Linear regression model or logistic models

More suitable is PCA because it provides individuality between the genotype and reduce bianess and noise between the genotypes.So you can go for PCA although both are used for genetic variability analysis.

For genetic diversity analysis, several statistical methods can be used, depending on the specific goals and data available. Some commonly used methods include: 1. Principal Component Analysis (PCA): PCA is used to reduce the dimensionality of genetic data while preserving important variation. It can help visualize genetic relationships between individuals or populations. 2. Cluster Analysis: Methods like hierarchical clustering or model-based clustering (e.g., STRUCTURE) can group individuals or populations based on genetic similarity, providing insights into population structure and admixture. 3. F-statistics (FST, FIS, FIT): These statistics quantify genetic differentiation, inbreeding, and total genetic diversity, respectively. They are often used in conjunction with other methods to assess genetic diversity and population structure.