Predicting Phenotype from Multi-Scale Genomic and Environment Data using Neural Networks and Knowledge Graphs: An Introduction to the NSF GenoPhenoEnvo Project
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The Genophenoenvo project aims to develop a machine learning framework to predict phenotypes by integrating multi-scale genomic and environmental data. It addresses the challenges of translating gene information into phenotypes, utilizing knowledge graphs and advanced modeling techniques to enhance predictive accuracy. The project is in its early stages, focusing initially on sorghum and expanding to include wheat and other species, while emphasizing the importance of preparing data for machine learning applications.
![Training Data - Genomic
● Use VCF files and phenotype data from
TERRA-REF
● SNP to phenotype associations with p values and
effect sizes (GWAS)
● Manhattan plot for all phenotype data
List 1: TERRA-REF data for Y1
Gene Information (G)
● Sorghum whole genome
● Sorghum genotypes[219]
Phenotype Information (P)
● Emergence Date
● End of Season Biomass
● End of Season Height
● Flowering Date
Environment Information (E)
● Soil moisture
● Air temperature
● PAR (Irradiance)
● Wind speed
● Humidity
● Precipitation and Irrigation
● Fertilizer inputs
By M. Kamran Ikram et al - Ikram MK et al (2010) Four Novel Loci (19q13, 6q24, 12q24, and
5q14) Influence the Microcirculation In Vivo. PLoS Genet. 2010 Oct 28;6(10):e1001184.
doi:10.1371/journal.pgen.1001184.g001, CC BY 2.5,
https://commons.wikimedia.org/w/index.php?curid=18056138
*example
GWAS results can be combined
with the knowledge graph results to
reduce input variables for ML](https://image.slidesharecdn.com/thessenpag2020-200114152546/85/Predicting-Phenotype-from-Multi-Scale-Genomic-and-Environment-Data-using-Neural-Networks-and-Knowledge-Graphs-An-Introduction-to-the-NSF-GenoPhenoEnvo-Project-12-320.jpg)
![Training Data - Phenomic
● Days to flowering
● Growing Degree Days to flowering
● Days to flag leaf emergence
● Growing Degree Days to flag leaf emergence
● Canopy height
● End of season canopy height
● Above ground dry biomass at harvest
List 1: TERRA-REF data for Y1
Gene Information (G)
● Sorghum whole genome
● Sorghum genotypes[219]
Phenotype Information (P)
● Emergence Date
● End of Season Biomass
● End of Season Height
● Flowering Date
Environment Information (E)
● Soil moisture
● Air temperature
● PAR (Irradiance)
● Wind speed
● Humidity
● Precipitation and Irrigation
● Fertilizer inputs
Phenotypes can be
represented as
categories or
integers (10 cm or
decreased height?)](https://image.slidesharecdn.com/thessenpag2020-200114152546/85/Predicting-Phenotype-from-Multi-Scale-Genomic-and-Environment-Data-using-Neural-Networks-and-Knowledge-Graphs-An-Introduction-to-the-NSF-GenoPhenoEnvo-Project-13-320.jpg)
![Training Data - Environmental
● Air temperature
● Relative humidity
● Precipitation
● Wind speed and direction
● Growing degree days
● Cumulative precipitation
List 1: TERRA-REF data for Y1
Gene Information (G)
● Sorghum whole genome
● Sorghum genotypes[219]
Phenotype Information (P)
● Emergence Date
● End of Season Biomass
● End of Season Height
● Flowering Date
Environment Information (E)
● Soil moisture
● Air temperature
● PAR (Irradiance)
● Wind speed
● Humidity
● Precipitation and Irrigation
● Fertilizer inputs
Data from weather station and gantry
Abstracted to daily average, min, and max](https://image.slidesharecdn.com/thessenpag2020-200114152546/85/Predicting-Phenotype-from-Multi-Scale-Genomic-and-Environment-Data-using-Neural-Networks-and-Knowledge-Graphs-An-Introduction-to-the-NSF-GenoPhenoEnvo-Project-14-320.jpg)
Predicting Phenotype from Multi-Scale Genomic and Environment Data using Neural Networks and Knowledge Graphs: An Introduction to the NSF GenoPhenoEnvo Project
- 1. Predicting Phenotype from Multi-Scale Genomic and Environment Data using Neural Networks and Knowledge Graphs: An Introduction to the NSF GenoPhenoEnvo Project Anne E Thessen, Michael Behrisch, Emily J Cain, Remco Chang, Bryan Heidorn, Pankaj Jaiswal, David LeBauer, Ab Mosca, Monica C Munoz-Torres, Arun Ross, Tyson Swetnam
- 2. Acknowledgements ● NSF Ideas Lab ● NSF Award 1940330 Harnessing the Data Revolution ● Translational and Integrative Sciences Lab OSU (tislab.org) ● Two new members: Ishita Debnath (MSU) and Ryan Bartelme (UA)
- 3. Predicting Phenotype from Genes and Environments ● G + E = P only works in the simplest systems, if at all ● There’s a lot we don’t know about how genes are translated into phenotypes ● How do phenotypes affect the ecosystem?
- 4. How can we predict phenotype given an organism’s environmental conditions and genomic endowment? ● State-of-the-art statistical modeling has led to many insights, but has been applied to very controlled systems. ● Getting the phenotype is only part of the answer. ● Can we use the predictive model to reveal hidden processes? Critical variables?
