inv4m Analysis Pipeline

Modulation of Flowering Time, Plant Height, and Growth Regulation Gene Networks

Comprehensive analysis of the maize chromosomal inversion Inv4m: phenotypic effects on flowering and growth, gene-by-environment interactions, JMJ candidate genes, and WGCNA network perturbation

2 Research Papers
8 Main Figures
6 Supplementary Figures
20+ Analysis Scripts
1

Inversion Paper

Complete (8 main figures, 6 supplementary)

Genomic Analysis

Genotype & Breakpoint Analysis

Inv4m delimitation, breakpoint identification, SNP distribution and correlation

Figure 1, Figure S1, Table 1, Table S1

Phenotypic Effects

Phenotype Analysis

Effect of Inv4m on plant height, flowering time (DTA, DTS), and harvest index

Figure 2

SAM Morphology

DIC microscopy analysis of shoot apical meristem dimensions

Figure 2 (SAM panels)

Internode Analysis

Internode length profiles, node count, and height validation

Figure S4

GxE Analysis

Genotype × Environment interactions across 3 field environments (PSU2022, PSU2025, CLY2025)

Figure S2, Figure S3, Table S3

Transcriptomics

Figure 3 Assembly

8-panel composite figure showing global and local transcriptomic effects

Figure 3 (complete assembly)

Differential Expression

Limma analysis of Inv4m effects on gene expression with plant-level blocking

Figure 3 (MDS panel), Table 2, Table S2

Volcano Plots

Visualization of DEGs with labeled candidate genes

Figure 3C, Figure 4

Manhattan Plots

Genome-wide visualization of expression effects by chromosome

Figure 3 (panels D, E, G, H)

Phenotype Association Filter

Overlap of Inv4m DEGs with published flowering time and plant height candidate gene lists (GWAS Atlas, tibbs-cortes, wang2021, li2022)

Table 2

Network Analysis

Trans Coexpression Network

MaizeNetome-validated trans-regulatory network of Inv4m DEGs

Figure 5

Network GO Enrichment

Gene Ontology enrichment of trans-regulated gene networks

Figure 5 (GO panel)

WGCNA Module Perturbation

Consensus network analysis showing module disruption in Inv4m

Figure 6, Figure S5, Figure S6, Table S4

WGCNA Field Perturbation Pipeline

7-step consensus WGCNA pipeline with bootstrap support and preservation analysis

Figure 6, Figure S5 (bootstrap), Table S4 (preservation) - 7 Rmd scripts

JMJ Cluster Analysis

JMJ Cluster Expression

Expression analysis of jmj2/jmj4 across tissues and experiments

Figure 7 (Panel A)

Pink Module Characterization

30 DEGs co-expressed with jmj2/jmj4 in the pink WGCNA module (growth network)

Table S5

5-Genome Microsynteny

jcvi pipeline for JMJ cluster synteny across B73, PT, TIL18, CML457, CML459

Figure 7 (Panel B) - Bash script

JMJ Paralog Expression

Transcript-level expression of all 5 JMJ paralogs (jmj9, psi, jmj6, jmj2, jmj4)

Supporting analysis

Crow 2020 Reanalysis

Reanalysis of growth chamber dataset for JMJ expression validation

Figure 7 (Panel A, Crow data)
2

Phosphorus Paper

Complete (12 scripts)

Foundation

Spatial Correction

Spatial correction for field phenotypes using SpATS models

Differential Expression

RNA-seq analysis identifying DEGs under leaf × phosphorus treatment interaction

Differential Lipid Analysis

Lipidomics analysis of membrane remodeling under phosphorus stress

Phenotypes

Growth Curves

Longitudinal growth analysis comparing Inv4m and control genotypes

Ionome Profiling

Mineral nutrient analysis (P, K, Ca, Mg, Fe, Zn, etc.) across genotypes

Phenotype Marginal Means

Estimated marginal means for phenotypic traits with statistical comparisons

Enrichment

GO Enrichment

Gene Ontology enrichment analysis of differentially expressed genes

KEGG Enrichment

KEGG pathway analysis linking DEGs to metabolic pathways

LION Enrichment

Lipid ontology enrichment for differentially abundant lipids

Synthesis

Transcription Indices

Gene expression indices for photosynthesis, senescence, and stress response

Volcano Plots

Visualization of differential expression results with annotated genes