About

Jean-Luc Jannink is a faculty member associated with the Cornell Center for Comparative and Population Genomics. The specific research focus, background, and key contributions of Professor Jannink are not detailed in the provided page text.

Research topics

• Computer Science
• Computational biology
• World Wide Web
• Biology
• Genetics
• Database

Selected publications

• GrainGenes: genetics, genomes, and pangenomes

  Genetics · 2025-12-16 · 3 citations

  article

  As one of the flagship databases of the US Department of Agriculture, GrainGenes is positioned at the critical juncture of agricultural data crossroads. GrainGenes (https://graingenes.org; https://wheat.pw.usda.gov) is a centralized location for curated data and web-based tools for wheat, barley, rye, and oat in the service of a global user base. Since 1992, GrainGenes has been serving plant researchers in their quest to improve traits, including biotic and abiotic resistance, as well as high nutrition content. Starting with genetic markers and maps, GrainGenes has evolved to acquire genomic sequences, assemblies, and annotations, leading to an ever-increasing number of pangenomes. Over the years, new web-based tools and capabilities were added to the website to increase the access and utility of peer-reviewed datasets for researchers, plant geneticists and breeders at various stages of their careers, from high school students to emeritus professors. Here we provide a comprehensive overview of the curated content and customized tools available in GrainGenes, whose resources are designed to benefit researchers, growers, and farmers in their efforts to develop more nutritious food for the growing human population and high-quality animal feed.

  PublisherDOI

• The Impact of Sparse Testing design on Prediction in Wheat: An Empirical Study Using T3/Wheat data

  2025-09-09

  preprintOpen accessSenior author

  Sparse testing is a multi-environment trial design in which different subsets of all accessions are tested in each environment. Particularly at early stages of breeding programs, when many accessions need to be evaluated, sparse testing can be an efficient way to allocate resources to maximize genetic gain under genotype x environment interactions. Appropriate sparse testing designs can increase the prediction accuracy of unobserved accession x environment combinations. This study was done to evaluate the prediction accuracy of four sparse testing designs: pure sparse (Mpure), overlap sparse (Mover), sparse with common accessions (Mcomm), and overlap sparse with common accessions (Movco) using five groups of trials sourced from the Wheat instance of The Triticeae Toolbox database. The MegaLMM package was used to predict the performance of untested accessions within a known environment using the factor analytic model. Mover, Mcomm, and Movco gave better prediction accuracy than Mpure. Across all tested scenarios, the use of 90 accessions per trial as the training set increased the prediction accuracy as compared to 60 accessions per trial. Given these results, we recommend the use of sparse testing with some level of overlapping accessions across trials to evaluate breeding lines at an early stage of the breeding program and to impute missing accession x environment combinations.

  PublisherOA PDFDOI

• Genomic strategies to facilitate breeding for increased β-Glucan content in oat (Avena sativa L.)

  BMC Genomics · 2025-01-14 · 9 citations

  articleOpen access

  BACKGROUND: Hexaploid oat (Avena sativa L.) is a commercially important cereal crop due to its soluble dietary fiber β-glucan, a hemicellulose known to prevent cardio-vascular diseases. To maximize health benefits associated with the consumption of oat-based food products, breeding efforts have aimed at increasing the β-glucan content in oat groats. However, progress has been limited. To accelerate oat breeding efforts, we leveraged existing breeding datasets (1,230 breeding lines from South Dakota State University oat breeding program grown in multiple environments between 2015 and 2022) to conduct a genome-wide association study (GWAS) to increase our understanding of the genetic control of beta-glucan content in oats and to compare strategies to implement genomic selection (GS) to increase genetic gain for β-glucan content in oat. RESULTS: Large variation for β-glucan content was observed with values ranging between 3.02 and 7.24%. An independent GWAS was performed for each breeding panel in each environment and identified 22 loci distributed over fourteen oat chromosomes significantly associated with β-glucan content. Comparison based on physical position showed that 12 out of 22 loci coincided with previously identified β-glucan QTLs, and three loci are in the vicinity of cellulose synthesis genes, Cellulose synthase-like (Csl). To perform a GWAS analysis across all breeding datasets, the β-glucan content of each breeding line was predicted for each of the 26 environments. The overall GWAS identified 73 loci, of which 15 coincided with loci identified for individual environments and 37 coincided with previously reported β-glucan QTLs not identified when performing the GWAS in single years. In addition, 21 novel loci were identified that were not reported in the previous studies. The proposed approach increased our ability to detect significantly associated markers. The comparison of multiple GS scenarios indicated that using a specific set of markers as a fixed effect in GS models did not increase the prediction accuracy. However, the use of multi-environment data in the training population resulted in an increase in prediction accuracy (0.61-0.72) as compared to single-year (0.28-0.48) data. The use of USDA-SoyWheOatBar-3 K genotyping array data resulted in a similar level of prediction accuracy as did genotyping-by-sequencing data. CONCLUSION: This study identified and confirmed the location of multiple loci associated with β-glucan content. The proposed genomic strategies significantly increase both our ability to detect significant markers in GWAS and the accuracy of genomic predictions. The findings of this study can be useful to accelerate the genetic improvement of β-glucan content and other traits.

