In genetic analysis, sometimes we come across low-quality genotyping data in insects like honeybees. Lack of Identity by State matrix (IBS) analysis in any kind of genetic studies can lead to several issues that may compromise the integrity of the research outcomes, especially in low-quality data. Therefore, IBS matrix analysis can be used for quality control in genetic studies to ensure data integrity and minimize false discoveries. In this study, we performed a detailed IBS matrix analysis, comparing raw data to data after outlier deletion. Key quality control metrics—including PI_HAT distribution, Distance (DST) values, PPC distribution, and RATIO density—were evaluated before and after filtering. The raw genotypic data exhibited notable deviations, with elevated PI_HAT values, increased DST dispersion, irregular PPC patterns, and a wider RATIO distribution, indicative of technical artifacts, cryptic relatedness, or sample contamination. Following outlier removal, the dataset demonstrated improvements: PI_HAT values normalized, DST and PPC distributions became more uniform, and RATIO density plots showed reduced skewness. These results confirm that stringent outlier filtering enhances the accuracy of genetic relatedness estimates and population structure inference by eliminating spurious variation while retaining true biological diversity. Our analysis underscores the importance of robust quality control in quantitative genetics to ensure the validity of downstream genomic analyses like genome-wide association studies.
