🔍 1. Descriptive Statistics & Trait Analysis

Used to summarize and explore phenotypic data (e.g., yield, plant height, flowering time):

Boxplots – Compare trait distributions across genotypes, treatments, or locations.
Histograms – Show the frequency distribution of traits.
Violin plots – Similar to boxplots but show kernel density.
Bar charts – Compare means of traits (often with error bars).
Scatter plots – Show relationships between two continuous traits.
Dot plots – Visualize trait values for each genotype.

🌾 2. Genotype × Environment Interaction (G×E)

To evaluate how genotypes perform across environments:

GGE Biplot (Genotype + Genotype × Environment) – Visualize performance and stability of genotypes.
AMMI Biplot (Additive Main Effects and Multiplicative Interaction) – Partition and display G×E interaction.
Interaction plots – Line plots showing performance across environments.

Heatmaps of trait performance across environments – Easily spot high- and low-performing combinations.

🧬 3. Genetic Diversity & Population Structure

Helpful when assessing variability, clustering, and population stratification:

  • Principal Component Analysis (PCA) plots – Show genetic variation among genotypes.
  • Dendrograms (Hierarchical Clustering) – Group genotypes based on genetic similarity.
  • STRUCTURE bar plots – Visualize population structure (from STRUCTURE or ADMIXTURE outputs).
  • Discriminant Analysis of Principal Components (DAPC) – Genetic clustering visualization.
  • Multidimensional Scaling (MDS) plots – Similar to PCA for distance-based visualization.

🧪 4. QTL Mapping / GWAS (Genetic Association Studies)

Used to identify genetic markers associated with traits:

  • Manhattan plots – Show significant associations between markers and traits.
  • QQ plots (Quantile-Quantile plots) – Assess false positive rates in GWAS.
  • Linkage disequilibrium (LD) heatmaps – Visualize the degree of linkage between markers.
  • Circos plots – Integrate QTL/GWAS results across chromosomes.

📈 5. Time-Series / Growth Analysis

For analyzing growth over time or developmental stages:

  • Growth curves / line plots – Trait progression over time.
  • Area plots – Useful for cumulative growth or production.
  • Spaghetti plots – Overlay individual genotype trends.

📊 6. Heritability & Variance Component Analysis

To assess trait control and genetic contribution:

  • Variance partitioning bar plots – Show % contribution of genetic, environmental, and error variances.
  • Error bar plots – Show confidence intervals around heritability estimates.
  • Pie charts / stacked bar plots – Visual breakdown of variance components.

🧪 7. Experimental Design & Field Layout

Useful for visualizing trial setup and data collection:

  • Field layout maps / grid plots – Show design (e.g., RCBD, alpha lattice).
  • Heatmaps of plot values – Spot spatial variability in fields.
  • 3D surface plots – Depict field variability in traits like yield.

🔄 8. Correlation & Multivariate Analysis

To explore relationships between multiple traits or variables:

  • Correlation matrix heatmaps – Trait-to-trait relationships.
  • Pairwise scatterplot matrix – Multiple scatter plots with correlation overlays.
  • Biplots (PCA or PLS) – Multivariate representation of genotypes and traits.
  • Cluster heatmaps – Combine trait clustering and visualization.

🧬 9. Genomic Selection

Visualizations used in prediction modeling and model evaluation:

  • Predicted vs. Observed scatter plots – Assess accuracy of genomic predictions.
  • Cross-validation performance plots – Compare models using RMSE, R², etc.
  • Feature importance bar plots – Show most important markers or traits in prediction.

📌 Bonus: Specialized Visualizations

  • Sankey diagrams – Flow of genetic material across generations (e.g., pedigree tracking).
  • Alluvial plots – Changes in group membership or trait classification over time.
  • UpSet plots – Better alternative to Venn diagrams for multiple set intersections (e.g., shared QTLs).