Peeling back the layers of the onion genome

“In this study, we challenged the limits of how far we can go in deciphering a DNA sequence consisting of 16 billion base pairs,” said paper author Masayoshi Shigyo, professor in the Laboratory of Vegetable Crop Science, Graduate School of Sciences and Technology for Innovation, Yamaguchi University, and researcher at the National Institutes of Natural Sciences.

In typical genome mapping, scientists curate a reference assembly of the genes of what is considered the ideal representation of that species. They can then compare how chunks of genetic information from other members of the species, or closely related species, links to specific traits and which traits are expressed from what information. The reference assembly is the key to the genetic map. However, according to Shigyo, the onion genome is far too large and complex to develop a traditional reference assembly, so researchers developed a genetic map to detail the links between genes that code for specific physical traits, such as size.

“In previous work, we collected the sequence information on expressed genes, determined the locations of 25,000 expressed genes and succeeded in developing the world’s first high-density genetic map that accurately reflects the order of expressed genes,” Shigyo said.

In the current paper, researchers began assembling the genetic information from onion by processing cell samples taken from plants grown in the lab. Using this process, called sequencing, the researchers obtained about 2.2 gigabytes of genetic data from the total 16 gigabytes comprising the full onion genome.

They then compared this information with the high-density genetic map developed previously to create eight molecular sequences, called chromosomes, representing half of the genetic material a single onion plant would have. They then analyzed the chromosomal sequences to see how many total genes the total onion genome may contain.

“The prediction method detected 540,925 putative possible genes,” Shigyo said. “This number was much higher than expected, suggesting there are many pseudogenes in the genome.”

Pseudogenes are incomplete or incorrect copies of functional genes, resulting in nonsensical, non-coding “junk DNA,” according to Shigyo. The researchers also found that the functional gene regions appeared to be evenly distributed across the genome, so all of the information would need to be carefully examined to determine if a gene was junk or functional.

“In short, due to the large amount of junk DNA in the genome, computer sequence assembly cannot be accomplished,” Shigyo said. “However, in these difficult circumstances, our high-density genetic map information served as a model, and we succeeded in beginning to decode the onion genome for the first time.”

Shigyo said the researchers hope to continue decoding the genome, the information from which could help cultivate better production for onion and related crops, such as garlic and asparagus; however, the funding for research on such an old vegetable is limited.

“Humans have a long history of cultivating onions, dating back to 2300 BC in Egypt, and the vegetable is believed to have contributed to our health and society since then,” Shigyo said. “Yet onions tend to be thought of as suitable for high school biology experiments, but not more serious research. I hope the results of this work will bring more attention to onions, and the potential benefits of better understanding the crop, in the research community.”

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Other contributors include Richard Finkers, Martijin van Kaauwen, Karin Burger-Meijer, Linda Kodde, Ben Vosman and Olga Scholten, Plant Breeding, Wageningen University and Research Centre, the Netherlands; Kai Ament, Henk Huits and Laurens Kroon, Bejo Zaden B.V., the Netherlands; Raymond Egging, GenomeScan, the Netherlands; Shusei Sato, Graduate School of Life Science, Tohoku University, Japan; and Wilbert van Workum, Limes Innovations B.V., the Netherlands.

Top Sector Horticulture & Propagation Materials, Bejo Zaden B.V., De Groot en Slot B.V. and GenomeScan funded this work.