Spatial analysis of the genome: Method for detecting selection of gene deletions
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Human genome research grown rapidly as sequencing technology has evolved. These developments support comparisons between genes at the molecular level. Biologists have used this accomplishment to conduct health-related research, biological research, and research on human evolution. However, it can be difficult to infer microevolutionary processes from genomic data because of the huge amount of calculation needed. In order to identify microevolutionary processes, especially selection, spatial analysis methods and multiple cluster analysis methods are brought together to study gene frequency data. This research uses spatial correlograms to represent spatial characteristics of genes and genetic deletions. Multiple clustering analysis methods are used to interpret all the correlograms calculated from genetic frequency data. The final objective is to generate a diagnostic framework by combining the results from the previous analysis methods for detecting human microevolutionary processes. The results showed that the deletions could be categorized into three classes according to their correlograms. The analysis indicates that the deletions under migration could have high Moran’s I -values when the distance band is small and the values decrease as the distance band increases. The deletions under isolation by distance have small I-values no matter what the distance band is. Some others have several values increasing although the distance band decreases. These variants could have evolved through different adaptive forces, including selection. Based on the results, this research addresses some recommendations on classifying genetic deletions based on spatial autocorrelation information. As the final objective, this research aims to outline and validate a diagnostic framework that enables a faster and easier way to detect microevolutionary processes of genes to further enhance biological research and to have a better knowledge of human history.