Malachy Campbell

Hi, my name is

Malachy Campbell

Senior Scientist, Computational Biology & Quantitative Genetics

Quantitative geneticist and computational scientist developing statistical methods for genomic prediction, selection, and crop improvement. Author of 24 peer-reviewed publications and inventor on three patents.

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about

About me

I am a quantitative geneticist and computational scientist with more than ten years of experience developing and applying statistical methods to improve genetic evaluation, selection, and product-development decisions in plant breeding. My work sits at the intersection of genomics, statistics, and computing — building methods and workflows that translate biological data into better breeding decisions.

My expertise spans genomic prediction, mixed models, breeding simulations, multi-trait and longitudinal analysis, high-throughput phenotyping, and multi-omics integration. I have a proven record of leading cross-functional research programs, translating novel methods into scalable computational processes, and collaborating across quantitative genetics, molecular biology, data science, and genome-editing teams.

Cambridge, MA campbell.malachy@gmail.com

Skills & tools

Genomic Prediction Mixed Models & Bayesian Methods Quantitative Genetics R / Python / Bash Multi-omics Integration High-throughput Phenotyping AWS & HPC GWAS

work

Projects

A selection of things I have built. Source code and live demos are linked where available.

Trait-specific genomic relationship matrices improve seed quality prediction in oat

Led development of a multikernel BLUP approach using trait-specific genomic relationship matrices (TGRMs) constructed from intermediate molecular phenotypes. Applied to predict total lipid content in 210 independent oat lines, the method significantly outperformed conventional genomic BLUP and multi-trait approaches — showing that endophenotype information can meaningfully improve prediction of complex agronomic traits.

RGenomic PredictionMixed ModelsASReml

Seed metabolome latent factors improve genomic prediction for oat quality traits

Characterized natural variation in the mature oat seed metabolome across 378 diverse accessions using untargeted metabolomics and latent factor analysis. Identified 100 latent factors — 21% enriched for lipid metabolism — with 23% showing significant genetic associations. Incorporating these biologically-informed factors into a multi-kernel prediction model improved accuracy for 9 of 13 seed quality traits over conventional genomic prediction.

RLatent Factor AnalysisGenomic PredictionGWASMetabolomics

Random regression models improve genetic discovery for longitudinal plant traits

Developed a pipeline leveraging random regression breeding values derived from image-based phenotyping to improve GWAS resolution for dynamic shoot growth traits in rice. The approach identified both persistent loci affecting growth throughout development and transient, time-specific loci — providing considerably better power than conventional single time-point analyses.

RRandom RegressionGWASHigh-throughput PhenotypingASReml

research

Publications

Journal Articles (24)

  1. 2023

    Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa)

    Brzozowski, L. J., Campbell, M. T., Hu, H., Yao, L., Caffe, M., Gutiérrez, L., Smith, K. P., et al.

    The Plant Genome, 16(4), Article e20370

    Scholar
  2. 2022

    Generalizable approaches for genomic prediction of metabolites in plants

    Brzozowski, L. J., Campbell, M. T., Hu, H., Caffe, M., Gutiérrez, L., Smith, K. P., et al.

    The Plant Genome, 15(2), Article e20205

    Scholar

  3. Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.)

    Brzozowski, L. J., Hu, H., Campbell, M. T., Broeckling, C. D., Caffe, M., Gutierrez, L., et al.

    G3: Genes, Genomes, Genetics, 12(3), Article jkab419

    Scholar
  4. 2021

    Improving genomic prediction for seed quality traits in oat (Avena sativa L.) using trait-specific relationship matrices

    Campbell, M. T., Hu, H., Yeats, T. H., Brzozowski, L. J., Caffe-Treml, M., Gutiérrez, L., et al.

    Frontiers in Genetics, 12, Article 643733

    Scholar

  5. Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

    Campbell, M. T., Hu, H., Yeats, T. H., Caffe-Treml, M., Gutiérrez, L., Smith, K. P., et al.

    Genetics, 217(3), Article iyaa043

    Scholar

  6. Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

    Hu, H., Campbell, M. T., Yeats, T. H., Zheng, X., Runcie, D. E., et al.

    Theoretical and Applied Genetics, 134(12), 4043–4054

    Scholar

  7. Genome-wide discovery of natural variation in pre-mRNA splicing and prioritising causal alternative splicing to salt stress response in rice

    Yu, H., Du, Q., Campbell, M., Yu, B., Walia, H., & Zhang, C.

    New Phytologist, 230(3), 1273–1287

    Scholar
  8. 2020

    Characterization of the transcriptional divergence between the subspecies of cultivated rice (Oryza sativa)

    Campbell, M. T., Du, Q., Liu, K., Sharma, S., Zhang, C., & Walia, H.

    BMC Genomics, 21(1), Article 394

    Scholar

  9. Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice

    Campbell, M. T., Grondin, A., Walia, H., & Morota, G.

