Marcelo Mollinari

Quantitative genetics · Plant breeding · Scientific software

Marcelo Mollinari, PhD

Consultant, Quantitative Genetics & Bioinformatics at AbacusBio

I develop statistical methods for breeding data and build them into tools that breeders and researchers can use, from genetic mapping and QTL analysis to genomic prediction in diploid and polyploid crops.

Previously Research Assistant Professor, North Carolina State University.

Selected work Get in touch
Portrait of Marcelo Mollinari

  1. Statistical methods

    Hidden Markov, random-effect, and mixed models for mapping, QTL, and prediction.

  2. Working with breeders

    Built around real families in outcrossing diploid and polyploid crops.

  3. Usable tools

    Open R and C++ packages that support reproducible analyses.

Selected work

Public research and software projects, from statistical models to the tools that put them to use.

Genetic mapping · Polyploids

Multi-family linkage maps in polyploids

Breeding programs often work with several related crosses. MAPpoly2 combines information across interconnected diploid and autopolyploid families to build integrated linkage maps. It extends the single-family models in MAPpoly.

MAPpoly2 on GitHub MAPpoly method (G3, 2019)

Creator of MAPpoly and MAPpoly2.

Schematic: three populations each have markers mapped separately at partly different positions; an arrow leads to one consensus map containing the markers from all three.
Maps from separate populations combined into one consensus map (simplified).
How MAPpoly2 builds the map

A hidden Markov model builds a map for each parent. These are combined into a joint map, which is then recomputed to place the remaining markers. Ploidy levels 2, 4, and 6 can be combined in any configuration, and the computationally intensive steps are written in C++.

Original figure: markers mapped separately in three populations are linked through a hidden Markov model into a single consensus map, with the full caption below the diagram.
Original figure with its full caption.
Example output: a potato consensus map
MAPpoly2 output for a multiparental potato population: a population summary table, per-family and consensus maps for 12 chromosomes, and the final consensus linkage map.

Recent methods development

Maternal maps from open-pollinated families

In open-pollinated families the mother is known and genotyped, but the fathers are not observed. HalfSibMap is an R package that builds maternal linkage maps and reconstructs each offspring's maternal inheritance in diploid open-pollinated families, using the offspring genotypes and the genotyped mother.

Role
Author and maintainer, with co-authors Kim McKeever, Molly Irvin, and Fikret Isik.
Status
Available from GitHub; a methodological manuscript is in preparation.
Method details

A source-aware hidden Markov model pairs the transmitted maternal homolog with a paternal source state. Persistent paternal haplotype-sharing tracts, as arise when some pollen parents are related to the mother, are then modelled explicitly rather than absorbed into the maternal recombination map. These states describe shared tracts statistically; they do not identify fathers. Intervals without linkage are reported as gaps rather than as large distances.

HalfSibMap output showing a recombination heatmap, reconstructed maternal inheritance, and paternal-process summaries along a linkage group.
Example HalfSibMap output showing linkage mapping, reconstructed maternal inheritance, and paternal-process diagnostics.

View full-size figure (PDF)

Genomics · Sweetpotato

Inheritance and genome architecture of hexaploid sweetpotato

Hexaploid sweetpotato has six sets of chromosomes, making inheritance analysis more complex. Ultra-dense multilocus mapping in a full-sib population helped clarify it. Related work includes a chapter on genetic maps in sweetpotato and, as a co-author, a phased chromosome-level genome assembly.

Inheritance (G3, 2020) Genome assembly (Nature Plants, 2025) Genetic maps in sweetpotato (2024)

Visualization

Exploring polyploid genetic analyses in one place

Results from linkage, QTL, and genome analyses often sit in separate tools. VIEWpoly is an interactive application that brings them together, so users can explore candidate genes in a genome browser. It reads output from MAPpoly, polymapR, diaQTL, QTLpoly, and polyqtlR.

VIEWpoly paper (JOSS, 2022) Video tutorial

Conceptual lead of VIEWpoly.

Software

Open-source R packages I develop, maintain, and contribute to.

  • MAPpoly & MAPpoly2

    Creator

    MAPpoly

    Linkage analysis and haplotype phasing within diploid and autopolyploid families.

    MAPpoly on CRAN MAPpoly on GitHub

    MAPpoly2

    Integrated linkage maps across interconnected diploid and autopolyploid families.

    MAPpoly2 on GitHub

  • OneMap

    Co-creator

    Linkage maps in experimental crosses: full-sib, RIL, F2, and backcross.

    Current release on CRAN Development version

  • VIEWpoly

    Conceptual lead

    Interactive exploration of linkage, QTL, and genomic results in polyploids.

    CRAN GitHub

  • HalfSibMap

    Author & maintainer

    Maternal linkage maps in diploid open-pollinated families with unobserved fathers.