- 5. Machine Learning for Results and Process ● Pros ○ Capable of coping with non-linearity in biological systems ○ Find hidden relationships ● Cons ○ Output is opaque ○ Not enough curated data not available ○ Data that are available not “ML ready”
- 6. GenoPhenoEnvo Project ● Goal: Develop a machine learning framework capable of predicting phenotypes based on multi-scale data about genes and environments. ○ Leverage existing, well-structured, cross-species reference data about genes and phenotypes ○ Provide interactive data visualizations for examining and interpreting the “black-box” behavior of ML models and their results ○ Realize a new model for relating phenotypes, genetic endowment, and environmental characteristics ● Just started Oct 1 ● Now in Year 1 Q2
- 7. Training Data Year 1 ● TERRA-REF sorghum ● Heavily controlled and measured environment data ● Thorough genotyping and phenotyping ● Knowledge graph links ● How do we prepare these data to be ML ready? Year 2 ● TERRA-REF wheat ● NEON, EOS ● Citizen science phenology
- 8. What is a Knowledge Graph?
- 9. How Can Knowledge Graphs Help? DOI: 10.1126/scitranslmed.3009262
- 10. How Can Knowledge Graphs Help?
- 11. How Can Knowledge Graphs Help? ● Constrain ML and prioritize results ● Quality control - sanity check ● Integrate heterogeneous data ○ Manage terminology ○ Manage scale and granularity ● Find new relationships ● Fill in data gaps with inferencing
- 12. Training Data - Genomic ● Use VCF files and phenotype data from TERRA-REF ● SNP to phenotype associations with p values and effect sizes (GWAS) ● Manhattan plot for all phenotype data List 1: TERRA-REF data for Y1 Gene Information (G) ● Sorghum whole genome ● Sorghum genotypes[219] Phenotype Information (P) ● Emergence Date ● End of Season Biomass ● End of Season Height ● Flowering Date Environment Information (E) ● Soil moisture ● Air temperature ● PAR (Irradiance) ● Wind speed ● Humidity ● Precipitation and Irrigation ● Fertilizer inputs By M. Kamran Ikram et al - Ikram MK et al (2010) Four Novel Loci (19q13, 6q24, 12q24, and 5q14) Influence the Microcirculation In Vivo. PLoS Genet. 2010 Oct 28;6(10):e1001184. doi:10.1371/journal.pgen.1001184.g001, CC BY 2.5, https://commons.wikimedia.org/w/index.php?curid=18056138 *example GWAS results can be combined with the knowledge graph results to reduce input variables for ML
- 13. Training Data - Phenomic ● Days to flowering ● Growing Degree Days to flowering ● Days to flag leaf emergence ● Growing Degree Days to flag leaf emergence ● Canopy height ● End of season canopy height ● Above ground dry biomass at harvest List 1: TERRA-REF data for Y1 Gene Information (G) ● Sorghum whole genome ● Sorghum genotypes[219] Phenotype Information (P) ● Emergence Date ● End of Season Biomass ● End of Season Height ● Flowering Date Environment Information (E) ● Soil moisture ● Air temperature ● PAR (Irradiance) ● Wind speed ● Humidity ● Precipitation and Irrigation ● Fertilizer inputs Phenotypes can be represented as categories or integers (10 cm or decreased height?)
- 14. Training Data - Environmental ● Air temperature ● Relative humidity ● Precipitation ● Wind speed and direction ● Growing degree days ● Cumulative precipitation List 1: TERRA-REF data for Y1 Gene Information (G) ● Sorghum whole genome ● Sorghum genotypes[219] Phenotype Information (P) ● Emergence Date ● End of Season Biomass ● End of Season Height ● Flowering Date Environment Information (E) ● Soil moisture ● Air temperature ● PAR (Irradiance) ● Wind speed ● Humidity ● Precipitation and Irrigation ● Fertilizer inputs Data from weather station and gantry Abstracted to daily average, min, and max
- 15. Machine Learning - Preliminary 1. Regression Models 2. Simple Neural Networks 3. Deep Neural Networks
- 16. Year 2 - Expanding to Ecosystems (Preliminary) Leverage data and resources from multiple NSF supported programs: ● Analyze genetic and remote sensing data from NEON ● Utilize the CyVerse Data Science Workbench ● XSEDE for ML computations List 2: Observational & EOS data for Y2 Gene Information (G) ● Cottonwood whole genome ● Cottonwood genotypes ● Sorghum whole genome ● Sorghum genotypes ● Wheat whole genome ● Wheat genotypes Environment Information (E) ● Soil moisture (e.g., SMAP) ● Precipitation (Daymet) ● Air temperature (Daymet) ● PAR (NARR) ● Soil Type (USDA) Phenotype Information (P) ● Leafing date ● Flowering date ● Breaking leaf buds (#) ● Ripe fruits (#) ● Increasing leaf size (Y/N) ● Falling leaves (Y/N) ● Colored leaves (Y/N) ● Flowers or buds (#) ● Open flowers (#) ● Pollen release (Y/N) ● Recent fruit/seed drop (Y/N) ● Fruits (#) ● NDVI (EOS)
- 17. GenoPhenoEnvo Project Information ● Join our Google Group ● Watch our GitHub Repo github.com/genophenoenvo ● Search Twitter hashtag #GenoPhenoEnvo ● Visit the project web page ● Anne E Thessen ● annethessen@gmail.com Questions?