‘D2 Statistics’ Technique: Methodology and Applications in Plant Breeding

Agricultural scientists deals with diverse set of characters in respect to range of crops for making decisions through various field and laboratory experiments. These varying and extreme sets of data has to be analyzed and interpreted to draw valid conclusions, deciding the faith of long experimentations. In case of plant breeders, it found that the Mahalanobis distance (D2 statistics) is an effective multivariate distance/metric that measures the distance between a point and a distribution. It is an extremely useful metric having, excellent applications in multivariate anomaly detection, classification on highly imbalanced datasets and one-class classification. Crop Improvement consists of selection of parents, crossing those parents to create variability, selection of elite genotypes and synthesis of suitable cultivar(s) from the selection. The choice of parental germplasm with which to begin a hybridization program is the most important decision a breeder makes. However, it is only relatively recent that quantitative genetic theory has been applied to this question. Recent years it has become imperative to use the modern techniques and genetic methods to fasten the process of improvement. Mahalanobis D2 Statistics is one of the powerful statistical tools for estimating the genetic distances between the germplasm. These may be utilized for selecting parents in crossing program. In this paper the attempt made to discuss the procedural aspect of Mahalanobis D2 useful for conventional breeding along with its applicability over range of experiments. Keywords: ANOVA, Clustering, D2 statistics, Genetic Divergence, Intra and Inter Cluster Distances, Mahalanobis D2 analysis, MANOVA.