‘Map’ of Sweetpotato Genome to Improve Breeding Efforts
When it comes to certain aspects of genetics, sweetpotatoes are a lot more complicated than humans. But NC State scientists have created complex genetic maps enabling them to untangle which genes are associated with economically important traits such as higher yield and disease resistance. In other words, their genetic maps may lead to better sweetpotatoes.
In addition to being economically important for North Carolina growers, sweetpotatoes are an important crop for farmers and families in Sub-Saharan Africa. That’s why scientists from NC State and the International Potato Center (CIP) have been working to advance food security in the region through improved sweetpotato breeding efforts. The sweetpotato breeding work has been supported by the Bill & Melinda Gates Foundation for more than 10 years; NC State received complementary funding for genomic tools for the past five years.
However, sweetpotatoes are hexaploid, meaning they have six copies of each gene, or three from each parent, rather than the two copies humans have. This makes tracking the genes associated with desirable traits — and breeding better varieties — very complicated.
“We’re the first ones to sort and map out inheritance for a autohexaploid plant,” said Zhao-Bang Zeng, a William Neal Reynolds Distinguished Professor of horticultural science. “Before us, people mostly studied diploid or tetraploid species, with two or four copies of each gene. Figuring out how the polyploid genome, with so many copies, transmits desirable traits from parents to offspring in practice is very challenging. This is what we did.”
For more than 10 years Marcelo Mollinari, a researcher working with Zeng in the Department of Horticultural Science, has been developing sophisticated computational tools to tease apart the complex inheritance of polypoid plants, that have more than two sets of each gene.
In a recent study published in the journal Genetics, Mollinari, Zeng and Guilherme Pereira, who started the project as a postdoctoral fellow in Zeng’s lab and recently joined the International Potato Center’s regional office in Kenya as a molecular breeding scientist, mapped the genetic locations in sweetpotatoes associated with important agronomic traits such as the yield of commercial-sized roots per hectare. This study built upon the algorithm package the group designed to map the genome of polypoid organisms.
The backbone of our algorithm is a hidden Markov model, Zeng said. This model links all possible ‘hidden’ genotypes from two parents and their offspring to observable data, in the form of genetic markers.
In the example of two brown-eyed parents having a blue-eyed child, there are four different ways to mix and match the two genes from the two parents producing four different genotypes, or genetic make-ups, but only two different phenotypes: brown or blue eyes. A genetic marker with two states would be informative for tracking inheritance in the case of a less visible trait.
Now, sweetpotatoes have 6 copies of each gene, meaning there are 400 different ways to mix and match the six genes from the two parents instead, Zeng said. The information of a genetic marker with only two states is now very limited. However, by combining many linked genetic markers, the team could untangle the hidden information about the genotypes.
“The key to the hidden Markov model algorithm is that by linking the 400 states of one genome location, with neighboring genome locations, which each also has 400 states, throughout whole chromosomes, we can then infer the parental origin of each offspring genome segment,” Zeng said.
In the recent study, the team looked for associations between all of the genetic markers in the inferred genetic map and an observable trait, such as yield. If yield is significantly associated with a genotype at a certain location in genome, a QTL, or quantitative trait locus, is mapped to the genome location, Zeng said. The algorithm can associate one trait with multiple QTL in multiple chromosomes, if that’s what the data supports.
The research team grew over 300 offspring plants from the same two parents, the North Carolina staple “Beauregard” and an African variety known as “Tanzania” in several different locations in Peru. They collected DNA from the plants as well as data on eight different yield-related traits.
Then using their genetic map and specialized algorithms, Pereira, Mollinari and Zeng were able to link several regions of the genome with variation in traits such as yield of commercial-sized roots. By taking a closer look at a few regions associated with a higher number of storage roots, the scientists were able to find some genes worth further study.
Now that they have identified specific regions of the genome associated with important agronomic traits, breeders can use the information for marker-assisted breeding. Instead of having to grow up many offspring from test crosses, they can look at the genetic markers of the seedings, and select the ones most likely to have the traits desired to grow up and test, and discard the rest.
In several years, Zeng and Mollinari aim to have a complete package of tools to make genetic mapping, quantitative trait loci analysis and genomic selection methods available for sweetpotato breeders – and breeders of other polypoid crops.
Scientists studying other crops with two, four, six and even eight sets of genes, which includes potatoes, kiwifruit, blueberries, strawberries and sugarcane, have already begun to use the algorithms to construct genetic maps of their crop of choice, said Mollinari.
In addition to Mollinari, Pereira and Zeng, other authors on the paper include Craig Yencho at NC State; Dorcus Gemenet, Federico Diaz, Veronica Mosquera and Wolfgang Gruneberg with CIP; Bode Olukolu at the University of Tennessee; Robin Buell and Joshua Wood at Michigan State University; and Awais Khan at Cornell University.
The research was supported by the Bill & Melinda Gates Foundation under the Genomic Tools for Sweetpotato Improvement project, which was led by Yencho.