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Fig. 1 | Genetics Selection Evolution

Fig. 1

From: Locally epistatic models for genome-wide prediction and association by importance sampling

Fig. 1

Many of the models used in genomic prediction and association analyses are additive: These include ridge regression-best linear unbiased prediction (rr-BLUP) [31, 32], Lasso [33], Bayesian–Lasso [34], Bayesian ridge regression, Bayesian alphabet [35, 36], GBLUP and EMMA [47]. Several scientists have also developed methods to use genome-wide epistatic effects: RKHS [37, 38]), RF [39], SVM. The dendrogram on the left was obtained based on a table of the properties of different models, this table included variables such as “additive-epistatic”, “global-local”, “marker-kernel based”; it should not be taken as a formal clustering of models. The colors attached to the groups in the dendrogram are matched with different parts of the genome to illustrate the focus of each of these groups. Locally epistatic kernels (LEK) and locally epistatic rules (LER) models that use local epistasis

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