  PublisherOA PDFDOI

• Evaluation of six sugar kelp crosses selected for high yield at three Northeastern US farms

  Aquaculture · 2025-01-22 · 5 citations

  articleOpen access

  Sugar kelp ( Saccharina latissima ), a brown macroalga, is a vital crop in the burgeoning seaweed aquaculture industry. As seaweed farms expand, the traditional practice of collecting wild sporophytes will be unsustainable. Developing new kelp cultivars that suit multiple farm conditions is necessary. To address this challenge, our breeding project selected six sugar kelp crosses to be grown in New Castle, New Hampshire; Duxbury, Massachusetts; and Moriches, New York, in the 2022–2023 growing season . We measured four plot level traits (wet weight, dry weight, sporophyte density, and percent dry weight), five single blade level traits (blade length, blade maximum width, blade thickness, stipe length, and stipe diameter), and three tissue composition traits (ash content, carbon content, and nitrogen content). All plot level traits except for the percent dry weight were affected by both crosses/genotypes (G) and farm site/environments (E). All blade level traits were significantly affected by crosses. Farm effects were only detected on blade maximum width and stipe diameter. For the tissue composition traits, ash content was not affected by either cross or farm site. Carbon content was only significantly affected by the farm site, while the nitrogen content was affected by farm site, cross and their interaction effects. These findings suggest that multi-farm testing for sugar kelp breeding programs is important for determining the best crosses for various growers. Understanding G by E effects can advance sugar kelp breeding for targeted traits and farms that will facilitate the adoption of cultivars toward sustainable economic growth on diverse kelp farms. • Six sugar kelp crosses selected for high yield were evaluated across three different farms during the same growing season. • Yield and sporophyte density were significantly influenced by farm site. • Blade width and stipe diameter were also significantly affected by farm site. • Tissue carbon and nitrogen content showed significant variation depending on farm site. • Future sugar kelp breeding programs can leverage genotype-by-environment interactions to enhance specific traits.

  PublisherDOI

• DeltaBreed: A BrAPI-centric breeding data information system

  PLoS ONE · 2025-12-12 · 2 citations

  articleOpen accessCorresponding

  DeltaBreed is a unified breeding data management system designed by Breeding Insight (BI, Cornell University) to serve the wide diversity of USDA-ARS specialty crop and livestock breeding programs. DeltaBreed has a RESTful microservice architecture that utilizes the BrAPI v2.1 Java Test Server as its primary database. The system is interoperable with many BrAPI-compliant applications (BrApps), including Field Book v6.1.0, and is continually aligned with the most recent BrAPI specifications (BrAPI v2.1). Here we describe the features of DeltaBreed v1.0, a minimum viable product, and how we aligned data capture and validation with community standards. We highlight the modules for management of germplasm, observation variables, experiments and observations, genotypic sample submission, and a prototype genomic database that supports polyploid and multiallelic genomic data, as well as SNP data. Several test cases are illustrated to demonstrate the successes and challenges of interoperability with other open-source BrAPI-enabled software packages. We also discuss expansion and enhancement plans for future DeltaBreed versions, as well as outline possible solutions to known limitations. To our knowledge, DeltaBreed is the first species-agnostic, fully BrAPI-compliant breeding data management system built for transactional use.

  PublisherDOI

• Author Correction: A pangenome and pantranscriptome of hexaploid oat

  Nature · 2025-11-20

  articleOpen access

  In the version of the article initially published, the wrong caption was included for Extended Data Fig. 10. The correct caption is now available in the HTML and PDF versions of the article, and the original, incorrect caption is available for comparison in the Supplementary information accompanying this notice.

  PublisherDOI

• A Unified Crop Ontology for Standardizing Phenotypic Data Collection in Bottle Gourd [Lagenaria siceraria (Molina) Standl.]

  HortScience · 2025-08-29

  articleOpen accessSenior author

  Underutilized crops have considerable cultural, culinary, and historical value but lack widespread cultivation or extensive research. With these gaps in commercialization efforts, maximizing available information is crucial for breeding adapted and improved cultivars, conserving genetic resources, and, ultimately, promoting broader adoption. Crop ontologies provide a framework for describing a crop’s relevant attributes by standardizing data collection protocols across various research endeavors. These ontologies enhance the value of the broader pool of genetic resources and facilitate greater interoperability among collaborative conservation and improvement efforts. To maximize impact, a crop ontology should prioritize the inclusion of traits from markets, cultures, and regions with connections to the given crop. The bottle gourd [ Lagenaria siceraria (Molina) Standl.] is an underutilized, under-researched crop originating in Africa, but has regional significance across Europe, Asia, and the Americas. We developed a crop ontology for bottle gourds via literature reviews using 35 previous characterization studies from 10 countries, related cucurbit ontologies, and multisite and multiyear collaborative phenotyping efforts with Kasetsart University (Thailand), the US Department of Agriculture National Plant Germplasm System (United States), Cornell University (United States), and the University of Erciyes Melikgazi Kayseri-Türkiye (Turkey). The crop ontology emphasizes traits important for localized use and includes 300 traits that describe vegetative, floral, fruit, and seed phenotypes critical to horticultural variation and culturally diverse uses. Furthermore, our bottle gourd ontology provides a foundation for future conservation and improvement efforts, as well as a framework for creating ontologies that could be applied to other underutilized crops.

