This talk aims to dive into technical details in machine learning model development, implementation and values it bring to Monsanto breeding pipeline. We genotype over 100 million seeds a year in order to save field resources and product development cycle time. Automation and high throughput production from the lab becomes key to R&D success. In house predictive model development incorporated random forest ensemble based approach with additional features derived from gaussian mixture model. The results show over 95% accuracy with less than 1% false positives/negatives. Model is highly generalizable with over 10 million data points being trained and tested on. The model also offers probabilistic approach to present genotypes in a more meaningful way and help enhanced downstream genomics analyses. The talk targets audience who are in breeding, genetics, molecular biology, and data scientists who are interested in practical applications.

1. Nan Newton
Data Scientist, Global IT Analytics
StampedeCon 2017, St. Louis MO
Novel Semi-Supervised Probabilistic ML
approach to SNP Variant Calling

2. DNA Analysis
Through
Advanced ML
Automated
Seed Chipping
Performance
Evaluation
Superior
Seeds
Selected
Today’s Digital Plant Breeding
is Powered by Our Knowledge of Genetics & Advanced Analytics
LABFIELD

3. Single Nucleotide
Polymorphism (SNP)
Variant detects seeds
with desired traits
C
T

4. SNP or Molecular Markers serve as signposts
Monsanto Company Confidential4

5. Genotypes-Phenotype Association
helps breeders reduce spending on field resources by only selecting
seeds with desired phenotypes
Parent generation
P1 P2
CC TT
F1 generation
CT CT CT CT
F2 generation
CC CT CT TT
CC CT CT TT
Homozygous C
Heterozygous
Homozygous T
Genotypes
Goal: Predict Genotypes for any seeds

6. Genotypes Detection
through Molecular Biology knowledge in high throughput genotyping labs
Seeds sent to lab Part of seeds are chipped
DNA molecule obtained
for each seed
A A T C A T G T
A A T C A T G T
allele1
allele2
A A C T A C G A
A A C T A C G A
allele1
allele2
Uncoil double helix DNA
A A T C A T G T
A A T C A T G T
allele1
allele2
A A C T A C G A
A A C T A C G A
allele1
allele2
Add fluorophores
FAM
FAM
VIC
VIC
Make a bunch of DNA copies
to generate stronger signal
of fluorophores

7. Genotypes Calls
through fluorescence signals and controls information
Controls
HOM_FAM
HET
HOM_VIC
MISSING

8. Plate-to-Plate Variations
in regards to clusters behaviors, controls performance, intensities distribution
Monsanto Company Confidential8

10. Impute MISSING label using
k-Nearest Neighbor algorithm
MISSING Less
Confident

11. Predict FAIL samples using training
model from another lab
MISSING

12. Semi-supervised Machine Learning
Random
Forest
Normalized
Fluorescence Intensities
Create fluorescence-
based features
Create controls-
based features
Create positions-
based features
Create unsupervised
clustering based features
Predict
Probabilistic
Genotypes
Predict FAIL
samples

13. Probabilistic Genotypes Prediction
LOWER
CONFIDENCE

14. Model Scalability and Extensibility: AWS Cloud
Integration with Enterprise Digital Architecture
Input Data from
any databases
Breeding
Biotech
Supply Chain
Customized
Training Models
Predictive Model Execution

15. 4

16. Better data… Better Decisions
Linkage
Disequilibrium
Genotype-Phenotype
Association
Haplotype
Mapping
Genetic
Mapping
Probabilistic impact on downstream genetic analytics
aa Aa AA
Short Tall

17. Further Improvement
17 Acknowledgement: Jeff Pobst, Bryan Dannowitz, Chris Schlosberg, Shane Ryerson

18. Further Improvement
18

19. Acknowledgement
Molecular Breeding Technology
Global Breeding
Cloud Analytics
Global IT Analytics
Products & Engineering Lab Platform
Product 360 Data Asset
Data Science Center of Excellence