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Crop genome sequencing reads genetic information; traditional plant breeding is the process of crossing, growing, evaluating and selecting plants to develop improved varieties. Sequencing can give breeders useful information for choosing plants, but it does not replace the breeding program or tell them on its own how a crop will perform in a field. Its value depends on how the genetic data are used alongside plant traits, breeding goals and local conditions.
Sequencing is a tool; breeding is a process
Genome sequencing determines the order and arrangement of genetic material. It can help researchers identify genes and genetic variation, develop DNA markers, or generate data for prediction. By itself, however, a sequence is not a new crop variety and does not specify how plants should be crossed, grown or selected.
Plant breeding aims to develop plants with combinations of characteristics that meet particular needs: for example, improved yield, resistance to a disease, or adaptation to a production environment. Breeders use methods suited to the crop and the type of cultivar they are developing. They make selections using observed plant characteristics—known as phenotypes—and may also use genetic information to guide those decisions.
Doreen Ware, a computational biologist at the USDA Agricultural Research Service’s Robert W. Holley Center for Agriculture & Health, described sequencing as a way “to study how plants grow and adapt to the environment, and to guide the way that we breed plants.” The distinction matters: sequencing can inform a decision, while breeding entails making and evaluating generations of plants.
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How traditional breeding and sequencing-informed selection differ
| Comparison | Traditional breeding | Sequencing-informed selection |
|---|---|---|
| Main information | Observed plant traits, breeding history and program goals | DNA markers or sequence-derived genotypes, often used with observed traits and data from a reference or training population |
| Selection approach | Select plants or families based on performance and breeding objectives | Marker-assisted selection tracks markers linked to chosen traits; genomic selection uses genome-wide markers to predict breeding value or performance |
| Potential advantage | Directly evaluates the plants and environments relevant to the program | Can help with traits that are difficult to screen and may help select for complex traits influenced by many genes |
| What the approach still needs | Appropriate trait evaluation, including evaluation across environments where needed | Reliable genotyping, suitable reference or training data, predictive models and validation using phenotypes |
| Cost and complexity | Field-program costs and time, which vary by crop and program | Genotyping or sequencing and analysis costs, which vary by genome, sample scale, method and information required |
This is not a universal cost comparison: the available evidence does not establish that either approach always costs less. A breeding program may also combine them rather than choose one in place of the other.
Three terms that are easy to confuse
Genome sequencing
Sequencing produces information about the sequence and arrangement of genetic material. It can support the discovery of genetic variation and markers, studies of how genes are configured, or the development of data used in prediction. Ware described sequencing as “the process of discovering and describing the layout of the genome – which parts are active, which genes are present, and how they are configured.” The term names a way of obtaining genetic information, not a complete selection strategy.
Marker-assisted selection
Marker-assisted selection (MAS) uses DNA markers associated with particular traits to help identify plants carrying desired alleles. It can be especially useful when a trait is difficult or impossible to evaluate directly in every plant. The FAO background paper discusses screening for pest or disease resistance as a case where marker information can help breeders make selections without relying exclusively on time-consuming field screening.
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A marker is evidence about a genetic region associated with a trait, not the trait itself. MAS is most directly suited to a program that can identify useful associations and apply them to its breeding material. It does not mean that every plant with a selected marker will perform equally well in every environment.
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Genomic selection
Genomic selection (GS) uses marker information across the genome in statistical models to estimate a plant’s breeding value or likely performance. Rather than concentrating only on a small number of selected loci, it draws on genome-wide patterns. That makes it a candidate approach for complex traits such as yield or stress tolerance, where many genes may each have a small effect.
GS depends on a model trained with genetic and observed-trait information from an appropriate population. The model’s predictions are useful only to the extent that its data, relationships and target conditions suit the plants being considered. A prediction is a guide for selection, not a substitute for confirming crop performance.
What a breeding program does with genomic information
The precise workflow varies with the crop, the trait and the breeding objective. A general sequence is to define what the program is trying to improve, obtain suitable genetic and phenotype data, use markers or a prediction model to rank candidate plants, and evaluate promising material for the traits and conditions that matter. Crossing, growing and advancing plants remain part of the breeding process.
- Set a specific breeding goal. Decide which traits and target production conditions matter. A useful selection decision depends on the intended crop use and environment, not simply on having more sequence data.
- Collect genetic and trait information. Genotype plants with markers or sequence-derived data, and record observed traits in the populations used to identify associations or train a prediction model.
- Choose an appropriate selection method. Use MAS when informative markers for particular traits are available; consider GS when genome-wide information and suitable training data can support prediction for the traits of interest.
- Use the results to prioritize plants or families. Genetic results can help decide which material to advance or cross. The result is a selection aid, not a finished variety or a guarantee of field success.
- Evaluate selected material phenotypically. Grow and assess promising plants in relevant conditions, using the field and trait evaluation appropriate to the program. This checks whether genetic expectations translate into useful performance.