    Journal of Experimental Botany, 71(18), 5669–5679

    Scholar

  10. Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping

    Baba, T., Momen, M., Campbell, M. T., Walia, H., & Morota, G.

    PLOS ONE, 15(2), Article e0228118

    Scholar

  11. Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions

    Hussain, W., Campbell, M. T., Jarquin, D., Walia, H., & Morota, G.

    The Plant Genome, 13(1), Article e20011

    Scholar
  12. 2019

    Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits

    Campbell, M., Momen, M., Walia, H., & Morota, G.

    The Plant Genome, 12(2), Article 180075

    Scholar

  13. Network-based feature selection reveals substructures of gene modules responding to salt stress in rice

    Du, Q., Campbell, M., Yu, H., Liu, K., Walia, H., Zhang, Q., & Zhang, C.

    Plant Direct, 3(8), Article e00154

    Scholar

  14. Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic Legendre polynomials and B-splines

    Momen, M., Campbell, M. T., Walia, H., & Morota, G.

    G3: Genes, Genomes, Genetics, 9(10), 3369–3380

    Scholar

  15. Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies

    Momen, M., Campbell, M. T., Walia, H., & Morota, G.

    Plant Methods, 15(1), Article 107

    Scholar

  16. Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes

    Yu, H., Campbell, M. T., Zhang, Q., Walia, H., & Morota, G.

    G3: Genes, Genomes, Genetics, 9(6), 1975–1986

    Scholar
  17. 2018

    Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping

    Campbell, M. T., Walia, H., & Morota, G.

    Plant Direct, 2, 1–11

    Scholar

  18. ShinyAIM: Shiny-based application of interactive Manhattan plots for longitudinal genome-wide association studies

    Hussain, W., Campbell, M., Walia, H., & Morota, G.

    Plant Direct, 2(10), Article e00091

    Scholar
  19. 2017

    A comprehensive image-based phenomic analysis reveals the complex genetic architecture of shoot growth dynamics in rice (Oryza sativa)

    Campbell, M. T., Du, Q., Liu, K., Brien, C. J., Berger, B., Zhang, C., & Walia, H.

    The Plant Genome, 10(2)

    Scholar

  20. Allelic variants of OsHKT1;1 underlie the divergence between indica and japonica subspecies of rice (Oryza sativa) for root sodium content

    Campbell, M. T., Bandillo, N., Al Shiblawi, F. R. A., Sharma, S., Liu, K., Du, Q., et al.

    PLoS Genetics, 13(6), Article e1006823

    Scholar
  21. 2016

    Image Harvest: an open-source platform for high-throughput plant image processing and analysis

    Knecht, A. C., Campbell, M. T., Caprez, A., Swanson, D. R., & Walia, H.

    Journal of Experimental Botany, 67(11), 3587–3599

    Scholar
  22. 2015

    Genetic and molecular characterization of submergence response identifies Subtol6 as a major submergence tolerance locus in maize

    Campbell, M. T., Proctor, C. A., Dou, Y., Schmitz, A. J., Phansak, P., Kruger, G. R., et al.

    PLOS ONE, 10(3), Article e0120385

    Scholar

  23. Integrating image-based phenomics and association analysis to dissect the genetic architecture of temporal salinity responses in rice

    Campbell, M. T., Knecht, A. C., Berger, B., Brien, C. J., Wang, D., & Walia, H.

    Plant Physiology, 168(4), 1476–1489

    Scholar
  24. 2013

    Introgression of novel traits from a wild wheat relative improves drought adaptation in wheat

    Placido, D. F., Campbell, M. T., Folsom, J. J., Cui, X., Kruger, G. R., Baenziger, P. S., et al.

    Plant Physiology, 161(4), 1806–1819

    Scholar

Book Chapters

  • Gene co-expression network analysis and linking modules to phenotyping response in plants

    Du, Q., Campbell, M. T., Yu, H., Liu, K., Walia, H., Zhang, Q., & Zhang, C.

    In High-Throughput Plant Phenotyping: Methods and Protocols (pp. 261–268). Springer US (2022)

    Scholar

  • Statistical methods for the quantitative genetic analysis of high-throughput phenotyping data

    Morota, G., Jarquin, D., Campbell, M. T., & Iwata, H.

    In High-Throughput Plant Phenotyping: Methods and Protocols (pp. 269–296). Springer US (2022)

    Scholar

Patent Applications

  • Uorf editing to improve plant traits

    Altman, R. E., Campbell, M. T., Kremling, K. A. G., & Li, R.

    US Patent App. 19/474,124 (2026)

  • Identity by function based BLUP method for genomic improvement in animals

    Campbell, M. T., Kremling, K., Lippman, Z., & Li, R.

    US Patent App. 18/865,414 (2025)

  • Identity by function based BLUP method for genomic improvement

    Campbell, M. T., Kremling, K., Lippman, Z., & Li, R.

