Center for Quantitative Genetics and Genomics

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About QGG

Guillaume Ramstein

Assistant professor, tenure track, Tenure Track assistant professor

Curriculum Vitae

PROFESSIONAL EXPERIENCE

Tenure-Track Assistant Professor at the Center for Quantitative Genetics and Genomics, Aarhus University (Aarhus, Denmark)
Application of machine learning techniques to infer effects of mutations in plants

  • Inference of mutation effects by evolutionary constraint and mutagenesis
  • Integrative analyses of multi-omics data
  • Cross-population and cross-species analyses in quantitative genetics

Feb. 2021 – Present

USAID-funded project between Cornell University and CHIBAS (Port-au-Prince, Haiti)
Bioinformatics and implementation of genomic prediction in a sorghum breeding program

  • Five cycles of genomic selection in sorghum, more than 2000 plants per cycle
  • Testing of statistical models and genotyping protocols in genomic prediction
  • Coordination with breeders, laboratory technicians (DNA extraction and sequencing) and bioinformaticians

Oct. 2017 ­–
Jan. 2021

Breeding project at USDA-ARS (Madison, Wisconsin, USA)
Implementation of genomic selection in a switchgrass breeding program

  • Two cycles of genomic selection in switchgrass, more than 2000 plants per cycle
  • Data processing and prediction of breeding values

Apr. 2016 – May 2017

Fixed-term contract at Arvalis (Paris, France)
Study of genotype-by-environment interactions in wheat

  • Analysis of sensitivities of cultivars to different types of abiotic stress in historical datasets
  • Development of predictive models of genotype-by-environment interactions for grain yield

Nov. 2011 ­–
May 2012

EDUCATION

Postdoctoral Research, Cornell University

Department: Institute of Biotechnology

Supervisor: Dr. Edward S. Buckler

Topics:Computational biology and quantitative genomics to understand genetic effects in maize and sorghum

  • Application of machine learning, bioinformatics and network analysis for functional annotation of genes and DNA polymorphisms in maize
  • Incorporation of annotation into genomic models for investigation of genetic architectures in maize and sorghum
  • Implementation of haplotype imputation methods and application in genomic prediction in sorghum

Oct. 2017 ­–
Jan. 2021

PhD, University of Wisconsin – Madison

Major: Plant Breeding and Genetics (Quantitative Genetics, Breeding Methods, Plant Molecular Biology)

Minor: Statistics (Probability and Mathematics, Experimental Designs, Applied Methods)

Department: Agronomy (Plant Breeding and Plant Genetics Program)

Supervisor: Dr. Michael D. Casler

Thesis:Inference of candidate causal variants and assessment of genomic prediction for bioenergy traits in switchgrass (Panicum virgatum L.)

  • Extension of genomic prediction models to account for linkage disequilibrium
  • Development of novel genomic models to account for population differentiation
  • Detection and analysis of interactive genetic effects on biofuel traits

June 2012 ­–
Sept. 2017

Licence and Master’s degree, Montpellier SupAgro – International University Centre for Advanced Studies in Agricultural Sciences and Rural Development

Major: Agronomy (Ecophysiology, Plant and Animal Breeding, Molecular Biology, Statistics, Agricultural Engineering, Social and Economic Sciences)

  • Exchange semester at the University of Wisconsin-Madison (Statistics, Evolutionary Science, Plant Pathology)
  • Specialization in Agronomy and Plant Genetics in AgroParisTech, post-graduate engineering school for life sciences

Sept. 2007 ­–
Oct. 2011

 

PUBLICATIONS

1.   Ramstein,G. P., & Buckler, E. S. (2022). Prediction of evolutionary constraintby genomic annotations improves functional prioritization of genomic variantsin maize. Genome Biology, 23(1), 1–26. https://doi.org/10.1186/s13059-022-02747-2

2.   Khaipho-Burch,M., Ferebee, T., Giri, A., Ramstein, G. P., Monier, B., Yi, E., CintaRomay, M., & Buckler, E. S. (2022). Elucidating the patterns of pleiotropyand its biological relevance in maize. In bioRxiv (p.2022.07.20.500810). https://doi.org/10.1101/2022.07.20.500810

