New faculty member Frank Albert aims to answer fundamental open questions in genetics by cleverly recombining emergent biotechnologies.
Your genetic makeup has big consequences. It influences your appearance, your metabolism and your risk for disease. But precisely how do the millions of differences in genome sequence among people influence their bodies? For a long time, geneticists have been working out a broad outline of the answer. Now, the advent of next-gen tools for reading, writing and editing DNA opens up a new window into this decades-old question.
New faculty member Frank Albert, who recently took up residence in a new cross-disciplinary Computational Biology lab in the Molecular and Cellular Biology building, investigates genetic variation by analyzing differences in genome sequence between individuals. “My lab finds creative ways to pull together the extremely exciting new technologies that are just exploding right now to experimentally probe complex trait genetics in ways that historically haven't been possible,” he says.
“For many, many traits, most of the genetic variants that have an effect are regulatory,” says Albert. “They don't necessarily change the protein sequence. Instead, they change how much a gene is expressed.” This is particularly true for many common human diseases. Albert is working on deciphering how the information encoded by DNA sequence differences flows through the cell to influence the organism.
The Central Dogma of Molecular Biology states that (1) DNA gets transcribed into RNA, and (2) RNA gets translated into protein. It is therefore natural to assume that genetic differences that influence mRNA should also have an effect on protein — but there’s a hitch. Nowadays, DNA and RNA are so easy and cheap to work with that most studies focus on step one. However, a handful of studies into step two revealed an unsettling trend. It seemed that only about a quarter of genetic differences that influenced how much RNA is made actually caused the same effect on protein levels. This implies that there are important subtleties in how the Central Dogma actually plays out in cells.
“Enormous population size is very important because many of the effects we look at are very small,” says Albert, who uses yeast as a model organism for its small genome, short life cycle and massive population sizes. Precisely because of the high number of individuals analyzed, Albert’s techniques have far greater statistical power than can be achieved with larger, more complex organisms.
For instance, the number of people in the United States is approximately 300 million. Albert can fit that many yeast cells in a single flask and routinely manipulate and gather genomic information from successive generations — a feat that is clearly impossible with humans.
By using huge yeast populations, Albert was able to detect a better match between genetic influences on RNA and protein production, raising their concordance from 25 percent to 60 percent. Plenty of mystery remains around the other 40 percent. “We're still in the stage of understanding quantitatively what's happening in the cell,” says Albert, who hopes to eventually develop predictive models of which genetic differences affect which traits, and how.
Albert’s work touches not only on the Central Dogma, but also evolutionary genetics and functional genomics. “One motivation for me to come to this department is that there's a lot of deep expertise here in how cells work,” he says. “We use genomics approaches to identify the genes and variants that are the most interesting from the perspective of complex trait genetics, and the Department of Genetics, Cell Biology and Development is an outstanding environment to investigate their biology further. – Colleen Smith