    US Patent App. 18/833,594 (2025)

speaking

Talks & Presentations

  1. Invited Seminar

    Introducing the time axis in genomic prediction and association studies

    Faculty of Agriculture, Food and Environment, Hebrew University

    February 2019 Rehovot, Israel
  2. Invited Conference Talk

    Utilizing longitudinal phenotypes derived from high-throughput phenotyping to study and improve adaptation to water limitation in rice

    Gordon Research Conference — Salt and Water Stress in Plants

    June 2018 Waterville Valley, NH
  3. Department Workshop

    Utility of longitudinal traits derived from high-throughput phenotyping platforms for genomic prediction and GWAS

    Trait Prediction in Agriculture, Department of Statistics, University of Nebraska–Lincoln

    April 2018 Lincoln, NE
  4. Invited Conference Talk

    Genetic and computational approaches for studying plant development and abiotic stress responses using image-based phenotyping

    American Geophysical Union Fall Meeting

    December 2017 New Orleans, LA
  5. Invited Conference Talk

    Allelic variants of OsHKT1;1 underlie the divergence between indica and japonica subspecies of rice for root sodium content

    Nebraska Plant Breeding Symposium

    March 2017 Lincoln, NE
  6. Conference Talk

    Allelic variants of OsHKT1;1 underlie the divergence between indica and japonica subspecies of rice for root sodium content

    Plant Science Retreat

    October 2016 Nebraska City, NE
  7. Department Seminar

    Natural variation in a rice sodium transporter, OsHKT1.1, provides insight into origins of salinity tolerance in rice

    University of Nebraska Animal Breeding and Genetics Seminars

    April 2016 Lincoln, NE

teaching

Workshops & Courses

2020

Advanced Statistics and Experimental Design

Instructor

NSF Digital Plant Science Initiative

An overview of the statistical analyses and experimental designs commonly used in plant science, designed to give first- and second-year graduate students from a variety of backgrounds a practical foundation.

2019

GWAS Workshop @ VT

Instructor (with Gota Morota and Haipeng Yu)

Virginia Tech

A three-day workshop covering genotyping quality control, single-marker regression, whole-genome regression, and advanced topics for genome-wide association studies.

2018

Statistical Methods for Omics-Assisted Breeding

Instructor (with H. Iwata, G. Morota, J. Tressou, D. Jarquin, and E. Tanaka)

A three-day workshop introducing quantitative genetics and teaching participants to leverage omics datasets for GWAS and genomic prediction.

background

Experience & Achievements

  1. Feb. 2021 — present · Cambridge, MA

    Senior Scientist, Computational Biology & Quantitative Genetics

    Inari Agriculture

    • Lead quantitative genetics and computational biology programs supporting product design, genome editing, and crop improvement.
    • Develop genomic prediction, mixed-model, and biologically informed methods for genetic evaluation, candidate prioritization, and selection.
    • Integrate genomic, phenotypic, transcriptomic, and environmental data to improve candidate gene selection and prediction of complex agronomic traits.
    • Lead cross-functional collaborations spanning quantitative genetics, breeding, computational biology, molecular biology, data science, and genome editing.
    • Translate statistical methods into scalable, reproducible workflows on AWS and high-performance computing infrastructure.
    • Develop simulation and optimization frameworks for experimental design, resource allocation, and research prioritization.
    • Mentor scientists and contribute to intellectual property as an inventor on three patent applications.

  2. Oct. 2019 — Feb. 2021 · Ithaca, NY

    Postdoctoral Research Associate

    Cornell University — Jannink/Gore Labs, School of Integrative Plant Science

  3. Jan. 2019 — Oct. 2019 · Blacksburg, VA

    Postdoctoral Research Associate

    Virginia Tech — Morota Lab, Department of Animal and Poultry Sciences

  4. Aug. 2017 — Jan. 2019 · Lincoln, NE

    Postdoctoral Research Associate

    University of Nebraska Lincoln — Morota Lab, Department of Animal Science

  5. Sept. 2011 — Aug. 2017 · Lincoln, NE

    Graduate Research Assistant

    University of Nebraska Lincoln — Walia Lab, Department of Agronomy and Horticulture

  6. 2011 — 2017 · Lincoln, NE

    Ph.D., Plant Breeding & Genetics

    University of Nebraska Lincoln

    • Thesis: Dissecting the Genetic Basis of Salt Tolerance in Rice (Oryza sativa)
    • Advisor: Dr. Harkamal Walia

  7. 2008 — 2010 · New Brunswick, NJ

    B.S., Plant Biology

    Rutgers University

    • Graduated summa cum laude (GPA: 3.87)
    • Advisors: Drs. Richard Merritt & Gojko Jelenkovic

contact

Get in touch

Whether it is an opportunity, a collaboration, or a question about my work — my inbox is open. You can also reach me directly at campbell.malachy@gmail.com.