Statistical methods of genomic prediction, or the mathematics behind the farm, are crucial in creating more efficient seeds and improving overall seed performance.

Like humans, individual seeds have specific genetic makeups. For example, a seed’s genotype is its DNA sequence and its phenotype is the physical traits. Genomic prediction is the process of utilizing this genotypic data (DNA) to predict an individual seed’s phenotype (physical traits).

Reka Howard, associate professor in the Department of Statistics at the University of Nebraska-Lincoln, explores ways to use DNA data from seeds to save farmers time and money.

“Genomic prediction allows for predicting the performance of plants that have molecular marker information without physically planting seeds in the field. It enables plant breeders to select parents for crossing without the burden of planting and harvesting.” Howard said.

George Graef, professor in the Department of Agronomy and Horticulture at the university also studies the application of genomic prediction to produce higher-performing soybean cultivars for farmers.   

Together, this work enhances desirable characteristics in crops such as drought resistance and high yield through efficient seed breeding practices – ultimately helping Nebraska growers produce more while using less. 

“Looking at the historical rate of gains, the agricultural industry is behind where it needs to be,” Graef said. “But using methods like genomic selection, can hopefully increase the rate of gain and continue to improve yield and productivity for farmers.”

The Science Behind The Yield

The advancement of plant performance comes by crossing the best characteristics together to form an optimal product. Genomic prediction’s ability to accurately predict a plant’s phenotype, or the performance of the plant, depends on how closely the plant’s genotype is related to the final phenotype.

“Without genomic prediction, plant breeding is a longer-term endeavor that requires years to make the crosses, develop the populations, evaluate the progenies and select the superior individuals from those,” Graef said.

That is why Dr. Howard’s work with statistical methods in genomic prediction is crucial. The challenge at hand is to determine what other information is useful to include in these models and how to construct the equations to use all the information and provide a more accurate prediction of the phenotype.

“The value of being able to predict performance based on genotype information, even if that prediction is less than perfect, is huge. If one can obtain a prediction accuracy of even 60%, that could eliminate the need for a huge amount of plot testing and make the whole process more efficient,” Graef said.

“Now, the genomic data can be collected and through statistical methods, the performance of the plant can be predicted before even planting it,” Howard said.

This process is much quicker and more efficient for growers, saving both time and money.

Influencing Plant Genetics

Collaboration is key to innovation – and Howard and Graef’s teams are eliminating the unknowns in plant breeding.

“When selecting parents of a cross, it is crucial to have the best genotypes,” Graef said. “By breeding the best genotypes (DNA), the hope is that they will produce progenies with best phenotypes (physical traits).”

That said, the impact the environment has on performance is a key element not to overlook, he said. For example, a variety that excels in irrigated production in central Nebraska may not excel in eastern Iowa due to the different weather and other environmental conditions.

Genomic prediction’s accuracy increases when breeders analyze the relationships between specific genotypes and phenotypes across multiple environments.

“It is in the combination of the genotypic data and the environmental data collected over the years where the significance of these methods starts to gain traction,” Graef said.

“When a number of individual varieties are evaluated across multiple environments, one can better define relationships between genotype and phenotype,” Graef said. “Then one can compare them to the untested set of genotypes and make predictions about which of those untested genotypes would be expected to have the best phenotype (like yield) in a certain environment or across environments.”  

These methods allow breeders to select the best parents to cross for specific environments, which influences plant genetics and overall seed performance.

A Lasting Impact

Although utilizing genomic prediction on a large scale will not happen immediately, there is no doubting its long-term benefits in plant breeding.

Through her research, Howard takes great pride in knowing the work she performs has a lasting impact on the agricultural industry.

“Genomic prediction is now one of the main drivers for advancing genetic gains at a faster rate, moving the seed industry forward,” Howard said. “This work is neat because it is not trivial. Intentionally combining the data makes a difference.”

According to the United Nations, the world’s population is projected to reach 9.8 billion people by the year 2050. Utilizing tools like genomic prediction will allow the agricultural industry to continue to innovate and meet the increasing demand. 

Challenges like loss of productive farmland worldwide, losses due to unpredictable and changing climate patterns, and a growing world population emphasize the need to increase crop production per unit land area to meet future needs for food, feed, fiber and fuel. While the agricultural industry may be behind, using technological advancements like statistical methods in genomic prediction will help close the gap.

“Even a 1-2% change in the annual gain over time is going to be significant because it changes the production outcome,” Graef said. “Instead of being below the projected demand levels, in 50 years, we could be meeting or exceeding them.” 

For more information about statistical methods in genomic prediction and the research Howard is conducting, please visit https://statistics.unl.edu/reka-howard. For more information on the research Graef and Howard conduct together please visit https://journals.sagepub.com/doi/full/10.1177/1176934319831307.

Key Takeaways

1. Statistical methods of genomic prediction, or the mathematics behind the farm, are crucial in creating more efficient varieties and improving overall crop performance.
2. Like humans, individual seeds have specific genetic makeups. Genomic prediction is the process of utilizing genotypic data (DNA) to predict an individual’s phenotype (physical traits).
3. The advancement of seed technology comes by crossing the best characteristics of different varieties together to form an optimal product.
4. The development of more accurate prediction models by combing genotypic data with environmental and other data may enable breeders to select more optimal varieties for specific environments.
5. For more information about statistical methods in genomic prediction and the research, Howard is conducting, please visit https://statistics.unl.edu/reka-howard. For more information on the research Graef and Howard conduct together please visit https://journals.sagepub.com/doi/full/10.1177/1176934319831307.