genetic diversity across various pig breeds (Table 3). In the study by [PERSON] et al. [15], the authors used an integrated approach of microsatellite markers, SNP data, and pedigree information to analyze the genetic diversity and relationships of autochthonous pig breeds from Croatia, Slovenia, and Serbia. The authors found that although SNP panels provide a broader distribution of informativeness for assignment compared to microsatellite markers, both methods successfully distinguish distinct genetic clusters of individual breeds. [PERSON] et al. [112], [PERSON] et al. [71], and [PERSON] et al. [61] used pedigree and SNP analysis to analyze the Iberian pig breed, Landrace and Large White breeds, and the Prestice Black-Pied pig breed. Also, [PERSON] et al. [110] found that inbreeding coefficients derived from pedigree data were lower than those estimated from SNP data. A combined approach to analyzing genetic diversity is useful in populations with shallow pedigrees. Most researchers have shown that genetic diversity declines as pedigree completeness increases. This could also be attributed to the homogenization of breed characteristics and the limited number of available breeding individuals [113]. In order to obtain a reliable comparison of population parameters from different sources, it is necessary to calculate the correlations between the different results. [PERSON] et al. [61] reported moderate correlations between pedigree-based inbreeding (Fped) and SNP marker-based inbreeding (Froh), while other authors reported low correlations between Fped and Froh [71]. These studies, such as those based on SNP data, suggest that genomic inbreeding may provide a more accurate assessment of inbreeding levels compared to traditional pedigree-based methods, especially in breeds where pedigree recording is not precise or complete. These discrepancies highlight the importance of integrating genomic information into breeding programs to more effectively control inbreeding and preserve genetic diversity. In future perspectives of molecular methods, an increase in the application of artificial intelligence (AI) techniques is expected. However, AI has already been present in the analysis of pig genome in various ways. One of the first applications of artificial neural networks was carried out on a European pig population [115], where the application of the method provided additional information on the within- and between-population diversity, allowed differences between similar populations to be highlighted, and helped differentiate different groups of populations. A machine learning approach using the random forest method was tested by [PERSON] et al. [116] to analyze genotyping data of pigs, with the task of assigning animals to breed; this method showed advantages compared to earlier methods. In the future, AI can be used to build predictive models that forecast genetic diversity outcomes under different breeding strategies. Machine learning algorithms can analyze large datasets from genomic, pedigree, and phenotypic data to predict the impact of various selection and breeding programs on genetic variation and inbreeding levels. AI can also help in the identification and tracking inbreeding patterns using genomic data. Machine learning algorithms can detect runs of homozygosity (ROH) and predict inbreeding coefficients more accurately than traditional methods. This allows breeders to manage and mitigate the risk of inbreeding and reduce associated health problems