3.   Lin,M., Qiao, P., Matschi, S., Vasquez, M., Ramstein, G. P., Bourgault, R.,Mohammadi, M., Scanlon, M. J., Molina, I., Smith, L. G., & Gore, M. A.(2022). Integrating GWAS and TWAS to elucidate the genetic architecture ofmaize leaf cuticular conductance. Plant Physiology. https://doi.org/10.1093/plphys/kiac198

4.   Giri,A., Khaipho-Burch, M., Buckler, E. S., & Ramstein, G. P. (2021).Haplotype associated RNA expression (HARE) improves prediction of complextraits in maize. PLoS Genetics, 17(10), e1009568. https://doi.org/10.1371/journal.pgen.1009568

5.   Washburn,J. D., Cimen, E., Ramstein, G. P., Reeves, T., O’Briant, P., McLean, G.,Cooper, M., Hammer, G., & Buckler, E. S. (2021). Predicting phenotypes fromgenetic, environment, management, and historical data using CNNs. Theoreticaland Applied Genetics. https://doi.org/10.1007/s00122-021-03943-7

6.   Wu,D., Tanaka, R., Li, X., Ramstein, G. P., Cu, S., Hamilton, J. P., Buell,C. R., Stangoulis, J., Rocheford, T., & Gore, M. A. (2021). High-resolutiongenome-wide association study pinpoints metal transporter and chelator genesinvolved in the genetic control of element levels in maize grain. G3, 11(4).https://doi.org/10.1093/g3journal/jkab059 

7.   Ramstein,G. P., Larsson, S. J., Cook, J. P., Edwards, J. W., Ersoz, E. S.,Flint-Garcia, S., Gardner, C. A., Holland, J. B., Lorenz, A. J., McMullen, M.D., Millard, M. J., Rocheford, T. R., Tuinstra, M. R., Bradbury, P. J.,Buckler, E. S., & Romay, M. C. (2020). Dominance Effects and FunctionalEnrichments Improve Prediction of Agronomic Traits in Hybrid Maize. Genetics,215(1), 215–230. https://doi.org/10.1534/genetics.120.303025

8.   Jensen, S., Charles, J. R., Muleta, K.,Bradbury, P., Casstevens, T., Deshpande, S. P., Gore, M. A., Gupta, R., Ilut,D. C., Johnson, L., Lozano, R., Miller, Z., Ramu, P., Rathore, A., Cinta Romay,M., Upadhyaya, H. D., Varshney, R., Morris, G. P., Pressoir, G., Buckler, E.S., Ramstein, G. P. (2020). A sorghum practical haplotype graphfacilitates genome‐wideimputation and cost‐effectivegenomic prediction. The Plant Genome, 13(1), 1687. https://doi.org/10.1002/tpg2.20009

9.   Washburn, J. D., Mejia-Guerra, M. K., Ramstein,G. P., Kremling, K. A., Valluru, R., Buckler, E. S., & Wang, H. (2019).Evolutionarily informed deep learning methods for predicting relativetranscript abundance from DNA sequence. Proceedings of the National Academyof Sciences of the United States of America, 116(12), 5542–5549. https://doi.org/10.1073/pnas.1814551116

10. Ramstein, G. P., & Casler, M.D. (2019). Extensions of BLUP Models for Genomic Prediction in HeterogeneousPopulations: Application in a Diverse Switchgrass Sample. G3, 9(3),789–805. https://doi.org/10.1534/g3.118.200969

11. Ramstein, G. P., Jensen, S. E.,& Buckler, E. S. (2019). Breaking the curse of dimensionality to identifycausal variants in Breeding 4. Theoretical and Applied Genetics, 132(3),559–567. https://doi.org/10.1007/s00122-018-3267-3

12. Ramstein,G. P., Evans, J., Nandety, A., Saha, M. C., Brummer, E. C., Kaeppler, S.M., Buell, C. R., & Casler, M. D. (2018). Candidate Variants for Additiveand Interactive Effects on Bioenergy Traits in Switchgrass (Panicum virgatumL.) Identified by Genome-Wide Association Analyses. The Plant Genome, 11(3).https://doi.org/10.3835/plantgenome2018.01.0002

