Dr. Pardis Sabeti is the computational geneticist heading the Sabeti Lab which uses computational methods and genomics to study of humans and pathogens
Sabeti was convinced that there was a way to pinpoint when more recent changes in the human genome had occurred and that this knowledge could lead to breakthroughs in fighting disease. Specifically, she wanted to use the makeup of neighborhoods of genes (called haplotypes) to determine if a specific gene variation (called an allele) in a given neighborhood had recently come to prominence in a population because it conferred an evolutionary advantage. This should be possible, she thought, by using the never-ending process of genetic recombination—the breaking and rejoining of DNA strands—as a kind of clock to measure how long ago a given mutation had swept through a population. If a widespread mutation had appeared recently—for instance, the mutation that enabled adult human beings to digest the lactose in cow’s milk, a nutritional advantage for many people in Europe after cows became common there—fewer recombination events would have occurred since it was introduced. As a result, the mutated version of that allele should be on a stretch of DNA that was more or less identical for everyone in a population. If the mutation had appeared a longer time ago, recombination would dictate that the area around the mutated allele would have gone through more random recombination events and it would be on a stretch of DNA that was more varied across the population.
It was a radical approach: Instead of using existing tools to analyze new data, she was trying to develop new tools to use on available data. When she was at Oxford, “Everybody thought what I was trying to look for was dumb,” Sabeti says. “It seemed as if I was just going to go nowhere. I know everyone has a hard time at some point when they’re in graduate school, but I was on the higher end of the hard time early on in my PhD.”
Nevertheless, Sabeti returned to Boston to attend Harvard Medical School and kept at it, taking “a series of little steps,” she says. “I was just charting my path in my own weird ways.” Then, early one morning, she plugged a large data set related to the DC40L gene, which she’d already linked to malaria resistance, into an algorithm she’d developed and watched results showing it was associated with a common haplotype—indicating it had recently been selected for—come into focus on her computer screen.
“I was just sort of beside myself with excitement,” she says. “It’s a really exciting moment when you know something about the whole world that no one else does. I wanted to call somebody, but didn’t know anybody I felt comfortable calling at 3 a.m.”