Getting the Right Goat: Genetic Mapping Helps Meet Food Needs of World Regions

Posted: May 14, 2012 at 10:10 am

Print Friendly

By Michele McDonald

Equipped with measuring tape, camera and a few other tools, a Mason doctoral student is applying lessons learned from the U.S dairy industry to goats in Africa as one way to combat hunger.

African goat market. USDA photo

Jennifer Woodward-Greene is traveling to Ethiopia and Kenya in early June to measure goats and sample their DNA as part of her work with the global hunger and food security program Feed the Future, with the U.S. Department of Agriculture, Agricultural Research Service (ARS), and for her PhD in Mason’s Bioinformatics and Computational Biology Department.

“One of the main objectives is to work with the people in their own country to develop sustainable solutions to the problem of hunger, rather than only providing short-term aid,” Woodward-Greene says of the United States Agency for International Development (USAID)-funded Feed the Future initiative.

Researchers working in goats can learn a thing of two from experience with cows. U.S. dairy cows have increased milk production more than fourfold over the past 40 years due to better breeding, says Curt Van Tassell, a leading bovine geneticist with the USDA’s ARS and an affiliate professor at Mason.

Woodward-Greene adds, “We have fewer cows to make much more milk, leaving less of an environmental footprint.”

The U.S. dairy industry draws upon more than 100 years of phenotype data, which details bovine physical attributes such as milk yield, fertility, height, length and chest depth. Plus, now genetic mapping adds to the mix by associating these observed traits with specific locations in the animal’s DNA. Woodward-Greene’s task is to begin a similar phenotype database for African goats. She plans to record “traits you can see by looking at the goat.”

Woodward-Greene takes pictures of the goats for another step in the project. She’s working with Jason Kinser, an associate professor in the School of Physics, Astronomy and Computational Sciences, to develop a simple analytical system for future researchers to use. Researchers will be able to share data from anywhere in the world using photos, without having to physically handle every goat themselves or rely on measurements by various handlers and measuring tools.

“Analyzing images is very standard,” says Kinser, who’s been teaching Advanced Programming for Bioinformatics for a decade. “But this is the first time I’ve seen it applied to goats.”

A trip to Nairobi in 2009 sparked the goat idea for Van Tassell. He also noted the U.K.-based FarmAfrica project, which focuses on improving Africa’s agriculture. “Driving around Nairobi made me unequivocally aware of the role goats had in the urban environment,” he says.

A new breed of goat selected and bred to thrive in the African environment could be forthcoming in the next 20 years, Van Tassell says. Merely shipping high-milk producing goats from other regions to Africa won’t work, if the dairy industry is any indication. “Holstein cows from temperate climates were introduced in Africa, with sometimes catastrophic results,” he says. “We’re trying to avoid that.”

Genetic mapping has cut some of the guesswork out of breeding the best animals. Van Tassell adds, “Genetic mapping has completely revolutionized dairy cattle breeding around the globe.”

SNP, or single-nucleotide polymorphism, can lead the way to finding the best traits for breeding. “Some people have described SNPs like mile markers on the road,” Woodward-Greene says. “The SNPs make it easier to see if the cows will likely make more milk or they won’t.”

SNPs also speed up the process of finding animals carrying the best traits to pass on to future generations for both goats and cattle. Before DNA testing, farmers would look at a young bull from productive parents to decide if “he looks good on the ground,” Woodward-Greene says.

But then it would take another three to five years to find out if the bull could pass along the traits that make a cow a good milk producer. The cost could tally $50,000, she says.

“You could spend five years and $25,000 to $50,000 to find he’s a dud,” she says. “Now with SNPs, you have more information to decide right there from the day he’s born.”

Goats will get similar treatment so they can be bred to meet the needs of specific regions. For example, goats in western Kenya are bigger and better milk producers while goats in East Africa are smaller and used for their meat, Van Tassell says. “There’s a lot of range in goats but not a clear distinction into breeds,” he says.

Van Tassell is optimistic about the goat program’s success to help decrease hunger in Africa by improving goatherds.

“Once we understand the genetic differences among African goat populations and breeds, we’ll know which goats are genetically rare or unique, and which are more common,” Woodward-Greene says. “Then, we can associate desired phenotypic traits with the goat genetic map, and goat farmers can use this information to make breeding and conservation decisions to maintain genetic resources, and improve their goats as they see fit.”

Additional information on the project can be found in the Feed the Future Newsletter or on the USDA Blog.

Write to Michele McDonald at mmcdon15@gmu.edu

Construction Updates

Leave a Comment