This outline is not a claim that every program follows one fixed sequence or uses every kind of data. The design must fit the crop, the breeding population and the decision breeders need to make.
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Can sequencing make crop breeding faster?
It can contribute to shorter selection cycles under suitable conditions, but it does not guarantee faster variety development. Genomic selection can allow a program to estimate breeding value using genome-wide marker data and, where the approach is effective, make selection decisions without waiting for every candidate to complete lengthy evaluation. The benefit depends on prediction accuracy, the population and data available, breeding resources, and how the method fits the program.
A USDA ARS record of a 2010 analysis by Heffner, Lorenz, Jannink and Sorrells describes equal-budget modeled strategies for a high-investment maize program and a low-investment winter-wheat program. In those scenarios, genomic selection could outperform marker-assisted selection in gain per year through a shorter breeding cycle. These are modeled program scenarios, not a measured guarantee for all maize or wheat programs, and they do not establish a universal speed advantage over conventional breeding.
The 2010 review by Lorenz and colleagues explains why the number and quality of genetic and observed-trait records matter. It describes 200 to 10,000 markers and observations as a range for typical crops in the context of genomic-selection requirements. That range is context, not a universal minimum or design prescription: the useful data quantity depends on factors such as the genetic map and effective population size.
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Why field evaluation still matters
Genetic information cannot capture every factor that determines how a crop performs in a production system. Field phenotyping—the observation and measurement of plant traits—provides direct evidence about the plants in the environments and conditions that matter. It helps breeders assess whether predicted or marker-associated differences translate into useful traits.
The FAO background paper presents both the opportunity and a caution. MAS can expand the scope and efficiency of breeding and reduce some time-consuming field evaluation. But a narrow reliance on markers may lose the holistic gains of phenotypic selection. It may also leave varieties less prepared for changes in climate, production systems or end use if selection does not account for those changes.
For that reason, marker or genome-wide predictions should be interpreted in relation to the populations and conditions behind them. A plant selected for a genetic signal still needs evaluation against the program’s goals. Where performance across environments matters, the evaluation must be appropriate to those environments; a sequence does not show that a variety will perform identically everywhere.
Crop genomes affect the methods and the costs
There is no single sequencing setup or cost that applies to every crop. Genome size and structure affect the amount and kind of information researchers need to obtain, while method, sample scale and analytical goals also affect the work. In a USDA ARS interview, Ware notes that wheat is hexaploid and that corn has a history of genome duplication; she also describes more than 80% of the corn genome as mobile elements. These details illustrate why genomic work can require crop-specific approaches rather than one standard recipe.
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A wheat study provides a more specific example. The USDA ARS record for Wang and colleagues’ 2023 paper in The Plant Genome describes wheat’s genome as 16 Gb, with approximately 85% repetitive material. The researchers developed skim exome capture, a targeted sequencing-based genotyping method. They report a repeatable approach that can scale to a desired price point and costs less per sample than traditional resequencing and capture approaches.
That finding is specific to the method and study described. It is not a commercial service quote, a current price for sequencing a wheat genome, or proof that the same approach will be cheaper for every crop or research question. The record identifies downstream uses including quantitative trait locus mapping, genome-wide association studies and genomic selection.
Ware also discussed the historical decline in maize genome sequencing costs: more than $50 million fifteen years before the interview, compared with as low as $1,000 at the time of the interview. The interview page does not state its publication date, so the comparison cannot be assigned a calendar year or treated as a current price. It illustrates how costs changed over the period Ware described, not what a breeder should expect to pay now.
Does sequencing replace breeding, or mean genetic engineering?
No. Sequencing and selection are not themselves a breeding scheme, and the methods discussed here are not genetic engineering. Sequencing reads genetic information; MAS and GS use genetic data to inform which plants breeders select. The evidence described here concerns those information and selection methods, not the safety or regulatory status of engineered crops. A sequence alone does not create a variety, establish how it was produced, or settle questions beyond the breeding methods covered here.
How to judge whether genomic selection is useful
For a breeder, researcher or reader assessing a claim about sequencing-based crop improvement, the key question is not whether a program uses DNA data, but whether those data improve a well-defined selection decision. Consider:
- Trait: Is the target trait difficult to screen, associated with a few useful markers, or complex enough to motivate genome-wide prediction?
- Data fit: Do the genetic and phenotype data represent the population and conditions where the program will use its predictions?
- Validation: Are predicted gains checked against observed plant performance rather than treated as outcomes in themselves?
- Breeding objective: Does selection account for the intended production system, climate, and end use, in addition to the genetic signal?
- Resources: Do genotyping, analysis and field evaluation fit the program’s budget and capacity? Costs vary, and the available evidence does not identify one approach as universally less expensive.
Genome sequencing can reveal information that breeders could not see by observing a plant alone, and selection methods can make that information actionable. The useful comparison is therefore not DNA versus fieldwork. It is how a breeding program combines genetic evidence with the evaluation needed to identify plants that meet its goals.
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