13. Taylor, M., Tornqvist, C.-E., Zhao, X.,Grabowski, P., Doerge, R., Ma, J., Volenec, J., Evans, J., Ramstein, G. P.,Sanciangco, M. D., Buell, C. R., Casler, M. D., & Jiang, Y. (2018).Genome-Wide Association Study in Pseudo-F2 Populations of SwitchgrassIdentifies Genetic Loci Affecting Heading and Anthesis Dates. Frontiers inPlant Science, 9, 1250. https://doi.org/10.3389/fpls.2018.01250

14. Jabbour, F., Gaudeul, M., Lambourdière,J., Ramstein, G. P., Hassanin, A., Labat, J.-N., & Sarthou, C.(2018). Phylogeny, biogeography and character evolution in the tribe Desmodieae(Fabaceae: Papilionoideae), with special emphasis on the New Caledonian endemicgenera. Molecular Phylogenetics and Evolution, 118, 108–121. https://doi.org/10.1016/j.ympev.2017.09.017

15. Casler,M. D., & Ramstein, G. P. (2018). Breeding for biomass yield inswitchgrass using surrogate measures of yield. Bioenergy Research, 11,1–12. https://doi.org/10.1007/s12155-017-9867-y

16. Grabowski, P. P., Evans, J., Daum, C.,Deshpande, S., Barry, K. W., Kennedy, M., Ramstein, G. P., Kaeppler, S.M., Buell, C. R., Jiang, Y., & Casler, M. D. (2017). Genome-wideassociations with flowering time in switchgrass using exome-capture sequencingdata. The New Phytologist, 213(1), 154–169. https://doi.org/10.1111/nph.14101

17. Ramstein, G. P., Evans, J.,Kaeppler, S. M., Mitchell, R. B., Vogel, K. P., Buell, C. R., & Casler, M.D. (2016). Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L.)Improved by Accounting for Linkage Disequilibrium. G3, 6(4),1049–1062. https://doi.org/10.1534/g3.115.024950

18. Ramstein, G. P., Lipka, A. E., Lu,F., Costich, D. E., Cherney, J. H., Buckler, E. S., & Casler, M. D. (2015).Genome-wide association study based on multiple imputation with low-depthsequencing data: application to biofuel traits in reed canarygrass. G3, 5(5),891–909. https://doi.org/10.1534/g3.115.017533


INVITED PRESENTATIONS

Sept. 2022Beyond QTL effects in quantitative genetics: comparative genomics and machine learning techniques for prediction across populations. Eucarpia Biometrics 2022
Aug. 2022Detecting genomic effects at high resolution: functional prioritization of genomic variants by evolutionary constraint. Evolution and Population genetICs in DenmarK
June 2022Current limitations in quantitative genetics, and potential solutions for robust genomic prediction and biological inference. Eucarpia Maize and Sorghum 2022
June 2021Identifying causal variants by evolutionary constraint: Prediction at single-site resolution and application in maize. Limagrain Seminar on Molecular Characterization of Genetic Variability
Mar. 2021Prediction of evolutionary constraint by genomic annotations improves prioritization of causal variants in maize. 63rd Maize Genetics Conference
Jan. 2020Prioritization of genetic variants by biological and evolutionary annotation: functional assessments in diverse maize populations. Plant and Animal Genome XXVIII Conference
Sept. 2019An overview of machine learning principles and their applications in biology. Boyce Thompson Institute Symposium
Aug. 2019Breaking the curse of dimensionality to identify causal variants in Breeding 4. GOBii/Excellence in Breeding Webinar
May 2016Genomic selection in switchgrass: proofs of concept and applications. 2016 GLBRC Annual Science Meeting
Jan. 2016Accuracy of genomic prediction in switchgrass improved by accounting for linkage disequilibrium. Plant and Animal Genome XXIV Conference
May 2015Genomic selection for biofuel traits in switchgrass: evaluation of procedures. 2015 GLBRC Annual Science Meeting
Jan. 2015Genome-wide association study based on multiple imputation with low-depth sequencing data: application to biofuel traits in reed canarygrass. Plant and Animal Genome XXIII Conference
May 2014Genome-wide association studies in bioenergy grasses for biofuel quality traits. 2014 GLBRC Annual Science Meeting

