Figure 1
From: A joint learning approach for genomic prediction in polyploid grasses
Multivariate analyses performed on both genotypic and phenotypic data for the populations of (A) sugarcane (stalk diameter (SD) and stalk height (SH)), (B) Urochloa decumbens (field green weight (FGW), leaf dry matter (LDM), leaf percentage (LP), leaf stem ratio (LSR), regrowth capacity (RC), and total dry matter (TDM)), and (C) Megathyrsus maximus (green matter (GM), LDM, percentage of leaf blade (PLB), RC, stem dry matter (SDM), and TDM). Principal component analyses (PCAs) were performed with phenotypic data; t-distributed stochastic neighbor embedding (t-SNE), with genotypic data; and redundancy analyses (RDAs), with both datasets.
