1. Complex Genomic Trait Predictions to Accelerate Plant Breeding Programs
Kelci Miclaus1, Luciano da Costa e Silva1 , and Lauro Jose Moreira Guimaraes2
1JMP Life Sciences, SAS Institute Inc. and 2Embrapa Maize and Sorghum, Brazil
Abstract
Case Study in Maize
Methods
Predictive Models: Build, Cross-Validate, Compare
Models
Ridge Regression (RR-BLUP)
Genomic BLUP (GBLUP)
Predictor Filtering
Forest
Statistical T-tests
Cross-Validation
5 Iterations of 5-fold cross validation for each model
Score, Evaluate and Select Line Crosses
Summary statistics analytically derived for all pair-wise crosses of each line (16,290 crosses)
Distributions of the scored predicted yield and height used to select potential crosses using JMP Pareto-Frontier Dominant Points to Maximize Yield and Minimize Height
Multi-Generation Progeny Simulation
Recombinant Inbred Lines were simulated by selfing and selecting new lines
Line selection at each generation was based on optimizing high yield vs. low height
5 generations of a virtual breeding cycle were performed
Feeding the world’s population is one of the most demanding problems of our generation
Modern breeding programs for crop improvement leverage genomic variability to develop new plant varieties that optimize physical traits (i.e. yield, disease-resistance, vitamin content) using patterns of genomic inheritance
This poster will highlight the genomic prediction, cross-evaluation, and progeny simulation methods available in JMP Genomics software to perform multi-trait optimization and line selection to accelerate plant breeding programs
Researchers at the maize breeding program of Embrapa in Brazil conducted an experiment with 181 inbred lines which were field-evaluated for grain yield and plant height under well-watered (WW) and water-stressed (WS) conditions.
Lines were genotyped via GBS producing SNPs after quality control
While scatterplots and Venn diagrams show positive correlation and significantly associated genetic variants common in height and yield, there is potential in genomic selection to select varieties that can decrease height while increasing yield
Objectives
Complex traits like yield and height are driven by a genetic variability across many loci as shown by the results of the Genome-Wide Association (GWAS) Manhattan Plot above
The goal of the breeding program is to utilize the genetic variability to create a set of lines that maximize yield while reducing height.
Prediction models built from the SNP data set were used to score, cross, evaluate and simulate future breeding lines to perform multi-trait genomic selection
The classic “Breeders Equation” shows genetic gain driven by heritability, selection intensity and cycle length
Genomic prediction models can accelerate genetic gain P1 P2

2. Complex Genomic Trait Predictions to Accelerate Plant Breeding Programs
Kelci Miclaus1, Luciano da Costa e Silva1 , and Lauro Jose Moreira Guimaraes2
1JMP Life Sciences, SAS Institute Inc. and 2Embrapa Maize and Sorghum, Brazil
Results: Prediction
Results: Progeny Simulation
Conclusions
The Predictive Modeling Review builder in JMP Genomics was used to fit and compare the RR-BLUP and GBLUP models under varying predictor filtering settings.
The final models built used RR-BLUP with ~900 SNP predictors for scoring lines and creating cross evaluation summary statistics
Genomic Selection, the use of whole-genome molecular markers in prediction methods to improve quantitative traits, shows gains can be achieved with maize breeding programs based on models that capture the genetic variability, even with smaller subsets of genetic markers from predictor filtering
Line 41 selected by the software via cross-evaluation for potential crosses with other lines is corroborated as a known drought-tolerant line
The predictive modeling suite with cross-validation and model comparison, cross-evaluation and progeny simulation reports in JMP Genomics provide an end-to-end analysis tool for virtual breeding cycles based on molecular marker variability and prediction
Lines 41, 45, 85, 99, 110, 113, and 148 show promising expectation for the extraction of new lines when selecting for high WS-yield and low WS-height.
# SNPs 50
100
500
1000
1500
57047
3000
5000
RR-BLUP (A + D)
G-BLUP (A + D)
Model
Marker Reduction
Forest
t-test
Recombinant Inbred Lines (RILs) derived from selected crosses are shown above
Several key lines, namely 41, showed promise for drought-tolerant hybrids. Caterpillar plots below show this improvement after 5 generations of virtual breeding
WS-Height
Mother / Father / Generation
Results: Cross Evaluation WS-Yield and WS-Height
Acknowledgements and Funding
Pareto Frontier dominant point selection was used on the predicted means for WS Yield and Height from the RR-BLUP models to select from the potential 16,290 crosses
Embrapa researchers involved in this study:
Maria Marta Pastina, Claudia Teixeira Guimarães, Sidney Netto Parentoni, Paulo Evaristo de Oliveira Guimarães, Newton Portilho Carneiro, Roberto Noda, and Lauro José Moreira Guimarães gratefully acknowledge the financial support of Embrapa, FAPEMIG, CNPq and CAPES
Mother / Father / Generation
Possibility to increase yield while reducing height for crosses involving line 41.