POSTER PRESENTATIONS

Mar. 2022Prediction of evolutionary constraint by genomic annotations improves prioritization of causal variants in maize. Probabilistic Modelling in Genomics 2022
Nov. 2020Prediction of evolutionary constraint by genomic annotations: Functional enrichment in maize. 6th International Conference on Quantitative Genetics
Mar. 2019Functional basis for hybrid vigor in maize: directional and polygenic effects on yield, height and flowering time. 61st Maize Genetics Conference
Feb. 2019Dominance gene action and gene proximity capture the genetic basis of heterosis in diverse maize panels. 2019 Gordon Research Conference in Quantitative Genetics and Genomics
May 2018Expression levels and gene annotation for transcriptomic prediction in maize. The Biology of Genomes 2018
Mar. 2018Incorporation of functional information into genomic prediction models in maize. 60th Maize Genetics Conference
Mar. 2017Deterministic optimization algorithms and alternate BLUP models for genomic prediction in heterogeneous populations: application in switchgrass (Panicum virgatum L.). 2017 Gordon Research Conference in Quantitative Genetics and Genomics
June 2016The use of marker-data transformations to account for linkage disequilibrium in genomic selection: a case study in switchgrass (Panicum virgatum L.). 5th International Conference on Quantitative Genetics
May 2016Genomic selection and genome-wide association analyses for biofuel traits in switchgrass. 2016 GLBRC Annual Science Meeting
June 2014Causal variants for biofuel traits of reed canarygrass based on multiple imputation and GWAS analysis of low-quality GBS data. Pan-American Congress on Plants and BioEnergy

AWARDS AND HONORS

2016Henry Steenbock Academic Merit Award - College of Agricultural and Life Sciences at the University of Wisconsin-Madison
2016Award for Participation in the 5th International Conference on Quantitative Genetics USDA, National Institute of Food and Agriculture
2015Royce Bringhurst Memorial Scholarship - College of Agricultural and Life Sciences at the University of Wisconsin-Madison
2015O. N. Allen Scholarship - Department of Agronomy at the University of Wisconsin-Madison
2014-2016Gabelman-Shippo Distinguished Graduate Fellowship - Plant Breeding and Plant Genetics Program at the University of Wisconsin-Madison

GRANTS

Current grants

Oct. 2022 – Sept. 2025

Industrial Postdoc Grant 2022 (Innovation Fund Denmark)

Funding: DKK 2,470,000

Title: Improvement of winter barley by efficient genomics-based hybrid breeding

Role: Academic Mentor


Jan. 2022 – Dec. 2024

AUFF Starting Grant 2021 – Assistant Professor (Aarhus University Research Foundation)

Funding: DKK 1,500,000

Role: Principal Investigator


July 2021 – June 2025

Emerging Investigator 2021 – Research within Plant Science, Agriculture and Food Biotechnology (Novo Nordisk Foundation)

Funding: DKK 7,993,851

Title: Selection of mutations by in silico and experimental variant effects (SIEVE): a new strategy to improve fitness in cool-season grasses

Role: Principal Investigator



OUTREACH AND SERVICE TO THE PROFESSION

2015 - present
Ad Hoc Scientific Reviews
- PNAS (Fall 2022)

- Trendsin Plant Sciences (Spring 2022)

- Nature Communications (Summer 2021)

- GENETICS (Summer 2021)

- Genome Biology (Spring 2021)

- New Phytologist (Winter 2020-2021)

- Cell (Summer 2020)

- The Plant Genome (Summer 2020)

- G3 (Spring 2020)

- PLoS One (Spring 2019, Summer 2019)

- Theoretical and Applied Genetics (Spring 2018)

- Heredity (Spring 2018)

- Molecular Breeding (Spring 2016)

- Crop Science (Fall 2015, Fall 2017, Fall 2018, Summer 2020)


2015
Member of the Plant Sciences Graduate Student Council
Association of graduate students in the departments of Agronomy, Horticulture and Plant Pathology at the University of Wisconsin-Madison
- Promotion of graduate student social interactions
- Organization of the 5th Annual Plant Sciences Symposium at the University of Wisconsin-Madison in collaboration with Pioneer Hybrid (now Corteva)

2015
Journal Club Chair
Organization of scientific discussions for the departments of Agronomy, Horticulture and Plant Pathology at the University of Wisconsin-Madison
- Supervision of weekly discussions about scientific articles