Now that the 33rd Plant and Animal Genome Conference (PAG 33) is behind us, it is a good moment to reflect on ten key trends that emerged from the meeting. Together, these themes reinforced PAG 33’s role as a global hub for researchers connecting genomics research with practical, translatable innovation.
1) Advancements in genome sequencing technologies, assembly, and pangenome resources.
Long read sequencing, improved error correction, and graph-based references now enable chromosome scale assemblies and comprehensive pangenomes. If we take a pause to reflect, it’s amazing to hear researchers now speak of nodes, edges, and paths when revealing structural variation of genetic information. And notably, that these could underlie traits and diversity in populations that single references may miss.
In parallel, groups are benchmarking available platforms and analysis/graphing tools for haplotype phasing and to close sequence gaps. By building full sequences and pangenomes to capture trait-linked variants and enhance genome-wide association studies (GWAS), researchers are polishing genomics-assisted breeding programs.
2) Quantitative trait discovery and genetic mapping that maximize desirable traits.
Linking measurable phenotypes, including complex traits, to genotypic data remains fundamental. Statistical methods such as QTL analysis and GWAS help advance collaborative work in agriculture, evolution, and ecology. For agrigenomics, the exchange of knowledge helps those in their industry stay informed about selecting traits that would improve (or help maintain) yield, quality, and resilience to environmental or urbanization pressures.
These trait studies, high-throughput phenotyping experiments, and AI-enhanced bioinformatics workflows will continue to be leveraged for diverse applications across crops, livestock, insects, aquaculture, companion animal health, and more.
3) Modernizing genomics-assisted breeding pipelines.
Modern breeding integrates dense genotyping, high-quality phenotyping, environmental profiling (envirotyping), and predictive modeling to select earlier, with greater confidence. Genomic prediction has long been used to quicken decisions by estimating success and value from markers or at least from marker-selected target regions, whatever technology permitted at the time.
Speed breeding for crops now offers compressed generation times to achieve doubling or tripling of generations per year. Programs could focus resources on lines deemed likely to succeed, quicker. Together, these advancements allow for shorter cycle length, maintained selection intensity, and higher prediction accuracy that increases genetic gain per unit of time.
This hyper-acceleration, coupled with the immensity of genetic data, begs for standardization of processes where possible. This starts with effective sample prep and lysis with solutions durable enough to support the influx of a range of sample types to feed into technologies like sequencing. Labs also look to streamline manual processes to fully leverage their analytics pipelines that offer quicker computation, enabled by machine-learning tools and AI enhancements.
4) Functional genomics, regulation, and networks underlying traits.
Sequence/structural variations are intertwined with layers of regulation before being translated into an expressed phenotype. Functional genomics integrates events that affect transcriptome profiles, chromatin accessibility, protein interactions, and other mechanisms that impact the omics. Researchers leverage these molecular elements and their networks to reconstruct and influence their target traits, whether flowering time, stress responses, or another well-defined product quality metric.
For agronomics and in the management of animal husbandry and land/ecosystems, researchers perform perturbation experiments in the lab to identify these mechanisms and follow their effectiveness into real-world conditions.
5) Epigenetic tags and chromatin states within adaptation, stress response, and trait stability.
Epigenetic marks such as DNA methylation and histone modifications are some of the regulatory elements studied for their dynamic ability to fine-tune how genes are accessed and read. In addition, it also helps with plasticity of genetic information outside the rigidity of the AGCTs. They are another toolset leveraged to introduce heritable or embryonic/early-life differences that could affect product resilience and quality. In response to environmental changes, research is clarifying how these chemical tags and their associated degrees of generational persistence/penetrance might influence phenotypes to inform epigenetics-mediated breeding (epibreeding) programs.
6) Interplay of abiotic and biotic factors impacting stress resilience.
Climate variability and rapid adaptation by pathogens/pests can create a complex combination of abiotic and biotic stresses, where not a single elixir likely exists. There is an effort, therefore, to build collective tolerance. Central to this idea is understanding and engineering resilient genes and networks that are durable but adaptable enough to changing environments.
The genomic reach of this area of research can span from upstream sensing (stress genomics) through physiological adjustment. The work of mapping interactions helps build strategies that prioritize selective alleles, regulatory modules, or genes stacking for durability and plant-trait-mechanism of action (MOA) combinations as outcomes that impact global issues such as food security.
7) Metagenomics insights into the microbiome for plant and animal health.
Microbiomes influence the health and productivity of agricultural products. By integrating metagenomic profiles with host genomic data, researchers are exploring the manipulation of the microbiome to help with product traits.
Within crop genomics, soil and root-associated (rhizosphere) microbiomes often come to mind; but, leaf surfaces (phyllosphere) and air also house microbial communities that may be contributing to disease resistance and plant growth. Metagenomics continues to also be a core in ecology as a data set that helps balance conservation and resource consumption within natural systems.
8) Deepened understanding of evolutionary genomics.
Genomic interrogation in evolution and biodiversity helps identify genetic variations and how they were incorporated. Basic research inspires translational applicability; understanding past events has helped us decipher some of the mechanisms behind selective sweeps, genetic hitchhiking, introgression, or rapid adaptation. This work helps us map and perform comparative analyses for data-driven strategies across the board for conservation efforts, in landraces or wild accessions, as well as in intentional selection employed in domestication or applied breeding for trait improvement or survival.
9) Implementation strategies from discovery to deployment.
Delivering discoveries to growers and agribusiness requires intentional, actionable, and coordinated steps. A realistic adoption timeline needs to be considered, especially if frameworks include multi environment trials (METs). As prediction models are being validated under diverse conditions, it can also help define go-to-market choices. The decisions take into consideration factors such as latest regulatory requirements, responsible stewardship, and operational scaling.
This allows a collaborative framework that maintains a pipeline-oriented approach that can help accelerate delivery of climate-smart cultivars, for example, with preemptive discussions on practical and budgetary bottlenecks.
10) Reproducibility and consistency for actionable insights.
When advancing the fields of plant and animal genomics, high-volume data is implied. Sequencing data along with archives of associated phenotypic readouts demand reproducibility and scalability in their results. For wet lab experiments, this starts with sample prep leveraging automation before entering analytical assessment and data analysis. Processes also need to account for interoperability, which is key to successful collaboration across studies and global partnerships.
Smart sample preparation is the practical starting point for agrigenomics.
We are excited by the research driving the field forward - and by the role our homogenization and automated preparation technologies can play in accelerating discovery.