    GitHub Manuscript in preparation

  • QTLpoly

    Contributor

    Random-effect multiple QTL mapping in autopolyploid full-sib families.

    GitHub Method (Genetics, 2020)

Selected publications

  1. Crowley J, Bjelland D, Hely F, Valente T, Mollinari M. (2026). Agricultural genomics data: assessing the opportunity for data standards. Canadian Standards Association, Toronto, ON.
  2. Wu S, Sun H, Zhao X, Hamilton JP, Mollinari M, Gesteira GDS, Kitavi M, Yan M, Wang H, Yang J, Yencho GC, Buell CR, Fei Z. (2025). Phased chromosome-level assembly provides insight into the genome architecture of hexaploid sweetpotato. Nature Plants, 11, 1951–1959. doi:10.1038/s41477-025-02079-6
  3. Gesteira GS, Pereira GS, Zeng ZB, Mollinari M. (2024). Genetic maps in sweetpotato. In The Sweetpotato Genome (pp. 45–68). Springer. doi:10.1007/978-3-031-65003-1_5
  4. Taniguti CH, Gesteira GS, Lau J, Pereira GS, Zeng ZB, Byrne D, Riera-Lizarazu O, Mollinari M. (2022). VIEWpoly: a visualization tool to integrate and explore results of polyploid genetic analysis. Journal of Open Source Software, 7(74), 4242. doi:10.21105/joss.04242
  5. Pereira GS, Gemenet DC, Mollinari M, Olukolu BA, Wood JC, Diaz F, Mosquera V, Gruneberg WJ, Khan A, Buell CR, Yencho GC, Zeng ZB. (2020). Multiple QTL mapping in autopolyploids: a random-effect model approach with application in a hexaploid sweetpotato full-sib population. Genetics, 215(3), 579–595. doi:10.1534/genetics.120.303080
  6. Mollinari M, Olukolu BA, Pereira GS, Khan A, Gemenet D, Yencho GC, Zeng ZB. (2020). Unraveling the hexaploid sweetpotato inheritance using ultra-dense multilocus mapping. G3: Genes, Genomes, Genetics, 10(1), 281–292. doi:10.1534/g3.119.400620
  7. Mollinari M, Garcia AAF. (2019). Linkage analysis and haplotype phasing in experimental autopolyploid populations with high ploidy level using hidden Markov models. G3: Genes, Genomes, Genetics, 9(10), 3297–3314. doi:10.1534/g3.119.400378

Full publication list on Google Scholar

About

I am a quantitative geneticist trained in plant genetics and breeding. My doctoral work at the University of São Paulo developed models for building genetic maps in autopolyploids, with applications in sugarcane. Since then I have focused on algorithms for genomic analysis in complex family structures, especially outcrossing diploid and polyploid crops.

At North Carolina State University I worked with breeders and collaborators across several species, combining statistical models with R and C++ software.

At AbacusBio, I work on genetic evaluation, genotype data quality, and statistical analysis for breeding programs, developing reproducible workflows and communicating results to project teams.

Areas of focus

  • Statistical & quantitative genetics
  • Genomic prediction & mixed models
  • Linkage analysis & haplotype phasing
  • QTL mapping
  • Polyploid genetics
  • R & C++ package development

Experience

  1. Consultant, Quantitative Genetics & Bioinformatics

    AbacusBio · October 2025–present

  2. Research Assistant Professor

    Bioinformatics Research Center & Department of Horticultural Science, North Carolina State University · 2021–October 2025

    Earlier at NC State: Senior Research Assistant (2018–2021) and Postdoctoral Fellow (2016–2017)

  3. Postdoctoral Fellow

    Department of Genetics, ESALQ, University of São Paulo · 2012–2015

    Visiting Scholar, Department of Statistics, Purdue University (2014–2015)

  4. Research Intern, eScience Group

    Microsoft Research · 2010

Education

  • PhD, Plant Genetics and Breeding, ESALQ, University of São Paulo (2012)
  • MSc, Plant Genetics and Breeding, ESALQ, University of São Paulo (2008)
  • BSc, Agronomic Engineering, ESALQ, University of São Paulo (2005)

Talks, workshops & resources

Slides from talks, workshops, and lectures (PDF).

Scripts and guides

Contact

I welcome conversations about quantitative genetics, plant breeding, and breeding analytics, whether you work in a breeding program or company, are a researcher interested in collaborating, or use one of my software packages.

Email
marcelo@marcelomollinari.com
LinkedIn
linkedin.com/in/mmollina
GitHub
github.com/mmollina
Google Scholar
Scholar profile

For software questions or bug reports, the issue tracker in each package's repository is usually the quickest route.