  PublisherDOI

• Field trial analyses of wheat and cassava benefit from spatial correction

  2025-06-04

  preprintOpen accessSenior author

  Spatial variation is a major source of error in agricultural field experiments affecting genotype performance prediction. Implementing statistical models that account for spatial effects can improve the prediction of genotype performance. This study evaluated the impact of the B-spline spatial correction method on the estimation of genetic parameters and AIC values in two distinct crops – wheat and cassava – using four models: Block, Block + Spatial, Block + Marker, and Block + Marker + Spatial. Analyses were performed on data from 136 and 68 trials obtained from the T3/WheatCAP and Cassavabase databases, respectively. The results demonstrated that correcting for spatial variation, regardless of marker information, increased the heritability estimate of grain yield, test weight, plant height, powdery mildew, stripe rust, and bacterial streak disease in wheat. Similar increases were observed in cassava for dry matter content, dry yield, and plant height. However, no increase was observed for cassava mosaic disease or bacterial blight. Models incorporating spatial correction in both crops consistently provided the best fit based on AIC values across all traits in wheat and cassava. These results were consistent whether or not marker effects were fitted in the models. This showed the importance of spatial correction in field experiment analysis.

  PublisherOA PDFDOI

• Computational design for more engaged, impactful, and dynamic agricultural research

  Crop Science · 2025-03-01 · 1 citations

  article

  Abstract Computational design in agriculture is the use of data‐driven systems and tools to propose and evaluate alternative configurations of agricultural systems. It is unique from digital agriculture in that it integrates computational and crop science approaches to formulate problems rather than mitigating problems by applying digital technologies. In this special issue, we highlight how computational design could be used to adapt agricultural systems to better meet societal goals more rapidly and at lower cost. Many disciplines within crop sciences are represented, from breeding to cropping systems agronomy. Using a symposium at a major scientific conference as a case study, we also demonstrate how this framing of computational design can facilitate transdisciplinary research. Critically, all participants highlighted the potential of computational design to facilitate stakeholder engagement through eliciting, formalizing, and evaluating their values and experiences. This is especially important within the grand challenge contexts of changing climates and market demands, where intuition developed in the past may break down. By leveraging the power of computational design, we can make informed decisions to create agricultural systems that maximize productivity while minimizing environmental impact under current and future environments.

  PublisherDOI

• A pangenome and pantranscriptome of hexaploid oat

  Nature · 2025-10-29 · 7 citations

  articleOpen access

  Abstract Oat grain is a traditional human food that is rich in dietary fibre and contributes to improved human health 1,2 . Interest in the crop has surged in recent years owing to its use as the basis for plant-based milk analogues 3 . Oat is an allohexaploid with a large, repeat-rich genome that was shaped by subgenome exchanges over evolutionary timescales 4 . In contrast to many other cereal species, genomic research in oat is still at an early stage, and surveys of structural genome diversity and gene expression variability are scarce. Here we present annotated chromosome-scale sequence assemblies of 33 wild and domesticated oat lines, along with an atlas of gene expression across 6 tissues of different developmental stages in 23 of these lines. We construct an atlas of gene-expression diversity across subgenomes, accessions and tissues. Gene loss in the hexaploid is accompanied by compensatory upregulation of the remaining homeologues, but this process is constrained by subgenome divergence. Chromosomal rearrangements have substantially affected recent oat breeding. A large pericentric inversion associated with early flowering explains distorted segregation on chromosome 7D and a homeologous sequence exchange between chromosomes 2A and 2C in a semi-dwarf mutant has risen to prominence in Australian elite varieties. The oat pangenome will promote the adoption of genomic approaches to understanding the evolution and adaptation of domesticated oats and will accelerate their improvement.

  PublisherOA PDFDOI

Frequent coauthors

• Mark E. Sorrells

  Cornell University

  141 shared

• Chiedozie Egesi

  National Root Crops Research Institute

  111 shared

• Ismail Rabbi

  International Institute of Tropical Agriculture

  78 shared

• Marnin Wolfe

  Auburn University

  77 shared

• Peter Kulakow

  International Institute of Tropical Agriculture

  71 shared

• Jesse Poland

  King Abdullah University of Science and Technology

  61 shared

• Edward S. Buckler

  Cornell University

  54 shared

• Deniz Akdemir

  University College Dublin

  45 shared

Labs

• Cornell Center for Comparative and Population GenomicsPI

Education

• Ph.D., Agronomy and Plant Genetics

  University of Minnesota System

  1999