Prioritizing entire plant genome may improve crops

Prioritizing entire plant genome may improve crops

A NEW study by Kansas State University plant geneticists may help scientists produce better climate-resistant corn and other food production plants by putting a spin on the notion that "we are what we eat."

Kansas State associate professor of agronomy Jianming Yu, a co-senior author on the study, and colleagues found that by applying a genetic analysis method used to study and prioritize the genes in people, it improved the likelihood of finding critical genes in food production plants. These genes control quantitative traits in plants, such as how the plants grow and when they flower.

Additionally, this method can be used to study how food production plants respond to drought, heat and other factors, thus giving scientists a greater chance at improving crops' resistance to harsh weather and environments, the university said.

"Right now, we know most of the genes that make up several of these food production plants, but finding the right genes to increase food yield or heat tolerance is like finding a needle in a haystack," Yu said.

For the study, the researchers looked at the sequenced genome of corn. Staple food crops like corn, wheat, barley and oats have comparable and sometimes larger, more complex genomes than people and mammals. That poses a challenge for scientists attempting to modify the plant to improve aspects like production and heat tolerance.

"Like humans, plants have complex traits and complex diseases," Kansas State agronomy research associate Xianran Li said. "In plants, those are things like drought tolerance and grain yield. Sometimes, one specific gene can make a big change. Frequently, though, it involves multiple genes. Each gene has a small, modest effect on the trait, and many genes are involved. This makes it really difficult to study."

Historically, scientists have analyzed an isolated region of a plant genome -- often taking a trial-and-error approach at finding which genes control which traits.

Instead, in this study, the researchers approached the corn genome with a relatively new analysis method that is used to study the human genome. The method, called genome-wide associate studies (GWAS), searches the entire genome for small, frequent variations that may influence the risk of a certain disease. This helps researchers pinpoint genes that are potentially problematic and may be the key in abnormal traits and diseases.

"Conducting routine, full-scale, genome-wide studies in crop plants remains challenging due to cost and genome complexity," Patrick Schnable, Baker professor of agronomy at Iowa State University and co-senior author on the study, explained. "What we tried to get out of this study was a broad view of which regions of crop genomes should be examined in detail."

Using GWAS for multiple analyses and complementary methods in identifying genetic variants, the researchers were able to find that, on average, 79% of detectable genetic signals are concentrated at previously defined genes and their promoter regions.

According to Yu, the percentage is a significant increase compared to looking at the gene regions alone.

"We used to think that genes were the only search priority and there were just many other less-important or useless DNA sequences," Yu said. "Now, we are starting to see that these other regions harbor some important genetic codes in them. Canvassing without prioritizing can be cost prohibitive, however, and efficient GWAS in crops with complex genomes still need to be carried out by taking advantage of a combination of the genome technologies available."

"Genic & Non-Genic Contributions to Natural Variation of Quantitative Traits in Maize" was recently published in the journal Genome Research. The National Science Foundation funded the research.

Researchers with Cornell University, Cold Spring Harbor Laboratory, University of Minnesota and the U.S. Department of Agriculture-Agricultural Research Service also participated in the project.

Volume:84 Issue:51

Hide comments

Comments

  • Allowed HTML tags: <em> <strong> <blockquote> <br> <p>

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.
Publish